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Wyse, Jason, and Nial Friel. "Block clustering with collapsed latent block models." Statistics and Computing 22, no. 2 (May 5, 2011): 415–28. http://dx.doi.org/10.1007/s11222-011-9233-4.
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Bartolucci, Francesco, Silvia Pandolfi, and Fulvia Pennoni. "Discrete Latent Variable Models." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 425–52. http://dx.doi.org/10.1146/annurev-statistics-040220-091910.
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We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested.
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Watanabe, Chihiro, and Taiji Suzuki. "Goodness-of-fit test for latent block models." Computational Statistics & Data Analysis 154 (February 2021): 107090. http://dx.doi.org/10.1016/j.csda.2020.107090.
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Norget, Julia, and Axel Mayer. "Block-Wise Model Fit for Structural Equation Models With Experience Sampling Data." Zeitschrift für Psychologie 230, no. 1 (January 2022): 47–59. http://dx.doi.org/10.1027/2151-2604/a000482.
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Abstract. Common model fit indices behave poorly in structural equation models for experience sampling data which typically contain many manifest variables. In this article, we propose a block-wise fit assessment for large models as an alternative. The entire model is estimated jointly, and block-wise versions of common fit indices are then determined from smaller blocks of the variance-covariance matrix using simulated degrees of freedom. In a first simulation study, we show that block-wise fit indices, contrary to global fit indices, correctly identify correctly specified latent state-trait models with 49 occasions and N = 200. In a second simulation, we find that block-wise fit indices cannot identify misspecification purely between days but correctly rejects other misspecified models. In some cases, the block-wise fit is superior in judging the strength of the misspecification. Lastly, we discuss the practical use of block-wise fit evaluation and its limitations.
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Moron-Lopez, Sara, Sushama Telwatte, Indra Sarabia, Emilie Battivelli, Mauricio Montano, Amanda B. Macedo, Dvir Aran, et al. "Human splice factors contribute to latent HIV infection in primary cell models and blood CD4+ T cells from ART-treated individuals." PLOS Pathogens 16, no. 11 (November 30, 2020): e1009060. http://dx.doi.org/10.1371/journal.ppat.1009060.
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It is unclear what mechanisms govern latent HIV infection in vivo or in primary cell models. To investigate these questions, we compared the HIV and cellular transcription profile in three primary cell models and peripheral CD4+ T cells from HIV-infected ART-suppressed individuals using RT-ddPCR and RNA-seq. All primary cell models recapitulated the block to HIV multiple splicing seen in cells from ART-suppressed individuals, suggesting that this may be a key feature of HIV latency in primary CD4+ T cells. Blocks to HIV transcriptional initiation and elongation were observed more variably among models. A common set of 234 cellular genes, including members of the minor spliceosome pathway, was differentially expressed between unstimulated and activated cells from primary cell models and ART-suppressed individuals, suggesting these genes may play a role in the blocks to HIV transcription and splicing underlying latent infection. These genes may represent new targets for therapies designed to reactivate or silence latently-infected cells.
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Mariadassou, Mahendra, and Catherine Matias. "Convergence of the groups posterior distribution in latent or stochastic block models." Bernoulli 21, no. 1 (February 2015): 537–73. http://dx.doi.org/10.3150/13-bej579.
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SANTOS, Naiara Caroline Aparecido dos, and Jorge Luiz BAZÁN. "RESIDUAL ANALYSIS IN RASCH POISSON COUNTS MODELS." REVISTA BRASILEIRA DE BIOMETRIA 39, no. 1 (March 31, 2021): 206–20. http://dx.doi.org/10.28951/rbb.v39i1.531.
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A Rasch Poisson counts (RPC) model is described to identify individual latent traits and facilities of the items of tests that model the error (or success) count in several tasks over time, instead of modeling the correct responses to items in a test as in the dichotomous item response theory (IRT) model. These types of tests can be more informative than traditional tests. To estimate the model parameters, we consider a Bayesian approach using the integrated nested Laplace approximation (INLA). We develop residual analysis to assess model t by introducing randomized quantile residuals for items. The data used to illustrate the method comes from 228 people who took a selective attention test. The test has 20 blocks (items), with a time limit of 15 seconds for each block. The results of the residual analysis of the RPC were promising and indicated that the studied attention data are not well tted by the RPC model.
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Kihal-Talantikite, Wahida, Pauline Le Nouveau, Pierre Legendre, Denis Zmirou Navier, Arlette Danzon, Marion Carayol, and Séverine Deguen. "Adverse Birth Outcomes as Indicators of Poor Fetal Growth Conditions in a French Newborn Population—A Stratified Analysis by Neighborhood Deprivation Level." International Journal of Environmental Research and Public Health 16, no. 21 (October 23, 2019): 4069. http://dx.doi.org/10.3390/ijerph16214069.
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Background: Adverse birth outcomes are related to unfavorable fetal growth conditions. A latent variable, named Favorable Fetal Growth Condition (FFGC), has been defined by Bollen et al., in 2013; he showed that this FFGC latent variable mediates the effects of maternal characteristics on several birth outcomes. Objectives: The objectives of the present study were to replicate Bollen’s approach in a population of newborns in Paris and to investigate the potential differential effect of the FFGC latent variable according to the neighborhood socioeconomic level. Methods: Newborn health data were available from the first birth certificate registered by the Maternal and Child Care department of the City of Paris. All newborns (2008–2011) were geocoded at the mother residential census block. Each census block was assigned a socioeconomic deprivation level. Several mothers’ characteristics were collected from the birth certificates: age, parity, education and occupational status and the occupational status of the father. Three birth outcomes were considered: birth weight (BW), birth length (BL) and gestational age (GA). Results: Using a series of structural equation models, we confirm that the undirected model (that includes the FFGC latent variable) provided a better fit for the data compared with the model where parental characteristics directly affected BW, BL, and/or GA. However, the strength, the direction and statistical significance of the associations between the exogenous variables and the FFGC were different according to the neighborhood deprivation level. Conclusion: Future research should be designed to assess the how robust the FFGC latent variable is across populations and should take into account neighborhood characteristics to identify the most vulnerable group and create better design prevention policies.
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Xie, Fangzheng, and Yanxun Xu. "Optimal Bayesian estimation for random dot product graphs." Biometrika 107, no. 4 (July 6, 2020): 875–89. http://dx.doi.org/10.1093/biomet/asaa031.
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Summary We propose and prove the optimality of a Bayesian approach for estimating the latent positions in random dot product graphs, which we call posterior spectral embedding. Unlike classical spectral-based adjacency, or Laplacian spectral embedding, posterior spectral embedding is a fully likelihood-based graph estimation method that takes advantage of the Bernoulli likelihood information of the observed adjacency matrix. We develop a minimax lower bound for estimating the latent positions, and show that posterior spectral embedding achieves this lower bound in the following two senses: it both results in a minimax-optimal posterior contraction rate and yields a point estimator achieving the minimax risk asymptotically. The convergence results are subsequently applied to clustering in stochastic block models with positive semidefinite block probability matrices, strengthening an existing result concerning the number of misclustered vertices. We also study a spectral-based Gaussian spectral embedding as a natural Bayesian analogue of adjacency spectral embedding, but the resulting posterior contraction rate is suboptimal by an extra logarithmic factor. The practical performance of the proposed methodology is illustrated through extensive synthetic examples and the analysis of Wikipedia graph data.
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Gong, Shiqi, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhiming Ma, Hao Ni, and Tie-Yan Liu. "Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7740–47. http://dx.doi.org/10.1609/aaai.v37i6.25938.
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Stochastic partial differential equations (SPDEs) are crucial for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly popular. However, SPDEs have two unique properties that require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over both initial conditions and the random sampled forcing term. Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white noise). To deal with the problems, in this work, we design a deep neural network called \emph{Deep Latent Regularity Net} (DLR-Net). DLR-Net includes a regularity feature block as the main component, which maps the initial condition and the random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to represent the SPDE solution. The regularity features are then fed into a small backbone neural operator to get the output. We conduct experiments on various SPDEs including the dynamic $\Phi^4_1$ model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that the proposed DLR-Net can achieve SOTA accuracy compared with the baselines. Moreover, the inference time is over 20 times faster than the traditional numerical solver and is comparable with the baseline deep learning models.
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Kharchevnikova, A. S., and A. V. Savchenko. "Visual preferences prediction for a photo gallery based on image captioning methods." Computer Optics 44, no. 4 (August 2020): 618–26. http://dx.doi.org/10.18287/2412-6179-co-678.
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The paper considers a problem of extracting user preferences based on their photo gallery. We propose a novel approach based on image captioning, i.e., automatic generation of textual descriptions of photos, and their classification. Known image captioning methods based on convolutional and recurrent (Long short-term memory) neural networks are analyzed. We train several models that combine the visual features of a photograph and the outputs of an Long short-term memory block by using Google's Conceptual Captions dataset. We examine application of natural language processing algorithms to transform obtained textual annotations into user preferences. Experimental studies are carried out using Microsoft COCO Captions, Flickr8k and a specially collected dataset reflecting the user’s interests. It is demonstrated that the best quality of preference prediction is achieved using keyword search methods and text summarization from Watson API, which are 8 % more accurate compared to traditional latent Dirichlet allocation. Moreover, descriptions generated by trained neural models are classified 1 – 7 % more accurately when compared to known image captioning models.
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Fazeli, N., M. Oller, J. Wu, Z. Wu, J. B. Tenenbaum, and A. Rodriguez. "See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion." Science Robotics 4, no. 26 (January 30, 2019): eaav3123. http://dx.doi.org/10.1126/scirobotics.aav3123.
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Humans are able to seamlessly integrate tactile and visual stimuli with their intuitions to explore and execute complex manipulation skills. They not only see but also feel their actions. Most current robotic learning methodologies exploit recent progress in computer vision and deep learning to acquire data-hungry pixel-to-action policies. These methodologies do not exploit intuitive latent structure in physics or tactile signatures. Tactile reasoning is omnipresent in the animal kingdom, yet it is underdeveloped in robotic manipulation. Tactile stimuli are only acquired through invasive interaction, and interpretation of the data stream together with visual stimuli is challenging. Here, we propose a methodology to emulate hierarchical reasoning and multisensory fusion in a robot that learns to play Jenga, a complex game that requires physical interaction to be played effectively. The game mechanics were formulated as a generative process using a temporal hierarchical Bayesian model, with representations for both behavioral archetypes and noisy block states. This model captured descriptive latent structures, and the robot learned probabilistic models of these relationships in force and visual domains through a short exploration phase. Once learned, the robot used this representation to infer block behavior patterns and states as it played the game. Using its inferred beliefs, the robot adjusted its behavior with respect to both its current actions and its game strategy, similar to the way humans play the game. We evaluated the performance of the approach against three standard baselines and show its fidelity on a real-world implementation of the game.
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Marcellos, Luiza, Adriana Faria, Marcus Souza, Mariana Almeida, Guilherme Sabin, Ronei Poppi, and Marcone Oliveira. "Simultaneous analysis of first-line anti-tuberculosis drugs in tablets by UV spectrophotometry compared to capillary zone electrophoresis." Open Chemistry 10, no. 6 (December 1, 2012): 1808–16. http://dx.doi.org/10.2478/s11532-012-0102-6.
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AbstractThe development and optimization of a novel UV spectrophotometric methodology was proposed for simultaneous analysis of ethambutol (ETB), isoniazid (ISO), rifampicin (RIF) and pyrazinamide (PYR), using multivariate calibration based on the partial least squares method (PLS). The methodology was successfully applied for analysis of four-drug fixed dose combination (4-FDC) tablets used for tuberculosis treatment. A 34 Box-Behnken design, with triplicate in central point, was used for sample preparation in the calibration step. In the present case, nine latent variables were chosen for the model development that presented the smallest RMSECV and explain 98.76% of data variance in Y block (concentrations of ETB ISO, RIF and PYR) and 99.93% of data variance in X block (spectral data). PLS models for ETB, ISO, RIF and PYR presented RMSEP and R2 values of 0.23 mg L−1 and 0.971; 0.14 mg L−1 and 0.731; 0.11 mg L−1 and 0.990 and 0.57 mg L−1 and 0.972, respectively. A validation step was performed based on the comparison between the UV spectrophotometric proposed methodology and capillary zone electrophoresis (CZE) in 4-FDC real samples and no significant difference was found between two methodologies at 95% of confidence level.
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Dong, Qian, Shuzi Niu, Tao Yuan, and Yucheng Li. "Disentangled Graph Recurrent Network for Document Ranking." Data Science and Engineering 7, no. 1 (February 15, 2022): 30–43. http://dx.doi.org/10.1007/s41019-022-00179-3.
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AbstractBERT-based ranking models are emerging for its superior natural language understanding ability. All word relations and representations in the concatenation of query and document are modeled in the self-attention matrix as latent knowledge. However, some latent knowledge has none or negative effect on the relevance prediction between query and document. We model the observable and unobservable confounding factors in a causal graph and perform do-query to predict the relevance label given an intervention over this graph. For the observed factors, we block the back door path by an adaptive masking method through the transformer layer and refine word representations over this disentangled word graph through the refinement layer. For the unobserved factors, we resolve the do-operation query from the front door path by decomposing word representations into query related and unrelated parts through the decomposition layer. Pairwise ranking loss is mainly used for the ad hoc document ranking task, triangle distance loss is introduced to both the transformer and refinement layers for more discriminative representations, and mutual information constraints are put on the decomposition layer. Experimental results on public benchmark datasets TREC Robust04 and WebTrack2009-12 show that DGRe outperforms state-of-the-art baselines more than 2% especially for short queries.
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Messick, Troy E., Garry R. Smith, Samantha S. Soldan, Mark E. McDonnell, Julianna S. Deakyne, Kimberly A. Malecka, Lois Tolvinski, et al. "Structure-based design of small-molecule inhibitors of EBNA1 DNA binding blocks Epstein-Barr virus latent infection and tumor growth." Science Translational Medicine 11, no. 482 (March 6, 2019): eaau5612. http://dx.doi.org/10.1126/scitranslmed.aau5612.
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Epstein-Barr virus (EBV) is a DNA tumor virus responsible for 1 to 2% of human cancers including subtypes of Burkitt’s lymphoma, Hodgkin’s lymphoma, gastric carcinoma, and nasopharyngeal carcinoma (NPC). Persistent latent infection drives EBV-associated tumorigenesis. Epstein-Barr nuclear antigen 1 (EBNA1) is the only viral protein consistently expressed in all EBV-associated tumors and is therefore an attractive target for therapeutic intervention. It is a multifunctional DNA binding protein critical for viral replication, genome maintenance, viral gene expression, and host cell survival. Using a fragment-based approach and x-ray crystallography, we identify a 2,3-disubstituted benzoic acid series that selectively inhibits the DNA binding activity of EBNA1. We characterize these inhibitors biochemically and in cell-based assays, including chromatin immunoprecipitation and DNA replication assays. In addition, we demonstrate the potency of EBNA1 inhibitors to suppress tumor growth in several EBV-dependent xenograft models, including patient-derived xenografts for NPC. These inhibitors selectively block EBV gene transcription and alter the cellular transforming growth factor–β (TGF-β) signaling pathway in NPC tumor xenografts. These EBNA1-specific inhibitors show favorable pharmacological properties and have the potential to be further developed for the treatment of EBV-associated malignancies.
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He, Anzheng, Zishuo Dong, Hang Zhang, Allen A. Zhang, Shi Qiu, Yang Liu, Kelvin C. P. Wang, and Zhihao Lin. "Automated Pixel-Level Detection of Expansion Joints on Asphalt Pavement Using a Deep-Learning-Based Approach." Structural Control and Health Monitoring 2023 (May 23, 2023): 1–15. http://dx.doi.org/10.1155/2023/7552337.
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Pixel-level detection of expansion joints on complex pavements is significant for traffic safety and the structural integrity of highway bridges. This paper proposed an improved HRNet-OCR, named as expansion joints segmentation network (EJSNet), for automated pixel-level detection of the expansion joints on asphalt pavement. Different from the high-resolution network (HRNet), the proposed EJSNet modifies the residual structure of the first stage by conducting a Conv. + BN + ReLU (convolution + batch normalization + rectified linear unit) operation for each shortcut connection, which can avoid the network degradation. The feature selection module (FSM) and receptive field block (RFB) module are incorporated into the proposed EJSNet model to learn and extract the contexts at different resolution levels for enhanced latent representations. The convolutional block attention module (CBAM) is introduced to enhance the adaptive feature refinement of the network. Moreover, the shared multilayer perceptron (MLP) architecture of the channel attention module (CAM) is also modified in this paper. Experimental results demonstrate that the F-measure and intersection-over-union (IOU) attained by the proposed EJSNet model on 500 testing image sets are 95.14% and 0.9036, respectively. Compared with four state-of-the-art models for semantic segmentation (i.e., SegNet, DeepLabv3+, dual attention network (DANet), and HRNet-OCR), the proposed EJSNet model can yield higher detection accuracy on both private and public datasets.
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Saxena, Divya, and Jiannong Cao. "Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–23. http://dx.doi.org/10.1145/3458025.
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Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.
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Kolodziej, Andrew, Eric Haines, Richard Morse, and Eugene Zhukovsky. "Abstract 5611: CRB-601: A highly potent and selective integrin αvβ8 blocking antibody with anti-tumoral properties." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5611. http://dx.doi.org/10.1158/1538-7445.am2022-5611.
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Abstract TGFβ is a secreted protein produced by tumors that promotes cancer progression primarily via the suppression of both the innate and adaptive immune systems. This makes TGFβ a promising immunotherapeutic target in cancer. It is ubiquitously expressed in a latent (L-TGFβ) form and the latent form has been shown to promote an immune suppressive phenotype within the tumor microenvironment. Integrin αvβ8 specifically binds to L-TGFβ. This interaction is essential for the activation of L-TGFβ-mediated signals in a variety of immune cell types. Interestingly, it has been recently shown that integrin αvβ8-mediated TGFβ activation can active directly through L-TGFβ and does not require the release of active TGFβ (1). Inhibition of integrin αvβ8-mediated TGFβ activation has been shown to block immunosuppressive regulatory T cell differentiation and enhance the recruitment of cytotoxic T cells into the tumor microenvironment (2). Here, we demonstrate by Surface Plasmon Resonance (SPR) that CRB-601, our selective inhibitor of integrin αvβ8 monoclonal antibody candidate, has a high affinity and specificity for the integrin αvβ8 complex. Moreover, in comparison to competitor molecules, such as ADWA-11, CRB-601 substantially blocks TGFβ activation in a reporter cell assay system. Additionally, using syngeneic mouse models, we evaluated the anti-tumoral properties of CRB-601 as a monotherapy, as well as in combination with immune checkpoints therapies. Findings from this study highlight the importance of integrin αvβ8 blockade in mediating the immune landscape within the tumor and leads to an enhanced response to immune checkpoint therapy. In conclusion, CRB-601 is a potent and selective integrin αvβ8 blocking monoclonal antibody that enhances the activity of immune checkpoint inhibitors in vivo and holds promise as a potential combination partner for immunotherapy. Investigational New Drug (IND) enabling studies are currently underway. References: 1: Campbell MG. et al. (2020) Cyro-EM reveals integrin-mediated TGF-β activation without release from latent TGF-β. Cell 180, 490-501. 2: Mariathasan S. et al. (2018) TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544-48. Citation Format: Andrew Kolodziej, Eric Haines, Richard Morse, Eugene Zhukovsky. CRB-601: A highly potent and selective integrin αvβ8 blocking antibody with anti-tumoral properties [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5611.
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Hernández-Sánchez, Natalia, Lourdes Lleó, Belén Diezma, Eva Cristina Correa, Blanca Sastre, and Jean-Michel Roger. "Multiblock Analysis Applied to Fluorescence and Absorbance Spectra to Estimate Total Polyphenol Content in Extra Virgin Olive Oil." Foods 10, no. 11 (October 23, 2021): 2556. http://dx.doi.org/10.3390/foods10112556.
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A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and absorbance spectra. The hypothesis of this work stated that inner-filter effects in fluorescence spectra that would reduce the estimation performance of a single block model could be overcome by incorporating the absorbance spectral information of the compounds causing them. Different spectral preprocessing algorithms were applied. Double cross-validation with 50 iterations was implemented to improve the robustness of the obtained results. The PLSR model on the single block of fluorescence raw spectra achieved an RMSEP of 177.11 mg·kg−1 as the median value, and the complexity of the model was high, as the median value of latent variables (LVs) was eight. Multiblock SO-PLS models with pretreated fluorescence and absorbance spectra provided better performance, although artefacts could be introduced by transformation. The combination of fluorescence and absorbance raw data decreased the RMSEP median to 134.45 mg·kg−1. Moreover, the complexity of the model was greatly reduced, which contributed to an increase in robustness. The median value of LVs was three for fluorescence data and only one for absorbance data. Validation of the methodology could be addressed by further work considering a higher number of samples and a detailed composition of polyphenols.
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Budhu, Sadna, Aditi Gupta, Kelly Fitzgerald, Rachel Giese, Adam Michel, Aliya Holland, Luis Felipe Campesato, et al. "567 Isoform specific anti-TGFβ therapy enhances antitumor efficacy in mouse models of stroma poor cancers." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A596. http://dx.doi.org/10.1136/jitc-2021-sitc2021.567.
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BackgroundTGFβ is a potential target in cancer treatment due to its dual role in tumorigenesis and homeostasis. There are three isoforms of TGFβ (TGFβ1, TGFβ2 and TGFβ3), which are secreted by immune and non-immune cells as an inactive latent complex. Depending on the local context and players, TGFβ can adopt opposing roles in carcinogenesis and in modulating the immune system. However, the expression of TGFβ and its inhibition within the tumor microenvironment has mainly been investigated in stroma-rich tumors.MethodsWe examined expression of TGFβ1 and TGFβ3 isoforms on immune cells in two stroma-poor mouse tumor models (B16 melanoma and CT26 colon carcinoma) and investigated the anti-tumor efficacy of antibodies that block TGFβ1 and TGFβ3 in these two models.ResultsDepending on local expression of TGFβ isoforms, specific inhibition of either TGFβ1 or TGFβ3 may be effective. The ”TGFβ signature” of CT26 colon carcinoma is defined by TGFβ1 expression on immune cells and TGFβ1 inhibition results in tumor delay; B16 melanoma has equal expression of both TGFβ1 or TGFβ3 isoforms and inhibition of either TGFβ1 or TGFβ3 controls tumor growth. We show that the mechanism of tumor growth delay is enhanced CD8+ T cell activation and effector function. In addition, we found that combining TGFβ inhibition with immune checkpoint blockade results in improved tumor control and survival.ConclusionsOur findings suggests that expression of TGFβ isoforms in the TME is variable in different tumor types and their expression may be used to predict anti-tumor responses to TGFβ inhibition. Isoform specific TGFβ inhibition in stroma poor tumors shifts the local immune environment to favor tumor regression alone or in combination with immune checkpoint blockade.
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Alsayat, Ahmed, Mahmoud Elmezain, Saad Alanazi, Meshrif Alruily, Ayman Mohamed Mostafa, and Wael Said. "Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation." Diagnostics 13, no. 21 (November 1, 2023): 3364. http://dx.doi.org/10.3390/diagnostics13213364.
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Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step.
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Monaco, Maria Chiara G., Samantha S. Soldan, Chenhe Su, Annaliese Clauze, John F. Cooper, Rishi J. Patel, Fang Lu, et al. "EBNA1 Inhibitors Block Proliferation of Spontaneous Lymphoblastoid Cell Lines From Patients With Multiple Sclerosis and Healthy Controls." Neurology - Neuroimmunology Neuroinflammation 10, no. 5 (August 10, 2023): e200149. http://dx.doi.org/10.1212/nxi.0000000000200149.
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Background and ObjectivesEpstein-Barr virus (EBV) is a ubiquitous herpesvirus that establishes lifelong latency in memory B cells and has been identified as a major risk factor of multiple sclerosis (MS). B cell depletion therapies have disease-modifying benefit in MS. However, it is unclear whether this benefit is partly attributable to the elimination of EBV+B cells. Currently, there are no EBV-specific antiviral therapies available for targeting EBV latent infection in MS and limited experimental models to study EBV in MS.MethodsIn this study, we describe the establishment of spontaneous lymphoblastoid cell lines (SLCLs) generated ex vivo with the endogenous EBV of patients with MS and controls and treated with either an Epstein-Barr virus nuclear antigen 1 (EBNA1) inhibitor (VK-1727) or cladribine, a nucleoside analog that eliminates B cells.ResultsWe showed that a small molecule inhibitor of EBNA1, a critical regulator of the EBV life cycle, blocks the proliferation and metabolic activity of these SLCLs. In contrast to cladribine, a highly cytotoxic B cell depleting therapy currently used in MS, the EBNA1 inhibitor VK-1727 was cytostatic rather than cytotoxic and selective for EBV+cells, while having no discernible effects on EBV−cells. We validate that VK-1727 reduces EBNA1 DNA binding at known viral and cellular sites by ChIP-qPCR.DiscussionThis study shows that patient-derived SLCLs provide a useful tool for interrogating the role of EBV+B cells in MS and suggests that a clinical trial testing the effect of EBNA1 inhibitors in MS may be warranted.
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Pugalendhi, Ganesh Kumar, Shanmugapriya Kumaresan, and Anand Paul. "FMC2 model based perception grading for dark insurgent network analysis." PeerJ Computer Science 9 (December 5, 2023): e1644. http://dx.doi.org/10.7717/peerj-cs.1644.
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The burgeoning role of social network analysis (SNA) in various fields raises complex challenges, particularly in the analysis of dark and dim networks involved in illicit activities. Existing models like the stochastic block model (SBM), exponential graph model (EGM), and latent space model (LSM) are limited in scope, often only suitable for one-mode networks. This article introduces a novel fuzzy multiple criteria multiple constraint model (FMC2) tailored for community detection in two-mode networks, which are particularly common in dark networks. The proposed method quantitatively determines the relationships between nodes based on a probabilistic measure and uses distance metrics to identify communities within the network. Moreover, the model establishes fuzzy boundaries to differentiate between the most and least influential nodes. We validate the efficacy of FMC2 using the Noordin Terrorist dataset and conduct extensive simulations to evaluate performance metrics. The results demonstrate that FMC2 not only effectively identifies communities but also ranks influential nodes within them, contributing to a nuanced understanding of complex networks. The method promises broad applicability and adaptability, particularly in intelligence and security domains where identifying influential actors within covert networks is critical.
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Hutcheon, B., R. M. Miura, and E. Puil. "Models of subthreshold membrane resonance in neocortical neurons." Journal of Neurophysiology 76, no. 2 (August 1, 1996): 698–714. http://dx.doi.org/10.1152/jn.1996.76.2.698.
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1. We obtained whole cell data from sensorimotor cortical neurons of in vitro slices (juvenile rats) and observed a low-frequency resonance (1-2 Hz) in their voltage responses. We constructed models of subthreshold membrane currents to determine whether a hyperpolarization-activated cation current (IH) is sufficient to account for this resonance. 2. Parameter values for a basic IH (BH) model were estimated from voltage-clamp experiments at room temperature. The BH model formed a component of a reduced membrane (RM) model. On numerical integration, the RM model exhibited voltage sags and rebounds to injected current pulses; maximal voltage responses to injected oscillatory currents occurred near 2 Hz. 3. We compared the experimentally measured frequency-response curves (FRCs) at room temperature with the theoretical FRCs derived from the RM model. The theoretical FRCs exhibited resonant humps with peaks between 1 and 2 Hz. At 36 degrees C, the theoretical FRCs peaked near 10 Hz. The characteristics of theoretical and observed FRCs were in close agreement, demonstrating that IH is sufficient to cause resonance. Thus we classified IH as a resonator current. 4. We developed a technique, the reactive current clamp (RCC), to inject a computer-calculated current corresponding to a membrane ionic current in response to the membrane potential of the neuron. This enabled us to study the interaction of an artificial ionic current with living neurons (electronic pharmacology or EP-method). Using the RCC, a simplified version of the BH model was used to cancel an endogenous IH (electronic antagonism) and to introduce an artificial IH (electronic expression) when an endogenous IH was absent. Antagonism of IH eliminated sags and rebounds, whereas expression of IH endowed neurons with resonance and the frequency-selective firing that accompanies resonance in neurons with an endogenous IH. Previous investigations have relied on the specificity of pharmacological agents to block ionic channels, e.g., Cs+ to block IH. However, Cs+ additionally affects other currents. This represents the first time an in vitro modeling technique (RCC) has been used to antagonize a specific endogenous current, IH. 5. A simplified RM model yielded values of the resonant frequency and Q (an index of the sharpness of resonance), which rose almost linearly between -55 and -80 mV. Resonant frequencies could be much higher than fH = (2 pi tau H) - 1 where tau H is the activation time constant for IH. 6. In the FRCs, resonance appeared as a hump at intermediate frequencies because of low- and high-frequency attenuations due to IH and membrane capacitance, respectively. Changing the parameters of IH altered the low-frequency attenuation and, hence, the resonance. Changes in the leak conductance affected both the low- and high-frequency attenuations. 7. We modeled an inwardly rectifying K+ current (IIR) and a persistent Na+ current (INaP) to study their effects on resonance. Neither current produced resonance in the absence of IH. We found that IIR attenuated, whereas INaP amplified resonance. Thus IIR and INaP are classified as attenuator and amplifier currents, respectively. 8. Resonators and attenuators differ in that they have longer and shorter time constants, respectively, compared with the membrane time constant. Therefore, an increase in the leak conductance decreases the membrane time constant, which can transform an attenuator into a resonator, altering the frequency response. This suggests a novel mechanism for modulating the frequency responses of neurons to inputs. 9. These investigations have provided a theoretical framework for detailed understanding of mechanisms that produce resonance in cortical neurons. Resonance is one aspect of the intrinsic rhythmicity of neurons. The rhythmicity due to IH resonance is latent until it is revealed by oscillatory inputs. (ABSTRACT TRUNCATED)
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Gong, Letian, Youfang Lin, Shengnan Guo, Yan Lin, Tianyi Wang, Erwen Zheng, Zeyu Zhou, and Huaiyu Wan. "Contrastive Pre-training with Adversarial Perturbations for Check-In Sequence Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4276–83. http://dx.doi.org/10.1609/aaai.v37i4.25546.
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A core step of mining human mobility data is to learn accurate representations for user-generated check-in sequences. The learned representations should be able to fully describe the spatial-temporal mobility patterns of users and the high-level semantics of traveling. However, existing check-in sequence representation learning is usually implicitly achieved by end-to-end models designed for specific downstream tasks, resulting in unsatisfactory generalizable abilities and poor performance. Besides, although the sequence representation learning models that follow the contrastive learning pre-training paradigm have achieved breakthroughs in many fields like NLP, they fail to simultaneously consider the unique spatial-temporal characteristics of check-in sequences and need manual adjustments on the data augmentation strategies. So, directly applying them to check-in sequences cannot yield a meaningful pretext task. To this end, in this paper we propose a contrastive pre-training model with adversarial perturbations for check-in sequence representation learning (CACSR). Firstly, we design a novel spatial-temporal augmentation block for disturbing the spatial-temporal features of check-in sequences in the latent space to relieve the stress of designing manual data augmentation strategies. Secondly, to construct an effective contrastive pretext task, we generate “hard” positive and negative pairs for the check-in sequence by adversarial training. These two designs encourage the model to capture the high-level spatial-temporal patterns and semantics of check-in sequences while ignoring the noisy and unimportant details. We demonstrate the effectiveness and versatility of CACSR on two kinds of downstream tasks using three real-world datasets. The results show that our model outperforms both the state-of-the-art pre-training methods and the end-to-end models.
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Chen, Yue, Peibin Yue, Wenzhen Fu, Weiliang Chen, Kathleen M. Kershaw, Stephen L. Shiao, Marcus A. Tius, Francisco Lopez-Tapia, and James Turkson. "Abstract 2782: Small molecule H182 suppresses Stat3 activation in tumor cells and combines with radiation therapy to block breast tumor growth in mouse syngeneic models." Cancer Research 83, no. 7_Supplement (April 4, 2023): 2782. http://dx.doi.org/10.1158/1538-7445.am2023-2782.
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Abstract Signal transducer and activator of transcription 3 (Stat3) is a latent transcription factor that contributes to tumor cell growth and survival in constitutively-active form in several types of human cancers, and hence, serves as a therapeutic target. The azetidine-based compound, H182 irreversibly binds to Stat3. In cell-free DNA-binding assay, H182 selectively inhibited Stat3 DNA-binding activity (IC50 0.38-0.66 μM) over Stat1 or Stat5 (IC50>15.8 μM) in vitro. In treated pancreatic cancer cells, H182 specifically blocked the association of Stat3 with gp130 and JAK2, and inhibited Stat3 tyrosine phosphorylation and DNA-binding activity. Coimmunoprecipitation and colocalization studies of hemagglutinin (HA)-tagged Stat3 and EGFP-tagged Stat3 expressed in prostate cancer cells showed that treatment with H182 blocked the HA-Stat3:EGFP-Stat3 interactions in intact cells. Immunofluorescence staining with laser-scanning confocal microscopy analysis for the intracellular localization of Stat3 showed treatment of H182 disrupted Stat3 nuclear accumulation and promoted the aggregation of Stat3 at the perinuclear region. H182 consequently suppressed Stat3-dependent transcriptional activity and the expression of Stat3 downstream genes, including Cyclin A, Bcl-2, Cyclin B1, and Mcl-1. Moreover, H182 significantly inhibited the colony survival, migration, and invasion in vitro of breast, pancreatic and prostate cancer cells harboring aberrant Stat3 activation. Significantly, in vivo administration of H182 in combination with radiation induced a strong antitumor response against mouse triple-negative breast cancer in syngeneic models and prolonged survival. Thus, our study provides a novel Stat3 inhibitor with significant antitumor activity against human tumors cancer harboring persistently active STAT3. Citation Format: Yue Chen, Peibin Yue, Wenzhen Fu, Weiliang Chen, Kathleen M. Kershaw, Stephen L. Shiao, Marcus A. Tius, Francisco Lopez-Tapia, James Turkson. Small molecule H182 suppresses Stat3 activation in tumor cells and combines with radiation therapy to block breast tumor growth in mouse syngeneic models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2782.
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Flores-Valdez, Mario Alberto, Andreas Kupz, and Selvakumar Subbian. "Recent Developments in Mycobacteria-Based Live Attenuated Vaccine Candidates for Tuberculosis." Biomedicines 10, no. 11 (October 29, 2022): 2749. http://dx.doi.org/10.3390/biomedicines10112749.
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Vaccination is an excellent approach to stimulating the host immune response and reducing human morbidity and mortality against microbial infections, such as tuberculosis (TB). Bacillus Calmette–Guerin (BCG) is the most widely administered vaccine in the world and the only vaccine approved by the World Health Organization (WHO) to protect against TB. Although BCG confers “protective” immunity in children against the progression of Mycobacterium tuberculosis (Mtb) infection into active TB, this vaccine is ineffective in protecting adults with active TB manifestations, such as multiple-, extensive-, and total-drug-resistant (MDR/XDR/TDR) cases and the co-existence of TB with immune-compromising health conditions, such as HIV infection or diabetes. Moreover, BCG can cause disease in individuals with HIV infection or other immune compromises. Due to these limitations of BCG, novel strategies are urgently needed to improve global TB control measures. Since live vaccines elicit a broader immune response and do not require an adjuvant, developing recombinant BCG (rBCG) vaccine candidates have received significant attention as a potential replacement for the currently approved BCG vaccine for TB prevention. In this report, we aim to present the latest findings and outstanding questions that we consider worth investigating regarding novel mycobacteria-based live attenuated TB vaccine candidates. We also specifically discuss the important features of two key animal models, mice and rabbits, that are relevant to TB vaccine testing. Our review emphasizes that the development of vaccines that block the reactivation of latent Mtb infection (LTBI) into active TB would have a significant impact in reducing the spread and transmission of Mtb. The results and ideas discussed here are only based on reports from the last five years to keep the focus on recent developments.
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Liang, Ge, Zhenglin Ji, Qunhong Zhong, Yong Huang, and Kun Han. "Vector Quantized Variational Autoencoder-Based Compressive Sampling Method for Time Series in Structural Health Monitoring." Sustainability 15, no. 20 (October 13, 2023): 14868. http://dx.doi.org/10.3390/su152014868.
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The theory of compressive sampling (CS) has revolutionized data compression technology by capitalizing on the inherent sparsity of a signal to enable signal recovery from significantly far fewer samples than what is required by the Nyquist–Shannon sampling theorem. Recent advancement in deep generative models, which can represent high-dimension data in a low-dimension latent space efficiently when trained with big data, has been used to further reduce the sample size for image data compressive sampling. However, compressive sampling for 1D time series data has not significantly benefited from this technological progress. In this study, we investigate the application of different architectures of deep neural networks suitable for time series data compression and propose an efficient method to solve the compressive sampling problem on one-dimensional (1D) structural health monitoring (SHM) data, based on block CS and the vector quantized–variational autoencoder model with a naïve multitask paradigm (VQ-VAE-M). The proposed method utilizes VQ-VAE-M to learn the data characteristics of the signal, replaces the “hard constraint” of sparsity to realize the compressive sampling signal reconstruction and thereby does not need to select the appropriate sparse basis for the signal. A comparative analysis against various CS methods and other deep neural network models was performed in both synthetic data and real-world data from two real bridges in China. The results have demonstrated the superiority of the proposed method, with achieving the smallest reconstruction error of 0.038, 0.034 and 0.021, and the highest reconstruction accuracy of 0.882, 0.892 and 0.936 for compression ratios of 4.0, 2.66, and 2.0, respectively.
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Moghaddam, Amir, Joachim Koch, Bethany Annis, and Fred Wang. "Infection of Human B Lymphocytes with Lymphocryptoviruses Related to Epstein-Barr Virus." Journal of Virology 72, no. 4 (April 1, 1998): 3205–12. http://dx.doi.org/10.1128/jvi.72.4.3205-3212.1998.
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ABSTRACT Lymphocryptoviruses (LCVs) naturally infecting Old World nonhuman primates are closely related to the human LCV, Epstein-Barr virus (EBV), and share similar genome organization and sequences, biologic properties, epidemiology, and pathogenesis. LCVs can efficiently immortalize B lymphocytes from the autologous species, but the ability of a given LCV to immortalize B cells from other Old World primate species is variable. We found that LCV from rhesus monkeys did not immortalize human B cells, and EBV did not immortalize rhesus monkey B cells. In this study, baboon LCV could not immortalize human peripheral blood B cells but could readily immortalize rhesus monkey B cells. Thus, efficient LCV-induced B-cell immortalization across distant Old World primate species appears to be restricted by a species-specific block. To further characterize this species restriction, we first cloned the rhesus monkey LCV major membrane glycoprotein and discovered that the binding epitope for the EBV receptor, CD21, was highly conserved. Stable infections of human B cells with recombinant amplicons packaged in rhesus monkey or baboon LCV envelopes were also consistent with a species-restricted block occurring after virus binding and penetration. Transient infections of human B cells with simian LCV resulted in latent LCV EBNA-2 gene expression and activation of cell CD23 gene expression. EBV-immortalized human B cells could be coinfected with baboon LCV, and the simian virus persisted and replicated in human B cells. Thus, several lines of evidence indicate that the species restriction for efficient LCV-induced B-cell immortalization occurs beyond virus binding and penetration. This has important implications for the study of LCV infection in Old World primate models and for human xenotransplantation where simian LCVs may be inadvertently introduced into humans.
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Li, Anqi, Yuzhou Chang, No-Joon Song, Xingjun Wu, Dongjun Chung, Brian P. Riesenberg, Maria Velegraki, et al. "Selective targeting of GARP-LTGFβ axis in the tumor microenvironment augments PD-1 blockade via enhancing CD8+ T cell antitumor immunity." Journal for ImmunoTherapy of Cancer 10, no. 9 (September 2022): e005433. http://dx.doi.org/10.1136/jitc-2022-005433.
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BackgroundImmune checkpoint blockade (ICB) has revolutionized cancer immunotherapy. However, most patients with cancer fail to respond clinically. One potential reason is the accumulation of immunosuppressive transforming growth factor β (TGFβ) in the tumor microenvironment (TME). TGFβ drives cancer immune evasion in part by inducing regulatory T cells (Tregs) and limiting CD8+ T cell function. Glycoprotein-A repetitions predominant (GARP) is a cell surface docking receptor for activating latent TGFβ1, TGFβ2 and TGFβ3, with its expression restricted predominantly to effector Tregs, cancer cells, and platelets.MethodsWe investigated the role of GARP in human patients with cancer by analyzing existing large databases. In addition, we generated and humanized an anti-GARP monoclonal antibody and evaluated its antitumor efficacy and underlying mechanisms of action in murine models of cancer.ResultsWe demonstrate that GARP overexpression in human cancers correlates with a tolerogenic TME and poor clinical response to ICB, suggesting GARP blockade may improve cancer immunotherapy. We report on a unique anti-human GARP antibody (named PIIO-1) that specifically binds the ligand-interacting domain of all latent TGFβ isoforms. PIIO-1 lacks recognition of GARP-TGFβ complex on platelets. Using human LRRC32 (encoding GARP) knock-in mice, we find that PIIO-1 does not cause thrombocytopenia; is preferentially distributed in the TME; and exhibits therapeutic efficacy against GARP+ and GARP- cancers, alone or in combination with anti-PD-1 antibody. Mechanistically, PIIO-1 treatment reduces canonical TGFβ signaling in tumor-infiltrating immune cells, prevents T cell exhaustion, and enhances CD8+ T cell migration into the TME in a C-X-C motif chemokine receptor 3 (CXCR3)-dependent manner.ConclusionGARP contributes to multiple aspects of immune resistance in cancer. Anti-human GARP antibody PIIO-1 is an efficacious and safe strategy to block GARP-mediated LTGFβ activation, enhance CD8+ T cell trafficking and functionality in the tumor, and overcome primary resistance to anti-PD-1 ICB. PIIO-1 therefore warrants clinical development as a novel cancer immunotherapeutic.
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He, Haoyang, Jiangning Zhang, Hongxu Chen, Xuhai Chen, Zhishan Li, Xu Chen, Yabiao Wang, Chengjie Wang, and Lei Xie. "A Diffusion-Based Framework for Multi-Class Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 8472–80. http://dx.doi.org/10.1609/aaai.v38i8.28690.
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Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image’s semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset. Code will be available at https://lewandofskee.github.io/projects/diad.
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Wenta, Marta, Christian M. Grams, Lukas Papritz, and Marc Federer. "Linking Gulf Stream air–sea interactions to the exceptional blocking episode in February 2019: a Lagrangian perspective." Weather and Climate Dynamics 5, no. 1 (February 8, 2024): 181–209. http://dx.doi.org/10.5194/wcd-5-181-2024.
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Abstract. The development of atmospheric blocks over the North Atlantic–European region can lead to extreme weather events like heat waves or cold air outbreaks. Despite their potential severe impact on surface weather, the correct prediction of blocking lifecycles remains a key challenge in current numerical weather prediction (NWP) models. Increasing evidence suggests that latent heat release in cyclones, the advection of cold air (cold air outbreaks, CAOs) from the Arctic over the North Atlantic, and associated air–sea interactions over the Gulf Stream are key processes contributing to the onset, maintenance, and persistence of such flow regimes. To better understand the mechanism connecting air–sea interactions over the Gulf Stream with changes in the large-scale flow, we focus on an episode between 20 and 27 February 2019, when a quasi-stationary upper-level ridge was established over western Europe accompanied by an intensified storm track in the northwestern North Atlantic. During that time, a record-breaking winter warm spell occurred over western Europe bringing temperatures above 20 ∘C to the United Kingdom, the Netherlands, and northern France. The event was preceded and accompanied by the development of several rapidly intensifying cyclones that originated in the Gulf Stream region and traversed the North Atlantic. To explore the mechanistic linkage between the formation of this block and air–sea interactions over the Gulf Stream, we adopt a Lagrangian perspective, using kinematic trajectories. This allows us to study the pathways and transformations of air masses that form the upper-level potential vorticity anomaly and interact with the ocean front. We establish that more than one-fifth of these air masses interact with the Gulf Stream in the lower troposphere, experiencing intense heating and moistening over the region due to the frequent occurrence of CAOs behind the cold front of the cyclones. Trajectories moistened by the advection of cold air over a warm ocean by one cyclone later ascend into the upper troposphere with the ascending airstream of a subsequent cyclone, fueled by the strong surface fluxes. These findings highlight the importance of CAOs in the Gulf Stream region, indicating that their intense coupling between the ocean and atmosphere plays a role in block development. Additionally, they provide a mechanistic pathway linking air–sea interactions in the lower troposphere and the upper-level flow.
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Schmitt, Clemens A., Maja Milanovic, Henry Daebritz, Zhen Zhao, and Andreas Trumpp. "Chemotherapy-Induced Senescence Reprograms Lymphoma and Leukemia Cells into Latent Cancer Stem Cells That Are Susceptible to Conceptually Novel Treatments." Blood 124, no. 21 (December 6, 2014): 4788. http://dx.doi.org/10.1182/blood.v124.21.4788.4788.
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Abstract Cellular senescence is a stress-responsive cell-cycle arrest program that terminates further expansion of (pre-)malignant cells. Senescence imposes a tumor-suppressive barrier in lymphomagenesis, and acts as an effector program in response to chemotherapy in various hematological malignancies. Interestingly, many key signaling components of the senescence machinery also operate as critical regulators of stem cell functions (collectively termed ‘stemness’), among them the p53 axis and control of lysine 9 trimethylation at histone H3 (H3K9). We investigated here in vitro and in vivo whether chemotherapy-induced senescence may change stem cell-related functionalities in aggressive B-cell lymphomas and acute leukemias of murine and human origin. Gene expression and functional analyses comparing senescent vs. non-senescent Eµ-myc transgenic B-cell lymphomas unveiled massive upregulation of an adult tissue stem cell signature, activated Wnt signaling, and de novo expression of distinct stem cell markers in senescence. Utilizing Suv39h1- (an H3K9-targeting methyltransferase) and p53-based genetically switchable ‘matched pair’ on/off models of senescence to mimic spontaneous escape (‘previously senescent’, PS), we found PS cells to re-enter the cell-cycle with strongly enhanced and Wnt-dependent clonogenic growth when compared to their equally chemotherapy-exposed but never senescent (NS) counterparts.In vivo, these PS lymphoma cells presented with a much higher tumor initiation potential, which was neutralized upon pharmacological or genetic Wnt inhibition. Strikingly, temporary enforcement of senescence in a p53-regulatable leukemia model reprogrammed non-stem bulk PS leukemia cells into leukemia-initiating stem cells, whose de novo self-renewing potential was also Wnt-dependent. In contrast, equally chemotherapy-exposed NS bulk cells (i.e. p53 always ‘off’) did not acquire stemness potential. Our data, further supported by consistent findings in various human cancer cell lines and primary patient-derived lymphoma and leukemia samples, characterize senescence as a fundamentally reprogrammed cellular condition, and uncover senescence-associated stemness as an unexpected, cell-autonomous feature that exerts its detrimental potential upon escape from the cell-cycle block. These findings raise concerns about the long-term benefit of senescence-inducing cancer therapies, and provide new mechanistic insights into the plasticity of the “cancer stem cell” condition. In turn, we present synthetic lethal targeting of senescence-associated stemness as a conceptually novel, outcome-improving treatment strategy in lymphoma, leukemia and possibly other cancer entities as well. Disclosures No relevant conflicts of interest to declare.
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Mollereau, B., M. Deckert, O. Déas, F. Rieux-Laucat, F. Hirsch, A. Bernard, A. Fischer, et al. "CD2-induced apoptosis in activated human peripheral T cells: a Fas-independent pathway that requires early protein tyrosine phosphorylation." Journal of Immunology 156, no. 9 (May 1, 1996): 3184–90. http://dx.doi.org/10.4049/jimmunol.156.9.3184.
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Abstract Short-term activated peripheral T lymphocytes are susceptible to apoptotic cell death triggered by CD2 mAbs. The aim of this study was to examine whether the CD2-mediated pathway of apoptosis is linked to the Fas death pathway, as this is the case for CD3/TCR-triggered apoptosis in several models of T cells. Using T lymphocytes from patients harboring Fas gene mutations and displaying a profound defect in Fas signaling of cell death, we show that CD2- (but not CD3-) mediated apoptosis still proceeds normally. In normal activated T cells, CD3-mediated apoptosis is prevented by reagents that block the Fas/Fas-ligand interaction, namely soluble M3 (an antagonistic anti-Fas mAb) and soluble human Fas.Fc, a fusion protein able to bind released Fas-ligand. In contrast, CD2 signaling of apoptosis resists these blocking agents. Neither new protein synthesis nor the activation of calcineurin was required for CD2- and Fas-mediated apoptosis, suggesting that latent cytoplasmic "death" molecules were activated upon stimulation of the cells. In both cases, protein tyrosine kinases were transiently activated, as is exemplified by the autophosphorylation and exokinase activity of p56lck, yielding overlapping yet nonidentical profiles of protein tyrosine phosphorylation. Pretreating the cells with herbimycin A, before the addition of the apoptotic stimuli, almost completely inhibited CD2 transmembrane signaling of apoptosis, but left intact Fas-induced apoptosis. Our data suggest that CD2 is a Fas-independent cell death pathway that might contribute directly to the elimination of T cells expanding during an immune reaction.
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Cofre-Martel, Sergio, Enrique Lopez Droguett, and Mohammad Modarres. "Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables." Shock and Vibration 2021 (May 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/9937846.
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Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.
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Nawalany, Marek, and Grzegorz Sinicyn. "Scale problems in assessment of hydrogeological parameters of groundwater flow models." Geologos 21, no. 3 (September 1, 2015): 179–85. http://dx.doi.org/10.1515/logos-2015-0012.
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Abstract An overview is presented of scale problems in groundwater flow, with emphasis on upscaling of hydraulic conductivity, being a brief summary of the conventional upscaling approach with some attention paid to recently emerged approaches. The focus is on essential aspects which may be an advantage in comparison to the occasionally extremely extensive summaries presented in the literature. In the present paper the concept of scale is introduced as an indispensable part of system analysis applied to hydrogeology. The concept is illustrated with a simple hydrogeological system for which definitions of four major ingredients of scale are presented: (i) spatial extent and geometry of hydrogeological system, (ii) spatial continuity and granularity of both natural and man-made objects within the system, (iii) duration of the system and (iv) continuity/granularity of natural and man-related variables of groundwater flow system. Scales used in hydrogeology are categorised into five classes: micro-scale – scale of pores, meso-scale – scale of laboratory sample, macro-scale – scale of typical blocks in numerical models of groundwater flow, local-scale – scale of an aquifer/aquitard and regional-scale – scale of series of aquifers and aquitards. Variables, parameters and groundwater flow equations for the three lowest scales, i.e., pore-scale, sample-scale and (numerical) block-scale, are discussed in detail, with the aim to justify physically deterministic procedures of upscaling from finer to coarser scales (stochastic issues of upscaling are not discussed here). Since the procedure of transition from sample-scale to block-scale is physically well based, it is a good candidate for upscaling block-scale models to local-scale models and likewise for upscaling local-scale models to regional-scale models. Also the latest results in downscaling from block-scale to sample scale are briefly referred to.
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Ugale, Amol Sanjay, Gudmundur Logi Norddahl, Martin Wahlestedt, Petter Säwén, Pekka Jaako, Cornelis J. H. Pronk, Shamit Soneji, Jorg Cammenga, and David Bryder. "Hematopoietic Stem Cells Are Intrinsically Protected Against MLL-ENL Mediated Transformation." Blood 124, no. 21 (December 6, 2014): 839. http://dx.doi.org/10.1182/blood.v124.21.839.839.
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Abstract Studies on the developmental pathways of hematopoietic stem cells (HSCs) have led to roadmaps of differentiation and resulted in key information concerning lineage relationships and restriction points in the blood system. This knowledge is also central to understand the etiology of acute myeloid leukemia (AML), where recent work has proposed that the heterogeneity and aggressiveness of AML can associate with the developmental stage of transformation. Balanced chromosomal translocations that result in fusion proteins with aberrant transcriptional regulatory activities are frequent initiating events in acute myeloid leukemia, and a prototype family of such chimeric transcription factors is represented by fusions involving the mixed lineage leukemia-1 (MLL1) gene. Previous work using mouse models have suggested that at some stage of normal differentiation there is a loss of competence to induce AML. However discrepancies exists between these mouse models concerning the target cells of MLL fusion genes. While it is clear that cells can lose competence for leukemic transformation as part of their normal differentiation, the question remains whether the most primitive HSCs are always imbued with leukemogenic competency as part of their normal biology. To address this, we developed a Doxycycline inducible transgenic mouse model of the human chimeric transcription factor Mixed Lineage Leukemia-Eleven Nineteen Leukemia (MLL-ENL). Prospective isolations of candidate leukemia-initiating cells followed by adoptive transfers allowed us to detail leukemia-initiation and competence throughout the hematopoietic hierarchy. We show that AML can origin from multiple HPC subsets with intrinsic granulocytic/monocytic potential. Closely related myeloid progenitors displayed distinct leukemic- and functional capacity in response to physiological levels of MLL-ENL, highlighting the importance of a careful prospective isolation of progenitor populations. AML could also develop efficiently from common lymphoid progenitors, supporting a latent myeloid potential of these cells. By contrast, early commitment to the megakaryocytic/erythroid lineages was incompatible with leukemic development. By contrast, disease failed to arise from the most primitive progenitor subsets, including HSCs. Investigations of the immediate transcriptional responses to MLL-ENL showed evidence for a block in differentiation in both myeloid progenitors and HSCs, while MLL-ENL restricted cell cycle progression uniquely in HSCs. Our study highlights how an oncogene can exert unique functions depending on the developmental position of its cellular targets and demonstrate the existence of a mechanism, operational at the level of immature HSCs/progenitors, which act to prevent leukemic development. Figure 1 Graphical abstract Figure 1. Graphical abstract Disclosures No relevant conflicts of interest to declare.
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Sridhar, Dhanya, Hal Daumé, and David Blei. "Heterogeneous Supervised Topic Models." Transactions of the Association for Computational Linguistics 10 (2022): 732–45. http://dx.doi.org/10.1162/tacl_a_00487.
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Abstract Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to both uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.
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S. Andrei, Mihnea, Sujit K. Ghosh, and Jian Zou. "Dynamic Correlation Multivariate Stochastic Volatility Black-Litterman With Latent Factors." International Journal of Statistics and Probability 10, no. 2 (January 12, 2021): 1. http://dx.doi.org/10.5539/ijsp.v10n2p1.
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In finance, it is often of interest to study market volatility for portfolios that may consist of a large number of assets using multivariate stochastic volatility models. However, such models, though useful, do not usually incorporate investor views that might be available. In this paper we introduce a novel hierarchical Bayesian methodology of modeling volatility for a large portfolio of assets that incorporates investor’s personal views of the market via the Black-Litterman (BL) model. We extend the scope and use of BL models by using it within a multivariate stochastic volatility model based on latent factors for dimensionality reduction but allows for time varying correlations. Detailed derivations of MCMC algorithm are provided with an illustration with S&P500 asset returns. Moreover, sensitivity analysis for the confidence levels that the investor has in their personal views is also explored. Numerical results show that the proposed method provides flexible interpretation based on the investor’s uncertainty in personal beliefs, and converges to the empirical sample estimate when their confidence level of the market becomes weak.
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Ensinger, Katharina, Sebastian Ziesche, and Sebastian Trimpe. "Learning Hybrid Dynamics Models with Simulator-Informed Latent States." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 11892–900. http://dx.doi.org/10.1609/aaai.v38i11.29075.
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Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this problem is to train recurrent models. However, predictions with these models are often not physically meaningful. Further, they suffer from deteriorated behavior over time due to accumulating errors. Often, simulators building on first principles are available being physically meaningful by design. However, modeling simplifications typically cause inaccuracies in these models. Consequently, hybrid modeling is an emerging trend that aims to combine the best of both worlds. In this paper, we propose a new approach to hybrid modeling, where we inform the latent states of a learned model via a black-box simulator. This allows to control the predictions via the simulator preventing them from accumulating errors. This is especially challenging since, in contrast to previous approaches, access to the simulator's latent states is not available. We tackle the task by leveraging observers, a well-known concept from control theory, inferring unknown latent states from observations and dynamics over time. In our learning-based setting, we jointly learn the dynamics and an observer that infers the latent states via the simulator. Thus, the simulator constantly corrects the latent states, compensating for modeling mismatch caused by learning. To maintain flexibility, we train an RNN-based residuum for the latent states that cannot be informed by the simulator.
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Gadetsky, Artyom, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, and Dmitry Vetrov. "Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10126–35. http://dx.doi.org/10.1609/aaai.v34i06.6572.
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Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates. Modern variance reduction techniques mostly consider categorical distributions and have limited applicability when the number of possible outcomes becomes large. In this work, we consider models with latent permutations and propose control variates for the Plackett-Luce distribution. In particular, the control variates allow us to optimize black-box functions over permutations using stochastic gradient descent. To illustrate the approach, we consider a variety of causal structure learning tasks for continuous and discrete data. We show that our method outperforms competitive relaxation-based optimization methods and is also applicable to non-differentiable score functions.
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Falcke, Heino, Sera Markoff, Peter L. Biermann, Thomas P. Krichbaum, Fulvio Melia, Eric Agol, and Geoffrey Bower. "Sgr A*: Observations, Models, and Imaging of the event horizon with VLBI." Symposium - International Astronomical Union 205 (2001): 28–31. http://dx.doi.org/10.1017/s0074180900220330.
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We show and discuss results and prospects of high-resolution imaging of the supermassive black hole candidate Sgr A*. We also briefly review the latest observational and theoretical progress for this source. The latest millimeter-VLBI observations show compact radio emission from within a region of about 15 Schwarzschild radii. This compact component is most likely responsible for the so-called sub-mm bump in the spectrum and perhaps even for the recently discovered circular polarization discovered up to 43 GHz and some X-ray emission through synchrotron self-Compton emission. Most importantly, however, the sub-mm emission from Sgr A* opens the door to observe, for the first time, the event horizon of a black hole directly with VLBI at sub-mm wavelengths.
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Soulos, Paul, and Leyla Isik. "Disentangled deep generative models reveal coding principles of the human face processing network." PLOS Computational Biology 20, no. 2 (February 26, 2024): e1011887. http://dx.doi.org/10.1371/journal.pcbi.1011887.
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Despite decades of research, much is still unknown about the computations carried out in the human face processing network. Recently, deep networks have been proposed as a computational account of human visual processing, but while they provide a good match to neural data throughout visual cortex, they lack interpretability. We introduce a method for interpreting brain activity using a new class of deep generative models, disentangled representation learning models, which learn a low-dimensional latent space that “disentangles” different semantically meaningful dimensions of faces, such as rotation, lighting, or hairstyle, in an unsupervised manner by enforcing statistical independence between dimensions. We find that the majority of our model’s learned latent dimensions are interpretable by human raters. Further, these latent dimensions serve as a good encoding model for human fMRI data. We next investigate the representation of different latent dimensions across face-selective voxels. We find that low- and high-level face features are represented in posterior and anterior face-selective regions, respectively, corroborating prior models of human face recognition. Interestingly, though, we find identity-relevant and irrelevant face features across the face processing network. Finally, we provide new insight into the few "entangled" (uninterpretable) dimensions in our model by showing that they match responses in the ventral stream and carry information about facial identity. Disentangled face encoding models provide an exciting alternative to standard “black box” deep learning approaches for modeling and interpreting human brain data.
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Henderson, R. C., P. Williams, J. Gabbidon, S. Farrelly, O. Schauman, S. Hatch, G. Thornicroft, D. Bhugra, and S. Clement. "Mistrust of mental health services: ethnicity, hospital admission and unfair treatment." Epidemiology and Psychiatric Sciences 24, no. 3 (March 17, 2014): 258–65. http://dx.doi.org/10.1017/s2045796014000158.
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Aims.To explore the role of psychiatric admission, diagnosis and reported unfair treatment in the relationship between ethnicity and mistrust of mental health services.Methods.The Mental Illness-Related Investigations on Discrimination (MIRIAD) study was a cross-sectional study of 202 individuals using secondary mental health services in South London. Two structural equation models were estimated, one using Admission (whether admitted to hospital for psychiatric treatment in the past 5 years) and one using involuntary admission to hospital in the past 5 years.Results.Increased mistrust was directly associated with the latent variable ‘unfair treatment by mental health services and staff’ and with Black or mixed ethnicity in both models. Those with a diagnosis of schizophrenia spectrum (as compared to depression and bipolar disorder) had a lower average score on the latent variable, suggesting that on average they reported less unfair treatment. We found evidence of increased reporting of unfair treatment by those who had an admission in the past 5 years, had experienced involuntary admission, and for people of Black of mixed Black and White ethnicity.Conclusions.Neither prevalence of schizophrenia spectrum nor rates of hospital admission explained the greater mistrust of mental health services found among people of Black and mixed Black and White ethnicity compared with White ethnicity. Rather, people of Black and mixed Black and white ethnicity may be more likely to experience unfair treatment, generating mistrust; furthermore, this group is more likely to express mistrust even after accounting for reporting of unfair treatment by mental health services and staff.
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Namatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš, and Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning." Information Technology and Management Science 26 (November 30, 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.
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Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.
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Yan, Jinpei, Yong Qi, and Qifan Rao. "LSTM-Based Hierarchical Denoising Network for Android Malware Detection." Security and Communication Networks 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/5249190.
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Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks) to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM) for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.
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Kaldunski, Pawel, and Leon Kukielka. "Numerical Analysis and Simulation of Drawpiece Forming Process by Finite Element Method." Applied Mechanics and Materials 474 (January 2014): 153–58. http://dx.doi.org/10.4028/www.scientific.net/amm.474.153.
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This paper shows the application of an incremental modelling and numerical solution of the contact problem between movable elastic or rigid tool and elastic/visco-plastic bodies developed in [ to the numerical simulation of drawpiece forming process for the case of rigid tool (punch and die block) and elastic-plastic body (drawpiece). Also the current state of knowledge of the subject matter of the drawing process, modelling and simulation of this process is discussed. The latest and unconventional methods of drawpiece forming have been presented. The important factors determining the proper formation of drawpiece and the ways of their determination have been described. Three types of material models have been used: elastic-plastic model with the linear hardening, elastic-plastic model with the power-law hardening and Frederic's Barlat model which takes into account the anisotropy in three main directions and three tangents. For an example of selected simulations, dependence of punch force from its displacement for different types of die blocks has been presented.
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Wöber, Wilfried, Lars Mehnen, Manuel Curto, Papius Dias Tibihika, Genanaw Tesfaye, and Harald Meimberg. "Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning." Applied Sciences 12, no. 6 (March 20, 2022): 3158. http://dx.doi.org/10.3390/app12063158.
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The biological investigation of a population’s shape diversity using digital images is typically reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that 80% of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where 16% of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model.
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Williams, Harold, and M. A. J. Piasecki. "The Cold Spring Melange and a possible model for Dunnage–Gander zone interaction in central Newfoundland." Canadian Journal of Earth Sciences 27, no. 8 (August 1, 1990): 1126–34. http://dx.doi.org/10.1139/e90-117.
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Structural relationships at Cold Spring Pond and the recognition of ophiolitic melange bear on the important questions of timing and style of structural superpositioning of Dunnage Zone rocks above Gander Zone rocks in central Newfoundland. The latest models emphasize ductile shear boundaries and orogen-parallel movements. Previous models proposed west-to-east or head-on obduction of Dunnage ophiolitic rocks across the Gander Zone.At the Dunnage (Exploits Subzone) – Gander (Meelpaeg Subzone) boundary at Cold Spring Pond, discrete, outcrop-size ultramafic blocks and smaller quartzite blocks are randomly distributed, and they are surrounded by, or are embedded in, homogeneous black graphitic shale or phyllite. The ultramafic blocks are typical of nearby Early Ordovician Dunnage ophiolite suites, the quartzite blocks are typical of adjacent Early Ordovician or earlier Gander clastic rocks, and the matrix black shales are similar to those of Middle or Early Ordovician age that occur throughout central Newfoundland. This chaotic mixture of almost coeval lithologies at Cold Spring Pond is interpreted as an olistostromal melange; the Cold Spring Melange. It resembles melanges that are dated as Ordovician elsewhere in Newfoundland.The Cold Spring Melange is overprinted by the full range of structures and metamorphic effects evident in adjacent rocks of the Exploits (Dunnage) and Meelpaeg (Gander) subzones. These include the development of lineations, cleavages, schistosities, zones of ductile shearing, regional metamorphism, and contact metamorphism. The oldest of these effects are interpreted as Silurian, based on isotopic dating in southern Newfoundland.The formation of olistostromal, ophiolitic melange implies disruption of the oceanic tract (Exploits Subzone of the Dunnage Zone), and in the case of the Cold Spring example, juxtapositioning or transport of Exploits Subzone ophiolite suites against or across the supracrustal rocks of the Meelpaeg Subzone (Gander Zone). The age and provenance of Cold Spring components, lack of post-Ordovician components, overprinting structural relationships, and comparison with other Newfoundland melanges all support an Ordovician age of formation. Overprinting relationships indicate that major ductile shears at other Dunnage–Gander zone boundaries postdate initial Dunnage–Gander superpositioning.
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Yeo, Cheng Hong, A. Jariwala, N. Pourgiezis, and A. Pillai. "Assessing the Accuracy of Bone Resection by Cutting Blocks in Patient-Specific Total Knee Replacements." ISRN Orthopedics 2012 (May 20, 2012): 1–4. http://dx.doi.org/10.5402/2012/509750.
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Introduction. The key to a successful total knee arthroplasty (TKA) is the restoration of the mechanical axis with balanced flexion and extension gaps. Patient-specific cutting block technique has been the latest development in total knee arthroplasty. This technique uses a magnetic resonance image (MRI) of the patient's symptomatic knee to create bone models and cutting jigs. This study was designed to evaluate the intraoperative accuracy of the patient-specific cutting block as compared to the preoperative template. Methods. Visionaire (Smith and Nephew, Genesis 2 Knee Arthroplasty) patient-specific TKA was used in all patients. An independent research officer was responsible for measuring all the resected articular surfaces of femur and tibia during surgery and compared it to the cutting block manufactured according to the preoperative template. Seven different measurements from each patient were obtained; four different measurements from the femur and three from the tibia were recorded. The differences between the actual resections made intraoperatively, as compared to the original pre-operative templates, were noted as the error. The surgical team was blinded to the measurements of the resections and the calculations of the errors. Results. Twenty-six Visionaire patient-specific TKA were included in the study. A total of 182 readings of bone resections made intraoperatively (seven for each patient). Eighty five percent of all collected readings were below the error margin of ≤1.5 mm. Size of resection had no effect on the error margin. All patients had satisfactory post-operative alignment, and at discharge all 26 patients achieved more than 90° of knee flexion. Conclusion. This observational study provides evidence that patient-specific TKA is comparable to other forms of TKA and may have some distinct advantages. In addition, we have shown that the cutting blocks are able to consistently deliver accurate cuts that are reproducible. We recommend intra-operative measurement of the bone resection and its comparison with the cutting block as a routine surgical step to confirm the MRI scan data, block placement, and instant validation of the bony resection before implant placement.