Artículos de revistas sobre el tema "Algorithmes de mariage"

Résumé De Santis (Gustavo), Livi Bacci (Massimo). - La reproduction des populations : une méthode de décomposition et d'estimation Dans cet article, on montre comment, sous certaines hypothèses peu restrictives, on peut décomposer une mesure classique de la reproduction Ro en une série de composantes multiplicatives, chacune reflétant une dimension spécifique (intensité ou fréquence) de la nuptialité, de la mortalité, de la fécondité, et si l'on veut, de la migration. Cette idée peut être exploitée de deux façons : - soit on peut obtenir des données ou des estimations relativement complètes des processus démographiques, à partir, par exemple, d'un recensement ou d'un état des âmes où il est possible d'appliquer la méthode des enfants présents au ménage et de calculer la proportion des célibataires aux différents âges ; - soit, au contraire, on ne peut pas calculer ces éléments, comme, par exemple, quand on obtient les données par reconstitution nominative des familles. Dans ce cas, il est préférable de suivre une méthode légèrement différente. Elle consiste à estimer le nombre moyen de filles naissant d'une génération de femmes soumises à la mortalité, à la migration et au mariage. En multipliant cette valeur par la durée de mariage fécond, on obtient une estimation de l'effectif total de la génération des filles et donc de la valeur de Ro. Quelques algorithmes simples sont proposés pour le calcul de l'âge moyen à l'accouchement et pour l'estimation, à cet âge, de la proportion de femmes mariées. Une application aux cas de l'Angleterre, de la France et de l'Allemagne révèle l'existence et la nature des différents régimes démographiques des XVIIe et XVIIIe siècles.

Résumé De Santis (Gustavo), Livi Bacci (Massimo). - La reproduction des populations: une méthode de décomposition et d'estimation Dans cet article, on montre comment, sous certaines hypothèses peu restrictives, on peut décomposer une mesure classique de la reproduction Ro en une série de composantes multiplicatives, chacune reflétant une dimension spécifique (intensité ou fréquence) de la nuptialité, de la mortalité, de la fécondité, et si l'on veut, de la migration. Cette idée peut être exploitée de deux façons : - soit on peut obtenir des données ou des estimations relativement complètes des processus démographiques, à partir, par exemple, d'un recensement ou d'un état des âmes où il est possible d'appliquer la méthode des enfants présents au ménage et de calculer la proportion des célibataires aux différents âges ; - soit, au contraire, on ne peut pas calculer ces éléments, comme, par exemple, quand on obtient les données par reconstitution nominative des familles. Dans ce cas, il est préférable de suivre une méthode légèrement différente. Elle consiste à estimer le nombre moyen de filles naissant d'une génération de femmes soumises à la mortalité, à la migration et au mariage. En multipliant cette valeur par la durée de mariage fécond, on obtient une estimation de l'effectif total de la génération des filles et donc de la valeur de Ro. Quelques algorithmes simples sont proposés pour le calcul de l'âge moyen à l'accouchement et pour l'estimation, à cet âge, de la proportion de femmes mariées. Une application aux cas de l'Angleterre, de la France et de l'Allemagne révèle l'existence et la nature des différents régimes démographiques des XVIIe et XVIIIe siècles.

Algorithmic versions of stand density management diagrams (SDMDs) were developed for natural and managed black spruce (Picea mariana (Mill.) B.S.P.) stands. Specifically, the IBM-compatible PC-based algorithms (1) graphically illustrate site-specific size-density trajectories for eight user-specified initial density regimes, (2) given (1), calculate and subsequent tabulate periodic yield estimates (mean dominant height, density, mean volume, total volume, total merchantable volume, quadratic mean diameter, and basal area), and (3) given (2), graphically illustrate empirically-derived yield production curves for total merchantable volume ha−1 and stems m−3 with user-specified operability criteria superimposed. Instructions on acquiring the executable algorithmic versions including the required graphical subroutines via the Internet are described. Currently, the algorithms are restricted in applicability to central insular Newfoundland. Key words: stand density management diagrams, black spruce, algorithms, microcomputer, World-Wide Web (WWW), hypertext browser, file transfer protocol (FTP).

Abstract A regional-specific algorithmic stand density management diagram was developed for managed upland black spruce (Picea mariana [Mill.] B.S.P.) stands applicable to Newfoundland, New Brunswick, Quebec, and Ontario. Specifically, the IBM-compatible PC-based algorithm (1) graphically illustrates site-specific size-density trajectories for 8 user-specified initial density regimes, (2) calculates and subsequently tabulates periodic yield estimates from 5-50 yr by 5 yr intervals for mean dominant height, density, mean volume, total volume, total merchantable volume, quadratic mean diameter, and basal area, (3) given (2), graphically illustrates empirically derived yield production curves for total merchantable volume per hectare and number of stems per cubic meter with user-specified operability criteria superimposed, and (4) given (3), calculates and subsequently tabulates the minimum time required to attain operability status. The utility of the algorithm is demonstrated within the context of determining site-specific initial densities required to attain user-specified operability criteria. Procedures for acquiring the executable version via the Internet are also included. North. J. Appl. For. 15(2):94-97.

AbstractThe paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air–sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air–sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints—are the community-standard precipitation and surface radiation products pairwise compatible?

Daster Hits pontianak has a need to expand its market share and manage valid data systems. The sales system currently used is traditional sellers and social media such as Instagram and WhatsApp, therefore the interaction between the store and customers is still limited, recording of transactions is still manual using a transaction book, and if customers want to find products they have to scroll Instagram or come directly to the store. The purpose of this research is to apply the CodeIgniter framework to build an online shop website which is expected to help the shop in promoting the product, manage product data, and expedite the product sales process. The research method used in this research is Design Science Research and the design method uses Extreme Programming. The system modeling used is the Unified Modeling Language (UML) and the software testing method uses white-box testing. The advantages of Daster Hits Pontianak Online Shop website are the features such as managing user profiles, product sales, detailed product information, online product orders and payment confirmation, searches using sequential search algorithms, managing product stocks using the Apriori algorithm, and notifications using push notifications. The process of making this program uses the PHP programming language and the MariaDB database.

The hypothesis underlying this article is that new documentary practices potentially enable new forms of mediation allowing all interactors to experience complexity and deal with contingency in a world of polyvocality through multilayered configurations. An exploration of the Korsakow documentary Racing Home (Marianne McMahon and Phil Hoffman, 2014) brings into focus the epistemological and ontological status of these assemblages. A central issue is the question of how significantly specific modes of editing in Korsakow affect the overall experience for both the authoring instances and for the user-interactor: not only the authorship is shared between the interrelated agents, but also the heterogeneity of materials from various sources adds to the complexity of the assemblages. Central research questions include the role that polyvocality and algorithmic editing play in Korsakow, its specific embracement of contingency and its probing of nonlinear narratives. The answers provided through the analysis of the case study lead to the conclusion that Korsakow is more than just a tool to promote a special purpose and/or a platform to distribute material. Rather, Korsakow is best seen as a methodology to approach complex matters and provide multiple affective and cognitive ways to respond to them.

Abstract Cervical cancer is one of the most common cancers in women. Despite progress in prevention through Human Papilloma Virus [HPV] vaccination and success in early detection of cervical cancer through cytologic screening and HPV detection, there remains an unequal burden in low-resource settings in developed and developing countries. Applying novel methylation-based detection methods, we validated a panel of three human methylated genes (ZNF516, FKBP6 and INTS1) on discarded cervical liquid cytology samples from clinical laboratories in the United States during the development of the CervicalMethDx test. We hypothesized that the CervicalMethDx test can identify HPV positive women most likely to be diagnosed with Cervical Intraepithelial Neoplasia (CIN) grades 2 and 3 by anatomic pathologists, before they are referred to colposcopy-driven biopsies. We assessed DNA methylation by Quantitative Real Time Methylation Specific PCR (QMSP) analysis of sodium bisulfite-modified genomic DNA. Primers and probes were previously designed to specifically amplify the promoters of the 3 genes of interest and the promoter of a reference gene, β-actin, to assess DNA input. We performed blinded retrospective studies on well-characterized, discarded, HPV-positive clinical samples in PreservCyt sample transport media (Thin Prep, Hologic), comparing DNA methylation levels in samples from CIN2 and CIN3 cases (346), and 120 controls without evidence of Intraepithelial Lesions or Malignancy (NILM) as an endpoint. Our results showed that the CervicalMethDx test can correctly classify 96% of CIN2 (n=197) samples with 93% Sensitivity, 100% Specificity, and an AUC of 0.96 as well as 95% of CIN3 (n=149) samples with 91% Sensitivity, 100% Specificity, and an AUC of 0.96. Moreover, the assay correctly classified 94% of CIN2-CIN3 samples combined (n=346) with 92% Sensitivity, 100% Specificity, an AUC of 0.96, and a 100% positive predictive value (PPV), when compared to samples with NILM (n=120). The CervicalMethDx test high PPV supports the addition of this low-cost molecular test to cervical cancer screening algorithms worldwide. Our results suggest that the CervicalMethDx test is a new and valuable tool to stratify HPV positive women prior to colposcopy-driven biopsies in developed countries and ablative treatment in developing countries, most of which are unnecessary. These results warrant further evaluation of the CervicalMethDX test in prospective, population-based studies, assessing the use of precision DNA methylation algorithms to triage women before colposcopy-driven biopsies or ablative treatments worldwide. Citation Format: Laura Palmieri, Fernando T. Zamuner, Dieila Giomo De Lima, Keerthana Gosala, Eli Winkler, Yash Prashar, Ana Purcell-Wiltz, Amanda García-Negrón, Ashley Ramos-Lopez, David Sidransky, Mariana Brait, Rafael Guerrero-Preston. CervicalMethDx: A precision DNA methylation test to identify advanced disease risk in cervical cancer screening algorithms. [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 4192.

Abstract Background: Ductal carcinoma in situ (DCIS) of the breast is a heterogeneous group with variable clinical presentation. 70-80% of DCIS are classified as grade 3 (Van Nuys Classification). They are treated according to grade and extension of the lesion, as was the standard treatment for invasive breast cancer in the 1970`s. Molecular genetic studies of invasive breast carcinomas (BC) have defined a molecular subclassification. In routine diagnostics, we use surrogate markers like estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) as tools to stratify for treatment. The individual results of these surrogate markers have resulted in a complex treatment algorithm. In contrast, still all DCIS grade 3 are given the same treatment. Aim: To investigate the molecular subtypes of DCIS diagnosed at the Department of Pathology, Akershus University Hospital (Norway). Materials: 483 histological specimens of DCIS at Akershus University Hospital, Dep. of Pathology, during 1996-2018. All relevant background information was recorded. Methods: All histological sections have been reassessed, and the grading has been confirmed independently by two experienced pathologists. An eventual peritumoral inflammatory component was made note of. All grade 3 DCIS with representative paraffin blocks were submitted for immunohistochemistry (IHC). The investigated markers were ER, PR, Ki-67 and HER-2. Results: The age ranged from 33-90, with a mean and median of 57 years. 10.4% were grade 1, 3.9% as grade 2 and 85.7% as grade 3. The size of DCIS ranged from 0,5 to150 mm, with mean 28 mm and median of 20 mm. 64 patients had an invasive component, 51 of them with a size of <5 mm (pT1a), and 13 > 5 mm but < 10 mm (pT1b). 33 (6,8%) patients were diagnosed with a recurrent DCIS or cancer (minimum 1year after primary DCIS diagnosis). Six patients were deceased (1 was diagnosed with new ipsilateral DCIS 3 years later, 2 with cancer in the contralateral breast, 2 with cancer in ipsilateral breast and 1 with new DCIS in the ipsilateral breast). The preliminary results of immunohistochemical studies of 245 cases indicate: Luminal A: 30.7% (Ki-67 <14%), Luminal B: 42% (Ki-67> 14%) and non-luminal 27.3%. Around 33% of the non-luminal cases are expected to be triple negative. Histomorphologically, we have found peritumoral lymphoid cells in 31% of cases, and all of these (100%) are DCIS grade 3. Conclusions: The molecular subtypes identified in invasive breast carcinomas are all represented in DCIS G3 and further studies might reveal the possibility of a more adapted treatment algorithm, as in invasive BC. The peritumoral inflammatory cell infiltrates will be investigated in future studies. Citation Format: Hossein Schandiz, Daehoon Park, Yan Liu Kaiser, Marianne Lyngra, Ingrid Solvang Talleraas, Jürgen Geisler, Torill Sauer. A novel diagnostics approach to Ductal Carcinoma In Situ (DCIS) with potential impact on the therapeutic algorithms [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr A015.

The paper presents the development of a cyber-physical system based on the HomeAssistant platform for efficient automation and control of Internet of Things (IoT) devices. The architectural features, technical implementation and prospects for the development of the system are considered, with an emphasis on the ability to integrate a wide range of sensors and IoT devices into a single network to create adaptive and intelligent solutions. The main focus is on the implementation of automation scenarios for indoor climate control, which optimise living conditions depending on the internal needs of users and external weather conditions, significantly increasing energy efficiency and overall comfort. Data storage and analysis strategies are described in detail, including the use of NAS servers for backup, the use of MariaDB for storage and InfluxDB and Grafana for analytics and visualisation, ensuring a high level of reliability and availability of information. Testing was conducted to assess the delay of notifications transmitted via Telegram and the internal network of the cyber-physical system. The obtained results confirm the high efficiency of the implemented cyber-physical system in ensuring instant delivery of notifications, which is a key aspect for rapid response to critical situations in a dynamic environment. The Home Assistant platform has been found to have extensive capabilities to support artificial intelligence services. It integrates with a variety of services such as Google Assistant, TensorFlow, DeepStack, Amazon Alexa, and allows you to easily expand its functionality with additional components and plug-ins. Using intelligent algorithms and data analysis, the system can independently make decisions on the optimal use of resources, monitor devices in accordance with changing conditions and user needs, and respond to dangerous or unpredictable situations.

The objectives of this study were to develop and subsequently demonstrate a parameter prediction approach for estimating black spruce (Picea mariana (Mill. ) BSP) diameter frequency distributions within the context of a stand density management diagram (SDMD). The approach consisted of three sequential steps: (1) obtaining maximum likelihood estimates for the location, scale and shape parameters of the Weibull probability density function for 153 empirical diameter frequency distributions; (2) developing and evaluating parameter prediction equations in which the Weibull parameter estimates were expressed as functions of stand-level variables based on step-wise regression and seemingly unrelated regression techniques; and (3) explicitly incorporating the parameter prediction equations into the SDMD modelling framework. The results indicated that the Weibull function was successful in characterizing the diameter distributions within the sample stands: the fitted distributions exhibited no significant (p ≤ 0. 05) differences in relation to their corresponding observed distributions, based on the Kolmogorov-Smirnov test. The parameter prediction equations described 94, 94 and 89% of the variation in the location, scale and shape parameter estimates, respectively. Furthermore, evaluation of the recovered distributions in terms of prediction error indicated minimal biases and acceptable accuracy. As demonstrated, incorporating the parameter prediction equations into an algorithmic version of the SDMD enabled the prediction of the temporal dynamics of the diameter frequency distribution by initial density regime and site quality. Additionally, an executable version of the resultant algorithm with instructions on acquiring it via the Internet is provided. Key words: 3-parameter Weibull probability density function, stepwise and seemingly unrelated regression, predictive error, product value, algorithm, Internet

After decades of research, constructing call graphs for modern C-based software remains either imprecise or inefficient when scaling up to the ever-growing complexity. The main culprit is the difficulty of resolving function pointers, as precise pointer analyses are cubic in nature and become exponential when considering calling contexts. This paper takes a practical stance by first conducting a comprehensive empirical study of function pointer manipulations in the wild. By investigating 5355 indirect calls in five popular open-source systems, we conclude that, instead of the past uniform treatments for function pointers, a cocktail approach can be more effective in “squeezing” the number of difficult pointers to a minimum using a potpourri of cheap methods. In particular, we decompose the costs of constructing highly precise call graphs of big code by tailoring several increasingly precise algorithms and synergizing them into a concerted workflow. As a result, many indirect calls can be precisely resolved in an efficient and principled fashion, thereby reducing the final, expensive refinements. This is, in spirit, similar to the well-known cocktail medical therapy. The results are encouraging — our implemented prototype called Coral can achieve similar precision versus the previous field-, flow-, and context-sensitive Andersen-style call graph construction, yet scale up to millions of lines of code for the first time, to the best of our knowledge. Moreover, by evaluating the produced call graphs through the lens of downstream clients (i.e., use-after-free detection, thin slicing, and directed grey-box fuzzing), the results show that Coral can dramatically improve their effectiveness for better vulnerability hunting, understanding, and reproduction. More excitingly, we found twelve confirmed bugs (six impacted by indirect calls) in popular systems (e.g., MariaDB), spreading across multiple historical versions.

Abstract Capture-based targeted sequencing is routinely used for small variant detection in cancer clinical care. Alongside targeted DNA, off-target DNA is also sequenced. These off-target reads are distributed across the genome, allowing for a whole genome copy number profile to be derived without the need for a SNP backbone. Here we present CopyRight, a method that generates robust genome-wide copy number profiles, which can be used for downstream chromosomal instability (CIN) quantification, overcoming various sources of technical noise with a novel approach that only requires a single tumor sample. We analyzed a variety of capture-based NGS protocols using our novel computational method, including the TruSight Oncology 500 (TSO500) panel, and compared them to the current gold standard for genome-wide copy number profiling from FFPE tissues: shallow whole genome sequencing (sWGS). Comparable results were obtained from targeted sequencing depending on sample purity, preservation method, and read depth. Additionally, benchmarks of CopyRight against other computational algorithms show an improvement in performance without the need for a matched normal tissue, the usual drawback for other methods in the cancer field. CIN signatures are a new set of emerging biomarkers that reflect the diversity of defective pathways that have operated in a tumor, which require robust genome-wide copy number profiles. These CIN signatures can be used to predict response to cytotoxic agents and targeted therapies and could ultimately help guide therapy selection in patients. As most clinical sequencing workflows rely on targeted sequencing, CopyRight can be included in current clinical assays, enabling CIN biomarker quantification with no extra technical or experimental requirements. Citation Format: David Gómez-Sánchez, Joe Sneath Thompson, Barbara Hernando, Diego García-López, Hector de Galard, Abhipsa Roy, Amy Cullen, Laura Madrid, José Teles, Ania Piskorz, Jason Yip, Alice Cádiz, Maria Escobar-Rey, Roberto Moreno-Vellisca, Nuria Carrizo, Eva Álvarez, Miguel Quintela-Fandino, Mariano Barbacid, Javier Ramos-Paradas, Juan Manuel Coya, Irene Ferrer, Jon Zugazagoitia, Luis Paz-Ares, Geoff Macintyre. Enabling biomarkers of chromosomal instability for tumor only targeted gene panel sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7422.

The objectives of this study were to develop a stand density management decision-support software suite for boreal conifers and demonstrate its potential utility in crop planning using practical deployment exemplifications. Denoted CPDSS (CroPlanner Decision-support Software Suite), the program was developed by transcribing algorithmic analogues of structural stand density management diagrams previously developed for even-aged black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) stand-types into an integrated software platform with shared commonalities with respect to computational structure, input requirements and generated numerical and graphical outputs. The suite included 6 stand-type-specific model variants (natural-origin monospecific upland black spruce and jack pine stands, mixed upland black spruce and jack pine stands, and monospecific lowland black spruce stands, and plantation-origin monospecific upland black spruce and jack pine stands), and 4 climate-sensitive stand-type-specific model variants (monospecific upland black spruce and jack pine natural-origin and planted stands). The underlying models which were equivalent in terms of their modular structure, parameterization analytics and geographic applicability, were enabled to address a diversity of crop planning scenarios when integrated within the software suite (e.g., basic, extensive, intensive and elite silvicultural regimes). Algorithmically, the Windows® (Microsoft Corporation, Redmond, WA, USA) based suite was developed by recoding the Fortran-based algorithmic model variants into a collection of VisualBasic.Net® (Microsoft Corporation, Redmond, WA, USA) equivalents and augmenting them with intuitive graphical user interfaces (GUIs), optional computer-intensive optimization applications for automated crop plan selection, and interactive tabular and charting reporting tools inclusive of static and dynamic stand visualization capabilities. In order to address a wide range of requirements from the end-user community and facilitate potential deployment within provincially regulated forest management planning systems, a participatory approach was used to guide software design. As exemplified, the resultant CPDSS can be used as an (1) automated crop planning searching tool in which computer-intensive methods are used to find the most appropriate precommercial thinning, commercial thinning and (or) initial espacement (spacing) regime, according to a weighted multivariate scoring metric reflective of attained mean tree size, operability status, volumetric productivity, and economic viability, and a set of treatment-related constraints (e.g., thresholds regarding intensity and timing of thinning events, and residual stocking levels), as specified by the end-user, or (2) iterative gaming-like crop planning tool where end-users simultaneously contrast density management regimes using detailed annual and rotational volumetric yield, end-product and ecological output measures, and (or) an abbreviated set of rotational-based performance metrics, from which they determine the most applicable crop plan required for attaining their specified stand-level objective(s). The participatory approach, modular computational structure and software platform used in the formulation of the CPDSS along with its exemplified utility, collectively provides the prerequisite foundation for its potential deployment in boreal crop planning.

Abstract Exemestane, an aromatase inactivator of the third generation, plays a crucial role in breast cancer therapy by targeting the P450 aromatase enzyme and, thus, decreasing estrogen synthesis. Exemestane (Aromasin™) is currently the only steroidal aromatase inactivator widely used in clinical routine treatment of ER+ breast cancer in all phases of the disease in a global perspective. However, the complex mechanisms underlying its therapeutic effects, besides being an aromatase inhibitor, remain incompletely understood. In this study, we employed a combination of human samples and in vitro data to unveil a compelling insight: Exemestane (EXE) and its primary metabolite, 17β-hydroxyexemestane (HEXE), exhibit potent inhibitory effects on tumor growth when present together in patient serum. Our biochemical analysis establishes a critical threshold—20% HEXE metabolite of the total EXE in patient serum—to trigger a tumor growth inhibition exceeding 90%, as evidenced by Ki67 staining. Mechanistically, our data reveals that both HEXE and EXE bind to the Androgen Receptor (AR), triggering a synergistic activation that induces a transcriptional program leading to cell death while diminishing the intracellular signaling activated by the oncogene Ras. Notably, patients with tumors characterized by a minimum of 20% AR+ epithelial cancer cells stand to benefit the most from exemestane and HEXE. Intriguingly, the binding of AR to chromatin in tumors gives rise to a molecular signature capable of distinguishing responsive from non-responsive patients to both EXE and HEXE. Collectively, our findings elucidate a dual therapeutic role for Exemestane in selected patients: it not only restrains estrogen-driven proliferation by estrogen suppression but also stimulates cell death by establishing a specific interactome with the AR at the genomic level. These insights suggest that both EXE and HEXE are necessary to achieve the best therapeutic effects in ER+/AR+ breast cancer tumors. Moreover, our findings suggest that AR-expression and targeting should be investigated further to potentially add novel strategies to our existing algorithms in ER+/AR+ MBC. Citation Format: Gemma Santacana-Font, Darek Kedra, Maria del Carmen García-Macías, Laurens Cornelus-Reitsma, Marianne Lyngra, Torill Sauer, Vessela Kristensen, Antoni Hurtado, Jürgen Geisler. Exemestane and its primary metabolite 17-hydroexemestane inhibit synergically the tumor growth of ER/AR positive breast cancer tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7576.

Background:To explore the full therapeutic spectrum of a drug it is crucial to consider its potential effectiveness in all diseases. Serendipitous clinical observations have often shown that approved drugs and those in development to be efficacious in indications different to those originally tested for. Traditional approaches to match a drug candidate with possible indications are mostly based on matching drug mechanistic knowledge with disease pathophysiology. Proof-of-concept trials or elaborate pre-clinical studies in animal models do not allow for a broad assessment due to high costs and slow progress. Gene expression changes in patients or animal models represent a good proxy to comprehensively assess both disease and drug effects. Furthermore, this data type can be integrated with a plethora of publicly available data.Objectives:Generation of a novel in silico framework to support the selection and expansion of potential indications which associate with a compound or approved drug. The framework was exemplified by the clinical compound cenerimod, a potent, selective, and orally active sphingosine-1-phosphate receptor 1 modulator (Piali et al., 2017).Methods:A total of ~13’000 public patient gene expression datasets from ~140 diseases were evaluated against cenerimod gene expression data generated in mouse disease models. To improve comparability of studies across platforms and species, computer algorithms (neural networks) were trained and employed to reduce noise within the data sets and improve signal. The predicted response to cenerimod for individual patients was contrasted against clinical patient characteristics.Results:The neural network algorithm efficiently reduced experimental noise and improved sensitivity in the gene expression data. The results predicted cenerimod to be efficacious in several auto-immune diseases foremost SLE. Additionally, focused analysis on individual patients rather than disease cohorts revealed potential determinants predictive of maximal clinical response, with the highest predicted clinical response for cenerimod in patients with severe inflammatory endotype and/or high SLE Disease Activity Index (SLEDAI).Conclusion:Combining preclinical compound data with the wealth of public disease gene expression data, provides great potential to support indication selection. The novel in silico framework identified SLE as a prime potential indication for cenerimod and supported the cenerimod phase 2b clinical trial in patients with SLE (CARE study,NCT03742037).References:[1]Piali, L., Birker-Robaczewska, M., Lescop, C., Froidevaux, S., Schmitz, N., Morrison, K., … Nayler, O. (2017). Cenerimod, a novel selective S1P1 receptor modulator with unique signaling properties. Pharmacology Research & Perspectives, 5(6), 1–12.https://doi.org/10.1002/prp2.370Disclosure of Interests:Dominik Hartl Shareholder of: Idorsia shares, Employee of: Idorsia employee, Marcel Keller Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Axel Klenk Shareholder of: Idorsia option/shares, Employee of: Idorsia employee, Mark Murphy Shareholder of: Idorsia shares and stock options, Employee of: Idorsia employee, Marianne Martinic Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Gabin Pierlot Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Peter Groenen Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Daniel Strasser Shareholder of: Idorsia options/shares, Employee of: Idorsia employee

Abstract Background The recent introduction of CDK4/6 inhibitors has been one of the most pivotal breakthroughs in breast cancer therapy during the last decades. A growing body of evidence proposes that CDK4/6 inhibitors influence the recruitment of immune cells in the tumor microenvironment with potential effects on the outcome. Study design The NeoLetRib-study is a multicenter, single-arm, open-label, neoadjuvant, phase II trial aiming at treating 100 locally advanced luminal-A and luminal-B breast cancer patients, defined as either large T2, or T3/T4, and/or N2-3. Patients receive neoadjuvant therapy for 6 months: ribociclib (600 mg daily, 21 days on/7 days off) and letrozole (2.5 mg daily). Peri- and premenopausal women also receive therapy with goserelin 3.6 mg s.c every 28 days. Methods Three sequencial tumor biopsies were collected: pre-treatment and on-treatment (cycle 1 - day 21 and cycle 6 - day 21 with ribociclib). These biopsies were subjected to single-cell transcriptome, T cell receptor and B cell receptor profiling using the Chromium Single-Cell v2 5′ Chemistry (10x Genomics). Libraries were paired-end sequenced on a NovaSeq6000. Single cell gene expression matrices were analyzed with the Seurat package (v4.0.2). After filtering out stressed/dying cells or cells with low quality sequencing, gene expression of the remaining good quality cells was normalized and scaled to construct principal components and further cluster cells. Results In this planned interim analysis, we clustered 242315 cells from longitudinal tumor biopsies from 18 patients at pre-treatment, 18 at cycle 1 - day 21 and 9 at cycle 6 - day 21, respectively. We identified 8 main cell types: T cells, B cells, epithelial cells, fibroblasts, endothelial cells, macrophages, mast cells and dendritic cells. To further identify specific and specialized cell subtypes, we clustered the cells belonging to the above-mentioned cell types and annotated the clusters obtained using validated marker genes. Statistical methods and algorithms were then used to characterize how the proportion of different cell types changes in tumors under treatment pressure. We identified significant changes in immune cell proportions, including regulatory T cells, CD14 monocytes, SLC2A1-Macrophages among others. Conclusions In this unique patient cohort, we used single cell transcriptome profiling to obtain a high-resolution map of cell types found in tumor biopsies from the NeoLetRib trial. We characterized the effects of the combination of ribociclib and letrozole on the tumor microenvironment and identified cells sensitive and resistant to treatment. In this interim analysis, the observed longitudinal changes of immune cell types proportions in the tumor microenvironment might suggest immune related effects of the treatment combination. Citation Format: Marie Fongård, Chloé Steen, Salim Ghannoum, Marius Bjørnstad, Barbro Holm, Tatjana Bosnjak, Laurens Reitsma, Stephanie Geisler, Kamilla Fjermeros, Johannes Bruteig, Manouchehr Seyedzadeh, Unn-Cathrin Buvarp, Marie Loeng, Aino Vuoriluoto, Torben Lüders, Diether Lambrechts, Marianne Lyngra, Vessela Kristensen, Jürgen Geisler, Xavier Tekpli. Single cell characterization of longitudinal biopsies from breast cancer patients treated neoadjuvantly with the aromatase inhibitor letrozole and the CDK4/6 inhibitor ribociclib in concert [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT2-19-01.

Abstract Background About 70% of breast cancer cases are hormone receptor positive, indicating that cancer cells exploit estrogens for their growth. Postmenopausal estrogen receptor positive breast cancer patients are currently often treated with aromatase inhibitors suppressing serum and tumor tissue estradiol levels by >90%. Two widely used aromatase inhibitors are letrozole, a nonsteroidal inhibitor and exemestane, a steroidal aromatase inactivator. While the mechanisms of action of these two drugs are well studied, their effects on the tumor immune microenvironment and mechanisms of resistance to these drugs are still not sufficiently elucidated. Study design The NEOLETEXE trial1 was a neoadjuvant, randomized, open-label, intra-patient, cross-over, single center clinical trial aiming at treating postmenopausal patients with locally advanced breast cancer defined primarily as large T3/T4 and or N2/N3. However, patients with large T2 tumors were also eligible. Patients were randomized to neoadjuvant therapy with either letrozole (2.5 mg daily) or exemestane 25 mg daily for about 3 months, followed by a cross-over to the alternative drug for another 3 months prior to surgery. 102 patients were enrolled in NEOLETEXE. Methods Pre-treatment, on-treatment (3 months) and end-of-treatment (6 months of therapy) biopsies were subjected to single-cell transcriptome, T cell receptor and B cell receptor profiling using the Chromium Single-Cell v2 5′ Chemistry (10x Genomics). Libraries were paired-end sequenced on a NovaSeq6000. Single cell gene expression matrices were analyzed with the Seurat package (v4.0.2). After filtering out stressed/dying cells or cells with low quality sequencing, gene expression of the remaining good quality cells was normalized and scaled to construct principal components and further cluster cells. Results We clustered 362.762 cells from 26 pre-treatment, 20 on-treatment and 19 end-of-treatment biopsies and identified 8 main cell types to be present: T cells, B cells, epithelial cells, fibroblasts, endothelial cells, macrophages, mast cells and dendritic cells. To further identify specific and specialized cell subtypes, we clustered the cells belonging to the above-mentioned cell types independently and annotated the clusters obtained using validated marker genes. Finally, we use statistical methods and algorithms to characterize how the proportion of the different cell types changes in tumors under treatment pressure. Specifically, we show changes in the proportion of CD8 effector memory cells under treatment pressure, with a significant increase in cytotoxic T cell proportions after two months of treatment with aromatase inhibitor. Conclusions We use single cell profiling to obtain a high-resolution map of the cell types found in tumor biopsies of the NEOLETEXE trial to characterize the effects of aromatase inhibitors on the tumor microenvironment and to identify the cancer cell signatures during treatment with letrozole or exemestane. 1. Bahrami N., Sauer T., Engebretsen S., Aljabri B., Bemanian V.,Lindstrøm J., Lüders T., Kristensen V.N., Lorentzen A., Loeng M., Ødegård H.P., Kvaløy J.Ø., Vestøl I.B., Geisler S.B., Gravdehaug B., Gundersen J.M., Geisler J. The NEOLETEXE trial: a neoadjuvant cross-over study exploring the lack of cross resistance between aromatase inhibitors. Future Oncology, 15 (32), 3675-3682, 2019. Citation Format: Salim Ghannoum, Chloé Steen, Marie Fongård, Marius Bjørnstad, Laurens Reitsma, Stephanie Geisler, Manouchehr Seyedzadeh, Unn-Cathrin Buvarp, Marie Loeng, Torben Lüders, Diether Lambrechts, Marianne Lyngra, Vessela Kristensen, Jürgen Geisler, Xavier Tekpli. PD10-10 Single cell characterization of longitudinal biopsies from breast cancer patients treated with the aromatase inhibitors letrozole and exemestane in sequence [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD10-10.

Abstract Cervical cancer is one of the most common cancers in women. Despite progress in prevention through Human Papilloma Virus (HPV) vaccination and success in early detection of cervical cancer through cytologic screening and HPV detection, there remains an unequal cervical cancer burden in low-resource settings, both in developed and developing countries. We have previously shown that the CervicalMethDx test can provide a Cervical Intraepithelial Neoplasia (CIN) grade 2-3 risk score, by assessing DNA methylation in a panel of three human genes (ZNF516, FKBP6 and INTS1) in samples from the United States (US), Puerto Rico, and Chile. We now tested the performance of the CervicalMethDx test on HPV-positive CIN2 and CIN3 cases (n=113) from Honduras collected from participants in the ESTAMPA clinical trial (NCT01881659). ESTAMPA (EStudio multicéntrico de TAMizaje y triaje de cáncer de cuello uterino con pruebas del virus del PApiloma humano; Spanish acronym) is a multicentric study of cervical cancer screening with HPV testing and assessment of triage methods in Latin America, which has accrued close to 50,000 participants from twelve recruitment centers in nine Latin American countries since 2013. We hypothesized that the CervicalMethDx test can identify HPV positive women most likely to be diagnosed with CIN grades 2 and 3 by anatomic pathologists, before they are referred to colposcopy-driven biopsies. We assessed DNA methylation by quantitative Real Time Methylation Specific PCR (qMSP) analysis of sodium bisulfite-modified genomic DNA. Primers and probes were previously designed to specifically amplify the promoters of the 3 genes of interest and the promoter of a reference gene, β-actin, to assess DNA input. We performed blinded retrospective studies on well-characterized clinical samples in PreservCyt sample transport media (ThinPrep, Hologic), comparing DNA methylation levels in samples from Honduras and 88 HPV-positive previously tested US samples with No Intraepithelial Lesions or Malignancy (NILM), as controls. Our results showed that the CervicalMethDx test can correctly classify 96% of CIN2 (n=62) samples with 92% Sensitivity, 98% Specificity, and an AUC of 0.96 as well as 97% of CIN3 (n=51) samples with 96% Sensitivity, 98% Specificity, and an AUC of 0.97. Moreover, the assay correctly classified 96% of CIN2-CIN3 samples combined (n=113) with 95% Sensitivity, 98% Specificity, and AUC of 0.97, when compared to samples with NILM (n=88). Our results suggest that the CervicalMethDx test is a new and valuable tool to stratify HPV positive women prior to colposcopy-driven biopsies in developed countries and ablative treatment in developing countries, most of which are unnecessary. These results warrant further evaluation of the CervicalMethDX test in prospective, population-based studies, assessing the use of precision DNA methylation algorithms to triage HPV positive women before referral to colposcopy-driven biopsies or ablative treatments in cervical cancer screening clinics world-wide. Citation Format: Laura Palmieri, Fernando Zamuner, Yessy Cabrera, Jafet Ortiz-Quintero, Dieila Giomo De Lima, Ana Purcell-Wiltz, Amanda García-Negrón, Ashley Ramos-López, Mariana Brait, David Sidransky, Annabelle Ferrera, Rafael E. Guerrero-Preston. A precision DNA methylation test to triage HPV positive women before referral to colposcopy-driven biopsies or ablative treatment in cervical cancer screening clinics worldwide [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB107.

Abstract Introduction: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) in combination with endocrine therapy (ET) are the standard first-line treatment for patients with hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (HR+/HER2- aBC). HER2-low BC, which is defined by an IHC score for HER2 of 1+ or 2+ with negative ISH assay, accounts for more than half of all HR+/HER2- aBC cases, and it is associated with remarkable clinical benefit from the novel anti-HER2 antibody drug conjugate (ADC) trastuzumab-deruxtecan. Evidence on the prognostic impact of HER2-low status is controversial in both limited-stage and advanced BC. Here, we sought to investigate the possible prognostic relevance of HER2-low status in a population of aBC patients treated with CDK4/6i plus ET. Methods: We conducted a retrospective-prospective study in six Italian Cancer Centers to investigate the impact of HER2 status (low vs. 0) on the progression-free survival (PFS) and overall survival (OS) of consecutive HR+/HER2- aBC patients treated with CDK4/6i plus ET (aromatase inhibitors or fulvestrant) as a first-line therapy. In the main study analysis, we considered HER2 status in the last tumor assessment (i.e., primary tumor, or, when available, a metastatic lesion). We also performed a subgroup analysis including only patients with HER2 status evaluation in a metastatic lesion collected before CDK4/6i plus ET therapy initiation. The association between HER2 status (low vs. 0) and PFS or OS was evaluated using log-rank test and Cox regression modeling. Results: We evaluated 767 consecutive HR+/HER2- aBC patients treated with CDK4/6i plus ET between January 2017 and January 2022. Of these, 436 patients (56.8%) received CDK4/6i plus ET as a first-line therapy, and they were included in this analysis. Median age was 63 years (range 27-87), and 362 patients (83.0%) were postmenopausal. The majority of patients were treated with palbociclib (68.3%), while 91 (20.9%) and 47 (10.8%) patients received ribociclib and abemaciclib, respectively. Regarding HER2 status, 269 (62.9%) patients had HER2-low tumors, while 159 (37.1%) patients had HER2-0 neoplasms. HER2-low status was associated with significantly lower PFS when compared to HER2-0 status [median PFS (mPFS) 23.6 vs. 32.3 months, respectively; p=0.014]. HER2-low status was also associated with significantly worse OS (mOS 48.7 vs 58.3 months, respectively; p=0.025). These results were confirmed in multivariable models adjusting the impact of HER2 status for clinically-relevant covariates, namely estrogen receptor status, Ki-67, age, number of metastatic sites, presence of liver metastases, disease free interval, ECOG Performance Status. In this analysis, HER2-low status, compared with HER2-0 status, was independently associated with worse PFS [adjusted Hazard Ratio (aHR): 1.62; 95% confidence interval (CI): 1.17-2.24; p< 0.01] and OS (aHR: 1.74; 95% CI: 1.09-2.76; p=0.019). Subgroup analysis conducted in the subset of 256 patients with available metastatic tumor samples collected before CDK4/6i plus ET initiation confirmed that HER2-low status (n=157), when compared to HER2-0 status (n=99), was independently associated with worse PFS (mPFS 24.5 vs 35.2 months, p=0.01; aHR 2.07; 95% CI: 1.28-3.34, p< 0.01) and worse OS (mPFS 48.7 vs 72.3 months, p=0.027; aHR 3.12; 95% CI 1.44-6.77, p< 0.01). Conclusions: This multicenter Italian study revealed that HER2-low status has independent, negative prognostic value in patients with HR+/HER2- aBC treated with CDK4/6i plus ET in the first-line setting. Our results suggest that HER2-low status might be associated with different clinical benefit from standard anticancer therapies in specific clinical settings. The definition of treatment algorithms also taking into account HER2 status is a clinical priority in patients with HR+/HER2- aBC. Citation Format: Emma Zattarin, Caterina Sposetti, Rita Leporati, Luigi Mariani, Alice Menichetti, Chiara Corti, Chiara Benvenuti, Giovanni Fucà, Riccardo Lobefaro, Francesca Ligorio, Daniele Presti, Leonardo Provenzano, Andrea Vingiani, Gaia Griguolo, Marianna Sirico, Ottavia Bernocchi, Antonio Marra, Paola Zagami, Elisa Agostinetto, Flavia Jacobs, Pierluigi Di Mauro, Andrea Esposito, Carlo Alberto Giorgi, Luca Lalli, Laura Boldrini, Pier Paolo Maria Berton Giachetti, Ambra Carnevale Schianca, Valentina Guarneri, Rebecca Pedersini, Agnese Losurdo, Alberto Zambelli, Daniele Giulio Generali, Giuseppe Curigliano, Giancarlo Pruneri, Filippo de Braud, Maria Vittoria Dieci, Claudio Vernieri. HER2-02 HER2-Low Status is Associated with Worse Clinical Outcomes in Hormone Receptor-Positive, HER2-Negative Advanced Breast Cancer Patients Treated With First-Line Cyclin-Dependent Kinase 4/6 Inhibitors Plus Endocrine Therapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr HER2-02.

Abstract Background Bone and joint infections (BJI) comprise a series of disorders, including septic arthritis, osteomyelitis, and prosthetic joint infections. Dalbavancin (DALBA) is a lipoglycopeptide with a very long half-life that allows the treatment of serious infections with once weekly or biweekly administration. We evaluated the activity of DALBA against pathogens isolated from BJI. Methods A total of 798 organisms were collected from 62 US and 28 European (EU) hospitals in 2016-2020, including 503 S. aureus, 140 β-haemolytic streptococci (BHS), 71 coagulase-negative staphylococci (CoNS), 57 Enterococcus spp. (ESP), 22 viridans group streptococci (VGS), and 5 S. pneumoniae. Bacteria were identified by standard algorithms and MALDI-TOF-MS. Susceptibility testing was performed by the reference broth microdilution method in a central laboratory. Results S. aureus (63.0%) was the most common pathogen associated with BJI, followed by BHS (17.5%), CoNS (8.9%), and ESP (7.1%). All S. aureus isolates were susceptible (S) to DALBA (MIC50/90, 0.03/0.03 mg/L), linezolid (LNZ; MIC50/90, 1/2 mg/L), teicoplanin (TEI; MIC50/90, ≤0.5/1 mg/L), vancomycin (VAN; MIC50/90, 1/1 mg/L), and daptomycin (DAPTO; MIC50/90, 0.25/0.0.5 mg/L. DALBA was 8- to 16-fold more potent than DAPTO and 32- to 64-fold more potent than LNZ, VAN, and TEI against S. aureus. Oxacillin resistance (OXA-R) rates among S. aureus (MRSA rates) were 35.5% and 15.4% in the US and EU, respectively. Ceftaroline (CPT) was active against 98.6% of S. aureus (MIC50/90, 0.25/1 mg/L) and 94.7% of MRSA (MIC50/90, 1/1 mg/L) isolates. Doxycycline and levofloxacin were active against 97.0% and 76.5% of S. aureus, respectively. Among CoNS, (54.9% OXA-R), DALBA (MIC50/90, 0.03/0.03 mg/L; highest MIC, 0.12 mg/L) was the most potent agent, followed by DAPTO (MIC50/90, 0.25/0.5 mg/L), CPT (MIC50/90, 0.25/0.5 mg/L) and LNZ (MIC50/90, 0.5/1 mg/L). The highest DALBA MIC value among BHS and VGS was 0.12 mg/L (MIC90, 0.03 mg/L for both groups). VAN was active against 82.4% of ESP and DALBA inhibited all VAN-S ESP at ≤0.06 mg/L. Conclusion DALBA demonstrated potent in vitro activity against common gram-positive organisms (GP) causing BJI and appears to be a valuable option to treat BJI/osteomyelitis caused by GP. Disclosures Helio S. Sader, MD, PhD, FIDSA, AbbVie (formerly Allergan) (Research Grant or Support)Basilea Pharmaceutica International, Ltd. (Research Grant or Support)Cipla Therapeutics (Research Grant or Support)Cipla USA Inc. (Research Grant or Support)Department of Health and Human Services (Research Grant or Support, Contract no. HHSO100201600002C)Melinta Therapeutics, LLC (Research Grant or Support)Nabriva Therapeutics (Research Grant or Support)Pfizer, Inc. (Research Grant or Support)Shionogi (Research Grant or Support)Spero Therapeutics (Research Grant or Support) Leonard R. Duncan, PhD, AbbVie (formerly Allergan) (Research Grant or Support)Basilea Pharmaceutica International, Ltd. (Research Grant or Support)Cipla Therapeutics (Research Grant or Support)Cipla USA Inc. (Research Grant or Support)Department of Health and Human Services (Research Grant or Support, Contract no. HHSO100201600002C)Shionogi (Research Grant or Support) Mariana Castanheira, PhD, AbbVie (formerly Allergan) (Research Grant or Support)Bravos Biosciences (Research Grant or Support)Cidara Therapeutics, Inc. (Research Grant or Support)Cipla Therapeutics (Research Grant or Support)Cipla USA Inc. (Research Grant or Support)GlaxoSmithKline (Research Grant or Support)Melinta Therapeutics, Inc. (Research Grant or Support)Melinta Therapeutics, LLC (Research Grant or Support)Pfizer, Inc. (Research Grant or Support)Qpex Biopharma (Research Grant or Support)Shionogi (Research Grant or Support)Spero Therapeutics (Research Grant or Support) Mariana Castanheira, PhD, Affinity Biosensors (Individual(s) Involved: Self): Research Grant or Support; Allergan (Individual(s) Involved: Self): Research Grant or Support; Amicrobe, Inc (Individual(s) Involved: Self): Research Grant or Support; Amplyx Pharma (Individual(s) Involved: Self): Research Grant or Support; Artugen Therapeutics USA, Inc. (Individual(s) Involved: Self): Research Grant or Support; Astellas (Individual(s) Involved: Self): Research Grant or Support; Basilea (Individual(s) Involved: Self): Research Grant or Support; Beth Israel Deaconess Medical Center (Individual(s) Involved: Self): Research Grant or Support; BIDMC (Individual(s) Involved: Self): Research Grant or Support; bioMerieux Inc. (Individual(s) Involved: Self): Research Grant or Support; BioVersys Ag (Individual(s) Involved: Self): Research Grant or Support; Bugworks (Individual(s) Involved: Self): Research Grant or Support; Cidara (Individual(s) Involved: Self): Research Grant or Support; Cipla (Individual(s) Involved: Self): Research Grant or Support; Contrafect (Individual(s) Involved: Self): Research Grant or Support; Cormedix (Individual(s) Involved: Self): Research Grant or Support; Crestone, Inc. (Individual(s) Involved: Self): Research Grant or Support; Curza (Individual(s) Involved: Self): Research Grant or Support; CXC7 (Individual(s) Involved: Self): Research Grant or Support; Entasis (Individual(s) Involved: Self): Research Grant or Support; Fedora Pharmaceutical (Individual(s) Involved: Self): Research Grant or Support; Fimbrion Therapeutics (Individual(s) Involved: Self): Research Grant or Support; Fox Chase (Individual(s) Involved: Self): Research Grant or Support; GlaxoSmithKline (Individual(s) Involved: Self): Research Grant or Support; Guardian Therapeutics (Individual(s) Involved: Self): Research Grant or Support; Hardy Diagnostics (Individual(s) Involved: Self): Research Grant or Support; IHMA (Individual(s) Involved: Self): Research Grant or Support; Janssen Research & Development (Individual(s) Involved: Self): Research Grant or Support; Johnson & Johnson (Individual(s) Involved: Self): Research Grant or Support; Kaleido Biosceinces (Individual(s) Involved: Self): Research Grant or Support; KBP Biosciences (Individual(s) Involved: Self): Research Grant or Support; Luminex (Individual(s) Involved: Self): Research Grant or Support; Matrivax (Individual(s) Involved: Self): Research Grant or Support; Mayo Clinic (Individual(s) Involved: Self): Research Grant or Support; Medpace (Individual(s) Involved: Self): Research Grant or Support; Meiji Seika Pharma Co., Ltd. (Individual(s) Involved: Self): Research Grant or Support; Melinta (Individual(s) Involved: Self): Research Grant or Support; Menarini (Individual(s) Involved: Self): Research Grant or Support; Merck (Individual(s) Involved: Self): Research Grant or Support; Meridian Bioscience Inc. (Individual(s) Involved: Self): Research Grant or Support; Micromyx (Individual(s) Involved: Self): Research Grant or Support; MicuRx (Individual(s) Involved: Self): Research Grant or Support; N8 Medical (Individual(s) Involved: Self): Research Grant or Support; Nabriva (Individual(s) Involved: Self): Research Grant or Support; National Institutes of Health (Individual(s) Involved: Self): Research Grant or Support; National University of Singapore (Individual(s) Involved: Self): Research Grant or Support; North Bristol NHS Trust (Individual(s) Involved: Self): Research Grant or Support; Novome Biotechnologies (Individual(s) Involved: Self): Research Grant or Support; Paratek (Individual(s) Involved: Self): Research Grant or Support; Pfizer (Individual(s) Involved: Self): Research Grant or Support; Prokaryotics Inc. (Individual(s) Involved: Self): Research Grant or Support; QPEX Biopharma (Individual(s) Involved: Self): Research Grant or Support; Rhode Island Hospital (Individual(s) Involved: Self): Research Grant or Support; RIHML (Individual(s) Involved: Self): Research Grant or Support; Roche (Individual(s) Involved: Self): Research Grant or Support; Roivant (Individual(s) Involved: Self): Research Grant or Support; Salvat (Individual(s) Involved: Self): Research Grant or Support; Scynexis (Individual(s) Involved: Self): Research Grant or Support; SeLux Diagnostics (Individual(s) Involved: Self): Research Grant or Support; Shionogi (Individual(s) Involved: Self): Research Grant or Support; Specific Diagnostics (Individual(s) Involved: Self): Research Grant or Support; Spero (Individual(s) Involved: Self): Research Grant or Support; SuperTrans Medical LT (Individual(s) Involved: Self): Research Grant or Support; T2 Biosystems (Individual(s) Involved: Self): Research Grant or Support; The University of Queensland (Individual(s) Involved: Self): Research Grant or Support; Thermo Fisher Scientific (Individual(s) Involved: Self): Research Grant or Support; Tufts Medical Center (Individual(s) Involved: Self): Research Grant or Support; Universite de Sherbrooke (Individual(s) Involved: Self): Research Grant or Support; University of Iowa (Individual(s) Involved: Self): Research Grant or Support; University of Iowa Hospitals and Clinics (Individual(s) Involved: Self): Research Grant or Support; University of Wisconsin (Individual(s) Involved: Self): Research Grant or Support; UNT System College of Pharmacy (Individual(s) Involved: Self): Research Grant or Support; URMC (Individual(s) Involved: Self): Research Grant or Support; UT Southwestern (Individual(s) Involved: Self): Research Grant or Support; VenatoRx (Individual(s) Involved: Self): Research Grant or Support; Viosera Therapeutics (Individual(s) Involved: Self): Research Grant or Support; Wayne State University (Individual(s) Involved: Self): Research Grant or Support Rodrigo E. Mendes, PhD, AbbVie (Research Grant or Support)AbbVie (formerly Allergan) (Research Grant or Support)Cipla Therapeutics (Research Grant or Support)Cipla USA Inc. (Research Grant or Support)ContraFect Corporation (Research Grant or Support)GlaxoSmithKline, LLC (Research Grant or Support)Melinta Therapeutics, Inc. (Research Grant or Support)Melinta Therapeutics, LLC (Research Grant or Support)Nabriva Therapeutics (Research Grant or Support)Pfizer, Inc. (Research Grant or Support)Shionogi (Research Grant or Support)Spero Therapeutics (Research Grant or Support)

ABSTRACTThe topological local cluster (or Schläfli cluster) concept of Marians and Hobbs is used to detect topologically crystalline regions in models of disordered tetrahedral semiconductors. We present simple algorithms for detecting both Wells-type shortest circuits and O'Keeffe-type rings, which can be used to delineate alternative forms of the Schläfli cluster in models.

This review focuses on recent advances in process-based numerical models of the impact of extreme storms on sandy coasts. Driven by larger-scale models of meteorology and hydrodynamics, these models simulate morphodynamics across the Sallenger storm-impact scale, including swash, collision, overwash, and inundation. Models are becoming both wider (as more processes are added) and deeper (as detailed physics replaces earlier parameterizations). Algorithms for wave-induced flows and sediment transport under shoaling waves are among the recent developments. Community and open-source models have become the norm. Observations of initial conditions (topography, land cover, and sediment characteristics) have become more detailed, and improvements in tropical cyclone and wave models provide forcing (winds, waves, surge, and upland flow) that is better resolved and more accurate, yielding commensurate improvements in model skill. We foresee that future storm-impact models will increasingly resolve individual waves, apply data assimilation, and be used in ensemble modeling modes to predict uncertainties. Expected final online publication date for the Annual Review of Marine Science, Volume 14 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Abstract Several outbreaks of mosquito-borne diseases have taken place in Europe in recent years. In Spain, both active and passive surveillance have demonstrated that dengue and West Nile viruses are currently circulating, and seven autochthonous dengue cases have been reported in the last 2 yr. The effectiveness of vector control programs largely depends on the accuracy of the taxonomic identification of the species. However, in Spain, identification almost completely relies on the use of morphological keys to characterize the mosquito fauna. This study investigates the congruence between molecular and morphological species boundaries in 13 Spanish mosquito taxa. The Cytochrome c oxidase subunit I (COI) gene region was sequenced from 60 adult specimens collected in Mallorca, plus several representatives from other Spanish regions for comparative purposes. Phylogenetic relationships were established using Bayesian and maximum-likelihood approaches. Using three species delimitation algorithms (ABGD, mPTP, and GMYC), we found strong evidence for cryptic speciation within Anopheles algeriensis Theobald, a widespread mosquito in the Mediterranean basin. We also delimited the Mallorcan rock pool mosquito Aedes mariae (Sergent & Sergent), from mainland European populations. Finally, we found difficulties in the use of wing characters in species keys to distinguish Culiseta annulata (Schrk) from Culiseta subochrea (Edwards). Given that these species are vectors of pathogens of medical relevance and have veterinary importance, their accurate taxonomic identification is essential in European vector surveillance programs.

In this paper, a short-term wind speed prediction model, called CEEMDAN-SE-Improved PIO-GRNN, is proposed to optimize the accuracy of the short-term wind speed forecast. This model is established on account of the optimized General Regression Neural Network (GRNN) method optimized by three algorithms, which are Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Pigeon Inspired Optimization (PIO), separately. Firstly, decomposing the original wind speed sequences into several subsequences with different complexity by CEEMDAN. Then, the complexity of each subsequence is judged by SE and the similar subsequences are combined into a new sequence to reduce the scale of calculation. Afterwards, the GRNN model optimized by improved PIO is used to predict each new sequence. Finally, the predicted results are superposed as the eventual predicted value. Implementing the prediction for the wind speed data of a wind field in north China within 30 days by applying the different prediction models, namely, GRNN, CEEMDAN-GRNN, Improved PIO-GRNN, and CEEMDAN-SE-Improved PIO-GRNN which are proposed in this paper. Comparing the prediction curves of different models with the fitting curve of the actual wind speed shows that the optimal fitting effect and minimum error value are included in CEEMDAN-SE-Improved PIO-GRNN model. Specifically, the values of mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) separately decrease by 0.6222, 0.3334, and 8.5766%, which compare with the single prediction model GRNN. Meanwhile, diebold-mariano (DM) test shows that the prediction ability of the two models is significantly different. The above statements indicate the proposed model does great advance in the precision of short-term wind speed prediction.

Summary Deep slow slip events (SSEs) at subduction zones have significantly contributed to refining our understanding of the megathrust earthquake cycle at the brittle-ductile transition. However, the specific combination of factors that determine their occurrence has not yet been fully explored. Here we evaluate the contribution of several of these characteristics using globally mapped geophysical data that are used as proxies for physical properties of the subducting plate. This is performed by classifying 25 km-wide, trench-parallel segments into binary classes based on the observation (or lack thereof) of deep, short- or long-term SSEs. The five characteristics explored here include subducting plate age, sediment thickness, relative plate velocity, slab dip, and plate surface roughness. We use these characteristics to train six Machine Learning models based on different learning algorithms: Gaussian Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Random Forest, Support Vector Machine, and K-Nearest Neighbour. Short-term SSE models show that subducting plate age, relative velocity, and sediment thickness have the strongest predictive power with the first two characteristics negatively correlating and sediment thickness positively correlating with SSE occurrence, respectively. These results are consistent with a conceptual model where slow slip is controlled by conditions favoring the enduring release (and possible storage) of fluids near the source region. However, the relationship between these features and elevated pore fluid pressures is not established here and further evidence is needed to validate this hypothesis. We then use a final model constructed as a weighted average of the best performing models to make predictions on the probability of SSE occurrence, with predicted short-term SSE occurrence in South America, the Aleutians, Sumatra, Vanuatu and Solomon, as well as long-term SSE occurrence in the Aleutians, Izu-Bonin, Kuril-Kamchatka, Mariana, and Tonga-Kermadec. Overall, long-term SSE models do not perform as well as the short-term SSE models which may indicate that long-term SSEs are controlled by a different and/or extended set of physical characteristics than the short-term SSEs.

Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world’s inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R2 for Aus; 0.92 MSE, 90% R2 for Aman; 0.246 MSE, 99% R2 for Boro; 0.062 MSE, 99% R2 for wheat; and 0.016 MSE, 99% R2 for potato production prediction. The Diebold–Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.