Journal articles: 'Hotspot selection'

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Luo, Pan. "A Deep Neural Network-Based Approach to Media Hotspot Discovery." Advances in Multimedia 2023 (February 21, 2023): 1–9. http://dx.doi.org/10.1155/2023/3438025.

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In recent years, with the rapid development of social network media, it has become a valuable research direction to quickly analyze these texts and find out the current hotspots from them in real time. To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. Then, the K-means algorithm is optimized for the initial point selection problem, and a clustering algorithm based on the maximum density selection method in the moving range is proposed. Finally, the text feature representation method based on graph convolutional neural network is combined with the clustering algorithm based on the moving range density maximum selection method to build a deep learning-based media hotspot discovery framework. The accuracy of the proposed media hotspot discovery method and the comprehensive evaluation of the computing time have been verified experimentally.

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Mishra, Ahan, Ke Chen, Subhadipto Poddar, Emmanuel Posadas, Anand Rangarajan, and Sanjay Ranka. "Using Video Analytics to Improve Traffic Intersection Safety and Performance." Vehicles 4, no. 4 (November 10, 2022): 1288–313. http://dx.doi.org/10.3390/vehicles4040068.

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Road safety has always been a crucial priority for municipalities, as vehicle accidents claim lives every day. Recent rapid improvements in video collection and processing technologies enable traffic researchers to identify and alleviate potentially dangerous situations. This paper illustrates cutting-edge methods by which conflict hotspots can be detected in various situations and conditions. Both pedestrian–vehicle and vehicle–vehicle conflict hotspots can be discovered, and we present an original technique for including more information in the graphs with shapes. Conflict hotspot detection, volume hotspot detection, and intersection-service evaluation allow us to understand the safety and performance issues and test countermeasures comprehensively. The selection of appropriate countermeasures is demonstrated by extensive analysis and discussion of two intersections in Gainesville, Florida, USA. Just as important is the evaluation of the efficacy of countermeasures. This paper advocates for selection from a menu of countermeasures at the municipal level, with safety as the top priority. Performance is also considered, and we present a novel concept of a performance–safety trade-off at intersections.

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Long, Z., N. Yang, Y. Huang, Y. Chao, and L. Wan. "QUANTITATIVE EVALUATION METHOD OF ELEMENTS PRIORITY OF CARTOGRAPHIC GENERALIZATION BASED ON TAXI TRAJECTORY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 65–69. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-65-2017.

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Considering the lack of quantitative criteria for the selection of elements in cartographic generalization, this study divided the hotspot areas of passengers into parts at three levels, gave them different weights, and then classified the elements from the different hotspots. On this basis, a method was proposed to quantify the priority of elements selection. Subsequently, the quantitative priority of different cartographic elements was summarized based on this method. In cartographic generalization, the method can be preferred to select the significant elements and discard those that are relatively non-significant.

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Kumar, Sushant, Declan Clarke, and Mark B. Gerstein. "Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures." Proceedings of the National Academy of Sciences 116, no. 38 (August 28, 2019): 18962–70. http://dx.doi.org/10.1073/pnas.1901156116.

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Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

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Arunachalam, Vanathi, and Nagamalleswara Nallamothu. "Load Balancing in RPL to Avoid Hotspot Problem for Improving Data Aggregation in IoT." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 528–40. http://dx.doi.org/10.22266/ijies2021.0228.49.

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Data aggregation plays a vital role in the Internet of Things (IoT), and it aggregates the collected sensor data from devices to suppress redundant data transmissions. Many-to-one traffic pattern in the IoT induces hotspot problem and inefficient data aggregation. The Routing protocol for low-power and lossy networks (RPL) in the network layer impacts the hotspot problem due to the frequent usage of forwarding nodes and load imbalance. The processes of network layer protocol, such as trickle algorithm and Objective Functions (OF) for Destination Oriented Directed Acyclic Graph (DODAG) construction, need more attention to avoid hotspot for efficient data aggregation. This work proposes a Load Balanced RPL (LoB-RPL) protocol to avoid hotspot creation using a composite metric based parent selection, DODAG construction, and local topology adaptive decision on trickle parameters. The LoBRPL improves the Minimum Hop with Hysteresis Objective Function(MRHOF) using the composite metric based parent selection and tunes the parameters of the Trickle algorithm. It ensures efficient maintenance of DODAG structure, hotspot avoidance, and unnecessary DIO transmissions. Beyond the advantages of composite metric based parent selection, consideration of dynamic parameters may induce frequent parent switching in RPL. To avoid frequent changes in the DODAG structure, the LoB-RPL optimally decides the parent switching threshold. Thus, the proposed work ensures a load-balanced and an energy-efficient RPL routing as well as data aggregation in the IoT environment. The LoB-RPL delivers outperforming results compared to the base RPL under various inter-packet interval time over 50 node topologies.

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Autika, Yotta, Aras Mulyadi, and Yusni Ikhwan Siregar. "Pemetaan Indek Kekeringan dan Sebaran Titik Hotspot Daerah Potensi Kebakaran Hutan dan Lahan di Propinsi Riau." Dinamika Lingkungan Indonesia 5, no. 1 (January 28, 2018): 1. http://dx.doi.org/10.31258/dli.5.1.p.1-11.

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Riau is one of the most vulnerable provinces to forest and land fires in Indonesia. The potency for forest and land fires is inseparable from the presence of peatlands and exacerbated by drought. The purpose of this research is to know the characteristics of meteorological drought using SPI (Standardized Precipitation Index) method and its relation with forest and peatland fire as one of disaster management effort in Riau Province. The data used in this research are monthly rainfall data from meteorology station and rainfall posts of BMKG, hotspot data from NOAA satellite, map of Forest Use Agreement (TGHK), peat land map and land use map. Analysis of drought characteristics was done by calculating monthly SPI-1 then determining the maximum duration, intensity, severity and drought exposure. Determination of the severity of the drought by weighting and suspension method was based on duration and intensity while drought exposure was done by overlaying the map of the severity of the drought with the land use map. Meanwhile, to know the potential of forest and land fires began with the selection of hotspots on peatlands and forest areas every month then created a graph of the relationship of meteorological drought with the number of hotspots. Then, to see the relationship of drought distribution to the distribution of hotspots in dry season (MK) and wet season (MH) of 2015 was done by overlaying cover the drought distribution with hotspot distribution. The result shows that drought characteristic in the most of Riau province has maximum duration around 4-6 months, dry category of intensity, high category of severity with exposure area in paddy field, field, habitation, and plantation. Then, negative SPI Index (dry condition) has potential to increase the number of hotspots otherwise positive SPI index (wet condition) leads to low occurrence of hotspot. The drought distribution in the dry season (July, August, September) of 2015 triggers the number of hotspots during drought conditions, while in wet season (April, November, December) of 2015 are dominated by normal conditions, some areas are dry and wet, resulting in lower hotspots distribution compared to the dry season.

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Tuna, Musaffe, Zhenlin Ju, Kosuke Yoshihara, Christopher I. Amos, Janos L. Tanyi, and Gordon B. Mills. "Clinical relevance of TP53 hotspot mutations in high-grade serous ovarian cancers." British Journal of Cancer 122, no. 3 (November 29, 2019): 405–12. http://dx.doi.org/10.1038/s41416-019-0654-8.

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Abstract Background Mutation of TP53 is the most frequent genetic alteration in high-grade serous ovarian cancer (HGSOC). The impact of hotspot mutations of TP53 and protein levels on patient outcomes in HGSOC has not been fully elucidated. Methods The study population (n = 791) comprised of HGSOC samples with TP53 mutation from TCGA and other publicly available data. Univariate and multivariate cox proportional hazards regression analyses were used to select variables that were correlated with patient survival. Results We assessed the effects of TP53 mutations based on type and individual hotspot mutations on patient outcomes in HGSOC. Only hotspot mutations were associated with outcomes. Three hotspot mutations: G266, Y163C, and R282, in aggregate were associated with a worsened overall and recurrence-free survival compared with other hotspot mutations (p < 0.0001 and p = 0.001), other non-hotspot missense mutations (p < 0.0001 and p = 0.008), truncated mutations (p < 0.0001 and p = 0.001), and all other mutations (p < 0.0001 and p = 0.001). Specific hotspot mutations were associated with different protein expression patterns consistent with different functions. Conclusions This study provides evidence that individual TP53 hotspot mutations have different impact on HGSOC patient outcomes and potentially TP53 function. Thus the status of particular TP53 aberrations could influence response to therapy and selection of therapeutic agents.

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Sukojo, Bangun Muljo, and Diya Rochima Lisakiyanto. "Web-Based Geographic Information System Development of Hotspots Distribution for Monitoring Forest and Land Fires Using Leaflet JavaScript Library (Case Study: Ogan Komering Ilir Regency, South Sumatera)." IOP Conference Series: Earth and Environmental Science 936, no. 1 (December 1, 2021): 012010. http://dx.doi.org/10.1088/1755-1315/936/1/012010.

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Abstract Forest and land fires are a disaster that occurs almost every year on Sumatra Island. Ogan Komering Ilir is one of the regencies in South Sumatra Province with a high number of hotspots causing forest and land fires every year. Prevention efforts are important to reduce the impact caused by forest and land fires on various aspects of life. One of them is by building a web-based Geographic Information System (WebGIS) for the distribution of hotspots as a form of early warning and detection system by utilizing VIIRS Nightfire (VNF) data from the remote sensing technology of the Suomi-NPP satellite which has Visible Infrared Imaging Radiometer Suite (VIIRS) active sensors which have been processed with the Nightfire algorithm. The Leaflet JavaScript library plays an important role in adding to the functionality of WebGIS with a wide selection of available plugins and easy-to-read source code to make web-based spatial information more interactive. The prototype of WebGIS with the name OKIApi has been successfully developed and has several key features such as displaying information on the distribution of hotspots that have been classified by temperature; the priority level of firefighting areas and the vulnerability level of flammable areas based on the type of land cover; route to the hotspot or the fire department locations; a chart of the estimated burned area from the source footprint of hotspot; and a chart of the number of hotspots per day that have been classified by temperature. The percentage value of the web feasibility for the functionality test to 13 WebGIS features is 100% with a very good predicate, the usability test is 91.5% with a very good predicate, and the portability test on 18 web browsers applications is 100% with a very good predicate.

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NirmalaDevi, K., and V. Murali Bhaskaran. "Rough Set and Entropy based Feature Selection for Online Forums Hotspot Detection." International Journal of Computer Applications 117, no. 10 (May 20, 2015): 37–41. http://dx.doi.org/10.5120/20593-3087.

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Moreno-García, Roberto A., Ricardo Zamora, and Miguel A. Herrera. "Habitat selection of endemic birds in temperate forests in a biodiversity "Hotspot"." Forest Systems 23, no. 2 (August 1, 2014): 216. http://dx.doi.org/10.5424/fs/2014232-03700.

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Jamil, Farhan, and Farrukh Zeeshan Khan. "Multi-criteria-Based Mobile Hotspot Selection in IoT-Based Highly Dense Network." Wireless Personal Communications 112, no. 3 (January 22, 2020): 1689–704. http://dx.doi.org/10.1007/s11277-020-07122-7.

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Yang, Siming, Zheng Shan, Jiang Cao, Yuan Gao, Yang Guo, Ping Wang, Jing Wang, and Xiaonan Wang. "A Novel Path Planning and Node Selection Method Using Reinforcement Learning in NTN IoT Networks." Wireless Communications and Mobile Computing 2022 (September 16, 2022): 1–14. http://dx.doi.org/10.1155/2022/5265038.

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With the rapid deployment of 5G networks in recent years, the characteristics of high bandwidth, low latency, and low energy consumption of 5G networks have enabled the rapid development of IoT (Internet of things) technology. However, 5G networks cannot provide high-quality wireless coverage for many IoT devices in border areas and hotspots with a high signal density that lack fixed infrastructure. Therefore, this paper uses the UAV (unmanned aerial vehicle) to carry the communication platform to build the NTN (nonterrestrial network) to provide wireless coverage for terrestrial fixed and mobile IoT devices. Meanwhile, since the NTN needs to provide wireless coverage for many IoT devices, we use deep reinforcement learning to provide path planning for the UAV communication platform to improve the efficiency of wireless coverage. We build a simulation environment to evaluate the performance of the NTN network for wireless coverage of IoT devices in urban hotspot areas. Experimental results show that the method proposed in this paper can provide higher downlink rates for more IoT devices than NB-IoT (narrowband Internet of things).

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Zhang, Wei, Xin Rong Wu, Wei Han, Ting Liu, and Shun Jiang. "A Novel Differentiate Weight Algorithm on Heterogeneous Network Selection." Advanced Materials Research 709 (June 2013): 593–98. http://dx.doi.org/10.4028/www.scientific.net/amr.709.593.

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How to select a target network meeting the needs of Quality of Service (QoS) guarantee in heterogeneous network environment has become a research hotspot. In this paper, a novel differentiate weight algorithm on heterogeneous network selection is proposed. As regard of the different needs of QoS for each class of traffic in heterogeneous network, the QoS parameter weight for each class of traffic is calculated by G1 method, and then the target network is selected by technique for order preference by similarity to ideal solution (TOPSIS). At last, simulation results show that the algorithm proposed in this paper is better than average weight algorithm on selecting target network reasonably and balancing network loads.

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Wang, Gensheng. "Research on Hotspot Discovery in Internet Public Opinions Based on Improved -Means." Computational Intelligence and Neuroscience 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/230946.

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How to discover hotspot in the Internet public opinions effectively is a hot research field for the researchers related which plays a key role for governments and corporations to find useful information from mass data in the Internet. An improved -means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original -means algorithm. First, some new methods are designed to preprocess website texts, select and express the characteristics of website texts, and define the similarity between two website texts, respectively. Second, clustering principle and the method of initial classification centers selection are analyzed and improved in order to overcome the limitations of original -means algorithm. Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice.

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Khalaf, Osamah Ibrahim, Carlos Andrés Tavera Romero, Shahzad Hassan, and Muhammad Taimoor Iqbal. "Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks." Journal of Sensors 2022 (February 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/7909472.

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Wireless Sensor Networks (WSNs) consist of a spatially distributed set of autonomous connected sensor nodes. The deployed sensor nodes are extensively used for sensing and monitoring for environmental surveillance, military operations, transportation monitoring, and healthcare monitoring. The sensor nodes in these networks have limited resources in terms of battery, storage, and processing. In some scenarios, the sensor nodes are deployed closer to the base station and responsible to forward their own and neighbor nodes’ data towards the base station and depleted energy. This issue is called a hotspot in the network. Hotspot issues mainly appear in those locations where traffic load is more on the sensor nodes. The dynamic and unequal clustering techniques have been used and mitigate the hotspot issues. However, with few benefits, these solutions have suffered from coverage overhead, network connection issues, unbalanced energy utilization among the sink nodes, and network stability issues. In this paper, a comprehensive review of various equal clustering, unequal clustering, and hybrid clustering approaches with their clustering attributes is presented to mitigate hotspot issues in heterogeneous WSNs by using various parameters such as cluster head selection, number of clusters, zone formation, transmission, and routing parameters. This review provides a detailed platform for new researchers to explore the new and novel solutions to solve the hotspot issues in these networks.

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Hofmann, Sylvia, Chitra Bahadur Baniya, Matthias Stöck, and Lars Podsiadlowski. "De novo Assembly, Annotation, and Analysis of Transcriptome Data of the Ladakh Ground Skink Provide Genetic Information on High-Altitude Adaptation." Genes 12, no. 9 (September 16, 2021): 1423. http://dx.doi.org/10.3390/genes12091423.

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The Himalayan Arc is recognized as a global biodiversity hotspot. Among its numerous cryptic and undiscovered organisms, this composite high-mountain ecosystem harbors many taxa with adaptations to life in high elevations. However, evolutionary patterns and genomic features have been relatively rarely studied in Himalayan vertebrates. Here, we provide the first well-annotated transcriptome of a Greater Himalayan reptile species, the Ladakh Ground skink Asymblepharus ladacensis (Squamata: Scincidae). Based on tissues from the brain, an embryonic disc, and pooled organ material, using pair-end Illumina NextSeq 500 RNAseq, we assembled ~77,000 transcripts, which were annotated using seven functional databases. We tested ~1600 genes, known to be under positive selection in anurans and reptiles adapted to high elevations, and potentially detected positive selection for 114 of these genes in Asymblepharus. Even though the strength of these results is limited due to the single-animal approach, our transcriptome resource may be valuable data for further studies on squamate reptile evolution in the Himalayas as a hotspot of biodiversity.

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Mangal, Ankita, and Elizabeth A. Holm. "A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials." Integrating Materials and Manufacturing Innovation 7, no. 3 (June 15, 2018): 87–95. http://dx.doi.org/10.1007/s40192-018-0109-8.

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Doughty, Kevin J., Helge Sierotzki, Martin Semar, and Andreas Goertz. "Selection and Amplification of Fungicide Resistance in Aspergillus fumigatus in Relation to DMI Fungicide Use in Agronomic Settings: Hotspots versus Coldspots." Microorganisms 9, no. 12 (November 26, 2021): 2439. http://dx.doi.org/10.3390/microorganisms9122439.

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Aspergillus fumigatus is a ubiquitous saprophytic fungus. Inhalation of A. fumigatus spores can lead to Invasive Aspergillosis (IA) in people with weakened immune systems. The use of triazole antifungals with the demethylation inhibitor (DMI) mode of action to treat IA is being hampered by the spread of DMI-resistant “ARAf” (azole-resistant Aspergillus fumigatus) genotypes. DMIs are also used in the environment, for example, as fungicides to protect yield and quality in agronomic settings, which may lead to exposure of A. fumigatus to DMI residues. An agronomic setting can be a “hotspot” for ARAf if it provides a suitable substrate and favourable conditions for the growth of A. fumigatus in the presence of DMI fungicides at concentrations capable of selecting ARAf genotypes at the expense of the susceptible wild-type, followed by the release of predominantly resistant spores. Agronomic settings that do not provide these conditions are considered “coldspots". Identifying and mitigating hotspots will be key to securing the agronomic use of DMIs without compromising their use in medicine. We provide a review of studies of the prevalence of ARAf in various agronomic settings and discuss the mitigation options for confirmed hotspots, particularly those relating to the management of crop waste.

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Hwang, Joyce K., Chong Wang, Zhou Du, Robin M. Meyers, Thomas B. Kepler, Donna Neuberg, Peter D. Kwong, et al. "Sequence intrinsic somatic mutation mechanisms contribute to affinity maturation of VRC01-class HIV-1 broadly neutralizing antibodies." Proceedings of the National Academy of Sciences 114, no. 32 (July 26, 2017): 8614–19. http://dx.doi.org/10.1073/pnas.1709203114.

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Variable regions of Ig chains provide the antigen recognition portion of B-cell receptors and derivative antibodies. Ig heavy-chain variable region exons are assembled developmentally from V, D, J gene segments. Each variable region contains three antigen-contacting complementarity-determining regions (CDRs), with CDR1 and CDR2 encoded by the V segment and CDR3 encoded by the V(D)J junction region. Antigen-stimulated germinal center (GC) B cells undergo somatic hypermutation (SHM) of V(D)J exons followed by selection for SHMs that increase antigen-binding affinity. Some HIV-1–infected human subjects develop broadly neutralizing antibodies (bnAbs), such as the potent VRC01-class bnAbs, that neutralize diverse HIV-1 strains. Mature VRC01-class bnAbs, including VRC-PG04, accumulate very high SHM levels, a property that hinders development of vaccine strategies to elicit them. Because many VRC01-class bnAb SHMs are not required for broad neutralization, high overall SHM may be required to achieve certain functional SHMs. To elucidate such requirements, we used a V(D)J passenger allele system to assay, in mouse GC B cells, sequence-intrinsic SHM-targeting rates of nucleotides across substrates representing maturation stages of human VRC-PG04. We identify rate-limiting SHM positions for VRC-PG04 maturation, as well as SHM hotspots and intrinsically frequent deletions associated with SHM. We find that mature VRC-PG04 has low SHM capability due to hotspot saturation but also demonstrate that generation of new SHM hotspots and saturation of existing hotspot regions (e.g., CDR3) does not majorly influence intrinsic SHM in unmutated portions of VRC-PG04 progenitor sequences. We discuss implications of our findings for bnAb affinity maturation mechanisms.

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McVean, Gil. "What drives recombination hotspots to repeat DNA in humans?" Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1544 (April 27, 2010): 1213–18. http://dx.doi.org/10.1098/rstb.2009.0299.

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Recombination between homologous, but non-allelic, stretches of DNA such as gene families, segmental duplications and repeat elements is an important source of mutation. In humans, recent studies have identified short DNA motifs that both determine the location of 40 per cent of meiotic cross-over hotspots and are significantly enriched at the breakpoints of recurrent non-allelic homologous recombination (NAHR) syndromes. Unexpectedly, the most highly penetrant form of the motif occurs on the background of an inactive repeat element family (THE1 elements) and the motif also has strong recombinogenic activity on currently active element families including Alu and LINE2 elements. Analysis of genetic variation among members of these repeat families indicates an important role for NAHR in their evolution. Given the potential for double-strand breaks within repeat DNA to cause pathological rearrangement, the association between repeats and hotspots is surprising. Here we consider possible explanations for why selection acting against NAHR has not eliminated hotspots from repeat DNA including mechanistic constraints, possible benefits to repeat DNA from recruiting hotspots and rapid evolution of the recombination machinery. I suggest that rapid evolution of hotspot motifs may, surprisingly, tend to favour sequences present in repeat DNA and outline the data required to differentiate between hypotheses.

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Li, Da, and Yi Zong. "External Windows Selection in Hot-Summer and Cold-Winter Areas." Applied Mechanics and Materials 448-453 (October 2013): 1301–7. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1301.

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Building energy conservation is a research hotspot at present. In this paper, using energy simulation software DesignBuilder, we analyzed the influence of different external window materials on energy consumption of residential buildings and finally studied the energy saving rate and economical efficiency. The economical efficiency was analyzed from two aspects: the difference of net present value and the pay back period. The result shows that Low-e insulating glazing is good at both energy-saving potential and economic benefits. This paper provides a basis for the external window selection in hot summer and cold winter areas like Wuhan.

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Zhang, Jing, Jia Jia Bi, Ning Sun, and Xue Gang Hu. "Multi-Relational Naïve Bayesian Classification Based on the Selection of Relations." Advanced Materials Research 1070-1072 (December 2014): 2066–72. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.2066.

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Nowadays, multi-relational classification has become a hotspot for research and application in the field of data mining. Compared to the single table with simple structure, multi-relational tables is more complicated. However, not all of the information in the tables has good effects on classification. It may decrease the classification accuracy of the algorithm when irrelevant relations are added. In this article, we optimized the multi-relational tables using the usefulness of the backgrounds to remove those relations which have little effect on the classification. The results show that, this method is effective.

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Zhang, Lihong, Shuqian Chen, and Yanglie Fu. "Fast Elliptic Curve Algorithm of Embedded Mobile Equipment." Open Electrical & Electronic Engineering Journal 7, no. 1 (December 13, 2013): 138–42. http://dx.doi.org/10.2174/1874129001307010138.

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Selection Algorithm and Generation Algorithm of elliptic curves have been the focus of research and hotspot of the Elliptic Curve Cryptosystem. This paper discusses a random elliptic curve realization method of Embedded Mobile Equipment, the SEA algorithm and its improved algorithm from Elliptic Curve's selection, Elliptic Curve's structure and Elliptic Curve's generation. Ensuring that the embedded system in the security situation goes through invariable situation causes the embedded system to realize a fast elliptic curve realization method, which enhances the efficiency of embedded system.

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Yao, Song, Lipeng Cui, and Sining Ma. "The Sparse Group Log Ridge for the Selection of Variable Groups." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012012. http://dx.doi.org/10.1088/1742-6596/2078/1/012012.

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Abstract In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.

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Zhang, Ru, Zi-ang Lin, Shaozhen Chen, Zhixuan Lin, and Xingwei Liang. "Multi-factor Stock Selection Model Based on Kernel Support Vector Machine." Journal of Mathematics Research 10, no. 5 (June 28, 2018): 9. http://dx.doi.org/10.5539/jmr.v10n5p9.

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In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

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Chen, Chao, and Hao Dong Zhu. "Feature Selection Method Based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization." Advanced Materials Research 546-547 (July 2012): 1538–43. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1538.

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In order to enhance the operating speed and reduce the occupied memory space and filter out irrelevant or lower degree of features, feature selection algorithms must be used. However, most of existing feature selection methods are serial and are inefficient timely to be applied to massive text data sets, so it is a hotspot how to improve efficiency of feature selection by means of parallel thinking. This paper presented a feature selection method based on Parallel Binary Immune Quantum-Behaved Particle Swarm Optimization (PBIQPSO). The presented method uses the Binary Immune Quantum-Behaved Particle Swarm Optimization to select feature subset, takes advantage of multiple computing nodes to enhance time efficiency, so can acquire quickly the feature subsets which are more representative. Experimental results show that the method is effective.

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Ke, Hong, Chuan Wang, and Gao Feng Luo. "The Research on Selection of Estimate Methods of Artificial Man-Days Unit Price of Power Construction Project." Applied Mechanics and Materials 405-408 (September 2013): 3437–41. http://dx.doi.org/10.4028/www.scientific.net/amm.405-408.3437.

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The selection of caculation method of the artificial man-days unit price of power construction projects is the focus and hotspot problem of the parties of power construction project and cost management departments. With domestic typical provinces, cities, and industry as the research object, this paper carried out case study and comprehensive analysis of three typical estimate methods of the basic principle, characteristics, application scope etc. The paper gives reference for the selection of estimate method of artificial man-days unit price of power construction project and this research conclusion to estimate method has strong pertinence and guidance.

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Rodgers-Melnick, Eli, Peter J. Bradbury, Robert J. Elshire, Jeffrey C. Glaubitz, Charlotte B. Acharya, Sharon E. Mitchell, Chunhui Li, Yongxiang Li, and Edward S. Buckler. "Recombination in diverse maize is stable, predictable, and associated with genetic load." Proceedings of the National Academy of Sciences 112, no. 12 (March 9, 2015): 3823–28. http://dx.doi.org/10.1073/pnas.1413864112.

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Among the fundamental evolutionary forces, recombination arguably has the largest impact on the practical work of plant breeders. Varying over 1,000-fold across the maize genome, the local meiotic recombination rate limits the resolving power of quantitative trait mapping and the precision of favorable allele introgression. The consequences of low recombination also theoretically extend to the species-wide scale by decreasing the power of selection relative to genetic drift, and thereby hindering the purging of deleterious mutations. In this study, we used genotyping-by-sequencing (GBS) to identify 136,000 recombination breakpoints at high resolution within US and Chinese maize nested association mapping populations. We find that the pattern of cross-overs is highly predictable on the broad scale, following the distribution of gene density and CpG methylation. Several large inversions also suppress recombination in distinct regions of several families. We also identify recombination hotspots ranging in size from 1 kb to 30 kb. We find these hotspots to be historically stable and, compared with similar regions with low recombination, to have strongly differentiated patterns of DNA methylation and GC content. We also provide evidence for the historical action of GC-biased gene conversion in recombination hotspots. Finally, using genomic evolutionary rate profiling (GERP) to identify putative deleterious polymorphisms, we find evidence for reduced genetic load in hotspot regions, a phenomenon that may have considerable practical importance for breeding programs worldwide.

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Souza-Silva, Marconi, Roberta Fernanda Ventura Cerqueira, Thais Giovannini Pellegrini, and Rodrigo Lopes Ferreira. "Habitat selection of cave-restricted fauna in a new hotspot of subterranean biodiversity in Neotropics." Biodiversity and Conservation 30, no. 14 (October 8, 2021): 4223–50. http://dx.doi.org/10.1007/s10531-021-02302-8.

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Mangal, Ankita, and Elizabeth A. Holm. "Correction to: A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials." Integrating Materials and Manufacturing Innovation 7, no. 3 (July 19, 2018): 96. http://dx.doi.org/10.1007/s40192-018-0114-y.

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Freudenberg, Jan, Ying-Hui Fu, and Louis J. Ptác̆ek. "Enrichment of HapMap recombination hotspot predictions around human nervous system genes: evidence for positive selection ?" European Journal of Human Genetics 15, no. 10 (June 13, 2007): 1071–78. http://dx.doi.org/10.1038/sj.ejhg.5201876.

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Sharma, Mahendra, and Santhosh Kumar Singh. "Tentative Route Selection aApproach for Irregular Clustered Wireless Sensor Networks." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (December 1, 2017): 715. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp715-718.

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Wireless Sensor Networks (WSNs) assume a crucial part in the field of mechanization and control where detecting of data is the initial step before any automated job could be performed. So as to encourage such perpetual assignments with less vitality utilization proportion, clustering is consolidated everywhere to upgrade the system lifetime. Unequal Cluster-based Routing (UCR) [7] is a standout amongst the most productive answers for draw out the system lifetime and to take care of the hotspot issue that is generally found in equivalent clustering method. In this paper, we propose Tentative Route (TRS) Selection approach for irregular Clustered Wireless Sensor Networks that facilitates in decision an efficient next relay to send the data cumulative by Cluster Heads to the Base Station. Simulation analysis is achieved using the network simulator to demonstrate the effectiveness of the TRS method.

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Huang, Tianyu, Xijuan Guo, Yue Zhang, and Zheng Chang. "Collaborative Content Downloading in VANETs with Fuzzy Comprehensive Evaluation." Symmetry 11, no. 4 (April 6, 2019): 502. http://dx.doi.org/10.3390/sym11040502.

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Vehicle collaborative content downloading has become a hotspot in current vehicular ad-hoc network (VANET) research. However, in reality, the highly dynamic nature of VANET makes users lose resources easily, and the transmission of invalid segment data also wastes valuable bandwidth and storage of the users’ vehicles. In addition, the individual need of each customer vehicle should also be taken into consideration when selecting an agent vehicle for downloading. In this paper, a novel scheme is proposed for vehicle selection in the download of cooperative content from the Internet, by considering the basic evaluation information of the vehicle. To maximize the overall throughput of the system, a collaborative content downloading algorithm is proposed, which is based on fuzzy evaluation and a customer’s own expectations, in order to solve the problems of agent vehicle selection. With the premise of ensuring successful downloading and the selection preferences of customer vehicles, linear programming is used to optimize the distribution of agent vehicles and maximize customer’s satisfaction. Simulation results show that the proposed scheme works well in terms of average quality of service, average bandwidth efficiency, failure frequency, and average consumption.

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Qing-dao-er-ji, Ren, Rui Pang, and Yue Chang. "An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia." Mathematical Problems in Engineering 2020 (April 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/4020723.

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HotSpot is an algorithm that can directly mine association rules from real data. Aiming at the problem that the support threshold in the algorithm cannot be set accurately according to the actual scale of the dataset and needs to be set artificially according to experience, this paper proposes a dynamic optimization algorithm with minimum support threshold setting: S_HotSpot algorithm. The algorithm combines simulated annealing algorithm with HotSpot algorithm and uses the global search ability of simulated annealing algorithm to dynamically optimize the minimum support in the solution space. Finally, the Inner Mongolia sandstorm dataset is used for experiment while the wine quality dataset is used for verification, and the association rules screening indicators are set for the mining results. The results show that S_HotSpot algorithm can not only dynamically optimize the selection of support but also improve the quality of association rules as it is mining reasonable number of rules.

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Song, Wentao. "MIMO Antenna Array in 5G Communication." Highlights in Science, Engineering and Technology 27 (December 27, 2022): 600–612. http://dx.doi.org/10.54097/hset.v27i.3823.

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The main topics of this study are the fundamental operation of a MIMO antenna array and the benefits of using MIMO technology for 5G communication. Three significant works are recognized as benchmarks by outlining the component sets and spatial reuse that designers should consider in MIMO antenna design. For 5G low and medium frequencies, these include an 8x8 large-scale MIMO antenna array and a circular binary MIMO antenna array. In these designs, the design of the MIMO antenna array will be carried out based on the introduction of the design and parameter selection of the antenna array elements as a priority. Additionally, this study conducts a performance analysis of a multi-cell, Massive MIMO antenna array system, which serves as a high reference point for the Analysis of this system. Numerous possible application domains exist for the study object of this work, including hotspot situations and 3D coverage scenarios. Campus, shopping malls, and halls are examples of hotspot environments.

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Platts, Philip J., Colin J. McClean, Jon C. Lovett, and Rob Marchant. "Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty." Ecological Modelling 218, no. 1-2 (October 2008): 121–34. http://dx.doi.org/10.1016/j.ecolmodel.2008.06.028.

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Chen, Hao-xuan, Fei Tao, Pei-long Ma, Li-na Gao, and Tong Zhou. "Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory." ISPRS International Journal of Geo-Information 10, no. 3 (March 12, 2021): 161. http://dx.doi.org/10.3390/ijgi10030161.

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Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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Zhang, Bei, Zhan Ding, Liang Li, Ling-Kun Xie, Yu-Jie Fan, and Yong-Zhen Xu. "Two oppositely-charged sf3b1 mutations cause defective development, impaired immune response, and aberrant selection of intronic branch sites in Drosophila." PLOS Genetics 17, no. 11 (November 1, 2021): e1009861. http://dx.doi.org/10.1371/journal.pgen.1009861.

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SF3B1 mutations occur in many cancers, and the highly conserved His662 residue is one of the hotspot mutation sites. To address effects on splicing and development, we constructed strains carrying point mutations at the corresponding residue His698 in Drosophila using the CRISPR-Cas9 technique. Two mutations, H698D and H698R, were selected due to their frequent presence in patients and notable opposite charges. Both the sf3b1-H698D and–H698R mutant flies exhibit developmental defects, including less egg-laying, decreased hatching rates, delayed morphogenesis and shorter lifespans. Interestingly, the H698D mutant has decreased resistance to fungal infection, while the H698R mutant shows impaired climbing ability. Consistent with these phenotypes, further analysis of RNA-seq data finds altered expression of immune response genes and changed alternative splicing of muscle and neural-related genes in the two mutants, respectively. Expression of Mef2-RB, an isoform of Mef2 gene that was downregulated due to splicing changes caused by H698R, partly rescues the climbing defects of the sf3b1-H698R mutant. Lariat sequencing reveals that the two sf3b1-H698 mutations cause aberrant selection of multiple intronic branch sites, with the H698R mutant using far upstream branch sites in the changed alternative splicing events. This study provides in vivo evidence from Drosophila that elucidates how these SF3B1 hotspot mutations alter splicing and their consequences in development and in the immune system.

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Zvinoera, Katherine, J. Mutsvangwa, E. Chikaka, T. D. Coutinho, V. Kampira, and S. Mharakurwa. "Geo-Spatial Distribution of Frequencies of MTB/RIF Detected Specimens based on Requesting Health Facilities in Manicaland Zimbabwe for 2017 and 2018." Medical Journal of Zambia 48, no. 2 (August 10, 2021): 78–84. http://dx.doi.org/10.55320/mjz.48.2.867.

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Objectives: The aim of this study was to produce Geo Spatial Distribution of Frequencies of MTB/RIF Detected Specimens based on RequestingHealth Facilities in Manicaland Zimbabwe for 2017 and 2018, so as to give insight to TB program managers. Focusing elimination interventions onhot pockets of Tuberculosis (TB) strengthens rationale use of resources in resource limited countries like Zimbabwe. Early detection and earlytreatment is backbone of breaking TB transmission. Drug resistant tuberculosis (DRTB) control interventions like Programmatic Management of Drug Resistant TB or mentoring on Short, all Oral Regimen for Rifampicin resistant Tuberculosis (ShORRT) will be driven by science. Materials and Methods: The retrospective study was carried out in Manicaland, Zimbabwe. Manicaland one of the 10 provinces in Zimbabwe, has 7 districts with 308 health facilities. During this retrospective cross sectional study 2221 MTB detected results of 2017 and 2018, downloaded from 14 of the 15 Genexpert sites in Manicaland were employed to generate hotspot maps. Fifteenth Genexpert site lost its electronic records when Genexpert CPU crushed. Geographical Positioning System (GPS) of the health facilities were recorded.The study used MTB detected frequencies at a facility in relation to surrounding facilities inManicaland, then ran optimised hotspot analysis function in Arc Map 10.5 to implement the Gi* statistic. Results: Overall provincial MTB detected positivity was 2221/36055 (6.2%).Overall provincial Rifampicin Resistant (RR) positivity was .111.2221(5.0%).Geo-spatial map of Manicaland showed 10 facilities that are RR hotspots with 7/10 (70%) of the facilities in Buhera district. Chipinge district had facilities that were MTB detected high hotspots.For the whole of Manicaland, Buhera district had100% MTB detected low hotspots facilities. Ninety percent hotspots were clusteredaround 2 of the 15 Genexpert Sites in Manicaland, namely Murambinda Mission Hospital and Chipinge District Hospital. Conclusion: Study identified health facilities with high frequencies of RR areas. For the identified health facilities with high frequencies of RR specimens, NTP may focus DRTB control interventions like PMDT, or mentoring on ShORRT. For the health facilities with high frequencies of MTB detected NTP can focus trainings in TB Case Management. Instead of uniformly spreading the limited resources to all 325 facilities, efforts streamlined to manageable number of 20 facilities incommensurate with identified gap( e.g. objective selection of cadres for training, data driven supportive supervision & targeted awarenesscampaigns).

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Zou, Yajie, Xinzhi Zhong, John Ash, Ziqiang Zeng, Yinhai Wang, Yanxi Hao, and Yichuan Peng. "Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5230248.

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Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

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Niño, Carlos A., Rossella Scotto di Perrotolo, and Simona Polo. "Recurrent Spliceosome Mutations in Cancer: Mechanisms and Consequences of Aberrant Splice Site Selection." Cancers 14, no. 2 (January 7, 2022): 281. http://dx.doi.org/10.3390/cancers14020281.

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Splicing alterations have been widely documented in tumors where the proliferation and dissemination of cancer cells is supported by the expression of aberrant isoform variants. Splicing is catalyzed by the spliceosome, a ribonucleoprotein complex that orchestrates the complex process of intron removal and exon ligation. In recent years, recurrent hotspot mutations in the spliceosome components U1 snRNA, SF3B1, and U2AF1 have been identified across different tumor types. Such mutations in principle are highly detrimental for cells as all three spliceosome components are crucial for accurate splice site selection: the U1 snRNA is essential for 3′ splice site recognition, and SF3B1 and U2AF1 are important for 5′ splice site selection. Nonetheless, they appear to be selected to promote specific types of cancers. Here, we review the current molecular understanding of these mutations in cancer, focusing on how they influence splice site selection and impact on cancer development.

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Grant, Sydney R., Megan E. Fitzgerald, Barbara A. Foster, Wendy J. Huss, Lei Wei, and Gyorgy Paragh. "Abstract 1909: Comparison of mutational burden hotspots in cutaneous squamous cell carcinoma and UV-exposed healthy skin for development of optimal targeted sequencing panels." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1909. http://dx.doi.org/10.1158/1538-7445.am2022-1909.

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Abstract Cutaneous squamous cell carcinoma (cSCC) is the second most common human malignancy in the United States. Chronic long-term ultraviolet (UV) exposure drives cSCC development. Early clonal mutation (CM) accumulation is the first step in photocarcinogenesis and is the first known manifestation of field cancerization. Understanding CM accumulation and development is expected to improve the prevention of field cancerization and cSCC. Nevertheless, CMs are poorly understood, and mutations in cSCC have yet to be systematically compared to CM in sun-exposed skin. Key steps for studies to detect and compare CMs are to design optimal targeted sequencing panels and compare mutational hotspot areas. The ideal panel should cover genomic regions containing maximum numbers of mutations within a given number of amplicons. Currently there is no computational tool to help optimize target area selection. Therefore, we aimed to create an algorithm to optimize sequencing target area design for capturing high mutation frequency hotspot areas. An R Shiny web application was created to identify an optimal sequencing target panel from an input mutation dataset and compare the distribution of mutational hotspots. The tool optimizes sequencing target areas based on preset amplicon length by identifying the best fitting panel of amplicons to capture mutations efficiently. Besides identifying optimal sequencing target areas, the developed software also efficiently identifies the most highly mutated genomic areas and compares target area overlaps. The developed algorithm was used to compare the mutational hotspots of cSCC and CM in clinically normal-appearing skin or cSCC from previous publications (cBioPortal, Martincorena et al, Hernando et al, Wei et al, Fowler et al). The current method was more efficient than tested previously available alternative methods by increasing target areas capture efficacy by 1.05 - 8.1-fold. Using the developed tool, we found that frequently mutated areas of CMs in normal skin significantly overlap (p < 0.005) with those of cSCC. Mutational hotspots of normal skin with a history of frequent UV exposure had 1.2-fold greater overlap with cSCC than skin with minimal UV exposure, suggesting the frequently UV-exposed skin carries a greater number of cSCC-related mutations. Although we found that normal skin CM hotspots better predict capture efficiency in sun-exposed normal skin than cSCC mutation hotspots, cSCC mutation hotspots can still design efficient sequencing targets in genomic areas where we do not have deep sequencing data on CM in normal skin. Our work provides a framework for helping design efficient, customized sequencing panels covering the genomic regions with the highest number of mutations. We expect the current algorithm to be a valuable tool for similar studies of other cancers. Citation Format: Sydney R. Grant, Megan E. Fitzgerald, Barbara A. Foster, Wendy J. Huss, Lei Wei, Gyorgy Paragh. Comparison of mutational burden hotspots in cutaneous squamous cell carcinoma and UV-exposed healthy skin for development of optimal targeted sequencing panels [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 1909.

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Chen, Yili, Congdong Li, and Han Wang. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)." Forecasting 4, no. 4 (September 23, 2022): 767–86. http://dx.doi.org/10.3390/forecast4040042.

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Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

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Chattopadhyay, S., S. J. Weissman, V. N. Minin, T. A. Russo, D. E. Dykhuizen, and E. V. Sokurenko. "High frequency of hotspot mutations in core genes of Escherichia coli due to short-term positive selection." Proceedings of the National Academy of Sciences 106, no. 30 (July 15, 2009): 12412–17. http://dx.doi.org/10.1073/pnas.0906217106.

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Gordon, Alasdair J. E., William E. Schy, and Barry W. Glickman. "Non-phenotypic selection of N-methyl-N′-nitro-N-nitrosoguanidine-directed mutation at a predicted hotspot site." Mutation Research Letters 243, no. 2 (February 1990): 145–49. http://dx.doi.org/10.1016/0165-7992(90)90037-k.

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Rajagopal, Srivats, Roberto Meza-Romero, and Indraneel Ghosh. "Dual surface selection methodology for the identification of thrombin binding epitopes from hotspot biased phage-display libraries." Bioorganic & Medicinal Chemistry Letters 14, no. 6 (March 2004): 1389–93. http://dx.doi.org/10.1016/j.bmcl.2003.09.098.

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Zhao, Ruonan, Lize Gu, and Xiaoning Zhu. "Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection." Applied Sciences 9, no. 4 (February 16, 2019): 679. http://dx.doi.org/10.3390/app9040679.

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With the rapid development of the network, data fusion becomes an important research hotspot. Large amounts of data need to be preprocessed in data fusion; in practice, the features of datasets can be filtered to reduce the amount of data. The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification but less dependent on other features. In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection. Different from the existing research, our method takes full advantage of knowledge about the dataset itself and the differences between datasets, which makes the features selected have a higher correlation with the classification, improves the classification accuracy, and reduces the data dimension. Experimental results on the UCI machine learning repository datasets confirmed the performance and effectiveness of our method. Compared to the existing method, smaller subsets of features and an average of 1% higher classification accuracies were achieved.

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Bergom, Hannah E., Eamon P. Toye, Xiaolei Shi, Charles J. Ryan, Emmanuel S. Antonarakis, and Justin Hwang. "Pan-cancer analysis of BRAF alterations and tumor-specific mechanisms of activation." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e17005-e17005. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e17005.

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e17005 Background: BRAF is a proto-oncogene that is altered in various cancers and is a druggable target. Particularly, the selective BRAF kinase inhibitors Dabrafenib and Vemurafenib are FDA approved for melanoma and thyroid cancer that harbor the activating BRAF V600 hotspot mutations. In pre-clinical studies, activated BRAF promotes tumor cell function across many additional cancer types in which therapeutics have not been considered. We propose that identifying BRAF-activated cancers will guide future pre-clinical investigations and potentially expand the usage of existing BRAF-targeting therapeutics. Methods: We utilized bioinformatics to investigate the molecular profiles of pan-cancer cohorts from two platforms based on distinct sequencing technology. This included a custom curated dataset (whole exome, 6 studies, 39 cancer types, n = 12,019) and GENIE9.1 (targeted gene panel, 62 cancer types, n = 78,619). Studies were included based on genomic modalities (copy number alterations, mutations, gene fusions). We cataloged the frequency and all types of BRAF genomic alterations pan-cancer. We also examined other genomic features, including tumor mutational burden (TMB) and fraction of the genome altered (FGA), and their association with types of BRAF alterations across cancer types. Results: In both platforms, we found that BRAF alterations were observed across over 80% of cancer types. Melanoma, thyroid, and colorectal tumors exhibited the greatest rates of mutations. Regarding gene body rearrangements, we noted endocrine-driven cancers including ovarian and metastatic prostate tumors (mPC) exhibited relative robust rates of amplification. In addition, thyroid carcinoma and mPC harbored recurrent BRAF gene fusion events. In mPC, we found that the BRAF gene fusions often included an N-terminus gene fragment from known androgen-target genes (TMPRSS2 and SND1). These fusion events represented a dysregulated hormone-driven mechanism of BRAF activation. We also found that hotspot missense mutations were cancer-type specific, including K601 hotspots in mPC, G469 hotspots in non-small cell lung cancers, and G466 hotspots in ovarian cancers. Lastly, BRAF-amplified endocrine tumors harbored a 1.5-fold increase in FGA medians, whereas BRAF-mutated lung and colorectal tumors exhibited a 1.6-fold increase in TMB medians. Conclusions: Our molecular analysis revealed that cancers accrued activated BRAF through cancer-type-specific and divergent genomic mechanisms. These molecular features provide genomic evidence to expand the application of BRAF-targeted therapies in additional cancer lineages, including mPC. BRAF mutations were also associated with genomic biomarkers, such as TMB, which may predict response to immune therapies. This suggests that BRAF alterations can be considered as an additional feature to aid therapy selection.

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Bergom, Hannah E., Eamon P. Toye, Xiaolei Shi, Charles J. Ryan, Emmanuel S. Antonarakis, and Justin Hwang. "Pan-cancer analysis of BRAF alterations and tumor-specific mechanisms of activation." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e17005-e17005. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e17005.

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e17005 Background: BRAF is a proto-oncogene that is altered in various cancers and is a druggable target. Particularly, the selective BRAF kinase inhibitors Dabrafenib and Vemurafenib are FDA approved for melanoma and thyroid cancer that harbor the activating BRAF V600 hotspot mutations. In pre-clinical studies, activated BRAF promotes tumor cell function across many additional cancer types in which therapeutics have not been considered. We propose that identifying BRAF-activated cancers will guide future pre-clinical investigations and potentially expand the usage of existing BRAF-targeting therapeutics. Methods: We utilized bioinformatics to investigate the molecular profiles of pan-cancer cohorts from two platforms based on distinct sequencing technology. This included a custom curated dataset (whole exome, 6 studies, 39 cancer types, n = 12,019) and GENIE9.1 (targeted gene panel, 62 cancer types, n = 78,619). Studies were included based on genomic modalities (copy number alterations, mutations, gene fusions). We cataloged the frequency and all types of BRAF genomic alterations pan-cancer. We also examined other genomic features, including tumor mutational burden (TMB) and fraction of the genome altered (FGA), and their association with types of BRAF alterations across cancer types. Results: In both platforms, we found that BRAF alterations were observed across over 80% of cancer types. Melanoma, thyroid, and colorectal tumors exhibited the greatest rates of mutations. Regarding gene body rearrangements, we noted endocrine-driven cancers including ovarian and metastatic prostate tumors (mPC) exhibited relative robust rates of amplification. In addition, thyroid carcinoma and mPC harbored recurrent BRAF gene fusion events. In mPC, we found that the BRAF gene fusions often included an N-terminus gene fragment from known androgen-target genes (TMPRSS2 and SND1). These fusion events represented a dysregulated hormone-driven mechanism of BRAF activation. We also found that hotspot missense mutations were cancer-type specific, including K601 hotspots in mPC, G469 hotspots in non-small cell lung cancers, and G466 hotspots in ovarian cancers. Lastly, BRAF-amplified endocrine tumors harbored a 1.5-fold increase in FGA medians, whereas BRAF-mutated lung and colorectal tumors exhibited a 1.6-fold increase in TMB medians. Conclusions: Our molecular analysis revealed that cancers accrued activated BRAF through cancer-type-specific and divergent genomic mechanisms. These molecular features provide genomic evidence to expand the application of BRAF-targeted therapies in additional cancer lineages, including mPC. BRAF mutations were also associated with genomic biomarkers, such as TMB, which may predict response to immune therapies. This suggests that BRAF alterations can be considered as an additional feature to aid therapy selection.

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Iqbal, Javed. "Impact of silvicultural system on natural regeneration in Western Himalayan moist temperate forests of Pakistan." Journal of Forest Science 67, No. 3 (March 5, 2021): 101–12. http://dx.doi.org/10.17221/124/2020-jfs.

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Site conditions (topography, aspect, moisture availability, humus thickness, light exposure, and grazing activities) play a vital role in the germination and regeneration process. The research was conducted in the Himalayan moist temperate forest. The research site was divided based on the silvicultural system (group selection system and single-tree selection system) into 148 plots and 150 plots, respectively. The group selection system was examined on the site of 2 ha which was clear-felled under a project in the 1980's. The present study examined the impact of silvicultural systems on regeneration. The frequency table was used, and relative frequency was calculated for the species and silvicultural system, density per m2 was also calculated. Diversity indices were calculated through taxa, dominance, Simpson’s index, Shannon index, evenness, equitability, and fisher alpha. Ten taxa were found in both silvicultural systems, with individual repetition of 17 and 15 taxa, respectively. Group selection is more compact visibly as compared to the single-tree selection system. The single-tree selection system is more diversified in species composition, stand structure, moisture availability, and less humus availability. The study also highlights future predictions for the conservation of these forests, which are highly sensitive and a hotspot for wildlife and climate change phenomena. Silvicultural practices such as silvicultural system, cleaning, weeding, thinning operations are regularly practiced, which can reduce the negative impact on these productive forests.

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Journal articles on the topic 'Hotspot selection'

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