Community-Wide Prevention of COVID-19: A Systematic Analysis of Hot vs. Cold Spots

Drawing on 34 semi-structured interviews, this study investigates the temporality of family practices taking place in the hot spot. It does so by looking at how breakfast is inserted in the economy of family time in Italy. Our data show that breakfast, contrary to other meals, allows the adoption of more individualised and asynchronous practices, hinged on the consumption of convenience products. These time-saving strategies are normalised as part of doing family. Although the existing literature suggests that convenience and care are in opposition, and consumers of convenience products can experience anxiety and a lack of personal integrity, such features were not a dominant feature of our participants’ accounts. These findings suggest that the dichotomies of hot/cold spots and care/convenience are not always experienced in opposition when embedded within family practices. Hence, this study furthers understandings of family meals, temporality and the distinction between hot and cold spots.

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The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

10.21203/rs.3.rs-16301/v1 ◽

2020 ◽

Author(s):

Dingwen Lin ◽

Zhezhe Cui ◽

Virasakdi Virasakdi ◽

Prasit Palittapongarnpim ◽

Angkana Chaiprasert ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Hot Spots ◽

Hot Spot ◽

Cold Spot ◽

Sequencing Analysis ◽

Spot Area ◽

H37rv Strain ◽

Lineage 2 ◽

Cold Spots ◽

Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. Objective This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots.Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

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The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

10.21203/rs.3.rs-16301/v3 ◽

2020 ◽

Author(s):

Dingwen Lin ◽

Zhezhe Cui ◽

Virasakdi Virasakdi ◽

Prasit Palittapongarnpim ◽

Angkana Chaiprasert ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Hot Spots ◽

Hot Spot ◽

Cold Spot ◽

Sequencing Analysis ◽

Spot Area ◽

H37rv Strain ◽

Lineage 2 ◽

Cold Spots ◽

Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

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Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach

BMC Bioinformatics ◽

10.1186/s12859-020-03675-3 ◽

2020 ◽

Vol 21 (S13) ◽

Author(s):

Yuliang Pan ◽

Shuigeng Zhou ◽

Jihong Guan

Keyword(s):

Dna Binding ◽

Hot Spots ◽

Large Scale ◽

Binding Free Energy ◽

Hot Spot ◽

Dna Interaction ◽

Computational Method ◽

Dna Complexes ◽

Binding Interface ◽

Binding Interfaces

Abstract Background Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. Results Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. Conclusions PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/.

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Identifying diabetes management opportunity areas in the USA

Family Medicine and Community Health ◽

10.1136/fmch-2019-000293 ◽

2020 ◽

Vol 8 (1) ◽

pp. e000293 ◽

Cited By ~ 1

Author(s):

Michael Topmiller ◽

Kyle Shaak ◽

Peter J Mallow ◽

Autumn M Kieber-Emmons

Keyword(s):

Hot Spots ◽

Diabetes Management ◽

Hot Spot ◽

The United States ◽

Cold Spot ◽

United States Census ◽

Preventable Hospitalisation ◽

Cold Spots ◽

Medicare Spending ◽

Management Services

Using adherence to diabetes management guidelines as a case study, this paper applied a novel geospatial hot-spot and cold-spot methodology to identify priority counties to target interventions. Data for this study were obtained from the Dartmouth Atlas of Healthcare, the United States Census Bureau’s American Community Survey and the University of Wisconsin County Health Rankings. A geospatial approach was used to identify four tiers of priority counties for diabetes preventive and management services: diabetes management cold-spots, clusters of counties with low rates of adherence to diabetes preventive and management services (Tier D); Medicare spending hot-spots, clusters of counties with high rates of spending and were diabetes management cold-spots (Tier C); preventable hospitalisation hot-spots, clusters of counties with high rates of spending and are diabetes management cold-spots (Tier B); and counties that were located in a diabetes management cold-spot cluster, preventable hospitalisation hot-spot cluster and Medicare spending hot-spot cluster (Tier A). The four tiers of priority counties were geographically concentrated in Texas and Oklahoma, the Southeast and central Appalachia. Of these tiers, there were 62 Tier A counties. Rates of preventable hospitalisations and Medicare spending were higher in Tier A counties compared with national averages. These same counties had much lower rates of adherence to diabetes preventive and management services. The novel geospatial mapping approach used in this study may allow practitioners and policy makers to target interventions in areas that have the highest need. Further refinement of this approach is necessary before making policy recommendations.

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The Long-Term and System-Level Impacts of Institutionalizing Hot Spot Policing in a Small City

Policing A Journal of Policy and Practice ◽

10.1093/police/paaa096 ◽

2021 ◽

Author(s):

Christopher S Koper ◽

Cynthia Lum ◽

Xiaoyun Wu ◽

Tim Hegarty

Keyword(s):

Hot Spots ◽

Large Scale ◽

Hot Spot ◽

System Level ◽

Small City ◽

Police Agency ◽

Large Cities ◽

Quasi Experimental

Abstract Numerous studies have shown that hot spot policing (HSP) is effective in reducing crime in small high-risk locations. However, questions remain about the efficacy of HSP outside large cities, its long-term sustainability and effects, and its ability to produce aggregate reductions in crime across large areas. This study highlights a small city police agency that has sustained a systematic, citywide HSP patrol strategy since 2013. A quasi-experimental assessment using nearly 7 years of follow-up data shows the programme reduced crime in targeted hot spots without displacement. Citywide, citizen calls about crime and disorder fell by 14%, with reductions ranging from 12% for disorder calls to 41% for violence calls. This study shows the value of HSP in smaller jurisdictions and supports the theory that HSP can produce large-scale, long-term reductions in crime and disorder when practiced in a manner that has sufficient targeting, dosage, tracking, management, and commitment from leadership.

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Clustering of Mycobacterium avium subsp. paratuberculosis in Rabbits and the Environment: How Hot Is a Hot Spot?

Applied and Environmental Microbiology ◽

10.1128/aem.71.10.6033-6038.2005 ◽

2005 ◽

Vol 71 (10) ◽

pp. 6033-6038 ◽

Cited By ~ 25

Author(s):

Johanna Judge ◽

Ilias Kyriazakis ◽

Alastair Greig ◽

David J. Allcroft ◽

Michael R. Hutchings

Keyword(s):

Mycobacterium Avium ◽

Hot Spots ◽

Large Scale ◽

Temporal Dynamics ◽

Hot Spot ◽

Temporal Distribution ◽

Interspecies Transmission ◽

Environmental Distribution ◽

Cyclical Pattern ◽

Mycobacterium Avium Subsp Paratuberculosis

ABSTRACT Clustering of pathogens in the environment leads to hot spots of diseases at local, regional, national, and international levels. Scotland contains regional hot spots of Johne's disease (caused by Mycobacterium avium subsp. paratuberculosis) in rabbits, and there is increasing evidence of a link between paratuberculosis infections in rabbits and cattle. The spatial and temporal dynamics of paratuberculosis in rabbits within a hot spot region were studied with the overall aim of determining environmental patterns of infection and thus the risk of interspecies transmission to livestock. The specific aims were to determine if prevalence of paratuberculosis in rabbits varies temporally between seasons and whether the heterogeneous spatial environmental distribution of M. avium subsp. paratuberculosis on a large scale (i.e., regional hot spots) is replicated at finer resolutions within a hot spot. The overall prevalence of M. avium subsp. paratuberculosis in rabbits was 39.7%; the temporal distribution of infection in rabbits followed a cyclical pattern, with a peak in spring of 55.4% and a low in summer of 19.4%. Spatially, M. avium subsp. paratuberculosis-infected rabbits and, thus, the risk of interspecies transmission were highly clustered in the environment. However, this is mostly due to the clustered distribution of rabbits. The patterns of M. avium subsp. paratuberculosis infection in rabbits are discussed in relation to the host's socioecology and risk to livestock.

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Bifurcations and mutation hot-spots in SARS-CoV-2 spike protein

10.1101/2020.11.11.378828 ◽

2020 ◽

Author(s):

Xubiao Peng ◽

Antti J. Niemi

Keyword(s):

Hot Spots ◽

Large Scale ◽

Hot Spot ◽

Data Bank ◽

High Rate ◽

Spike Protein ◽

Novel Approach ◽

Promising Target ◽

Protein Research ◽

Local Topology

AbstractThe spike protein is a most promising target for the development of vaccines and therapeutic drugs against the SARS-CoV-2 infection. But the apparently high rate of mutations makes the development of antiviral inhibitors a challenge. Here a methodology is presented to try and predict mutation hot-spot sites, where a small local change in spike protein’s structure can lead to a large scale conformational effect, and change the protein’s biological function. The methodology starts with a systematic physics based investigation of the spike protein’s Cα backbone in terms of its local topology. This topological investigation is then combined with a statistical examination of the pertinent backbone fragments; the statistical analysis builds on a comparison with high resolution Protein Data Bank (PDB) structures. Putative mutation hot-spot sites are identified as proximal sites to bifurcation points that can change the local topology of the Cα backbone in an essential manner. The likely outcome of a mutation, if it indeed occurs, is predicted by a comparison with residues in best-matching PDB fragments together with general stereochemical considerations. The detailed methodology is developed using the already observed D614G mutation as an example. This is a mutation that could have been correctly predicted by the present approach. Several additional examples of potential hot-spot residues are identified and analyzed in detail, some of them are found to be even better candidates for a mutation hot-spot than D614G.Significance statementA novel approach to predict mutation hot-spots in SARS-CoV-2 spike protein is presented. The approach introduces new topology based techniques to biophysical protein research. For a proof-of-concept the approach is described with the notorious D614G mutation of the spike protein as an example. It is shown that this mutation could have been correctly predicted by the present methods. Several additional mutation hot-spots are then identified and a number of them are shown to be topologically similar to the observed D614G mutation. The methodology can be used to design effective drugs and antibodies against the spike protein. It can also be employed more generally, whenever one needs to search for and identify mutation hot-spots in a protein.

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Potential Application of Hough Transform to Extract Hot Spot Delineated Boundaries using Landsat-8 Satellite Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7342.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3550-3557

Keyword(s):

Hough Transform ◽

Hot Spots ◽

Large Scale ◽

Hot Spot ◽

Satellite Image ◽

Detection Algorithm ◽

Landsat 8 ◽

Spot Detection ◽

Canny Edge ◽

Satellite Image Analysis

Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculated, which suggest that hot spots were detected accurately in successful and efficient way. In second stage, the Canny edge detection algorithm is applied to detect the edges of the hot spot regions and then the binary image is generated, which is later converted into a vector image. Finally Hough transform is implemented on the obtained vector images for delineating hot spot boundaries. In future, delineated hot spot boundaries may be used to obtain the expansion or shrinking information of hot spot regions and it can be used for area estimation also.

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The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China

10.21203/rs.3.rs-16301/v2 ◽

2020 ◽

Author(s):

Dingwen Lin ◽

Zhezhe Cui ◽

Virasakdi Virasakdi ◽

Prasit Palittapongarnpim ◽

Angkana Chaiprasert ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Hot Spots ◽

Hot Spot ◽

Cold Spot ◽

Sequencing Analysis ◽

Spot Area ◽

H37rv Strain ◽

Lineage 2 ◽

Cold Spots ◽

Genomic Clusters

Abstract Background At present, there are few studies on polymorphism of Mycobacterium tuberculosis (Mtb) gene and how it affects the TB epidemic. This study aimed to document the differences of polymorphisms between tuberculosis hot and cold spot areas of Guangxi Zhuang Autonomous Region, China. Methods The cold and hot spot areas, each with 3 counties, had been pre-identified by TB incidence for 5 years from the surveillance database. Whole genome sequencing analysis was performed on all sputum Mtb isolates from the detected cases during January and June 2018. Single nucleotide polymorphism (SNP) of each isolate compared to the H37Rv strain were called and used for lineage and sub-lineage identification. Pairwise SNP differences between every pair of isolates were computed. Analyses of Molecular Variance (AMOVA) across counties of the same hot or cold spot area and between the two areas were performed. Results As a whole, 59.8% (57.7% sub-lineage 2.2 and 2.1% sub-lineage 2.1) and 39.8% (17.8% sub-lineage 4.4, 6.5% sub-lineage 4.2 and 15.5% sub-lineage 4.5) of the Mtb strains were Lineage 2 and Lineage 4 respectively. The percentages of sub-lineage 2.2 (Beijing family strains) are significantly higher in hot spots. Through the MDS dimension reduction, the genomic population structure in the three hot spot counties is significantly different from those three cold spot counties (T-test p = 0.05). The median of SNPs distances among Mtb isolates in cold spots was greater than that in hot spots (897 vs 746, Rank-sum test p < 0.001). Three genomic clusters, each with genomic distance ≤ 12 SNPs, were identified with 2, 3 and 4 consanguineous strains. Two clusters were from hot spots and one was from cold spots. Conclusion Narrower genotype diversity in the hot area may indicate higher transmissibility of the Mtb strains in the area compared to those in the cold spot area.

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