Fuzzy-Set Qualitative Comparative Analysis (fsQCA) in Organizational Psychology: Theoretical Overview, Research Guidelines, and A Step-By-Step Tutorial Using R Software

Fuzzy set qualitative comparative analysis (fsQCA) is a method for assessing the effects of configurations of variables leading to an outcome. The recent growth of interest in this technique in organizational psychology is proving this method to be an important tool for addressing new and decisive research hypotheses. However, the effectiveness of fsQCA is dictated not only by its general principles, but also by how well these are understood and applied in the research community. Consequently, a guide that covers the fundamental ideas and tenets of the approach is required to aid the research community in its comprehension and practical application. The current study seeks to offer an understanding of FsQCA by providing: (a) A complete description of the method highlighting some of the most important theoretical-methodological aspects; (b) a perspective on the most used guidelines and recommendations, and (c) step-by-step instructions on how to carry out FsQCA in R using the QCA package. Data from 120 employees and supervisors derived from a company based in central Italy were used o best to illustrate how to carry out fsQCA. Codes for conducting the analyses from the QCA package for R accompany the tutorial and can be adapted to a new dataset. (AU)

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences.

Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server.

There's an App for That: Development of an Application to Operationalize the Global Diet Quality Score.

BACKGROUND: The global diet quality score (GDQS) is a simple, standardized metric appropriate for population-based measurement of diet quality globally. OBJECTIVES: We aimed to operationalize data collection by modifying the quantity of consumption cutoffs originally developed for the GDQS food groups and to statistically evaluate the performance of the operationalized GDQS relative to the original GDQS against nutrient adequacy and noncommunicable disease (NCD)-related outcomes. METHODS: The GDQS application uses a 24-h open-recall to collect a full list of all foods consumed during the previous day or night, and automatically classifies them into corresponding GDQS food group. Respondents use a set of 10 cubes in a range of predetermined sizes to determine if the quantity consumed per GDQS food group was below, or equal to or above food group-specific cutoffs established in grams. Because there is only a total of 10 cubes but as many as 54 cutoffs for the GDQS food groups, the operationalized cutoffs differ slightly from the original GDQS cutoffs. RESULTS: A secondary analysis using 5 cross-sectional datasets comparing the GDQS with the original and operationalized cutoffs showed that the operationalized GDQS remained strongly correlated with nutrient adequacy and was equally sensitive to anthropometric and other clinical measures of NCD risk. In a secondary analysis of a longitudinal cohort study of Mexican teachers, there were no differences between the 2 modalities with the beta coefficients per 1 SD change in the original and operationalized GDQS scores being nearly identical for weight gain (-0.37 and -0.36, respectively, P < 0.001 for linear trend for both models) and of the same clinical order of magnitude for waist circumference (-0.52 and -0.44, respectively, P < 0.001 for linear trend for both models). CONCLUSION: The operationalized GDQS cutoffs did not change the performance of the GDQS and therefore are recommended for use to collect GDQS data in the future.

Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model.

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

MutVis: Automated framework for analysis and visualization of mutational signatures in pathogenic bacterial strains.

In recent years, mutational signature analysis has become a routine practice in cancer genomics for classification and diagnosis. Characterizing mutational signatures across species or within genomes of a bacteria helps in understanding their evolution and adaptation. However, an integrated framework for analysis and visualization of mutational signatures in bacterial genome is lacking. Hence, we aim to develop an integrated, automated, open-source and user-friendly framework called MutVis to analyze mutational signatures from bacterial whole genome next generation sequencing data. The current framework integrates various publicly available packages using Snakemake workflow management software, Python and R scripting. MutVis supports variant calling, transition (Ti) and transversion (Tv) graphical representation, generation of mutational count matrix, graphical visualization of base-pair substitution spectrum (BPSs) and mutation signatures extraction. TvTi plots provide the 6 base substitution classification for both genome and gene level. Further resolution of base pair substitution classification is provided as 96-profile BPSs plot. Mutation signatures is derived based on the characteristic pattern observed in BPSs using non-negative matrix factorization. Relative contribution of signatures is given as hierarchically clustered heatmap. This provides information on active signatures in the individual given sample and classify samples according to signature contributions. We demonstrated the MutVis framework using geographically different strains of Mycobacterium tuberculosis, downloaded from PATRIC TB-ARC Antibiotic Resistance Catalog (n = 963). The current framework can be used to study mutation biases and characteristic mutational signatures in bacterial genomes and is freely available at https://github.com/AkshathaPrasanna/MutVis.

Shortening the Early Treatment Diabetic Retinopathy Study visual acuity test utilizing a novel computer software: reproducibility in control and patient eyes.

PURPOSE: To describe and compare a method of computerized visual acuity (VA) testing software to the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. METHODS: Setting: Single tertiary institution. STUDY POPULATION: Prospective study including right eyes of volunteers (N = 109) and patients (N = 126). INTERVENTION: Subjects were tested in a random order twice with the ETDRS chart and twice with the VA software. For ETDRS, we calculated the final VA separately for each run, using four different test termination criteria (1-miss in a row, 2-miss in a row, 50% miss and per-letter). For software testing, we calculated final VA with a variety of number of letters presented. MAIN OUTCOME MEASURES: The main outcome measures were reproducibility and number of letters required to exceed ETDRS reproducibility. RESULTS: For ETDRS, the average number of letters presented was 55.1 ± 9, 54.3 ± 10, 53.1 ± 10 and 70 for the 1-miss, 2-miss, 50% termination and per-letter criterion. The test-retest variability (TRV) of ETDRS was 0.29, 0.42, 0.17 and 0.141 for the 1-miss in a row, 2-miss in a row, 50% and per-letter termination criteria. For the software VA test, TRV was 0.202, 0.138 and 0.112 after presenting 6, 11 and 20 letters. The reproducibility of the software was equal to the ETDRS at 11 letters and thereafter surpassed. Similar results were achieved in the patient group. CONCLUSIONS: This study demonstrates that by utilizing a VA testing software, based on advanced threshold testing algorithms we were able to duplicate, and surpass, the reproducibility of the ETDRS chart while presenting much fewer letters.

Instantaneous and averaged heart rate profiles: Developing strategies for programming pacing rates in dogs.

Pacemakers use heart rate histograms (% beats) and sensor indicated rate histograms (% time) to illustrate rate distributions. When programmed to the rate adaptive modes, these data are used to determine the appropriateness of rate response to activity. These histograms are generated from instantaneous heart rate calculations. In humans, such data are compared to known histographic rate profiles. Such rate profiles during 24 h in the dog are not available. Moreover, data representation differ between Holter monitoring and pacemakers making comparisons challenging. The rate distribution in dogs >7-years of age was determined over 24 h using instantaneous and rolling average heart rate. Such data could serve as a guide to programming pacing rates for dogs. Sinus arrhythmia resulted in dissimilar heart rate profiles depending on the method of determining rate. The long intervals of sinus arrhythmia resulted in median values for the percent of time with an instantaneous heart rate of <50 beats/min (bpm) of 15%, whereas a rolling average heart rate of <50 bpm was 0.2%. Based on the cumulative time of the rolling average rate, dogs spent 26.3% of the day between 70-90 bpm with rates <65 bpm and >90 bpm approximating 30% for each. Rates >160 bpm were uncommon (<1%). However, high variability existed between dogs. This study demonstrated the shortcomings of both instantaneous and averaging methods to evaluate heart rate profiles in the dog and that both methods should be incorporated when making pacing rate decisions during programming.

TRsandflies: A Web-Based Software for the Morphometric Identification of Sand Flies in Turkey.

Sand flies are vector of several diseases, mostly cutaneous and visceral leishmaniasis (CL and VL). Also, 29 sand fly species have been identified in previous fauna studies carried out in 40 provinces of Turkey. Totally, 24 sand flies species belonging to Phlebotomus (Ph.) (Diptera: Psychodidae) genus have been proven or reported as possible vector species. This study aimed to develop a new software which could contribute to researchers' decision making about the identification of sand flies with obtained data from entomological surveys conducted before in Turkey. Developed software called TRsandflies included 35 textbox created with parameters obtained from caught sand flies specimens by the above-mentioned surveys. It also contained 130 photos and distribution maps related to 24 sand flies species. In addition, C# language and MYSQL database were used in the program. TRsandflies had three different forms (pages) allowing the user to compare the specimens and known species. In the species identification trials with three repetitions carried out in the program, except for the specimens belonging to the Transphlebotomus Artemiev & Neronov, 1984 subgenus, morphometric data of all previously collected sand fly species specimens were included. The process of running the morphometric measurement results of predetermined specimens in the program provided us with an accurate prediction rate of 86.66% in male specimens and 71.66% in female specimens. We concluded that the web-based software developed could play an important role in reducing the rate of possible errors that might be encountered by conventional identification methods.

COVID-19: atenção hospitalar na urgência e emergência

O Debate Virtual do Conass desta sexta-feira (7/8), às 17 horas irá tratar da atenção à Covid-19 nas urgências e emergências dos estados e municípios brasileiros, enfatizando a importância da integração da atenção hospitalar à Rede de Atenção à Saúde (RAS), considerando a continuidade do cuidado dos pacientes curados pela Atenção Primária à Saúde (APS). A iniciativa faz parte da implantação do Guia Orientador do Enfrentamento da Covid-19 na Rede de Atenção à Saúde, elaborado em parceria com o Conselho Nacional de Secretarias Municipais de Saúde (Conasems). Participarão do debate o médico especialista em terapia intensiva e medicina de emergência do Hospital Sírio-Libanês, Welfane Cordeiro Júnior; o diretor geral do Hospital Regional de Nova Andradina/Mato Grosso do Sul, Norberto Fabri Junior; o diretor geral do Hospital Municipal de Teixeira de Freitas/Bahia, Allan Jacqueson Barbosa Lobo; e a coordenadora estadual do Apoio Rede Colaborativa do Espírito Santo, Marfiza Machado de Novaes. A mediação do debate será feita pela assessora técnica do Conass, Rita Catanelli. Perguntas poderão ser enviadas pelo www.conass.org.br/participe O debate será transmitido pelo Portal do Conass: www.conass.org.br e pelos canais do Conselho no YouTube: www.youtube.com/conassoficial; e no FaceBook: www.facebook.com/conassoficial. Participe!

Automated data extraction from historical city directories: The rise and fall of mid-century gas stations in Providence, RI.

The location of defunct environmentally hazardous businesses like gas stations has many implications for modern American cities. To track down these locations, we present the directoreadr code (github.com/brown-ccv/directoreadr). Using scans of Polk city directories from Providence, RI, directoreadr extracts and parses business location data with a high degree of accuracy. The image processing pipeline ran without any human input for 94.4% of the pages we examined. For the remaining 5.6%, we processed them with some human input. Through hand-checking a sample of three years, we estimate that ~94.6% of historical gas stations are correctly identified and located, with historical street changes and non-standard address formats being the main drivers of errors. As an example use, we look at gas stations, finding that gas stations were most common early in the study period in 1936, beginning a sharp and steady decline around 1950. We are making the dataset produced by directoreadr publicly available. We hope it will be used to explore a range of important questions about socioeconomic patterns in Providence and cities like it during the transformations of the mid-1900s.

The Essential Role of Technology in the Public Health Battle Against COVID-19.

Technology has played an important role in responding to the novel coronavirus (SARS-CoV-2) and subsequent COVID-19 pandemic. The virus's blend of lethality and transmissibility have challenged officials and exposed critical limitations of the traditional public health apparatus. However, throughout this pandemic, technology has answered the call for a new form of public health that illustrates opportunities for enhanced agility, scale, and responsiveness. The authors share the Microsoft perspective and illustrate how technology has helped transform the public health landscape with new and refined capabilities - the efficacy and impact of which will be determined by history. Technologies like chatbot and virtualized patient care offer a mechanism to triage and distribute care at scale. Artificial intelligence and high-performance computing have accelerated research into understanding the virus and developing targeted therapeutics to treat infection and prevent transmission. New mobile contact tracing protocols that preserve patient privacy and civil liberties were developed in response to public concerns, creating new opportunities for privacy-sensitive technologies that aid efforts to prevent and control outbreaks. While much progress is still needed, the COVID-19 pandemic has highlighted technology's importance to public health security and pandemic preparedness. Future multi-stakeholder collaborations, including those with technology organizations, are needed to facilitate progress in overcoming the current pandemic, setting the stage for improved pandemic preparedness in the future. As lessons are assessed from the current pandemic, public officials should consider technology's role and continue to seek opportunities to supplement and improve on traditional approaches.

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.

Importance: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. Objective: To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. Design, Setting, and Participants: This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017. Main Outcomes and Measures: The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis. Results: A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001). Conclusions and Relevance: The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.

Refinement and validation of the IDIOM score for predicting the risk of gastrointestinal cancer in iron deficiency anaemia.

OBJECTIVE: To refine and validate a model for predicting the risk of gastrointestinal (GI) cancer in iron deficiency anaemia (IDA) and to develop an app to facilitate use in clinical practice. DESIGN: Three elements: (1) analysis of a dataset of 2390 cases of IDA to validate the predictive value of age, sex, blood haemoglobin concentration (Hb), mean cell volume (MCV) and iron studies on the probability of underlying GI cancer; (2) a pilot study of the benefit of adding faecal immunochemical testing (FIT) into the model; and (3) development of an app based on the model. RESULTS: Age, sex and Hb were all strong, independent predictors of the risk of GI cancer, with ORs (95% CI) of 1.05 per year (1.03 to 1.07, p<0.00001), 2.86 for men (2.03 to 4.06, p<0.00001) and 1.03 for each g/L reduction in Hb (1.01 to 1.04, p<0.0001) respectively. An association with MCV was also revealed, with an OR of 1.03 for each fl reduction (1.01 to 1.05, p<0.02). The model was confirmed to be robust by an internal validation exercise. In the pilot study of high-risk cases, FIT was also predictive of GI cancer (OR 6.6, 95% CI 1.6 to 51.8), but the sensitivity was low at 23.5% (95% CI 6.8% to 49.9%). An app based on the model was developed. CONCLUSION: This predictive model may help rationalise the use of investigational resources in IDA, by fast-tracking high-risk cases and, with appropriate safeguards, avoiding invasive investigation altogether in those at ultra-low predicted risk.

Utilization of health information technology among cancer genetic counselors.

BACKGROUND: Health information technology (IT) is becoming increasingly utilized by cancer genetic counselors (CGCs). We sought to understand the current engagement, satisfaction, and opportunities to adopt new health IT tools among CGCs. METHODS: We conducted a mixed-mode survey among 128 board-certified CGCs using both closed- and open-ended questions. We then evaluated the utilization and satisfaction among 10 types of health IT tools, including the following: cancer screening tool, family health history (FHx) collection tools, electronic health records (EHRs), telegenetics software, pedigree drawing software, genetic risk assessment tools, gene test panel ordering tools, electronic patient education tools, patient communication tools, and family communication tools. RESULTS: Seven of 10 health IT tools were used by a minority of CGCs. The vast majority of respondents reported using EHRs (95.2%) and genetic risk assessment tools (88.6%). Genetic test panel ordering software had the highest satisfaction rate (very satisfied and satisfied) at 80.0%, followed by genetic risk assessment tools (77.1%). EHRs had the highest dissatisfaction rate among CGCs at 18.3%. Dissatisfaction with a health IT tool was associated with desire to change: EHRs (p < .001), cancer screening tools (p = .010), genetic risk assessment tools (p = .024), and family history collection tools (p = .026). We found that nearly half of CGCs were considering adopting or changing their FHx tool (49.2%), cancer screening tool (44.9%), and pedigree drawing tool (41.8%). CONCLUSION: Overall, CGCs reported high levels of satisfaction among commonly used health IT tools. Tools that enable the collection of FHx, cancer screening tools, and pedigree drawing software represent the greatest opportunities for research and development.

Empirical study of the relationship between design patterns and code smells.

Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical study using 20 design patterns and 13 code smells in ten small-size to medium-size, open source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be associated with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit.

The awareness about software vulnerabilities is crucial to ensure effective cybersecurity practices, the development of high-quality software, and, ultimately, national security. This awareness can be better understood by studying the spread, structure and evolution of software vulnerability discussions across online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms-Twitter, GitHub, and Reddit. Moreover, we measure how user-level e.g., bot or not, and content-level characteristics e.g., vulnerability severity, post subjectivity, targeted operating systems as well as social network topology influence the rate of vulnerability discussion spread. To lay the groundwork, we present a novel fundamental framework for measuring information spread in multiple social platforms that identifies spread mechanisms and observables, units of information, and groups of measurements. We then contrast topologies for three social networks and analyze the effect of the network structure on the way discussions about vulnerabilities spread. We measure the scale and speed of the discussion spread to understand how far and how wide they go, how many users participate, and the duration of their spread. To demonstrate the awareness of more impactful vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber-attacks and those exploited by advanced persistent threat groups. One of our major findings is that most discussions start on GitHub not only before Twitter and Reddit, but even before a vulnerability is officially published. The severity of a vulnerability contributes to how much it spreads, especially on Twitter. Highly severe vulnerabilities have significantly deeper, broader and more viral discussion threads. When analyzing vulnerabilities in software products we found that different flavors of Linux received the highest discussion volume. We also observe that Twitter discussions started by humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by bots. On Reddit, discussion threads of positive posts are larger, wider, and deeper than negative or neutral posts. We also found that all three networks have high modularity that encourages spread. However, the spread on GitHub is different from other networks, because GitHub is more dense, has stronger community structure and assortativity that enhances information diffusion. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform the existing and new analytical frameworks for simulating information spread e.g., disinformation across multiple social environments online.

A comparison of imaging software and conventional cell counting in determining melanocyte density in photodamaged control sample and melanoma in situ biopsies.

BACKGROUND: Objective methods for distinguishing melanoma in situ (MIS) from photodamaged skin (PS) are needed to guide treatment in patients with melanocytic proliferations. Melanocyte density (MD) could serve as an objective histopathological criterion in difficult cases. Calculating MD via manual cell counts (MCC) with immunohistochemical (IHC)-stained slides has been previously published. However, the clinical application of this method is questionable, as quantification of MD via MCC on difficult cases is time consuming, especially in high volume practices. METHODS: ImageJ is an image processing software that uses scanned slide images to determine cell count. In this study, we compared MCC to ImageJ calculated MD in microphthalmia transcription factor-IHC stained MIS biopsies and control PS acquired from the same patients. RESULTS: We found a statistically significant difference in MD between PS and MIS as measured by both MCC and ImageJ software (P < 0.01). Additionally, no statistically significant difference was found when comparing MD measurements recorded by ImageJ vs those determined by the MCC method. CONCLUSION: MD as determined by ImageJ strongly correlates with the MD calculated by MCC. We propose the use of ImageJ as a time-efficient, objective, and reproducible tool to assess MD.