35 research outputs found
Forecasting dengue fever in Brazil: An assessment of climate conditions
No full textLocal climate conditions play a major role in the biology of the Aedes aegypti mosquito, the main vector responsible for transmitting dengue, zika, chikungunya and yellow fever in urban centers. For this reason, a detailed assessment of periods in which changes in climate conditions affect the number of human cases may improve the timing of vector-control efforts. In this work, we develop new machine-learning algorithms to analyze climate time series and their connection to the occurrence of dengue epidemic years for seven Brazilian state capitals. Our method explores the impact of two key variables—frequency of precipitation and average temperature—during a wide range of time windows in the annual cycle. Our results indicate that each Brazilian state capital considered has its own climate signatures that correlate with the overall number of human dengue-cases. However, for most of the studied cities, the winter preceding an epidemic year shows a strong predictive power. Understanding such climate contributions to the vector’s biology could lead to more accurate prediction models and early warning systems. [ © 2019 Stolerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. DOI: https://doi.org/10.1371/journal.pone.0220106
Authorship Verification
No full textIn recent years, stylometry, the study of linguistic style, has become more prominent in security and privacy applications involving written language, mostly in digital and online domains. Although literature is abundant with computational stylometry research, the field of authorship verification is relatively unexplored. Authorship verification is the binary semi-open-world problem of determining whether a document is written by a given author or not. A key component in authorship verification techniques is confidence measurement, on which verification decisions are based, expressed by acceptance thresholds selected and tuned per need. This thesis demonstrates how utilization of confidence-based approaches in stylometric applications, and their combination with traditional approaches, can benefit classification accuracy, and allow new domains and problems to be analyzed. We start by motivating the usage of authorship verification approaches with two stylometric applications: native-language identification from non-native text and active linguistic user authentication. Next, we introduce the Classify-Verify algorithm, which integrates classification with binary verification, applied to several stylometric problems. Classify-Verify is proposed as an open-world alternative to restricted closed-world attribution methods, and is shown effective in dealing with possibly missing candidate authors by thwarting misclassifications, coping with various domains and scales, and even adversarial authors who try to fool the classifier.Ph.D., Computer Science -- Drexel University, 201
Breaking the Closed-World Assumption in Stylometric Authorship Attribution
No full textPart 2: Forensic TechniquesInternational audienceStylometry is a form of authorship attribution that relies on the linguistic information found in a document. While there has been significant work in stylometry, most research focuses on the closed-world problem where the author of the document is in a known suspect set. For open-world problems where the author may not be in the suspect set, traditional classification methods are ineffective. This paper proposes the “classify-verify” method that augments classification with a binary verification step evaluated on stylometric datasets. This method, which can be generalized to any domain, significantly outperforms traditional classifiers in open-world settings and yields an F1-score of 0.87, comparable to traditional classifiers in closed-world settings. Moreover, the method successfully detects adversarial documents where authors deliberately change their styles, a problem for which closed-world classifiers fail
RNA analysis of intronic variants in the LAMA2 gene detected by whole genome sequencing confirms a rare dual diagnosis of incontinentia pigmenti with limb‐girdle muscular dystrophy
No full textAbstract We see that a multiple methods approach to diagnosis remains necessary in the era of whole genome sequencing. We also observe that reproductive risk genetic counseling can be a motivating factor for further testing along the diagnostic odyssey
Active Linguistic Authentication Using Real-Time Stylometric Evaluation for Multi-Modal Decision Fusion
No full textPart 2: Forensic TechniquesInternational audienceActive authentication is the process of continuously verifying a user based on his/her ongoing interactions with a computer. Forensic stylometry is the study of linguistic style applied to author (user) identification. This paper evaluates the Active Linguistic Authentication Dataset, collected from users working individually in an office environment over a period of one week. It considers a battery of stylometric modalities as a representative collection of high-level behavioral biometrics. While a previous study conducted a partial evaluation of the dataset with data from fourteen users, this paper considers the complete dataset comprising data from 67 users. Another significant difference is in the type of evaluation: instead of using day-based or data-based (number-of-characters) windows for classification, the evaluation employs time-based, overlapping sliding windows. This tests the ability to produce authentication decisions every 10 to 60 seconds, which is highly applicable to real-world active security systems. Sensor evaluation is conducted via cross-validation, measuring the false acceptance and false rejection rates (FAR/FRR). The results demonstrate that, under realistic settings, stylometric sensors perform with considerable effectiveness down to 0/0.5 FAR/FRR for decisions produced every 60 seconds and available 95% of the time
Increased degradation and alteered tissue distribution of cartilage oligomeric matrix protein in human rheumatoid and osteoarthritic cartilage
No full textDe novo coding variants in the AGO1 gene cause a neurodevelopmental disorder with intellectual disability
No full textBackground: High-impact pathogenic variants in more than a thousand genes are involved in Mendelian forms of neurodevelopmental disorders (NDD).
Methods: This study describes the molecular and clinical characterisation of 28 probands with NDD harbouring heterozygous AGO1 coding variants, occurring de novo for all those whose transmission could have been verified (26/28).
Results: A total of 15 unique variants leading to amino acid changes or deletions were identified: 12 missense variants, two in-frame deletions of one codon, and one canonical splice variant leading to a deletion of two amino acid residues. Recurrently identified variants were present in several unrelated individuals: p.(Phe180del), p.(Leu190Pro), p.(Leu190Arg), p.(Gly199Ser), p.(Val254Ile) and p.(Glu376del). AGO1 encodes the Argonaute 1 protein, which functions in gene-silencing pathways mediated by small non-coding RNAs. Three-dimensional protein structure predictions suggest that these variants might alter the flexibility of the AGO1 linker domains, which likely would impair its function in mRNA processing. Affected individuals present with intellectual disability of varying severity, as well as speech and motor delay, autistic behaviour and additional behavioural manifestations.
Conclusion: Our study establishes that de novo coding variants in AGO1 are involved in a novel monogenic form of NDD, highly similar to the recently reported AGO2-related NDD
Haplotype Structure of the ENPP1 Gene and Nominal Association of the K121Q Missense Single Nucleotide Polymorphism With Glycemic Traits in the Framingham Heart Study
No full textOBJECTIVE—A recent meta-analysis demonstrated a nominal association of the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) K→Q missense single nucleotide polymorphism (SNP) at position 121 with type 2 diabetes. We set out to confirm the association of ENPP1 K121Q with hyperglycemia, expand this association to insulin resistance traits, and determine whether the association stems from K121Q or another variant in linkage disequilibrium with it. RESEARCH DESIGN AND METHODS—We characterized the haplotype structure of ENPP1 and selected 39 tag SNPs that captured 96% of common variation in the region (minor allele frequency ≥5%) with an r2 value ≥0.80. We genotyped the SNPs in 2,511 Framingham Heart Study participants and used age- and sex-adjusted linear mixed effects (LME) models to test for association with quantitative metabolic traits. We also examined whether interaction between K121Q and BMI affected glycemic trait levels. RESULTS—The Q allele of K121Q (rs1044498) was associated with increased fasting plasma glucose (FPG), A1C, fasting insulin, and insulin resistance by homeostasis model assessment (HOMA-IR; all P = 0.01–0.006). Two noncoding SNPs (rs7775386 and rs7773477) demonstrated similar associations, but LME models indicated that their effects were not independent from K121Q. We found no association of K121Q with obesity, but interaction models suggested that the effect of the Q allele on FPG and HOMA-IR was stronger in those with a higher BMI (P = 0.008 and 0.01 for interaction, respectively). CONCLUSIONS—The Q allele of ENPP1 K121Q is associated with hyperglycemia and insulin resistance in whites. We found an adiposity-SNP interaction, with a stronger association of K121Q with diabetes-related quantitative traits in people with a higher BMI.Version of Recor
The study to understand the genetics of the acute response to metformin and glipizide in humans (SUGAR-MGH): design of a pharmacogenetic resource for type 2 diabetes.
No full textGenome-wide association studies have uncovered a large number of genetic variants associated with type 2 diabetes or related phenotypes. In many cases the causal gene or polymorphism has not been identified, and its impact on response to anti-hyperglycemic medications is unknown. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH, NCT01762046) is a novel resource of genetic and biochemical data following glipizide and metformin administration. We describe recruitment, enrollment, and phenotyping procedures and preliminary results for the first 668 of our planned 1,000 participants enriched for individuals at risk of requiring anti-diabetic therapy in the future.All individuals are challenged with 5 mg glipizide × 1; twice daily 500 mg metformin × 2 days; and 75-g oral glucose tolerance test following metformin. Genetic variants associated with glycemic traits and blood glucose, insulin, and other hormones at baseline and following each intervention are measured.Approximately 50% of the cohort is female and 30% belong to an ethnic minority group. Following glipizide administration, peak insulin occurred at 60 minutes and trough glucose at 120 minutes. Thirty percent of participants experienced non-severe symptomatic hypoglycemia and required rescue with oral glucose. Following metformin administration, fasting glucose and insulin were reduced. Common genetic variants were associated with fasting glucose levels.SUGAR-MGH represents a viable pharmacogenetic resource which, when completed, will serve to characterize genetic influences on pharmacological perturbations, and help establish the functional relevance of newly discovered genetic loci to therapy of type 2 diabetes.ClinicalTrials.gov NCT01762046
