Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering

Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review could improve the program repair further. Large language models, trained with Natural Language (NL) and Programming Language (PL), can contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of PL and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models. With the advent of code generative models like Codex and GPT-3.5-Turbo, we also performed zero-shot and few-shots learning-based prompt engineering to assess their performance on these datasets. However, the practical application of using LLMs in the context of automated program repair is still a long way off based on our manual analysis of the generated repaired codes by the learning models.Comment: 12 pages, 2 figures, 4 table

An Empirical Study of Using Large Language Models for Unit Test Generation

A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g. GitHub Copilot) are increasingly being adopted in practice, it is unclear whether they can successfully be used for unit test generation without fine-tuning. We investigated how well three generative models (Codex, GPT-3.5-Turbo, and StarCoder) can generate test cases to fill this gap. We used two benchmarks (HumanEval and Evosuite SF110) to investigate the context generation's effect in the unit test generation process. We evaluated the models based on compilation rates, test correctness, coverage, and test smells. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. The generated tests also suffered from test smells, such as Duplicated Asserts and Empty Tests.Comment: Preprint submitted to Journal of Systems and Software; 36 pages, 4 figures, 7 table

Thalidomide is Associated With Increased T Cell Activation and Inflammation in Antiretroviral-naive HIV-infected Individuals in a Randomised Clinical Trial of Efficacy and Safety

Trial Design: Open-label, randomised, controlled, pilot proof-of-concept clinical trial. Methods: Participants: Antiretroviral naive adult males with CD4 count >= 350 cells/mm(3). Interventions: Patients were randomised to receive thalidomide 200 mg QD for 3 weeks (Thalidomide group) or not (Control group) and followed for 48 weeks. Objective: We hypothesized that short-term Thalidomide use would reduce HIV related inflammation and HIV replication among antiretroviral naive HIV infected individuals. Outcome: Viral loads, CD4/CD8 counts, ultra-sensitive C-reactive protein (US-CRP), cell activation markers, and plasma lipopolysaccharide (LPS) were analyzed. Randomisation: Unrestricted randomisation. Blinding: No blinding was used. Results: Numbers randomised: Thirty recruited individuals were randomised to Thalidomide (16 patients) or Control (14 patients) groups. Recruitment: Patients were recruited from April 2011 to January 2013. Outcome: Viral loads remained stable in both groups. During thalidomide treatment, a decrease in CD4/CD8 ratio (p = 0.04), a decrease in CD4 count (p = 0.04), an increase in cell activation calculated by the percentage of CD38 (+)/HLA-DR+ CD8 cells (p < 0.05) and an increase in US-CRP (p < 0.01) were observed in the Thalidomide group, with all parameters returning to baseline levels after thalidomide interruption. We confirmed that thalidomide increased HIV replication in vitro of 6 of 7 samples from long-term antiretroviral suppressed individuals. Harms: No class 3/4 adverse events occurred. Conclusions: Short-termuse of thalidomide led to an intense transient increase in T cell activation and inflammation, with a decrease in the CD4(+) cell count without changes to the CD8(+) cell count. We confirmed that thalidomide acts in vitro as a latency reversal agent and speculate that the in vivo results obtained were due to an increase in HIV replication. (C) 2017 The Authors. Published by Elsevier B.V.Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP)Univ Fed São Paulo, Lab Retrovirol, São Paulo, BrazilFiocruz MS, Inst Oswaldo Cruz, Lab Interdisciplinar Pesquisas Med, Rio De Janeiro, BrazilSecretaria Municipal Saude Antonio Ribeiro Neto, Rio De Janeiro, BrazilOncohiv, Rio De Janeiro, BrazilUniv Fed Rio de Janeiro, Rio De Janeiro, BrazilInst Fed Educ Ciencia & Tecnol Rio de Janeiro IF, Rio De Janeiro, BrazilUniv Fed São Paulo, Lab Retrovirol, São Paulo, BrazilFAPESP: 04/15856-9Web of Scienc

Hair cortisol concentration, weight loss maintenance and body weight variability: A prospective study based on data from the european nohow trial

Several cross-sectional studies have shown hair cortisol concentration to be associated with adiposity, but the relationship between hair cortisol concentration and longitudinal changes in measures of adiposity are largely unknown. We included 786 adults from the NoHoW trial, who had achieved a successful weight loss of ≥5% and had a body mass index of ≥25 kg/m2 prior to losing weight. Hair cortisol concentration (pg/mg hair) was measured at baseline and after 12 months. Body weight and body fat percentage were measured at baseline, 6-month, 12-month and 18-month visits. Participants weighed themselves at home ≥2 weekly using a Wi-Fi scale for the 18-month study duration, from which body weight variability was estimated using linear and non-linear approaches. Regression models were conducted to examine log hair cortisol concentration and change in log hair cortisol concentration as predictors of changes in body weight, change in body fat percentage and body weight variability. After adjustment for lifestyle and demographic factors, no associations between baseline log hair cortisol concentration and outcome measures were observed. Similar results were seen when analysing the association between 12-month concurrent development in log hair cortisol concentration and outcomes. However, an initial 12-month increase in log hair cortisol concentration was associated with a higher subsequent body weight variability between month 12 and 18, based on deviations from a nonlinear trend (β: 0.02% per unit increase in log hair cortisol concentration [95% CI: 0.00, 0.04]; P =0.016). Our data suggest that an association between hair cortisol concentration and subsequent change in body weight or body fat percentage is absent or marginal, but that an increase in hair cortisol concentration during a 12-month weight loss maintenance effort may predict a slightly higher subsequent 6-months body weight variability. Clinical Trial Registration: ISRCTN registry, identifier ISRCTN88405328. [ABSTRACT FROM AUTHOR]info:eu-repo/semantics/publishedVersio

Evaluation of Cellular Phenotypes Implicated in Immunopathogenesis and Monitoring Immune Reconstitution Inflammatory Syndrome in HIV/Leprosy Cases

BACKGROUND: It is now evident that HAART-associated immunological improvement often leads to a variety of new clinical manifestations, collectively termed immune reconstitution inflammatory syndrome, or IRIS. This phenomenon has already been described in cases of HIV coinfection with Mycobacterium leprae, most of them belonging to the tuberculoid spectrum of leprosy disease, as observed in leprosy reversal reaction (RR). However, the events related to the pathogenesis of this association need to be clarified. This study investigated the immunological profile of HIV/leprosy patients, with special attention to the cellular activation status, to better understand the mechanisms related to IRIS/RR immunopathogenesis, identifying any potential biomarkers for IRIS/RR intercurrence. METHODS/PRINCIPAL FINDINGS: Eighty-five individuals were assessed in this study: HIV/leprosy and HIV-monoinfected patients, grouped according to HIV-viral load levels, leprosy patients without HIV coinfection, and healthy controls. Phenotypes were evaluated by flow cytometry for T cell subsets and immune differentiation/activation markers. As expected, absolute counts of the CD4+ and CD8+ T cells from the HIV-infected individuals changed in relation to those of the leprosy patients and controls. However, there were no significant differences among the groups, whether in the expression of cellular differentiation phenotypes or cellular activation, as reflected by the expression of CD38 and HLA-DR. Six HIV/leprosy patients identified as IRIS/RR were analyzed during IRIS/RR episodes and after prednisone treatment. These patients presented high cellular activation levels regarding the expression of CD38 in CD8+ cells T during IRIS/RR (median: 77,15%), dropping significantly (p<0,05) during post-IRIS/RR moments (median: 29,7%). Furthermore, an increase of cellular activation seems to occur prior to IRIS/RR. CONCLUSION/SIGNIFICANCE: These data suggest CD38 expression in CD8+ T cells interesting tool identifying HIV/leprosy individuals at risk for IRIS/RR. So, a comparative investigation to leprosy patients at RR should be conducted

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment