An Empirical Study of Off-by-one Loop Mutation

Context: Developing test cases that are measurably effective in finding faults in programs is a very challenging research problem. Mutation testing, a prominent technique developed to address this challenge, often becomes com- putationally too expensive for practical use due to the very large number of mutants that need to be analyzed. Objective: This paper evaluates the impact of One-by-one (OBO) loop mutation in reducing the cost of mutation analysis and investigates this technique\u27s effectiveness in measuring the strength or weakness of test suites. Method: A set of Java and C programs have been used to generate both OBO and traditional mutants. Mutation scores are computed and analyzed for both sets of mutants. An analysis of first order vs. higher order loop mutations have also been performed. Results: On average, 89.15% fewer mutants are generated by OBO op- erator in comparison to traditional operators while the two sets of muta- tion scores still remain highly positively correlated (correlation coefficient of .9228) indicating the usefulness of OBO operator in measuring test suite\u27s ef- fectiveness of finding faults in programs. We also investigate the relationship between first order OBO mutation (FOM) and their corresponding higher order mutations (HOM). We have found that OBO HOMs do not subsume their corresponding FOMs. Conclusion: We conclude that One-by-one (OBO) loop mutant operator, which targets specific program elements for mutation, can greatly reduce the number of mutants generated, and thus make the mutation analysis relatively inexpensive and practical while still being capable of providing useful measurement of the strength or weakness of a test suite. Our investigation into the relationship between higher order OBO mutants (HOM) and first order OBO mutants (FOM) has revealed that OBO HOMs usually do not add any value to the mutation analysis over the corresponding FOMs

Post craniectomy paradoxical brain herniation: a case report with radiological review

Sinking skin flap (SSF) syndrome and paradoxical brain herniation are rare complications after craniectomy. On CT scan, there is shrunken appearance of the skin flap at craniectomy site. The meningogaleal complex is drawn inwards and is resting on underlying deformed brain with resultant concave surface. It results due to altered CSF hydrodynamics. Paradoxical brain herniation is rare complications which occur in patients who undergo cerebrospinal fluid (CSF) drainage procedures like lumbar puncture (LP), external ventricular drainage, ventriculo-peritoneal shunting and post craniectomy. Its early detection on imaging is essential as it is a neurosurgical emergency. We report a case of 75 year old male previously operated for left chronic subdural hematoma in the left fronto-temporo-parietal region presenting with altered consciousness and inability to walk. Plain CT scan showed craniectomy defect in the left fronto-temporo-parietal region with indrawing of meningogaleal complex suggestive of Shrunken Skin Flap. There was mass effect on the left lateral ventricle and third ventricle with shift of the midline structures towards right (1cm) with evidence of subfalcine herniation suggestive of paradoxical brain herniation

Status of major diseases of brinjal and tomato in charland of Jamalpur and Sherpur districts of Bangladesh

Brinjal and tomato, both of these Solanaceous crops, suffer from different diseases based on their surrounding environment. In charland ecosystem, due to the current trend of climate change these two crops have become vulnerable to disease infection. A comprehensive survey was conducted in Jamalpur and Sherpur districts to measure the severity of major diseases of brinjal and tomato in this region during the rabi season of 2018-19. This survey was based on farmers’ fields targeting 10 different locations where brinjal and tomato were grown extensively. Data were collected from randomly selected farmer’s vegetable fields where incidence and severity were recorded. Through the survey, five (5) diseases based on their incidence and severity were categorized as major diseases in studied areas. Bacterial wilt of brinjal (60%) and viral disease of tomato (41.67%) were found to be higher in incidence. This was a major limiting factor for decreasing total brinjal and tomato production in the charland of Jamalpur and Sherpur. Wilting of tomato (20%), viral disease of brinjal (20%) and phomopsis blight of brinjal (13.33%) were medium in incidence at different locations. Farmers of these areas found these diseases to be a serious threat to future cultivation and expansion of brinjal and tomato in charland

Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments

Pathway and network analysis of more than 2500 whole cancer genomes.

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

The 5-Hydroxymethylcytosine Landscape of Prostate Cancer

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. SIGNIFICANCE: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880.publishedVersionPeer reviewe

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis