CodePlan: Repository-level Coding using LLMs and Planning

Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them

Transcultural Diabetes Nutrition Therapy Algorithm: The Asian Indian Application

India and other countries in Asia are experiencing rapidly escalating epidemics of type 2 diabetes (T2D) and cardiovascular disease. The dramatic rise in the prevalence of these illnesses has been attributed to rapid changes in demographic, socioeconomic, and nutritional factors. The rapid transition in dietary patterns in India—coupled with a sedentary lifestyle and specific socioeconomic pressures—has led to an increase in obesity and other diet-related noncommunicable diseases. Studies have shown that nutritional interventions significantly enhance metabolic control and weight loss. Current clinical practice guidelines (CPGs) are not portable to diverse cultures, constraining the applicability of this type of practical educational instrument. Therefore, a transcultural Diabetes Nutrition Algorithm (tDNA) was developed and then customized per regional variations in India. The resultant India-specific tDNA reflects differences in epidemiologic, physiologic, and nutritional aspects of disease, anthropometric cutoff points, and lifestyle interventions unique to this region of the world. Specific features of this transculturalization process for India include characteristics of a transitional economy with a persistently high poverty rate in a majority of people; higher percentage of body fat and lower muscle mass for a given body mass index; higher rate of sedentary lifestyle; elements of the thrifty phenotype; impact of festivals and holidays on adherence with clinic appointments; and the role of a systems or holistic approach to the problem that must involve politics, policy, and government. This Asian Indian tDNA promises to help guide physicians in the management of prediabetes and T2D in India in a more structured, systematic, and effective way compared with previous methods and currently available CPGs

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Using machine learning as a tool to help guide undeclared/undecided first-year engineering students toward a discipline

Supervised Machine Learning classification algorithms are used to analyze the potential inclination of the undecided/undeclared first-year engineering students. The data exploration task is possible by building a dataset that comprises of questions based on significant attributes. These attributes hover around different disciplines of engineering being offered at the University of Toronto. This qualitative survey is distributed to upperclassmen students (3rd, 4th year and graduate students, N = 54) and undecided first-year engineering students (N = 29) Multi-class classification is a technique that is used to categorize the data into two or more classes, in this case, the different disciplines of engineering at University of Toronto. The dataset that is built, based on the answers provided by the upperclassmen, is programmed into different classification algorithms such as Logistic Regression, KNN (K-nearest neighbors), Decision Tree and Random Forest classifier. The algorithms are compared so as to identify the most appropriate one that can determine the specific class label of the upperclassmen based on the answers provided in the qualitative survey. The accuracy of the various algorithms is an indicator of the favorable algorithm that can serve as a tool to suggest the potential majors that could be pursued by the undecided/undeclared students. Moreover, the answers given by the upperclassmen is visually analyzed for identifying the patterns of inclination of the students belonging to different disciplines of engineering

Dental applications of ozone therapy: A review of literature

Ozone has been used successfully for the treatment of various diseases for more than a decade. Its unique properties include immunostimulant, analgesic, antihypnotic, detoxicating, antimicrobial, bioenergetic and biosynthetic actions. Its atraumatic, painless, non invasive nature, and relative absence of discomfort and side effects increase the patient’s acceptability and compliance thus making it an ideal treatment choice specially for pediatric patients. This review is an attempt to highlight various treatment modalities of ozone therapy and its possible clinical applications in future

SMARCAD1 Phosphorylation and Ubiquitination Are Required for Resection during DNA Double-Strand Break Repair

Summary: The chromatin remodeling factor SMARCAD1, an SWI/SNF ATPase family member, has a role in 5′ end resection at DNA double-strand breaks (DSBs) to produce single-strand DNA (ssDNA), a critical step for subsequent checkpoint and repair factor loading to remove DNA damage. However, the mechanistic details of SMARCAD1 coupling to the DNA damage response and repair pathways remains unknown. Here we report that SMARCAD1 is recruited to DNA DSBs through an ATM-dependent process. Depletion of SMARCAD1 reduces ionizing radiation (IR)-induced repairosome foci formation and DSB repair by homologous recombination (HR). IR induces SMARCAD1 phosphorylation at a conserved T906 by ATM kinase, a modification essential for SMARCAD1 recruitment to DSBs. Interestingly, T906 phosphorylation is also important for SMARCAD1 ubiquitination by RING1 at K905. Both these post-translational modifications are critical for regulating the role of SMARCAD1 in DNA end resection, HR-mediated repair, and cell survival after DNA damage. : In Vitro Toxicology Including 3D Culture; Bioengineering; Tissue Engineering Subject Areas: In Vitro Toxicology Including 3D Culture, Bioengineering, Tissue Engineerin