Judicial Accountability in India: Issues and Challenges

The proper administration of justice system being an intrinsic value of rule of law and constitutional governance fundamentally lies with judicial accountability. Though the Indian constitutional jurisprudence is considerably developed in line with international judicial standards, still Indian legal system lacks adequate standard relating to judicial accountability and Code of Ethics. The inadequacy and inefficiency of the system are evident from the very few cases reported against judges despite prevalence of suspicion of corrupt and unethical practices among the judges. This tendency of non-reporting of the cases is reasonably high and relatively complex on account of apparently proved unworkable and unfeasible constitutional mechanism against judges in India. Longstanding judicial reforms of the country and recent constitutional indiscipline of the judges of Supreme Court of India in expressing their anguish over the functioning of the highest apex Court of country by breaking down the constitutional culture has further aggravated the situation. This uncultured constitutional practice has intensified suspicion of efficiency of constitutional governance in infusing propriety and probity to the judicial system of the nation. In this context, this paper examines judicial accountability system of the country and identifies legal deficiencies in judicial accountability. The researcher argues that the Indian legal system shall be further streamlined in line with the best practices of the other countries

Statistical Analysis and Deep Learning Associated Modeling for Early stage Detection of Carinoma

The high death rate and overall complexity of the cancer epidemic is a global health crisis. Progress in cancer prediction based on gene expression has increased in light of the speedy advancement using modern high-throughput sequencing methods and a wide range of machine learning techniques, bringing insights into efficient and precise treatment decision-making. Therefore, it is of significant interest to create machine learning systems that accurately identify cancer patients and healthy people. Although several classification systems have been applied to cancer prediction, no single strategy has proven superior. This research shows how to apply deep learning to an optimization method that uses numerous machine learning models. Statistical analysis has helped us choose informative genes, and we've been feeding those to five different categorization models. The results from the five different classifiers are ensembled in the next step using a deep learning technique. The three most common types of adenocarcinoma are those of the lungs, stomach, and breasts. The suggested deep learning-based inter-ensembles model was tested with deep learning-based algorithms on Carcinoma data. The results of the tests show that relative to using only one set of classifiers or the simple consensus algorithm, it improves the precision of cancer prognosis in every analyzed carcinoma dataset. The suggested deep learning-based inter-ensemble approach is demonstrated to be reliable and efficient for cancer diagnosis by entirely using diverse classifiers

Observations on the nuclear interactions of cosmic ray pions and nucleons at energies ≳ 20 GeV Part II. The extremely collimated nuclear interactions

Detailed features of extremely collimated nuclear interactions induced by cosmic ray particles in carbon and brass (belonging to group I as classified in Part I of this series of papers) are presented. These extremely collimated nuclear interactions seem to be preferentially induced by pions rather than by nucleons; also the relative frequency of these seems to be less when brass is used as target compared to the case with carbon as target. The distribution of multiplicities of secondary particles emitted in the forward direction show certain regularities in the case of interactions induced by charged primaries. Observations on the γ-rays associated with these events give support to the interpretation that in these inelastic collisions pions are produced in pairs in the forward direction with low transverse momentum. It is suggested that such a low energy di-pion system could be the same as found in the so-called ABC effect

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Epidemiology of Untreated Psychoses in 3 Diverse Settings in the Global South: The International Research Program on Psychotic Disorders in Diverse Settings (INTREPID II).

IMPORTANCE: Less than 10% of research on psychotic disorders has been conducted in settings in the Global South, which refers broadly to the regions of Latin America, Asia, Africa, and Oceania. There is a lack of basic epidemiological data on the distribution of and risks for psychoses that can inform the development of services in many parts of the world. OBJECTIVE: To compare demographic and clinical profiles of cohorts of cases and rates of untreated psychoses (proxy for incidence) across and within 3 economically and socially diverse settings in the Global South. Two hypotheses were tested: (1) demographic and clinical profiles of cases with an untreated psychotic disorder vary across setting and (2) rates of untreated psychotic disorders vary across and within setting by clinical and demographic group. DESIGN, SETTING, AND PARTICIPANTS: The International Research Program on Psychotic Disorders in Diverse Settings (INTREPID II) comprises incidence, case-control, and cohort studies of untreated psychoses in catchment areas in 3 countries in the Global South: Kancheepuram District, India; Ibadan, Nigeria; and northern Trinidad. Participants were individuals with an untreated psychotic disorder. This incidence study was conducted from May 1, 2018, to July 31, 2020. In each setting, comprehensive systems were implemented to identify and assess all individuals with an untreated psychosis during a 2-year period. Data were analyzed from January 1 to May 1, 2022. MAIN OUTCOMES AND MEASURES: The presence of an untreated psychotic disorder, assessed using the Schedules for Clinical Assessment in Neuropsychiatry, which incorporate the Present State Examination. RESULTS: Identified were a total of 1038 cases, including 64 through leakage studies (Kancheepuram: 268; median [IQR] age, 42 [33-50] years; 154 women [57.5%]; 114 men [42.5%]; Ibadan: 196; median [IQR] age, 34 [26-41] years; 93 women [47.4%]; 103 men [52.6%]; Trinidad: 574; median [IQR] age, 30 [23-40] years; 235 women [40.9%]; 339 men [59.1%]). Marked variations were found across and within settings in the sex, age, and clinical profiles of cases (eg, lower percentage of men, older age at onset, longer duration of psychosis, and lower percentage of affective psychosis in Kancheepuram compared with Ibadan and Trinidad) and in rates of untreated psychosis. Age- and sex-standardized rates of untreated psychoses were approximately 3 times higher in Trinidad (59.1/100 000 person-years; 95% CI, 54.2-64.0) compared with Kancheepuram (20.7/100 000 person-years; 95% CI, 18.2-23.2) and Ibadan (14.4/100 000 person-years; 95% CI, 12.3-16.5). In Trinidad, rates were approximately 2 times higher in the African Trinidadian population (85.4/100 000 person-years; 95% CI, 76.0-94.9) compared with the Indian Trinidadian (43.9/100 000 person-years; 95% CI, 35.7-52.2) and mixed populations (50.7/100 000 person-years; 95% CI, 42.0-59.5). CONCLUSIONS AND RELEVANCE: This analysis adds to research that suggests that core aspects of psychosis vary by historic, economic, and social context, with far-reaching implications for understanding and treatment of psychoses globally

Increased Resting-State Functional Connectivity in Obese Adolescents; A Magnetoencephalographic Pilot Study

BACKGROUND: Obesity is not only associated with metabolic abnormalities, but also with cognitive dysfunction and changes in the central nervous system. The present pilot study was carried out to investigate functional connectivity in obese and non-obese adolescents using magnetoencephalography (MEG). METHODOLOGY/PRINCIPAL FINDINGS: Magnetoencephalographic recordings were performed in 11 obese (mean BMI 38.8+/-4.6 kg/m(2)) and 8 lean (mean BMI 21.0+/-1.5 kg/m(2)) female adolescents (age 12-19 years) during an eyes-closed resting-state condition. From these recordings, the synchronization likelihood (SL), a common method that estimates both linear and non-linear interdependencies between MEG signals, was calculated within and between brain regions, and within standard frequency bands (delta, theta, alpha1, alpha2, beta and gamma). The obese adolescents had increased synchronization in delta (0.5-4 Hz) and beta (13-30 Hz) frequency bands compared to lean controls (P(delta total) = 0.001; P(beta total) = 0.002). CONCLUSIONS/SIGNIFICANCE: This study identified increased resting-state functional connectivity in severe obese adolescents. Considering the importance of functional coupling between brain areas for cognitive functioning, the present findings strengthen the hypothesis that obesity may have a major impact on human brain function. The cause of the observed excessive synchronization is unknown, but might be related to disturbed motivational pathways, the recently demonstrated increase in white matter volume in obese subjects or altered metabolic processes like hyperinsulinemia. The question arises whether the changes in brain structure and communication are a dynamic process due to weight gain and whether these effects are reversible or not

Collaborative community based care for people and their families living with schizophrenia in India: protocol for a randomised controlled trial

BACKGROUND: There is a large treatment gap with few community services for people with schizophrenia in low income countries largely due to the shortage of specialist mental healthcare human resources. Community based rehabilitation (CBR), involving lay health workers, has been shown to be feasible, acceptable and more effective than routine care for people with schizophrenia in observational studies. The aim of this study is to evaluate whether a lay health worker led, Collaborative Community Based Care (CCBC) intervention, combined with usual Facility Based Care (FBC), is superior to FBC alone in improving outcomes for people with schizophrenia and their caregivers in India. METHODS/DESIGN: This trial is a multi-site, parallel group randomised controlled trial design in India.The trial will be conducted concurrently at three sites in India where persons with schizophrenia will be screened for eligibility and recruited after providing informed consent. Trial participants will be randomly allocated in a 2:1 ratio to the CCBC+FBC and FBC arms respectively using an allocation sequence pre-prepared through the use of permuted blocks, stratified within site. The structured CCBC intervention will be delivered by trained lay community health workers (CHWs) working together with the treating Psychiatrist. We aim to recruit 282 persons with schizophrenia. The primary outcomes are reduction in severity of symptoms of schizophrenia and disability at 12 months. The study will be conducted according to good ethical practice, data analysis and reporting guidelines. DISCUSSION: If the additional CCBC intervention delivered by front line CHWs is demonstrated to be effective and cost-effective in comparison to usually available care, this intervention can be scaled up to expand coverage and improve outcomes for persons with schizophrenia and their caregivers in low income countries. TRIAL REGISTRATION: The trial is registered with the International Society for the Registration of Clinical Trials and the allocated unique ID number is ISRCTN 56877013