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Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through classification models that do not consider the highly imbalanced class distribution. Therefore, they are particularly ineffective in detecting the minority classes (for instance, ‘disagree’), even though such instances are crucial for tasks such as fact-checking by providing evidence for detecting false claims. In this paper, we exploit the hierarchical nature of stance classes which allows us to propose a modular pipeline of cascading binary classifiers, enabling performance tuning on a per step and class basis. We implement our approach through a combination of neural and traditional classification models that highlight the misclassification costs of minority classes. Evaluation results demonstrate state-of-the-art performance of our approach and its ability to significantly improve the classification performance of the important ‘disagree’ class.

The gender inequality gap has widened in recent years, despite significant global awareness and efforts to address the issue. This indicates the possibility that there is still uncertainty about the selection of the most important levers for reducing gender inequality. While economic empowerment has been analysed and discussed as an important input into women’s empowerment, evidence remains inconclusive and interventions sparse, especially in the context of large populous lower middle-income countries like India. The paper examines the impact of economic empowerment on a woman’s overall ability to take decision using data from the National Family Health Survey in India. Data on decision-making, economic empowerment and other socioeconomic variables of currently married women, aged 15–49 years, are used to analyse to whether and to what extent economic empowerment has an impact on women’s agency. Nine decision-making areas were used cumulatively in an ordered logit model, and the results indicated that economic empowerment was important in improving women’s decision-making abilities, including other key variables on the socioeconomic status of the women. The results imply that while education would remain a key policy tool, policies on women’s empowerment need to incorporate programmes and interventions on women’s economic empowerment, and programmes guaranteeing women employment and focusing on their employment conditions need to get much higher budget allocations within the government’s overall budget.

Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One cause for this is the encoded societal biases in the training data (e.g., under-representation of females in the tech workforce), which is aggravated in the presence of unbalanced class distributions (e.g., when “hired” is the minority class in a hiring application). State-of-the-art fairness-aware machine learning approaches focus on preserving the overall classification accuracy while mitigating discrimination. In the presence of class-imbalance, such methods may further aggravate the problem of discrimination by denying an already underrepresented group (e.g., females) the fundamental rights of equal social privileges (e.g., equal access to employment). To this end, we propose AdaFair, a fairness-aware boosting ensemble that changes the data distribution at each round, taking into account not only the class errors but also the fairness-related performance of the model defined cumulatively based on the partial ensemble. Except for the in-training boosting of the group discriminated over each round, AdaFair directly tackles imbalance during the post-training phase by optimizing the number of ensemble learners for balanced error performance. AdaFair can facilitate different parity-based fairness notions and mitigate effectively discriminatory outcomes.

Background The burden from non-communicable diseases and injuries (NCDI) in India is increasing rapidly. With low public sector investment in the health sector generally, and a high financial burden on households for treatment, it is important that economic evidence is used to set priorities in the context of NCDI. Objective Our objective was to understand the extent to which economic analysis has been used in India to (1) analyze the impact of NCDI and (2) evaluate prevention and treatment interventions. Specifically, this analysis focused on the type of economic analysis used, disease categories, funding patterns, authorship, and author characteristics. Methods We conducted a systematic review based on economic keywords to identify studies on NCDI in India published in English between January 2006 and November 2016. In all, 96 studies were included in the review. The analysis used descriptive statistics, including frequencies and percentages. Results A majority of the studies were economic impact studies, followed by economic evaluation studies, especially cost-effectiveness analysis. In the costing/partial economic evaluation category, most were cost-description and cost-analysis studies. Under the economic impact/economic burden category, most studies investigated out-of-pocket spending. The studies were mostly on cardiovascular disease, diabetes, and neoplasms. Slightly over half of the studies were funded, with funding coming mainly from outside of India. Half of the studies were led by domestic authors. In most of the studies, the lead author was a clinician or a public health professional; however, most of the economist-led studies were by authors from outside India. Conclusions The results indicate the lack of engagement of economists generally and health economists in particular in research on NCDI in India. Demand from health policy makers for evidence-based decision making appears to be lacking, which in turn solidifies the divergence between economics and health policy, and highlights the need to prioritize scarce resources based on evidence regarding what works. Capacity building in health economics needs focus, and the government’s support in this is recommended.

Abstract Results from a pilot, 6-week, randomized, open-label, rater-blinded study, with 46-week extension, indicate very good tolerability with exceptional, clinically important, increasing efficacy of evenamide (7.5, 15, and 30 mg bid), a glutamate modulator, as add-on treatment to antipsychotics in 161 treatment-resistant, schizophrenia patients. Ninety-five percent of patients completed 6 weeks (1 discontinued for adverse event), and 89% continued in the extension. Results from the first 100 patients enrolled showed very low attrition over 1 year (77 completers); data pooled from all dose groups showed the Positive and Negative Syndrome Scale total score improved significantly (P < .001; paired t test; last observation carried forward [LOCF]) from baseline at 6 weeks (−9.4), 6 months (−12.7), and 1 year (−14.7); similarly, the proportion of responders (≥20% improvement) increased over time from 6 weeks (16.5%) to 6 months (39%) to 1 year (47.4%). Noteworthy improvement was also observed at each timepoint on the Clinical Global Impression - Severity scale and Clinical Global Impression of Change, indicating progressively increasing efficacy of evenamide up to 1 year.

Abstract The results from a pilot, 1-year, randomized, open-label, add-on treatment study in treatment-resistant schizophrenia (TRS) with evenamide, a glutamate modulator, were not associated with any safety abnormalities at all doses (7.5-30 mg bid), with a high retention rate even at 6-month (~85%), and 1-year (~75%), and the absence of psychotic relapses during the 1-year treatment period. Overall, treatment with evenamide showed a gradual, sustained, and clinically important improvement up to 1 year in all efficacy measures (eg, PANSS mean change ~ −20%; CGI-S mean change ~ −1.0). In addition, compared to the results at Week 6, the responder rates generally more than doubled at 1-year (PANSS “≥20% improvement from baseline” = ~45%; CGI-S “2-category of improvement” = ~25%; CGI-C “much improved” = ~40%). These results, rarely replicated in other trials in TRS, support the use of evenamide as an add-on treatment in patients who are not benefiting from their current first- or second-generation antipsychotic medication.

The submental artery island flap has been used for reconstruction of mild to moderate size oral defects after resection despite its controversy of oncological safety.Our study aims to assess the efficiency and oncological safety of submental artery flap in oral reconstruction.Twenty-three oral cancer patients who underwent resection and reconstruction using the submental artery island flap at State Cancer Institute, Guwahati, India, between February 2021 and February 2022 were retrospectively studied for the flap viability, complications, and function and locoregional recurrence.There were 9 men and 14 women with mean age of 58.56 years. The follow up period ranged from 7 months to 18 months with a median of 12 months. There was no loss of flap, i.e., 100% success rate of flap survival. One patient presented with locoregional recurrence at 11 months of follow up. Three male patients had hair growth on the flap inside oral cavity. There was one case of marginal nerve palsy and one case of donor site wound dehiscence which healed conservatively. The functions and donor site cosmesis were good in all the patients.Submental artery island flap is a good option for reconstruction in select cases of oral cancer.

The proliferation of distributed internet services has reaffirmed the need for reliable and high-performance networks, not only in the WAN bringing users to the services, but within the datacenters where services themselves reside. Services consist of distributed applications running across thousands of servers within datacenters, with stringent performance, scaling and reliability requirements. To support these requirements, datacenter networks are comprised of thousands of servers, links and ports and over hundreds of switches providing multiple paths between any pair of servers. Because any given component has a small but non-zero failure rate, the large number of components means that failures are endemic inside datacenters. Unfortunately, not all failures are easily diagnosable within datacenter environments. In particular, datacenters are susceptible to insidious parasitic performance loss due to a class of network component fault known as partial faults—where a component is nominally healthy, but intermittently drops or delays traffic. These faults have been noted as being particularly difficult to detect and localize, though mitigation can be straightforward once the faulty component is determined. Pinpointing partial faults quickly is crucial, because they are capable of inflicting a disproportionately high toll on application performance. Unfortunately, partial faults can confound existing fault detection methods in several ways, including interactions between the fault itself, application traffic characteristics, and networking hardware. For example, network switches may fail to detect a fault due to unreliable or otherwise insensitive monitoring capabilities. Traffic volume and variability may complicate analysis of server-based application and network metrics, as well as mask fault impact. Moreover, the myriad paths available to network flows complicate localization even if servers do detect partial faults. However, this work shows that the scale and regular design of contemporary datacenters can simplify partial-fault localization. In particular, the combination of large-scale load-balanced multipath topologies and high-volume datacenter traffic enables simple, low-overhead, application-agnostic, and root-cause-agnostic partial-fault localization via passive, link-by-link outlier analysis of application network performance. I validate the effectiveness of my approach within large-scale first-party production datacenters, and examine the additional challenges and complexities raised by third-party cloud datacenters.

Background. Artemisinins, which are derived from plants, are subject to risk of supply interruption due to climatic changes. Consequently, an effort to identify a new synthetic antimalarial was initiated. A fixed-dose combination of arterolane maleate (AM), a new synthetic trioxolane, with piperaquine phosphate (PQP), a long half-life bisquinoline, was evaluated in patients with uncomplicated Plasmodium falciparum malaria. Methods. In this multicenter, randomized, double-blind, comparative, parallel-group trial, 1072 patients aged 12–65 years with P. falciparum monoinfection received either AM–PQP (714 patients) once daily or artemether–lumefantrine (A–L; 358 patients) twice daily for 3 days. All patients were followed up until day 42. Results. Of the 714 patients in the AM–PQP group, 638 (89.4%) completed the study; of the 358 patients in the A–L group, 301 (84.1%) completed the study. In both groups, the polymerase chain reaction corrected adequate clinical and parasitological response (PCR–corrected ACPR) on day 28 in intent-to-treat (ITT) and per-protocol (PP) populations was 92.86% and 92.46% and 99.25% and 99.07%, respectively. The corresponding figures on day 42 in the ITT and PP populations were 90.48% and 91.34%, respectively. After adjusting for survival ITT, the PCR-corrected ACPR on day 42 was >98% in both groups. The overall incidence of adverse events was comparable. Conclusions. AM–PQP showed comparable efficacy and safety to A–L in the treatment of uncomplicated P. falciparum malaria in adolescent and adult patients. AM–PQP demonstrated high clinical and parasitological response rates as well as rapid parasite clearance. Clinical Trials Registration. India. CTRI/2009/091/000101.