Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence

In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in the ReScience journal. We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI study programs. We hope this can be a useful resource for instructors who want to set up similar courses in the future

Micro-Actuators and Implementation

Miniaturization of devices inculcates the need for small-sized actuators. Actuators in the size of a few centimeters are not uncommon but miniature devices need ones that are less than a few centimeters in dimension. Market for such actuators is rather small and information pertaining to their implementation is limited. This paper talks about various actuators and their actuation mechanism for the design of small-scale electronic devices. Not only are the small-sized actuators used for designing miniature devices, but also used for precise movements in the range of a few millimeters. We have included a procedure-wise description on how to implement these actuators. An in-depth analysis of their mechanical, electrical and chemical characteristics is elaborated in this paper

Hybrid Autonomous Vehicle (Aerial and Grounded)

This work discusses hybrid autonomous vehicles that are grounded and aerial vehicles that are utilized to select their course based on their environmental characteristics. It includes algorithms for path planning, obstacle avoidance, and trajectory planning. It also has a microcontroller, known as the PIXHAWK Flight Controller, for various transmissions and configurations. Calibration and testing are performed using Mission Planner software. This article shows the different problematic features of an autonomous vehicle with several functionalities

Assessing labour migration patterns in marine fisheries sector across the coastal States of Gujarat and Maharashtra

Assessing the demographic characteristics of migrant fisher folks of coastal states of Gujarat and Maharashtra, with special focus on causes of migration, challenges and achievements in livelihood. Methods: The primary statistical tool of descriptive analysis, percentage analysis, Garrette ranking etc. have been carried out to assess various parameters of the study. Binary logistic regression was carried out to analyse the determinants for predicting willingness for continuing migration of fishermen. Findings: The expected rise in income and anticipated desire of enjoying a better standard of living and the seasonality are the key factors which necessitate the fishermen to migrate . The study identified unemployment as the main problem faced by the workers in fishing industry which necessitates the need for the labour migration. Moreover, income inequality, climate change, demographic shift and conflicts also contributed towards labour migration, in search of employability and security. Novelty: Marine fisheries sector is one of the most important sectors contributing much for the Indian economy but still facing disguised unemployment. Income inequalities, climate change, demographic shift and conflicts had contributed much for the migration of labour in search of employment and security. This proposed study is an investigative research over the labour migration and alternative avocation in the marine fisheries sector of the districts of Gujarat and Maharashtra as labour migration has become an adaptive or coping strategy of fishermen of the study area which scrutinizes the relevance of this study

Leveraging Existing Cohorts to Study Health Effects of Air Pollution on Cardiometabolic Disorders:India Global Environmental and Occupational Health Hub

Air pollution is a growing public health concern in developing countries and poses a huge epidemiological burden. Despite the growing awareness of ill effects of air pollution, the evidence linking air pollution and health effects is sparse. This requires environmental exposure scientist and public health researchers to work more cohesively to generate evidence on health impacts of air pollution in developing countries for policy advocacy. In the Global Environmental and Occupational Health (GEOHealth) Program, we aim to build exposure assessment model to estimate ambient air pollution exposure at a very fine resolution which can be linked with health outcomes leveraging well-phenotyped cohorts which have information on geolocation of households of study participants. We aim to address how air pollution interacts with meteorological and weather parameters and other aspects of the urban environment, occupational classification, and socioeconomic status, to affect cardiometabolic risk factors and disease outcomes. This will help us generate evidence for cardiovascular health impacts of ambient air pollution in India needed for necessary policy advocacy. The other exploratory aims are to explore mediatory role of the epigenetic mechanisms (DNA methylation) and vitamin D exposure in determining the association between air pollution exposure and cardiovascular health outcomes. Other components of the GEOHealth program include building capacity and strengthening the skills of public health researchers in India through variety of training programs and international collaborations. This will help generate research capacity to address environmental and occupational health research questions in India. The expertise that we bring together in GEOHealth hub are public health, clinical epidemiology, environmental exposure science, statistical modeling, and policy advocacy

Advances in computational and translational approaches for malignant glioma

Gliomas are the most common primary brain tumors in adults and carry a dismal prognosis for patients. Current standard-of-care for gliomas is comprised of maximal safe surgical resection following by a combination of chemotherapy and radiation therapy depending on the grade and type of tumor. Despite decades of research efforts directed towards identifying effective therapies, curative treatments have been largely elusive in the majority of cases. The development and refinement of novel methodologies over recent years that integrate computational techniques with translational paradigms have begun to shed light on features of glioma, previously difficult to study. These methodologies have enabled a number of point-of-care approaches that can provide real-time, patient-specific and tumor-specific diagnostics that may guide the selection and development of therapies including decision-making surrounding surgical resection. Novel methodologies have also demonstrated utility in characterizing glioma-brain network dynamics and in turn early investigations into glioma plasticity and influence on surgical planning at a systems level. Similarly, application of such techniques in the laboratory setting have enhanced the ability to accurately model glioma disease processes and interrogate mechanisms of resistance to therapy. In this review, we highlight representative trends in the integration of computational methodologies including artificial intelligence and modeling with translational approaches in the study and treatment of malignant gliomas both at the point-of-care and outside the operative theater in silico as well as in the laboratory setting

Towards pain-free diagnosis of skin diseases through multiplexed microneedles: biomarker extraction and detection using a highly sensitive blotting method

Immunodiagnostic microneedles provide a novel way to extract protein biomarkers from the skin in a minimally invasive manner for analysis in vitro. The technology could overcome challenges in biomarker analysis specifically in solid tissue, which currently often involves invasive biopsies. This study describes the development of a multiplex immunodiagnostic device incorporating mechanisms to detect multiple antigens simultaneously, as well as internal assay controls for result validation. A novel detection method is also proposed. It enables signal detection specifically at microneedle tips and therefore may aid the construction of depth profiles of skin biomarkers. The detection method can be coupled with computerised densitometry for signal quantitation. The antigen specificity, sensitivity and functional stability of the device were assessed against a number of model biomarkers. Detection and analysis of endogenous antigens (interleukins 1α and 6) from the skin using the device was demonstrated. The results were verified using conventional enzyme-linked immunosorbent assays. The detection limit of the microneedle device, at ≤10 pg/mL, was at least comparable to conventional plate-based solid-phase enzyme immunoassays

The reactive metabolite target protein database (TPDB) – a web-accessible resource

BACKGROUND: The toxic effects of many simple organic compounds stem from their biotransformation to chemically reactive metabolites which bind covalently to cellular proteins. To understand the mechanisms of cytotoxic responses it may be important to know which proteins become adducted and whether some may be common targets of multiple toxins. The literature of this field is widely scattered but expanding rapidly, suggesting the need for a comprehensive, searchable database of reactive metabolite target proteins. DESCRIPTION: The Reactive Metabolite Target Protein Database (TPDB) is a comprehensive, curated, searchable, documented compilation of publicly available information on the protein targets of reactive metabolites of 18 well-studied chemicals and drugs of known toxicity. TPDB software enables i) string searches for author names and proteins names/synonyms, ii) more complex searches by selecting chemical compound, animal species, target tissue and protein names/synonyms from pull-down menus, and iii) commonality searches over multiple chemicals. Tabulated search results provide information, references and links to other databases. CONCLUSION: The TPDB is a unique on-line compilation of information on the covalent modification of cellular proteins by reactive metabolites of chemicals and drugs. Its comprehensiveness and searchability should facilitate the elucidation of mechanisms of reactive metabolite toxicity. The database is freely available a

Solid-state fermentation of oil palm frond petiole for lignin peroxidase and xylanase-rich cocktail production

In current practice, oil palm frond leaflets and stems are re-used for soil nutrient recycling, while the petioles are typically burned. Frond petioles have high commercialization value, attributed to high lignocellulose fiber content and abundant of juice containing free reducing sugars. Pressed petiole fiber is the subject of interest in this study for the production of lignocellulolytic enzyme. The initial characterization showed the combination of 0.125 mm frond particle size and 60% moisture content provided a surface area of 42.3 m2/g, porosity of 12.8%, and density of 1.2 g/cm3, which facilitated fungal solid-state fermentation. Among the several species of Aspergillus and Trichoderma tested, Aspergillus awamori MMS4 yielded the highest xylanase (109 IU/g) and cellulase (12 IU/g), while Trichoderma virens UKM1 yielded the highest lignin peroxidase (222 IU/g). Crude enzyme cocktail also contained various sugar residues, mainly glucose and xylose (0.1–0.4 g/L), from the hydrolysis of cellulose and hemicellulose. FT-IR analysis of the fermented petioles observed reduction in cellulose crystallinity (I900/1098), cellulose–lignin (I900/1511), and lignin–hemicellulose (I1511/1738) linkages. The study demonstrated successful bioconversion of chemically untreated frond petioles into lignin peroxidase and xylanase-rich enzyme cocktail under SSF condition

Effectiveness of a brief lay counsellor-delivered, problem-solving intervention for adolescent mental health problems in urban, low-income schools in India: a randomised controlled trial

Background Mental health problems are a leading cause of disability in adolescents worldwide. Problem solving is a well-tested mental health intervention in many populations. We aimed to investigate the effectiveness of a brief, transdiagnostic problem-solving intervention for common adolescent mental health problems when delivered by non-specialist school counsellors in New Delhi, India. Methods This randomised trial was done in six government-run schools (three all-boys schools, two all-girls schools, and one co-educational school) that serve low-income communities. We recruited participants from grades 9 to 12 (ages 12–20 years) by selecting students with persistently elevated mental health symptoms accompanied by distress or functional impairment. Clinical eligibility criteria were assessed by research assistants using the Hindi-language version of the Strengths and Difficulties Questionnaire (SDQ), with reference to locally validated borderline cutoff scores of 19 or greater for boys and 20 or greater for girls on the SDQ Total Difficulties scale, an abnormal score of 2 or more on the SDQ Impact scale, and persistence of more than 1 month on the SDQ Chronicity index. Participants were randomly allocated (1:1) to problem solving delivered through a brief (2–3 week) counsellor-led intervention with supporting printed materials (intervention group), or problem solving delivered via printed booklets alone (control group). Primary outcomes were adolescent-reported mental health symptoms (SDQ Total Difficulties scale) and idiographic psychosocial problems (Youth Top Problems [YTP]) at 6 weeks. Primary analyses were done on an intention-to-treat basis at the 6-week endpoint. The trial is registered with ClinicalTrials.gov, NCT03630471. Findings Participants were enrolled between Aug 20, and Dec 4, 2018. 283 eligible adolescents were referred to the trial, and 251 (89%) of these were enrolled (mean age 15·61 years; 174 [69%] boys). 125 participants were allocated to each group (after accounting for one participant in the intervention group who withdrew consent after randomisation). Primary outcome data were available for 245 (98%) participants. At 6 weeks, the mean YTP scores were 3·52 (SD 2·66) in the intervention group and 4·60 (2·75) in the control group (adjusted mean difference –1·01, 95% CI –1·63 to –0·38; adjusted effect size 0·36, 95% CI 0·11 to 0·61; p=0·0015). The mean SDQ Total Difficulties scores were 17·48 (5·45) in the intervention group and 18·33 (5·45) in the control group (–0·86, –2·14 to 0·41; 0·16, –0·09 to 0·41; p=0·18). We observed no adverse events. Interpretation A brief lay counsellor-delivered problem-solving intervention combined with printed booklets seemed to have a modest effect on psychosocial outcomes among adolescents with diverse mental health problems compared with problem-solving booklets alone. This counsellor-delivered intervention might be a suitable first-line intervention in a stepped care approach, which is being evaluated in ongoing studies