Application of Machine Learning Techniques to Forecast Harmful Algal Blooms in Gulf of Mexico

The Harmful Algal Blooms (HABs) forecast is crucial for the mitigation of health hazards and to inform actions for the protection of ecosystems and fisheries in the Gulf of Mexico (GoM). For the sake of simplicity of our application we assume ocean color satellite imagery from the National Oceanic and Atmospheric Administration as a proxy for HABs. In this study we use a deep neural network trained on the 2-Dimensional time series proxy data to provide a forecast of the HABs’ manifestations in the GoM.Our approach analyzes between both spatial and temporal features simultaneously. In addition, the network also helps to fill in the gaps of the time series data along the way. We use Long Short Term Memory (LSTM) layers to learn the underlying trends in the time series data and Convolutional layers to decode the spatial trends in the 2-Dimensional gridded data. Our unique contribution is an iterative, bidirectional training scheme, where we train two models: for forward and backward prediction. The intention is that if there is a functional dependence within the data in the forward time direction, then such a dependence may also exist in the backward time direction, which may be leveraged for predictions to fill the gaps in the data. We train each model to predict the next data point in their respective time-direction, based on an LSTM recurrence over the “lookback” data points. Since there are missing cells in the grid within each data point, we use a custom loss function that ignores prediction errors on missing cells. Thus the loss function critiques the models based on known cells alone, while the models act with (forward/backward) predictions that are spatiotemporally consistent across both missing and visible cells, thus updating the input training data, and consequently changing the object of critique. This actor-critic training scheme progresses iteratively, leading to the iterative improvement of the models/actors. Several models are developed with varying combinations of convolutional layers and max pooling layers to enable the model to learn the spatial and temporal trends within the month-long training data. The most effective model performs reasonably well with prediction of chlorophyll intensities

Understanding host pathogen interactions in mycobacteria using CRISPR

Tuberculosis (TB) is one of the leading causes of death from a single infectious agent (Mycobacterium tuberculosis), second only to Coronavirus disease 2019 (COVID-19). The emergence of drug resistant strains, the variable efficacy of the BCG vaccination worldwide and the vulnerability of immunocompromised individuals infected with Human immunodeficiency virus suffering from acquired immune deficiency syndrome (HIV/AIDS) has prompted the scientific community to develop new strategies towards the development of novel anti-tubercular drugs, vaccine candidates and treatment strategies. The genus Mycobacterium has an elaborate and highly impermeable cell envelope, with additional molecular decorations contributing to its virulence and pathogenesis. Presence of this highly evolved cell envelope adds additional complexities in its treatment strategies, with the ongoing global pandemic exacerbating the situation, with tuberculosis claiming 1.5 million lives in 2020. An understanding of mycobacterial cell wall assembly and host pathogen interactions are key aspects to tackle this disease. This thesis addresses these aspects in two separate projects with an overall aim to advance research on TB; and aid in the development of novel anti-tubercular therapeutics. Firstly, an essential enzyme involved in mycobacterial cell wall assembly has been characterised for its role in growth, biofilm formation and pathogenesis of M. bovis BCG - the vaccine strain of TB. Secondly, a host immune response regulator protein which was reported to encourage intracellular mycobacterial growth and proliferation has been studied to contributing to research on the development of host directed therapies to combat tuberculosis

ASSESSMENT AND COMPARISON OF INJECTION TECHNIQUES USING MANNEQUIN AS A LEARNING TOOL AND OSPE AS AN EVALUATION METHOD

Objective: The aim of the current study was to compare the effectiveness of only demonstration and demonstration coupled with the powerpoint method (intervention) in acquiring the knowledge of injection technique using objective structured practical examination (OSPE) as an evaluation tool. Methods: The present study was conducted among IInd professional medical undergraduates (N=80). Identification of medical devices, parts of a syringe and intravenous (IV) infusion set, intramuscular (IM) injection and intravenous infusion techniques were taught using demonstration and intervention method. Participants were then evaluated for their knowledge by OSPE method using validated checklists. Participants were also asked to give feedback for the teaching and evaluation method. Data were analyzed using SPSS 20.0.0, IBM Corporation. Results: After the intervention method 100% participant could identify needle, cannula, and IV infusion set. Noticeable difference was found in identifying parts of a syringe and IV infusion set after intervention method. OSPE evaluation post-intervention showed that more number of participants could perform the steps of injection correctly and in sequence. OSPE scores post-intervention differed significantly (<0.001) with demonstration method. Conclusion: Demonstration coupled with the powerpoint teaching method was found better than the demonstration method alone. This method should be used to impart practical knowledge of injection technique

Micronutrient supplementation in pregnancy: a KAP survey with healthcare consultants in India

Background: Maternal nutrition during pregnancy is a serious public health issue as it negatively impacts women and their children. The most commonly used nutritional interventions during pregnancy are folic acid and omega-3 fatty acid (n-3 FA). This survey aimed to evaluate the knowledge, attitude and perception towards folic acid, n-3 FA and other supplementation amongst healthcare practitioners (HCPs). Methods: A close-ended questionnaire-based survey was distributed among obstetricians-gynaecologists and HCPs overlooking pregnant and lactating women between July and September 2022 in India. An excel based survey analysis was performed once the survey completed. Results: A total of 500 valid questionnaires were collected. Only 55% of them recommended n-3 FA, whereas 45% did not recommend as they believed that the typical Indian diet provides enough n-3 FA and supplements are not necessary. The majority (58.91%) of prescribers prescribed n-3 FA to all pregnant women, followed by older women with a history of abortion and high-risk pregnancy. Both eicosapentaenoic acid and docosahexaenoic acid were favoured in clinical practice. In addition, 56.8% of HCPs recommended folic acid at a dose of 5 mg/day for patients with a bad obstetric history, while 43.2% of HCPs recommended folic acid at a dose of 1 mg/day. Conclusions: Supplements and adequate nutrition can reduce the likelihood of poor maternal and foetal outcomes in high-risk pregnancies. Nutritional supplementation is a cost-effective and safe risk-reduction method, given the high prevalence of pregnancy complications. However, more knowledge dissemination on n-3 FA supplements, folic acid and micronutrients is essential

High Speed Level Converters With Short Circuit Current Reduction

The level converter is used as interface between low voltages to high voltage boundary. The efficient level converter has less power consumption and less delay are the design considerations of the level shifter. In this paper two new CMOS level converters are presented with high driving capability with low propagation delay. The proposed level converters are simulated using Cadence software with 0.18 µm CMOS technology. The simulation result shows that the proposed circuits have less propagation delay than the existing ones. The circuits are simulated for different load capacitor values and different voltages. The proposed level converters operate for different input pulse signal amplitude values are +0.8 V, +1 V, +1.2 V and VDDH values of +1.8 V and +3.3 V.</p