Our study objectively demonstrates significant association of ESAP and early mortality with primary cytokine response, and development of IPN with persistent HLA-DR down-regulation.

We discuss the impact of a Covid-19–like shock on a simple model economy, described by the previously developed Mark-0 Agent-Based Model. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our model economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the economy getting trapped in a self-sustained “bad” state. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough. We highlight the potential danger of terminating these policies too early, although inflation is substantially increased by lax access to credit. Finally, we consider the impact of a second lockdown. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to accommodate a wide variety of situations, thus serving as a useful exploratory tool for a qualitative, scenario-based understanding of post-Covid recovery. The corresponding code is available on-line.

The present work was aimed to investigate helminth biodiversity among rodents in order to evaluate the threat for helminth transmission to humans since they act as a potential source of parasitic zoonoses. In this study, faeces of 43 black rats (Rattus rattus) and 35 house mice (Mus musculus) were collected from various habitats viz. domestic places and agricultural fields of different parts of tarai region of Uttarakhand. These faecal samples were examined for the presence of parasitic eggs, adult and segments of the worms. The study revealed that the rodents were infected with 5 genera of helminth parasites, i.e. Hymenolepis nana, Hymenolepis diminuta, Syphacia muris, Capillaria hepatica, Trichuris muris and other strongyle eggs (2 species of cestodes and 4 species of nematodes). Adult Syphacia muris and segments of Hymenolepis nana were also recovered from faecal droppings. Of the 43 samples of black rat, all (100 %) and of the 35 samples of mice 9 (25.71 %) were found positive for one or more than one species of parasitic infections. Greater infection of H. diminuta 19 (44.18 %) followed by H. nana 17 (39.53 %) was seen in rat whereas mice were mostly infected with H. nana. The diversity and prevalence of various parasites reported here within domestic habitats may suggest that these can pose a high risk of helminth transmission to human population and are thus of considerable public health importance.

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Motivated by a potential application in economics, we investigate a simple dynamical scheme to produce planted solutions in optimization problems with continuous variables. We consider the perceptron model as a prototypical model. Starting from random input patterns and perceptron weights, we find a locally optimal assignment of weights by gradient descent; we then remove misclassified patterns (if any), and replace them by new, randomly extracted patterns. This "remove and replace" procedure is iterated until perfect classification is achieved. We call this procedure "self-planting" because the "planted" state is not pre-assigned but results from a co-evolution of weights and patterns. We find an algorithmic phase transition separating a region in which selfplanting is efficiently achieved from a region in which it takes exponential time in the system size. We conjecture that this transition might exist in a broad class of similar problems.

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Medical images are difficult to comprehend for a person without expertise. The scarcity of medical practitioners across the globe often face the issue of physical and mental fatigue due to the high number of cases, inducing human errors during the diagnosis. In such scenarios, having an additional opinion can be helpful in boosting the confidence of the decision maker. Thus, it becomes crucial to have a reliable visual question answering (VQA) system to provide a ‘second opinion’ on medical cases. However, most of the VQA systems that work today cater to real-world problems and are not specifically tailored for handling medical images. Moreover, the VQA system for medical images needs to consider a limited amount of training data available in this domain. In this paper, we develop MedFuseNet, an attention-based multimodal deep learning model, for VQA on medical images taking the associated challenges into account. Our MedFuseNet aims at maximizing the learning with minimal complexity by breaking the problem statement into simpler tasks and predicting the answer. We tackle two types of answer prediction—categorization and generation. We conducted an extensive set of quantitative and qualitative analyses to evaluate the performance of MedFuseNet. Our experiments demonstrate that MedFuseNet outperforms the state-of-the-art VQA methods, and that visualization of the captured attentions showcases the intepretability of our model’s predicted results.