EgoEnv: Human-centric environment representations from egocentric video

First-person video highlights a camera-wearer's activities in the context of their persistent environment. However, current video understanding approaches reason over visual features from short video clips that are detached from the underlying physical space and capture only what is immediately visible. To facilitate human-centric environment understanding, we present an approach that links egocentric video and the environment by learning representations that are predictive of the camera-wearer's (potentially unseen) local surroundings. We train such models using videos from agents in simulated 3D environments where the environment is fully observable, and test them on human-captured real-world videos from unseen environments. On two human-centric video tasks, we show that models equipped with our environment-aware features consistently outperform their counterparts with traditional clip features. Moreover, despite being trained exclusively on simulated videos, our approach successfully handles real-world videos from HouseTours and Ego4D, and achieves state-of-the-art results on the Ego4D NLQ challenge. Project page: https://vision.cs.utexas.edu/projects/ego-env/Comment: Published in NeurIPS 2023 (Oral

Contact tracing for COVID-19 in a healthcare institution: Our experience and lessons learned

During the initial phases of the COVID-19 pandemic contact tracing was used to control spread of the disease. It played a key role in health care institute which continued to work even during lockdown. In this piece of work, we share the lessons learnt from the contact tracing activity done in the health care institution during April to July 2020. The training needs of persons involved in contact tracing, the follow of activities, use of technology, methods to fill the missing gaps were the key lessons learnt. Its documentation supports in setting up contact tracing activity for any emerging infectious disease outbreaks in future

Properties and customization of sensor materials for biomedical applications.

Low-power chemo- and biosensing devices capable of monitoring clinically important parameters in real time represent a great challenge in the analytical field as the issue of sensor calibration pertaining to keeping the response within an accurate calibration domain is particularly significant (1–4). Diagnostics, personal health, and related costs will also benefit from the introduction of sensors technology (5–7). In addition, with the introduction of Registration, Evaluation, Authorization, and Restriction of Chemical Substances (REACH) regulation, unraveling the cause–effect relationships in epidemiology studies will be of outmost importance to help establish reliable environmental policies aimed at protecting the health of individuals and communities (8–10). For instance, the effect of low concentration of toxic elements is seldom investigated as physicians do not have means to access the data (11)

Server-Less Rule-Based Chatbot Using Deep Neural Network

Customer support entails multi-faceted benefits for IT businesses. Presently, the business depends upon on conventional channels like e-mail, customer care and web interface to provide customer support services. However, with the advent of new developments in Scania IT, different IT business units is driving a shift towards automated chatbot solutions to provide flexible responses to the user's questions. This thesis presents a practical study of such chatbot solution for the company SCANIA CV AB, Södertälje. The objective of the research work presented in this thesis is to analyze several deep learning approaches in order to develop a chatbot prototype using serverless Amazon Web Services components. The proposed bot prototype includes two main Natural Language Understanding (NLU) tasks: Intent classification and Intent fulfilment. This is a two-step process, focusing first on Recurrent Neural Network (RNN) to perform a sentence classification (intent detection task). Then, a slot filling mechanism is used for intent fulfilment task for the extraction of parameters. The results from several neural network structures for user intent classification are analyzed and compared. It is found that the bidirectional Gated Recurrent units (GRU) were shown to be the most effective for the classification task. The concluded model is then deployed on the designed AWS stack. They demonstrate that the bot behaves as expected and it places more insistence on the structure of the neural network and word embeddings for future advancements in order to find an even better neural network structure

Server-Less Rule-Based Chatbot Using Deep Neural Network

Customer support entails multi-faceted benefits for IT businesses. Presently, the business depends upon on conventional channels like e-mail, customer care and web interface to provide customer support services. However, with the advent of new developments in Scania IT, different IT business units is driving a shift towards automated chatbot solutions to provide flexible responses to the user's questions. This thesis presents a practical study of such chatbot solution for the company SCANIA CV AB, Södertälje. The objective of the research work presented in this thesis is to analyze several deep learning approaches in order to develop a chatbot prototype using serverless Amazon Web Services components. The proposed bot prototype includes two main Natural Language Understanding (NLU) tasks: Intent classification and Intent fulfilment. This is a two-step process, focusing first on Recurrent Neural Network (RNN) to perform a sentence classification (intent detection task). Then, a slot filling mechanism is used for intent fulfilment task for the extraction of parameters. The results from several neural network structures for user intent classification are analyzed and compared. It is found that the bidirectional Gated Recurrent units (GRU) were shown to be the most effective for the classification task. The concluded model is then deployed on the designed AWS stack. They demonstrate that the bot behaves as expected and it places more insistence on the structure of the neural network and word embeddings for future advancements in order to find an even better neural network structure

Claudin-10b cation channels in tight junction strands: Octameric-interlocked pore barrels constitute paracellular channels with low water permeability

Claudin proteins constitute the backbone of tight junctions (TJs) regulating paracellular permeability for solutes and water. The molecular mechanism of claudin polymerization and paracellular channel formation is unclear. However, a joined double-rows architecture of claudin strands has been supported by experimental and modeling data. Here, we compared two variants of this architectural model for the related but functionally distinct cation channel-forming claudin-10b and claudin-15: tetrameric-locked-barrel vs octameric-interlocked-barrels model. Homology modeling and molecular dynamics simulations of double-membrane embedded dodecamers indicate that claudin-10b and claudin-15 share the same joined double-rows architecture of TJ-strands. For both, the results indicate octameric-interlocked-barrels: Sidewise unsealed tetrameric pore scaffolds interlocked with adjacent pores via the β1β2 loop of the extracellular segment (ECS) 1. This loop mediates hydrophobic clustering and, together with ECS2, cis- and trans-interaction between claudins of the adjacent tetrameric pore scaffolds. In addition, the β1β2 loop contributes to lining of the ion conduction pathway. The charge-distribution along the pore differs between claudin-10b and claudin-15 and is suggested to be a key determinant for the cation- and water permeabilities that differ between the two claudins. In the claudin-10b simulations, similar as for claudin-15, the conserved D56 in the pore center is the main cation interaction site. In contrast to claudin-15 channels, the claudin-10b-specific D36, K64 and E153 are suggested to cause jamming of cations that prevents efficient water passage. In sum, we provide novel mechanistic information about polymerization of classic claudins, formation of embedded channels and thus regulation of paracellular transport across epithelia

Toward a better understanding of the interaction between somatostatin receptor 2 and its ligands: a structural characterization study using molecular dynamics and conceptual density functional theory

<p>This study is a part of the extensive research intending to provide the structural insights on somatostatin and its receptor. Herein, we have studied the structural complexity involved in the binding of somatostatin receptor 2 (SSTR2) with its agonists and antagonist. A 3D QSAR study based on comparative molecular field analysis and comparative molecular similarity analysis (CoMSIA) discerned that a SSTR2 ligand with electronegative, less-bulkier, and hydrogen atom donating/accepting substitutions is important for their biological activity. A conceptual density functional theory (DFT) study was followed to study the chemical behavior of the ligands based on the molecular descriptors derived using the Fukui’s molecular orbital theory. We have performed molecular dynamics simulations of receptor–ligand complexes for 100 ns to analyze the dynamic stability of the backbone Cα atoms of the receptor and strength and approachability of the receptor–ligand complex. The findings of this study could be efficacious in the further studies understanding intricate structural features of the somatostatin receptors and in discovering novel subtype-specific ligands with higher affinity.</p> <p>Communicated by Ramaswamy H. Sarma</p

Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation

Abstract Infections by multidrug resistant bacteria (MDR) are becoming increasingly difficult to treat and alternative approaches like phage therapy, which is unhindered by drug resistance, are urgently needed to tackle MDR bacterial infections. During phage therapy phage cocktails targeting different receptors are likely to be more effective than monophages. In the present study, phages targeting carbapenem resistant clinical isolate of E. coli U1007 was isolated from Ganges River (U1G), Cooum River (CR) and Hospital waste water (M). Capsid architecture discerned using TEM identified the phage families as Podoviridae for U1G, Myoviridae for CR and Siphoviridae for M phage. Genome sequencing showed the phage genomes varied in size U1G (73,275 bp) CR (45,236 bp) and M (45,294 bp). All three genomes lacked genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by receptor binding protein of all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen, LamB or OmpC for U1G; FhuA, OmpC for CR phage; and FhuA, LamB, TonB or OmpF for the M phage. OmpC was validated as receptor for U1G by physiological experiments. In vivo intramuscular infection study in zebrafish showed that cocktail of dual phages (U1G + M) along with colsitin resulted in a significant 3.5 log decline in cell counts. Our study highlights the potential of ML tool to predict host receptor and proves the utility of phage cocktail to restrict E. coli U1007 in vivo