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Background: COVID-19 pandemic has affected mankind globally. After the three waves since March 2020, the threat continues instilling fear in the minds. Vital parameter monitoring through remote health monitoring system (RHMS) becomes critical for effective disease management and manpower safety and confidence. In a low resource setting like India, a comprehensive, wearable, and remotely operable device that is economical was required to be introduced for COVID-19 care. Present study validated the remote health monitoring device named COVIDBEEP with gold standard equipment. Materials and Methods: Six parameters, namely heart rate, SpO2, respiratory rate, temperature, blood pressure, and ECG were acquired in the supine position using the devices. Result: Analysis was performed using Graph Pad Prism. Intraclass correlation coefficients were used to measure concurrent validity. Bland–Altman graphs were plotted to know the agreement for each vital parameter. Confidence limits were set at 95%. All the parameters recorded from the devices showed a significant correlation with an “r” value between 0.5 and 0.9 with P value between 0.001 and 0.0002. Bland–Altman plots showed a minimum bias of 0.033 for heart rate and maximum of 3.5 for systolic blood pressure and respiratory rate. Conclusion: The association between the parameters recorded by the devices strengthened as the time of collection of data increased. Agreement between the two methods in 95% confidence interval was also proven to be significant for the parameters. Therefore, the indigenously developed COVIDBEEP has shown good validity in comparison to standard monitoring device.

Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism’s functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.

Even though diesel-powered Heavy Goods Vehicles (HGVs) are major contributors of greenhouse gas emission, HGV electrification is still in its infancy owing to various challenges. Costlier and heavier batteries and lack of charging infrastructure along long routes are the major bottlenecks in the realisation of HGV electrification for long-haul freight transportation. In this regard, adopting technologies that can make HGV electrification viable is vital to aid the transportation sector decarbonisation. Benefiting from approaches such as the use of overhead catenary power, and conforming to operate in a platoon formation, it would be possible to considerably reduce the cost of HGV electrification and make it economically feasible. Utilising overhead catenary infrastructure would result in requiring economical charging infrastructure, shorter charging cycles, and smaller batteries, while not compromising on the overall payload of HGVs. HGVs in platoon formation result in energy consumption reduction and increased traffic throughput. This paper investigates the benefits of using overhead catenary-powered electric HGVs for freight transportation and also explores the advantages associated with electric HGV platoon formations on the same. The feasibility analysis has been done with the support of close to real physics-based electric HGV models on realistic operating scenarios adopting an in-service highway drive cycle. From the analysis, electric HGV platooning using overhead catenary as the power source was found to be economical with $\approx 10.4\%$ reduction in electricity cost when compared to without platooning while devising electrification strategies for long-haul freight transportation.

Background: Diabetes can be managed well by adherence to prescribed oral hypoglycemic agents (OHAs) and/or insulin. Between 33% and 69% of all medication-related hospital admissions in the US are due to poor medication adherence, at a cost ranging from ≥100 to ≥300 annually. Objective: The objective of this study was to study the prevalence of poor medication adherence among known diabetics and its association between poor medication adherence and glycemic control among adults known type II diabetes. Materials and Methods: A cross-sectional study was carried out among 225 adults with known type II diabetes. Known diabetics with type II and adults above the age of 30 years taking treatment at Malla Reddy Hospital were included in the study. Morisky Green Levine Medication Adherence Scale (MGLS) was used to assess patients' adherence to diabetic medications. The American College of Physicians guidelines on glycated hemoglobin (HbA1c) was used to classify glycemic control. Results: The majority (40%) were 60–69 years, males (64%) and (69.3%) were from urban areas. 37.8% were illiterate. 47.6% were doing business or service. The majority (82.7%) had diabetes for >5 years. As per the American College of Physicians guidelines, HbA1c should be kept at 7%–8% for diabetics. Accordingly, only 16.4% had HbA1c levels of

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

Present study examines friction and wear characteristics for Ni-P-SiC coated SS 304 steel sample and It is used in conveyors in terms of increment in life cycle of conveyors under dry condition with cost effective manner. It is one of the most commonly produced and used stainless steel grades. So, this research is focused on SS304. The surface of SS 304 steel is finished by Ni-P-SiC electroless coating, and its characteristics have been studied through different analyses. FESEM with EDAX has been used to identify the elemental distribution of samples. Taguchi L16 orthogonal array, ANOVA, and regression analysis have been used to obtain experiment layout, significant process parameters, and linear relationship between input and output variables, respectively, under the experiment of design and validate with confirmatory test. Combination of operating parameters which minimize both coefficient of friction and wear is generated using multi-objective optimization. Experiments are carried out according to L16 orthogonal array and the significant parameters among the chosen, which influence wear and coefficient of friction of Ni-P-SiC electroless coated SS 304 under dry conditions are determined. Relations between the output variable and input variable is established by linear regression equations of the friction coefficient between interacting components and wear under the dry condition in terms of load, speed, and temperature. 10 N, 100 rpm, and 90°C are found to be an optimum set of operating parameters among selected levels which minimize both wear and coefficient of friction of sample.

Abstract Determining emerging trends of clinical psychiatric diagnoses among patients infected with the SARS-CoV-2 virus is important to understand post-acute sequelae of SARS-CoV-2 infection or long COVID. However, published reports accounting for pre-COVID psychiatric diagnoses have usually relied on self-report rather than clinical diagnoses. Using electronic health records (EHRs) among 2,358,318 patients from the New York City (NYC) metropolitan region, this time series study examined changes in clinical psychiatric diagnoses between March 2020 and August 2021 with month as the unit of analysis. We compared trends in patients with and without recent pre-COVID clinical psychiatric diagnoses noted in the EHRs up to 3 years before the first COVID-19 test. Patients with recent clinical psychiatric diagnoses, as compared to those without, had more subsequent anxiety disorders, mood disorders, and psychosis throughout the study period. Substance use disorders were greater between March and August 2020 among patients without any recent clinical psychiatric diagnoses than those with. COVID-19 positive patients (both hospitalized and non-hospitalized) had greater post-COVID psychiatric diagnoses than COVID-19 negative patients. Among patients with recent clinical psychiatric diagnoses, psychiatric diagnoses have decreased since January 2021, regardless of COVID-19 infection/hospitalization. However, among patients without recent clinical psychiatric diagnoses, new anxiety disorders, mood disorders, and psychosis diagnoses increased between February and August 2021 among all patients (COVID-19 positive and negative). The greatest increases were anxiety disorders (378.7%) and mood disorders (269.0%) among COVID-19 positive non-hospitalized patients. New clinical psychosis diagnoses increased by 242.5% among COVID-19 negative patients. This study is the first to delineate the impact of COVID-19 on different clinical psychiatric diagnoses by pre-COVID psychiatric diagnoses and COVID-19 infections and hospitalizations across NYC, one of the hardest-hit US cities in the early pandemic. Our findings suggest the need for tailoring treatment and policies to meet the needs of individuals with pre-COVID psychiatric diagnoses.

This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to adopt rooftop solar power generation in their premises. The proposed policy framework optimizes Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Case studies with three residential prosumers of different demand and generation profiles are presented. The models for utility profit and prosumer savings are developed, and a multi-objective problem is formulated with FiT, generation capacity (as a function of demand) and ABR as decision variables. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoffs are selected based on sensitivity analysis of the impact on collective welfare. The suitability of prevailing tariff and FiT rates of two Indian utilities namely, MSEDCL and TATA POWER, Delhi are studied, and their impact on prosumer savings and utility profits is brought out. The workflow to fix tariff, FiT and local PV capacities in active residential distribution systems is devised, providing the policymakers an effective decision-making tool.

Interleukin-2 (IL-2), along with T-cell receptor (TCR) signaling, are required to control regulatory T cell (Treg) homeostasis and function in vivo. Due to the heightened sensitivity to IL-2, Tregs retain the ability to respond to low-dose or attenuated forms of IL-2, as currently being developed for clinical use to treat inflammatory diseases. While attenuated IL-2 increases Treg selectivity, the question remains as to whether a weakened IL-2 signal sufficiently enhances Treg suppressive function(s) toward disease modification. To understand this question, we characterized the in vivo activity and transcriptomic profiles of two different attenuated IL-2 muteins in comparison with wildtype (WT) IL-2. Our study showed that, in addition to favoring Tregs, the attenuated muteins induced disproportionately robust effects on Treg activation and conversion to effector Treg (eTreg) phenotype. Our data furthermore suggested that Tregs activated by attenuated IL-2 muteins showed reduced dependence on TCR signal, at least in part due to the enhanced ability of IL-2 muteins to amplify the TCR signal in vivo. These results point to a new paradigm wherein IL-2 influences Tregs’ sensitivity to antigenic signal, and that the combination effect may be leveraged for therapeutic use of attenuated IL-2 muteins.

Background: Athletes have changes that can mimic pathological cardiomyopathy. Methods: Echocardiographic study of 50 male, female athletes (MA, FA) and non-athletes (MNA, FNA) age 18 to 30 years. These athletes participate in sports with predominantly endurance component. All participants exhibit no known medical illnesses or symptoms. Results: MA have thicker wall (IVSd) than MNA. No MA have IVSd > 1.2 cm and no FA have IVSd > 1.0 cm. Left ventricle internal dimension (LVIDd), left ventricle end diastolic volume index (LVEDVi) is bigger in athletes. None have LVIDd > 5.8 cm. Right ventricle fractional area change (FAC) is lower in athletes. (MA vs MNA, p = 0.013, FA vs FNA, p = 0.025). Athletes have higher septal and lateral e’ (Septal e’; MA 13.57 ± 2.66 cm/s vs MNA 11.46 ± 2.93 cm/s, p 1.2 cm and/or LVIDd > 5.8 cm. There is no difference in GLS, RVFWS and GCS but athletes have smaller LArS and LAbS.

Background: Spirituality and spiritual health are an integral component of an individual’s health and wellbeing. Among medical undergraduates and professionals, spiritual health has positive effects on the individual by decreasing burnout, psychological distress, and rates of substance abuse. Spiritual health is also correlated with increased satisfaction and meaning in life. Spiritual health also influences future patient care, builds patient- provider communication channels, and improves patient outcomes. Methods: A cross-sectional study was conducted to assess the spiritual health levels of medical undergraduates in a medical college, in Karnataka, India, and to identify the factors associated with it. Using a pre-designed, validated semi-structed questionnaire, 600 medical undergraduates were approached and provided the forms. Spiritual health was assessed across the three domains of self-development, self-actualization, and self-realization using the Spiritual Health Assessment Scale (SHAS). Results: A total of 436 medical undergraduates participated. Majority (67.7%) of participants were girls. Most (62%) were found to have fair spiritual health with a large portion of the rest (36%) having good spiritual health and 2% had poor spiritual health. A significant association was found between spiritual health and the father’s (p=0.005) and mother’s (p=0.012) education levels. Spiritual health was also found to be associated with living in a nuclear family (p=0.04). Conclusions: Majority of the medical undergraduates had fair spiritual health. Parents' education levels and nuclear family were significantly associated with spiritual health.

Objective This study explored multistakeholder perspectives on existing adolescent-specific tobacco control policies and programmes, to advance India’s transition towards a tobacco-free generation.Design Qualitative semi-structured interviews.Setting Interviews were conducted with officials involved in tobacco control at the national (India), state (Karnataka), district (Udupi) and village level. Interviews were audio recorded, transcribed verbatim and analysed thematically.Participants Thirty-eight individuals representing national (n=9), state (n=9), district (n=14) and village (n=6) levels, participated.Results The study findings highlighted the need to strengthen and amend the existing Tobacco Control Law (2003) provisions, particularly in the vicinity of schools (Sections 6a and 6b). Increasing the minimum legal age to buy tobacco from 18 to 21 years, developing an ‘application’ for ‘compliance and monitoring indicators’ in Tobacco-Free Educational Institution guidelines were proposed. Policies to address smokeless tobacco use, stricter enforcement including regular monitoring of existing programmes, and robust evaluation of policies was underscored. Engaging adolescents to co-create interventions was advocated, along with integrating national tobacco control programmes into existing school and adolescent health programmes, using both an intersectoral and whole-societal approach to prevent tobacco use, were recommended. Finally, stakeholders mentioned that when drafting and implementing a comprehensive national tobacco control policy, there is a need to adopt a vision striving toward a tobacco-free generation.Conclusion Strengthening and developing tobacco control programmes and policies are warranted which are monitored and evaluated rigorously, and where adolescents should be involved, accordingly.

This paper explores a novel method to achieve negative group delay-assisted anomalous propagation without significant signal attenuation using stacked Split-Ring Resonator (SRR) array in the microwave regime. A sub-wavelength stacking thickness between the SRR layers creates transverse magnetic dipole moments due to the excitation of anti-parallel currents on these layers resulting in increased transmission amplitude. Multipole scattering theory has been utilized to extract the reason behind anomalous propagation by evaluating the scattered power from the principal electric and magnetic dipole moments excited on the structure. Time-domain analysis are performed on the samples, and negative group delay behavior is confirmed by observing the precedence of the output electromagnetic Gaussian pulse within the anomalous dispersion regime. The coupling effects are studied numerically using full-wave solvers and verified in experiments by performing transmission measurements inside an anechoic chamber.

ECM is widely regarded as a highly promising and cost-effective manufacturing technique, especially for processing hard-to-machine materials that are challenging to shape using conventional methods. The machining operations were carried out using an ECM machine with a working voltage range of 0.6 to 1.0 V and a feed rate between 15 and 25 mm/min. A copper electrode was employed alongside an NaCl electrolyte solution for calculating material removal rate on AA5083/MoS2 composites. The Highest MRR is observed when voltage 1.0 V, feed rate 25 mm/min and Electrolyte Concentration 400 g/Lit. To improve the accuracy of the predicted output responses, an artificial neural network (ANN) model was designed using the Levenberg-Marquardt algorithm. The structure with a configuration of 3–10–1, confirmed strong regression fit outcomes, The overall correlation coefficients (R) calculated at 0.96348, confirmed a high level of consistency between the experimental data and the predicted value.

In the present study, Cu _2 SnSe _3 / x % Ni ( x = 0, 1, 2 and 3 wt%) composite thermoelectric were synthesized using solid-state reaction followed by sintering in the mid-temperature range 300–570 K. XRD studies revealed that the samples have a diamond cubic structure with the $F\bar{4}3m$ space group. Scanning Electron Microscopy indicates that the synthesized samples possess homogeneous surfaces with fewer pores. The sample with 3% Ni exhibits an approximately eight-fold increase in electrical conductivity than the pure sample at 307 K. However, the addition of Ni decreased the Seebeck coefficient, resulting in a lower overall power factor ( PF ) for the composites. The highest PF of ∼420 μ W/mK ^2 , was observed in the pristine sample at 570 K. Although Ni addition improves electrical properties, it negatively impacts the overall thermoelectric performance due to the significant reduction in Seebeck coefficient, thereby lowering the overall power factor.

As the construction industry is moving towards sustainable infrastructure, the need for green construction materials has gained importance in current scenario. Geopolymer concrete is considered to be an alternate to conventional concrete with cement as cement production is considered to emit more greenhouse gases into the atmosphere. This work has still raised the sustainable part of the geopolymer concrete by using steel sludge generated from automobile industry as partial replacement for fine aggregate. The experimental results were designed using fractional factorial design. The results were analysed for statistical integrity with ANOVA. It has been observed that the maximum compressive strength achieved with steel sludge up to 30% replacement is 48 Mpa with flexural strength of 5.16 Mpa and split tensile strength of 4.6 Mpa. Optimisation was done to find the proportion that gives maximum strength and it was ascertained with experimental validation. XRD and SEM analysis were carried out for studying the microstructure and reaction products formed in the process.

A combination of their unique stiffness and strength, hybrid composites has great potential in a variety of industries, including medium-duty automotive, aircraft, and submarines. In order to evaluate the performance of kevlar/E-glass epoxy composites in this article concentrated on their fabrication and testing. It is well known that the composition has a major impact on the mechanical properties and functional traits of the composite. In order to ascertain the ideal epoxy mix, we thus used a manual layup technique to produce three unique composite sheets designated as P (2:3), Q (2:4), and R (3:2). Research follow ASTM guidelines while analyzing the mechanical parameters, concentrating on tensile strength and binding strength to assess overall structural integrity. As a way to find the interlayer laminar strength between Kevlar fibre and epoxy provide information on interfacial properties, delamination experiments were also carried out. Comparing composite sample P (2:3) to other compositions, our results showed that it had the lowest swelling ratio and the best mechanical qualities. Composite sample Q (2:4), on the other hand, showed the best interlaminar characteristics and adhesion capabilities. The significance of composition mix in attaining targeted performance attributes in hybrid composites is highlighted by these findings.

The global demand for functionally graded materials (FGMs) has grown rapidly. This research reviews FGMs and the production technologies that affect their physical, structural, and manufacturing properties. We discuss the aluminium alloys and ceramics used in the fabrication process based on their engineering uses in many industries. Centrifugal casting is the versatile and commercial viable method to manufacture FGMs. These FGMs possess a variety of applications in automobile and aerospace industries owing to their enhanced mechanical strength and thermal and corrosion resistance.

Many AC Microgrids required an inverter for converting power from DC to AC. Many control techniques are available and need a flexible control method which can able to regulate both the voltage at DC and AC side. The DQ method is developed in this paper by incorporated with space vector pulse width modulation (SVPWM) technique. A distribution generator (DG) is considered in this paper for connecting to utility grid through an inverter controlled by proposed double loop control technique. One voltage controlled loop and one current controlled loop are used in proposed control method to regulate both voltage and current. This paper showcases comprehensive findings using MATLAB/Simulink.

This study numerically and experimentally presents a novel approach to excite bound state in the continuum (BIC) mode with a high Q-factor in the THz meta-molecule (composition of meta-atoms) system, leveraging a unique method of selective symmetry breaking in a ring-shaped metamolecule system. Unlike conventional strategies that uniformly disrupt the symmetry across all resonators to excite a quasi-BIC mode, this innovative technique targets only half of the unit cell for symmetry perturbation. This selective symmetry breaking minimizes radiative losses and enhances the Q-factor of the quasi-bound states in continuum (quasi-BIC) modes. The selective symmetry breaking is achieved in a ring-shaped metamolecule system by simple radial perturbation. The results depict a notable improvement in the Q-factor, achieving values as high as 107 in simulation, a significant enhancement compared to the uniformly symmetry-breaking approach, which exhibits Q-factors around 25.80. The experimental transmission spectrum and the near-field scanning images firmly validate the existence of the high Q BIC mode under this strategic symmetry-breaking approach. This work may open new avenues for developing advanced THz devices with promising applications in sensing, filtering, and non-linearity in the THz domain.

This project shows a robotic arm that can pick up objects. It was made with accuracy and speed in mind for use in factories. The robotic arm is very good at picking up metal nuts. It uses cutting edge technologies, like Haar cascade to find objects and inverse kinematics to figure out angles very accurately, to make its moves more exact and dexterous. A powerful computer vision method called Haar cascade is used to find metal nuts in the robotic arm's working environment. To do this, positive and negative pictures are used to train a Haar cascade classifier, which makes a model that can recognize the unique traits of metal nuts. The object recognition process makes sure that the nuts are located correctly, which lets the robotic arm deal with them in a controlled and precise way. The robotic arm uses inverse kinematics along with Haar cascading to figure out the exact joint angles needed for smooth, controlled movement. Inverse kinematics is very important for figuring out the joint setups that are needed to place the robotic arm's end-effector exactly on top of the metal nuts that have been found. Haar cascading for finding objects and inverse kinematics for accurate angle estimates work together to make sure that the robotic arm can pick up metal nuts in a variety of space arrangements. This combined system is a high-tech way to automatethe picking of specific items in manufacturing and assembly lines. It shows how industrial automation can be used to improve accuracy, efficiency, and flexibility.

In the dynamic landscape of our hyper-connected digital world, social media platforms play a dual role as facilitators of global interaction and breeding grounds for harmful behaviors. Cyberbullying, an insidious online menace, inflicts emotional distress and psychological trauma on numerous individuals, underscoring the urgent need for advanced tools to detect and prevent such malevolent actions. This innovative project harnesses the power of artificial intelligence and text analysis to illuminate the dark corners of social media where cyberbullying thrives, offering hope to countless victims. At its core, this endeavor utilizes cutting-edge ensemble techniques, a fusion of diverse machine learning algorithms, to analyze textual content across social media platforms. This approach ensures unparalleled accuracy in identifying and flagging cyberbullying instances, enhancing the efficiency of the detection process while minimizing false positives. The project adopts a multifaceted approach to text analysis, examining explicit language, sentiments, context, and behavioral patterns in online interactions. By delving into the intricacies of human communication, the system distinguishes between genuine expressions and malicious intent, providing a nuanced and accurate assessment.

Depression is a widespread mental health disorder that affects millions of individuals globally. Early and accurate detection of depression is essential for timely intervention and effective treatment. The abstract outlines the key steps involved in developing a depression detection system using EEG, starting with data collection from individuals with and without depression. Preprocessing techniques are applied to clean and normalize the EEG signals, ensuring the removal of artifacts and noise. Feature extraction is a critical phase where relevant information is derived from EEG signals to characterize brain activity patterns associated with depression. After that, the extracted features are used to train machine learning models for the categorization of depression, such as support vector machines (SVMs), random forests, or deep learning architectures (CNN). This highlights the importance of addressing challenges like small and imbalanced datasets, inter-subject variability, and generalizability across diverse populations. Additionally, the model emphasizes the importance of interpretability in machine learning models for depression detection, as it aids in understanding the underlying neural correlates of depression. The abstract gives underscoring the promising prospects of EEG-based depression detection in early diagnosis, personalized treatment, and improved management of depression, ultimately contributing to enhanced mental health care and patient well-being.

The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be retrieved using orders of randomly generated moves, posing unique challenges for machine learning. The proposed work is above to solve the Rubik’s cube with recursion and DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100% of all test patterns, finding a shortest path to the goal state 60.3% of the time. Deep Cube A generalizes to other combinatorial puzzles andis able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority of verifiable cases. These models were trained with 1 4 GPUs and 20 30 CPUs. This varies throughout training as the training is often stopped and started again to make room for other processes. Further our experimentation compares the results of Rubik’s cube solving among both recursion and DeepCubeA and also with the state of art models. Later, we intend to develop a new deep learning model with an application.

BackgroundEngineered vaccine proteins incorporating both antigen and adjuvant components are constructed with the aim of combining functions to induce effective protective immunity. Bacterial flagellin is a strong candidate for an engineered vaccine scaffold as it is known to provide adjuvant activity through its TLR5 and inflammasome activation. Moreover, polymerized flagellin filaments can elicit a more robust immunoglobulin response than monomeric flagellin, and the multimeric antigen form can also promote T cell-independent antibody responses. Here, we aim to produce and test a covalently stabilized polymerized flagellar filament, providing additional immune efficacy through stabilization of its polymeric filament structure, as well as stabilization for long-term storage.ResultsComputational modeling of monomer packing in flagellin filaments helped identify amino acids with proximity to neighboring flagella protofilaments. Paired cysteine substitutions were made at amino acids predicted to form inter-monomer disulfide cross-links, and these substitutions were capable of forming flagella when transfected into a flagellin-negative strain of Salmonella enterica subspecies Typhimurium. Interestingly, each paired substitution stabilized different helical conformational polymorphisms; the stabilized filaments lost the ability to transition between conformations, reducing bacterial motility. More importantly, the paired substitutions enabled extensive disulfide cross links and intra-filament multimer formation, and in one of the three variants, permitted filament stability in high acidic and temperature conditions where wild-type filaments would normally rapidly depolymerize. In addition, with regard to potential adjuvant activity, all crosslinked flagella filaments were able to induce wild-type levels of epithelial NF-κB in a cell reporter system. Finally, bacterial virulence was unimpaired in epithelial adherence and invasion, and the cysteine substitutions also appeared to increase bacterial resistance to oxidizing and reducing conditions.ConclusionsWe identified amino acid pairs, with cysteine substitutions, were able to form intermolecular disulfide bonds that stabilized the resulting flagellar filaments in detergent, hydrochloric acid, and high temperatures while retaining its immunostimulatory function. Flagellar filaments with disulfide-stabilized protofilaments introduce new possibilities for the application of flagella as a vaccine adjuvant. Specifically, increased stability and heat tolerance permits long-term storage in a range of temperature environments, as well as delivery under a range of clinical conditions.

Despite intensive study for 50 years, the biochemical and genetic links between lysine metabolism and central metabolism in Pseudomonas putida remain unresolved. To establish these biochemical links, we leveraged random barcode transposon sequencing (RB-TnSeq), a genome-wide assay measuring the fitness of thousands of genes in parallel, to identify multiple novel enzymes in both l- and d-lysine metabolism. We first describe three pathway enzymes that catabolize l-2-aminoadipate (l-2AA) to 2-ketoglutarate (2KG), connecting d-lysine to the TCA cycle. One of these enzymes, P. putida 5260 (PP_5260), contains a DUF1338 domain, representing a family with no previously described biological function. Our work also identified the recently described coenzyme A (CoA)-independent route of l-lysine degradation that results in metabolization to succinate. We expanded on previous findings by demonstrating that glutarate hydroxylase CsiD is promiscuous in its 2-oxoacid selectivity. Proteomics of selected pathway enzymes revealed that expression of catabolic genes is highly sensitive to the presence of particular pathway metabolites, implying intensive local and global regulation. This work demonstrated the utility of RB-TnSeq for discovering novel metabolic pathways in even well-studied bacteria, as well as its utility a powerful tool for validating previous research.IMPORTANCEP. putida lysine metabolism can produce multiple commodity chemicals, conferring great biotechnological value. Despite much research, the connection of lysine catabolism to central metabolism in P. putida remained undefined. Here, we used random barcode transposon sequencing to fill the gaps of lysine metabolism in P. putida We describe a route of 2-oxoadipate (2OA) catabolism, which utilizes DUF1338-containing protein P. putida 5260 (PP_5260) in bacteria. Despite its prevalence in many domains of life, DUF1338-containing proteins have had no known biochemical function. We demonstrate that PP_5260 is a metalloenzyme which catalyzes an unusual route of decarboxylation of 2OA to d-2-hydroxyglutarate (d-2HG). Our screen also identified a recently described novel glutarate metabolic pathway. We validate previous results and expand the understanding of glutarate hydroxylase CsiD by showing that can it use either 2OA or 2KG as a cosubstrate. Our work demonstrated that biological novelty can be rapidly identified using unbiased experimental genetics and that RB-TnSeq can be used to rapidly validate previous results.

Abstract Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk has been attributed to viral infection, anti-retroviral therapy, chronic inflammation, and lifestyle factors. However, the rates at which PLWH develop AS vary even after controlling for length of infection, treatment duration, and for lifestyle factors. To investigate the molecular signaling underlying this variation, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from eight PLWH, four of whom have atherosclerosis (AS+). Additionally, a publicly available dataset of PBMCs from persons before and after HIV infection was used to investigate the effect of acute HIV infection. To characterize dysregulation of pathways rather than just measuring enrichment, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. scBONITA infers executable dynamic pathway models and performs a perturbation analysis to identify high impact genes. These dynamic models are used for pathway analysis and to map sequenced cells to characteristic signaling states (attractor analysis). scBONITA revealed that lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Pathways implicated included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Attractor analysis with scBONITA facilitated the pathway-based characterization of cellular states in CD8+ T cells and monocytes. In this manner, we identify critical cell-type specific molecular mechanisms underlying HIV-associated atherosclerosis using a novel computational method.

Abstract Suppose we are given a system of coupled oscillators on an unknown graph along with the trajectory of the system during some period. Can we predict whether the system will eventually synchronize? Even with a known underlying graph structure, this is an important yet analytically intractable question in general. In this work, we take an alternative approach to the synchronization prediction problem by viewing it as a classification problem based on the fact that any given system will eventually synchronize or converge to a non-synchronizing limit cycle. By only using some basic statistics of the underlying graphs such as edge density and diameter, our method can achieve perfect accuracy when there is a significant difference in the topology of the underlying graphs between the synchronizing and the non-synchronizing examples. However, in the problem setting where these graph statistics cannot distinguish the two classes very well (e.g., when the graphs are generated from the same random graph model), we find that pairing a few iterations of the initial dynamics along with the graph statistics as the input to our classification algorithms can lead to significant improvement in accuracy; far exceeding what is known by the classical oscillator theory. More surprisingly, we find that in almost all such settings, dropping out the basic graph statistics and training our algorithms with only initial dynamics achieves nearly the same accuracy. We demonstrate our method on three models of continuous and discrete coupled oscillators—the Kuramoto model, Firefly Cellular Automata, and Greenberg-Hastings model. Finally, we also propose an “ensemble prediction” algorithm that successfully scales our method to large graphs by training on dynamics observed from multiple random subgraphs.

Coronaviruses (CoVs) use -1 programmed ribosomal frameshifting stimulated by RNA pseudoknots in the viral genome to control expression of enzymes essential for replication, making CoV pseudoknots a promising target for anti-coronaviral drugs. Bats represent one of the largest reservoirs of CoVs and are the ultimate source of most CoVs infecting humans, including those causing SARS, MERS, and COVID-19. However, the structures of bat-CoV frameshift-stimulatory pseudoknots remain largely unexplored. Here we use a combination of blind structure prediction followed by all-atom molecular dynamics simulations to model the structures of eight pseudoknots that, together with the SARS-CoV-2 pseudoknot, are representative of the range of pseudoknot sequences in bat CoVs. We find that they all share some key qualitative features with the pseudoknot from SARS-CoV-2, notably the presence of conformers with two distinct fold topologies differing in whether or not the 5' end of the RNA is threaded through a junction, and similar conformations for stem 1. However, they differed in the number of helices present, with half sharing the 3-helix architecture of the SARS-CoV-2 pseudoknot but two containing 4 helices and two others only 2. These structure models should be helpful for future work studying bat-CoV pseudoknots as potential therapeutic targets.