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Abstract Single-point mutations are pivotal in molecular zoology, shaping functions and influencing genetic diversity and evolution. Here we study three such genetic variants of a mechano-responsive protein, cadherin-23, that uphold the structural integrity of the protein, but showcase distinct genotypes and phenotypes. The variants exhibit subtle differences in transient intra-domain interactions, which in turn affect the anti-correlated motions among the constituent β-strands. In nature, the variants experience declining functions with aging at different rates. We expose these variants to constant and oscillatory forces using magnetic tweezer, and measure variations in stochastic folding dynamics. All variants exhibit multiple microstates under force. However, the protein variant with higher number of intra-domain interactions exhibits transitions among the heterogeneous microstates for larger extent of forces and persisted longer. Conversely, the protein variant with weaker inter-strand correlations exhibits greater unfolding cooperativity and faster intrinsic folding, although its folding-energy landscape is more susceptible to distortion under tension. Our study thus deciphers the molecular mechanisms underlying the variations in force-adaptations and proposes a mechanical relation between genotype and phenotype.

The proper disposal of solid waste represents one of the most intricate environmental challenges encountered by the municipalities of West Bengal. The municipality of Cooch Behar, a rapidly developing part of the Koch Bihar district of West Bengal, was selected as a case study. A health and environmental threat is created by the disposal of solid waste from the municipality area in an open place in Guriahati G.P., 6 km from the town and near the Torsa River. This study aims to identify the most suitable areas for the disposal and dumping of municipal solid waste. Using the Analytic Hierarchy Process, the weights of the seventeen causative factors were allocated based on their relevance to identify the appropriate locations. The present study also introduces an innovative methodology employing GIS-based sensitivity analyses to enhance the optimisation of factors influencing suitable solid waste disposal site selection. Three possible waste disposal sites have been identified based on low land costs, the availability of barren land and the distance to settlements and sensitive places. The area under the ROC curve reveals that the AUC value is 0.895, which indicates that the level of accuracy of the model is 89.50 %. With a weight of 0.184, land value was found to be the most important criterion in the model, followed by distance to settlement (0.135) and distance to road (0.123). By carefully choosing suitable locations, this research could assist urban planners in developing a waste management system that is resilient and environmentally sustainable.

This paper aims to engage in an ethical discussion of de in the Laozi and guṇa in the Bhagavad-Gītā to expand the horizon of our ethical understanding of Chinese and Indian philosophy. First, this paper will explore the different ethical levels of de and guṇa and discuss how these levels operate and are bound together. From an ethical perspective, this paper points out that de and guṇa can each be divided into two parts—higher de and lower de, and higher guṇa and lower guṇa. It will further explain the ethical significance of de and guṇa, arguing that they offer a unique contribution to developing our contemporary understanding of ethics. We will also seek to discover the interplay of de and/with the dao in the Laozi and that among the three guṇas in the Gītā from the perspective of human behaviors. In the conclusion, we will provide a comparative discussion of our findings on the ethics of de and guṇa in the Laozi and the Gītā. These findings suggest that de and guṇa promote a shared philosophical outlook within their respective traditions and thereby make a contribution to post-comparative ethical approaches.

Objectives: Lead (Pb), a toxic heavy metal, is a serious concern for poultry that negatively affects their productivity and health. To combat those issues efficiently, it is necessary to include feed supplements that have rich antioxidant properties for satisfactory health and productivity. Spirulina platensis (Sp), a microalgae, is a compound that provides several health benefits for humans and animals. This study explores that supplementation of Sp in diet as well as in water reduces the burden of Pb in different tissues, improves hematology, and improves the productive performance of advanced-age laying hens. Materials and methods: Forty birds were separated into four groups: the control (C), Spirulina (Sp), Pb, and (Pb + Sp) groups. The Pb group was given Pb acetate at a dose of 2 gm/l in water ad libitum for 4 weeks. Sp group was fed Sp at a dose of 4 gm/kg feed. The Pb + Sp group was given Pb and Sp as in the previous groups. Results: Productive performance and hematology such as hemoglobin (Hb), packed cell volume, red blood cell, mean corpuscular volume, mean corpuscular Hb (MCH) concentration, and MCH were significantly (p < 0.05) decreased in Pb-treated groups compared to controls. The distribution of Pb concentration was highest in the bones and lowest in the gizzard. However, Sp treatment significantly (p < 0.05) increased the productive performance and the hematological parameters. Moreover, Pb concentration in different organs significantly decreased in the group treated with Sp. Conclusion: This study indicates that Sp can possibly be used as a natural and powerful dietary additive to mitigate heavy metal intoxication in chickens, thereby being efficient and effective for production. [J Adv Vet Anim Res 2023; 10(4.000): 794-800]

The unobservable use of hormones in fish production is becoming an alarming issue worldwide. To reveal the fact in Bangladesh, 144 fish samples (rui (Labeo rohita), catla (Catla catla), and monosex tilapia (Oreochromis niloticus)) were collected from different fish farms and markets of Mymensingh district. The market samples had two sources (Mymensingh and Rajshahi district). The steroid hormonal (testosterone, estrogen, and progesterone) residue was analyzed by HPLC-UV detection. A standard questionnaire survey was conducted where most farmers (80%) denied using the hormone in fish production. Among the analyzed samples of all three fishes, hormonal residues were detected in approximately 98% of samples, and around 92% contained residues above the ADI. Among the contaminated samples, 70% of samples had a single residue and 30% had multiple residues. The testosterone and progesterone hormonal residue was detected in all three fishes in both farm and market samples and ranged (above ADI) from 2.1 to 16.96 μg/kg and 31.47–731.57 μg/kg (p

This paper presents an evaluation of risk factors for highway crashes under mixed traffic conditions. The basis of selecting study sites was abutting land use, roadway, and traffic characteristics. Accordingly, the study selected thirteen segments on the existing highway network in the state of West Bengal of India, covering a wide spectrum of such road attributes. A systematic investigation based on site-specific accident data to capture the highway sections' safety features revealed that the crash rate has steadily increased for years with traffic regardless of roadway category and conditions. A number of risk factors that affect road accidents were identified; they are mid-block access, pavement and shoulder conditions, vehicle involvement, time of day, and road configuration, i.e., two and multi-lane. The empirical observation indicates that the crash rate is relatively lower on multi-lane highways; however, the severity of any crash on such a road is relatively high. Notably, the crash frequencies on such roads are less during daylight hours due to the lane-based unidirectional traffic movement. This is quite the opposite during nighttime when drivers exhibit an inability to meet traffic contingencies, thereby increasing crash risk. The majority of crashes on two-lane highways are, on the other hand, due to unsafe driving manoeuvers. The study also observed that frequent mid-block accesses and poor shoulder conditions reduce scopes to rectify driving errors and increase crash risk as a consequence. The paper subsequently suggests proactive approaches to identify safety deficits at the time of planning and designing.

Abstract In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of original data. Class imbalance is another reason for applying data augmentation techniques. Generative Adversarial Networks (GANs) are beneficial for synthetic image generation in various fields. However, stand-alone GANs may only fetch the localized features in the latent representation of an image, whereas combining different GANs might understand the distributed features. To this end, we have proposed AGGrGAN, an aggregation of three base GAN models—two variants of Deep Convolutional Generative Adversarial Network (DCGAN) and a Wasserstein GAN (WGAN) to generate synthetic MRI scans of brain tumors. Further, we have applied the style transfer technique to enhance the image resemblance. Our proposed model efficiently overcomes the limitation of data unavailability and can understand the information variance in multiple representations of the raw images. We have conducted all the experiments on the two publicly available datasets - the brain tumor dataset and the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. Results show that the proposed model can generate fine-quality images with maximum Structural Similarity Index Measure (SSIM) scores of 0.57 and 0.83 on the said two datasets.

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

Background Cigarette smoking remains a primary cause of chronic lung diseases. After a steady decline, smoking rates have recently increased especially with the introduction of newer electronic nicotine delivery devices, and it is also emerging that dual- or poly-product usage is on the rise. Additionally, with the introduction of IQOS (a heated tobacco product) globally, its impact on human health needs to be investigated. In this study we tested if dual exposure (cigarette smoke (CS)+IQOS) is detrimental to lung epithelial cells when compared with CS or IQOS exposure alone. Methods Human airway epithelial cells (BEAS-2B) were exposed to either CS, IQOS or their dual combination (CS+IQOS) at concentrations of 0.1%, 1.0%, 2.5% and 5.0%. Cytotoxicity, oxidative stress, mitochondrial homeostasis, mitophagy and effects on epithelial–mesenchymal transition (EMT) signalling were assessed. Results Both CS and IQOS alone significantly induced loss of cell viability in a concentration-dependent manner which was further enhanced by dual exposure compared with IQOS alone (p

Abstract COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .

COVID-19 takes a gigantic form worldwide in a short time from December, 2019. For this reason, World Health Organization (WHO) declared COVID-19 as a pandemic outbreak. In the early days when this outbreak began, the coronavirus spread rapidly in the community due to a lack of knowledge about the virus and the unavailability of medical facilities. Therefore it becomes a significant challenge to control the influence of the disease outbreak. In this situation, mathematical models are an important tool to employ an effective strategy in order to fight against this pandemic. To study the disease dynamics and their influence among the people, we propose a deterministic mathematical model for the COVID-19 outbreak and validate the model with real data of Italy from 15th Feb 2020 to 14th July 2020. We establish the positivity and boundedness of solutions, local stability of equilibria to examine its epidemiological relevance. Sensitivity analysis has been performed to identify the highly influential parameters which have the most impact on basic reproduction number (R0). We estimate the basic reproduction number (R0) from available data in Italy and also study effective reproduction numbers based on reported data per day from 15th Feb 2020 to 14th July 2020 in Italy. Finally, the disease control policy has been summarized in the conclusion section.

Time headway is an important microscopic traffic flow parameter, which affects safety, level-of-service, capacity and traffic simulation. It is, therefore, important to know the specific distribution for a particular roadway and traffic condition. Further, headway between two vehicles depends on the type of lead vehicle and is influenced by its size and dynamics. Such impact is considerably high on two-lane roads with mixed traffic composed of a wide variety of vehicle types. This paper identified sixteen combinations of vehicle pairs and analysed vehicle-type-specific headways using field data. Appropriate distribution functions were fitted to field data and predictive models were used in understanding carfollowing behaviour. Observations indicate that quite often bike riders become reluctant in obeying lane discipline. However, car drivers show conservative attitude and usually, keep safe distance from the lead vehicle except the case when they follow another car. In addition, while following Non-Motorized Vehicles (NMV), most of the drivers keep reasonably safe distances. In this paper, a comparison of computed headway probabilities was also made with those obtained from more or less homogeneous traffic. It was found that values obtained in current study are high in most of the instances. This indicates risk-taking behaviour of driver population, which eventually affects safety of such roads. The present study, thus, demonstrates the need of investigating vehicle-type-specific headways under mixed traffic based on comprehensive field data. First published online 7 January 2021

With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves semantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.

Editor's Note: this Article has been retracted; the Retraction Note is available at https://doi.org/10.1038/s41598-021-87523-1

Abstract Introduction The time headway of vehicles is of fundamental importance in traffic engineering applications like capacity, level-of-service and safety studies. Further, the performance of traffic simulation depends on inputs into the simulation process and ‘accurate vehicle generation’ is critical in this context. Thus, it is important to define headway distribution pattern for the purpose of analyzing traffic and subsequently, taking infrastructure related decisions. In so far, majority of the researches on this subject are based on homogeneous traffic and effects of mixed traffic especially on two-lane roads are yet to be culminated. The present study, thus, aimed at investigating headway distributions on such roads under mixed traffic situation. Methods Field study was conducted on two-lane highways in India that exhibits heterogeneity in its traffic composition. Contestant headway distribution models were evaluated and four distribution functions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 were considered while modeling the headway data. The appropriate models were selected using a methodology based on K-S test and subsequent field validation. Results Log-logistic distribution was found appropriate at moderate flow whereas, at congested state of flow it was Pearson 5. However, at unstable flow nearing capacity, both, following and non-following components of headways were observed to follow different distributional characteristics. Nomographs are developed for calculating the headway probabilities at different flow levels considering the appropriate distribution models. ‘Probability of headway less than‘t’s’ increases with the flow rate and rate of such increase is considerably high for headways 7.5 s or more. This attributes to the fact that at heavy flow more vehicles are entrapped inside platoons and they move in following with shorter headways. Further, a comparison was made between the headway probabilities obtained in the current study and road segments that exhibit more or less homogeneous traffic. It was found that at moderate flow level proportion of shorter headways are considerably high under mixed traffic. Conclusions The present paper demonstrates the effect of mixed traffic on distribution of time headways of two-lane roads. Presence of slower vehicles in such traffic leads to frequent formation of platoons, thereby, increases the risk taking behavior of drivers’ while overtaking. As a result, proportion of shorter headways increases resulting in highly skewed observations. Thus, the study proposed Log-logistic distribution under medium flow since it can model events which have ‘increased initial rate’ and Pearson 5 under heavy flow to model ‘highly skewed data’. The model outputs were accordingly compared with other studies and were found to explain the mixed traffic characteristics satisfactorily. The present study, thus, creates a starting point of further initiatives aimed at establishing a robust method of modeling headways on two-lane roads with mixed traffic.

The HCM (Highway Capacity Manual 2010) classifies two-lane rural highway that passes through developed areas as ‘Class III’ and suggests using Percent Free-Flow Speed (PFFS) as performance measure to define Level Of Service (LOS). Apparently, this performance measure addresses the main limitation associated with using Average Travel Speed (ATS) as a measure of performance. However, larger speed differential under heterogeneous traffic causes error in estimating Free-Flow Speed (FFS) and thus affects PFFS. This implication was examined in the present study using field data collected on a national highway approaching a city. Speed–flow equations were developed and intercept values were compared to the FFS obtained according to HCM guidelines. Comparison confirms a very close agreement between average FFS value and those obtained for different types of vehicle separately except for car. This consequently causes an error in estimating PFFS since the traffic composition consists of significant proportion of car. The possible capacity of the highway section was observed to be around 2300 pc/h for mixed traffic situation. First published online: 28 Jan 2015

The solubility of tugarinovite (MoO2) in pure water was investigated at temperatures between 400 and 800°C and at pressures ranging between 95 and 480 MPa by using in situ synchrotron X-ray fluorescence (SXRF) to separately analyze high temperature aqueous solutions in a hydrothermal diamond anvil cell (HDAC). The concentration of molybdenum in the fluid at 400 and 500°C was below detection; however, at temperatures between 600 and 800°C, the solubility of tugarinovite increased with increasing temperature by two orders of magnitude. The molybdenum concentration at 600°C and 800°C is 44 ppm and 658 ppm, respectively. The results complement the data of Kudrin (1985) and provide the first measurement of MoO2 solubility at pressure and temperature conditions comparable to intrusion-related Mo deposit formation. The data are also relevant to the study of water chemistry and corrosion product transport in supercritical-water-cooled reactors, where Mo-bearing steel alloys interact with aqueous solutions at temperatures greater than 600°C. The application of in situ SXRF to solubility measurements of sparingly soluble minerals is recommended because it circumvents analytical uncertainties inherent in determinations obtained by quenching and weight loss measurements.

Air pollution has become an emerging invisible killer in recent years and is a major cause of morbidity and mortality globally. More than 90% of the world’s children breathe toxic air every day. India is among the top ten most highly polluted countries with an average PM10 level of 134 μg/m3 per year. It is reported that 99% of India’s population encounters air pollution levels that exceed the World Health Organization Air Quality Guideline, advising a PM2.5 permissible level of 10 μg/m3. Maternal exposure to air pollution has serious health outcomes in offspring because it can affect embryonic phases of development during the gestation period. A fetus is more prone to effects from air pollution during embryonic developmental phases due to resulting oxidative stress as antioxidant mechanisms are lacking at that stage. Any injury during this vulnerable period (embryonic phase) will have a long-term impact on offspring health, both early and later in life. Epidemiological studies have revealed that maternal exposure to air pollution increases the risk of development of airway disease in the offspring due to impaired lung development in utero. In this review, we discuss cellular mechanisms involved in maternal exposure to air pollution and how it can impact airway disease development in offspring. A better understanding of these mechanisms in the context of maternal exposure to air pollution can offer a new avenue to prevent the development of airway disease in offspring.

Introduction: CausalPath compares experimentally measured changes in molecular profiles against curated biological pathways and infers causality between changes in measured features from profiling experiments (e.g., RNA-seq or proteomics from total or phospho-protein levels). Methods: We developed the CausalPath Cytoscape App, an app (i.e., plugin) for visualizing results from the CausalPath method within the Cytoscape Java-based desktop network analysis and visualization platform. Use Cases: Users are given instruction that represents use cases in multiple cancer research areas through the visualization of CausalPath analysis results generated from data by the Clinical Proteomic Tumor Analysis Consortium. Discussion: The CausalPath Cytoscape App visualizes the set of known interactions that are supported by molecular profiling data via the CausalPath method. This integration of CausalPath and Cytoscape benefits users interested in performing secondary analyses (e.g., module detection) on the sub-networks that result from CausalPath analysis by utilizing the many analytical features available in the Cytoscape software ecosystem.

Introduction: CausalPath compares experimentally measured changes in molecular profiles against curated biological pathways and infers causality between changes in measured features from profiling experiments (e.g., RNA-seq or proteomics from total or phospho-protein levels). Methods: We developed the CausalPath Cytoscape App, an app (i.e., plugin) for visualizing results from the CausalPath method within the Cytoscape Java-based desktop network analysis and visualization platform. Use Cases: Users are given instruction that represents use cases in multiple cancer research areas through the visualization of CausalPath analysis results generated from data by the Clinical Proteomic Tumor Analysis Consortium. Discussion: The CausalPath Cytoscape App visualizes the set of known interactions that are supported by molecular profiling data via the CausalPath method. This integration of CausalPath and Cytoscape benefits users interested in performing secondary analyses (e.g., module detection) on the sub-networks that result from CausalPath analysis by utilizing the many analytical features available in the Cytoscape software ecosystem.