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Abstract Cerebral dopamine neurotrophic factor (CDNF) is an unconventional neurotrophic factor that is a disease-modifying drug candidate for Parkinson’s disease. CDNF has pleiotropic protective effects on stressed cells, but its mechanism of action remains incompletely understood. Here, we use state-of-the-art advanced structural techniques to resolve the structural basis of CDNF interaction with GRP78, the master regulator of the unfolded protein response (UPR) pathway. Subsequent binding studies confirm the obtained structural model of the complex, eventually revealing the interaction site of CDNF and GRP78. Finally, mutating the key residues of CDNF mediating its interaction with GRP78 not only results in impaired binding of CDNF but also abolishes the neuroprotective activity of CDNF-derived peptides in mesencephalic neuron cultures. These results suggest that the molecular interaction with GRP78 mediates the neuroprotective actions of CDNF and provide a structural basis for development of next generation CDNF-based therapeutic compounds against neurodegenerative diseases. more...

Water pollution caused by emerging contaminants such as pharmaceutical compounds is a growing problem worldwide. In this reported work, graphene oxide (GO) was directly used to remove an antihistamine drug, cetirizine. GO was prepared from graphite using a modified Hummer’s method and was characterized by UV–vis spectroscopy, Fourier-transformed infrared spectroscopy (FTIR), thermogravimetric analyzer (TGA), field scanning electron microscope (FE-SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), etc. GO was demonstrated to be a highly efficient adsorbent for removing cetirizine from an aqueous solution. The adsorption of cetirizine on GO at various pH levels showed that in acidic pH with the adsorption shows faster kinetics and complete removal of cetirizine within 10 min, followed by neutral pH, which showed relatively slower kinetics but complete removal of cetirizine. However, at basic pH, GO could not completely remove cetirizine after 24 h. At a neutral pH, GO showed maximum adsorption of 81.30 mg g−1 of cetirizine. The adsorption isotherm results showed good agreement with the Langmuir isotherm. The BET surface area analysis showed the presence of mesoporosity in GO. In addition, the BET analysis further revealed a type IV isotherm curve being followed. A plausible mechanism is also discussed in the paper. The recyclability experiment demonstrates an adsorption efficiency of 85% after four cycles. The thermodynamic study reveals that adsorption is thermodynamically less favorable at higher temperatures. Hence, the current study successfully demonstrates the use of GO as an efficient adsorbent in removing cetirizine. It also studies the various factors and interactions affecting adsorption. Thus, this study sheds light on the adsorption characteristics of cetirizine on graphene oxide. more...

Abstract Background The number of food banks (charitable outlets of emergency food parcels) and the volume of food distributed by them increased multi-fold in the United Kingdom (UK) since 2010. The overwhelming majority of users of food banks are severely food insecure. Since food insecurity implies a nutritionally inadequate diet, and poor dietary intake has been linked to a number of diseases and chronic conditions, the rise in the number of people using food banks is a phenomenon of significant importance for public health. However, there is a shortage of robust, causal statistical analyses of drivers of food bank use, hindering social and political action on alleviating severe food insecurity. Methods A panel dataset of 325 local authorities in England was constructed, spanning 9 years (2011/12–2019/20). The dataset included information about the volume of parcels and the number of food banks in the Trussell Trust network, as well as economy-related, welfare system-related and housing-related variables. A quasi-experimental approach was employed in the form of a ‘first differencing’ ecological model, predicting the number of food parcels distributed by food banks in the Trussell Trust network. This neutralised bias from omitting time-constant unobserved confounders. Results Seven predictors in the model were statistically significant, including four related to the welfare system: the value of the main out-of-work benefit; the roll-out of Universal Credit; benefit sanctions; and the ‘bedroom tax’ in social housing. Of the remaining three significant predictors, one regarded the ‘supply’ side (the number of food banks in the area) and two regarded the ‘demand’ side (the proportion of working age population on out-of-work benefits; the proportion of working age population who were unemployed). Conclusion The structure of the welfare system has been partly responsible for driving food bank use in the UK since 2011. Severe food insecurity could be alleviated by reforming aspects of the benefit system that have been evidenced to be implicated in the rise in food bank use. More broadly, the findings provide support for ‘Health and Health Equity in All Policies’ approach to policymaking. more...

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - https://github.com/arnabkmondal/xViTCOS. Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources. more...

IntroductionIntercity mobility restriction, physical distancing, and mask-wearing are preventive behaviors to reduce the transmission of COVID-19. However, strong cultural and religious traditions become particular challenges in Indonesia. This study uses the Behavior Change Wheel to explore barriers and facilitators for intercity mobility restriction, physical distancing, and mask-wearing during Ramadan.MethodsSemi-structured in-depth interviews with 50 Indonesian adults were conducted between 10 April and 4 June 2020. Having mapped codes into the Capacity, Opportunity, Motivation – Behavior (COM-B), and Theoretical Domain Framework (TDF) model, we conducted summative content analysis to analyze the most identified factors to preventive behaviors and proposed interventions to address those factors.ResultsBelief about the consequence of preventive behaviors was the most mentioned facilitator to all preventive behaviors among compliers. However, optimism as a TDF factor was commonly mentioned as a barrier to preventive behaviors among non-compliers, while environmental context and resources were the most commonly mentioned factors for intercity mobility restriction.ConclusionsPublic health intervention should be implemented considering the persuasion and involvement of religious and local leaders. Concerning job and economic context, policy related to the intercity mobility restriction should be reconsidered to prevent a counterproductive effect. more...

Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications - device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution. more...

Mason introduced the notion of reflexive property of rings as a generalization of reduced rings. For a ring endomorphism α, Krempa studied α-rigid rings as an extension of reduced rings. In this note, we introduce the notion of α-quasi reflexive rings as a generalization of α-rigid rings and a natural extension of the reflexive property to ring endomorphisms. We investigate various properties of these rings and also study ring theoretic extensions such as polynomial rings, trivial extensions, right (left) quotient rings, Dorroh extensions etc. over these rings. Keywords: α-quasi reflexive ring, Matrix ring, Polynomial ring, Right (left) quotient ring, Dorroh extension, Mathematics Subject Classification: 16W20, 16S99 more...

Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy. more...

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy. more...

The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T. more...

The empirical link between children's cognitive ability and parental risk attitudes has been understudied. Specifically, an individual's educational outcome may reflect the decisions made on their behalf by parents, reflecting their risk attitudes. This paper aims to fill gaps in the existing literature by investigating whether parental risk preferences influence the cognitive ability of children in Indonesia. Using a dataset from the fifth wave of the Indonesia Family Life Survey (IFLS-5), we find an inverse relation between parental risk aversion and children's cognitive test scores. However, the effect of parental risk attitudes is only prominent for daughters. more...

Predominantly identified in pathogenic Gram-positive bacteria, sortase-dependent pili are also found in commensal species, such as the probiotic-marketed strain Lactobacillus rhamnosus strain GG. Pili are typically associated with host colonization, immune signalling and biofilm formation. Comparative analysis of the N-terminal domains of pilin-specific sortases from various piliated Gram-positive bacteria identified a conserved motif, called GYPSY, within the signal sequence. We investigated the function and role of the GYPSY residues by directed mutagenesis in homologous (rod-shaped) and heterologous (coccoid-shaped) expression systems for pilus formation. Substitutions of some of the GYPSY residues, and more specifically the proline residue, were found to have a direct impact on the degree of piliation of Lb. rhamnosus GG. The present findings uncover a new signalling element involved in the functionality of pilin-specific sortases controlling the pilus biogenesis of Lb. rhamnosus GG and related piliated Gram-positive species. more...