Three-Dimensional Modeling of Polygonal Ridges in Salt Playas.

In this work, we investigate the formation of the curious polygonal salt ridges that tessellate salt playas worldwide using suitable three-dimensional modeling and simulation of the dynamical processes that are responsible for their formation. We employ the principles of fracture mechanics under cyclic wetting and drying, fluid and mass transport in fracture channels, and processes of crystallization and self-organization to finally replicate the almost Voronoidal pattern of salt ridge mosaics observed in playas. The model is generic and applicable to playas having different salt compositions, as the effect of the salt diffusion coefficient and critical salinity for crystallization are factored in. The final pattern of polygonal salt ridges obtained by simulation visually resembles the geometry of the salt ridges reported in the literature. A single equation describing the time of first crystallization of salt in terms of evaporation suction pressure P, diffusion coefficient D of salt, and relative salinity Δc with respect to critical salinity at saturation, has been proposed. The saturation of crystal growth rate is shown to be a dynamic equilibrium between advection and diffusion processes. We show that the stable polygonal geometry of the salt playas is an effort toward the total minimization of system energy.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

Fingerprint image enhancement using multiple filters.

Biometrics is the measurement of an individual's distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger's skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed via visual comparison with coherence diffusion used disjointedly and with the Gabor filter.

Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.

Increasing selection differential and decreasing cycle time, the rate of genetic improvement can be accelerated. Creating and capturing higher genetic with higher accuracy within the shortest possible time is the prerequisite for enhancing genetic gain for any trait. Comprehensive yield testing at multi-locations at early generations together with the shortest line fixation time can expedite the rapid recycling of parents in the breeding program through recurrent selection. Genomic selection is efficient in capturing high breeding value individuals taking additive genetic effects of all genes into account with and without extensive field testing, thus reducing breeding cycle time enhances genetic gain. In the Bangladesh Rice Research Institute, GS technology together with the trait-specific marker-assisted selection at the early generation of RGA-derived breeding lines showed a prediction accuracy of 0.454-0.701 with 0.989-2.623 relative efficiency over the four consecutive years of exercise. This study reports that the application of GS together with trait-specific MAS has expedited the yield improvement by 117 kg ha-1·year-1, which is around seven-fold larger than the baseline annual genetic gain and shortened the breeding cycle by around 1.5 years from the existing 4.5 years.

Fabrication and investigation of the physico-mechanical properties of Jute-PALF reinforced LLDPE hybrid composites: Effect of gamma irradiation.

The hybridization effect of agro-waste pineapple leaf fibre (PALF) and jute fibre as reinforcement in linear low-density polyethylene (LLDPE) composites was investigated in this work. The samples were fabricated by using the heat press compression moulding. The effect of gamma irradiation on composite physico-mechanical properties was also investigated in order to determine the best gamma dose among 2.50, 5.00, 7.50, and 10.00 kGy. The composite sample containing 40% PALF and 60% jute (with a total weight of 50% fibres) demonstrated the most feasible tensile strength (33.36 ± 0.59 MPa), tensile modulus (1494.41 ± 10.94 MPa), elongation at break (50.92 ± 0.77%), bending strength (82.58 ± 0.49 MPa), bending modulus (4932.46 ± 96.12 MPa), and impact strength (34.38 ± 0.42 kJ/m2) at 7.50 kGy irradiation. Thermogravimetric analysis (TGA) determined the thermal performance of the samples. Scanning electron microscopy (SEM) images at the tensile fracture surfaces of composites revealed the interfacial interaction between reinforcement fibres and matrix.

Synthesis of nanoscale CuO via precursor method and its application in the catalytic epoxidation of styrene.

Nanoscale CuO with diameters in the range of 7-8 nm has been synthesized via a two-step precipitation-calcination method using copper(ii) isonicotinate tetrahydrate as the precursor. The first step involves the room temperature stirring of an alkaline ethanolic solution of the precursor which gives a non-crystalline CuO species, while the second step involves the calcination of the product of the first step at 180 °C to form nanocrystalline CuO which has been characterized by PXRD, TEM, SEM, H2-TPR and Raman spectroscopy, etc. The CuO material has shown excellent catalytic activity in the oxidation of styrene using TBHP as the oxidizing agent leading to complete styrene conversion with more than 95% styrene oxide selectivity at the end of 6 h. The oxide catalyst can be reused for at least 6 successive runs without significant loss in activity.

Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).

Hippocampus is a part of the limbic system in human brain that plays an important role in forming memories and dealing with intellectual abilities. In most of the neurological disorders related to dementia, such as, Alzheimer's disease, hippocampus is one of the earliest affected regions. Because there are no effective dementia drugs, an ambient assisted living approach may help to prevent or slow the progression of dementia. By segmenting and analyzing the size/shape of hippocampus, it may be possible to classify the early dementia stages. Because of complex structure, traditional image segmentation techniques can't segment hippocampus accurately. Machine learning (ML) is a well known tool in medical image processing that can predict and deliver the outcomes accurately by learning from it's previous results. Convolutional Neural Networks (CNN) is one of the most popular ML algorithms. In this work, a U-Net Convolutional Network based approach is used for hippocampus segmentation from 2D brain images. It is observed that, the original U-Net architecture can segment hippocampus with an average performance rate of 93.6%, which outperforms all other discussed state-of-arts. By using a filter size of [Formula: see text], the original U-Net architecture performs a sequence of convolutional processes. We tweaked the architecture further to extract more relevant features by replacing all [Formula: see text] kernels with three alternative kernels of sizes [Formula: see text], [Formula: see text], and [Formula: see text]. It is observed that, the modified architecture achieved an average performance rate of 96.5%, which outperforms the original U-Net model convincingly.

Did national holidays accelerate COVID-19 diffusion in Bangladesh? A case study

Bangladesh registered 1573828 confirmed cases of COVID-19 and the death toll crossed the grim milestone of 27946 across the country as of 9th December, 2021. Despite the enforcement of stringent COVID-19 measures, including nationwide lockdowns, travel bans, tighter curbs on nonessential activities, and social distancing, the country witnessed an accelerated diffusion of coronavirus cases during the national events and festivals in 2020. The present study aims to examine the association between the national events / festivals and the transmission dynamics of COVID-19 by looking at the instantaneous reproduction number, Rt, of the 64 districts in Bangladesh. We further illustrate the COVID-19 diffusion explicitly in Dhaka Division at the first phase of the pandemic. The comprehensive analysis shows an escalation of Rt value in Dhaka and in all industrialized cities during the major events such as, Garments reopening and religious holidays in Bangladesh. Based on the analysis, a set of array measurements has been also suggested to evade the future pandemic risks while running the national festival activities. HighlightsO_LIBangladesh confirmed 1573828 coronavirus cases and 27946 deaths due to the current COVID-19 outbreak. C_LIO_LICountry observed significant COVID-19 diffusion in its business hubs during national holidays. C_LIO_LIDhaka, the capital of Bangladesh, is the epicenter of the ongoing pandemic. C_LIO_LICalculated Rt value illustrates its escalation in Dhaka and its neighboring cities at the time of national events. C_LIO_LIBangladesh Government needs to consider interdisciplinary approaches and contextual policies to contain the future pandemic during any national events. C_LI

The synergistic efficacy of hydroxychloroquine with methotrexate is accompanied by increased erythrocyte mean corpuscular volume.

OBJECTIVES: To determine whether concomitant HCQ modulates the increase in erythrocyte mean corpuscular volume (MCV) caused by MTX therapy, and whether this is associated with improved clinical response in RA. METHODS: A retrospective observational analysis was conducted on two independent hospital datasets of biologic-naïve, early-RA patients who started oral MTX. Baseline characteristics, DAS28-ESR and monthly MCV after starting MTX were obtained. Conventional and machine-learning statistical approaches were applied to the discovery cohort (Cohort 1, 655 patients) and results validated using Cohort 2 (225 patients). RESULTS: HCQ therapy with MTX was associated with a 2-fold increase in the likelihood of response defined in this study as clinical remission or low disease activity at 6 months (P <0.001). The improved clinical outcome of combination HCQ and MTX therapy was associated with an accelerated rise in MCV from 2 months after commencing therapy. The increase in MCV at 3 months was equivalent to the contemporaneous reduction in the DAS (DAS28-ESR) in predicting clinical response at 6 months. Using latent class mixed modelling, five trajectories of MCV change over 6 months from baseline were identified. The odds ratio of response to treatment was 16.2 (95% CI 5.7, 46.4, P <0.001) in those receiving combination therapy classified within the MCV elevation >5 fl class, which contained the most patients, compared with MTX alone. CONCLUSION: Our data provide mechanistic insight into the synergistic clinical benefit of concomitant HCQ with MTX, boosting the rise in MCV, which could serve as a companion biomarker of treatment response.

Detection and Spatial Correlation Analysis of Infectious Diseases Using Wireless Body Area Network Under Imperfect Wireless Channel.

The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients.

Challenges in Tracking the Risk of COVID-19 in Bangladesh: Evaluation of A Novel Method

Identifying actual risk zones in a country where the overall test positive rate (TPR) is higher than 5% is crucial to contain the pandemic. However, TPR-based risk zoning methods are debatable since they do not consider the rate of infection in an area and thus, it has been observed to overestimate the risk. Similarly, the rate of infection in an area has been noticed to underestimate the risk of COVID-19 spreading for the zones with higher TPR. In this article, we discuss the shortcomings of currently available risk zoning methods that are followed in the lower-middle-income countries (LMIC), especially in Bangladesh. We then propose to determine a risk zone by combining the rate of infection with TPR and effective reproduction number, Rt in a distinct manner from existing methods. We evaluate the efficacy of the proposed method with respect to the mass-movement events and show its application to track the evolution of COVID-19 pandemic by identifying the risk zones over time. Demo website for the visualization of the analysis can be found at: http://erdos.dsm.fordham.edu:3000 CCS CONCEPTSO_LIApplied computing [->] Health informatics. C_LI ACM Reference FormatMd. Enamul Hoque, Md. Shariful Islam, Arnab Sen Sharma, Rashedul Islam, and Mohammad Ruhul Amin. 2021. Challenges in Tracking the Risk of COVID-19 in Bangladesh: Evaluation of A Novel Method. In Proceedings of August 15 (KDD Workshop on Data-driven Humanitarian Mapping, 27th ACM SIGKDD Conference). ACM, New York, NY, USA, 7 pages.

Peritraumatic Distress of COVID-19 on Physicians in Bangladesh: Implications and Policy Recommendations

COVID-19 pandemic has been an ultimate test of resource management for any governance, especially in the healthcare system. Bangladesh, being a developing country and with very limited resources, is fighting the COVID-19 pandemic. The frontline workers, especially the physicians and nurses are going through immense physical and psychological stress during the pandemic. Social unawareness, the absence of strict preventive policies, increasing workload, and the lack of resource management are making the frontline healthcare workers extremely vulnerable to COVID-19. In this paper, we present the outcome of our study on peritraumatic distress of COVID-19 among physicians in Bangladesh. Based on the user study, we have identified a number of key factors behind the peritraumatic distress and psychological stress caused by COVID-19. Our study shows, more than 78% respondents are suffering from peritraumatic psychological distress. We also recommended some very important and yet easy to implement policies to reduce the peritraumatic stress of the physicians of Bangladesh. These policy recommendations were a result of the survey analysis and the suggestions from the COVID-19 designated physicians.

Clinical profile, treatment and outcome of Kawasaki disease: A single-center experience from a tertiary care referral center of Assam, north-east India.

AIM: This is a retrospective study to report our experience with a cohort of 73 patients with Kawasaki disease (KD) over 2.5 years. METHOD: The study was conducted in the Department of Pediatrics. Data were retrieved from medical records of Pediatric Rheumatology and Immunodeficiency Clinic collected from April 2017 to October 2019 and analyzed. RESULTS: Male-to-female ratio in our cohort was 2:1. The median age at diagnosis of KD was 3 years (IQR, 4.25). Fever was present in all patients. Oral mucosal changes are the second most common symptom (N = 64, 87%) followed by extremity changes (N = 58, 79%), and rash (N = 56, 76%). Nineteen (26%) children had cardiovascular complications like coronary artery abnormalities (N = 15, 20%), cardiac tamponade (N = 2, 2%), and shock (N = 1, 1%). The effusion in the patients with cardiac tamponade contained inflammatory cells and plenty of red blood cells. Sixty-eight (93%) patients with KD had received treatment with IVIg. Patients in our cohort had completed a mean follow-up of 13.6 ± 9.4 months. No fatality or any long term adverse effects were observed on follow-up. CONCLUSION: Kawasaki disease is a common rheumatological disorder in children at our center with diverse clinical presentations. The disease needs to be considered as a differential diagnosis in an acute febrile illness in children persisting up to 5 days.

Gamma radiation application to rice: Reduced glycemic index in relation to modified carbohydrate observed in FTIR spectra.

This study was conducted with a popular, low price Bangladesh rice variety BRRI Dhan 29 with a view to possible carbohydrate modification targeting lower glycemic index using gamma radiation application. Irradiation process (5 and 10 â€‹kGy â€‹at a dose rate of 9.74kGy/h) altered amylose content, amylose/amylopectin ratio, swelling power, and solubility index. FTIR spectroscopy confirmed the changed molecular structures due to radiation application. Treatment with a dose of 5 and 10kGy caused increased amylose and amylose/amylopectin ratio significantly (p â€‹< â€‹0.02). The highest amylose content was found in 10 â€‹kGy irradiated rice (30.20%) compared to unirradiated one (17.27%). Gamma radiation caused decreased swelling and increased water solubility in rice powder. These features of carbohydrate amendment in irradiated rice lead to reduced glycemic index as investigated with the in vivo experiments. Therefore, this study suggests gamma-irradiated rice (10kGy) is beneficial for diabetic subjects keeping lower blood glucose levels.

Novel Mutations in NSP1 and PLPro of SARS-CoV-2 NIB-1 Genome Mount for Effective Therapeutics

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the etiologic agent of Coronavirus Disease-2019 (COVID-19), is rapidly accumulating new mutations. Analysis of these mutations is necessary for gaining knowledge regarding different aspects of therapeutic development. Recently, we have reported a Sanger method based genome sequence of a viral isolate named SARS-CoV-2 NIB-1, circulating in Bangladesh. The genome has four novel mutations in V121D, V843F, A889V and G1691C positions. V121D substitution has the potential to destabilize the Non-Structural Protein (NSP-1) which inactivates the type-1 Interferon-induced antiviral system hence this mutant could be the basis of attenuated vaccines against SARS-CoV-V843F, A889V and G1691C are all located in NSP3. G1691C can decrease the flexibility of the protein while V843F and A889V changed the binding pattern of SARS-CoV-2 Papain-Like protease (PLPro) inhibitor GRL0617. V843F PLPro showed reduced affinity for Interferon Stimulating Gene-15 (ISG-15) protein whereas V843F+A889V double mutants exhibited the same binding affinity as wild type PLPro. Here, V843F is a conserved position of PLPro that damaged the structure but A889V, a less conserved residue, most probably neutralized that damage. Mutants of NSP1 could provide attenuated vaccines against coronavirus. Also, these mutations of PLPro could be targeted to develop anti-SARS therapeutics.

Integration of Kalman filter in the epidemiological model: a robust approach to predict COVID-19 outbreak in Bangladesh

As one of the most densely populated countries in the world, Bangladesh have been trying to contain the impact of a pandemic like COVID-19 since March, 2020. Although government announced an array of restricted measures to slow down the diffusion in the beginning of the pandemic, the lockdown has been lifted gradually by reopening all the industries, markets and offices with a notable exception of educational institutes. As the physical geography of Bangladesh is highly variable across the largest delta, the population of different regions and their lifestyle also differ in the country. Thus, to get the real scenario of the current pandemic across Bangladesh, it is essential to analyze the transmission dynamics over the individual districts. In this article, we propose to integrate the Unscented Kalman Filter (UKF) with classic SIRD model to explain the epidemic evolution of individual districts in the country. We show that UKF-SIRD model results in a robust prediction of the transmission dynamics for 1-4 months. Then we apply the robust UKF-SIRD model over different regions in Bangladesh to estimates the course of the epidemic. Our analysis demonstrate that in addition to the densely populated areas, industrial areas and popular tourist spots are in the risk of higher COVID-19 transmission. In the light of these outcomes, we provide a set of suggestions to contain the pandemic in Bangladesh. All the data and relevant codebase is available at https://mjonyh.github.io. HighlightsO_LIWe integrate the UKF with classic SIRD model for the better estimation of the COVID-19 diffusion of 64 districts in Bangladesh. C_LIO_LINationwide analysis show the strong correlation between population density and the number of COVID-19 positive cases in the country. C_LIO_LIIndustrial zones and popular tourists spots are at greater risk of spreading the Coronavirus. C_LIO_LIWith the better assessment of the COVID-19 cases dynamics, the Government will find effective policies to contain the current pandemic. C_LI

Adjusted Dynamics of COVID-19 Pandemic due to Herd Immunity in Bangladesh

Amid growing debate between scientists and policymakers on the trade-off between public safety and reviving economy during the COVID-19 pandemic, the government of Bangladesh decided to relax the countrywide lockdown restrictions from the beginning of June 2020. Instead, the Ministry of Public Affairs officials have declared some parts of the capital city and a few other districts as red zones or high-risk areas based on the number of people infected in the late June 2020. Nonetheless, the COVID-19 infection rate had been increasing in almost every other part of the country. Ironically, rather than ensuring rapid tests and isolation of COVID-19 patients, from the beginning of July 2020, the Directorate General of Health Services restrained the maximum number of tests per laboratory. Thus, the health experts have raised the question of whether the government is heading toward achieving herd immunity instead of containing the COVID-19 pandemic. In this article, the dynamics of the pandemic due to SARS-CoV-2 in Bangladesh are analyzed with the SIRD model. We demonstrate that the herd immunity threshold can be reduced to 31% than that of 60% by considering age group cluster analysis resulting in a total of 53.0 million susceptible populations. With the data of Covid-19 cases till July 22, 2020, the time-varying reproduction numbers are used to explain the nature of the pandemic. Based on the estimations of active, severe, and critical cases, we discuss a set of policy recommendations to improve the current pandemic control methods in Bangladesh.

Tunable nanomechanical performance regimes in ceramic nanowires.

At the macroscopic size regime, ceramic materials exhibit brittle fracture and catastrophic failure when they are subjected to mechanical loads that exceed their characteristic strength. In this report, we present recoverable plasticity in alpha-phase, potassium stabilized manganese dioxide nanowire (α-K0.13MnO2 NW) crystals when they are subjected to atomic force microscopy (AFM) based three-point bending tests at very low loading rates. The force-deflection curves and AFM scans obtained from these measurements reveal yielding and extended plasticity in the NWs during the loading process, while the large plastic deformation is recovered spontaneously during the unloading process. However, the same material system exhibits failure via fracture at substantially higher strengths when it is subjected to bending tests at nearly an order of magnitude higher loading rates. These results highlight an important new pathway to controllably tune the nanomechanical performance of these technologically important nanoceramics for application-specific needs: either achieve self-reversible and ultra-large plasticity, or achieve substantially higher fracture strengths that approach the intrinsic limits of the material system.