First report on black spot disease of Phyllanthus emblica L. fruits caused by Thielaviopsis paradoxa in Bangladesh

Fruit spot disease of Phyllanthus emblica L. is one of theproblems, which reduce the quality of the fruits at pre-harvest period. Fungal pathogen was isolated using tissue planting methods during November 2016 to December 2017. The fungus, Thielaviopsis paradoxa was identified using both morphological and molecular characterization based on internal transcribe spacer (ITS) region of ribosomal DNA (rDNA). Mycelial growth of the isolated fungus was evaluated on six different fungal culture media viz, potato sucrose agar (PSA), Richard agar (RA), carrot agar (CA), potato dextrose agar (PDA), honey peptone agar (HPA) and Hansen’s agar (HA) in which RA and HPA media provided the utmost growth. The optimum temperature of the fungus was recorded at 25 to 35ºC. Alternate cycle of 12h/12h light dark and neutral to basic pH was preferred by the studied fungus. Aqueous crude extracts of three plants (garlic, black cumin, and turmeric) were evaluated against the isolated fungus in which the highest inhibition was recorded due to garlic extract. Two food preservatives (sodium benzoate and vinegar) were also tested in which sodium benzoate (100 mM) was most efficient for the inhibition of T. paradoxa. Therefore, garlic and food preservative-sodium benzoate could be used to control this fungal growth associated with amla fruits. To the best of our knowledge, occurrence of T. paradoxa on amla fruits is a new record in Bangladesh. Int. J. Agril. Res. Innov. Tech. 10(2): 38-46, December 202

Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos

Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a particular dataset, however, perform poorly when tested on another dataset. This occurs due to a significant domain shift between the datasets resulting from the use of different tools, sensors, data resolution etc. In this paper, we highlight this domain shift in the commonly performed cataract surgery and propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor that addresses the problem of distribution shift without requiring any labels from another domain. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains while reducing redundancy and the need for higher batch sizes, thus improving cross-dataset performance. The use of BFAL is a novel approach to address the challenge of domain shift in cataract surgery data. Extensive experiments are conducted on two cataract surgery datasets and it is shown that the proposed method outperforms the state-of-the-art UDA methods by 6%. The code can be found at https://github.com/JayParanjape/Barlow-AdaptorComment: MICCAI 202

Augmenting EV charging infrastructure towards transformative sustainable cities: An equity-based approach

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: We maintain all dataset, code and interactive visualizaiton for this paper in an open repository. This can be accessed at: https://doi.org/10.5281/zenodo.8335314An increasing number of studies have reported on the need to augment public electric vehicle (EV) charging points (E-CPs) in areas with growing demand for parking. However, the focus on E-CP infrastructure equity has largely been ignored. For increased uptake of EVs, we argue that future E-CP infrastructure augmentation (EIA) will necessitate the identification of the optimal locations based on a need-focused strategic approach. Our work utilises open datasets and presents a generic multicriteria-based modelling framework for EIA framework. The E-CP Infrastructure Framework a two-stage framework. The first stage assesses the existing infrastructure gap and spatial disparity of E-CP allocation at the city scale. Next, guided by the information from stage one, stage two identifies the optimal E-CP candidate locations for future EIA expansion. The locations are determined using a parametric scoring approach that includes ease of access, available bays for parked vehicles, and potential congestion risk. We take the example of Dresden city to demonstrate the applicability of the EIA framework. Our findings show the wide prevalence of spatial disparities in E-CPs across nine of the ten wards in the city. Our proposed city-scale approach for identifying candidate locations could help policymakers decide on the augmentation strategies of E-CP infrastructure in a spatially equitable and cost-effective manner

Study of the Variation of Resistivity, Permeability and Curie Temperature of Rare Earth Metal lanthanum (la) Substitution on Ni0.60Zn0.40-xLax Fe2O4(x=0.05, 0.10, 0.15) Ferrites.

In the present work, ferrites with compositions of Ni0.60Zn0.40-xRexFe2O4where x=0.05, 0.10, 0.15 were prepared by conventional Solid State Reaction Method. The samples were pre sintered at 10000 C for 4 hours in air and sintered at 12500 C for 3 hours. The influence of Lanthanum (la) substitution on various properties of Ni-Zn ferrites have been studied in this work.  Investigations were carried out by the measurements of AC resistivity, Permeability and Curie temperature of the sample. AC resistivity has been found to be decreased of the samples. The initial magnetic permeability remains constant up to 10 MHz thenceforth sharply fall to very low values at higher frequencies and again remain constant from 9 MHz to 120 MHz and onward due to Zn deficient of Ni -Zn ferrites with substituting of La.  The sharp directress of permeability at T = Tc indicates the samples good homogeneity. The TC is found to increase with increasing Zn-deficient by substituting rare earth metal lanthanum (La). 

Longitudinal borehole functionality in 15 rural Ghanaian towns from three groundwater quality clusters

OBJECTIVE: In sub-Saharan Africa, 45% of the rural population uses boreholes (BHs). Despite recent gains in improved water access and coverage, parallel use of unimproved sources persists. Periodic infrastructure disrepair contributes to non-exclusive use of BHs. Our study describes functionality of BHs in 2014, 2015, and 2016 in 15 rural towns in the Eastern Region of Ghana sourced from three groundwater quality clusters (high iron, high salinity, and control). We also assess factors affecting cross-sectional and longitudinal functionality using logistic regression. RESULTS: BH functionality rates ranged between 81 and 87% and were similar across groundwater quality clusters. Of 51 BHs assessed in all three years, 34 (67%) were consistently functional and only 3 (6%) were consistently broken. There was a shift toward proactive payment for water over the course of the study in the control and high-salinity clusters. Payment mechanism, population served, presence of nearby alternative water sources, and groundwater quality cluster were not significant predictors of cross-sectional or longitudinal BH functionality. However, even in the high iron cluster, where water quality is poor and no structured payment mechanism for water exists, BHs are maintained, showing that they are important community resources

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

BACKGROUND: Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. RESULTS: Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. CONCLUSION: Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset

An approach to converting raw animal waste to fish feed formulation: a case study for sustainable industrial waste management using acid silage methods

590-595Acid silage is a convenient method for converting raw poultry wastes (i.e., chicken offal) to fish feed ingredients. To investigate the potentiality of chicken offal for fish feed formulation; combination of two acids were used in the trail (90-days). The proximate compositions of raw offal contained 37.22 % moisture, 37.24 % protein, 18.80 % fat,19.04 % ash and 62.78 % dry matter; ensiled offal contained 25.02 % moisture, 47.31 % protein, 13.79 % fat, 13.45 % ash and 74.98 % dry matter; and post storage offal contained 23.05 % moisture, 44.33 % protein, 13.10 % fat, 12.75 % ash and 76.95 % dry matter. It took 13 days to convert raw offal into final product that was confirmed by physical observation in necked eyes (i.e., raw smell converted into pungent acidic; thick solid form liquefied; raw pink color converted to bright brownish and absence of microorganisms). No significant difference was observed during trail and storage period for all components as moisture, protein, fat, ash and dry matter. The pH value was found to be stable at 1.90 in 90-days storage period. These results suggest that chicken offal could be potentially used for aqua feed formulation that would be a cost effective means for industrial waste management

Psychological responses during the COVID-19 outbreak among university students in Bangladesh

Mental health problems in students are considered a public health challenge. We assessed the prevalence of depression, anxiety, and stress (DAS) with the DASS-21, as well as associated factors, among university students in Bangladesh early in the COVID-19 outbreak. We hypothesized high levels of DAS and their associations with previously reported factors (e.g., poor sleep, lack of exercise, heavy internet use) and those linked to disadvantage (e.g., low monthly family income). We also enquired about participants’ satisfaction with their pursuit of their academic studies while living under COVID-19 restrictions. An internet-based survey was conducted during the month of April 2020, involving 3,122 Bangladeshi university students aged 18 to 29 years (59.5% males; mean age 21.4±2 years). Prevalence estimates of depression, anxiety and stress were, respectively, 76.1%, 71.5% and 70.1% for at least mild symptoms, 62.9%, 63.6% and 58.6% for at least moderate symptoms, 35.2%, 40.3%, and 37.7% for at least severe symptoms and 19.7%, 27.5% and 16.5% for at least very severe symptoms. The present estimates of DAS were more prevalent than in previous pre-COVID-19 studies among Bangladeshi university students. Regression analyses with DASS-21-score as a dependent variable revealed associations with factors mostly as hypothesized. The largest effect size on DAS symptoms was related to students’ satisfaction with their academic studies during the pandemic. As this survey used cross-sectional and self-reported methods, causality cannot be inferred. Mental health monitoring of students attempting to cope with the impacts of the COVID-19 outbreak may be useful and feasible

Psychometric validation of the Bangla fear of COVID-19 Scale: confirmatory factor analysis and Rasch analysis

The recently developed Fear of COVID-19 Scale (FCV-19S) is a seven-item uni-dimensional scale that assesses the severity of fears of COVID-19. Given the rapid increase of COVID-19 cases in Bangladesh, we aimed to translate and validate the FCV-19S in Bangla. The forward-backward translation method was used to translate the English version of the questionnaire into Bangla. The reliability and validity properties of the Bangla FCV-19S were rigorously psychometrically evaluated (utilizing both confirmatory factor analysis and Rasch analysis) in relation to socio-demographic variables, national lockdown variables, and response to the Bangla Health Patient Questionnaire. The sample comprised 8550 Bangladeshi participants. The Cronbach α value for the Bangla FCV-19S was 0.871 indicating very good internal reliability. The results of the confirmatory factor analysis showed that the uni-dimensional factor structure of the FCV-19S fitted well with the data. The FCV-19S was significantly correlated with the nine-item Bangla Patient Health Questionnaire (PHQ-90) (r = 0.406,

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd