Contribution of morpho-physiological attributes in determining the yield of mungbean

Field experiments were conducted in 2006 and 2007 under subtropical conditions to investigate the variations in growth and reproductive characters, and yield attributes for selection of important source and sinks characters using correlation and path coefficient analyses in 45 mungbean genotypes. Large genetic variability existed in source characters viz., leaf area index (LAI) (1.22 to 3.80) and sink characters viz., number of racemes plant-1 (6.30 to 22.9), flowers plant-1 (18.1 to 51.9) and pods plant-1 (9.6 to 22.1). Genotypic correlation study revealed that among the traits investigated, LAI was the most important source that determined total dry mass (TDM) yield, and reproductive characters like number of racemes, flowers and pods plant-1 were the most important sinks that determined seed yield. Contrarily, reproductive efficiency (RE, % pod set to opened flowers) did not show significant relationship with pod number and seed yield, indicating that selection of high yield based on RE may be misleading. Path coefficient analysis further revealed that number of flowers, pods and 100-seed weight constituted central important sinks which exerted direct positive influence on seed yield. The results indicated that pod yield could be increased by increased raceme and flower production, while seed yield could be increased by increasing pod production. High yielding genotypes, in general, possessed higher earlier mentioned source (LAI) and sink (flower and pod number) characters which resulted in higher seed yield in mungbean. This information could be exploited in the future plant breeding programmes.Key words: Source-sink, correlation, path analysis, mungbean

Ethanol-Induced Hepatic and Renal Histopathological Changes in BALB/c mice

This study was to investigate the histopathologic changes of different concentrations of ethanol on the mice liver and kidney. Forty albino mice of the Mus musculus species, BALB/c strain mice underwent this study and were divided into four groups; control, %20, %40 and %60 of ethanol administration groups. The mice of each group (%20, %40 and %60 of ethanol) were orally administered with 1ml of ethanol 4days/week for 3 weeks. Hematoxylin and eosin staining indicated development of mild to severe lesions in kidney and liver which included; In %20 of ethanol administration group there was mild lesion development; hydropic swelling in liver and swelling of kidney parenchyma while in %40 of ethanol administration group developed moderate changes; hydropic swelling of hepatocytes and kidney tubules with hyaline degeneration and in %60 of ethanol administration group produced severe lesion; focal macro and micro abscess in liver parenchyma and focal neutrophil infiltration within renal parenchyma and hyaline cast within renal tubules. Based on our study, it can be concluded that ethanol intoxication leads to a various disorders of the liver and kidney which arrange from mild to severe injury which was depended on the concentration of ethanol. Keywords: Ethanol, Mice, Kidney, Liver, H&E stain

Postpartum Sexual Function; Conflict in Marriage Stability: A Systematic Review

Background: One of the most important issues affecting the stability of marriage is sexual function, so its problem can lead to divorce or separation of the couple. Pregnancy and delivery as one the most important periods of women's life can have significant effects on sexual function. This study reviews the postpartum sexual function and its related factors in Iran.Methods: This study is a systematic review of the sexual function after childbirth in Iran. By using of valid keywords and searching in databases such as Google scholar, SID, Magiran, Medlib, Irandoc, Iranmedex, the total number of 15 articles between 2005 and 2012 years have been evaluated. Results were reported quantitatively and qualitatively.Results: Total Sample was 4109 women, with an average of 274 samples per study. Plenty of studies in Tehran was 46% and other cities was 54%. The majority of studies showed no relation between mode of delivery and sexual function (P=0.14), but there were significant relation between lactation and postpartum sexual function (P<0.05) as, breastfeeding decreased sexual function. Also sexual function score has decreased with increasing parity.Conclusion: According to the effects of lactation and parity on women sexual function, therefore high risk for divorce, sex education after childbirth, especially in the first six months after delivery, maybe helpful in prevention of sexual dysfunction after delivery

Multifocal Extensive Spinal Tuberculosis with Retropharyngeal Abscess

An unusual case of a young boy presenting with spinal tuberculosis involving cervical & thoracic vertebrae, along with retropharyngeal abscess is reported. The patient presented with progressive quadriparesis, fever, night sweat and cervical lymphadenopathy. The lab studies confirmed tuberculosis and patient received anti-tubercular chemotherapy. After development of quadriparesis, spinal surgery was done. The post operative course was uneventful and the patient is on gradual neurological recovery. DOI: http://dx.doi.org/10.3329/bsmmuj.v4i2.8646 BSMMU J 2011; 4(2):128-13

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

OBJECTIVE: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. METHODS: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. RESULTS: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. CONCLUSIONS: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resource

Modification of TiO_2 Nanoparticles with Organodiboron Molecules Inducing Stable Surface Ti^(3+) Complex

As one of the most promising semiconductor oxide materials, titanium dioxide (TiO_2) absorbs ultraviolet (UV) light but not visible light. To address this limitation, the introduction of Ti^(3+) defects represents a common strategy to render TiO_2 visible-light-responsive. Unfortunately, current hurdles in Ti^(3+) generation technologies impeded the widespread application of Ti^(3+) modified materials. Herein, we demonstrate a simple and mechanistically distinct approach to generating abundant surface-Ti^(3+) sites without leaving behind oxygen vacancy and sacrificing one-off electron donors. In particular, upon adsorption of organodiboron reagents onto TiO_2 nanoparticles, spontaneous electron injection from the dibron-bound O^(2-) site to adjacent Ti^(4+) site leads to an extremely stable blue surface Ti^(3+)‒O^(-•) complex. Notably, this defect generation protocol is also applicable to other semiconductor oxides including ZnO, SnO_2, Nb_2O_5 and In_2O_3. Furthermore, the as-prepared photoelectronic device using this strategy affords 10^3 fold higher visible light response, and the fabricated perovskite solar cell shows an enhanced performance

Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals

We investigate how various coarse-graining methods affect the scaling properties of long-range power-law correlated and anti-correlated signals, quantified by the detrended fluctuation analysis. Specifically, for coarse-graining in the magnitude of a signal, we consider (i) the Floor, (ii) the Symmetry and (iii) the Centro-Symmetry coarse-graining methods. We find, that for anti-correlated signals coarse-graining in the magnitude leads to a crossover to random behavior at large scales, and that with increasing the width of the coarse-graining partition interval $\Delta$ this crossover moves to intermediate and small scales. In contrast, the scaling of positively correlated signals is less affected by the coarse-graining, with no observable changes when $\Delta1$ a crossover appears at small scales and moves to intermediate and large scales with increasing $\Delta$. For very rough coarse-graining ($\Delta>3$) based on the Floor and Symmetry methods, the position of the crossover stabilizes, in contrast to the Centro-Symmetry method where the crossover continuously moves across scales and leads to a random behavior at all scales, thus indicating a much stronger effect of the Centro-Symmetry compared to the Floor and the Symmetry methods. For coarse-graining in time, where data points are averaged in non-overlapping time windows, we find that the scaling for both anti-correlated and positively correlated signals is practically preserved. The results of our simulations are useful for the correct interpretation of the correlation and scaling properties of symbolic sequences.Comment: 19 pages, 13 figure

Physical and hybrid modelling techniques for earth-air heat exchangers in reducing building energy consumption: Performance, applications, progress, and challenges

Noteworthy advancements are seen in developing the earth-air heat exchanger (EAHE) models in the past several decades to reduce building energy consumption. However, it is still an ongoing challenge in selecting and implementing the most suitable and appropriate EAHE modelling technique in buildings based on the climates, performance, and limitations of the techniques. Therefore, this paper aims to review the published research related to the physical, and hybrid EAHE modelling techniques used in buildings, and highlight the prospects, benefits, progress, and challenges of these techniques. This is the first study that comprehensively evidences the prospects and technical challenges caused by unmeasured disturbances, assumptions, or the uncertainties generated in experimental and numerical works of all EAHE modelling techniques. Nevertheless, this study found that hybrid modelling is more effective than physical models for accurate prediction. On the contrary, the hybrid models suffer from high complexity if EAHE operating conditions and all key parameters are considered during the model development. Regarding the generalization capability, the physical models offer improved performance followed by the hybrid models. A minimum number of training data is needed for developing physical models, whereas medium training data is required for the hybrid models. The outcome of this study also provides valuable information regarding the physical and hybrid EAHE modelling techniques to the scientists, researchers, and so on in adopting the most appropriate EAHE modelling technique for their climates

A Mini Review on the Cold Flow Properties of Biodiesel and its Blends

© Copyright © 2020 Hazrat, Rasul, Mofijur, Djavanroodi, Khan, Azad, Bhuiya and Silitonga. Biodiesels are renewable fuel that may be produced from various feedstock using different techniques. It is endorsed in some countries of the world as a viable substitute to diesel fuel. While biodiesel possesses numerous benefits, the cold flow properties (CFP) of biodiesel in comparison with petro-diesel are significantly less satisfactory. This is due to the presence of saturated and unsaturated fatty acid esters. The poor CFP of biodiesel subsequently affects performance in cold weather and damages the engine fuel system, as well as chokes the fuel filter, fuel inlet lines, and injector nozzle. Previously, attempts were made to minimize the damaging impact of bad cold flow through the reduction of pour point, cloud point, and the cold filter plugging point of biodiesel. This study is focused on the biodiesel CFP-related mechanisms and highlights the factors that initialize and pace the crystallization process. This review indicates that the CFP of biodiesel fuel can be improved by utilizing different techniques. Winterisation of some biodiesel has been shown to improve CFP significantly. Additives such as polymethyl acrylate improved CFP by 3-9 ° C. However, it is recommended that improvement methods in terms of fuel properties and efficiency should be carefully studied and tested before being implemented in industrial applications as this might impact biodiesel yield, cetane number, etc