Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student's t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis

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

The genome, transcriptome, and proteome of the nematode Steinernema carpocapsae: Evolutionary signatures of a pathogenic lifestyle

The entomopathogenic nematode Steinernema carpocapsae has been widely used for the biological control of insect pests. It shares a symbiotic relationship with the bacterium Xenorhabdus nematophila, and is emerging as a genetic model to study symbiosis and pathogenesis. We obtained a high-quality draft of the nematode’s genome comprising 84,613,633 bp in 347 scaffolds, with an N50 of 1.24 Mb. To improve annotation, we sequenced both short and long RNA and conducted shotgun proteomic analyses. S. carpocapsae shares orthologous genes with other parasitic nematodes that are absent in the free-living nematode C. elegans, it has ncRNA families that are enriched in parasites, and expresses proteins putatively associated with parasitism and pathogenesis, suggesting an active role for the nematode during the pathogenic process. Host and parasites might engage in a co-evolutionary arms-race dynamic with genes participating in their interaction showing signatures of positive selection. Our analyses indicate that the consequence of this arms race is better characterized by positive selection altering specific functions instead of just increasing the number of positively selected genes, adding a new perspective to these co-evolutionary theories. We identified a protein, ATAD-3, that suggests a relevant role for mitochondrial function in the evolution and mechanisms of nematode parasitism

Effects of potassium application on yield attributes, yield and grain quality of lentil in terrace soil of Joydebpur

An experiment was conducted in the research field of Pulses Research Sub-Station, BARI, Gazipur during two consecutive years of 2015-16 and 2016-17 to determine the suitable dose of potassium for achieving higher yield attributes, nodulation, nutrient concentration and yield maximization of lentil. There were 5 treatments viz. T1 = Control, T2 = 30 kg K ha-1, T3= 40 kg K ha-1, T4= 50 kg K ha-1 and T5= 60 kg K ha-1 along with the blanket dose of fertilizers of N, P, S, Zn and B @ 15, 20, 10, 2 and 1.5 kg ha-1, respectively for all treatments. The experiment was laid out in randomized complete block design (RCBD) with three replications. Results revealed that the highest seed yield (1092 kg ha-1) of lentil (mean of two years) was found in T4 followed by T5 treatment and the lowest (736 kg ha-1) was noted in K control (T1) treatment. The highest % yield increase over control (48.3%) was recorded from T4 treatment. The maximum nodulation was found in T5 followed by T4 treatment. The highest protein (26.9%), N, P, K, S, Zn and B concentrations of lentil seed were recorded in T4 treatment. Therefore, the results suggest that the appliction of 50 kg K ha-1 along with N15P20S10Zn2B1.5 kg ha-1 are optimum for achieving higher yield potential of lentil in terrace soils of Bangladesh.&#x0D; Bangladesh J. Agril. Res. 44(4): 599-607, December 2019</jats:p

Antagonistic potential of rhizosphere fungi against leaf spot and fruit rot pathogens of brinjal

Antagonistic potentials of seven rhizoshere soil fungi viz., Aspergillus flavus Link., A. fumigatus Fresen., A. niger Tiegh., A. terreus Thom., Penicillium sp., Trichoderma harzianum Refat. and T. viride Pers. were tested in opposition to six pathogenic fungi viz., Colletotrichum sp., Curvularia lunata, Fusarium moniliforme, F. oxysporum, F. semitectum and Phomopsis sp. isolated from different leaf spots and fruit rots of brinjal. Out of seven soil fungi, Trichoderma harzianum was found most effective to control the growth of all the test fungi in the study of colony interactions and effects of volatile and non-volatile metabolites. This fungus may be exploited commercially to biocontrol the diseases. DOI: http://dx.doi.org/10.3329/bjb.v43i2.21675 Bangladesh J. Bot. 43(2): 213-217, 2014 (September)</jats:p

Effect of flooding on growth and yield of mungbean genotypes

The field experiment was carried out with some selected mungbean genotypes viz. IPSA-13, VC-6173A, BU mug 2, BARI Mung-5 and IPSA-12 to observe the effect of 4-days flooding on their growth and yield of mungbean under field conditions at Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during September to November, 2011 maintaining 3-5 cm standing water at 24 days after emergence. Days to flowering and maturity delayed in flooded plants over control depending on the genotypes. Flooding significantly reduced Total Day Matters (TDM), number of pods per plant, seed size and seed yield of the mungbean genotypes over control. Considering higher seed yield, larger seed size and less yield reduction relative to control VC-6173A, BU mug 2 and IPSA-13 were found tolerant to soil flooding condition.Bangladesh J. Agril. Res. 41(1): 151-162, March 2016</jats:p