Genetic Parameters and Prediction of Genotypic Values for Postharvest Physiological Deterioration Tolerance and Root Traits in Cassava using REML/BLUP.

The study aimed to estimate the genetic parameters and predict the genotypic values of postharvest physiological deterioration and root characteristics in cassava (Manihot esculentaCrantz) using restricted maximum likelihood (REML) and the best linear unbiased prediction (BLUP). A total of 76 cassava accessions were evaluated over two growing seasons. The evaluated traits included postharvest physiological deterioration response (PPD), root length (RL), root diameter (RD), root weight (RW), dry matter content (DMC), total starch content (TS) and total sugar content (TSU).

Design, characterization and evaluation of a new Tc-labeled folate derivative with affinity towards folate receptor.

Folate receptors (FRs) are known to be over-expressed in several human malignancies and therefore serve as an important target for small radiolabeled folate derivatives for non-invasive imaging of tumor, which is an important tool for future treatment recourse. In the present article, we report the synthesis of a new Tc-labeled radiotracer for the aforementioned application following the well-established Tc-'4+1' chemistry. Formation of the desired [Tc]Tc-complex with >95% radiochemical purity was confirmed by radio-HPLC and its structure was ascertained by characterizing a natural rhenium analogue of the said complex.

Imaging and pathological discordance amongst the plethora of breast lesions in breast biopsies.

Introduction: Imaging-guided breast tissue biopsy has become an acceptable alternative to open surgical biopsy for nonpalpable breast lesions. Discussion of abnormal results of the correlation between imaging and pathological findings can be very challenging as it can assist in decision-making with regard to the further treatment options by arriving at a comprehensive diagnosis.

Materials And Methods: This was a retrospective study.

Hybrid PSO-SVM algorithm for Covid-19 screening and quantification.

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization-Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19.