Molecular characterization of Pectobacterium atrosepticum infecting potato and its management through chemicals.

Potato farming is a vital component of food security and the economic stability especially in the under developing countries but it faces many challenges in production, blackleg disease caused by Pectobacterium atrosepticum (Pa) is one of the main reason for damaging crop yield of the potato. Effective management strategies are essential to control these losses and to get sustainable potato crop yield. This study was focused on characterizing the Pa and the investigating new chemical options for its management. The research was involved a systematic survey across the three district of Punjab, Pakistan (Khanewal, Okara, and Multan) to collect samples exhibiting the black leg symptoms. These samples were analyzed in the laboratory where gram-negative bacteria were isolated and identified through biochemical and pathogenicity tests for Pa. DNA sequencing further confirmed these isolates of Pa strains. Six different chemicals were tested to control blackleg problem in both vitro and vivo at different concentrations. In vitro experiment, Cordate demonstrated the highest efficacy with a maximum inhibition zones of 17.139 mm, followed by Air One (13.778 mm), Profiler (10.167 mm), Blue Copper (7.7778 mm), Spot Fix (7.6689 mm), and Strider (7.0667 mm). In vivo, Cordate maintained its effectiveness with the lowest disease incidence of 14.76%, followed by Blue Copper (17.49%), Air One (16.98%), Spot Fix (20.67%), Profiler (21.45%), Strider (24.99%), and the control group (43.00%). The results highlight Cordate's potential as a most effective chemical against Pa, offering promising role for managing blackleg disease in potato and to improve overall productivity.

Investigation of the effect of Ni and Cu variant MOF-74 in the Polydimethylsiloxane (PDMS)-based Mixed Matrix Membranes (MMMs) for efficient gas separation applications.

Mixed matrix membranes (MMMs) containing metal-organic frameworks (MOFs) have been an emerging and promising membrane technology to contribute to different gas separation applications including carbon dioxide (CO2) and oxygen (O2) separation, because of their large surface areas and distinctive gas adsorption features. In this work, the fabrication process of Polydimethylsiloxane (PDMS)-based MMMs was reported, in which 0.5 to 2 wt.% of each type of (Cu, Ni)-based MOF-74 variants were incorporated into a PDMS matrix in order to achieve high CO2/N2, O2/N2, and CO2/O2 separation efficiency. These MMMs and their nanofillers (MOF-74) were extensively characterized using scanning electron microscopy (SEM) along with Energy Dispersive X-Ray (EDX) mapping, X-ray Diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), a single gas permeation testing system, and an ultimate tensile strength testing (UTS) unit in order to gain insight into their properties in relation to their gas separation performance. The 1 wt.% of both (Cu and Ni)-MOF-74@PDMS were selected as the most optimum MMMs due to their uniform morphology and enhanced tensile strength, which exhibited high CO2 permeabilities of 4432 Barrer (37.9% increase) and 4288 Barrer (33.5% increase), respectively. Furthermore, in the case of 1 wt.% Ni-MOF-74@PDMS, the CO2/N2, O2/N2, and CO2/O2 selectivities were also enhanced to 36.2 (141.6% increase), 3.2 (21.9% increase), and 11.25 (98.1% increase), respectively. While, in the case of 1 wt.% Cu-MOF-74@PDMS the CO2/N2 and O2/N2 selectivities showed an increment up-to 94.7 (531.5% increase) and 6.47 (145% increase), respectively, Whereas, at 0.5 wt.%, Cu-MOF-74@PDMS showed the best CO2/O2 selectivity of 25.26 (344.7% increase).

The enlightening role of explainable artificial intelligence in medical & healthcare domains: A systematic literature review.

In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or treatments, can have severe and even life-threatening consequences for patients. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a popular area of research, focused on understanding the black-box nature of complex and hard-to-interpret machine learning models. While humans can increase the accuracy of these models through technical expertise, understanding how these models actually function during training can be difficult or even impossible. XAI algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) can provide explanations for these models, improving trust in their predictions by providing feature importance and increasing confidence in the systems. Many articles have been published that propose solutions to medical problems by using machine learning models alongside XAI algorithms to provide interpretability and explainability. In our study, we identified 454 articles published from 2018-2022 and analyzed 93 of them to explore the use of these techniques in the medical domain.

Preventing/Reversing Adverse Effects of Endocrine Disruption on Mouse Testes by Normalizing Tissue Resident VSELs.

Reproductive health of men is declining in today's world due to increased developmental exposure to endocrine-disrupting chemicals (EDCs). We earlier reported that neonatal exposure to endocrine disruption resulted in reduced numbers of seminiferous tubules in Stage VIII, decreased sperm count, and infertility along with testicular tumors in 65% of diethylstilbestrol (DES) treated mice. Epigenetic changes due to EDCs, pushed the VSELs out of a quiescent state to enter cell cycle and undergo excessive self-renewal while transition of c-KIT- stem cells into c-KIT + germ cells was blocked due to altered MMR axis (Np95, Pcna, Dnmts), global hypomethylation (reduced expression of 5-methylcytosine) and loss of imprinting at Igf2-H19 and Dlk1-Meg3 loci. The present study was undertaken to firstly show similar defects in FACS sorted VSELs from DES treated testis and to further explore the reversal of these testicular pathologies by (i) oral administration of XAR (a nano-formulation of resveratrol) or (ii) inter-tubular transplantation of mesenchymal stromal cells (MSCs). Similar defects as reported earlier in the testes were evident, based on RNAseq data, on FACS sorted VSELs from DES treated mice. Both strategies were found effective, improved spermatogenesis, increased number of tubules in Stage VIII, normalized numbers of VSELs and c-KIT + cells, improved epigenetic status of VSELs to restore quiescent state, and reduced cancer incidence from 65% after DES to 13.33% and 20% after XAR treatment or MSCs transplantation respectively. Results provide a basis for initiating clinical studies and the study falls under the umbrella of United Nations Sustainable Development Goal 3 to ensure healthy lives and well-being for all of all ages.