Novel Insights to Assess Climate Resilience in Goats Using a Holistic Approach of Skin-Based Advanced NGS Technologies.

A novel study was conducted to elucidate heat-stress responses on a number of hair- and skin-based traits in two indigenous goat breeds using a holistic approach that considered a number of phenotypic and genomic variables. The two goat breeds, Kanni Aadu and Kodi Aadu, were subjected to a simulated heat-stress study using the climate chambers. Four groups consisting of six goats each (KAC, Kaani Aadu control; KAH, Kanni Aadu heat stress; KOC, Kodi Aadu control; and KOH, Kodi Aadu heat stress) were considered for the study. The impact of heat stress on caprine skin tissue along with a comparative assessment of the thermal resilience of the two goat breeds was assessed. The variables considered were hair characteristics, hair cortisol, hair follicle quantitative PCR (qPCR), sweating (sweating rate and active sweat gland measurement), skin histometry, skin-surface infrared thermography (IRT), skin 16S rRNA V3-V4 metagenomics, skin transcriptomics, and skin bisulfite sequencing. Heat stress significantly influenced the hair fiber characteristics (fiber length) and hair follicle qPCR profile (Heat-shock protein 70 (HSP70), HSP90, and HSP110). Significantly higher sweating rate, activated sweat gland number, skin epithelium, and sweat gland number (histometry) were observed in heat stressed goats. The skin microbiota was also observed to be significantly altered due to heat stress, with a relatively higher alteration being noticed in Kanni Aadu goats than in Kodi Aadi goats. Furthermore, the transcriptomics and epigenetics analysis also pointed towards the significant impact of heat stress at the cellular and molecular levels in caprine skin tissue. The higher proportion of differentially expressed genes (DEGs) along with higher differentially methylated regions (DMRs) in Kanni Aadu goats due to heat stress when compared to Kodi Aadu goats pointed towards the better resilience of the latter breed. A number of established skin, adaptation, and immune-response genes were also observed to be significantly expressed/methylated. Additionally, the influence of heat stress at the genomic level was also predicted to result in significant functional alterations. This novel study thereby highlights the impact of heat stress on the caprine skin tissue and also the difference in thermal resilience exhibited by the two indigenous goat breeds, with Kodi Aadu goats being more resilient.

De novo pathway is an active metabolic pathway of cysteine synthesis in Haemonchus contortus.

Haemonchus contortus, commonly known as Barber's pole worm, is an economically important gastrointestinal nematode of sheep and goats especially in tropical and sub-tropical regions of the world. Cysteine synthesis is a very important metabolic pathway for the parasite, however the functional aspects of cysteine synthesis in parasite are largely unknown. The key question which we have investigated in the study is; whether the parasite uses a de novo pathway of cysteine synthesis, which is unknown in multicellular organisms of the animal kingdom and known to be absent in mammals. Directional cloning of the cysteine synthase (CS) gene was done in pET303 champion vector using restriction sites XbaI and XhoI. The CS gene of the H.contortus was closely related to CS-A protein of Oesophagostomum dentatum and a hypothetical protein of Ancylostoma ceylanicum. Recombinant protein of the H contortus CS (rHC-CS) gene was expressed using pET303 vector in pLysS BL21 strain of E.coli and subsequently purified by Ni-NTA affinity chromatography. Western blot using anti-His tag antibody confirmed the presence of rHC-CS. Biochemical assay, FTIR and enzyme kinetics studies revealed that rHC-CS used O-acetyl serine as substrate to produce cysteine using de novo pathway and CS activity was also confirmed with the homogenate of H.contortus. Upregulation of CS transcripts in the adult and its downregulation in the L3 larval stage suggests that de novo pathway contributes to the cysteine requirement of mature H.contortus. It is concluded that de novo pathway is an active metabolic pathway in H.contortus.

Gelatin/ß-Cyclodextrin Bio-Nanofibers as respiratory filter media for filtration of aerosols and volatile organic compounds at low air resistance.

Air pollution is a universal concern. The suspended solid/liquid particles in the air and volatile organic compounds (VOCs) are ubiquitous. Synthetic polymer-based air filter media not only has disposal issues but also is a source of air and water pollution at the end of their life cycle. It has been a challenge to filter both particulate matter and VOC pollutants by a common biodegradable filter media having low air resistance. This study reports gelatin/ß-cyclodextrin composite nanofiber mats with dual function air filtration ability at reduced air resistance (148 Pa) and low basis weight (1 g/m²). Gelatin/ß-cyclodextrin nanofibers captured aerosols (0.3-5 µm) with < 95% filtration efficiency at 0.029/Pa quality factor. They adsorbed great amount of xylene (287 mg/g), benzene (242 mg/g), and formaldehyde (0.75 mg/g) VOCs. VOC adsorption of gelatin/ß-cyclodextrin nanofibers is found several times higher than a commercial face mask and pristine powder samples. This study provides a solution for a 'green' dual function respiratory air filtration at low resistance. Gelatin/ß-cyclodextrin nanofibers also have the potential to filter nano-sized viruses.

Wheat starch, gum arabic and chitosan biopolymer treatment of wool fabric for improved shrink resistance finishing.

The major concerns of today's textile wet processing are large use of chemicals and toxic effluents. Eco-friendly sustainable ways of textile processing which are safe to health and environment are receiving much attention. Wool fabric suffers from irreversible shrinkage during washing. In this study, sustainable biopolymers have been utilized to impart shrink resistance finish to wool fabric without affecting the original properties of the fabric. The wool fabric was coated with gum arabic, chitosan, and wheat starch biopolymers. The presence of biopolymers on the wool fabrics was confirmed by Fourier Transform Infrared (FTIR) spectroscopy and Field Emission Scanning Electron Microscopy (FE-SEM). The tensile, bending, friction, color strength, yellowness index and whiteness index was measured along with shrink resistance of wool fabric before and after the biopolymer treatment. The biopolymer treatment significantly reduced the area shrinkage (<4%) of wool fabric. The biopolymer coating could be a viable and sustainable alternative to conventional treatments for developing shrink-resistant wool fabric with no adverse effect on fabric properties.

Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression.

Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is detected at the beginning stage, it can often be cured. Many researchers proposed numerous methods for early prediction of this Cancer. In this paper, we proposed feature ensemble learning based on Sparse Autoencoders and Softmax Regression for classification of Breast Cancer into benign (non-cancerous) and malignant (cancerous). We used Breast Cancer Wisconsin (Diagnostic) medical data sets from the UCI machine learning repository. The proposed method is assessed using various performance indices like true classification accuracy, specificity, sensitivity, recall, precision, f measure, and MCC. Simulation and result proved that the proposed approach gives better results in terms of different parameters. The prediction results obtained by the proposed approach were very promising (98.60% true accuracy). In addition, the proposed method outperforms the Stacked Sparse Autoencoders and Softmax Regression based (SSAE-SM) model and other State-of-the-art classifiers in terms of various performance indices. Experimental simulations, empirical results, and statistical analyses are also showing that the proposed model is an efficient and beneficial model for classification of Breast Cancer. It is also comparable with the existing machine learning and soft computing approaches present in the related literature.

Blended Nanostructured Degradable Mesh with Endometrial Mesenchymal Stem Cells Promotes Tissue Integration and Anti-Inflammatory Response in Vivo for Pelvic Floor Application.

The current urogynecological clinical meshes trigger unfavorable foreign body response which leads to graft failure in the long term. To overcome the present challenge, we applied a tissue engineering strategy using endometrial SUSD2+ mesenchymal stem cells (eMSCs) with high regenerative properties. This study delves deeper into foreign body response to SUSD2+ eMSC based degradable PLACL/gelatin nanofiber meshes using a mouse model targeted at understanding immunomodulation and mesh integration in the long term. Delivery of cells with nanofiber mesh provides a unique topography that enables entrapment of therapeutic cells for up to 6 weeks that promotes substantial cellular infiltration of host anti-inflammatory macrophages. As a result, degradation rate and tissue integration are highly impacted by eMSCs, revealing an unexpected level of implant integration over 6 weeks in vivo. From a clinical perspective, such immunomodulation may aid in overcoming the current challenges and provide an alternative to an unmet women's urogynecological health need.