A novel framework based on explainable AI and genetic algorithms for designing neurological medicines.

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.

Optimizing coal mine planning and design for sustainable development in the context of mass exploitation of coal deposits.

Sustainable mining practices is a concept that embeds the principles of sustainable development into the whole mine life-cycle, from exploration, extraction and processing through to mine closure. The optimization of coal mine planning and the developing a standardized design for its sustainable development is very challenging and requires more effort. The present research attempts to address the conditions of sustainability and necessary measures for sustainable development, thereby providing appropriate solutions for each stage of mining operation besides expressing the necessity of sustainable development integration at different stages of mining life cycle (MLC). The approach of systems engineering is essential to assist the sustainability goals which are integrated with the expected results. Hence a method depending more on systems engineering principles and optimization can be incorporated to attain better results. Several socio-environmental factors associated with sustainability depends on the geographic condition and few mining engineering considerations such as mine location, topography, coal seam characteristics and so on. These systems engineering approach can be further enhanced by incorporating tools like Geographic Information System (GIS), which provides more accuracy and precision of the geographic conditions of the site identified for the coal mining plan. In order to begin this way of approach towards the sustainability development and mining planning, the appropriate optimization parameters should be identified. The outcome of these optimization parameters can be also achieved by optimizing coal mining system models.

Studies on pollen micro-morphology, pollen storage methods, and cross-compatibility among grape (Vitis spp.) genotypes.

The knowledge of pollen morphology, suitable storage condition, and species compatibility is vital for a successful grapevine improvement programme. Ten grape genotypes from three different species, viz., Vitis vinifera L., Vitis parviflora Roxb., and Vitis champini Planc., were studied for their pollen structure and pollen storage with the objective of determining their utilization in grape rootstock improvement programs. Pollen morphology was examined through the use of a scanning electron microscope (SEM). The viability of the pollen was assessed using 2,3,5-triphenyltetrazolium chloride (TTC). In vitro pollen germination was investigated using the semi-solid medium with 10 % sucrose, 100 mg/L boric acid, and 300 mg/L calcium nitrate. The results revealed variations in pollen micro-morphology in 10 genotypes, with distinct pollen dimensions, shapes, and exine ornamentation. However, species-wise, no clear difference was found for these parameters. Pollen of V. parviflora Roxb. and Dogridge was acolporated and did not germinate. The remaining eight genotypes exhibited tricolporated pollen and showed satisfactory in vitro pollen germination. Storage temperature and duration interactions showed that, at room temperature, pollen of most of the grape genotypes can be stored for up to 1 day only with an acceptable pollen germination rate (>30 %). However, storage for up to 7 days was successfully achieved at 4 °C, except for 'Pearl of Csaba'. The most effective storage conditions were found to be at -20 °C and -196 °C (in liquid N2), enabling pollen storage for a period of up to 30 days, and can be used for pollination to overcome the challenge of asynchronous flowering. Four interspecific combinations were studied for their compatibility, among which V. parviflora Roxb. × V. vinifera L. (Pusa Navrang) and V. parviflora Roxb. × V. champini Planc. (Salt Creek) showed high cross-compatibility, offering their potential use for grape rootstock breeding. However, V. parviflora Roxb. × V. vinifera L. (Male Hybrid) recorded the lowest compatibility index among studied crosses. In the case of self-pollinated flowers from V. parviflora Roxb. and V. parviflora Roxb. × V. champini Planc. (Dogridge), pollen failed to germinate on the stigma due to male sterility caused by acolporated pollen. As a result, the flowers of these genotypes functioned as females, which means they are ideal female parents for grape breeding without the need for the tedious process of emasculation.

Recent progress in molecular transition metal catalysts for hydrogen production from methanol and formaldehyde.

Hydrogen is considered as a potential alternative and sustainable energy carrier, but its safe storage and transportation are still challenging due to its low volumetric energy density. Notably, C1-based substrates, methanol and formaldehyde, containing high hydrogen contents of 12.5 wt% and 6.7 wt%, respectively, can release hydrogen on demand in the presence of a suitable catalyst. Advantageously, both methanol and aqueous formaldehyde are liquid at room temperature, and hence can be stored and transported considerably more safely than hydrogen gas. Moreover, these C1-based substrates can be produced from biomass waste and can also be regenerated from CO2, a greenhouse gas. In this review, the recent progress in hydrogen production from methanol and formaldehyde over noble to non-noble metal complex-based molecular transition metal catalysts is extensively reviewed. This review also focuses on the critical role of the structure-activity relationship of the catalyst in the dehydrogenation pathway.

Exploring the genotype-environment interaction of bread wheat in ambient and high-temperature planting conditions: a rigorous investigation.

The current study is carried out to find out the stable wheat genotype in ambient and high temperature planting conditions. The objective was to estimate the genotype x environment interactions through various univariates and multivariate techniques. Twenty wheat genotypes were evaluated at Jabalpur, Narmadapuram, and Sagar districts of Madhya Pradesh, India, across cropping years 2019-20 and 2021-21, considering both timely and late planting conditions. The univariate and multivariate stability analysis were performed based on per-plant grain yield and grain filling rate. Our result revealed that environment, genotype, and GEI effects were significant (P < 0.001) across all the environments. The wheat genotypes JW3288, L8, and L13 have been discerned as top performers, exhibiting remarkable stability in grain yield per plant. Similarly, for grain filling rate, genotypes L11 and L13 have emerged as superior and consistently stable performers. Notably, the AMMI and GGE models demonstrated superior effectiveness and accuracy compared to the linear regression model. In conclusion, based on thorough univariate and multivariate stability analyses, L13 emerges as the most stable genotype across all environments under both planting conditions. Consequently, L13 holds promise for inclusion in future breeding programs. It's noteworthy that Jabalpur stands out as the most discriminating and representative environment among all the conditions assessed.

Kisspeptin stimulates oestradiol biosynthesis by upregulating steroidogenic transcripts and proliferation markers in the bubaline granulosa cells in vitro.

Kisspeptin (Kp), an upstream regulator of GnRH release, is essential for the development and function of reproductive axis. Previously, we demonstrated the localization of Kp and its receptor (Kiss1r) in the active follicle in the bubaline ovary. Present study aimed to determine the effect of Kp on granulosa cell (GCs) functions, especially oestradiol (E2 ) and progesterone (P4 ) production, and differential expression of genes regulating the proliferation, apoptosis and steroidogenesis in the buffalo. The ovaries with 6-10 mm size follicles obtained from the cyclic buffaloes after slaughtering were used for isolation of GCs for in vitro study. The primary GCs culture was treated with Kp (0, 10, 50 and 100 nM) and incubated for 48 h. Production of E2 and P4 was estimated in the culture supernatant by ELISA. The expression of gonadotropin receptors (FSHR and LHR), steroidogenic genes (STAR, 3ß-HSD, CYP19A1), proliferation marker (PCNA), apoptotic factors (CASP3 and BCL2) and Kp signalling molecule (extracellular signal-regulated kinase 1/2, ERK1/2 and p-ERK1/2) was studied in the GCs by qPCR. Significant E2 production was found in the Kp 50 and 100 nM groups (p < .05), whereas P4 production was reduced in Kp 100 nM group (p < .05). There was concomitant upregulation of FSHR, ERK1/2, STAR and CYP19A1 in the Kp 100 nM treated GCs. In addition, Kp at 100 nM stimulated the proliferation of GCs by upregulating the expression of BCL2 (5.0 fold) and PCNA (94.9 fold). Further, high immunoreactivity of p-ERK1/2 was observed in the Kp-treated GCs. It was concluded that Kp at 100 nM concentration stimulated E2 production by upregulating the steroidogenic pathway through ERK1/2, STAR and CYP19A1 and modulating PCNA and BCL2 expressions in the GCs. Further experiments are warranted using Kp antagonist in different combinations to establish the signalling pathway in Kp-mediated steroidogenesis in the GCs for developing strategies to control ovarian functions.

Computer-assisted interpretation, in-depth exploration and single cell type annotation of RNA sequence data using k-means clustering algorithm.

At now, the majority of approaches rely on manual techniques for annotating cell types subsequent to clustering the data obtained from single-cell RNA sequencing (scRNA-seq). These approaches require a significant amount of physical exertion and depend substantially on the user's skill, perhaps resulting in uneven outcomes and inconsistency in treatment. In this paper, we provide a computer-assisted interpretation of every single cell of a tissue sample, along with an in-depth exploration of an individual cell's molecular, phenotypic and functional attributes. The paper will also perform k-means clustering followed by silhouette validation based on similar phenotype and functional attributes, and also, cell type annotation is performed, where we match a cell's gene profile against some known database by applying certain statistical conditions. Finally, all the genes are mapped spatially on the tissue sample. This paper is an aid to medicine to know which cells are expressed/not expressed in a tissue sample and their spatial location on the tissue sample.

Recent Developments in Reversible CO2 Hydrogenation and Formic Acid Dehydrogenation over Molecular Catalysts.

Carbon dioxide (CO2), a valuable feedstock, can be reutilized as a hydrogen carrier by hydrogenating CO2 to formic acid (FA) and releasing hydrogen by FA dehydrogenation in a reversible manner. Notably, FA is liquid at room temperature and can be stored and transported considerably more safely than hydrogen gas. Herein, we extensively reviewed transition-metal-based molecular catalysts explored for reversible CO2 hydrogenation and FA dehydrogenation. This Review describes different approaches explored for carbon-neutral hydrogen storage and release by applying CO2 hydrogenation to FA/formate and the subsequent release of H2 by the dehydrogenation of FA over a wide range of molecular catalysts based on noble and non-noble metals. Emphasis is also placed on the specific catalyst-to-substrate interaction by highlighting the specific role of the catalyst in the CO2 hydrogenation-FA dehydrogenation pathway.

Secretory leucocyte protease inhibitor (SLPI) as an adjunct prognostic biomarker for canine pyometra.

Pyometra is a potentially life-threatening condition that affects intact female dogs in their middle to advance age. Timely diagnosis and appropriate treatment are critical for the survival of patients, especially when pyometra advances to sepsis. This study aimed to investigate the prognostic potential of certain haematology, serum biochemical and inflammatory biomarker, secretory leucocyte protease inhibitor (SLPI) for pyometra in bitches (n = 41). Blood samples were collected after clinical diagnosis of pyometra for haematology and serum biochemistry. Based on the prognosis following medical/surgical treatment, animals were retrospectively categorized into survivor (n = 29) and dead (n = 12). Endometrial tissue sections were obtained from the bitches undergoing ovariohysterectomy (n = 21). Serum concentration of SLPI was quantified using sandwich ELISA and its expression in the endometrium was investigated using RT-qPCR. A marked increase in the total leucocyte count (TLC), neutrophils, blood urea nitrogen (BUN) and serum creatinine was observed in the female dogs that did not survive. Significant elevation in the serum SLPI concentration (3.49 ± 0.44 vs. 2.38 ± 0.13 ng/mL) was observed in the bitches those died after the treatment, in comparison to those survived (p < .01). Additionally, there was a notable upregulation of SLPI in the endometrium in the bitches those died due to pyometra. Based on the ROC analysis results, it was observed that a cut-off concentration of 2.93 ng/mL for SLPI, 27.77 mg/dL for BUN and 16.3 × 103 /µL for TLC could effectively distinguish the prognosis of pyometra-affected dogs. From this study, it can be concluded that upregulation of SLPI in the endometrium and its elevated concentration in peripheral circulation along with TLC and BUN concentration could serve as valuable indicators for predicting the prognosis of pyometra in bitches.

Characterization of bioactive and fruit quality compounds of promising mango genotypes grown in Himalayan plain region.

Twenty mango genotypes grown in the plains of the Himalayas were characterized by their physical, physiological, biochemical, mineral and organoleptic attributes: fruit firmness, weight, peel thickness, shape, dry seed weight, respiration rate, weight loss, and shelf life. Biochemical attributes such as soluble solids, total carotenoids, total phenolic content, antioxidant activity, titratable acidity, ascorbic acid and total sugars were also determined. In addition, mineral content and fruit-softening enzymes were measured, and an organoleptic evaluation was performed. Polygalactouronase (PG), pectin methylesterase (PME) and lipoxygenase (LOX) were measured from the pulp adjacent to the peel. Similarly, biochemical attributes and mineral content were evaluated using fruit pulp, while organoleptic evaluation included fruit pulp characters and the fruit's external appearance. The results of the study showed that the 'Malda' genotype exhibited the highest total phenolic content (560.60 µg/100 g), total antioxidant (5.79 µmol TE/g), and titratable acidity (0.37%) among the tested genotypes. 'Amrapali' had the highest soluble solid content (25.20 °B), 'Jawahar' had the highest ascorbic acid content (44.20 mg/100 g pulp), 'Mallika' had the highest total flavonoid content (700.00 µg/g) and 'Amrapali' had the highest total carotenoid content (9.10 mg/100 g). Moreover, the genotypes 'Malda', 'Safed Malda'and 'Suvarnarekha' had a shelf life of 4-5 days longer than other tested genotypes. The genotypes with high biochemical attributes have practical utility for researchers for quality improvement programmes and processing industries as functional ingredients in industrial products. This study provides valuable information on the nutritional and functional properties of different mango genotypes, which can aid in developing improved varieties with enhanced health benefits and greater practical utility for processing industries.

Partial desensitization of MYC2 transcription factor alters the interaction with jasmonate signaling components and affects specialized metabolism.

The activity of bHLH transcription factor MYC2, a key regulator in jasmonate signaling and plant specialized metabolism, is sensitive to repression by JASMONATE-ZIM-domain (JAZ) proteins and co-activation by the mediator subunit MED25. The substitution of a conserved aspartic acid (D) to asparagine (N) in the JAZ-interacting domain (JID) of Arabidopsis MYC2 affects interaction with JAZ, although the mechanism remained unclear. The effects of the conserved residue MYC2D128 on interaction with MED25 have not been investigated. Using tobacco as a model, we generated all possible substitutions of aspartic acid 128 (D128) in NtMYC2a. NtMYC2aD128N partially desensitized the repression by JAZ proteins, while strongly interacting with MED25, resulting in increased expression of nicotine pathway genes and nicotine accumulation in tobacco hairy roots overexpressing NtMYC2aD128N compared to those overexpressing NtMYC2a. The proline substitution, NtMYC2aD128P, negatively affected transactivation and abolished the interaction with JAZ proteins and MED25. Structural modeling and simulation suggest that the overall stability of the JID binding pocket is a predominant cause for the observed effects of substitutions at D128. The D128N substitution has an overall stabilizing effect on the binding pocket, which is destabilized by D128P. Our study offers an innovative tool to increase the production of plant natural products.

Data Augmentation for Medical Image Classification based on Gaussian Laplacian Pyramid Blending with a Similarity Measure.

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.

A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines.

An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.

Assessment of diagnostic accuracy and effectiveness of trans-abdominal real-time ultrasound imaging for pregnancy diagnosis in breeding sows under intensive management.

Early and accurate determination of pregnancy is critical to optimum reproductive performance in pigs and enables farmers to early rebreed or cull non-pregnant animals. Most of the conventional diagnostic methods are unsuitable for systematic application under practical conditions. The advent of real-time ultrasonography has made it possible to establish relatively more reliable pregnancy diagnosis. The present study was carried out to evaluate the diagnostic accuracy and effectiveness of trans-abdominal real-time ultrasound (RTU) imaging vis-à-vis pregnancy status in sows reared under intensive management. Trans-abdominal ultrasonographic examinations were performed using a mechanical sector array transducer and portable ultrasound system in crossbred sows from 20 days post-insemination for up to next 40 days. Animals were followed up for subsequent reproductive performance with farrowing data used as the definitive test for deriving predictive values. Accuracy for diagnosis was determined by diagnostic accuracy measures like sensitivity, specificity, predictive values, and likelihood ratios. Before 30 days of breeding, RTU imaging had 84.21% sensitivity and 75% specificity. Relatively higher false diagnosis rates were obtained in animals checked at or before 55 days after AI than in animals checked after 55 days (21.73% versus 9.09%). Negative pregnancy rate was low with 29.16% (7/24) false positives. Overall sensitivity and specificity, using farrowing history as the gold standard, were 94.74% and 70.83% respectively. The sensitivity of testing tended to be slightly lower in sows with litter size of less than 8 total born piglets, compared to sows with 8 or more piglets. Overall positive likelihood ratio was 3.25 while negative likelihood ratio was 0.07. The results indicate that pregnancy in swine herds can be reliably detected earlier in gestation by 30 days post-insemination using trans-abdominal RTU imaging. This non-invasive technique with portable imaging system can be used as an integral part of reproductive monitoring and sound management practices for profitable swine production systems.

High resolution mapping of QTLs for fruit color and firmness in Amrapali/Sensation mango hybrids.

Introduction: Mango (Mangifera indica L.), acclaimed as the 'king of fruits' in the tropical world, has historical, religious, and economic values. It is grown commercially in more than 100 countries, and fresh mango world trade accounts for ~3,200 million US dollars for the year 2020. Mango is widely cultivated in sub-tropical and tropical regions of the world, with India, China, and Thailand being the top three producers. Mango fruit is adored for its taste, color, flavor, and aroma. Fruit color and firmness are important fruit quality traits for consumer acceptance, but their genetics is poorly understood. Methods: For mapping of fruit color and firmness, mango varieties Amrapali and Sensation, having contrasting fruit quality traits, were crossed for the development of a mapping population. Ninety-two bi-parental progenies obtained from this cross were used for the construction of a high-density linkage map and identification of QTLs. Genotyping was carried out using an 80K SNP chip array. Results and discussion: Initially, we constructed two high-density linkage maps based on the segregation of female and male parents. A female map with 3,213 SNPs and male map with 1,781 SNPs were distributed on 20 linkages groups covering map lengths of 2,844.39 and 2,684.22cM, respectively. Finally, the integrated map was constructed comprised of 4,361 SNP markers distributed on 20 linkage groups, which consisted of the chromosome haploid number in Mangifera indica (n =20). The integrated genetic map covered the entire genome of Mangifera indica cv. Dashehari, with a total genetic distance of 2,982.75 cM and an average distance between markers of 0.68 cM. The length of LGs varied from 85.78 to 218.28 cM, with a mean size of 149.14 cM. Phenotyping for fruit color and firmness traits was done for two consecutive seasons. We identified important consistent QTLs for 12 out of 20 traits, with integrated genetic linkages having significant LOD scores in at least one season. Important consistent QTLs for fruit peel color are located at Chr 3 and 18, and firmness on Chr 11 and 20. The QTLs mapped in this study would be useful in the marker-assisted breeding of mango for improved efficiency.

Rootstock-mediated carbohydrate metabolism, nutrient contents, and physiological modifications in regular and alternate mango (Mangifera indica L.) scion varieties.

Most of the popular scion varieties of mango possess alternate/irregular bearing. There are many external and internal factors assigned, among them carbohydrate reserves, and nutrient content plays important roles in the floral induction process in many crop species. In addition to that rootstock can alter the carbohydrate reserve and nutrient acquisition of scion varieties in fruit crops. The present investigation was carried out to understand the effect of rootstocks on the physiochemical traits of leaf, and bud and nutrient content in regular and alternate bearing varieties of mango. The rootstock "Kurukkan" promoted starch content in leaves of both alternate bearing varieties 'Dashehari' (5.62 mg/g) and regular 'Amrapali' (5.49 mg/g) and encouraged higher protein content (6.71 mg/g) and C/N ratio (37.94) in buds of alternate bearing 'Dashehari'. While Olour rootstock upregulated the reducing sugar in leaves of 'Amrapali' (43.56 mg/g) and promoted K (1.34%) and B (78.58 ppm) content in reproductive buds of 'Dashehari'. Stomatal density in 'Dashehari' scion variety was found higher on Olour rootstock (700.40/mm 2), while the rootstock fails to modify stomatal density in the scion variety regular bearer 'Amrapali'. Further, a total of 30 carbohydrate metabolism-specific primers were designed and validated in 15 scion/rootstock combinations. A total of 33 alleles were amplified among carbohydrate metabolism-specific markers, which varied from 2 to 3 alleles with a mean of 2.53 per locus. Maximum and minimum PIC value was found for NMSPS10, and NMTPS9 primers (0.58). Cluster analysis revealed that scion grafted on Kurukkan rootstock clustered together except 'Pusa Arunima' on Olour rootstock. Our analysis revealed that Fe is the key component that is commonly expressed in both leaf and bud. Although Stomatal density (SD) and Intercellular CO2 Concentration (Ci) are more specific to leaf and Fe, B, and total sugar (TS) are abundant in buds. Based on the results it can be inferred that the physiochemical and nutrient responses of mango scion varieties are manipulated by the rootstock, hence, the scion-rootstock combination can be an important consideration in mango for selecting suitable rootstock for alternate/irregular bearer varieties.

Diruthenium Catalyst for Hydrogen Production from Aqueous Formic Acid.

Diruthenium complexes [{(η6-arene)RuCl}2(µ-κ2:κ2-benztetraimd)]2+ containing the bridging bis-imidazole methane-based ligand {1,4-bis(bis(2-ethyl-5-methyl-1H-imidazol-4-yl)methyl)benzene} (benztetraimd) are synthesized for catalytic formic acid dehydrogenation in water at 90 °C. Catalyst [{(η6-p-cymene)RuCl}2(µ-κ2:κ2-benztetraimd)]2+ [1-Cl2] exhibited a remarkably high turnover frequency (1993 h-1 per Ru atom) and long-term stability over 60 days for formic acid dehydrogenation, while the analogous (η6-benzene)diruthenium and mononuclear catalysts displayed low activity with poor long-term stability. Notably, catalyst [1-Cl2] also displayed an appreciably high turnover number of 93 200 for the bulk-scale reaction. In addition, the in-depth mass and nuclear magnetic resonance investigations under the catalytic and control experimental conditions revealed the active involvement of several crucial catalytic intermediate species, such as Ru-aqua species [{(η6-p-cymene)Ru(H2O)}2(µ-L)]2+ [1-(OH2)2], Ru-formato species [{(η6-p-cymene)Ru(HCOO)}2(µ-L)] [1-(HCOO)2], and Ru-hydrido species [{(η6-p-cymene)Ru(H)}2(µ-L)] [1-(H)2], in the catalytic formic acid dehydrogenation reaction.

Fungal alkaline proteases and their potential applications in different industries.

The consumption of various enzymes in industrial applications around the world has increased immensely. Nowadays, industries are more focused on incorporating microbial enzymes in multiple processes to avoid the hazardous effects of chemicals. Among these commercially exploited enzymes, proteases are the most abundantly used enzymes in different industries. Numerous bacterial alkaline proteases have been studied widely and are commercially available; however, fungi exhibit a broader variety of proteases than bacteria. Additionally, since fungi are often recognized as generally regarded as safe (GRAS), using them as enzyme producers is safer than using bacteria. Fungal alkaline proteases are appealing models for industrial use because of their distinct spectrum of action and enormous diversity in terms of being active under alkaline range of pH. Unlike bacteria, fungi are less studied for alkaline protease production. Moreover, group of fungi growing at alkaline pH has remained unexplored for their capability for the production of commercially valuable products that are stable at alkaline pH. The current review focuses on the detailed classification of proteases, the production of alkaline proteases from different fungi by fermentation (submerged and solid-state), and their potential applications in detergent, leather, food, pharmaceutical industries along with their important role in silk degumming, waste management and silver recovery processes. Furthermore, the promising role of alkali-tolerant and alkaliphilic fungi in enzyme production has been discussed briefly. This will highlight the need for more research on fungi growing at alkaline pH and their biotechnological potential.

Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Serum concentrations of secretory leukocyte protease inhibitor and IL6 can predict the onset of sepsis in pyometra bitches.

As onset of sepsis adversely affects the prognosis of canine pyometra, finding biomarkers that would distinguish sepsis status would be useful in the clinical management. Accordingly, we hypothesized that differential expression of endometrial transcripts and circulating concentration of certain inflammatory mediators would discriminate pyometra-led sepsis (P-sepsis+) from those of pyometra without sepsis (P-sepsis-). Bitches with pyometra (n = 52) were classified into P-sepsis+ (n = 28) and P-sepsis- (n = 24) based on vital clinical score and total leukocyte count. A group of non-pyometra bitches (n = 12) served as control. The relative fold changes in the transcripts of IL6, IL8, TNFα, IL10, PTGS2, mPGES1 and PGFS, SLPI, S100A8, S100A12 and eNOS were determined by quantitative polymerase chain reaction. Furthermore, the serum concentrations of IL6, IL8, IL10, SLPI and prostaglandin F2α metabolite (PGFM) were assayed by ELISA. The relative fold changes in S100A12 and SLPI and mean concentrations of IL6 and SLPI were significantly (p < .05) higher in P-sepsis+ than that of P-sepsis- group. Receiver operating characteristic analysis revealed that serum IL6 had a diagnostic sensitivity of 78.6% and a positive likelihood ratio (LR+) of 2.09, at a cut-off value of 15.7 pg/mL to diagnose P-sepsis+ cases. Similarly, serum SLPI had a sensitivity of 84.6% and an LR+ of 2.23, at a cut-off value of 2.0 pg/mL. It was concluded that SLPI and IL6 would serve as putative biomarkers for pyometra-led sepsis in bitches. Monitoring SLPI and IL6 would be a useful adjunct to the established haemato-biochemical parameters in customizing the treatment strategies and arriving at the decision for management of pyometra bitches with critical illness.