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[This corrects the article DOI: 10.1057/s41599-021-00944-1.].

Scientific collaborations among nations to address common problems and to build international partnerships as part of science diplomacy is a well-established notion. The international flow of people and ideas has played an important role in the advancement of the 'Sciences' and the current pandemic scenario has drawn attention towards the genuine need for a stronger role of science diplomacy, science advice and science communication. In dealing with the COVID-19 pandemic, visible interactions across science, policy, science communication to the public and diplomacy worldwide have promptly emerged. These interactions have benefited primarily the disciplines of knowledge that are directly informing the pandemic response, while other scientific fields have been relegated. The effects of the COVID-19 pandemic on scientists of all disciplines and from all world regions are discussed here, with a focus on early-career researchers (ECRs), as a vulnerable population in the research system. Young academies and ECR-driven organisations could suggest ECR-powered solutions and actions that could have the potential to mitigate these effects on ECRs working on disciplines not related to the pandemic response. In relation with governments and other scientific organisations, they can have an impact on strengthening and creating fairer scientific systems for ECRs at the national, regional, and global level.

Abstract Cyanobacterial photosynthesis (to produce ATP and NADPH) might have played a pivotal role in the endosymbiotic evolution to chloroplast. However, rather than meeting the ATP requirements of the host cell, the modern-day land plant chloroplasts are suggested to utilize photosynthesized ATP predominantly for carbon assimilation. This is further highlighted by the fact that the plastidic ADP/ATP carrier translocases from land plants preferentially import ATP. Here, we investigate the preferences of plastidic ADP/ATP carrier translocases from key lineages of photosynthetic eukaryotes including red algae, glaucophytes, and land plants. Particularly, we observe that the cyanobacterial endosymbionts expressing plastidic ADP/ATP carrier translocases from red algae and glaucophyte are able to export ATP and support ATP dependent endosymbiosis, whereas those expressing ADP/ATP carrier translocases from land plants preferentially import ATP and are unable to support ATP dependent endosymbiosis. These data are consistent with a scenario where the ancestral plastids may have exported ATP to support the bioenergetic functions of the host cell.

Abstract The emergence and spread of antibiotic resistance in microorganisms pose significant challenges to public health, especially in hospitals. This study investigated the existence or occurrence of bacterial bioaerosol and their antibiotic resistance patterns in particulate matter (PM) collected from hospitals in the greater Dhaka region, Bangladesh. The real-time particulate matter concentrations (PM1.0, PM2.5, and PM10) were measured in four hospitals and two ambient locations. Air sampling was conducted using a filter-based method with a low-volume air sampler, while AEROCET-531 S (USA) was employed to monitor particulate matter concentrations. Bacterial bioaerosol concentration was determined using a culture-based method, and eleven bacterial species, including nine individual species, i.e., Staphylococcus aureus, Pseudomonas aeruginosa, P. stutzeri, Bacillus cereus, Acinetobacter schindleri, Proteus vulgaris, B. subtilis, Escherichia coli, and B. aerius, were isolated. Antibiotic susceptibility testing was conducted using the Kirby-Bauer disk diffusion method with 21 antibiotics. Bacterial isolates were detected using partial sequencing of the 16 S rRNA gene. Bioaerosol concentration ranged from 194.65 ± 22.48 CFU/m3 to 948.39 ± 84.14 CFU/m3, showing significant correlations with PM1.0 and PM2.5 concentrations (R2 = 0.80 and 0.85, respectively). All bacterial isolates collected from the hospitals exhibited resistance against four or more antibiotics, indicating multidrug resistance (MDR). Notably, the bacterial isolates displayed the highest resistance rate against ampicillin (90.90%), azithromycin (81.81%), erythromycin (81.81%), cefixime (81.81%), and cotrimoxazole (54.54%), among the tested antibiotics. Except B. aerius, all other bacterial isolates were associated with hospital-acquired infections (HAIs). These findings highlight the high rates of antibiotic resistance, underscoring the pressing requirement for infection control measures and continuous surveillance strategies in hospital settings. These findings emphasize the necessity for global hospital infection control strategies focusing airborne multidrug-resistant microorganisms.

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive deterioration and changes in behavior. Acetylcholinesterase (AChE), which hydrolyzes acetylcholine, is a key drug target for treating AD. This research aimed to identify new AChE inhibitors using the IMPPAT database. We used known drugs as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Initial screening of these compounds was performed using molecular docking, followed by an analysis of their drug-likeness and ADMET properties. Compounds with favorable properties underwent density functional theory (DFT) calculations to assess their electronic properties such as HOMO-LUMO gap, electron density, and molecular orbital distribution. These descriptors provided insights into each compound’s reactivity, stability, and binding potential with AChE. Promising candidates were further evaluated through molecular dynamics (MD) simulations over 100 ns and MMPBSA analysis for the last 30 ns. Two compounds, Biflavanone (IMPHY013027) with a binding free energy of − 130.394 kcal/mol and Calomelanol J (IMPHY007737) with − 107.908 kcal/mol, demonstrated strong binding affinities compared to the reference molecule HOR, which has a binding free energy of − 105.132 kcal/mol. These compounds exhibited promising drug-ability profiles in both molecular docking and MD simulations, indicating their potential as novel AChE inhibitors for AD treatment. However, further experimental validation is necessary to verify their effectiveness and safety.

Abstract To safely and efficiently utilize porous reservoirs for underground hydrogen storage (UHS), it is essential to characterize hydrogen transport properties at multiple scales. In this study, hydrogen/brine multiphase flow at 50 bar and 25 °C in a 17 cm Berea sandstone rock core was characterized and visualized at the pore and core scales using micro X-ray CT. The experiment included a single drainage and imbibition cycle during which relative permeability hysteresis was measured, and two no-flow periods to study the redistribution of hydrogen in the pore space during storage periods. An end-point relative permeability of 0.043 was found at $$S_w=0.56$$ , and the residual gas saturation was measured to be 0.32. Despite extensive pre-equilibration, significant dissolution of hydrogen into brine occurred near the core inlet due to elevated pressures and the corresponding increase in hydrogen solubility. During drainage, many disconnected hydrogen ganglia were observed further down the core which could be explained by the exsolution of the dissolved hydrogen. During imbibition, the dissolution of hydrogen led to the formation of preferential flow paths near the inlet, and eventually removed most of the trapped hydrogen in the final stage of the experiment. The two no-flow periods were characterized by the fragmentation of medium-sized hydrogen ganglia and the growth of a few larger ganglia, providing evidence for hydrogen re-connection through the dissolution-driven process of Ostwald ripening. These results demonstrate that despite the low solubility of hydrogen in brine, hydrogen dissolution can significantly influence the observed multiphase flow and trapping behavior in the reservoir and should be considered in UHS modeling.

Abstract Transition metals like Zn are essential for all organisms including bacteria, but fluctuations of their concentrations in the cell can be lethal. Organisms have thus evolved complex mechanisms for cellular metal homeostasis. One mechanistic paradigm involves pairs of transcription regulators sensing intracellular metal concentrations to regulate metal uptake and efflux. Here we report that Zur and ZntR, a prototypical pair of regulators for Zn uptake and efflux in E. coli, respectively, can coordinate their regulation through DNA, besides sensing cellular Zn2+ concentrations. Using a combination of live-cell single-molecule tracking and in vitro single-molecule FRET measurements, we show that unmetallated ZntR can enhance the unbinding kinetics of Zur from DNA by directly acting on Zur-DNA complexes, possibly through forming heteromeric ternary and quaternary complexes that involve both protein-DNA and protein-protein interactions. This ‘through-DNA’ mechanism may functionally facilitate the switching in Zn-uptake regulation when bacteria encounter changing Zn environments, such as facilitating derepression of Zn-uptake genes upon Zn depletion; it could also be relevant for regulating the uptake-vs.-efflux of various metals across different bacterial species and yeast.

Abstract α,β-unsaturated carbonyl compounds have extensive applications in various fields, such as organic, inorganic, analytical, and biological. In the modern era, they offer excellent pharmacological application prospects and find widespread use in the pharmaceutical industry. The current study revealed the synthesis and characterization of a novel 3-(2-bromo-5-fluorophenyl)-1-(thiophen-2-yl) prop-2-en-1-one (CY3). In vitro their antimicrobial (Pseudomonas aeruginosa, Klebsiella pneumonia, Escherichia coli, Staphylococcus aureus, and Acinetobacter baumannii), antifungal ( Candida parapsilosis, Candida tropicalis, and Candida albicans), cytotoxicity (VERO and Hep-G2 cells), in silico, and molecular docking analysis were also performed. The in-silico analysis evaluated the drug-likeness properties of the compound CY3 using various filtering rules, including Lipinski’s, Ghose filter, Veber, Egan, Muegge, and Medicinal Chemistry alerts such as Pan Assay Interference Structures (PAINS), Brenk, and Lead-likeness. Then, molecular docking studies performed using the AutoDock (AD4), Vina, and iGEMDOCK tools to determine the mechanism by which the CY3 compound interact with the bacterial strains. Here, five different receptors were selected, such as DNA gyrase, glucose 6-phosphate synthase (GlmS), dihydrofolate reductase (DHFR), dehydrosqualene synthase (DHSS), and undecaprenyl pyrophosphate synthase (UDPPS), for molecular docking analysis. The CY3 compound showed a good binding affinity with the two target proteins, DHFR and DHSS, respectively, with maximum binding energies of about − 7.07 and − 7.05 kcal/mol. The synthesized CY3 compound exhibited moderate antibacterial activity with a MIC value > 100 µg/mL against all five bacterial strains and moderate antifungal activity with a MIC value > 50 µg/mL against all three fungal strains. Drug-likeness analyses also support their favourable bioavailability.

Abstract Viral diseases arising in farmed fish are an ongoing challenge to the aquaculture industry, causing severe mortality and economic losses. Recently, there has been a spike in the incidence of a viral disease caused by Tilapia Parvovirus (TiPV) inflicts irreparable damage, and large-scale fish kills in the farmed tilapia species. We investigated a case of disease outbreak and severe mortality in cage and wetland farms of tilapia in West Bengal and Odisha, India. The symptomatic fish showed clinical signs, including hemorrhage, discoloration, ulcer, and redness in the body surfaces. Further analysis revealed that Tilapia Parvovirus was associated (validated by PCR, phylogenetic analysis, and cell line assay) with the infection and mortality of tilapia. The virus was detected in gill, heart, spleen, liver, and kidney samples collected from apparently healthy (asymptomatic) and symptomatic tilapia samples from cage and wetland farms. At the same time, negative results were found in the brain and skin tissue samples. The histological analysis revealed that TiPV induces severe damage invariably in almost all studied tissue, including the liver, kidney, spleen, gill, heart, and brain of tilapia samples. The viral quantification analysis showed that the viral genome was higher in the liver, spleen, and heart than in the tilapia samples’ gill, kidney, or brain tissue. Furthermore, the study indicated that TiPV infection has a significant effect on the health of tilapia. The tilapia exhibited an immune reactivity toward TiPV infection (upregulation of chemokine receptors, CRs and interleukin 1β, IL-1β), the majority of the studied immune genes (interleukin 8, IL-8; Toll-like receptors 7, TLR7; tumour necrosis factor α, TNF-α; major histocompatibility complex II, MHC II and nuclear factor kappa B, NF-kB) were significantly downregulated in the kidney, spleen and liver tissue samples of symptomatic tilapia. Further, the in vivo challenge assay confirms that the isolated TiPV is a novel parvovirus pathogen that causes massive mortality in tilapia. The lessons learned from the first cellular and molecular description associated with TiPV epidemiology from wetland and cage farms of tilapia could be critical to developing the current state of the tilapia farming industry. Additionally, a holistic approach is needed to develop management measures to control the virulence and risk factors of TiPV.

Abstract This study investigated the isolation and bioactivity of Actinomycetia from the soil of Northeast India, a region rich in microbial diversity. A total of 187 presumptive Actinomycetia isolates were obtained and 53 were found to exhibit antimicrobial properties. Phylogenetic analysis based on 16 S rRNA gene sequencing revealed that the isolates were predominantly from the genus Streptomyces. Among these, the strain Streptomyces sp. NP14 (ANP14ARS) demonstrated specific and significant antimicrobial activity against gram-positive bacterial pathogens. The strain was further assayed against a panel of clinically important bacterial pathogens including Methicillin-resistant Staphylococcus aureus (MRSA). Its antimicrobial activity was confirmed using disc diffusion and membrane disruption assay, and its minimum inhibitory concentration was determined to be ≥ 3.12 ± 0.5 µg/ml against MRSA. Chemical analyses using FTIR and GC-MS identified key bioactive compounds, including Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)- and Phenol 3,5-bis(1,1-dimethylethyl), known for their antimicrobial properties. Toxicity studies against animal liver cell lines indicated that the ethyl acetate extract was non-toxic at concentrations below 125 µg/mL, compared to Vancomycin, which was found to be hepatotoxic at similar concentrations. These findings highlight the potential of ANP14ARS as a source of bioactive natural products that is specific towards priority pathogens such as MRSA.

Abstract Phase-separating peptides (PSPs) self-assembling into coacervate microdroplets (CMs) are a promising class of intracellular delivery vehicles that can release macromolecular modalities deployed in a wide range of therapeutic treatments. However, the molecular grammar governing intracellular uptake and release kinetics of CMs remains elusive. Here, we systematically manipulate the sequence of PSPs to unravel the relationships between their molecular structure, the physical properties of the resulting CMs, and their delivery efficacy. We show that a few amino acid alterations are sufficient to modulate the viscoelastic properties of CMs towards either a gel-like or a liquid-like state as well as their binding interaction with cellular membranes, collectively enabling to tune the kinetics of intracellular cargo release. We also demonstrate that the optimized PSPs CMs display excellent transfection efficiency in hard-to-transfect cells such as primary fibroblasts and immune cells. Our findings provide molecular guidelines to precisely program the material properties of PSP CMs and achieve tunable cellular uptake and release kinetics depending on the cargo modality, with broad implications for therapeutic applications such as protein, gene, and immune cell therapies.

Abstract Biomaterials can act as pro- or anti-inflammatory agents. However, effects of biomaterials crystallinity on immune responses are poorly understood. We demonstrate that the adjuvant-like behaviour of covalent organic framework (COF) biomaterial is dependent on its crystallinity. COF crystallinity is inversely correlated with the activation of mouse and human dendritic cells (DC), but with antigen presentation by mouse DCs only. Amorphous COFs upregulates NFkB, TNF, and RIG-I signalling pathways, as well as the chemotaxis-associated gene Unc5c, when compared to crystalline COFs. Meanwhile, Unc5c inhibition disrupts the correlation between crystallinity and DC activation. Furthermore, COFs with the lowest crystallinity admixed with chicken ovalbumin (OVA) antigen prevent OVA-expressing B16F10 tumour growth in 60% of mice, with this protection associated with the induction of antigen-specific, pro-inflammatory T cell. The lowest crystalline COFs admixed with TRP2 antigen can also prevent non-immunogenic YUMM1.1 tumour growth in 50% of mice. These findings demonstrate that the crystallinity of biomaterials is an important aspect to consider when designing immunotherapy for pro- or anti-inflammatory applications.

Abstract A growing number of humans have suffered severe chronic illnesses, which has caused a boost in the requirement for diagnostic and medical treatment procedures that are both accurate and fast. Improved patient conditions and enhanced Decision-Making Systems (DMS) for healthcare professionals are the primary objectives of the Clinical Decision Support System (CDSS) recommended in this research article. The main drawback of traditional Machine Learning (ML) techniques is their failure to predict reliably. To solve this problem, the proposed model creates an Ensemble Extreme Learning Machine (EN-ELM) algorithm that combines predictors trained on several different data sets. This lowers the chance of overfitting. The suggested CDSS uses many different data processing methods, including Adaptive Synthetic (ADASYN) and isolation Forest (iForest), which fix problems like outliers and class imbalance. This approach significantly enhances the framework’s classification performance. Also, the CDSS is compatible with an EC model, which enables real-time computation while minimizing the requirement for integrated systems. The recommended CDSS applies iForest and ADASYN to execute large-scale trials validating high standards of accuracy across numerous datasets. Researchers concluded that a suitable ELM classification threshold of 85% is the most effective, which substantially boosts the accuracy of the predictive model. When applied to various medical datasets, such as Hepatocellular Carcinoma (HCC), Cervical Cancer, Chronic Kidney Disease (CKD), Heart Disease, and Arrhythmia, the EN-ELM achieved accuracy rates of 99.36%, 98.15%, 97.85%, 97.06%, and 96.72%, respectively. By measuring this progress, the CDSS could dramatically improve the accuracy of chronic illness diagnosis and treatment, which similarly affects clinicians.

Abstract The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Abstract The effects of classical music on psychophysiological parameters are not well understood. This study aimed to investigate the impact of listening to raga Bhairavi, an Indian Classical Music for six days on anxiety, stress, depression, and heart rate variability (HRV) parameters. Forty-four individuals were randomly assigned to either the intervention group (VR-raga), where they listened to raga Bhairavi via 360° video in a virtual reality environment, or the control group, where there was no exposure to raga Bhairavi for six days. Before allocation, the HRV baselines (relax-baseline and stress-baseline) were recorded on the first day. On the first and sixth days of the intervention, HRV was monitored, and the Depression, Anxiety, and Stress Scale (DASS-21) questionnaire was administered before and after the intervention. After six days, all DASS-21 subscales were significantly reduced in the VR-raga group. A similar trend was observed in the seven HRV parameters evaluated in this study, which demonstrated reduced physiological stress and enhanced autonomic balance following the six-day intervention. The findings collectively indicated the efficacy of the VR-based raga Bhairavi intervention in reducing psychological stress markers and highlighted the potential applications of utilizing the VR-based raga intervention for improving mental well-being in the real-world context.

Abstract Alzheimer’s disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Abstract Chloroquine is still used as a first-line treatment for uncomplicated Plasmodium vivax malaria in India and resistance to this therapy can act as a major hurdle for malaria elimination. It is difficult to monitor drug-efficacy and drug resistance through in vivo and in vitro studies in case of Plasmodium vivax so analysis of molecular markers serves as an important tool to track resistance. Molecular methods that are currently in use for detecting single nucleotide polymorphisms in resistant genes including Polymerase chain reaction (PCR), Realtime-Polymerase chain reaction require highly sophisticated labs and are time consuming. So, with this background the study has been designed to optimize Loop Mediated Isothermal Amplification Assay to detect single nucleotide polymorphisms in chloroquine resistance gene of Plasmodium vivax in field settings. Eighty-eight Plasmodium vivax positive samples were collected. Pvmdr1 gene was amplified for all the samples and sequenced. Obtained sequences were analyzed for the presence of single nucleotide polymorphisms in the target gene. Further Loop Mediated Isothermal Amplification Assay primer sets were designed for the target mutants and the assay was optimized. Clinical as well as analytical sensitivity and specificity for the assay was calculated. Double mutants with variations at T958M and F1076L were detected in 100% of the Plasmodium vivax clinical isolates with haplotype M958 Y976 Y1028 L1076. Designed primers for Loop Mediated Isothermal Amplification Assay successfully detected both the mutants (T958M and F1076L) in 100% of the isolates and do not show cross-reactivity with other strains. So, the assay was 100% sensitive and specific for detecting single nucleotide polymorphisms in the target Pvmdr1 gene. Limit of detection was found to be 0.9 copies/µl and lowest DNA template concentration detected by designed assay was 1.5 ng/µL. Observed prevalence of single nucleotide polymorphisms in Pvmdr 1 gene is indicating a beginning of trend towards chloroquine resistance in Plasmodium vivax. The present study optimized LAMP for detecting single nucleotide polymorphisms in Plasmodium vivax cases in field settings, thus would help in finding significant hubs of emerging chloroquine drug resistance and ultimately helping in the management of suitable antimalarial drug policy.

Abstract Different species of aphids, responsible for severe yield losses of cereal crops including wheat, (Triticum aestivum L.) are managed by insecticides, which are harmful to organisms and the environment under field conditions. Therefore, an environment friendly aphidicidal product of plant origin is required. Mentha spicata oil was found to be rich in carvone (81.88%), but the use of its oil and carvone in crop protection is lacking due to their volatility, poor solubility, and stability. A nanoformulaton not only solves these problems but also improve the efficacy and dose of the bioactive compounds. Thus, nano-emulsions of the oil and carvone prepared were characterized, and evaluated against Rhopalosiphum maidis (corn aphid) and Sitobion avenae (wheat aphid) The average droplet size of nano-emulsions of the oil and carvone was found to be 22.1 and 41.21 nm. Nano-emulsion of carvone exhibited higher aphid mortality (LC50 = 0.87–1.94 mg/mL) at 24 h and acetylcholinesterase inhibitory activity (IC50 = 0.07–3.83 mg/mL) compared to the nano-emulsion of the oil (LC50 = 2.87–2.81 mg/mL; IC50 = 1.66–5.34 mg/mL). The repellence index (RI) in nano-emulsion of essential oil was found to be higher (84.73 and 81.72%) at the highest concentration (0.05 µL/cm2) than that of carvone (77.59 and 80.98%) for R. maidis and S. avenae. Further, in silico studies also revealed the favourable binding energy (− 6.6 to − 8.5 kcal/mol) of the main compounds in the oil with acetylcholinesterase, facilitated by hydrophobic interactions and hydrogen bonding. This study suggests that the nano-emulsions of the essential oil and carvone can be explored under field conditions to establish efficacy for their utilization as aphidicidal and repellent products against aphids. In the present study, aphidicial and repellent activities of its essential oil and carvone were reported for the first time against R.maidis and S.avenae.

Abstract Currently, polycyclic aromatic compounds in organic solar cells (OSCs) have gained substantial consideration in research communities due to their promising characteristics. Herein, polycyclic aromatic hydrocarbons (PAHs) core-based chromophores (TTFD1-TTFD6) were designed by structural modifications of peripheral acceptor groups into TTFR. The density functional theory (DFT) and time dependent density functional theory (TD-DFT) calculations were carried out at B3LYP/6-311G (d, p) functional to explore insights for their structural, electronic, and photonic characteristics. The structural modulation unveiled notable electronic impact on the HOMO and LUMO levels across all derivatives, leading to decreased band gaps. All the designed compounds exhibited band gap ranging from 2.246 to 1.957 eV, along with wide absorption spectra of 897.071-492.274 nm. An elevated exciton dissociation rate was observed due to the lower binding energy values (E b = 0.381 to 0.365 eV) calculated in the derivatives compared to the reference (E b = 0.394 eV). Furthermore, data from the transition density matrix (TDM) and density of states (DOS) also corroborated the effective charge transfer process. Comparable results of V oc for reference and designed chromophores were obtained via HOMOdonor−LUMOPC71BM. The declining V oc order values was noted as TTFD5 > TTFD6 > TTFD4 > TTFD3 > TTFD2 > TTFD1 > TTFR. Interestingly, TTFD5 was found with the smallest energy gap and highest absorption value, resulting in better charge transference among all the derivatives. The results illustrated that the modification in indenofluorene based chromophores with end-capped small acceptors proved to be a significant approach in achieving favorable photovoltaic properties.

Abstract Diffusion-weighted imaging (DWI) is a technique used to probe the random microscopic motion of water protons in living tissue, represented by a parameter measurement of apparent diffusion coefficient (ADC) values. This study aimed to measure the ADC values of various fetal organs and placenta using 3T at various gestational ages. This was a prospective observational study. A total of 103 singleton pregnancies from 20 to 38 weeks of gestational age were included. Diffusion-weighted imaging was performed in the axial plane from the fetal head to the trunk with the following parameters: TR: 2000–2500 ms; TE: 88 ms; FOV: 250 mm; 256 matrix; slice thickness: 4 mm with a 0 mm gap; acquisition time: 1 min, 18 s. Diffusion gradient values were b = 0 and b = 700 s/mm2. ADC was measured in fetal brain regions (frontal white matter, occipital white matter, centrum semiovale, pons, thalamus, cerebellum, and fetal organs (lungs, kidney, and placenta). ANOVA was used to calculate the mean ADC values. Karl Pearson’s coefficient of correlation was used to evaluate the correlation between ADC values and increasing gestational age. The mean ADC values of brain regions were: frontal white matter (1.64 ± 0.08 × 10− 3 mm2 /s, F-39.10,p-

Abstract Eukaryotic translation initiation factor eIF4B is required for efficient cap-dependent translation, it is overexpressed in cancer cells, and may influence stress granule formation. Due to the high degree of intrinsic disorder, eIF4B is rarely observed in cryo-EM structures of translation complexes and only ever by its single structured RNA recognition motif domain, leaving the molecular details of its large intrinsically disordered region (IDR) unknown. By integrating experiments and simulations we demonstrate that eIF4B IDR orchestrates and fine-tunes an intricate transition from monomers to a condensed phase, in which large-size dynamic oligomers form before mesoscopic phase separation. Single-molecule spectroscopy combined with molecular simulations enabled us to characterize the conformational ensembles and underlying intra- and intermolecular dynamics across the oligomerization transition. The observed sensitivity to ionic strength and molecular crowding in the self-association landscape suggests potential regulation of eIF4B nanoscopic and mesoscopic behaviors such as driven by protein modifications, binding partners or changes to the cellular environment.

Abstract Arrhythmogenic cardiomyopathy (AC) is a common cause of sudden cardiac arrest and death in young adults. It can be induced by different types of mutations throughout the desmoplakin gene including the R2834H mutation in the extreme carboxyterminus tail of desmoplakin (DP CT) which remains structurally uncharacterized and poorly understood. Here, we have created 3D models of DP CT which show the structural effects of AC-inducing mutations as well as the implications of post-translational modifications (PTMs). Our results suggest that, in absence of PTMs, positively charged wildtype DP CT likely folds back onto negatively-charged plectin repeat 14 of nearby plakin repeat domain C (PRD C) contributing to the recruitment of intermediate filaments (IFs). When phosphorylated and methylated, negatively-charged wildtype DP CT would then fold back onto positively-charged plectin repeat 17 of PRD C, promoting the repulsion of intermediate filaments. However, by preventing PTMs, the R2834H mutation would lead to the formation of a cytoplasmic mutant desmoplakin with a constitutively positive DP CT tail that would be aberrantly recruited by cytoplasmic IFs instead of desmosomes, potentially weakening cell-cell contacts and promoting AC. Virtual screening of FDA-approved drug libraries identified several promising drug candidates for the treatment of cardiocutaneous diseases through drug repurposing.

Abstract Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station’s data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+–HCO3 − types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3 − + CO3 2−) outperform strong acids (Cl− + SO4 2−). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p

Abstract Chirality at the nanoscale has emerged as a key area of interest in materials science and engineering, with significant implications for various fields such as spintronics, photonics, optoelectronics, quantum computing, and biomedicine. With their unique properties such as enantioselective interactions with light and spin-polarized electron transport, chiral nanomaterials are opening a new window of opportunities for the design of advanced functional devices. This editorial provides an overview of the current state of research in chirality in nanomaterials. We also showcase several papers from this collection that exemplify the breadth of current work, offering insights into the future directions of this rapidly evolving field.

Abstract Alpha-synuclein (αSyn) forms pathologic aggregates in Parkinson’s disease (PD) and is implicated in mechanisms underlying neurodegeneration. While pathologic αSyn has been extensively studied, there is currently no method to evaluate αSyn within the brains of living patients. Patients with PD are often treated with deep brain stimulation (DBS) surgery in which surgical instruments are in direct contact with neuronal tissue; herein, we describe a method by which tissue is collected from DBS surgical instruments in PD and essential tremor (ET) patients and demonstrate that αSyn is detected. 24 patients undergoing DBS surgery for PD (17 patients) or ET (7 patients) were enrolled; from patient samples, 81.2 ± 44.8 µg of protein (n = 15), on average, was collected from surgical instruments. Light microscopy revealed axons, capillaries, and blood cells as the primary components of purified tissue (n = 3). ELISA assay further confirmed the presence of neuronal and glial tissue in DBS samples (n = 4). Further analysis was conducted using western blot, demonstrating that multiple αSyn antibodies are reactive in PD (n = 5) and ET (n = 3) samples; truncated αSyn (1–125 αSyn) was significantly increased in PD (n = 5) compared to ET (n = 3), in which αSyn misfolding is not expected (0.64 ± 0.25 vs. 0.25 ± 0.12, P = 0.046), thus showing that multiple forms of αSyn can be detected from living PD patients with this method.

Abstract Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system’s interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system’s performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren’t needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA.

Abstract The kagome network is a unique platform that harbors a diversity of special electronic states due to its inherent band structure features comprising Dirac cones, van Hove singularities, and flat bands. Some kagome-based metals have recently been found to exhibit favorable properties, including superconductivity, charge order, and signatures of an anomalous Hall effect. The kagome system ScV6Sn6 is a promising candidate for studying the emergence of an unconventional charge order and accompanying effects. We use 51V nuclear magnetic resonance to explore the local properties of the charge ordered phase in single crystalline ScV6Sn6, aided by density functional theory. We show the local charge symmetry of V to reflect a commensurate modulation with $${{\bf{q}}}=\left(\frac{1}{3},\frac{1}{3},\frac{1}{3}\right)$$ q = 1 3 , 1 3 , 1 3 , the density of states to drop by about a factor of $$\sqrt{2}$$ 2 during the phase transition, and an unusual orientation dependent change in the shift splitting symmetry to reveal orbital selective modulations of the local density of states.

Abstract Background Pectin from Taiwan Citrus depressa Hayata’s peels (CDH pectin) and sodium alginate (Na alginate) were mixed in neutral acidity to produce microhydrogel beads or microspheres. The potential use of the microspheres such as encapsulation materials for quercetin and nobiletin, DPPH (2,2-diphenyl-1-picrylhydrazyl) scavenging activity, toxic elements absorption ability, and thermal characteristics were explored. Results Different ratios of CDH pectin and Na alginate produced microspheres of varying sizes and shapes. The highest yield (47.59%) with the broadest diameter was obtained at a Na alginate—CDH pectin ratio of 2:1, while the smallest yield was obtained from Na alginate—CDH pectin ratio of 1:3 (24.13%). Increasing the amount of Na alginate resulted in more spherical microspheres, higher heavy metals (cobalt and nickel) removal rates, yet a lower swelling ratio. A high pectin concentration also increased the encapsulation efficiency of quercetin and nobiletin, reaching 91.5% and 86.74%, respectively. Quercetin and nobiletin release analysis (in vitro) showed a slow release of drugs from the microspheres. Less than 20% quercetin and nobiletin were released from the microspheres in SGF (simulated gastric fluid) pH 1.2 solution after 2 h and more than 40% of the encapsulated drug was released in SIF (simulated intestinal fluid) pH 6.8 after 4 h. The strong DPPH scavenging activity of quercetin (99%) was not hindered by encapsulation materials. ICP-OES (inductively coupled plasma–optical emission spectrometry) analysis demonstrated that the biopolymer can absorb cobalt and nickel from water. Thermogravimetric analysis (TGA) result showed that the combination of CDH pectin and Na alginate produced a biopolymer that exhibited a weight loss of only 1.86–4.33% at 100 °C. Conclusions These findings suggest that microspheres produced from CDH pectin cross-linked with sodium alginate had potential in nobiletin and quercetin encapsulation. Moreover, the polymer could absorb heavy metals and exhibit an important characteristic for hot food and beverage packaging applications.

Abstract Symptoms of the Post-COVID-19 Condition are often non-specific making it a challenge to distinguish them from symptoms due to other medical conditions. In this study, we compare the proportion of emergency department patients who developed symptoms consistent with the World Health Organization’s Post-COVID-19 Condition clinical case definition between those who tested positive for Severe Acute Respiratory Syndrome Coronavirus-2 infection and time-matched patients who tested negative. Our results show that over one-third of emergency department patients with a proven acute infection meet Post-COVID-19 Condition criteria 3 months post-index visit. However, one in five test-negative patients who claim never having been infected also report symptoms consistent with Post-COVID-19 Condition highlighting the lack of specificity of the clinical case definition. Testing for SARS-CoV-2 during the acute phase of a suspected infection should continue until specific biomarkers of Post-COVID-19 Condition become available for diagnosis and treatment.

Abstract Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes.

Abstract Nitroaromatic compounds represent a class of highly toxic pollutants discharged into aquatic environments by various industrial activities, posing significant threats to ecological integrity and human health due to their persistent and hazardous nature. In this study, Pd-doped ZnO nanoparticles were investigated as a potential solution for the degradation of nitro organics, offering heightened photocatalytic efficacy and prolonged stability. The synthesis of Pd-doped ZnO NPs was achieved via the hydrothermal method, with subsequent analysis through XRD spectra and XPS confirming successful Pd doping within the ZnO matrix. Characterization through FESEM and HRTEM unveiled the heterogeneous morphologies of both undoped and Pd-doped ZnO nanoparticles. Additionally, UV–vis and PL spectroscopy provided insights into the optical properties, chemical bonding, and defect structures of the synthesized Pd-doped ZnO NPs. Pd doping induces a redshift in ZnO’s absorption spectra, reducing the bandgap from 3.12 to 2.94 eV as Pd concentration rises from 0 to 0.2 wt.%. The photocatalytic degradation, following pseudo-first-order kinetics, achieved 90% nitrobenzene abatement (200 µg/L, pH 7) under visible light within 320 min with a catalyst loading of 16 µg/mL. The photocatalytic efficacy of 0.08 wt% Pd-doped ZnO (k = 0.058 min⁻1) exhibited a 25-fold enhancement compared to bare ZnO (k = 3.1 × 10–4 min-1). Subsequent quenching and ESR experiments identified hydroxyl radicals (OH•) as the predominant active species in the degradation mechanism. Mass spectrometry analysis unveiled potential breakdown intermediates, illuminating a plausible degradation pathway. The investigated Pd-doped ZnO nanoparticles demonstrated reusability for up to five successive treatment cycles, offering a sustainable solution to nitro organics contamination challenges.

Abstract Background Heat stress is one of the abiotic stresses that make wheat crops vulnerable, which significantly impacts crop production around the world. An increase in temperature during the reproductive phase (anthesis) beyond the optimal range of 15–20 °C leads to decreased crop production, poor quality of the grain, and altered physiological and biochemical processes. To study the association between high temperature and physio-biochemical traits under normal and late sown, a set of fifteen genotypes was utilized. Results Relative water content under high temperatures had an overall decrease of 8.7%. However, grain protein and malondialdehyde content were higher in the stressed conditions than in the control, with increases of 20.2% and 38.9%, respectively. Marker Xgwm67, located on chromosome 5B, was found to be significantly associated with malondialdehyde content (R 2 = 21%) and Xgwm570, located on 6A, was closely linked to relative water content as well as grain protein content (R 2 = 16%) revealed by regression analysis. The correlation matrix displays a positive association between the control and stressed condition by R 2 = 0.92, 0.82, and 0.53 in malondialdehyde, relative water content, and grain protein, respectively. However, there was a negative correlation between water content–malondialdehyde and malondialdehyde–grain protein, though there was only a 4% correlation between grain protein content (control) and relative water content (stressed). Based on the tolerance matrix, WH730 and RAJ4079 were heat tolerant, and DBW173 and HD3086 were sensitive. Conclusions These findings indicate that to identify tolerant genotypes, physiological and biochemical traits can be utilized as an alternate criterion, and these closely associated markers can be applied for improved late-planted wheat production through MAS. The breeding scheme and genome editing by recognizing novel genes through physio-biochemical parameters, marker-assisted selection, and prospective screening of tolerant genotypes are proclaimed by the study.

Abstract This study investigates vertically stacked CVD grown ReS2/MoS2 unipolar heterostructure device as Field Effect Transistor (FET) device wherein ReS2 on top acts as drain and MoS2 at bottom acts as source. The electrical measurements of ReS2/MoS2 FET device were carried out and variation in Ids (drain current) Vs Vds (drain voltage) for different Vgs (gate voltage) revealing the n-type device characteristics. Furthermore, the threshold voltage was calculated at the gate bias voltage corresponding to maximum transconductance (gm) value which is ~ 12 V. The mobility of the proposed ReS2/MoS2 heterojunction FET device was calculated as 60.97 cm2 V−1 s−1. The band structure of the fabricated vDW heterostructure was extracted utilizing ultraviolet photoelectron spectroscopy and the UV–visible spectroscopy revealing the formation of 2D electron gas (2DEG) at the ReS2/MoS2 interface which explains the high carrier mobility of the fabricated FET. The field effect behavior is studied by the modulation of the barrier height across heterojunction and detailed explanation is presented in terms of the charge transport across the heterojunction.

Abstract It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.

Abstract Despite recent advancements, artificial muscles have not yet been able to strike the right balance between exceptional mechanical properties and dexterous actuation abilities that are found in biological systems. Here, we present an artificial magnetic muscle that exhibits multiple remarkable mechanical properties and demonstrates comprehensive actuating performance, surpassing those of biological muscles. This artificial muscle utilizes a composite configuration, integrating a phase-change polymer and ferromagnetic particles, enabling active control over mechanical properties and complex actuating motions through remote laser heating and magnetic field manipulation. Consequently, the magnetic composite muscle can dynamically adjust its stiffness as needed, achieving a switching ratio exceeding 2.7 × 10³. This remarkable adaptability facilitates substantial load-bearing capacity, with specific load capacities of up to 1000 and 3690 for tensile and compressive stresses, respectively. Moreover, it demonstrates reversible extension, contraction, bending, and twisting, with stretchability exceeding 800%. We leverage these distinctive attributes to showcase the versatility of this composite muscle as a soft continuum robotic manipulator. It adeptly executes various programmable responses and performs complex tasks while minimizing mechanical vibrations. Furthermore, we demonstrate that this composite muscle excels across multiple mechanical and actuation aspects compared to existing actuators.

Abstract Understanding and preserving the deep sea ecosystems is paramount for marine conservation efforts. Automated object (deep-sea biota) classification can enable the creation of detailed habitat maps that not only aid in biodiversity assessments but also provide essential data to evaluate ecosystem health and resilience. Having a significant source of labelled data helps prevent overfitting and enables training deep learning models with numerous parameters. In this paper, we contribute to the establishment of a significant deep-sea remotely operated vehicle (ROV) image classification dataset with 3994 images featuring deep-sea biota belonging to 33 classes. We manually label the images through rigorous quality control with human-in-the-loop image labelling. Leveraging data from ROV equipped with advanced imaging systems, our study provides results using novel deep-learning models for image classification. We use deep learning models including ResNet, DenseNet, Inception, and Inception-ResNet to benchmark the dataset that features class imbalance with many classes. Our results show that the Inception-ResNet model provides a mean classification accuracy of 65%, with AUC scores exceeding 0.8 for each class.

Abstract The use of antimicrobial drugs in food-producing animals contributes to the selection pressure on pathogenic and commensal bacteria to become resistant. This study aims to evaluate the existence of trade-offs between treatment effectiveness, cost, and the dynamics of resistance in gut commensal bacteria. We developed a within-host ordinary differential equation model to track the dynamics of antimicrobial drug concentrations and bacterial populations in the site of infection (lung) and the gut. The model was parameterized to represent enrofloxacin treatment for bovine respiratory disease (BRD) caused by Pastereulla multocida in cattle. Three approved enrofloxacin dosing regimens were compared for their effects on resistance on P. multocida and commensal E. coli: 12.5 mg/kg and 7.5 mg/kg as a single dose, and 5 mg/kg as three doses. Additionally, we explored non-FDA-approved regimes. Our results indicated that both 12.5 mg/kg and 7.5 mg/kg as a single dose scenario increased the most the treatment costs and prevalence of P. multocida resistance in the lungs, while 5 mg/kg as three doses increased resistance in commensal E. coli bacteria in the gut the most out of the approved scenarios. A proposed non-FDA-approved scenario (7.5 mg/kg, two doses 24 h apart) showed low economic costs, minimal P. multocida, and moderate effects on resistant E. coli. Overall, the scenarios that decrease P. multocida, including resistant P. multocida did not coincide with those that decrease resistant E. coli the most, suggesting a trade-off between both outcomes. The sensitivity analysis suggests that bacterial populations were the most sensitive to drug conversion factors into plasma ( $${\beta}$$ β ), elimination of the drug from the colon ( $$\vartheta$$ ϑ ), fifty percent sensitive bacteria (P. multocida) killing effect ( $${\text{L}}_{\text{s50}}$$ L s50 ), fifty percent of bacteria (E. coli) above ECOFF killing effect ( $${\text{C}}_{\text{r50}}$$ C r50 ), and net drug transfer rate in the lung ( $$\gamma$$ γ ) parameters.

Abstract An automated speaker verification system uses the process of speech recognition to verify the identity of a user and block illicit access. Logical access attacks are efforts to obtain access to a system by tampering with its algorithms or data, or by circumventing security mechanisms. DeepFake attacks are a form of logical access threats that employs artificial intelligence to produce highly realistic audio clips of human voice, that may be used to circumvent vocal authentication systems. This paper presents a framework for the detection of Logical Access and DeepFake audio spoofings by integrating audio file components and time-frequency representation spectrograms into a lower-dimensional space using sequential prediction models. Bidirectional-LSTM trained on the bonafide class generates significant one-dimensional features for both classes. The feature set is then standardized to a fixed set using a novel Bags of Auditory Bites (BoAB) feature standardizing algorithm. The Extreme Learning Machine maps the feature space to predictions that differentiate between genuine and spoofed speeches. The framework is evaluated using the ASVspoof 2021 dataset, a comprehensive collection of audio recordings designed for evaluating the strength of speaker verification systems against spoofing attacks. It achieves favorable results on synthesized DeepFake attacks with an Equal Error Rate (EER) of 1.18% in the most optimal setting. Logical Access attacks were more challenging to detect at an EER of 12.22%. Compared to the state-of-the-arts in the ASVspoof2021 dataset, the proposed method notably improves EER for DeepFake attacks by an improvement rate of 95.16%.

Abstract Achieving large-scale electrochemical CO2 reduction to multicarbon products with high selectivity using membrane electrode assembly (MEA) electrolyzers in neutral electrolyte is promising for carbon neutrality. However, the unsatisfactory multicarbon products selectivity and unclear reaction mechanisms in an MEA have hindered its further development. Here, we report a strategy that manipulates the interfacial microenvironment of Cu nanoparticles in an MEA to suppress hydrogen evolution reaction and enhance C2H4 conversion. In situ multimodal characterizations consistently reveal well-stabilized Cuδ+-OH species as active sites during MEA testing. The OH radicals generated in situ from water create a locally oxidative microenvironment on the copper surface, stabilizing the Cuδ+ species and leading to an irreversible and asynchronous change in morphology and valence, yielding high-curvature nanowhiskers. Consequently, we deliver a selective C2H4 production with a Faradaic efficiency of 55.6% ± 2.8 at 316 mA cm−2 in neutral media.

Abstract Recently, we have reported that extracellular vesicles (EVs) from the bone marrow mesenchymal stromal cells (BM-MSC) of aplastic anemia (AA) patients inhibit hematopoietic stem and progenitor cell (HSPC) proliferative and colony-forming ability and promote apoptosis. One mechanism by which AA BM-MSC EVs might contribute to these altered HSPC functions is through microRNAs (miRNAs) encapsulated in EVs. However, little is known about the role of BM-MSC EVs derived miRNAs in regulating HSPC functions in AA. Therefore, we performed miRNA profiling of EVs from BM-MSC of AA (n = 6) and normal controls (NC) (n = 6) to identify differentially expressed miRNAs. The Integrated DEseq2 analysis revealed 34 significantly altered mature miRNAs, targeting 235 differentially expressed HSPC genes in AA. Hub gene analysis revealed 10 HSPC genes such as IGF-1R, IGF2R, PAK1, PTPN1, etc., which are targeted by EV miRNAs and had an enrichment of chemokine, MAPK, NK cell-mediated cytotoxicity, Rap1, PI3k-Akt, mTOR signalling pathways which are associated with hematopoietic homeostasis. We further showed that miR-139-5p and its target, IGF-1R (hub-gene), might regulate HSPC proliferation and apoptosis, which may serve as potential therapeutic targets in AA. Overall, the study highlights that AA BM-MSC EV miRNAs could contribute to impaired HSPC functions in AA.

Abstract A major and irreversible complication of diabetes is diabetic peripheral neuropathy (DPN), which can lead to significant disability and decreased quality of life. Prior work demonstrates the peptide hormone Angiotensin II (Ang II) is released locally in neuropathy and drives inflammation and impaired endoneurial blood flow. Therefore, we proposed that by utilizing a local thermoresponsive hydrogel injection, we could deliver inhibitors of angiotensin-converting enzyme (ACE) to suppress Ang II production and reduce nerve dysfunction in DPN through local drug release. The ACE inhibitor captopril was encapsulated into a micelle, which was then embedded into a reversibly thermoresponsive pluronics-based hydrogel matrix. Drug-free and captopril-loaded hydrogels demonstrated excellent product stability and sterility. Rheology testing confirmed sol properties with low viscosity at ambient temperature and increased viscosity and gelation at 37 °C. Captopril-loaded hydrogels significantly inhibited Ang II production in comparison to drug-free hydrogels. DPN mice treated with captopril-loaded hydrogels displayed normalized mechanical sensitivity and reduced inflammation, without side-effects associated with systemic exposure. Our data demonstrate the feasibility of repurposing ACE inhibitors as locally delivered anti-inflammatories for the treatment of sensory deficits in DPN. To the best of our knowledge, this is the first example of a locally delivered ACE inhibitor for the treatment of DPN.

The service activity aims to increase students' knowledge and skills in processing natural coffee ingredients into soap to prevent disease transmission in the school environment. The method of implementing this service used training and education. This service partner was SD Negeri 5 Subagan, Karangasem District, Karangasem Regency, Bali Province. The evaluation instruments used in this service activity were pre-test and post-test instruments. The data analysis technique used statistical analysis, namely the Wilcoxon test (P

Abstract Obstructed urine flow is known to cause structural and functional kidney damage leading to renal fibrosis. However, limited information is available on the change in kidney lipids during urinary tract obstruction. In this study, we investigated the change in lipidome in a mouse model with unilateral ureteral obstruction (UUO). The establishment of the UUO model was confirmed by histopathological examination using transmission electron microscopy. Untargeted liquid chromatography/mass spectrometry was carried out over a time course of 4 and 7 days. Compared to the sham control, the UUO kidney at 7 days showed dilatation of the renal tubule with loss of brush borders and thickening of the capillary endothelium. In the kidney lipidomes obtained from the UUO 7 days group compared to the control, a significant decrease of ceramide, sphingomyelin, phosphatidylcholine, lysophospholipids, and phosphatidylethanolamine was observed, whereas cholesteryl esters, free fatty acids, phosphatidylglycerol, and cardiolipins were significantly increased. The present study revealed the disturbed lipid metabolism in the UUO model, which may provide a clue to potential lipid pathways and therapeutic targets for the early stage of renal fibrosis.

Abstract In awareness of industrial dye wastewater, carbon quantum dots (CQDs) and cobalt zinc ferrite (CZF) nanocomposites were synthesised for the making of carbon quantum dots coated cobalt zinc ferrite (CZF@CQDs) nanophotocatalyst using oxidative polymerization reaction. The results of TEM, zeta potential value, and FTIR confirm highly dispersed 1–4 nm particles with the − 45.7 mV carboxylic functionalized surface of CQDs. The results of the synthesised CZF@CQDs photocatalyst showed an average particle size of ~ 15 nm according to TEM, SEM, and XRD. The photocatalyst showed a 1.20 eV band gap, which followed the perfect visible light irradiation. TGA and DTA revealed the good thermal stability of the nanophotocatalyst. VSM was carried out, and the saturation magnetisations for CZF and CZF@CQDs were 42.44 and 36.14 emu/g, respectively. A multipoint study determined the BET-specific surface area of the CZF@CQDs photocatalyst to be 149.87 m2/g. Under visible light irradiation, the final CZF@CQDs nanophotocatalyst demonstrated remarkable efficiency (~ 95% within 25 min) in the photocatalytic destruction of Reactive Blue 222 (RB 222) and Reactive Yellow 145 (RY 145) dyes, as well as mechanical stability and recyclability. Even after the recycling of the degradation study, the nanophotocatalyst efficiency (~ 82%, 7th cycles) was predominantly maintained. The effects of several parameters were also investigated, including initial dye concentration, nanophotocatalyst concentration, CQD content, initial pH of the dye solution, and reaction kinetics. Degradation study data follow the first-order reaction rate (R2 > 0.93). Finally, a simple and low-cost synthesis approach, rapid degradation, and outstanding stability of the CQD-coated CZF nanophotocatalyst should make it a potential photocatalyst for dye wastewater treatment.

Abstract Linseed, also known as flax is an important oilseed crop with many potential uses in paint, textile, food and pharmaceutical industries. Susceptibility to bud fly (Dasyneura lini Barnes) infestation is a serious biotic concern leading to severe yield penalty in linseed. Protease inhibitors (PIs) are potential candidates that activate during the insect-pest attack and modulate the resistance. In the present study, we explored the PI candidates in the linseed genome and a total of 100 LuPI genes were identified and grouped into five distinct subgroups. The analysis of cis-acting elements revealed that almost all LuPI promoters contain several regulatory elementary related to growth and development, hormonal regulation and stress responses. Across the subfamilies of PIs, the specific domains are consistently found conserved in all protein sequences. The tissue-specific in-silico expression pattern via RNA-seq revealed that all the genes were regulated during different stress. The expression through qRT-PCR of 15 genes revealed the significant up-regulation of LuPI-24, LuPI-40, LuPI-49, LuPI-53, and LuPI-63 upon bud fly infestation in resistant genotype EC0099001 and resistant check variety Neela. This study establishes a foundation resource for comprehending the structural, functional, and evolutionary dimensions of protease inhibitors in linseed.

Abstract In the years 2021–2022 and 2022–2023, an experiment was carried out at the IFS Unit, College of Agriculture, PJTSAU, Rajendranagar in order to determine the best one-acre integrated farming system model for Telangana's small and marginal farmers. Seven farm models among which six models were developed by combining the various components i.e., cropping systems, fruit cropfodder crops and livestock components, in different proportions, and compared with rice-groundnut system which is a major farming approach in Telangana using randomized block design. The seven models were as follows: M1: Rice–Groundnut; M2: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Bt cotton + Greengram (1:2)—Maize; M3: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Pigeonpea + Maize (1:3)—Sunhemp; Napier grass, Sheep (5 + 1); M4: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Bt cotton + Greengram (1:2)—Maize, Pigeonpea + Maize (1:3)—Sunhemp, Poultry unit; M5: Guava, Hedge Lucerne, Napier grass, Bt cotton + Greengram (1:2)—Maize, Sheep (5 + 1); M6: Guava, Bt cotton + Greengram (1:2)—Maize, Rice–Groundnut, Poultry; M7: Rice–Groundnut, Pigeonpea + Sweetcorn (1:3)—Bajra, Pigeonpea + Maize (1:3)—Sunhemp; Napier grass, Hedge lucerne, Poultry (100), Sheep (5 + 1). Based on a 2-year average, the Model M7 system produced 9980 Rice Grain Equivalent Yield(RGEY)kg of output per acre, with gross and net returns of ₹210,439 and ₹124,953 respectively, and recovered a B:C ratio of 2.46. It has recorded highest sustainable yield index (SYI) of 0.673 and value index of 0.772 with the lowest water footprint of 259.0 L/kg. This study reveals that adopting an integrated farming system is the optimal approach for effectively combining productive, financially rewarding, and diversified enterprises within a single acre of land.d. This system should be recommended for maximum benefits to smallto small and marginal farmers in Telangana's southern hills and plateau.

Abstract In recent times, photovoltaic (PV) power generation has been growing due to increase in energy demand. In grid-connected mode, achieving maximum power (MP) from the PV array is difficult by using conventional techniques due to various reasons like low tracking efficiency, stability issues, etc. This motivates the design of an appropriate control strategy to obtain the maximum power point tracking (MPPT) to harvest MP from the PV array. This paper proposes a combined higher order sliding mode observer (HOSMO)–super-twisting control (STC) for a grid-connected scenario. A perturb and observe (P &O) technique is employed to generate reference voltage, and a HOSMO is proposed to drive the STC by estimating the inductor current of the PV boost converter. The proposed controller performance is evaluated based on response time across various scenarios, including generation changes, dynamic faults, islanding and resynchronization, and load variations in comparison to other existing controllers. These microgrid test cases have been thoroughly simulated, and their effectiveness has been validated in real-time using OPAL-RT (OP4510).