An effective tellurium surface modification strategy to enhance the capacity and rate capability of Ni-rich LiNi0.8Co0.1Mn0.1O2 cathode material.

LiNi0.8Co0.1Mn0.1O2 (NCM) layered oxide is contemplated as an auspicious cathode candidate for commercialized lithium-ion batteries. Regardless, the successful commercial utilization of these materials is impeded by technical issues like structural degradation and poor cyclability. Elemental doping is among the most viable strategies for enhancing electrochemical performance. Herein, the preparation of surface tellurium-doped NCM is done by utilizing the methodology solid-state route at high temperatures. Surface doping of the Te ions leads to structural stability owing to the inactivation of oxygen at the surface via the binding of slabs of transition metal-oxygen. Remarkably, 1 wt% of Te doping in NCM exhibits enhanced electrochemical characteristics with an excellent discharge capacity, i.e., 225.8 mAh/g (0.1C), improved rate-capability of 156 mAh/g (5C) with 82.2% retention in capacity (0.5C) over 100 cycles within 2.7-4.3V as compared to all other prepared electrodes. Hence, the optimal doping of Te is favorable for enhancing capacity, cyclability along with rate capability of NCM.

Sb-Doped Biphasic P2/O3-Type Mn-Rich Layered Oxide Cathode Material for High-Performance Sodium-Ion Batteries.

Mn-rich P2-type layered oxide cathode materials suffer from severe capacity loss caused by detrimental phase transition and transition metal dissolution, making their implementation difficult in large-scale sodium-ion battery applications. Herein, we introduced a high-valent Sb5+ substitution, leading to a biphasic P2/O3 cathode that suppresses the P2-O2 phase transformation in the high-voltage condition attributed to the stronger Sb-O covalency that introduces extra electrons to the O atom, reducing oxygen loss from the lattices and improving structural stability, as confirmed by first-principle calculations. Besides, the enhanced Na+ diffusion kinetics and thermodynamics in the modified sample are associated with the enlarged lattice parameters. As a result, the proposed cathode delivers a discharge capacity of 142.6 mAh g-1 at 0.1C between 1.5 and 4.3 V and excellent performance at a high mass loading of 8 mg cm3 with a specific capacity of 131 mAh g-1 at 0.2C. Furthermore, it also possesses remarkable rate capability (90.3 mAh g-1 at 5C), specifying its practicality in high-energy-density sodium-ion batteries. Hence, this work provides insights into incorporating high-valent dopants for high-performance Mn-rich cathodes.

G-ACP: a machine learning approach to the prediction of therapeutic peptides for gastric cancer.

Conventional Gastrointestinal (GI) cancer treatments are quite expensive and have major hazards. Nowadays, a different strategy places more emphasis on creating tiny biologically active peptides that do not cause severe poisoning. Anticancer peptides (ACPs) are found through experimental screening, which is time-dependent and frequently fraught with difficulties. Gastric ACPs are emerging as a promising GI cancer treatment in the current day. It is crucial to identify novel gastric ACPs to have an improved knowledge of their functioning processes and treatment of gastric cancer. As a result of the post-genomic era's massive production of peptide sequences, rapid and effective ACPs using a computational method are essential. Several adaptive statistical techniques for distinguishing ACPs and non-ACPs have recently been developed. A variety of adapted statistically significant methods have been developed to differentiate between ACPs and non-ACPs. Despite significant progress, there is no specific model for the prediction of gastric ACPs because the specific model will predict a particular type of peptide more accurately and quickly. To overcome this, an initiative is taken for the creation of a reliable framework for the accurate identification of gastric ACPs. The current technique in particular contains four possible features along with one hybrid feature encoding mechanisms which are the target-class motif previously indicated by Amino Acid Composition, Dipeptide Composition, Tripeptide Composition (TPC), Pseudo Amino Acid Composition (PAAC), and their Hybrid. Machine Learning algorithms make high-performance and accurate prediction tools. Moreover, highly variable and ideal deep feature selection is done using an ANOVA-based F score for feature pruning. Experiments on a range of algorithms are carried out to identify the optimal operating strategy due to the diverse nature of learning. Following analysis of the empirical results, Naïve Bayes with TPC and Hybrid feature space outperforms other methods with 0.99 accuracy score on the testing dataset. To find the model generalization an external validation is carried out. In external datasets, the Extra Trees with PAAC features outperforms with the accuracy of 0.94. The comparison study shows that our suggested model will predict gastric ACPs more accurately and will be useful in drug development and gastric cancer. The predictive model can be freely accessed at https://github.com/humeraazad10/G-ACP.git.Communicated by Ramaswamy H. Sarma.

Comprehensive Noise Modeling of Piezoelectric Charge Accelerometer with Signal Conditioning Circuit.

This paper reports on noise modeling of a piezoelectric charge accelerometer with a signal conditioning circuit. The charge output is converted into voltage and amplified using a JFET operational amplifier that has high input resistance and low noise. The noise sources in the whole system include electrical and mechanical thermal noises of the accelerometer, thermal noises of resistors, and voltage and current noises of the operational amplifier. Noise gain of each source is derived from small signal circuit analysis. It is found that the feedback resistor of the operational amplifier is a major source of noise in low frequencies, whereas electrical thermal noise of the accelerometer dominates the rest of spectrum. This method can be used to pair a highly sensitive sensor with a single JFET operational amplifier instead of a multi-stage signal conditioning circuit.

Tailoring Na+ Solvation Environment and Electrode-Electrolyte Interphases with Sn(OTf)2 Additive in Non-flammable Phosphate Electrolytes towards Safe and Efficient Na-S Batteries.

Room-temperature sodium-sulfur (RT Na-S) batteries are promising for low-cost and large-scale energy storage applications. However, these batteries are plagued by safety concerns due to the highly flammable nature of conventional electrolytes. Although non-flammable electrolytes eliminate the risk of fire, they often result in compromised battery performance due to poor compatibility with sodium metal anode and sulfur cathode. Herein, we develop an additive of tin trifluoromethanesulfonate (Sn(OTf)2 ) in non-flammable phosphate electrolytes to improve the cycling stability of RT Na-S batteries via modulating the Na+ solvation environment and interface chemistry. The additive reduces the Na+ desolvation energy and enhances the electrolyte stability. Moreover, it facilitates the construction of Na-Sn alloy-based anode solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI). These interphases help to suppress the growth of Na dendrites and the dissolution/shuttling of sodium polysulfides (NaPSs), resulting in improved reversible capacity. Specifically, the Na-S battery with the designed electrolyte boosts the capacity from 322 to 906 mAh g-1 at 0.5 A g-1 . This study provides valuable insights for the development of safe and high-performance electrolytes in RT Na-S batteries.

Scalable Precursor-Assisted Synthesis of a High Voltage LiNiyCo1-yPO4 Cathode for Li-Ion Batteries.

A solid-solution cathode of LiCoPO4-LiNiPO4 was investigated as a potential candidate for use with the Li4Ti5O12 (LTO) anode in Li-ion batteries. A pre-synthesized nickel-cobalt hydroxide precursor is mixed with lithium and phosphate sources by wet ball milling, which results in the final product, LiNiyCo1-yPO4 (LNCP) by subsequent heat treatment. Crystal structure and morphology of the product were analyzed by X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). Its XRD patterns show that LNCP is primarily a single-phase compound and has olivine-type XRD patterns similar to its parent compounds, LiCoPO4 and LiNiPO4. Synchrotron X-ray absorption spectroscopy (XAS) analysis, however, indicates that Ni doping in LiCoPO4 is unfavorable because Ni2+ is not actively involved in the electrochemical reaction. Consequently, it reduces the charge storage capability of the LNCP cathode. Additionally, ex situ XRD analysis of cycled electrodes confirms the formation of the electrochemically inactive rock salt-type NiO phase. The discharge capacity of the LNCP cathode is entirely associated with the Co3+/Co2+ redox couple. The electrochemical evaluation demonstrated that the LNCP cathode paired with the LTO anode produced a 3.12 V battery with an energy density of 184 Wh kg-1 based on the cathode mass.

The comparative transcriptome analysis of two green super rice genotypes with varying tolerance to salt stress.

BACKGROUND: Salinity is one of the main abiotic factors that restrict plant growth, physiology, and crop productivity is salt stress. About 33% of the total irrigated land suffers from severe salinity because of intensive underground water extraction and irrigation with brackish water. Thus, it is important to understand the genetic mechanism and identify the novel genes involved in salt tolerance for the development of climate-resilient rice cultivars. METHODS AND RESULTS: In this study, two rice genotypes with varying tolerance to salt stress were used to investigate the differential expressed genes and molecular pathways to adapt under saline soil by comparative RNA sequencing at 42 days of the seedling stage. Salt-susceptible (S3) and -tolerant (S13) genotypes revealed 3982 and 3463 differentially expressed genes in S3 and S13 genotypes. The up-regulated genes in both genotypes were substantially enriched in different metabolic processes and binding activities. Biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, and plant signal transduction mechanisms were highly enriched. Salt-susceptible and -tolerant genotypes shared the same salt adaptability mechanism with no significant quantitative differences at the transcriptome level. Moreover, bHLH, ERF, NAC, WRKY, and MYB transcription factors were substantially up-regulated under salt stress. 391 out of 1806 identified novel genes involved in signal transduction mechanisms. Expression profiling of six novel genes further validated the findings from RNA-seq data. CONCLUSION: These findings suggest that the differentially expressed genes and molecular mechanisms involved in salt stress adaptation are conserved in both salt-susceptible and salt-tolerant rice genotypes. Further molecular characterization of novel genes will help to understand the genetic mechanism underlying salt tolerance in rice.

Lyme rashes disease classification using deep feature fusion technique.

Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.

Bacopa monnieri: A promising herbal approach for neurodegenerative disease treatment supported by in silico and in vitro research.

Neurodegenerative disorders, caused by progressive neuron loss, are a global health issue. Among the various factors implicated in their pathogenesis, dysregulation of acetylcholinesterase activity has been recognized as a key contributor. Acetylcholinesterase breaks down the neurotransmitter acetylcholine, important for neural transmission. Evaluating phyto-compounds from Bacopa monnieri Linn. through in vitro and in silico analysis may expand their role as alternative therapeutic agents by modulating the function of acetylcholinesterase and complementing existing treatments. To accomplish this objective, chemical structures of phyto-compounds were retrieved from PubChem database and subjected to in silico and in vitro approaches. Virtual screening was performed through molecular docking and molecular dynamic simulation resulting in four top hit compounds including quercetin, apigenin, wogonin, and bacopaside X (novel lead compound for acetylcholinesterase inhibitor) with least binding score. Further, dose dependent acetylcholinesterase inhibition biochemical assay depicted that bacopaside X, apigenin, quercetin, and wogonin exhibited strong potential against acetylcholinesterase with IC50 values of 12.78 µM, 13.83 µM, 12.73 µM and 15.48 µM respectively, in comparison with the donepezil (IC50: 0.0204 µM). The in silico and in vitro research suggests that B. monnieri phyto-compounds have the potential to modulate molecular targets associated with neurodegenerative diseases and have a role in neuroprotection.

Synergistic neuroprotection by phytocompounds of Bacopa monnieri in scopolamine-induced Alzheimer's disease mice model.

BACKGROUND: Millions of people around the globe are affected by Alzheimer's disease (AD). This crippling condition has no treatment despite intensive studies. Some phytocompounds have been shown to protect against Alzheimer's in recent studies. METHODS: Thus, this work aimed to examine Bacopa monnieri phytocompounds' synergistic effects on neurodegeneration, antioxidant activity, and cognition in the scopolamine-induced AD mice model. The toxicity study of two phytocompounds: quercetin and bacopaside X revealed an LD50 of more than 2000 mg/kg since no deaths occurred. RESULTS: The neuroprotection experiment consists of 6 groups i.e., control (saline), scopolamine (1 mg/kg), donepezil (5 mg/kg), Q (25 mg/kg), BX (20 mg/kg), and Q + BX (25 mg/kg + 20 mg/kg). Visual behavioral assessment using the Morris water maze showed that animals in the diseased model group (scopolamine) moved more slowly toward the platform and exhibited greater thigmotaxis behavior than the treatment and control groups. Likewise, the concentration of biochemical NO, GSH, and MDA improved in treatment groups concerning the diseased group. mRNA levels of different marker genes including ChAT, IL-1α, IL-1 ß, TNF α, tau, and ß secretase (BACE1) improved in treatment groups with respect to the disease group. CONCLUSION: Both bacopaside X and quercetin synergistically have shown promising results in neuroprotection. Therefore, it is suggested that Q and BX may work synergistically due to their antioxidant and neuroprotective property.

An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images.

Spine image analysis is based on the accurate segmentation and vertebrae recognition of the spine. Several deep learning models have been proposed for spine segmentation and vertebrae recognition, but they are very computationally demanding. In this research, a novel deep learning model is introduced for spine segmentation and vertebrae recognition using CT images. The proposed model works in two steps: (1) A cascaded hierarchical atrous spatial pyramid pooling residual attention U-Net (CHASPPRAU-Net), which is a modified version of U-Net, is used for the segmentation of the spine. Cascaded spatial pyramid pooling layers, along with residual blocks, are used for feature extraction, while the attention module is used for focusing on regions of interest. (2) A 3D mobile residual U-Net (MRU-Net) is used for vertebrae recognition. MobileNetv2 includes residual and attention modules to accurately extract features from the axial, sagittal, and coronal views of 3D spine images. The features from these three views are concatenated to form a 3D feature map. After that, a 3D deep learning model is used for vertebrae recognition. The VerSe 20 and VerSe 19 datasets were used to validate the proposed model. The model achieved more accurate results in spine segmentation and vertebrae recognition than the state-of-the-art methods.

Potential of nanotechnology to replace cancer stem cells.

Stem cells, which were initially identified in the 1900s, are distinct cells with the potential to replenish themselves as well as differentiate into specialised cells with certain forms and functions. Cancer stem cells play a significant role in the growth and recurrence of the tumours and, similar to normal stem cells, are capable of proliferating and differentiating. Traditional cancer treatments are ineffective against cancer stem cells, which leads to tumour regrowth. Cancer stem cells are thought to emerge as a result of epithelial-to-mesenchymal transition pathways. Brain, prostate, pancreatic, blood, ovarian, lung, liver, melanomas, AML, and breast cancer stem cells are among the most prevalent cancer forms. This review aims to comprehend the possibility of using specific forms of nanotechnology to replace cancer stem cells. In terms of nanotechnology, magnetic nanoparticles can deliver medications, especially to the target region without harming healthy cells, and they are biocompatible. In order to kill glioma cancer stem cells, the gold nanoparticles bond with DNA and function as radio sensitizers. In contrast, liposomes can circulate and traverse biological membranes and exhibit high therapeutic efficacy, precise targeting, and better drug release. Similar to carbon nanotubes, grapheme, and grapheme oxide, these substances can be delivered specifically when utilized in photothermal therapy. Recent treatments including signaling pathways and indicators targeted by nanoparticles are being researched. Future research in nanotechnology aims to develop more effective and targeted medicinal approaches. The results of the current investigation also showed that this technology's utilization will improve medical therapy and treatment.

Screening and validation of salt-stress responsive cg-SSR markers in wheat (Triticum aestivum L.) germplasm of Pakistan.

BACKGROUND: Soil salinity has been affecting wheat production worldwide over past few decades. Evaluation of wheat genotypes for salinity tolerance at germination and vegetative growth level is crucial. Marker assisted selection is a technique used extensively for choosing salt-tolerant genotypes from breeding populations to introduce novel genes. METHODS AND MATERIALS: The current study's main goal was to discover salt-stress resistant genes; genetic divergence and genome-wide connection by using recently designed candidate gene-based simple-sequence-repeat markers (cg-SSRs). The phenotypic connection of morphological features during the germination growth stage i.e., germination period, root length/weight and shoot length/weight, and vegetative growth stages i.e., root length/weight and shoot length/weight were tested in a group of 50 wheat genotypes. Significant difference was observed in germination rate, root length and weight among control and saline treatments. CONCLUSION: Total 30 SSR markers were utilized to test salinity resistance genes in wheat genotypes. Three (10%) of which were monomorphic, one (3.34%) showed no result, and the other 26 (86%) were polymorphic. Using 30 polymorphic markers discovered total 37 alleles. The polymorphic information content (PIC), quantifies each SSR locus capacity to discriminate between wheat, varied from 0.00 to 0.38 with an average of 0.19. Association analysis revealed that 26 primers were associated with morphological features, 03 with root length and the remaining 23 with germination. Utilizing morphological data, stress tolerance index (STI) was designed concluding that Auqab-2000, Margala-99 and Ufaq showed better resistance against salinity among other wheat genotypes. Cluster analysis demonstrated that wheat genotypes have vast genetic variability.

Colossal Dielectric Perovskites of Calcium Copper Titanate (CaCu3 Ti4 O12 ) with Low-Iridium Dopants Enables Ultrahigh Mass Activity for the Acidic Oxygen Evolution Reaction.

Oxygen evolution reaction (OER) under acidic conditions becomes of significant importance for the practical use of a proton exchange membrane (PEM) water electrolyzer. In particular, maximizing the mass activity of iridium (Ir) is one of the maiden issues. Herein, the authors discover that the Ir-doped calcium copper titanate (CaCu3Ti4O12, CCTO) perovskite exhibits ultrahigh mass activity up to 1000 A gIr -1 for the acidic OER, which is 66 times higher than that of the benchmark catalyst, IrO2 . By substituting Ti with Ir in CCTO, metal-oxygen (M-O) covalency can be significantly increased leading to the reduced energy barrier for charge transfer. Further, highly polarizable CCTO perovskite referred to as "colossal dielectric", possesses low defect formation energy for oxygen vacancy inducing a high number of oxygen vacancies in Ir-doped CCTO (Ir-CCTO). Electron transfer occurs from the oxygen vacancies and Ti to the substituted Ir consequentially resulting in the electron-rich Ir and -deficient Ti sites. Thus, favorable adsorptions of oxygen intermediates can take place at Ti sites while the Ir ensures efficient charge supplies during OER, taking a top position of the volcano plot. Simultaneously, the introduced Ir dopants form nanoclusters at the surface of Ir-CCTO, which can boost catalytic activity for the acidic OER.

Temporal Comparative Transcriptome Analysis on Wheat Response to Acute Cd Toxicity at the Seedling Stage.

Cadmium (Cd) is a non-essential and toxic metal that accumulates in plant's tissues and diminishes plant growth and productivity. In the present study, differential root transcriptomic analysis was carried out to identify Cd stress-responsive gene networks and functional annotation under Cd stress in wheat seedlings. For this purpose, the Yannong 0428 wheat cultivar was incubated with 40 µm/L of CdCl2·2.5H2O for 6 h at three different seedling growth days. After the quality screening, using the Illumina Hiseq 2000 platform, more than 2482 million clean reads were retrieved. Following this, 84.8% to 89.3% of the clean reads at three time points under normal conditions and 86.5% to 89.1% of the reads from the Cd stress condition were mapped onto the wheat reference genome. In contrast, at three separate seedling growth days, the data analysis revealed a total of 6221 differentially expressed genes (DEGs), including 1543 (24.8%) up-regulated genes and 4678 (75.8%) down-regulated genes. In total, 120 DEGs were co-expressed throughout all the growth days, whereas 1096, 1088, and 2265 DEGs were found to be selectively up-/down-regulated at 7d, 14d, and 30d, respectively. However, the clustering of DEGs, through utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG), revealed that the DEGs in the metabolic category were frequently annotated for phenylpropanoid biosynthesis. In comparison, a considerable number of DEGs were linked to protein processing in the endoplasmic reticulum under the process of genetic information processing. Similarly, in categories in organismal systems and cellular processes, DEGs were found in plant hormone signal transduction pathways, and DEGs were identified in the plant-pathogen interaction pathway, respectively. However, DEGs in "endocytosis pathways" were enriched in environmental information processing. In addition, in-depth annotations of roughly specific heavy metal stress-response genes and pathways were also mined, and the expression patterns of eight DEGs were studied using quantitative real-time PCR. The results were congruent with the findings of RNA sequencing regarding transcript abundance in the studied wheat cultivar.

CRISPR/Cas9 editing of wheat Ppd-1 gene homoeologs alters spike architecture and grain morphometric traits.

Mutations in Photoperiod-1 (Ppd-1) gene are known to modify flowering time and yield in wheat. We cloned TaPpd-1 from wheat and found high similarity among the three homoeologs of TaPpd-1. To clarify the characteristics of TaPpd-1 homoeologs in different photoperiod conditions for inflorescence architecture and yield, we used CRISPR/Cas9 system to generate Tappd-1 mutant plants by simultaneous modification of the three homoeologs of wheat Ppd-1. Tappd-1 mutant plants showed no off-target mutations. Four T0-edited lines under short-day length and three lines under long-day length conditions with the mutation frequency of 25% and 21%, respectively. These putative transgenic plants of all the lines were self-fertilized and generated T1 and T2 progenies and were evaluated by phenotypic and expression analysis. Results demonstrated that simultaneously edited TaPpd-1- A1, B1, and D1 homoeologs gene copies in T2_SDL-8-4, T2_SDL-4-5, T2_SDL-3-9, and T2_LDL-10-9 showed similar spike inflorescence, flowering time, and significantly increase in 1000-grain weight, grain area, grain width, grain length, plant height, and spikelets per spike due to mutation in both alleles of Ppd-B1 and Ppd-D1 homoeologs but only spike length was decreased in T2_SDL-8-4, T2_SDL-4-5, and T2_LDL-13-3 mutant lines due to mutation in both alleles of Ppd-A1 homoeolog under both conditions. Our results indicate that all TaPpd1 gene homoeologs influence wheat spike development by affecting both late flowering and earlier flowering but single mutant TaPpd-A1 homoeolog affect lowest as compared to the combination with double mutants of TaPpd-B1 and TaPpd-D1, TaPpd-A1 and TaPpd-B1, and TaPpd-A1 and TaPpd-D1 homoeologs for yield enhancement. Our findings further raised the idea that the relative expression of the various genomic copies of TaPpd-1 homoeologs may have an impact on the spike inflorescence architecture and grain morphometric features in wheat cultivars.

Rumour identification on Twitter as a function of novel textual and language-context features.

Social microblogs are one of the popular platforms for information spreading. However, with several advantages, these platforms are being used for spreading rumours. At present, the majority of existing approaches identify rumours at the topic level instead of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic features for rumours identification without considering discrete positive and negative emotions and effective part-of-speech features in content-based approaches. Similarly, the majority of prior studies used content-based approaches for feature generation, and recent context-based approaches were not explored. To cope with these challenges, a robust framework for rumour detection at the tweet level is designed in this paper. The model used word2vec embeddings and bidirectional encoder representations from transformers method (BERT) from context-based and discrete emotions, linguistic, and metadata characteristics from content-based approaches. According to our knowledge, we are the first ones who used these features for rumour identification at the tweet/post level. The framework is tested on four real-life twitter microblog datasets. The results show that the detection model is capable of detecting 97%, 86%, 85%, and 80% of rumours on four datasets respectively. In addition, the proposed framework outperformed the three latest state-of-the-art baselines. BERT model presented the best performance among context-based approaches, and linguistic features are best performing among content-based approaches as a stand-alone model. Moreover, the utilization of two-step feature selection further improves the detection model performance.

High Genetic Diversity in the Himalayan Common Bean (Phaseolus vulgaris) Germplasm with Divergence from Its Center of Origin in the Mesoamerica and Andes.

The common bean is found in the Himalayan region of Pakistan with substantial morphological variability. Genetic diversity within any crop species is a precursor for genetic improvement; however, little is known about common bean genetic diversity in this region. We explored the genetic diversity in the common bean from the Himalayan region (Khyber Pakhtunkhwa, Gilgit-Baltistan, Kashmir) of Pakistan. Microsatellite genotyping was carried out for 147 samples with 40 simple sequence repeat (SSR) markers. The results revealed a clear divergence of the Pakistani population from the primary gene pool (with FST values of 0.2 with Andes and 0.27 with Mesoamerica). However, within the Himalayan germplasm, no clear evidence of spatial structure was observed (with the maximum FST values of only 0.025), probably due to the dispersal of seeds by human activity within the region. This was further elucidated by the discriminant analyses of principal components. Considering the diversity parameters, high genotypic diversity was observed for the indigenous lines (0.990), comparable to the primary gene pool (0.976 for Mesoamerica and 0.976 for Andes populations). A high genotypic diversity was observed within the Himalayan population (ranging from 0.500 for Upper Dir to 0.952 for Mansehra). Gene diversity across loci varied between 0.28 for Chitral to 0.38 for Kurram. Our results suggested a divergent and independent evolution of the Himalayan population, which might have led to the diversification of the common bean germplasm in the region postintroduction into the region. The diversity observed could also be exploited in future breeding programs for the development and introduction of climate-resilient varieties.

Fabrication of cellulose paper-based counter electrodes for flexible dye-sensitized solar cells.

Here, we introduce the synthesis and deposition of organic/inorganic composite ink on cellulose paper using a rapid ultrasonic spray deposition approach that can be incorporated as a counter electrode (CE) in flexible dye-sensitized solar cells (FDSSCs). The composite ink comprised a copper indium sulfide (CuInS2) nanostructure ink and dispersion of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) in water. Fabricated counter electrodes are biodegradable, environment-friendly, flexible, and economical and meet the requirements for sustainable green energy. To evaluate the catalytic activities and power conversion efficiencies of DSSCs, the produced CuInS2/PEDOT:PSS composite ink-based CEs were compared with PEDOT:PSS counter electrodes. Cyclic voltammetry studies found that CuInS2/PEDOT:PSS had a greater cathodic charge transfer current density (Jc) (-1.23 mA cm-2). Moreover, it was found that the potential separation values are small, which indicate a stronger catalytic activity than PEDOT:PSS counter electrodes. The observed exchange current density (J0) was 3.98 mA cm-2, while the limiting current density (Jlim) increased to 45.7 mA cm-2, indicating a fast redox diffusion rate of the CuInS2/PEDOT:PSS CE. The photovoltaic performances of CuInS2/PEDOT:PSS and PEDOT: PSS-based DSSC's were measured and determined to be 5.66% and 4.41%, respectively, while the performance of CuInS2/PEDOT:PSS FDSSC composed of cellulose paper was 1.06%.

Impact of cybersecurity measures on improving institutional governance and digitalization for sustainable healthcare.

Digitalization in healthcare through advanced methods, tools, and the Internet are prominent social development factors. However, hackers and malpractices through cybercrimes made this digitalization worrisome for policymakers. In this study, the role of E-Government Development as a proxy for digitalization and corruption prevalence has been analyzed in Healthcare sustainability in developing and underdeveloped countries of Asia from 2015 to 2021. Moreover, a moderator role of Cybersecurity measures has also been estimated on EGDI, CRP, and HS through the two-step system GMM estimation. The results show that EGDI and CRP control measures significantly improved HS in Asia. Furthermore, by deploying strong and effective Cybersecurity measures, Asia's digitalization and institutional practices are considerably enhanced, which also has an incremental impact on HS and ethical values. This present study added a novel contribution to existing digitalization and public health services literature and empirical analysis by comprehensively applying advanced econometric estimation. The study concludes that cybersecurity measures significantly improved healthcare digitalization and controlled the institutional malfunctioning in Asia. This study gives insight into how cybersecurity measures enhance the service quality and promote institutional quality of the health sector in Asia, which will help draft sustainable policy decisions and ethical values in the coming years.