Characterisation of the Muscle Protein Synthetic Response to Resistance Exercise in Healthy Adults: A Systematic Review and Exploratory Meta-Analysis.

Methods: Five electronic databases (PubMed (Medline), Web of Science, Embase, Sport Discus, and Cochrane Library) were searched for controlled trials that assessed the MPS response to RE in healthy, adult humans, postabsorptive state. Individual study and random-effects meta-analysis arewere used to inform the effects of RE and covariates on MPS. Results from 79 controlled trials with 237 participants were analysed. Results: Analysis of the pooled effects revealed robust increases in MPS following RE (weighted mean difference (WMD): 0.032% h-1, 95% CI: [0.024, 0.041] % h-1, I2 = 92%, k = 37, P < 0.001). However, the magnitude of the increase in MPS was lower in older adults (>50 y: WMD: 0.015% h-1, 95% CI: [0.007, 0.022] % h-1, I2 = 76%, k = 12, P = 0.002) compared to younger adults (<35 y: WMD: 0.041% h-1, 95% CI: [0.030, 0.052] % h-1, I2 = 88%, k = 25, P < 0.001). Individual studies have reported that the temporal proximity of the RE, muscle group, muscle protein fraction, RE training experience, and the loading parameters of the RE (i.e., intensity, workload, and effort) appeared to affect the MPS response to RE, whereas sex or type of muscle contraction does not. Conclusion: A single bout of RE can sustain measurable increases in postabsorptive MPS soon after RE cessation and up to 48 h post-RE. However, there is substantial heterogeneity in the magnitude and time course of the MPS response between trials, which appears to be influenced by participants' age and/or the loading parameters of the RE itself.

Silver nanoparticle-decorated NiFe2O4/CuWO4 heterostructure electrocatalyst for oxygen evolution reactions.

In this work, Ag nanoparticles decorated with NiFe2O4/CuWO4 heterostructure were synthesized using the step-wise precipitation method. The influence of varying Ag loading on the NiFe2O4/CuWO4 heterostructure and its electrochemical OER performance was extensively studied in 1 M KOH electrolyte. The obtained LSV profile was analyzed to determine the overpotential, Tafel slope, and onset potential. The heterostructure with an optimal Ag loading of 5 wt% required the least overpotential (1.60 V vs. RHE) for generating a current density of 10 mA cm-2 with a lower Tafel slope of 44.5 mV dec-1, indicating its faster OER kinetics. Furthermore, the composite remained stable over a period of 24 hours with a minimum rise in the overpotential after the stability test. The enhanced OER performance of the as-prepared catalyst can be attributed to the presence of multiple metallic elements in the Ag-loaded NiFe2O4/CuWO4 composite, which created a diverse array of oxygen-vacant sites with varying reactivity, enhancing the charge-transfer kinetics; and thus contributing to the overall efficiency of OER. Therefore, optimizing the Ag concentration and engineering a microstructure represents an encouraging strategy for developing cost-effective catalysts for next-generation energy-conversion applications.

Photocatalytic Degradation of Crystal Violet on Cu, Zn Doped BiVO4 Particles.

Fabrication of codoped photocatalysts is a developing area of research. Herein, we explore the visible light photocatalytic properties of Cu, Zn codoped BiVO4 particles. Doping lower valent cations (Cu and Zn) makes the BiVO4 surface more acidic and enables us to target the basic crystal violet (CV) dye. The adopted hydrothermal protocol of synthesis results in the formation of Cu-Zn codoped monoclinic BiVO4 particles. Undoped monoclinic BiVO4, prepared by the same protocol, showed significant formation of oxygen vacancies. XPS analyses confirm the coexistence of Cu2+/Cu+ and Zn2+ dopants. Increased dopant percentage reduced oxygen vacancies. XRD indicates that Cu2+/Cu+ or Zn2+ dopants generally substitute Bi3+ in BiVO4. All photocatalysis activities for CV degradation are reported under near-neutral pH conditions. A typical codoped BiVO4 photocatalyst with 1% Zn and 2% Cu demonstrated the best CV degradation photocatalytic activity. The activity of this Zn, Cu codoped photocatalyst is better than that of pure, Zn-doped, and Cu-doped BiVO4 samples. Active species trapping experiments indicated the possible photocatalysis mechanism. The photocatalysts exhibited appropriate recyclability and photostability.

Mapping the unique neural engagement in deaf individuals during picture, word, and sign language processing: fMRI study.

Employing functional magnetic resonance imaging (fMRI) techniques, we conducted a comprehensive analysis of neural responses during sign language, picture, and word processing tasks in a cohort of 35 deaf participants and contrasted these responses with those of 35 hearing counterparts. Our voxel-based analysis unveiled distinct patterns of brain activation during language processing tasks. Deaf individuals exhibited robust bilateral activation in the superior temporal regions during sign language processing, signifying the profound neural adaptations associated with sign comprehension. Similarly, during picture processing, the deaf cohort displayed activation in the right angular, right calcarine, right middle temporal, and left angular gyrus regions, elucidating the neural dynamics engaged in visual processing tasks. Intriguingly, during word processing, the deaf group engaged the right insula and right fusiform gyrus, suggesting compensatory mechanisms at play during linguistic tasks. Notably, the control group failed to manifest additional or distinctive regions in any of the tasks when compared to the deaf cohort, underscoring the unique neural signatures within the deaf population. Multivariate Pattern Analysis (MVPA) of functional connectivity provided a more nuanced perspective on connectivity patterns across tasks. Deaf participants exhibited significant activation in a myriad of brain regions, including bilateral planum temporale (PT), postcentral gyrus, insula, and inferior frontal regions, among others. These findings underscore the intricate neural adaptations in response to auditory deprivation. Seed-based connectivity analysis, utilizing the PT as a seed region, revealed unique connectivity pattern across tasks. These connectivity dynamics provide valuable insights into the neural interplay associated with cross-modal plasticity.

Neurotopographical Transformations: Dissecting Cortical Reconfigurations in Auditory Deprivation.

Within the intricate matrices of cognitive neuroscience, auditory deprivation acts as a catalyst, propelling a cascade of neuroanatomical adjustments that have, until now, been suboptimally articulated in extant literature. Addressing this gap, our study harnesses high-resolution 3 T MRI modalities to unveil the multifaceted cortical transformations that emerge in tandem with congenital auditory deficits. We conducted a rigorous cortical surface analysis on a cohort of 90 congenitally deaf individuals, systematically compared with 90 normoacoustic controls. Our sample encompassed both male and female participants, ensuring a gender-inclusive perspective in our analysis. Expected alterations within prototypical auditory domains were evident, but our findings transcended these regions, spotlighting modifications dispersed across a gamut of cortical and subcortical structures, thereby epitomizing the cerebral adaptive dynamics to sensory voids. Crucially, the study's innovative methodology integrated two pivotal variables: the duration of auditory deprivation and the extent of sign language immersion. By intersecting these metrics with structural changes, our analysis unveiled nuanced layers of cortical reconfigurations, elucidating a more granulated understanding of neural plasticity. This intersectional approach bestows a unique advantage, allowing for a discerning exploration into how varying durations of sensory experience and alternative communication modalities modulate the brain's morphological terrain. In encapsulating the synergy of neuroimaging finesse and incisive scientific rigor, this research not only broadens the current understanding of adaptive neural mechanisms but also paves the way for tailored therapeutic strategies, finely attuned to individual auditory histories and communicative repertoires.

Decoding auditory deprivation: resting-state fMRI insights into deafness and brain plasticity.

Deafness, as a profound manifestation of sensory deprivation, prompts a cascade of intricate cerebral adaptations. In this study, involving 35 deaf individuals and 35 hearing controls, we utilized resting-state functional magnetic resonance imaging (rs-fMRI) to delve into the depths of functional connectivity nuances distinguishing deaf individuals from their hearing counterparts. Leading our analytical approach was the application of multi-voxel pattern analysis (fc-MVPA). This advanced method provided a refined perspective, revealing amplified neural connectivity within the deaf population. Notably, regions such as the left postcentral somatosensory association cortex, the anterior and posterior corridors of the left superior temporal gyrus (STG), and the left mid-temporal lobe were identified as hotspots of heightened connectivity. Further, fc-MVPA shed light on intricate interaction effects, which became more pronounced when examining variables such as the duration of auditory deprivation and the extent of sign language exposure. These interactions were particularly evident in the premotor and left frontal mid-orbital regions. Complementing this, seed-based connectivity assessments illuminated pronounced coupling dynamics within the left STG spectrum. Concurrently, local correlation (LCOR) value analysis in the deaf group revealed significant shifts in the right superior STG and bilateral precuneus. In addition, amplitude of low-frequency fluctuation (ALFF) evaluations indicated modulations in the bilateral mid cingulum and left superior mid frontal gyrus. This comprehensive, fc-MVPA-driven exploration uncovers the multifaceted functional adaptations resulting from deafness, highlighting the profound plasticity of the human brain and its potential implications for targeted rehabilitative strategies.

Using ALE coordinate-based meta-analysis to observe resting-state brain abnormalities in subjective tinnitus.

The origin of tinnitus remains a topic of discussion; however, numerous resting-state functional magnetic resonance imaging (rsfMRI) studies interpret it as a disruption in neural functional connectivity. Yet, there's notable inconsistency in the resting-state data across these studies. To shed light on this discrepancy, we conducted a meta-analysis of extant rsfMRI studies, aiming to identify potential regions that consistently signify core abnormalities in individuals with tinnitus. METHODS: A systematic search on MEDLINE/PubMed, Google Scholar, and Scopus databases was performed to identify rsfMRI studies on tinnitus published up to October 2022. Coordinates related to the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) brain maps that showed significant differences between tinnitus patients and controls were extracted. Meta-analysis was performed using the activation likelihood estimation method. Data were included from 17 rsfMRI studies that reported a total of 63 distinct foci in ALFF and 46 foci in ReHo. RESULTS: Our meta-analysis revealed several regions where tinnitus patients demonstrated increased ALFF and ReHO values, both individually and collectively, when compared to control subjects. These regions encompassed the insula, middle temporal gyrus, and inferior frontal gyrus on both sides. Additionally, increased activity was also noted in the cerebellum posterior lobe bilaterally and the right superior frontal gyrus. CONCLUSIONS: This meta-analysis demonstrates a unique pattern of resting-state brain abnormalities involving both the auditory and non-auditory brain regions as neuroimaging markers, which helps understand the neuro-pathophysiological mechanisms of tinnitus.

Tailoring of Visible to Near-Infrared Active 2D MXene with Defect-Enriched Titania-Based Heterojunction Photocatalyst for Green H2 Generation.

A wide solar light absorption window and its utilization, long-term stability, and improved interfacial charge transfer are the keys to scalable and superior solar photocatalytic performance. Based on this objective, a noble metal-free composite photocatalyst is developed with conducting MXene (Ti3C2) and semiconducting cauliflower-shaped CdS and porous Cu2O. XPS, HRTEM, and ESR analyses of TiOy@Ti3C2 confirm the formation of enough defect-enriched TiOy (where y is < 2) on the surface of Ti3C2 during hydrothermal treatment, thus creating a third semiconducting site with enough oxygen vacancy. The final material, TiOy@Ti3C2/CdS/Cu2O, shows a broad absorption window from 300 to 2000 nm, covering the visible to near-infrared (NIR) range of the solar spectrum. Photocatalytic H2 generation activity is found to be 12.23 and 16.26 mmol g-1 h-1 in the binary (TiOy@Ti3C2/CdS) and tertiary composite (TiOy@Ti3C2/CdS/Cu2O), respectively, with good repeatability under visible-NIR light using lactic acid as the hole scavenger. A clear increase of efficiency by 1.53 mmol g-1 h-1 in the tertiary composite due to NIR light absorption supports the intrinsic upconversion of electrons, which will open a new prospective of solar light utilization. Decreased charge-transfer resistance from the EIS plot and a decrease in PL intensity established the improved interfacial charge separation in the tertiary composite. Compared to pure CdS, H2 generation efficiency is 29.6 times higher on the noble metal-free tertiary composite with an apparent quantum efficiency of 12.34%. Synergistic effect of defect-enriched TiOy formation, creation of proper dual p-n junction on a Ti3C2 sheet as supported by the Mott-Schottky plot, significant NIR light absorption, increased electron mobility, and charge transfer on the conductive Ti3C2 layer facilitate the drastically increased hydrogen evolution rate even after several cycles of repetition. Expectantly, the 2D MXene-based heterostructure with defect-enriched dual p-n junctions of desired interface engineering will facilitate scalable photocatalytic water splitting over a broad range of the solar spectrum.

Advances and opportunities in unraveling cold-tolerance mechanisms in the world's primary staple food crops.

Temperatures below or above optimal growth conditions are among the major stressors affecting productivity, end-use quality, and distribution of key staple crops including rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays L.). Among temperature stresses, cold stress induces cellular changes that cause oxidative stress and slowdown metabolism, limit growth, and ultimately reduce crop productivity. Perception of cold stress by plant cells leads to the activation of cold-responsive transcription factors and downstream genes, which ultimately impart cold tolerance. The response triggered in crops to cold stress includes gene expression/suppression, the accumulation of sugars upon chilling, and signaling molecules, among others. Much of the information on the effects of cold stress on perception, signal transduction, gene expression, and plant metabolism are available in the model plant Arabidopsis but somewhat lacking in major crops. Hence, a complete understanding of the molecular mechanisms by which staple crops respond to cold stress remain largely unknown. Here, we make an effort to elaborate on the molecular mechanisms employed in response to low-temperature stress. We summarize the effects of cold stress on the growth and development of these crops, the mechanism of cold perception, and the role of various sensors and transducers in cold signaling. We discuss the progress in cold tolerance research at the genome, transcriptome, proteome, and metabolome levels and highlight how these findings provide opportunities for designing cold-tolerant crops for the future.

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model.

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.

Meta-QTL analysis in wheat: progress, challenges and opportunities.

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Altered white matter integrity in euthymic children with bipolar disorder: A tract-based spatial statistical analysis of diffusion tensor imaging.

Pediatric Bipolar Disorder (BD) is a serious mental illness that affects children and adolescents, characterized by episodes of mania, depression, and mixed episodes. Recent studies have suggested that abnormalities in the white matter (WM) may be a contributing factor. The neuropathogenesis of BD in children is not well-described, and research in this area is limited. Euthymic phase is a period in which clinical symptoms are present but not severe enough to significantly impact mood and daily behavior. In order to better understand the WM changes associated with BD in children, this study utilized Diffusion Tensor Imaging (DTI), to investigate alterations in WM microstructure. 20 confirmed euthymic BD children (aged 7-16) and 20 typically developing children were included in the study. DTI scans were obtained using a 3 T Magnetom Skyra and were analyzed using tract-based spatial statistics (TBSS) to examine changes in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Results showed that compared to the healthy control group, the euthymic BD group exhibited increased FA, AD, RD, and MD values in several brain regions, including the thalamus, precentral corticospinal tract, and superior longitudinal fasciculus. Conversely, decreased values were observed in the body of the corpus callosum and inferior fronto-occipital fasciculus. These findings suggest that alterations in WM microstructure are a hallmark of pediatric bipolar disorder. These findings provide important insights into the brain changes associated with pediatric bipolar disorder and open the door for new avenues of research.

GWAS for Early-Establishment QTLs and Their Linkage to Major Phenology-Affecting Genes (Vrn, Ppd, and Eps) in Bread Wheat.

Farmers in northern and central Indian regions prefer to plant wheat early in the season to take advantage of the remaining soil moisture. By planting crops before the start of the season, it is possible to extend the time frame for spring wheat. The early-wheat-establishment experiment began in the 2017 growing season at the Borlaug Institute for South Asia (BISA) in Ludhiana, India, and, after three years of intensive study, numerous agronomic, physiological, and yield data points were gathered. This study aimed to identify wheat lines suitable for early establishment through an analysis of the agro-morphological traits and the genetic mapping of associated genes or quantitative trait loci (QTLs). Advancing the planting schedule by two-three weeks proved to be advantageous in terms of providing a longer duration for crop growth and reducing the need for irrigation. This is attributed to the presence of residual soil moisture resulting from the monsoon season. Early sowing facilitated the selection of genotypes able to withstand early elevated temperatures and a prolonged phenological period. The ideotype, which includes increased photo-growing degree days for booting and heading, as well as a longer grain-filling period, is better suited to early planting than timely planting. Senescence was delayed in combination with a slower rate of canopy temperature rise, which was an excellent trait for early-adapted ideotypes. Thus, a novel approach to wheat breeding would include a screening of genotypes for early planting and an ideotype design with consistent and appropriate features. A genome-wide association study (GWAS) revealed multiple QTLs linked to early adaptation in terms of the yield and its contributing traits. Among them, 44 novel QTLs were also found along with known loci. Furthermore, the study discovered that the phenology regulatory genes, such as Vrn and Ppd, are in the same genomic region, thereby contributing to early adaptation.

Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics.

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.

Performance of APSIM to Simulate the Dynamics of Winter Wheat Growth, Phenology, and Nitrogen Uptake from Early Growth Stages to Maturity in Northern Europe.

Performance of the APSIM (Agricultural Production Systems sIMulator) wheat model was assessed to simulate winter wheat phenology, biomass, grain yield, and nitrogen (N) uptake for its potential to optimize fertilizer applications for optimal crop growth and minimal environmental degradation. The calibration and evaluation dataset had 144 and 72 different field growing conditions (location (~7) × year (~5) × sowing date (2) × N treatment (7-13)), respectively, and included seven cultivars. APSIM simulated phenological stages satisfactorily with both model calibration and evaluation data sets with r2 of 0.97 and RMSE of 3.98-4.15 BBCH (BASF, Bayer, Ciba-Geigy, and Hoechst) scale. Simulations for biomass accumulation and N uptake during early growth stages (BBCH 28-49) were also reasonable with r2 of 0.65 and RMSE of 1510 kg ha-1, and r2 of 0.64-0.66 and RMSE of 28-39 kg N ha-1, respectively, with a higher accuracy during booting (BBCH 45-47). Overestimation of N uptake during stem elongation (BBCH 32-39) was attributed to (1) high inter-annual variability in simulations, and (2) high sensitivity of parameters regulating N uptake from soil. Calibration accuracy of grain yield and grain N was higher than that of biomass and N uptake at the early growth stages. APSIM wheat model showed high potential for optimizing fertilizer management in winter wheat cultivation in Northern Europe.

The bandgap of sulfur-doped Ag2O nanoparticles.

A narrow band gap restricts photocatalytic applications of Ag2O nanoparticles, but appropriate doping can favorably modify this aspect. Given this, density functional theory (DFT) calculations were conducted, revealing that substitutional sulfur doping of Ag2O could increase its bandgap and stabilize oxygen vacancies. A hydrothermal precipitation protocol was employed to prepare sulfur-doped (S-doped) Ag2O nanoparticles. The band gap of the prepared nanoparticles increased to 1.89 eV with 1.25-mole percent S-doping. XPS analysis of the samples also revealed that S-doping increased oxygen vacancies in the prepared Ag2O nanoparticles. Furthermore, S-doping caused a major shift in the valence band position to a negative value. These doped Ag2O nanoparticles showed an enhanced visible-light photocatalytic activity towards rhodamine B (RhB) degradation.

Validation of Novel spot blotch disease resistance alleles identified in unexplored wheat (Triticum aestivum L.) germplasm lines through KASP markers.

BACKGROUND: During the last few decades, the diverse sources of resistance, several genes and QTLs for spot blotch resistance have been identified. However, a large set of germplasm lines are still unexplored that have the potential to develop highly resistant wheat cultivars for the target environments. Therefore, the identification of new sources of resistance to spot blotch is essential for breeding programmes to develop spot blotch resistant cultivars and sustain wheat production. The association mapping panel of 294 diverse bread wheat accessions was used to explore new sources of spot blotch disease resistance and to identify genomic regions using genome wide association analysis (GWAS). The genotypes were tested in replicated trials for spot blotch disease at three major hot spots in India (Varanasi in UP, Pusa in Bihar, and Cooch Behar in West Bengal). The area under the disease progress curve (AUDPC) was calculated to assess the level of resistance in each genotype. RESULTS: A total of 19 highly and 76 moderately resistant lines were identified. Three accessions (EC664204, IC534306 and IC535188) were nearly immune to spot blotch disease. The genotyping of all accessions resulted in a total of 16,787 high-quality polymorphic SNPs. The GWAS was performed using a Compressed Mixed Linear Model (CMLM) and a Mixed Linear Model (MLM). A total of seven significant MTAs, common in both the models and consistent across the environment, were further validated to develop KASP markers. Four MTAs (AX-94710084, AX-94865722, AX-95135556, and AX-94529408) on three chromosomes (2AL, 2BL, and 3BL) have been successfully validated through the KASP marker. CONCLUSIONS: The new source of resistance was identified from unexplored germplasm lines. The genomic regions identified through GWAS were validated through KASP markers. The marker information and the highly resistant sources are valuable resources to rapidly develop immune or near immune wheat varieties.

Cd-doped Ag2O/BiVO4 visible light Z-scheme photocatalyst for efficient ciprofloxacin degradation.

Foreign element doping can produce new photocatalysts with different band edge positions and adsorption properties. A composite of such a doped semiconductor with another component should enhance its photocatalytic properties towards a target substrate. The present investigation used a simple hydrothermal protocol to prepare Cd-doped Ag2O nanoparticles. The Cd-doping of Ag2O nanoparticles changed its valence band maximum position from 0.8 eV (for undoped Ag2O nanoparticles) to 2.67 eV with a slight narrowing of the Ag2O bandgap. A combination of DFT calculation and XRD results showed that the dopant Cd substituted Ag in the Ag2O lattice. The doped material is an effective photocatalyst for ciprofloxacin degradation but with poor recyclability. The joining of a BiVO4 part to the Cd-doped Ag2O nanostructures gave a composite with improved photocatalytic activity and recyclability towards ciprofloxacin degradation. DFT calculations showed that BiVO4 has a higher oxygen affinity than Cd-doped Ag2O. The XPS characterization of the composite and appropriate active species scavenger experiments demonstrated a Z-scheme mechanism. Superoxide radicals play a critical role in CIP degradation.