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In-situ vaccination (ISV) utilizing nanoparticles (NPs) and therapeutic devices like focused ultrasound (FUS) can trigger immune-mediated killing of both treated and untreated cancer cells. However, the impact of confounding factors such as aging and gut microbiota composition on therapeutic outcomes remains poorly understood. In this study, we sequentially treated young mice (∼8 weeks) and old mice (>18 months) with bilateral melanoma using FUS and calreticulin nanoparticles (CRT-NP) to enhance immunogenic cell death. The combination of CRT-NP and FUS (CFUS) demonstrated greater efficacy in inducing regression of treated and untreated tumors in young mice compared to old mice. The diminished effectiveness in older mice was associated with significant differences in gut microbiome composition, characterized by alterations in bacterial species and splenic immune cells. Specifically, young mice exposed to CFUS exhibited higher abundance of Bacteroidetes and Verrucomicrobia, which was not observed in the aged cohorts. Turicibacter, Anaerotruncus, and Ruminiclostridium demonstrated negative correlations with CD8+ T cells but positive correlations with CD4+ T cells and MDSC cells in both age groups. Taxon set enrichment analysis revealed 58 significantly enriched host gene targets in the young cluster compared to only 11 in the aged cluster. These findings highlight the relationship between ISV treatment efficacy and gut microbiome composition, suggesting that interventions such as diet modification, probiotics, or fecal microbiota transplantation may hold potential as therapeutic strategies to enhance immune responses against solid tumors. • The influence of age and gut microbiota composition on the efficacy of immunomodulatory treatments is not fully understood. • We found age affects the in-situ efficacy of immunomodulatory nanoparticles combined with therapeutic ultrasound. • Age-associated modification of antitumor immunity is correlated with changes in the gut microbiome. • Our study identified key bacterial species that could boost immune-mediated tumor eradication from in-situ therapies. [ABSTRACT FROM AUTHOR]

Catalytic efficiency of gas phase and alumina-supported bimetallic Ag n Au m ( n + m = 2 − 4) alloy clusters is studied using density functional methods As a pre-requite, adsorption of O 2 and CO molecules, and co-adsorption of both molecules on these clusters are studied in detail. O 2 and CO are co-adsorbed on nearby sites on the gas phase tetramer clusters Ag 2 Au 2 and Ag 3 Au. But their catalytic efficiency is hindered by large barriers (1.55 eV and 1.44 eV, respectively) to the breaking of O O bond. Among the deposited clusters, Ag 2 Au and AgAu 2 have O 2 and CO co-adsorbed on nearby locations. Of these two, Ag 2 Au has a lower kinetic barrier for subsequent CO 2 formation. Thus Ag 2 Au looks the most promising candidate. [ABSTRACT FROM AUTHOR]

• Haplotype structure was elucidated in Vrindavani cattle via sliding window approach. • Haplotype-based association analysis was undertaken with (re)production traits. • Haplotypes showed significant association with AFC, TLMY, PY, DD, ICP and SP. • Overlapping candidate genes revealed pathways affecting the (re)production performance. Haplotype-based association analysis promises to reveal important information regarding the effect of genetic variants on economic traits of interest. The present study aimed to evaluate the haplotype structure of Vrindavani cattle and explore the association of haplotypes with (re)production traits of economic interest. Genotyping array data of medium density (Bovine50KSNP BeadChip) on 96 randomly selected Vrindavani cows was used in the present study. Genotypes were called in GenomeStudio program while quality control was undertaken in PLINK using standard thresholds. The phenotypic traits used in the present study included age at first calving, dry days, lactation length, peak yield, total lactation milk yield, inter-calving period and service period. The haplotype structure of Vrindavani population was assessed, using a sliding window of 20 SNP with a shift of 5 SNPs at a time, in terms of the size of haplotype blocks regarding their length (in Kb) and frequency in chromosome-wise fashion. Haplotype blocks were assessed for possible association with important production and reproduction traits across three lactation cycles in Vrindavani cattle population. The first ten principal components were included in the model for haplotype-based association analysis to correct for stratification effects of assessed individuals. Multiple haplotypes were found to be associated with age at first calving, total lactation milk yield, peak yield, dry days, inter-calving period and service period. Various candidate genes were found to overlap haplotypes that were significantly associated with age at first calving (CDH18, MARCHF11, MYO10, FBXL7), total lactation milk yield (TGF, PDE1A, and COL8A1), peak yield (PPARGC1A, RCAN1, KCNE1, SMIM34 and MRPS6), dry days (CPNE4, ACAD11 and MRAS), inter-calving period (ABCG5, ABCG8 and COX7A2L) and service period (FOXL2 and PIK3CB). The putative candidate genes overlapping the significantly associated haplotypes revealed important pathways affecting the production and reproduction performance of animals. The identified genes and pathways may serve as good candidate markers to select animals for improved production and reproduction performance in future generations. [ABSTRACT FROM AUTHOR]

• We elucidated putative CNVs and CNV regions in Vrindavani cattle using Bovine SNP50 genotyping data. • 252 putative CNVs were detected which further concatenated into 69 CNVRs. • Five CNVRs were significantly associated with 10 (re)production traits in Vrindavani population. • The study provides useful insights to establish the comprehensive CNV map of Vrindavani population for association studies. The present study was aimed to analyze the genome-wide copy number variations (CNVs) in Vrindavani composite cattle and concatenate them into CNV regions (CNVRs), and finally test the association of CNVRs with different production and reproduction traits. Genotypic data, generated on BovineSNP50 Beadchip (v3) array for 96 Vrindavani animals, was used to elucidate the CNVs at the genome level. Intensity data covering over 53,218 SNP genotypes on bovine genome was used. Algorithm based on Hidden Markov Model was employed in PennCNV program to detect, normalize and filter CNVs across the genome. 252 putative CNVs, detected via PennCNV program, in different individuals were concatenated into 71 CNV regions (CNVRs) using CNVRuler program. Association of CNVRs with important (re)production traits in Vrindavani animals was assessed using linear regression. Five CNVRs were found to be significantly associated with ten important (re)production traits. The genes harbored in these regions provided useful insights into the association of CNVRs with genes and ultimately the variation at phenotype level. Important genes that overlapped with CNVRs included WASHC4, HS6ST3, MBNL2, TOLLIP, PIDD1 and TSPAN4. Furthermore, the CNVRs were found to overlap with important QTLs available in AnimalQTL database which affect milk yield and composition along with reproduction and immune function traits. The copy number states of three enes were validated using digital droplet PCR technique. The results from the present study significantly enhance the understanding about CNVs in Vrindavani cattle and should help establish its CNV map. The study will also enable further investigation on association of these variants with important traits of economic interest including disease incidence. [ABSTRACT FROM AUTHOR]

Simultaneous nanometer-scale spatial resolution and fractions of a second temporal resolution at controlled temperatures are desired to directly investigate the earliest stages of materials transformations. In this work, laser annealing of an amorphous Fe-Si-Nb-Cu-B alloy within an atom probe instrument is used to induce and subsequently probe in 3D the initial crystallization of an amorphous phase. Methods of estimating the laser-induced temperature profile, determining the cumulative annealing time, and measuring the resultant nanoscale crystallization are explored. These are verified through atom probe tomography and transmission electron microscopy measurements of laser pulsing conditions above and below the crystallization temperature of the alloy. Evaluation of the two-dimensional distribution of copper-rich cluster sizes and densities measured by atom probe tomography show good qualitative and, in some instances, quantiative agreement with Thermo-Calc modeling results. COMSOL models provide support for the claimed sub-second annealing time and the observed spatial variations in cluster sizes and densities induced by spatial variations in thermal history. Unlabelled Image • Laser annealing within an atom probe controllably crystallizes an amorphous metal. • Crystallization is confined to a few hundred nanometer region. • Analysis and modeling indicate a cumulative annealing time of ~0.1 s. • The annealed region displays a graded thermal history. • Cluster size and number density related to temperature in a single specimen. [ABSTRACT FROM AUTHOR]

• Optimal usage and management of the available spectrum for 6 G networks. • A cooperative spectrum sensing technique using Manta Ray Foraging Algorithm (MRFO). • Computationally efficient algorithm. • Provides maximum probability of detection. The upcoming sixth generation (6 G) systems can meet the high user demands. The existing communication systems are becoming inefficient in meeting the user demands. The multifold growth in the usage of high-definition multimedia applications requires new capabilities. The users are looking forward to high throughput and low latency. The shift from 5 G to 6 G networks is since the 6 G networks are expected to combine the terrestrial, aerial, and maritime communications into a robust network. This will provide the users a faster network with high reliability, accommodation to a larger number of users, and ultra-low latency. However, the limited availability of spectrum is a bottleneck in enhancing the user experience. Therefore, advanced techniques like cognitive radios and cooperative spectrum sensing are critical in the design of future network. The optimal usage and management of the available spectrum is significant for the performance of the network. In this paper, a cooperative spectrum sensing technique using Manta Ray Foraging Algorithm (MRFO) is proposed. The weighting vector at the fusion center is optimized using MRFO. The allocation of the spectrum is done using the optimal weight vector for secondary users. The proposed work aims at finding the maximum probability of detection. Probability of detection is significant in spectrum sensing. The channel needs to be sensed for the presence or absence of primary users. If the detection probability is maximized, then the channel usage efficiency will increase. The proposed method is compared with other state of the art methods. The results show that MRFO can be used efficiently for spectrum sharing by cognitive radios. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel technique of resource recommendation and scheduling based propagation analysis using Federated Learning (FL). • Based on user-log record analysis and extraction of similar patterns. • Federated learning model based on resource patterns and resource event occurrence fetched from computational logs of user participation. • The framework uses resource-attribute optimization technique for customization. The addition of devices in the IoT framework has leveraged the performance and ability of medical device computation in developing the Internet of Medical Things (IoMT) framework. The 6G ecosystem needs to be redefined in comparison with earlier generation communication environment and setup. This article proposes a novel technique of resource recommendation and scheduling-based propagation analysis usingFederated Learning (FL). The technique is driven by user-log record analysis and extraction of similar patterns. The logs are further evaluated with an available spectrum and bandwidth ratio of 6G to compute the requirement and availability of resources. The aim of the federated learning model is based on resource patterns and resource event occurrence fetched from computational logs of user participation. The framework uses the resource-attribute optimization technique for customization. The recommendation is based on strategic evaluation and the Dynamic User Allocation (DUA) technique. The approach has integrated data evaluation from the Operating system, networking channel, and communication devices of IoMT and has compared performance over the standard resource recommendation model. The proposed technique has achieved 94.72% of accuracy over standard DUA datasets. [Display omitted] [ABSTRACT FROM AUTHOR]

● A modular multilevel inverter (MMI) integrated with photovoltaic (PV) is employed in the three-phase system for power quality (PQ) improvement. ● Spotted hyena optimization (SHO) technique is proposed for generating the reference current and tuning the parameter of the PI controller in MMI. ● MMI is designed to supply power produced from the PV system, to the grid and controlling the harmonics and reactive power. ● The auto-scaling incremental conductance (ASINC) based maximum power point tracking (MPPT) technique is employed for drawing maximum power from the solar PV system. In this work, a photovoltaic (PV) integrated modular multilevel inverter (MMI) is connected to a three-phase system for power quality (PQ) improvement. The MMI has modular design and high-quality output waveform. Thus, it is widely used for medium and high-power applications. The Spotted Hyena Optimization (SHO) technique is proposed for generating the reference current and tuning the parameter of the PI controller in MMI. The proposed SHO technique is operated under different loading conditions and is compared with the conventional particle swarm optimization (PSO) and firefly optimization (FO) technique. The controlling part of MMI is designed to supply power produced from the PV system to the grid and controlling the harmonics and reactive power. This paper employs an incremental conductance (INC) based maximum power point tracking (MPPT) technique for drawing maximum power. The performance of the proposed technique is analysed using the MATLAB/Simulink tool. [Display omitted] [ABSTRACT FROM AUTHOR]

• The Indian Ghurrah pig has a mixture of European and Asian inheritance. • Analysis traced European admixture to the recent past. • Signs of interspecies introgression was detected in admixture analysis. • Ghurrah and Asian pigs show divergence in genomic regions governing teeth development. The evolution and domestication of pigs is a complex and ongoing process. Despite its rich biodiversity and proximity to the geographical origins of Sus scrofa domesticus, the place of Indian pigs in the global phylogeny is unclear. Using microarray-derived (porcine 60K SNP chip) genotypes of 11 Ghurrah pigs from North-Western India and a public dataset comprising 2113 pigs of 146 breeds, we determined the genomic ancestry of Ghurrah pigs and compared their genetic constitution to European and Asian breeds to ascertain signatures of divergent selection. Results showed that Ghurrah pigs contain genes of Asian and European ancestry with signs of inter-species introgression. Using Admixture LD – decay statistics, the European admixture event was dated to the recent past, coinciding with the start of cross-breeding efforts in India. The complex Asian ancestry pattern of the breed resembled that of wild boars of South – Central China and Thailand, possibly suggesting introgression from an Indian wild boar relative. Signatures of selection analysis showed the divergence of Ghurrah pigs from Asian and European breeds in genomic regions associated with odontogenesis, and nervous system processess, respectively. Our results present the first genomic characterization of an Indian pig breed using dense microarray-derived genotypes and highlight the importance of further genomic characterization of Indian domestic and wild pigs. Given the limited sample size available for this study, validation of the findings in a larger population is suggested. [ABSTRACT FROM AUTHOR]

• Power Quality improvement is analysed in smart grid (micro-grid) integrated shunt hybrid filter system. • The performance of shunt hybrid filter is performed with Adaptive Fuzzy-Neural-Network Control (AFNN) for achieving an efficient smart grid operation under different power system scenarios. • For validating the working of the proposed technique, AFNN is compared with adaptive fuzzy sliding (AFS) control and adaptive fuzzy back stepping (AFBS) techniques. • Non-singular terminal SMC (NTSMC) is designed to solve singularity point problem and provide very high precision and finite-time control for shunt hybrid filter. The Smart Grid (SG) is treated as the next level of modern power system which uses the bilateral flow of power and information. The ability of the smart grid for two-way communication amid the utility and consumers makes the grid smart. For proper functioning, all the elements and parameters associated with it should work effectively and efficiently. Power Quality (PQ) is an important issue related to a modern power system. In this paper, more focus is given on PQ improvement in the microgrid (MG) system (which is a part of SG) using shunt hybrid filters (SHF). The performance of SHF is investigated using an improved and advanced controlling technique, i.e., Adaptive Fuzzy-Neural-Network (AFNN) Control for achieving an efficient SG operating under different scenarios of loads and supply voltages. The proposed controller is compared with the other controlling techniques like adaptive fuzzy sliding (AFS) control and adaptive fuzzy back stepping (AFBS). The analysis is performed with the MATLAB/ Simulink tool. [Display omitted] [ABSTRACT FROM AUTHOR]