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Force myography (FMG) represents a promising alternative to surface electromyography (EMG) in the context of controlling bio-robotic hands. In this study, we built upon our prior research by introducing a novel wearable armband based on FMG technology, which integrates force-sensitive resistor (FSR) sensors housed in newly designed casings. We evaluated the sensors’ characteristics, including their load–voltage relationship and signal stability during the execution of gestures over time. Two sensor arrangements were evaluated: arrangement A, featuring sensors spaced at 4.5 cm intervals, and arrangement B, with sensors distributed evenly along the forearm. The data collection involved six participants, including three individuals with trans-radial amputations, who performed nine upper limb gestures. The prediction performance was assessed using support vector machines (SVMs) and k-nearest neighbor (KNN) algorithms for both sensor arrangments. The results revealed that the developed sensor exhibited non-linear behavior, and its sensitivity varied with the applied force. Notably, arrangement B outperformed arrangement A in classifying the nine gestures, with an average accuracy of 95.4 ± 2.1% compared to arrangement A’s 91.3 ± 2.3%. The utilization of the arrangement B armband led to a substantial increase in the average prediction accuracy, demonstrating an improvement of up to 4.5%.

The phylum Chloroflexota (formerly Chloroflexi) encompasses metabolically diverse bacteria that often have high prevalence in terrestrial and aquatic habitats, some even with biotechnological application. However, there is substantial disagreement in public databases which lineage should be considered a member of the phylum and at what taxonomic level. Here, we addressed these issues through extensive phylogenomic analyses. The analyses were based on a collection of >5000 Chloroflexota genomes and metagenome-assembled genomes (MAGs) from public databases, novel environmental sites, as well as newly generated MAGs from publicly available sequence reads via an improved binning approach incorporating covariance information. Based on calculated relative evolutionary divergence, we propose that Candidatus Dormibacterota should be listed as a class (i.e., Ca. Dormibacteria) within Chloroflexota together with the classes Anaerolineae, Chloroflexia, Dehalococcoidia, Ktedonobacteria, Ca. Limnocylindria, Thermomicrobia, and two other classes containing only uncultured members. All other Chloroflexota lineages previously listed at the class rank appear to be rather orders or families in the Anaerolineae and Dehalococcoidia, which contain the vast majority of genomes and exhibited the strongest phylogenetic radiation within the phylum. Furthermore, the study suggests that a common ecophysiological capability of members of the phylum is to successfully cope with low energy fluxes.

Angiopoietin-like proteins (ANGPTL) constitute a family of eight proteins (1–8) which play a pivotal role in the regulation of various pathophysiological processes. The current study sought to identify high-risk, “non-synonymous, single-nucleotide polymorphisms” (nsSNPs) in both ANGPTL3 and ANGPTL8 to evaluate the role that these nsSNPs play in various types of cancer. We retrieved a total of 301 nsSNPs from various databases; 79 of these candidates constitute high-risk nsSNPs. Moreover, we identified eleven high-risk nsSNPs that cause various types of cancer: seven candidates for ANGPTL3 (L57H, F295L, L309F, K329M, R332L, S348C, and G409R) and four candidates for ANGPTL8 (P23L, R85W, R138S, and E148D). Protein–protein interaction analysis revealed a strong association of ANGPTL proteins with several tumor-suppressor proteins such as ITGB3, ITGAV, and RASSF5. ‘Gene-expression profiling interactive analysis’ (GEPIA) showed that expression of ANGPTL3 is significantly downregulated in five cancers: sarcoma (SARC); cholangio carcinoma (CHOL); kidney chromophobe carcinoma (KICH); kidney renal clear cell carcinoma (KIRC); and kidney renal papillary cell carcinoma (KIRP). GEPIA also showed that expression of ANGPTL8 remains downregulated in three cancers: CHOL; glioblastoma (GBM); and breast invasive carcinoma (BRCA). Survival rate analysis indicated that both upregulation and downregulation of ANGPTL3 and ANGPTL8 leads to low survival rates in various types of cancer. Overall, the current study revealed that both ANGPTL3 and ANGPTL8 constitute potential prognostic biomarkers for cancer; moreover, nsSNPs in these proteins might lead to the progression of cancer. However, further in vivo investigation will be helpful to validate the role of these proteins in the biology of cancer.

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.

Using force myography (FMG) to monitor volumetric changes in limb muscles is a promising and effective alternative for controlling bio-robotic prosthetic devices. In recent years, there has been a focus on developing new methods to improve the performance of FMG technology in the control of bio-robotic devices. This study aimed to design and evaluate a novel low-density FMG (LD-FMG) armband for controlling upper limb prostheses. The study investigated the number of sensors and sampling rate for the newly developed LD-FMG band. The performance of the band was evaluated by detecting nine gestures of the hand, wrist, and forearm at varying elbow and shoulder positions. Six subjects, including both fit and amputated individuals, participated in this study and completed two experimental protocols: static and dynamic. The static protocol measured volumetric changes in forearm muscles at the fixed elbow and shoulder positions. In contrast, the dynamic protocol included continuous motion of the elbow and shoulder joints. The results showed that the number of sensors significantly impacts gesture prediction accuracy, with the best accuracy achieved on the 7-sensor FMG band arrangement. Compared to the number of sensors, the sampling rate had a lower influence on prediction accuracy. Additionally, variations in limb position greatly affect the classification accuracy of gestures. The static protocol shows an accuracy above 90% when considering nine gestures. Among dynamic results, shoulder movement shows the least classification error compared to elbow and elbow–shoulder (ES) movements.

Despite their remarkable biosynthetic potential, Bacillus subtilis have been widely overlooked. However, their capability to withstand harsh conditions (extreme temperature, Ultraviolet (UV) and γ-radiation, and dehydration) and the promiscuous metabolites they synthesize have created increased commercial interest in them as a therapeutic agent, a food preservative, and a plant-pathogen control agent. Nevertheless, the commercial-scale availability of these metabolites is constrained due to challenges in their accessibility via synthesis and low fermentation yields. In the context of this rising in interest, we comprehensively visualized the antimicrobial peptides produced by B. subtilis and highlighted their prospective applications in various industries. Moreover, we proposed and classified these metabolites produced by the B. subtilis group based on their biosynthetic pathways and chemical structures. The biosynthetic pathway, bioactivity, and chemical structure are discussed in detail for each class. We believe that this review will spark a renewed interest in the often disregarded B. subtilis and its remarkable biosynthetic capabilities.

Recently, crop management has involved excessive use of chemical fertilizers and pesticides, compromising public health and environmental integrity. Rhizobacteria, which can enhance plant growth and protect plants from phytopathogen, are eco-friendly and have been attracting increasing attention. In the current study, Bacillus subtilis RS10 isolated from the rhizosphere region of Cynodon dactylon, inhibited the growth of indicator strains and exhibited in vitro plant growth-promoting traits. A whole-genome analysis identified numerous biosynthetic gene clusters encoding antibacterial and antifungal metabolites including bacillibactin, bogorol A, fengycin, bacteriocin, type III polyketides (PKs), and bacilysin. The plant growth-promoting conferring genes involved in nitrogen metabolism, phosphate solubilization, hydrogen sulfide, phytohormones, siderophore biosynthesis, chemotaxis and motility, plant root colonization, lytic enzymes, and biofilm formation were determined. Furthermore, genes associated with abiotic stresses such as high salinity and osmotic stress were identified. A comparative genome analysis indicated open pan-genome and the strain was identified as a novel sequence type (ST-176). In addition, several horizontal gene transfer events were found which putatively play a vital role in the evolution and new functionalities of a strain. In conclusion, the current study demonstrates the potential of RS10 antagonism against important pathogens and plant growth promotion, highlighting its application in sustainable agriculture.

In this article, we establish the weighted (k,s)-Riemann-Liouville fractional integral and differential operators. Some certain properties of the operators and the weighted generalized Laplace transform of the new operators are part of the paper. The article consists of Chebyshev-type inequalities involving a weighted fractional integral. We propose an integro-differential kinetic equation using the novel fractional operators and find its solution by applying weighted generalized Laplace transforms.

Plant-associated endophytic microorganisms are essential to developing successful strategies for sustainable agriculture. Grazing is an effective practice of grassland utilization through regulating multitrophic relationships in natural grasslands. This study was conducted for exploring the effects of grazing on the diversities and communities of bacteria and fungi presented in rhizosphere soils, roots, stems, and leaves of Leymus chinensis (L. chinensis), based on high-throughput sequencing. Grazing increased bacterial diversity but reduced fungal diversity in plant leaves. Further analysis confirmed that the abundance of Chloroflexi, Gemmatimonadota, Nitrospirota, Sordariales, and Pezizales in plant leaves was increased by grazing. The Bray–Curtis similarities of microbial communities in the endosphere were higher under grazing plots than non-grazing plots. Moreover, the bacterial communities were significantly correlated with ions, while the nutrient and negative ions exhibited strong influence on fungal communities. We concluded that grazing-induced changes of microbial diversities and communities in different compartments of a dominant perennial grass (L. chinensis) could be attributed to the nutrient and ion distribution in host plant. The current study highlights the importance of livestock in mediating diversities and communities of endophytic microbes, and will be useful for better understanding the complexity of multitrophic interactions in a grassland ecosystem.

This study deals with a stochastic reaction–diffusion biofilm model under quorum sensing. Quorum sensing is a process of communication between cells that permits bacterial communication about cell density and alterations in gene expression. This model produces two results: the bacterial concentration, which over time demonstrates the development and decomposition of the biofilm, and the biofilm bacteria collaboration, which demonstrates the potency of resistance and defense against environmental stimuli. In this study, we investigate numerical solutions and exact solitary wave solutions with the presence of randomness. The finite difference scheme is proposed for the sake of numerical solutions while the generalized Riccati equation mapping method is applied to construct exact solitary wave solutions. The numerical scheme is analyzed by checking consistency and stability. The consistency of the scheme is gained under the mean square sense while the stability condition is gained by the help of the Von Neumann criteria. Exact stochastic solitary wave solutions are constructed in the form of hyperbolic, trigonometric, and rational forms. Some solutions are plots in 3D and 2D form to show dark, bright and solitary wave solutions and the effects of noise as well. Mainly, the numerical results are compared with the exact solitary wave solutions with the help of unique physical problems. The comparison plots are dispatched in three dimensions and line representations as well as by selecting different values of parameters.

This article deals with the study of ultrasound propagation, which propagates the mechanical vibration of the molecules or of the particles of a material. It measures the speed of sound in air. For this reason, the third-order non-linear model of the Westervelt equation was chosen to be studied, as the solutions to such problems have much importance for physical purposes. In this article, we discuss the exact solitary wave solutions of the third-order non-linear model of the Westervelt equation for an acoustic pressure p representing the equation of ultrasound with high intensity, as used in acoustic tomography. Moreover, the non-linear coefficient B/A (being a part of space-dependent coefficient K), has also been investigated in this literature. This problem is solved using the Generalized Kudryashov method along with a comparison of the Modified Kudryashov method. All of the solutions have been discussed with both surface and contour plots, which shows the behavior of the solution. The images are prepared in a well-established way, showing the production of tissues inside the human body.

In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) for Air-Fuel Ratio (AFR) control of an Internal Combustion (IC) engine in a process plant to avoid engine shutdown, are presented. The proposed AFTCS employs a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR)-based observer model in the Fault Detection and Isolation (FDI) unit for analytical redundancy. A comparison between these three proposed techniques is carried out to determine the least expensive and most accurate approach. The results show that the nonlinear regression produces highly accurate results by consuming very low computational power, and its response time is also very low as compared to GA and PSO. The results obtained show that NLR requires 99.6% and 93.1% less computational time for throttle and MAP estimation, respectively, by reducing the estimation error to as low as 0.01. The simulation of the proposed system is carried out in the MATLAB/Simulink environment. The results prove the superior fault tolerance performance for sensor faults of the AFR control system, especially for the Manifold Absolute Pressure (MAP) sensor in terms of less oscillatory response as compared to that reported in existing literature.

Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring both enhanced productivity and environmental adaptability is one such prominent need. To address this requirement, a series of analyses were conducted in this study for exploring natural diploid wheat germplasm resources. The current study involved 98 Recombinant Inbred Lines (RILs) populations developed by crossing two diploid ‘A’ sub-genome wheat species, Triticummonococcum and Triticum boeoticum, enriched with valuable alleles controlling, in particular, biotic and abiotic stresses tolerance. Their 12 phenotypic traits were explored to reveal germplasm value. All traits exhibited vast diversity among parents and RILs via multivariate analysis. Most of the investigated traits depicted significant (p < 0.05) positive correlations enlightening spikelet per spike, total biomass, seed weight per spike, number of seeds per spike, plant height, and days to heading as considerably focused traits for improving hexaploid wheat. Principal component analysis (PCA) exhibited 61.513% of total variation with three PCs for 12 traits. Clustering of genotypes happened in three clades, and the two parents were separated into two extreme clusters, validating their enrichment of diversity. This study provided beneficial aspects of parental resources rich in diverse alleles. They can be efficiently exploited in wheat improvement programs focusing on introgression breeding and the recovery of eroded genetic factors in currently available commercial wheat cultivars to sustain calamities of environmental fluctuations.

The present study aimed to characterize the simple sequence repeat markers in cotton using the cotton expressed sequence tags. A total of 111 EST-SSR polymorphic molecular markers with trinucleotide motifs were used to evaluate the 79 accessions of Gossypium L., (G. darwinii, 59 and G. barbadense, 20) collected from the Galapagos Islands. The allele number ranged from one to seven, with an average value of 2.85 alleles per locus, while polymorphism information content values varied from 0.008 to 0.995, with an average of 0.520. The discrimination power ranks high for the majority of the SSRs, with an average value of 0.98. Among 111 pairs of EST-SSRs and gSSRs, a total of 49 markers, comprising nine DPLs, one each of MonCGR, MUCS0064, and NAU1028, and 37 SWUs (D-genome), were found to be the best matched hits, similar to the 155 genes identified by BLASTx in the reference genome of G. barbadense, G. arboreum L., and G. raimondii Ulbr. Related genes GOBAR_DD21902, GOBAR_DD15579, GOBAR_DD27526, and GOBAR_AA04676 revealed highly significant expression 10, 15, 18, 21, and 28 days post-anthesis of fiber development. The identified EST-SSR and gSSR markers can be effectively used for mapping functional genes of segregating cotton populations, QTL identification, and marker-assisted selection in cotton breeding programs.

This study evaluated the genetic diversity and population structures in a novel cotton germplasm collection comprising 132 diploids, including Glossypium klotzschianum and allotetraploid cotton accessions, including Glossypium barbadense, Glossypium darwinii, Glossypium tomentosum, Glossypium ekmanianum, and Glossypium stephensii, from Santa Cruz, Isabella, San Cristobal, Hawaiian, Dominican Republic, and Wake Atoll islands. A total of 111 expressed sequence tag (EST) and genomic simple sequence repeat (gSSR) markers produced 382 polymorphic loci with an average of 3.44 polymorphic alleles per SSR marker. Polymorphism information content values counted 0.08 to 0.82 with an average of 0.56. Analysis of a genetic distance matrix revealed values of 0.003 to 0.53 with an average of 0.33 in the wild cotton collection. Phylogenetic analysis supported the subgroups identified by STRUCTURE and corresponds well with the results of principal coordinate analysis with a cumulative variation of 45.65%. A total of 123 unique alleles were observed among all accessions and 31 identified only in G. ekmanianum. Analysis of molecular variance revealed highly significant variation between the six groups identified by structure analysis with 49% of the total variation and 51% of the variation was due to diversity within the groups. The highest genetic differentiation among tetraploid populations was observed between accessions from the Hawaiian and Santa Cruz regions with a pairwise FST of 0.752 (p < 0.001). DUF819 containing an uncharacterized gene named yjcL linked to genomic markers has been found to be highly related to tryptophan-aspartic acid (W-D) repeats in a superfamily of genes. The RNA sequence expression data of the yjcL-linked gene Gh_A09G2500 was found to be upregulated under drought and salt stress conditions. The existence of genetic diversity, characterization of genes and variation in novel germplasm collection will be a landmark addition to the genetic study of cotton germplasm.