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Randomized, crossover clinical trial on the safety, feasibility, and usability of the ABLE exoskeleton: A comparative study with knee-ankle-foot orthoses

Wearable exoskeletons are emerging as a new tool for gait training. However, comparisons between exoskeletons and conventional orthoses in terms of safety and feasibility are scarce. This study assessed the safety, feasibility, usability, and learning process of using the ABLE Exoskeleton in people with spinal cord injury (SCI) while comparing it with knee-ankle-foot orthoses (KAFOs). In this randomized, crossover clinical trial, 10 patients with chronic complete SCI (T4-T12) conducted a 10-session training and assessment protocol with each device: KAFOs and the ABLE Exoskeleton. Outcomes on safety (adverse events), and feasibility and usability (level of assistance, donning/doffing, therapy activities) were recorded for both devices. Evaluation sessions included standard clinical tests (Timed Up and Go, 10-Meter Walk Test, and 6-Minute Walk Test) to assess gait performance. The therapy metrics (number of steps, distance, gait speed, and standing and walking time) were recorded at each session for the robotic device. Participants quickly learned how to use the ABLE Exoskeleton, showing improvements in all therapy metrics (p<0.05) and the 6-Minute Walk Test (p<0.05). Participants reported less adverse events with the robotic device than KAFOs (17 and 31, respectively). Total donning and doffing time was 43 s faster with the robotic device using comparable levels of assistance. The time to complete the therapy activities was very similar between devices. Overall, participants needed 1 to 4 training sessions to perform essential therapy activities (sit/stand transitions, walking 10 meters, turning around) with both devices using minimum assistance or less. The results of this study show that it is feasible and safe for people with motor complete paraplegia due to SCI (T4-T12) to use the ABLE Exoskeleton for gait training in a rehabilitation hospital setting. The ABLE Exoskeleton proved to be as practical and easy to use as conventional orthoses, with fewer AEs reported when using the exoskeleton versus the KAFOs.

Room temperature, metal-free, CDI-promoted, ex-situ protocol for S-methyl thioester synthesis

Abstract S-methyl thioesters have proven to be useful intermediates in many biosynthetic reactions and their occurrence in bioactive molecules further enhance their appeal. Shortcomings in several reported synthetic protocols have led us to disclose herewith a mild, room temperature ex situ protocol that provides access to a large number of thioesters (aryl, heteroaryl, alkyl, amino acids). Late-stage functionalization strategies were also performed on commercial pharmaceutical drugs (containing carboxylic acid functionality) while further in situ conversion to valuable compounds has also been achieved.

Hancinone possesses potentials on increasing the ability of HMC3 cells to phagocytosis of Aβ1-42 via TREM2/Syk/PI3K/AKT/mTOR signaling pathway

Context The amyloid hypothesis is the most widely accepted explanation for Alzheimer’s disease (AD). Failure of microglia Amyloid β-protein (1–42) (Aβ1–42) oligomer clearance and secondary neuroinflammation play a crucial role in the etiology in sporadic AD. Piper kadsura (Choisy) Ohwi (PkO), an herb of Chinese medicine, has anti-inflammation, antioxidation effects. Objective To explore the impact of PkO and its active substances on Alzheimer’s disease. Materials and methods We integrated drug prediction, network pharmacology and molecular docking techniques to systematically examine multi-scale mechanisms of PkO. Moreover, human Microglia Clone 3 (HMC3) were respectively incubated for 24 hours in the presence or absence of Syk inhibitor (SI, 100 nmol/L), β-amyloid (1-42) oligomer mixtures (called as Aβ oligomer hereafter, Aβ, 2.5 µM), or hancinone (HAN, 0.5 µM, 2.5 µM, 10 µM) to verify the target of the effect of PkO on Aβ oligomer-induced microglia. Results Ultimately, we screened hancinone from PkO as a potential therapeutic agent for AD. Hancinone increased Triggering receptor expressed on myeloid cells 2 (TREM2), Syk, and p-Syk levels, up-regulated relative levels of p-PI3K, p-AKT, and mTOR, promoted the ability of HMC3 cells from the M1 phenotype to the M2 phenotype in Aβ or SI-stimulated HMC3 cells, and enhanced the phagocytic capacity of HMC3 cells to Aβ. Discussion and conclusions Hancinone could regulate the phenotype of HMC3 cells and promote cell phagocytosis of Aβ by modulating the TREM2/Syk/PI3K/AKT/mTOR signaling pathway. This systematic exploration indicates that hancinone has the therapeutic effect on AD.

Deep learning-based CAD system for Alzheimer’s diagnosis using deep downsized KPLS

Genome-wide association studies and transcriptome analysis reveal novel genes associated with freezing tolerance in rapeseed (Brassica napus L.)

Freezing stress is the main obstacle affecting the geographical distribution, growth, development, quality, and productivity of rapeseed (Brassica napus) in northern China. However, there is a little knowledge of rapeseed freezing tolerance mechanism. Here, 289 core germplasms collected from 36 countries were used to identify SNPs associated with freezing tolerance. We used RNA-seq data to narrow down the candidate genes identified by genome-wide association studies. The frequency distributions of phenotypic values and best linear unbiased estimates (BLUE) values for each trait conform to normal or approximately normal distributions, with good repeatability across various locations. The results showed that 594, 513, 7, and 45 SNPs were significantly associated with malondialdehyde, peroxidase, soluble protein, and relative electrolyte leakage, respectively. Based on these significantly associated SNPs, we identified 4,998 associated genes. Crossover analysis indicated that 73 genes were overlapped between GWAS and RNA-seq datasets, and 13 candidate genes involved in transmission and perception of freeze stress signals, lipid metabolism, reactive oxygen species (ROS) homeostasis, antifreeze proteins synthesis, and other metabolic processes. These results reveal novel genes associated with freezing tolerance in rapeseed, and provide a basis for further research and improvement of freezing tolerance in rapeseed.

Space-based natural and human-induced water storage change quantification

ClusterRadar: An interactive web-tool for the multi-method exploration of spatial clusters over time

Spatial cluster analysis is crucial for understanding localized patterns in geospatial data, with wide-ranging applications for scientific discovery and decision-making. However, the dynamic nature of spatial clusters and the diverse range of clustering methods available can make analysis and interpretation challenging. We introduce ClusterRadar, a web-based tool designed to streamline this process by uniquely prioritizing longitudinal analysis and multi-method comparison of spatial clusters. It empowers users to easily perform clustering with multiple methods, directly compare results, and visualize spatiotemporal patterns through a novel design of linked interactive visualizations. ClusterRadar aims to maximize utility to a broad user base by supporting various geospatial formats and executing entirely within the browser to ensure data privacy. ClusterRadar is available at https://episphere.github.io/ClusterRadar.

Nonlinear relationship between serum high sensitivity C reactive protein to high density lipoprotein cholesterol ratio with non-alcoholic fatty liver disease

Classification of mountain-based rail transit stations and analysis of passenger flow influencing mechanisms

Mountainous urban rail transit stations exhibit distinct characteristics. To investigate how these features affect passenger flow variations at rail stations, we analyze geographic-environmental data surrounding the stations and integrate road network topology, automatic fare collection data, and point-of-interest (POI) data. We propose a method to classify rail transit stations by considering the mountainous features and establish a multiscale geographically weighted regression (MGWR) model to assess the classification results. This study focuses on 189 rail stations in Chongqing, identifying six station categories: comprehensive mountainous, comprehensive non-mountainous, employment mountainous, employment non-mountainous, residential mountainous, and residential non-mountainous. The MGWR results show that road growth coefficients, average longitudinal slopes, and road lengths significantly influence station performance. For instance, the average longitudinal slope substantially affects employment in mountainous stations, particularly during the morning peak. The analysis reveals that the average longitudinal slope exerts a stronger negative effect on morning peak inbound passenger flow at employment mountainous stations (-0.949), indicating that commuters are more sensitive to travel time during the morning peak. In contrast, the evening peak inbound passenger flow is less impacted (-0.409), suggesting that evening commuters face fewer time constraints. These findings offer strategic insights for zoning transit stations to support transit-oriented development(TOD).

Design, synthesis, anticancer properties, and molecular docking of imidazolone derivatives with lipophilic moiety

Abstract As part of an ongoing investigation into imidazolone derivatives with anticancer activities, herein we present the synthesis of a new series of imidazolones with various substituents, including lipophilic and hydrophilic. All synthesized imidazolones (3a–3g and 5a–5g) were characterized by various spectroscopic methods (1H and 13C NMR, FT-IR, and mass spectrometry). The preparation was performed by condensation cyclization of vanillin-based oxazolones with various amines. The anticancer efficiencies of the prepared imidazolones were tested against four different cancer cell lines liver cancer cells (HepG2), cervical adenocarcinoma cells (HeLa), colon cancer cells (CaCo-2), breast cancer cells (MCF-7). Among the prepared imidazolones the one with dodecyl chain displayed excellent efficiency against the tested cancer cell lines with an IC50 value of 65.3 ± 3.2 µM against HepG2 and 20.02 ± 3.5 µM against MCF-7. Imidazolone 2d with amino alkyl moiety displayed an IC50 value of 35.6 ± 4.1 µM against HeLa cell and 24.6 ± 3.8 µM against CaCo-2 cell line. Imidazolone 5g with thiophene and pyridyl group showed the highest activity among all tested derivatives with IC50 value of 18.6 ± 2.3 µM and 5.9 ± 2.3 µM against HeLa and CaCo-2 cell lines, respectively. Imidazolone 5b with a chlorophenyl moiety displayed an IC50 value of 2.2 ± 0.7 µM and 5.5 ± 1.1 µM against HepG2 and Hela cell lines, respectively. The study used computational tools to assess the pharmacokinetics and antitumor potential of the synthesized imidazolone molecules with the highest activities. They were evaluated through ADME analysis and molecular docking. ADME properties confirm favorable drug-likeness under Lipinski’s guidelines, with molecular weights ranging from 357.43 (5d) to 468.65 g/mol (5f). Molecules 2g, 2f, and 5f show optimal hydrogen bonding, moderate bioavailability (0.55), and synthetic accessibility scores from 3.78 to 4.76. Docking studies with proteins 4MAN and 1HNJ highlight strong interactions for 2g, 2f, and 5f, with molecule 3g showing the best binding for 4MAN (− 52.13 kcal/mol) and 5f for 1HNJ (− 38.63 kcal/mol). These results identify 3g and 5f as promising candidates for targeted cancer therapy.

Robust sparse smooth principal component analysis for face reconstruction and recognition

Existing Robust Sparse Principal Component Analysis (RSPCA) does not incorporate the two-dimensional spatial structure information of images. To address this issue, we introduce a smooth constraint that characterizes the spatial structure information of images into conventional RSPCA, generating a novel algorithm called Robust Sparse Smooth Principal Component Analysis (RSSPCA). The proposed RSSPCA achieves three key objectives simultaneously: robustness through L1-norm optimization, sparsity for feature selection, and smoothness for preserving spatial relationships. Within the Minorization-Maximization (MM) framework, an iterative process is designed to solve the RSSPCA optimization problem, ensuring that a locally optimal solution is achieved. To evaluate the face reconstruction and recognition performance of the proposed algorithm, we conducted comprehensive experiments on six benchmark face databases. Experimental results demonstrate that incorporating robustness and smoothness improves reconstruction performance, while incorporating sparsity and smoothness improves classification performance. Consequently, the proposed RSSPCA algorithm generally outperforms existing algorithms in face reconstruction and recognition. Additionally, visualization of the generalized eigenfaces provides intuitive insights into how sparse and smooth constraints influence the feature extraction process. The data and source code from this study have been made publicly available on the GitHub repository: https://github.com/yuzhounh/RSSPCA.

The influence of habitual tooth brushing frequency on individuals diagnosed with coronary artery disease

Cu(I)-catalysed chemo-, regio-, and stereoselective radical 1,2-carboalkynylation with two different terminal alkynes

Analyzing Riemann-Liouville constraints in second-order Lagrangian fractional electrodynamic models

This study used second-order fractional derivatives to constrain singular Lagrangians to construct comprehensive Hamilton-Dirac equations. Notable contributions include resolving the difficulties associated with fractional derivatives. This modeling methodology efficiently covers non-local and non-differentiable fractional derivatives, giving a systematic strategy for dealing with modeling complexity. It establishes fractional equations that connect Coulomb’s law to the principle of superposition. Furthermore, we extended the Hamilton-Jacobi formalism by incorporating second-order derivatives in the context of Podolsky’s electrodynamics. This approach provides a solution for overcoming limitations in singular Lagrangians by linking the principles of Lagrangian fractional electrodynamics and classical field theory. The novelty of this work lies in its methodological approach to resolving the challenges of second-order fractional derivatives, particularly with respect to non-locality and memory effects, which have not been adequately addressed in previous models. This research offers new insights into expanding classical field theory using fractional calculus, opening new avenues for understanding interactions in electrodynamic systems. The results suggest that fractional formulations broaden the boundaries of traditional theories, providing a framework that encompasses a wider range of dynamic behaviors and advancing the understanding of fractional electrodynamics beyond previous studies. Furthermore, this approach paves the way for future research into fractional special relativity theories.

A qualitative analysis of Iranian consumers’ perspectives on improving sausage quality

Late Paleolithic whale bone tools reveal human and whale ecology in the Bay of Biscay

Abstract Reconstructing how prehistoric humans used the products obtained from large cetaceans is challenging, but key to understand the history of early human coastal adaptations. Here we report the multiproxy analysis (ZooMS, radiocarbon, stable isotopes) of worked objects made of whale bone, and unworked whale bone fragments, found at Upper Paleolithic sites (Magdalenian) around the Bay of Biscay. Taxonomic identification using ZooMS reveals at least five species of large whales, expanding the range of known taxa whose products were utilized by humans in this period. Radiocarbon places the use of whale products ca. 20–14 ka cal BP, with a maximum diffusion and diversity at 17.5–16 ka cal BP, making it the oldest evidence of whale-bone working to our knowledge. δ13C and δ15N stable isotope values reflect taxon-specific differences in foraging behavior. The diversity and chronology of these cetacean populations attest to the richness of the marine ecosystem of the Bay of Biscay in the late Paleolithic, broadening our understanding of coastal adaptations at that time.

Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm

Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases—Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew—and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.

A robust multi-scale clustering framework for single-cell RNA-seq data analysis

The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia xenografted into a mouse model of Alzheimer’s disease

Abstract Microglia play a key role in the response to amyloid beta in Alzheimer’s disease (AD). In this context, the major transcriptional response of microglia is the upregulation of APOE, the strongest late-onset AD risk gene. Of its three isoforms, APOE2 is thought to be protective, while APOE4 increases AD risk. We hypothesised that the isoforms change gene regulatory patterns that link back to biological function by shaping microglial transcriptomic and chromatin landscapes. We use RNA- and ATAC-sequencing to profile gene expression and chromatin accessibility of human microglia xenotransplantated into the brains of male APP NL-G-F mice. We identify widespread transcriptomic and epigenomic differences which are dependent on APOE genotype and are corroborated across the profiling assays. Our results indicate that impaired microglial proliferation, migration and immune responses may contribute to the increased risk for late-onset AD in APOE4 carriers, while increased phagocytic capabilities and DNA-binding of the vitamin D receptor in APOE2 microglia may contribute to the isoform’s protective role.

Post-abortion contraceptive use among currently married women in India: New evidence from National Family Health Survey 2019–2021 (NFHS-5)

Post-abortion contraceptive use is a critical element of reproductive healthcare aimed at preventing unintended pregnancies and promoting reproductive agency. This study investigates changes in post-abortion contraceptive use and factors associated with that use. We use reproductive calendars implemented in 2015–16 and 2019–21 National Family Health Surveys (NFHS) to investigate changes in post-abortion contraceptive use among currently married women age 15–49 in India. We then use 2019–21 NFHS to examine the factors associated with post-abortion contraceptive use. Our analysis is based on a weighted sample of 5,473 women from NFHS-4 and 5,103 women from NFHS-5. The study employs a two-stage estimation procedure using the Inverse Mills Ratio (IMR) framework to address potential biases in abortion reporting. In the second stage, we used a multinomial probit regression model to assess factors influencing post-abortion contraceptive use. Post-abortion contraceptive use increased from 49% in NFHS-4 to 57% in NFHS-5. Multinomial probit regression analysis revealed that gestational age of abortion was negatively associated with post-abortion contraceptive use, while factors such as having a son or prior contraceptive use increased the likelihood. Women who had abortions in private or non-health facilities were less likely to use post-abortion Long-Acting Reversible Contraceptives (LARC), compared to public health facilities. Those who reported unplanned pregnancy or contraceptive failure as the reason for abortion were more likely to use traditional methods of post-abortion contraception. Our findings highlight the importance of integrating family planning services into abortion care and ensuring comprehensive information and counselling on contraceptive options during the post-abortion period, as crucial measures to improve women’s health.