Bidirectional interaction between IL and 17A/IL-17RA pathway dysregulation and α-synuclein in the pathogenesis of Parkinson's disease.

Parkinson's disease (PD) pathogenesis is characterized by α-synuclein (α-syn) pathology, which is influenced by various factors such as neuroinflammation and senescence. Increasing evidence has suggested a pivotal role for Interleukin-17A(IL-17A) and Interleukin-17 Receptor A (IL-17RA) in PD, yet the trigger and impact of IL-17A/IL-17RA activation in PD remains elusive. This study observed an age-related increase in IL-17A and IL-17RA in the human central nervous system, accompanied by increased α-syn and senescence biomarkers. Interestingly, both levels of IL-17A and IL-17RA in PD patients were significantly elevated compared to age-matched controls, wherein the IL-17A was mainly present in neurons. This abnormal neuronal IL-17A activation in the PD brain was recapitulated in α-syn mouse models. Correspondingly, administration of recombinant IL-17A exacerbated pathological α-syn in both neuron and mouse models. Furthermore, IL-17A/IL-17RA pathway interventions via blocking antibody or shRNA-mediated knockdown can mitigate the effects of pathological α-syn. This study reveals an interplay between dysregulation of the IL-17A/IL-17RA pathway and α-syn, suggesting that regulating the IL-17A/IL-17RA pathway could modify PD progression by disrupting the detrimental cycle.

Cascade Conversion of CO2 to Ethylene Carbonate under Ambient Conditions.

Ethylene carbonate (EC) is the simplest cyclic carbonate with great industrial significance, most importantly as the vital electrolyte component for lithium-ion batteries. Its conventional synthesis generally involves the use of toxic precursors and requires elevated temperatures and pressures. Herein, we propose a cascade catalytic route for converting CO2 to EC under ambient conditions. Such a hybrid reaction scheme consists of the electrochemical reduction of CO2 to ethylene catalyzed by copper in a membrane electrode assembly reactor, the bromine-mediated conversion of ethylene to bromoethanol catalyzed by WO3 nanoarrays grown on carbon cloth, and the reaction between bromoethanol and CO2 to form EC. By separately optimizing individual catalytic steps and then integrating them together in series, we achieved the conversion of CO2 to EC at a good yield under room temperature and atmospheric pressure. Our study also represents the first demonstration about the successful synthesis of organic carbonates from CO2 as the exclusive carbon source.

Integration of texture analysis based on DCE-MRI Ktrans map and metabolomics of early bone marrow microvascular changes in alloxan-induced diabetic rabbits.

OBJECTIVE: To evaluate early bone marrow microvascular changes in alloxan-induced diabetic rabbits using IDEAL-IQ fat quantification, texture analysis based on DCE-MRI Ktrans map, and metabolomics. MATERIALS AND METHODS: 24 male Japanese rabbits were randomly divided into diabetic (n = 12) and control (n = 12) groups. All rabbits underwent sagittal MRI of the lumbar vertebrae at the 0th,4th, 8th, 12th, and 16th week, respectively. The fat fraction (FF) ratio and quantitative permeability of the lumbar bone marrow was measured. Texture parameters were extracted from DCE-MRI Ktrans map. At 16th week, lumbar vertebrae 5 and 6 were used for histological analysis. Lumbar vertebra 7 was crushed to obtain bone marrow for metabolomics research. RESULTS: The FF ratio and Ktrans of the lumbar bone marrow in diabetic group were increased significantly at 16th week (t = 2.226, P = 0.02; Z = -2.721, P < 0.01). Nine texture feature parameters based on DCE-MRI Ktrans map were significantly different between the groups at the 16th week (all P < 0.05). Pathway analysis showed that diabetic bone marrow microvascular changes were mainly related to linoleic acid metabolism. Differential metabolites were correlated with the number of adipocytes, FF ratio, and permeability parameters. CONCLUSION: The integration of metabolomics with texture analysis based on DCE-MRI Ktrans map may be used to evaluate diabetic bone marrow microvascular changes at an early stage. It remains to be validated in clinical studies whether the integration of metabolomics with texture analysis based on the DCE-MRI Ktrans map can effectively evaluate diabetic bone marrow.

Perfluoroalkyl-modified covalent organic frameworks for continuous photocatalytic hydrogen peroxide synthesis and extraction in a biphasic fluid system.

H2O2 photosynthesis represents an appealing approach for sustainable and decentralized H2O2 production. Unfortunately, current reactions are mostly carried out in laboratory-scale single-phase batch reactors, which have a limited H2O2 production rate (<100 µmol h-1) and cannot operate in an uninterrupted manner. Herein, we propose continuous H2O2 photosynthesis and extraction in a biphasic fluid system. A superhydrophobic covalent organic framework photocatalyst with perfluoroalkyl functionalization is rationally designed and prepared via the Schiff-base reaction. When applied in a home-built biphasic fluid photo-reactor, the superhydrophobicity of our photocatalyst allows its selective dispersion in the oil phase, while formed H2O2 is spontaneously extracted to the water phase. Through optimizing reaction parameters, we achieve continuous H2O2 photosynthesis and extraction with an unprecedented production rate of up to 968 µmol h-1 and tunable H2O2 concentrations from 2.2 to 38.1 mM. As-obtained H2O2 solution could satisfactorily meet the general demands of household disinfection and wastewater treatments.

Subendometrial blood flow detected by Doppler ultrasound associates with pregnancy outcomes of frozen embryo transfer in patients with thin endometrium.

PURPOSE: Multiple factors have been shown to influence the rate of clinical pregnancy after FET in IVF treatment, including embryo quality, synchronization of embryo and endometrium, and endometrial receptivity (ER). The subendometrial blood flow conditions could also contribute potentially major effects toward the establishment and maintenance of pregnancy. We conducted a retrospective cohort study to examine the correlation between subendometrial blood flow, as determined by Doppler ultrasound, and pregnancy outcomes in IVF patients with a thin endometrium (endometrium thickness [EMT] ≤ 0.7 cm). METHODS: This was a retrospective cohort study conducted at a university-affiliated reproductive hospital from January 2017 to April 2023. The EMT and subendometrial blood flows were assessed using transvaginal color Doppler ultrasound and evaluated by experienced clinical ultrasound physicians on the endometrial transformation day. The pregnancy outcomes were followed up and documented in clinical medical records through the IVF cohort study at our center. RESULTS: In the patients with 0.5 cm ≤ EMT ≤ 0.7 cm, the embryo implantation rate was statistically significant increased in the patients with the presence of subendometrial blood flow (OR 1.484; 95% CI, 1.001-2.200; P = 0.049; aOR 1.425; 95% CI, 1.030-2.123; P = 0.003). Patients with discernible subendometrial blood flow have superior live birth (P = 0.028), clinical pregnancy (P = 0.049), and embryo implantation (P = 0.027) compared to the patients without subendometrial blood flow when the EMT is ≤ 0.7 cm. CONCLUSIONS: The presence of subendometrial blood flow detected by ultrasound was positively associated with successful embryo implantation and favorable pregnancy outcomes in patients with thin endometrium undergoing FET.

Variation of the Tegmen and Cercus in Sinopodisma rostellocerca (Orthoptera: Acrididae: Melanoplinae) with Proposal of a New Synonym.

Intraspecific variation is ubiquitous from individual traits to population level and plays an important role in a variety of fields. However, it is often ignored by systematists and comparative evolutionary biologists. In view of the limited knowledge of intraspecific variation, morphology-based identification has hindered the recognition of species borders and led to a great number of problems in the field of taxonomy and systematics. In this study, the intraspecific variation of the tegmen and cercus in Sinopodisma rostellocerca was examined, the variation patterns were summarized and the relationship between S. rostellocerca and S. hengshanica was discussed. The results showed that the intraspecific variation in the tegmen and male cercus was mainly manifested in the length and shape of the apical margin and dorso- and ventro-apical angles; this substantial variation occurred not only among intrapopulation individuals but also between the different sides of the same individuals, and all types of variation in S. hengshanica fell into the range of variation in S. rostellocerca, leading to the disappearance of the boundary between the two species. Therefore, S. hengshanica was herein considered as a new junior synonym of S. rostellocerca.

Semi-TSGAN: Semi-Supervised Learning for Highlight Removal Based on Teacher-Student Generative Adversarial Network.

Despite recent notable advancements in highlight image restoration techniques, the dearth of annotated data and the lightweight deployment of highlight removal networks pose significant impediments to further advancements in the field. In this paper, to the best of our knowledge, we first propose a semi-supervised learning paradigm for highlight removal, merging the fusion version of a teacher-student model and a generative adversarial network, featuring a lightweight network architecture. Initially, we establish a dependable repository to house optimal predictions as pseudo ground truth through empirical analyses guided by the most reliable No-Reference Image Quality Assessment (NR-IQA) method. This method serves to assess rigorously the quality of model predictions. Subsequently, addressing concerns regarding confirmation bias, we integrate contrastive regularization into the framework to curtail the risk of overfitting on inaccurate labels. Finally, we introduce a comprehensive feature aggregation module and an extensive attention mechanism within the generative network, considering a balance between network performance and computational efficiency. Our experimental evaluations encompass comprehensive assessments on both full-reference and non-reference highlight benchmarks. The results demonstrate conclusively the substantive quantitative and qualitative enhancements achieved by our proposed algorithm in comparison to state-of-the-art methodologies.

Multi-mode adaptive control strategy for a lower limb rehabilitation robot.

Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients' motion intention and motor ability are evaluated based on the average human-robot interaction force per task cycle. Second, three kinds of rehabilitation training modes-robot-dominant, patient-dominant, and safety-stop-are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient's motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

BACKGROUND: The presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGEO and LGEDL, respectively) and evaluate optimal quantification parameters to enhance diagnosis and management of suspected patients with UMI. METHODS: This prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGEO and LGEDL images were obtained using conventional and commercially available inline DLR algorithms. The myocardial signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and percentage of enhanced area (Parea) employing the signal threshold versus reference mean (STRM) approach, which correlates the signal intensity (SI) within areas of interest with the average SI of normal regions, were analyzed. Analysis was performed using the standard deviation (SD) threshold approach (2SD-5SD) and full width at half maximum (FWHM) method. The diagnostic efficacies based on LGEDL and LGEO images were calculated. RESULTS: The SNRDL and CNRDL were two times better than the SNRO and CNRO, respectively (P < 0.05). Parea-DL was elevated compared to Parea-O using the threshold methods (P < 0.05); however, no intergroup difference was found based on the FWHM method (P > 0.05). The Parea-DL and Parea-O also differed except between the 2SD and 3SD and the 4SD/5SD and FWHM methods (P < 0.05). The receiver operating characteristic curve analysis revealed that each SD method exhibited good diagnostic efficacy for detecting UMI, with the Parea-DL having the best diagnostic efficacy based on the 5SD method (P < 0.05). Overall, the LGEDL images had better image quality. Strong diagnostic efficacy for UMI identification was achieved when the STRM was ≥ 4SD and ≥ 3SD for the LGEDL and LGEO, respectively. CONCLUSIONS: STRM selection for LGEDL magnetic resonance images helps improve clinical decision-making in patients with UMI. This study underscored the importance of STRM selection for analyzing LGEDL images to enhance diagnostic accuracy and clinical decision-making for patients with UMI, further providing better cardiovascular care.

The American Cherimoya Genome Reveals Insights into the Intra-Specific Divergence, the Evolution of Magnoliales, and a Putative Gene Cluster for Acetogenin Biosynthesis.

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya 'Booth' cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the 'Booth' cultivar has 200 Mb less repeats than the Spanish cultivar 'Fino de Jete', despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.

Microvascular permeability and texture analysis of bone marrow in diabetic rabbits with critical limb ischemia based on dynamic contrast-enhanced magnetic resonance imaging.

BACKGROUND: Early on in the development of diabetes, skeletal muscles can exhibit microarchitectural changes that can be detected using texture analysis (TA) based on volume transfer constant (Ktrans) maps. Nevertheless, there have been few studies and thus we evaluated microvascular permeability and the TA of the bone marrow in diabetics with critical limb ischemia (CLI). METHODS: Eighteen male rabbits were randomly assigned equally into an operation group with hindlimb ischemia and diabetes, a sham-operated group with diabetes only, and a control group. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) was performed on all rabbits at predetermined intervals (1, 5, 10, 15, 20, and 25 days post-surgery). The pharmacokinetic model was used to generate the permeability parameters, while the textural parameters were derived from the Ktrans map. Data analysis methods included the independent sample t-test, Mann-Whitney U test, repeated-measures analysis of variance, and Pearson correlation tests. RESULTS: The Ktrans values reached a minimum on day 1 after ischemia induction, then gradually recovered, but remained lower than those of the sham-operated group. The volume fraction only showed a significant difference between the operation group and the sham-operated group on day 5 post-surgery, but not in the extravascular extracellular space volume fraction at all time points. A significantly reduced Ktrans on day 1, a decreased number of bone trabeculae (Tb.N), and the area of bone trabeculae (Tb.Ar), and an increased microvessel density on day 25 in the operation group compared with the sham-operated group were observed. At each time point, there was a discernible difference between the two groups in the mean value, mean of positive pixels, and sumAverage. CONCLUSIONS: The early stages of diabetic bone marrow with CLI can be evaluated by DCE-MRI for microvascular permeability. Texture analysis based on DCE-MRI could act as an imaging discriminator and new radiological analysis tool for critical limb ischemia in diabetes mellitus.

Immobilized Tetraalkylammonium Cations Enable Metal-free CO2 Electroreduction in Acid and Pure Water.

Carbon dioxide reduction reaction (CO2 RR) provides an efficient pathway to convert CO2 into desirable products, yet its commercialization is greatly hindered by the huge energy cost due to CO2 loss and regeneration. Performing CO2 RR under acidic conditions containing alkali cations can potentially address the issue, but still causes (bi)carbonate deposition at high current densities, compromising product Faradaic efficiencies (FEs) in present-day acid-fed membrane electrode assemblies. Herein, we present a strategy using a positively charged polyelectrolyte-poly(diallyldimethylammonium) immobilized on graphene oxide via electrostatic interactions to displace alkali cations. This enables a FE of 85 %, a carbon efficiency of 93 %, and an energy efficiency (EE) of 35 % for CO at 100 mA cm-2 on modified Ag catalysts in acid. In a pure-water-fed reactor, we obtained a 78 % CO FE with a 30 % EE at 100 mA cm-2 at 40 °C. All the performance metrics are comparable to or even exceed those attained in the presence of alkali metal cations.

Carbohydrate-active enzyme annotation in microbiomes using dbCAN.

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes ∼33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

Cascade Electrocatalytic and Thermocatalytic Reduction of CO2 to Propionaldehyde.

Electrochemical CO2 reduction can convert CO2 to value-added chemicals, but its selectivity toward C3+ products are very limited. One possible solution is to run the reactions in hybrid processes by coupling electrocatalysis with other catalytic routes. In this contribution, we report the cascade electrocatalytic and thermocatalytic reduction of CO2 to propionaldehyde. Using Cu(OH)2 nanowires as the precatalyst, CO2 /H2 O is reduced to concentrated C2 H4 , CO, and H2 gases in a zero-gap membrane electrode assembly (MEA) reactor. The thermochemical hydroformylation reaction is separately investigated with a series of rhodium-phosphine complexes. The best candidate is identified to be the one with the 1,4-bis(diphenylphosphino)butane diphosphine ligand, which exhibits a propionaldehyde turnover number of 1148 under a mild temperature and close-to-atmospheric pressure. By coupling and optimizing the upstream CO2 electroreduction and downstream hydroformylation reaction, we achieve a propionaldehyde selectivity of ~38 % and a total C3 oxygenate selectivity of 44 % based on reduced CO2 . These values represent a more than seven times improvement over the best prior electrochemical system alone or over two times improvement over other hybrid systems.

dbAPIS: a database of anti-prokaryotic immune system genes.

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

X-ray-induced acoustic computed tomography and its applications in biomedicine.

Significance: X-ray-induced acoustic computed tomography (XACT) offers a promising approach to biomedical imaging, leveraging X-ray absorption contrast. It overcomes the shortages of traditional X-ray, allowing for more advanced medical imaging. Aim: The review focuses on the significance and draws onto the potential applications of XACT to demonstrate it as an innovative imaging technique. Approach: This review navigates the expanding landscape of XACT imaging within the biomedical sphere. Integral topics addressed encompass the refinement of imaging systems and the advancement in image reconstruction algorithms. The review particularly emphasizes XACT's significant biomedical applications. Results: Key uses, such as breast imaging, bone density maps for osteoporosis, and X-ray molecular imaging, are highlighted to demonstrate the capability of XACT. A unique niche for XACT imaging is its application in in vivo dosimetry during radiotherapy, which has been validated on patients. Conclusions: Because of its unique property, XACT has great potential in biomedicine and non-destructive testing. We conclude by casting light on potential future avenues in this promising domain.

Modeling and Analysis of Human Comfort in Human-Robot Collaboration.

The emergence and recent development of collaborative robots have introduced a safer and more efficient human-robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor-human comfort-has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human-robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC.

Lysosomal dysfunction in carbon black-induced lung disorders.

Carbon black (CB), a component of environmental particulate pollution derived from carbon sources, poses a significant threat to human health, particularly in the context of lung-related disease. This study aimed to investigate the detrimental effects of aggregated CB in the average micron scale on lung tissues and cells in vitro and in vivo. We observed that CB particles induced lung disorders characterized by enhanced expression of inflammation, necrosis, and fibrosis-related factors in vivo. In alveolar epithelial cells, CB exposure resulted in decreased cell viability, induction of cell death, and generation of reactive oxidative species, along with altered expression of proteins associated with lung disorders. Our findings suggested that the damaging effects of CB on the lung involved the targeting of lysosomes. Specifically, CB promoted lysosomal membrane permeabilization, while lysosomal alkalization mitigated the harmfulness of CB on lung cells. Additionally, we explored the protective effects of alkaloids derived from Nelumbinis plumula, with a focus on neferine, against CB-induced lung disorders. In conclusion, these findings contribute to a deeper understanding of the pathophysiological effects of CB particles on the lungs and propose a potential therapeutic approach for pollution-related diseases.