Remediation effect of walnut shell biochar on Cu and Pb co-contaminated soils in different utilization types.

Biochar, with its dual roles of soil remediation and carbon sequestration, is gradually demonstrating great potential for sustainability in agricultural and ecological aspects. In this study, a porous biochar derived from walnut shell wastes was prepared via a facile pyrolysis coupling with in-situ alkali etching method. An incubation study was conducted to investigate its performance in stabilizing copper (Cu) and lead (Pb) co-contaminated soils under different utilization types. The biochar effectively decreased the bioavailable Cu (8.5-91.68%) and Pb (5.03-88.54%), while increasing the pH, CEC, and SOM contents in both soils. Additionally, the results of sequential extraction confirmed that biochar promoted the transformation of the labile fraction of Cu and Pb to stable fractions. The mechanisms of Cu and Pb stabilization were found to be greatly dependent on the soil types. For tea plantation yellow soil, the main approach for stabilization was the complexation of heavy metals with abundant organic functional groups and deprotonation structure. Surface electrostatic adsorption and cation exchange contributed to the immobilization of Cu and Pb in vegetable-cultivated purple soil. This research provides valuable information for the stabilization of Cu and Pb co-contaminated soils for different utilization types using environmentally-friendly biochar.

G protein subunit Gγ13-mediated signaling pathway is critical to the inflammation resolution and functional recovery of severely injured lungs.

Tuft cells are a group of rare epithelial cells that can detect pathogenic microbes and parasites. Many of these cells express signaling proteins initially found in taste buds. It is, however, not well understood how these taste signaling proteins contribute to the response to the invading pathogens or to the recovery of injured tissues. In this study, we conditionally nullified the signaling G protein subunit Gγ13 and found that the number of ectopic tuft cells in the injured lung was reduced following the infection of the influenza virus H1N1. Furthermore, the infected mutant mice exhibited significantly larger areas of lung injury, increased macrophage infiltration, severer pulmonary epithelial leakage, augmented pyroptosis and cell death, greater bodyweight loss, slower recovery, worsened fibrosis and increased fatality. Our data demonstrate that the Gγ13-mediated signal transduction pathway is critical to tuft cells-mediated inflammation resolution and functional repair of the damaged lungs.To our best knowledge, it is the first report indicating subtype-specific contributions of tuft cells to the resolution and recovery.

Regulating Interfacial Microenvironment in Aqueous Electrolyte via a N2 Filtering Membrane for Efficient Electrochemical Ammonia Synthesis.

Electrochemical synthesis of ammonia (NH3) in aqueous electrolyte has long been suffered from poor nitrogen (N2) supply owing to its low solubility and sluggish diffusion kinetics. Therefore, creating a N2 rich microenvironment around catalyst surface may potentially improve the efficiency of nitrogen reduction reaction (NRR). Herein, a delicately designed N2 filtering membrane consisted of polydimethylsiloxane is covered on catalyst surface via superspreading. Because this membrane let the dissolved N2 molecules be accessible to the catalyst but block excess water, the designed N2 rich microenvironment over catalyst leads to an optimized Faradaic efficiency of 39.4% and an NH3 yield rate of 109.2 µg h-1 mg-1, which is superior to those of the most report metal-based catalysts for electrochemical NRR. This study offers alternative strategy for enhancing NRR performance.

A Highly Sensitive and Selective Fluorescent Sensor for Folic Acid Detection Based on D-penicillamine Stabilized Ag/Cu Alloy Nanoclusters.

In this work, a highly sensitive and selective method for detecting folic acid (FA) was developed using D-penicillamine (DPA) stabilized Ag/Cu alloy nanoclusters (DPA@Ag/Cu NCs). The yellow emission of DPA@Ag/Cu NCs was found to be quenched upon the addition of FA to the system. The fluorescence intensity quenching value demonstrated a linear relationship with FA concentrations ranging from 0.01 to 1200 µM, with a limit of detection (LOD) of 5.3 nM. Furthermore, the detection mechanism was investigated through various characterization analyses, including high resolution transmission electron microscopy, fluorescence spectra, ultraviolet-visible absorption spectra, and fluorescence lifetime. The results indicated that the fluorescence quenching induced by FA was a result of electron transfer from FA to the ligands of DPA@Ag/Cu NCs. The selectivity of the FA sensor was also evaluated, showing that common amino acids and inorganic ions had minimal impact on the detection of FA. Moreover, the standard addition method was successfully applied to detect FA in human serum, chewable tablets and FA tablets with promising results. The use of DPA@Ag/Cu NCs demonstrates significant potential for detecting FA in complex biological samples.

Optimization Design of CMUT Sensors with Broadband and High Sensitivity Characteristics Based on the Genetic Algorithm.

In this study, we propose a method for optimizing the design of CMUT sensors using genetic algorithms. Existing CMUT sensors face frequency response and sensitivity limitations, necessitating optimization to enhance their sensing performance. Traditional optimization methods are often intricate and time-consuming and may fail to yield the optimal solution. Genetic algorithms, which simulate the biological evolution process, offer advantages in global optimization and efficiency, making them widely utilized in the optimization design of Microelectromechanical Systems (MEMS) devices. Based on the theoretical framework and finite element model of CMUT sensors, we propose a CMUT array element optimization design method based on genetic algorithms. The optimization and validation results demonstrate that we have successfully designed a broadband CMUT array element consisting of four microelements with a 1-2 MHz frequency range. Compared with a randomly arranged array element, the optimized array shows a 63.9% increase in bandwidth and a 7.5% increase in average sensitivity within the passband. Moreover, the sensitivity variance within the passband is reduced by 50.2%. Our proposed method effectively optimizes the design of high sensitivity CMUT sensors with the desired bandwidth, thereby offering significant reference value for the optimization design of CMUT sensors.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases.

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.

OBJECTIVES: Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. METHODS: A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. RESULTS: The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. CONCLUSIONS: Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

C(sp)-C(sp) Lever-Based Targets of Orientational Chirality: Design and Asymmetric Synthesis.

In this study, the design and asymmetric synthesis of a series of chiral targets of orientational chirality were conducted by taking advantage of N-sulfinylimine-assisted nucleophilic addition and modified Sonogashira catalytic coupling systems. Orientational isomers were controlled completely using alkynyl/alkynyl levers [C(sp)-C(sp) axis] with absolute configuration assignment determined by X-ray structural analysis. The key structural element of the resulting orientational chirality is uniquely characterized by remote through-space blocking. Forty examples of multi-step synthesis were performed, with modest to good yields and excellent orientational selectivity. Several chiral orientational amino targets are attached with scaffolds of natural and medicinal products, showing potential pharmaceutical and medical applications in the future.

Heterogeneous Oxidase-Type Catalysis for H2 Generation at Low Temperatures.

The long-term objective in the field of heterogeneous catalysis is to develop an enzyme-like catalytic pathway that can achieve exceptional catalytic performance even at low temperatures. Herein, we have demonstrated a heterogeneous oxidase-type catalysis on the ZnO-supported Ru clusters (Ru/ZnO) for efficient H2 generation from an aqueous solution of formaldehyde (HCHO) at low temperatures. Due to its unique reaction pathway, the Ru/ZnO catalysts exhibited a temperature-insensitive activity for H2 generation at the temperature of 15 to 45 °C. Remarkably, even at a low temperature of 5 °C, the Ru/ZnO catalysts still enabled an H2 generation rate of 13.8 mmol gcat-1 h-1 with a turnover frequency (TOF) of 1678 h-1. Additionally, instead of producing a CO2/CO molecule, the HCHO molecule underwent a transformation into formic acid and/or formate as the byproduct. This finding presents a novel class of heterogeneous catalysts to expand the potential application scenarios of liquid hydrogen storage and transportation systems.

Semisynthesis and biological evaluation of novel honokiol thioethers against colon cancer cells HCT116 via inhibiting the transcription and expression of YAP protein.

Honokiol, derived from Magnolia officinalis (a traditional Chinese medicine), has been reported to have anticancer activity. Here, a series of novel honokiol thioethers bearing a 1,3,4-oxadiazole moiety were prepared and evaluated for their anticancer activities against three types of digestive system tumor cells. Biological evaluation showed that honokiol derivative 3k exhibited the best antiproliferative activity against HCT116 cells with an IC50 value of 6.1 µmol/L, superior to the reference drug 5-fluorouracil (IC50: 9.63 ± 0.27 µmol/L). The structure-activity relationships (SARs) indicated that the introduction of -(4-NO2)Ph, 3-pyridyl, -(2-F)Ph, -(4-F)Ph, -(3-F)Ph, -(4-Cl)Ph, and -(3-Cl)Ph groups was favorable for enhancing the anticancer activity of the title honokiol thioethers. Further study revealed that honokiol thioether 3k can well inhibit the proliferation of colon cancer cells HCT116, arresting the cells in G1 phase and inducing cell death. Moreover, a preliminary mechanism study indicated that 3k directly inhibits the transcription and expression of YAP protein without activating the Hippo signaling pathway. Thus, honokiol thioether 3k could be deeply developed for the development of honokiol-based anticancer candidates.

Effects of improved amino acid balance diet on lysine mammary utilization, whole body protein turnover and muscle protein breakdown on lactating sows.

BACKGROUND: The study objective was to test the hypothesis that low crude protein (CP) diet with crystalline amino acids (CAA) supplementation improves Lys utilization efficiency for milk production and reduces protein turnover and muscle protein breakdown. Eighteen lactating multiparous Yorkshire sows were allotted to 1 of 2 isocaloric diets (10.80 MJ/kg net energy): control (CON; 19.24% CP) and reduced CP with "optimal" AA profile (OPT; 14.00% CP). Sow body weight and backfat were recorded on d 1 and 21 of lactation and piglets were weighed on d 1, 14, 18, and 21 of lactation. Between d 14 and 18, a subset of 9 sows (CON = 4, OPT = 5) was infused with a mixed solution of 3-[methyl-2H3]histidine (bolus injection) and [13C]bicarbonate (priming dose) first, then a constant 2-h [13C]bicarbonate infusion followed by a 6-h primed constant [1-13C]lysine infusion. Serial blood and milk sampling were performed to determine plasma and milk Lys enrichment, Lys oxidation rate, whole body protein turnover, and muscle protein breakdown. RESULTS: Over the 21-d lactation period, compared to CON, sows fed OPT had greater litter growth rate (P < 0.05). Compared to CON, sows fed OPT had greater efficiency of Lys (P < 0.05), Lys mammary flux (P < 0.01) and whole-body protein turnover efficiency (P < 0.05). Compared to CON, sows fed OPT tended to have lower whole body protein breakdown rate (P = 0.069). Muscle protein breakdown rate did not differ between OPT and CON (P = 0.197). CONCLUSION: Feeding an improved AA balance diet increased efficiency of Lys and reduced whole-body protein turnover and protein breakdown. These results imply that the lower maternal N retention observed in lactating sows fed improved AA balance diets in previous studies may be a result of greater partitioning of AA towards milk rather than greater body protein breakdown.

Finite-Time Consensus Adaptive Neural Network Control for Nonlinear Multiagent Systems Under PDE Models.

In this article, a novel adaptive control method based on neural networks is proposed for a class of multiagent systems (MASs) with nonlinear functions and external disturbances. First, the approximation properties of neural networks are used to approximate the MAS partial differential equation (PDE) model with nonlinear terms containing two variables, time t, and spatial variable x. Second, an adaptive controller is constructed to actuate the parabolic MAS to reach consensus under external disturbances. Based on this, the finite-time theorem and special inequalities are applied to prove the stability of the closed-loop system. Thus, MAS that have nonlinear functions and external disturbances are enabled with finite-time consensus. Finally, the effectiveness of the proposed control method is demonstrated by numerical simulations.

New multiple-layered 3D polymers showing aggregation-induced emission and polarization.

An exceptional achiral and chiral multilayer 3D polymer has been created and controlled by uniform and distinct aromatic chromophore units that are multiply sandwiched by naphthyl berths. In order to put together this assembly, it was necessary to search for new catalytic Suzuki-Miyaura polycouplings among various catalytic systems, monomers, and catalysts. Gel Permeation Chromatography (GPC) was able to verify the presence of many framework layers. The resulting achiral and chiral polymers displayed notable optical characteristic.

KIF11 UFMylation Maintains Photoreceptor Cilium Integrity and Retinal Homeostasis.

The photoreceptor cilium is vital for maintaining the structure and function of the retina. However, the molecular mechanisms underlying the photoreceptor cilium integrity and retinal homeostasis are largely unknown. Herein, it is shown that kinesin family member 11 (KIF11) localizes at the transition zone (connecting cilium) of the photoreceptor and plays a crucial role in orchestrating the cilium integrity. KIF11 depletion causes malformations of both the photoreceptor ciliary axoneme and membranous discs, resulting in photoreceptor degeneration and the accumulation of drusen-like deposits throughout the retina. Mechanistic studies show that the stability of KIF11 is regulated by an interplay between its UFMylation and ubiquitination; UFMylation of KIF11 at lysine 953 inhibits its ubiquitination by synoviolin 1 and thereby prevents its proteasomal degradation. The lysine 953-to-arginine mutant of KIF11 is more stable than wild-type KIF11 and also more effective in reversing the ciliary and retinal defects induced by KIF11 depletion. These findings identify a critical role for KIF11 UFMylation in the maintenance of photoreceptor cilium integrity and retinal homeostasis.

A Comparative Study of Removal of Acid Red 27 by Adsorption on Four Different Chitosan Morphologies.

To investigate the relationship between structures and adsorption properties, four different morphologies of chitosan, with hydrogel (CSH), aerogel (CSA), powder (CSP), and electrospinning nanofiber (CSEN) characteristics, were employed as adsorbents for the removal of Acid Red 27. The structures and morphologies of the four chitosan adsorbents were characterized with SEM, XRD, ATR-FTIR, and BET methods. The adsorption behaviors and mechanisms of the four chitosan adsorbents were comparatively studied. All adsorption behaviors exhibited a good fit with the pseudo-second-order kinetic model (R2 > 0.99) and Langmuir isotherm model (R2 > 0.99). Comparing the adsorption rates and the maximum adsorption capacities, the order was CSH > CSA > CSP > CSEN. The maximum adsorption capacities of CSH, CSA, CSP, and CSEN were 2732.2 (4.523), 676.7 (1.119), 534.8 (0.885), and 215.5 (0.357) mg/g (mmol/g) at 20 °C, respectively. The crystallinities of CSH, CSA, CSP, and CSEN were calculated as 0.41%, 6.97%, 8.76%, and 39.77%, respectively. The crystallinity of the four chitosan adsorbents was the main factor impacting the adsorption rates and adsorption capacities, compared with the specific surface area. With the decrease in crystallinity, the adsorption rates and capacities of the four chitosan adsorbents increased gradually under the same experimental conditions. CSH with a low crystallinity and large specific surface area resulted in the highest adsorption rate and capacity.

Pyrrolic Nitrogen Boosted H2 Generation from an Aqueous Solution of HCHO at Room Temperature by Metal-Free Carbon Catalysts.

Hydrogen production from organic hydrides represents a promising strategy for the development of safe and sustainable technologies for H2 storage and transportation. Nonetheless, the majority of existing procedures rely on noble metal catalysts and emit greenhouse gases such as CO2/CO. Herein, we demonstrated an alternative N-doped carbon (CN) catalyst for highly efficient and robust H2 production from an aqueous solution of formaldehyde (HCHO). Importantly, this process generated formic acid as a valuable byproduct instead of CO2/CO, enabling a clean H2 generation process with 100% atom economy. Mechanism investigations revealed that the pyrrolic N in the CN catalysts played a critical role in promoting H2 generation via enhancing the transformation of O2 to generate •OO- free radicals. Consequently, the optimized CN catalysts achieved a remarkable H2 generation rate of 13.6 mmol g-1 h-1 at 30 °C. This finding is anticipated to facilitate the development of liquid H2 storage and its large-scale utilization.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.

Dopamine promotes osteogenic differentiation of PDLSCs by activating DRD1 and DRD2 during orthodontic tooth movement via ERK1/2 signaling pathway.

Introduction: Orthodontic tooth movement (OTM) involves complex interactions between mechanical forces and periodontal tissue adaptation, mainly mediated by periodontal ligament cells, including periodontal ligament stem cells (PDLSCs), osteoblasts, and osteoclasts. Dopamine (DA), a neurotransmitter known for its critical role in bone metabolism, is investigated in this study for its potential to enhance osteogenic differentiation in PDLSCs, which are pivotal in OTM. This study examined the potential of DA to facilitate OTM by binding to DA receptors (D1R and D2R) and activating the ERK1/2 signaling pathway. We propose that DA's interaction with these receptors on PDLSCs could enhance osteogenic differentiation, thereby accelerating bone remodeling and reducing the duration of orthodontic treatments, which offering a novel approach to improve clinical outcomes in orthodontic care. Methods: This study utilized a rat OTM model, micro-CT, histological analyses, and in vitro assays to investigate dopamine's effect on osteogenesis. PDLSCs were cultured and treated with DA, and cytotoxicity, osteogenic differentiation, gene and protein expression assessed. Results: Dopamine administration significantly increased trabecular bone density and osteogenic marker expression in an OTM rat model. In vitro, DA at 10 nM optimally promoted human PDLSCs osteogenesis without affecting proliferation. Blocking DA receptors or inhibiting the ERK1/2 pathway attenuated these effects, underscoring the importance of dopaminergic signaling in tension-induced osteogenesis during OTM. Conclusion: Taken together, our study reveals that local dopamine administration at a concentration of 10 nM not only enhances tension-induced osteogenesis in vivo but also significantly promotes osteogenic differentiation of PDLSCs in vitro through D1 and D2 receptor-mediated ERK1/2 signaling pathway activation.

Relation-Aware Heterogeneous Graph Network for Learning Intermodal Semantics in Textbook Question Answering.

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently. First, we design a multidomain consistent representation (MDCR) to eliminate semantic gaps by capturing intermodal features while maintaining lossless intramodal semantics in multidomains. Furthermore, we present neighbor-based relation inpainting (NRI) to reduce semantic ambiguity via repairing fuzzy relations with correlations of relations. Finally, we propose hierarchical multisemantics aggregation (HMSA) to guarantee the completeness of semantics by aggregating features of nodes and relations with a reconstruction network (RN). Experimental results show that IMR-HGN could extract the intermodal semantics of answers, achieving a 2.16% improvement on the validation set of the TQA dataset and a 3.04% increase on the test set of the AI2D dataset.