High-selective separation recovery of Ni, Co, and Mn from the spent LIBs via acid dissolution and multistage oxidation precipitation.

For recovering Ni, Co, and Mn from lithium-ion batteries, traditional chemical precipitation methods demonstrate low selectivity and significantly contribute to environmental pollution. This study proposes a separation recovery technique for transition metals, specifically Ni, Co, and Mn, from spent LIBs, involving "acid dissolution" and "multistage oxidation precipitation". More than 98% of transition metals can be extracted from spent LIBs using a low acid concentration (0.5 M) without reducing agents. The feasibility of separating different metals via multistage oxidation precipitation, based on their different electrode potentials for oxidizing Me2+ (Me = Mn/Co/Ni), was confirmed. The combination of oxidizing agent S2O82- and the precipitant OH- was universally applied to the fractional precipitation of Mn, Co, and Ni respectively. About 99% of Mn, 97.06% Co, and 96.62% Ni could be precipitated sequentially by changing the concentrations of S2O82- and the pH value of solution. XRD, XPS, XRF, ICP-MS and other methods were employed to elucidate the mechanism behind the multistage oxidation precipitation of target metal compounds, exploiting the differential electrode potentials for oxidizing Me2+ ions. This technique surpasses traditional solvent extraction in cost-effectiveness and selectivity, showing promise for large-scale industrial applications in recovering Mn, Co, and Ni.

Integration of proteomics and metabolomics analysis investigate mechanism of As-induced immune injury in rat spleen.

Arsenic (As) is a widespread metalloid and human carcinogen found in the natural environment, and multiple toxic effects have been shown to be associated with As exposure. As can be accumulated in the spleen, the largest peripheral lymphatic organ, and long-term exposure to As can lead to splenic injury. In this study, a Sprague-Dawley (SD) rat model of As-poisoned was established, aiming to explore the molecular mechanism of As-induced immune injury through the combined analysis of proteomics and metabolomics of rats' spleen. After feeding the rats with As diet (50 mg/kg) for 90 days, the spleen tissue of the rats in the As-poisoned group was damaged, the level of As was significantly higher than that of the control group (P < 0.001), and the level of inflammatory cytokine interleukin-6 (IL-6) was decreased (P < 0.01). Proteomics and metabolomics results showed that a total of 134 differentially expressed proteins (DEPs) (P < 0.05 and fold change > 1.2) and 182 differentially expressed metabolites (DEMs) (VIP >1 and P < 0.05) were identified in the spleens of the As poisoned group compared to the control group (As/Ctrl). The proteomic results highlight the role of hypoxia-inducible factors (HIF), natural killer cell mediated cytotoxicity, and ribosomes. The major pathways of metabolic disruption included arachidonic acid (AA) metabolism, glycerophospholipid metabolism and folate single-carbon pool. The integrated analysis of these two omics suggested that Hmox1, Stat3, arachidonic acid, phosphatidylcholine and leukotriene B4 may play key roles in the mechanism of immune injury to the spleen by As exposure. The results indicate that As exposure can cause spleen damage in rats. Through proteomic and metabolomic analysis, the key proteins and metabolites and their associated mechanisms were obtained, which provided a basis for further understanding of the molecular mechanism of spleen immune damage caused by As exposure.

Application of Proteomics and Machine Learning Methods to Study the Pathogenesis of Diabetic Nephropathy and Screen Urinary Biomarkers.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

Effects of arsenic exposure on blood trace element levels in rats and sex differences.

Arsenic (As) is a widespread environmental metalloid and human carcinogen, and its exposure is associated with a wide range of toxic effects, leading to serious health hazards. As poisoning is a complex systemic multi-organ and multi-system damage disease. In this study, a rat model of As poisoning was established to investigate the levels of trace elements in the blood of rats and sex differences in the effect of As on every trace elements in rat blood. Twenty 6-week-old SD (Sprague Dawley) rats were randomly divided into the control group and the As-exposed group. After 3 months, the contents of 19 elements including As in the blood were detected in these two groups by inductively coupled plasma mass spectrometry (ICP-MS). As levels in the blood of As-exposed rats were significantly higher than those in the control group, with increased levels of Rb, Sr, Cs and Ce, and decreased levels of Pd. As showed a significant positive correlation with Rb. There were significant sex differences in blood Se, Pd, Eu, Dy, Ho, and Au levels in the As-exposed group. The results showed that As exposure can lead to an increase of As content in blood and an imbalance of some elements. There were sex differences in the concentration and the correlation between elements of some elements. Elemental imbalances may affect the toxic effects of As and play a synergistic or antagonistic role in As toxicity.

Coaxial Electrospun Porous Core-Shell Nanofibrous Membranes for Photodegradation of Organic Dyes.

In this study, a series of AgCl/ZnO-loaded nanofibrous membranes were prepared using coaxial electrospinning. Their physical and chemical characteristics were evaluated by SEM, TEM, XRD, XPS, IR, PL, and UV-visible spectrometer, and the photocatalytic experiments using methylene blue (MB) as a model pollutant. The formation of AgCl/ZnO heterojunction and the structure of core-shell nanofibers with porous shell layer were confirmed. AgCl/ZnO photocatalysts were also effectively loaded onto the surfaces of the porous core-shell nanofibers. The results of photocatalytic experiments revealed that the AgCl/ZnO (MAgCl:MZnO = 5:5)-loaded nanofibrous membrane achieved a degradation efficiency of 98% in just 70 min and maintained a photocatalytic efficiency exceeding 95% over the first five experimental cycles, which successfully addressed the issues of photocatalytic efficiency loss during the photodegradation of MB with AgCl/ZnO nanoparticles as photocatalyst. The photodegradation mechanism was also researched and proposed.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy.

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Selective Recovery of Battery-Grade Li2CO3 from Spent NCM Cathode Materials Using a One-Step Method of CO2 Carbonation Recovery Without Acids or Bases.

The recovery of spent lithium-ion batteries by traditional acid leaching is limited by serious pollution, complicated technology, and the low purity of Li2CO3. To address the problems of the traditional acid leaching process and increasing demand for decarbonization, a technique for the selective carbonation leaching of Li and the recovery of battery-grade Li2CO3 by a simple concentration precipitation process without acids or bases was developed. The coupling of CO2 and reducing agents could effectively promote the precipitation of MCO3 (M=Ni/Co/Mn) and the selective leaching of Li by decreasing the reducing capability needed for transition metals and decreasing the pH of the solution. The optimal selective leaching process of Li was obtained under 1 MPa CO2 with 20 g/L Na2S2O3 at an L/S ratio of 30 mL/g for 1.5 h. FT-IR, XRD, ICP-MS and other methods were used to reveal the multiphase interfacial reaction mechanism of the carbonation reduction of layered cathode materials, which indicated that the reducing agent Na2S2O3 could promote lattice distortion of the cathode materials and effective separation of Li. In summary, a green and economical method for the selective recovery of battery-grade Li2CO3 using a one-step method of CO2 carbonation recovery in a near-neutral environment was proposed.

Research Advances in Superabsorbent Polymers.

Superabsorbent polymers are new functional polymeric materials that can absorb and retain liquids thousands of times their masses. This paper reviews the synthesis and modification methods of different superabsorbent polymers, summarizes the processing methods for different forms of superabsorbent polymers, and organizes the applications and research progress of superabsorbent polymers in industrial, agricultural, and biomedical industries. Synthetic polymers like polyacrylic acid, polyacrylamide, polyacrylonitrile, and polyvinyl alcohol exhibit superior water absorption properties compared to natural polymers such as cellulose, chitosan, and starch, but they also do not degrade easily. Consequently, it is often necessary to modify synthetic polymers or graft superabsorbent functional groups onto natural polymers, and then crosslink them to balance the properties of material. Compared to the widely used superabsorbent nanoparticles, research on superabsorbent fibers and gels is on the rise, and they are particularly notable in biomedical fields like drug delivery, wound dressing, and tissue engineering.

Application of Electrospun Drug-Loaded Nanofibers in Cancer Therapy.

In the 21st century, chemotherapy stands as a primary treatment method for prevalent diseases, yet drug resistance remains a pressing challenge. Utilizing electrospinning to support chemotherapy drugs offers sustained and controlled release methods in contrast to oral and implantable drug delivery modes, which enable localized treatment of distinct tumor types. Moreover, the core-sheath structure in electrospinning bears advantages in dual-drug loading: the core and sheath layers can carry different drugs, facilitating collaborative treatment to counter chemotherapy drug resistance. This approach minimizes patient discomfort associated with multiple-drug administration. Electrospun fibers not only transport drugs but can also integrate metal particles and targeted compounds, enabling combinations of chemotherapy with magnetic and heat therapies for comprehensive cancer treatment. This review delves into electrospinning preparation techniques and drug delivery methods tailored to various cancers, foreseeing their promising roles in cancer treatment.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Subcellular proteomics insights into Alzheimer's disease development.

Alzheimer's disease (AD), one of the most common dementias, is a neurodegenerative disease characterized by cognitive impairment and decreased judgment function. The expected number of AD patient is increasing in the context of the world's advancing medical care and increasing human life expectancy. Since current molecular mechanism studies on AD pathogenesis are incomplete, there is no specific and effective therapeutic agent. Mass spectrometry (MS)-based unbiased proteomics studies provide an effective and comprehensive approach. Many advances have been made in the study of the mechanism, diagnostic markers, and drug targets of AD using proteomics. This paper focus on subcellular level studies, reviews studies using proteomics to study AD-associated mitochondrial dysfunction, synaptic, and myelin damage, the protein composition of amyloid plaques (APs) and neurofibrillary tangles (NFTs), changes in tissue extracellular vehicles (EVs) and exosome proteome, and the protein changes in ribosomes and lysosomes. The methods of sample separation and preparation and proteomic analysis as well as the main findings of these studies are involved. The results of these proteomics studies provide insights into the pathogenesis of AD and provide theoretical resource and direction for future research in AD, helping to identify new biomarkers and drugs targets for AD.

Multi-Granularity Context Network for Efficient Video Semantic Segmentation.

Current video semantic segmentation tasks involve two main challenges: how to take full advantage of multi-frame context information, and how to improve computational efficiency. To tackle the two challenges simultaneously, we present a novel Multi-Granularity Context Network (MGCNet) by aggregating context information at multiple granularities in a more effective and efficient way. Our method first converts image features into semantic prototypes, and then conducts a non-local operation to aggregate the per-frame and short-term contexts jointly. An additional long-term context module is introduced to capture the video-level semantic information during training. By aggregating both local and global semantic information, a strong feature representation is obtained. The proposed pixel-to-prototype non-local operation requires less computational cost than traditional non-local ones, and is video-friendly since it reuses the semantic prototypes of previous frames. Moreover, we propose an uncertainty-aware and structural knowledge distillation strategy to boost the performance of our method. Experiments on Cityscapes and CamVid datasets with multiple backbones demonstrate that the proposed MGCNet outperforms other state-of-the-art methods with high speed and low latency.

Toosendanin Induces Hepatocyte Damage by Inhibiting Autophagic Flux via TFEB-Mediated Lysosomal Dysfunction.

Toosendanin (TSN) is a triterpenoid from the fruit or bark of Melia toosendan Sieb et Zucc, which has clear antitumor and insecticidal activities, but it possesses limiting hepatotoxicity in clinical application. Autophagy is a degradation and recycling mechanism to maintain cellular homeostasis, and it also plays an essential role in TSN-induced hepatotoxicity. Nevertheless, the specific mechanism of TSN on autophagy-related hepatotoxicity is still unknown. The hepatotoxicity of TSN in vivo and in vitro was explored in this study. It was found that TSN induced the upregulation of the autophagy-marker microtubule-associated proteins 1A/1B light chain 3B (LC3B) and P62, the accumulation of autolysosomes, and the inhibition of autophagic flux. The middle and late stages of autophagy were mainly studied. The data showed that TSN did not affect the fusion of autophagosomes and lysosomes but significantly inhibited the acidity, the degradation capacity of lysosomes, and the expression of hydrolase cathepsin B (CTSB). The activation of autophagy could alleviate TSN-induced hepatocyte damage. TSN inhibited the expression of transcription factor EB (TFEB), which is a key transcription factor for many genes of autophagy and lysosomes, such as CTSB, and overexpression of TFEB alleviated the autophagic flux blockade caused by TSN. In summary, TSN caused hepatotoxicity by inhibiting TFEB-lysosome-mediated autophagic flux and activating autophagy by rapamycin (Rapa), which could effectively alleviate TSN-induced hepatotoxicity, indicating that targeting autophagy is a new strategy to intervene in the hepatotoxicity of TSN.

Person Foreground Segmentation by Learning Multi-Domain Networks.

Separating the dominant person from the complex background is significant to the human-related research and photo-editing based applications. Existing segmentation algorithms are either too general to separate the person region accurately, or not capable of achieving real-time speed. In this paper, we introduce the multi-domain learning framework into a novel baseline model to construct the Multi-domain TriSeNet Networks for the real-time single person image segmentation. We first divide training data into different subdomains based on the characteristics of single person images, then apply a multi-branch Feature Fusion Module (FFM) to decouple the networks into the domain-independent and the domain-specific layers. To further enhance the accuracy, a self-supervised learning strategy is proposed to dig out domain relations during training. It helps transfer domain-specific knowledge by improving predictive consistency among different FFM branches. Moreover, we create a large-scale single person image segmentation dataset named MSSP20k, which consists of 22,100 pixel-level annotated images in the real world. The MSSP20k dataset is more complex and challenging than existing public ones in terms of scalability and variety. Experiments show that our Multi-domain TriSeNet outperforms state-of-the-art approaches on both public and the newly built datasets with real-time speed.

Exploring the origins of the indentation size effect at submicron scales.

The origin of the indentation size effect has been extensively researched over the last three decades, following the establishment of nanoindentation as a broadly used small-scale mechanical testing technique that enables hardness measurements at submicrometer scales. However, a mechanistic understanding of the indentation size effect based on direct experimental observations at the dislocation level remains limited due to difficulties in observing and quantifying the dislocation structures that form underneath indents using conventional microscopy techniques. Here, we employ precession electron beam diffraction microscopy to "look beneath the surface," revealing the dislocation characteristics (e.g., distribution and total length) as a function of indentation depth for a single crystal of nickel. At smaller depths, individual dislocation lines can be resolved, and the dislocation distribution is quite diffuse. The indentation size effect deviates from the Nix-Gao model and is controlled by dislocation source starvation, as the dislocations are very mobile and glide away from the indented zone, leaving behind a relatively low dislocation density in the plastically deformed volume. At larger depths, dislocations become highly entangled and self-arrange to form subgrain boundaries. In this depth range, the Nix-Gao model provides a rational description because the entanglements and subgrain boundaries effectively confine dislocation movement to a small hemispherical volume beneath the contact impression, leading to dislocation interaction hardening. The work highlights the critical role of dislocation structural development in the small-scale mechanistic transition in indentation size effect and its importance in understanding the plastic deformation of materials at the submicron scale.

Local Semantic Siamese Networks for Fast Tracking.

Learning a powerful feature representation is critical for constructing a robust Siamese tracker. However, most existing Siamese trackers learn the global appearance features of the entire object, which usually suffers from drift problems caused by partial occlusion or non-rigid appearance deformation. In this paper, we propose a new Local Semantic Siamese (LSSiam) network to extract more robust features for solving these drift problems, since the local semantic features contain more fine-grained and partial information. We learn the semantic features during offline training by adding a classification branch into the classical Siamese framework. To further enhance the representation of features, we design a generally focal logistic loss to mine the hard negative samples. During the online tracking, we remove the classification branch and propose an efficient template updating strategy to avoid aggressive computing load. Thus, the proposed tracker can run at a high-speed of 100 Frame-per-Second (FPS) far beyond real-time requirement. Extensive experiments on popular benchmarks demonstrate the proposed LSSiam tracker achieves the state-of-the-art performance with a high-speed. Our source code is available at.

Steam Oxidation of Austenitic Heat-Resistant Steels TP347H and TP347HFG at 650⁻800 °C.

Steam oxidation of austenitic heat-resistant steels TP347H and TP347HFG at 650⁻800 °C was investigated. Comprehensive micro-characterization technologies containing Scanning Electron Microscope (SEM), Energy Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS) were employed to observe and analyze the oxidation products. Results show that breakaway oxidation behaviors were observed on TP347H at 700 °C and 800 °C. The oxidation kinetics of TP347HFG at 650⁻800 °C followed a parabolic law. The oxide scales formed on TP347HFG were composed of MnCr2O4 and Cr2O3. A thin and protective Cr-rich oxide scale was replaced by Fe2O3 nodules due to the insufficient outward migration of metallic ions, including Cr and Mn at the subsurface of coarse-grain TP347H. Smaller grain of TP347HFG promoted the formation of the compact Cr-rich oxide scales. At higher temperatures, the incubation period for breakaway oxidation of the Cr-rich oxide scale was much shorter because of quick evaporation of the Cr2O3 oxide scale and the slower outward diffusion of metallic ions via the grain boundaries.