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Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.

Solid-state lithium (Li)–air batteries are recognized as a next-generation solution for energy storage to address the safety and electrochemical stability issues that are encountered in liquid battery systems 1 – 4 . However, conventional solid electrolytes are unsuitable for use in solid-state Li–air systems owing to their instability towards lithium metal and/or air, as well as the difficulty in constructing low-resistance interfaces 5 . Here we present an integrated solid-state Li–air battery that contains an ultrathin, high-ion-conductive lithium-ion-exchanged zeolite X (LiX) membrane as the sole solid electrolyte. This electrolyte is integrated with cast lithium as the anode and carbon nanotubes as the cathode using an in situ assembly strategy. Owing to the intrinsic chemical stability of the zeolite, degeneration of the electrolyte from the effects of lithium or air is effectively suppressed. The battery has a capacity of 12,020 milliamp hours per gram of carbon nanotubes, and has a cycle life of 149 cycles at a current density of 500 milliamps per gram and at a capacity of 1,000 milliamp hours per gram. This cycle life is greater than those of batteries based on lithium aluminium germanium phosphate (12 cycles) and organic electrolytes (102 cycles) under the same conditions. The electrochemical performance, flexibility and stability of zeolite-based Li–air batteries confer practical applicability that could extend to other energy-storage systems, such as Li–ion, Na–air and Na–ion batteries. Flexible, stable and energy-dense solid-state Li–air batteries are realised using ultrathin, chemically inert ion-conductive zeolite membranes as a solid electrolyte.

Chitooligosaccharide (COS), which is acknowledged for possessing multiple functions, is a kind of low-molecular-weight polymer prepared by degrading chitosan via enzymatic, chemical methods, etc. COS has comprehensive applications in various fields including food, agriculture, pharmacy, clinical therapy, and environmental industries. Besides having excellent properties such as biodegradability, biocompatibility, adsorptive abilities and non-toxicity like chitin and chitosan, COS has better solubility. In addition, COS has strong biological functions including anti-inflammatory, antitumor, immunomodulatory, neuroprotective effects, etc. The present paper has summarized the preparation methods, analytical techniques and biological functions to provide an overall understanding of the application of COS.

Tool wear is one of the important indicators to reflect the health status of a machining system. In order to obtain tool’s wear status, tool condition monitoring (TCM) utilizes advanced sensor techniques, hoping to find out the wear status through those sensor signals. In this paper, a novel weighted hidden Markov model (HMM)-based approach is proposed for tool wear monitoring and tool life prediction, using the signals provided by TCM techniques. To describe the dynamic nature of wear evolution, a weighted HMM is first developed, which takes wear rate as the hidden state and formulates multiple HMMs in a weighted manner to include sufficient historical information. Explicit formulas to estimate the model parameters are also provided. Then, a particular probabilistic approach using the weighted HMM is proposed to estimate tool wear and predict tool’s remaining useful life during tool operation. The proposed weighted HMM-based approach is tested on a real dataset of a high-speed CNC milling machine cutters. The experimental results show that this approach is effective in estimating tool wear and predicting tool life, and it outperforms the conventional HMM approach.

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.

Theranostic nanoplatforms integrating diagnostic and therapeutic functions have received considerable attention in the past decade. Among them, hollow manganese (Mn)-based nanoplatforms are superior since they combine the advantages of hollow structures and the intrinsic theranostic features of Mn 2+ . Specifically, the hollow cavity can encapsulate a variety of small-molecule drugs, such as chemotherapeutic agents, photosensitizers and photothermal agents, for chemotherapy, photodynamic therapy (PDT) and photothermal therapy (PTT), respectively. After degradation in the tumor microenvironment (TME), the released Mn 2+ is able to act simultaneously as a magnetic resonance (MR) imaging contrast agent (CA) and as a Fenton-like agent for chemodynamic therapy (CDT). More importantly, synergistic treatment outcomes can be realized by reasonable and optimized design of the hollow nanosystems. This review summarizes various Mn-based hollow nanoplatforms, including hollow Mn x O y , hollow matrix-supported Mn x O y , hollow Mn-doped nanoparticles, hollow Mn complex-based nanoparticles, hollow Mn-cobalt (Co)-based nanoparticles, and hollow Mn-iron (Fe)-based nanoparticles, for MR imaging-guided cancer therapies. Finally, we discuss the potential obstacles and perspectives of these hollow Mn-based nanotheranostics for translational applications. Graphical Abstract Mn-based hollow nanoplatforms such as hollow Mn x O y nanoparticles, hollow matrix-supported Mn x O y nanoparticles, Mn-doped hollow nanoparticles, Mn complex-based hollow nanoparticles, hollow Mn-Co-based nanoparticles and hollow Mn-Fe-based nanoparticles show great promise in cancer theranostics.

Dysregulated NLRP3 inflammasome activity results in uncontrolled inflammation, which underlies many chronic diseases. Although mitochondrial damage is needed for the assembly and activation of the NLRP3 inflammasome, it is unclear how macrophages are able to respond to structurally diverse inflammasome-activating stimuli. Here we show that the synthesis of mitochondrial DNA (mtDNA), induced after the engagement of Toll-like receptors, is crucial for NLRP3 signalling. Toll-like receptors signal via the MyD88 and TRIF adaptors to trigger IRF1-dependent transcription of CMPK2, a rate-limiting enzyme that supplies deoxyribonucleotides for mtDNA synthesis. CMPK2-dependent mtDNA synthesis is necessary for the production of oxidized mtDNA fragments after exposure to NLRP3 activators. Cytosolic oxidized mtDNA associates with the NLRP3 inflammasome complex and is required for its activation. The dependence on CMPK2 catalytic activity provides opportunities for more effective control of NLRP3 inflammasome-associated diseases. New mitochondrial DNA synthesis links the priming and activation of the NLRP3 inflammasome.

Implementing environmental, social and governance (ESG) disclosure is critical for manufacturers’ sustainable development and high-quality growth. Amid manufacturing agglomeration, firms’ spatial concentration reshapes value creation and risk exposure, affecting ESG performance. Using 2010–2023 data from Chinese A-share listed manufacturers, this study empirically examines agglomeration’s impact on corporate ESG performance, based on heterogeneous firm and stakeholder theories. Results show agglomeration significantly improves ESG performance, via enhanced productivity (internal) and greater compliance pressure (external). Further analysis finds ESG performance mitigates adverse selection in agglomeration ecosystems, while cluster peer effects strengthen long-term ESG engagement, aligning with stakeholders’ demands for transparency and accountability. This enriches manufacturing agglomeration-ESG literature, guiding policymakers and firms in integrating sustainability into clustered development.

The aim of this study was to investigate the relationship of insulin-like growth factor-1 (IGF-1) and mild subclinical hypothyroidism (MSH) in obese boys and to assess whether the presence of MSH exacerbates cardiovascular risk factors in obesity. This study collected cross-sectional dataset covering 141 obese boys and 47 healthy non-obese boys. The obese group was further subdivided into two groups based on their serum Thyroid Stimulating Hormone (TSH) levels: the MSH group (n = 47) and the non-MSH group (n = 94). The MSH group exhibited significantly lower IGF-1 standard deviation score (IGF-1 SDS) and significantly higher Body Mass Index standard deviation score (BMI SDS) compared to the non-MSH group. Additionally, the MSH group demonstrated elevated triglycerides (TG) and gamma-glutamyl transferase (GGT) levels relative to the non-MSH group, and the incidence of non-alcoholic fatty liver disease (NAFLD) and metabolic syndrome (MS) were also higher in the MSH group than in the non-MSH group. The results of multivariable logistic regression analysis indicated that lower IGF-1 SDS and higher BMI SDS are strongly associated with MSH in obese boys, independently of systolic blood pressure (SBP), diastolic blood pressure (DBP), alanine aminotransferase (ALT), GGT and uric acid. These findings underscore the clinical utility of IGF-1 SDS and BMI SDS as potential biomarkers for identifying MSH-related cardiovascular risks in obese pediatric populations, warranting targeted screening and intervention strategies.

Climate influences vegetation directly and through climate-mediated disturbance processes, such as wildfire. Temperature and area burned are positively associated, conditional on availability of vegetation to burn. Fire is a self-limiting process that is influenced by productivity. Yet, many fire projections assume sufficient vegetation to support fire, with substantial implications for carbon (C) dynamics and emissions. We simulated forest dynamics under projected climate and wildfire for the Sierra Nevada, accounting for climate effects on fuel flammability (static) and climate and prior fire effects on fuel availability and flammability (dynamic). We show that compared to climate effects on flammability alone, accounting for the interaction of prior fires and climate on fuel availability and flammability moderates the projected increase in area burned by 14.3%. This reduces predicted increases in area-weighted median cumulative emissions by 38.3 Tg carbon dioxide (CO 2 ) and 0.6 Tg particulate matter (PM1), or 12.9% and 11.5%, respectively. Our results demonstrate that after correcting for potential over-estimates of the effects of climate-driven increases in area burned, California is likely to continue facing significant wildfire and air quality challenges with on-going climate change.