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In the search for rational design strategies for oxygen evolution reaction (OER) catalysts, linking the catalyst structure to activity and stability is key. However, highly active catalysts such as IrOx and RuOx undergo structural changes under OER conditions, and hence, structure-activity-stability relationships need to take into account the operando structure of the catalyst. Under the highly anodic conditions of the oxygen evolution reaction (OER), electrocatalysts are often converted into an active form. Here, we studied this activation for amorphous and crystalline ruthenium oxide using X-ray absorption spectroscopy (XAS) and electrochemical scanning electron microscopy (EC-SEM). We tracked the evolution of surface oxygen species in ruthenium oxides while in parallel mapping the oxidation state of the Ru atoms to draw a complete picture of the oxidation events that lead to the OER active structure. Our data show that a large fraction of the OH groups in the oxide are deprotonated under OER conditions, leading to a highly oxidized active material. The oxidation is centered not only on the Ru atoms but also on the oxygen lattice. This oxygen lattice activation is particularly strong for amorphous RuOx. We propose that this property is key for the high activity and low stability observed for amorphous ruthenium oxide.

With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on Multiple Behavioral Features for Knowledge Tracing model (MBFKT), which models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability.

Bilateral theta-burst stimulation (biTBS, intermittent TBS over the left dorsolateral prefrontal cortex (DLPFC), and continuous TBS over the right DLPFC), has demonstrated efficacy in improving symptoms in patients with major depressive disorder (MDD). However, the underlying brain mechanisms remain unknown. We aimed to investigate the anti-depressant efficacy of biTBS monotherapy and its effects on the brain responses measured by functional magnetic resonance imaging (fMRI) during emotional processing in MDD.We conducted a double-blind, randomized, sham-controlled trial with MDD patients who exhibited no responses to at least one adequate anti-depressant treatment for the prevailing episode. Recruited patients were randomly assigned to 10 biTBS monotherapy or sham stimulation sessions. The fMRI scans during performing emotional recognition task were obtained at baseline and after 10 sessions of treatment. Depressive symptoms were assessed 21-item Hamilton Rating Scale for Depression (HAMD-21) at baseline, 4The biTBS group (n=17) exhibited significant decreases in depression scores compared with the sham group (n=11) at week 8 (70% vs. 40%; p = 0.02), and the significant differences persisted during the 24-week follow-up periods. At week 4, when the treatment course was completed, subjects in the biTBS, but not in the sham group exhibited increased brain activities over the left superior and middle frontal gyrus during negative emotional stimuli.Our findings provided the first evidence regarding the underlying neural mechanisms of biTBS therapy to improve clinical symptoms in MDD patients. This article is protected by copyright. All rights reserved.

This review addresses the applications of electrospinning in the thermal management of polymer matrix composites, mainly involving enhanced thermal conductivity, thermal insulation, and passive daytime radiative cooling.

Here, we developed an air-stable, earth-abundant cobalt(III)-catalyzed regioselective mono-olefination of arenes directed by urea under mild conditions through a cross-dehydrogenative coupling (CDC) process. Under the optimized conditions, a high regioselectivity of mono-olefination was achieved with various electron-rich and electron-deficient arenes, which afforded

Although the aberrant activity of fibroblast growth factor receptor 3 (FGFR3) is implicated in various cancers, the reported kinase inhibitors of FGFR3 tend to cause side effects resulting from the inhibitory activity on vascular endothelial growth factor receptor 2 (VEGFR2). Therefore, it is necessary to find a novel high-selective inhibitor of FGFR3 over VEGFR2 from the small-molecule compound database. In this study, integrated virtual screening protocols were established to screen for selective inhibitors of FGFR3 over VEGFR2 in Drugbank and Asinex databases by combining three-dimensional pharmacophore model, molecular docking, molecular dynamics (MD) simulation, and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations. Finally, it is found that Asinex-5082, as an octahydropyrrolo[3,2-b] pyridin derivative, has larger binding free energy with FGFR3 (-39.3 kcal/mol) than reference drug Erdafitinib (-29.9 kcal/mol), while cannot bind with VEGFR2, resulting in considerable inhibitory selectivity. This is because Asinex-5082, unlike Erdafitinib, has not m-dimethoxybenzene with large steric hindrance, thus can enter the larger ATP-binding pocket of FGFR3 with DFG-in conformation to form hydrophobic interaction with residues Met529, Ile539, and Tyr557 as well as hydrogen bond with Ala558. On the other hand, due to the fact that the benzodioxane and N-heterocyclic rings are connected by carbonyl (C=O), Asinex-5082 cannot rotate freely so as to enter the smaller ATP binding pocket of VEGFR2 on the DFG-out conformation. The lead molecule Asinex-5082 may facilitate the rational design and development of novel selective inhibitors of FGFR3 over VEGFR2 as anticancer drugs.

The NIST LightWeight Cryptography (LWC) selection process aims to standardise cryptographic functionality which is suitable for resource-constrained devices. Since the outcome is likely to have significant, long-lived impact, careful evaluation of each submission with respect to metrics explicitly outlined in the call is imperative. Beyond the robustness of submissions against cryptanalytic attack, metrics related to their implementation (e.g., execution latency and memory footprint) form an important example. Aiming to provide evidence allowing richer evaluation with respect to such metrics, this paper presents the design, implementation, and evaluation of one separate Instruction Set Extension (ISE) for each of the 10 LWC final round submissions, namely Ascon, Elephant, GIFT-COFB, Grain-128AEADv2, ISAP, PHOTON-Beetle, Romulus, Sparkle, TinyJAMBU, and Xoodyak; although we base the work on use of RISC-V, we argue that it provides more general insight.

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a K -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.

Microplastics represent an emerging environmental problem worldwide, raising ecological and food safety concerns. Compared to microplastics, there is growing evidence of an even higher abundance of submicro- and nanoplastics in the environment, but a reliable monitoring method for detecting these smaller-sized plastics is lacking. Herein we presented the application of surface-enhanced Raman scattering (SERS) for this purpose. Particles of polystyrene (PS; 600 nm) were used as the probe analyte. Gold nanourchins (AuNU; 50 nm), i.e. urchin-shaped nanoparticles with irregular spikes around the core, were used as the SERS-active substrate. The effectiveness of SERS on PS was evaluated at a single-particle level with different numbers of AuNU in order to determine the minimum conditions required for the onset of the SERS effect. Our findings suggest that SERS of a single particle of PS can be induced by as few as 1-5 particles of AuNU, and that the use of excitation wavelength at 785 nm is appropriate to meet the red-shifted surface plasmon resonance of AuNU upon aggregation. These specifications provide additional information for the development of SERS-based tools for detecting plastic particles1 µm in food and environmental samples.

Organic room-temperature phosphorescence, a spin-forbidden radiative process, has emerged as an interesting but rare phenomenon with multiple potential applications in optoelectronic devices, biosensing, and anticounterfeiting. Covalent organic frameworks (COFs) with accessible nanoscale porosity and precisely engineered topology can offer unique benefits in the design of phosphorescent materials, which are presently unexplored. Here, we report an original approach of covalent doping, whereby a bi-component COF is synthesized by copolymerization of halogenated and unsubstituted phenyldiboronic acids, allowing for random distribution of functionalized units at varying ratios, yielding highly phosphorescent COFs. Such controlled halogen doping enhances the intersystem crossing while minimizing the triplet-triplet annihilation by diluting the phosphors. The rigidity of the COF suppresses vibrational relaxation and allows high phosphorescence quantum yield (Φ(phos)

Enhanced recovery after surgery (ERAS) is a perioperative management protocol used to accelerate patient recovery. This study evaluated its benefits in patients with resectable esophageal cancer.This retrospective study compared patients before (January 2013 to December 2016) and after (June 2018 to December 2020) ERAS protocol implementation in our hospital. A propensity score-matched analysis was used to compare short-term surgical outcomes between ERAS and non-ERAS groups. After propensity score matching each group included 243 patients.There were significant differences in hospital length of stay after surgery (7.40 vs 11.17 days, P.001) and hospitalization cost (¥69380 vs ¥78075, P.001) between the ERAS and non-ERAS groups. The time to chest tube removal (4.91 vs 7.16 days, P.001) and first bowel movement (2.87 vs 3.97 days, P.001) was significantly shorter in the ERAS group. However there was no significant difference in total postoperative complication morbidity (20.2% vs 25.1%, P = .193). The complication of postoperative atelectasis or pneumonia was significantly lower in the ERAS group (P = .003), but there was no significant difference in occurrence of at least grade III complications between the 2 groups (12.3% vs 11.5%, P = .889).We demonstrated that ERAS could reduce hospital stay, numerical pain scores, and hospitalization costs without increasing postoperative complication and readmission. Furthermore subgroup analyses revealed that ERAS was safe for older people (70 years old).

The distinction between judgments of perception and judgments of experience in Kant’s Prolegomena has long been a controversial issue in Kantian studies. On the one hand, this distinction challenges the close connection between the synthetic unity of self-consciousness and the categories. On the other hand, a distinction between the subjective and the objective is unavoidable in our cognitive life. I will show in this paper that the interpretive difficulties arise from the ambiguity of Kant’s use of the concept of perception and his misleading formulation of the judgments of perception. After clarifying these problems and adopting the regulative use of the concept of the I in Kant’s theory of perception, the place of judgments of perception in his transcendental cognitive theory can be clarified, and the consistency of his system can be observed.

Although Large Language Models (LLMs) have shown great potential in Natural Language Generation, it is still challenging to characterize the uncertainty of model generations, i.e., when users could trust model outputs. Our research is derived from the heuristic facts that tokens are created unequally in reflecting the meaning of generations by auto-regressive LLMs, i.e., some tokens are more relevant (or representative) than others, yet all the tokens are equally valued when estimating uncertainty. It is because of the linguistic redundancy where mostly a few keywords are sufficient to convey the meaning of a long sentence. We name these inequalities as generative inequalities and investigate how they affect uncertainty estimation. Our results reveal that considerable tokens and sentences containing limited semantics are weighted equally or even heavily when estimating uncertainty. To tackle these biases posed by generative inequalities, we propose to jointly Shifting Attention to more Relevant (SAR) components from both the token level and the sentence level while estimating uncertainty. We conduct experiments over popular "off-the-shelf" LLMs (e.g., OPT, LLaMA) with model sizes up to 30B and powerful commercial LLMs (e.g., Davinci from OpenAI), across various free-form question-answering tasks. Experimental results and detailed demographic analysis indicate the superior performance of SAR. Code is available at https://github.com/jinhaoduan/shifting-attention-to-relevance.

Ribonucleotide reductases (RRs or RNRs) catalyze the reduction of the OH group on the 2nd carbon of ribose, reducing four ribonucleotides (NTPs) to the corresponding deoxyribonucleotides (dNTPs) to promote DNA synthesis. Large DNA viruses, such as herpesviruses and poxviruses, could benefit their replication through increasing dNTPs via expression of viral RRs. Little is known regarding the relationship between cellular RRs and RNA viruses. Mammalian RRs contain two subunits of ribonucleotide reductase M1 polypeptide (RRM1) and two subunits of ribonucleotide reductase M2 polypeptide (RRM2). In this study, expression of cellular RRMs, including RRM1 and RRM2, is found to be down-regulated in hepatitis C virus (HCV)-infected Huh7.5 cells and Huh7 cells with HCV subgenomic RNAs (HCVr). As expected, the NTP/dNTP ratio is elevated in HCVr cells. Compared with that of the control Huh7 cells with sh-scramble, the NTP/dNTP ratio of the RRM-knockdown cells is elevated. Knockdown of RRM1 or RRM2 increases HCV replication in HCV replicon cells. Moreover, inhibitors to RRMs, including Didox, Trimidox and hydroxyurea, enhance HCV replication. Among various HCV viral proteins, the NS5A and/or NS3/4A proteins suppress the expression of RRMs. When these are taken together, the results suggest that HCV down-regulates the expression of RRMs in cultured cells to promote its replication.

Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks (ANNs). While previous research has primarily focused on enhancing the performance of SNNs in classification tasks, the generative potential of SNNs remains relatively unexplored. In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality. To fully exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net architecture, which achieves comparable performance to its ANN counterpart using only 4 time steps, resulting in significantly reduced energy consumption. Extensive experimental results reveal that our approach achieves state-of-the-art on the generative tasks and substantially outperforms other SNN-based generative models, achieving up to $12\times$ and $6\times$ improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we propose a threshold-guided strategy that can further improve the performances by 16.7% in a training-free manner. The SDDPM symbolizes a significant advancement in the field of SNN generation, injecting new perspectives and potential avenues of exploration., Under Review

This study compares the accuracy and safety of pedicle screw placement using a 3D navigation template with the free-hand fluoroscopy technique in scoliotic patients. Fifteen scoliotic patients were recruited and divided into a template group (eight cases) and a free-hand group (seven cases). All patients received posterior corrective surgeries, and the pedicle screw was placed using a 3D navigation template or a free-hand technique. After surgery, the positions of the pedicle screws were evaluated using CT. A total of 264 pedicle screws were implanted in 15 patients. Both the two techniques were found to achieve satisfactory safety of screw insertion in scoliotic patients (89.9% vs. 90.5%). In the thoracic region, the 3D navigation template was able to achieve a much higher accuracy of screw than the free-hand technique (75.3% vs. 60.4%). In the two groups, the accuracy rates on the convex side were slightly higher than on the concave side, while no significance was seen. In terms of rotational vertebrae, no significant differences were seen in Grades I or II vertebrae between the two groups. In conclusion, the 3D navigation template technique significantly increased the accuracy of thoracic pedicle screw placement, which held great potential for extensively clinical application.

Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users' preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor).

To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in edible oils with consumption data. Initially, the k-means algorithm was used for risk classification; then the data were pre-processed and trained to predict the data using the Long Short-Term Memory (LSTM) and the eXtreme Gradient Boosting (XGBoost) models, respectively, and finally, the two models were combined using the inverse error method. To test the effectiveness of the prediction model, this study experimentally validated the model according to five evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), precision, recall, and F1 score. The variable-weight combined LSTM-XGBoost prediction model proposed in this paper achieved a precision of 94.62%, and the F1 score value reached 95.16%, which is significantly better than other neural network models; the results demonstrate that the prediction model has certain stability and feasibility. Overall, the combined model used in this study not only improves the accuracy but also enhances the practicality, real-time capabilities, and expandability of the model.

Background Hypertension and diabetes cause chronic kidney disease (CKD) and diastolic left ventricular dysfunction (DVD) as forerunners of disability and death. Home blood pressure telemonitoring (HTM) and urinary peptidomic profiling (UPP) are technologies enabling prevention. Methods UPRIGHT-HTM (Urinary Proteomics Combined with Home Blood Pressure Telemonitoring for Health Care Reform [NCT04299529]) is an investigator-initiated 5-year clinical trial with patient-centred design, which will randomise 1148 patients to be recruited in Europe, sub-Saharan Africa and South America. During the whole study, HTM data will be collected and freely accessible for patients and caregivers. The UPP, measured at enrolment only, will be communicated early during follow-up to 50% of patients and their caregivers (intervention), but only at trial closure in 50% (control). The hypothesis is that early knowledge of the UPP risk profile will lead to more rigorous risk factor management and result in benefit. Eligible patients, aged 55-75 years old, are asymptomatic, but have >= 5 CKD- or DVD-related risk factors, preferably including hypertension, type-2 diabetes, or both. The primary endpoint is a composite of new-onset intermediate and hard cardiovascular and renal outcomes. Demonstrating that combining UPP with HTM is feasible in a multicultural context and defining the molecular signatures of early CKD and DVD are secondary endpoints. Expected outcomes The expected outcome is that application of UPP on top of HTM will be superior to HTM alone in the prevention of CKD and DVD and associated complications and that UPP allows shifting emphasis from treating to preventing disease, thereby empowering patients. The authors gratefully acknowledge the enthusiasm of the patientsenrolled in UPRIGHT-HTM and the expert assistance of the consul-tants, residents and nursing staff supporting the trial at the clinicalsites. The Alliance for the Promotion of Preventive Medicine is anot-profitresearchinstitute(URL:www.appremed.org;Belgianregis-trationnumber,739849385),whichreceivedanon-bindinggrantfromOMRONHealthcareCo.Ltd.,Kyoto,Japan.UPRIGHT-HTMisaTopZprojectsupportedbyOMRONHealthcareCo.Ltd.,Kyoto,Japan

The problems of parallel two-sided assembly lines balancing (TALB) are nonpolynomial (NP-hard) problems that belong to the field of combinatorial optimization. In most cases, these problems can be resolved with algorithms. To this end, this research adopts colored Petri nets (CPN) and a minimal spanning tree (MST) to address the type I problems for two-sided assembly line balancing problem (TALBP). This research proposes a three-step method. In the first step, a reachability tree chart is built to show potential tasks order assignment. In the second step, the color firing mechanism is employed to determine if the colored conditions are satisfied. The final step uses a MST and a least-time prioritized rule to select the proper tasks for assignment efficiently. These steps will be performed repetitively until all the tasks are properly assigned. Since the whole assignment process is based on graphical and paradigm methods, it can be adopted to lessen the burdens of complex calculations. The end of the research will present the comparison results between the examples from the literature and the method proposed in this research. As indicated by the research findings, employing CPN and an MST for TALBPs (type-I) can obtain even favorable and consistent results over legacy methods, and further optimize workstation design and enhance balance efficiency.

This study uses the research structure of the experimental design, taking Taiwan specialty coffee beans as an example, and selects four aspects which are a coffee grind, brewing water temperature, coffee blooming, and coffee water powder ratio that is most likely to affect the coffee extraction rate and coffee concentration. Brewing with hand brewing equipment which is easier to buy in daily life, and obtain data analysis, and then conduct experiments and comparisons with coffee beans from other countries. The results of this study found that coffee beans from different producing areas can be slightly adjusted for different brewing techniques to reduce the consumption of coffee beans and reduce costs, to provide consumers and subsequent researchers with references and suggestions. JEL classification numbers: O13, P32, Q15. Keywords: Taiwan Coffee Bean, Brew Coffee, Variation Analysis, and Gold Cup.

The B-cell lymphoma 2 (BCL-2) protein family plays a pivotal role in regulating the apoptosis process. BCL-2, as an antiapoptotic protein in this family, mediates apoptosis resistance and is an ideal target for cell death strategies in cancer therapy. Traditional treatment modalities target BCL-2 by occupying the hydrophobic pocket formed by BCL-2 homology (BH) domains 1-3, while in recent years, the BH4 domain of BCL-2 has also been considered an attractive novel target. Herein, we describe the discovery and identification of DC-B01, a novel BCL-2 inhibitor targeting the BH4 domain, through virtual screening combined with biophysical and biochemical methods. Our results from surface plasmon resonance and cellular thermal shift assay confirmed that the BH4 domain is responsible for the interaction between BCL-2 and DC-B01. As evidenced by further cell-based experiments, DC-B01 induced cell killing in a BCL-2-dependent manner and triggered apoptosis via the mitochondria-mediated pathway. DC-B01 disrupted the BCL-2/c-Myc interaction and consequently suppressed the transcriptional activity of c-Myc. Moreover, DC-B01 inhibited tumor growth in vivo in a BCL‑2‑dependent manner. Collectively, these results indicate that DC-B01 is a promising BCL-2 BH4 domain inhibitor with the potential for further development.

Aim: The underlying mechanisms by which circular RNAs (circRNAs) regulate non-small-cell lung cancer (NSCLC) progression remain elusive. This study investigated the role of circRNA circTTBK2 in NSCLC tumorigenesis. Materials & methods: Quantitative reverse transcriptase polymerase chain reaction analysis of circTTBK2 in NSCLC tissues and cell lines was performed. Cell proliferation, migration, invasion and tumorigenesis were confirmed in vitro and in vivo using CCK-8, EdU incorporation, Transwell assays and xenograft technique. The circTTBK2/miR-873-5p/TEAD1/DERL1 axis was verified by RNA immunoprecipitation, chromatin immunoprecipitation and luciferase reporter assays. Results: Overexpressed circTTBK2 in NSCLC tissues indicates poor prognosis of NSCLC patients. circTTBK2 harbors miR-873-5p, and miR-873-5p directly targets TEAD1. TEAD1 transcriptionally activates DERL1. Conclusion: This study revealed a novel machinery of circTTBK2/miR-873-5p/TEAD1/DERL1 for NSCLC tumorigenesis.