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Based on 1,364 children across Grades 1, 3, and 5, the present study explored the mutualism of (externalizing and internalizing) behavioral problems and language, and whether social skills explained such mutualism. The random intercept cross-lagged panel models were conducted to control for between-person level variance and to explore mutualism of variables on the within-person level. Results indicated the mutualistic coupling of language and externalizing behavioral problems from Grades 3 to 5. When including social skills in the model, only social skills and externalizing behavioral problems formed the mutualism from Grades 3 to 5; language only predicted behavioral problems and social skills in the early elementary stage (Grades 1-3). These results suggest that language and behavioral management early on are important investing skills for the development of language, behavior skills, and social skills. With development, social skills may become more important than general language skills to manage problem behaviors. These findings indicated that interventions targeting behavioral problems may need to adapt their focus as children develop: fostering general language abilities early on but shifting to building social skills in later grades.

Materials-based H2 storage plays a critical role in facilitating H2 as a low-carbon energy carrier, but there remains limited guidance on the technical performance necessary for specific applications. Metal-organic framework (MOF) adsorbents have shown potential in power applications, but need to demonstrate economic promises against incumbent compressed H2 storage. Herein, we evaluate the potential impact of material properties, charge/discharge patterns, and propose targets for MOFs' deployment in long-duration energy storage applications including backup, load optimization, and hybrid power. We find that state-of-the-art MOF could outperform cryogenic storage and 350 bar compressed storage in applications requiring ≤8 cycles per year, but need ≥5 g/L increase in uptake to be cost-competitive for applications that require ≥30 cycles per year. Existing challenges include manufacturing at scale and quantifying the economic value of lower-pressure storage. Lastly, future research needs are identified including integrating thermodynamic effects and degradation mechanisms.

Externalizing behavior and low academic performance present key developmental challenges for school-age children, with the potential for these domains to predict each other over time, leading to worsened outcomes. Yet, previous studies have yielded inconsistent conclusions about the directional pathways between externalizing behaviors and academic performance. Moreover, the moderating factors influencing these predictive pathways remain unclear. To clarify these relations, The current study conducted a meta-analysis on the longitudinal predictive relation between externalizing behaviors and academic performance, incorporating data from 124,695 students without clinically diagnosed behavioral problems or learning disabilities across 70 independent studies. The results revealed a bidirectional predictive relation between externalizing behaviors and academic performance, with comparable effect sizes for both the externalizing behavior to academic performance pathway (r = -0.082, p < 0.001) and the reverse pathway (r = -0.076, p < 0.001). Moderation analysis revealed that inattention-related behavior, shorter time intervals, and fewer white participants strengthen the predictive effect of externalizing behaviors on academic performance. In particular, as child age increases, the moderating effect of time interval is even stronger. For the academic performance leading to externalizing behavior pathway, inattention-related behavior, teacher-reported externalizing behavior, literacy-related performance, and GPA/grades were identified as factors contributing to an augmentation in the longitudinal predictive effect of academic performance on externalizing behaviors. Overall, the negative cycle between externalizing behavior and academic performance may be influenced by various factors, providing targeted recommendations for intervention and prevention.

Abstract Seawater electrolysis using renewable electricity offers an attractive route to sustainable hydrogen production, but the sluggish electrode kinetics and poor durability are two major challenges. We report a molybdenum nitride (Mo2N) catalyst for the hydrogen evolution reaction with activity comparable to commercial platinum on carbon (Pt/C) catalyst in natural seawater. The catalyst operates more than 1000 hours of continuous testing at 100 mA cm−2 without degradation, whereas massive precipitate (mainly magnesium hydroxide) forms on the Pt/C counterpart after 36 hours of operation at 10 mA cm−2. Our investigation reveals that ammonium groups generate in situ at the catalyst surface, which not only improve the connectivity of hydrogen-bond networks but also suppress the local pH increase, enabling the enhanced performances. Moreover, a zero-gap membrane flow electrolyser assembled by this catalyst exhibits a current density of 1 A cm−2 at 1.87 V and 60 oC in simulated seawater and runs steadily over 900 hours.

The NiCoCrFeAlW eutectic high-entropy alloy exhibits a balanced combination of ductility and strength. This study examines the microstructure evolution and mechanical properties of the Ni30Co30Cr10Fe10Al18W2 alloy at varying growth velocities (10, 20, 50, 100, and 200 μm/s), which in its as-cast state, features a microstructure that includes both coarse dendrites and lamellar eutectic. The structure of the coarse dendrites is characterized by an ordered body-centered cubic (B2) phase, while the lamellar eutectic, features a combination of face-centered cubic (FCC) and ordered B2 phases. The microstructure, characterized by lamellar and dendritic structures that are parallel to the growth direction after directional solidification, undergoes a morphological transition at the solid-liquid interface from a planar to a dendritic form as growth velocity increases. The tensile fracture strength initially increases with the growth velocity, reaching a peak of 1286 MPa at a velocity of 50 μm/s, and then subsequently decreases as the growth velocity continues to increase. Directional solidification results in an increase in sample elongation with increasing growth velocity, achieving an elongation of 22.5% at 200 μm/s. An examination of the deformation mechanism reveals that the lamellar structure, composed of the more ductile FCC phase and the harder B2 phase, effectively harmonizes the plasticity and strength of the alloy. Therefore, directional solidification significantly adjusts the eutectic microstructure's orientation, thereby enhancing the mechanical properties along the direction of solidification.

Abstract Autophagy is closely associated with the onset and progression of steroid-induced osteonecrosis of the femoral head (SIONFH). SQSTM1/p62 is an important indicator of autophagic activity. The aim of this study was to investigate the role of SQSTM1/p62 in the development of SIONFH. From May 2021 through November 2021, 36 patients diagnosed with SIONFH and 36 healthy controls were recruited for this study. Evaluations included imaging and pathologic assessment of clinical bone tissue, location and level of SQSTM1/p62 expression, plasma SQSTM1/p62 levels, and receiver operating characteristic (ROC) curves. We observed that the expression level of SQSTM1/p62 in bone samples decreased with the Association Research Circulation Osseous (ARCO) phase. Plasma SQSTM1/p62 levels were significantly higher in the SIONFH group compared to healthy controls. Plasma SQSTM1/p62 levels were higher in pre-crash patients than in post-crash patients, and lower plasma SQSTM1/p62 levels were associated with elevated ARCO stage. Plasma SQSTM1/p62 may represent a potential biomarker for different stages during SIONFH. Lower plasma SQSTM1/p62 levels indicate an advanced stage of SIONFH. This study provides new clues for early diagnosis of SIONFH.

Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery’s characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multifaceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. To realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.

Abstract Background and Objective Tinengotinib, a novel multi-target small molecule kinase inhibitor, is currently undergoing phase II clinical trial in the USA and China. The purpose of this open-label study was to investigate the absorption, metabolism, and excretion of [14C]tinengotinib following a single oral dose in healthy subjects. Methods Six healthy male subjects received a single oral dose of [14C]tinengotinib capsules at 10 mg/100 µCi, and blood, urine, and feces samples were collected. Phenotyping experiments were further conducted to confirm the enzymes involved in its metabolism. Results Tinengotinib was rapidly absorbed in plasma with a time to peak drug concentration (T max) of 1.0–4.0 h post-dose and a long terminal half-life (t ½) of 23.7 h. Blood-to-plasma radioactivity concentration ratios across timepoints ranged from 0.780 to 0.827, which indicated minimal association of radioactivity with blood cells. The mean cumulative excreted radioactivity was 99.57% of the dose, including 92.46% (68.65% as unchanged) in feces and 7.11% (0.28% as unchanged) in urine. In addition to unchanged tinengotinib, a total of 11 radioactive metabolites were identified in plasma, urine, and feces. The most abundant circulating radioactivity was the parent drug in plasma, which comprised 88.23% of the total radioactivity area under the concentration–time curve (AUC). Metabolite M410-3 was a major circulating metabolite, accounting for 5.38% of the parent drug exposure and 4.75% of the total drug-related exposure, respectively. All excreted metabolites accounted for less than 5.10% and 1.82% of the dose in feces and urine, respectively. In addition, no unique metabolites were observed in humans. Tinengotinib was metabolized mainly via CYP3A4. Conclusions Overall, tinengotinib demonstrated a complete mass balance with limited renal excretion, no disproportionate blood metabolism, and slow elimination, primarily through the fecal route. The results of this study provide evidence to support the rational use of tinengotinib as a pharmacotherapeutic agent. Registration ChinadrugTrials.org.cn identifier: CTR20212852.

High-quality Geographic Knowledge Graphs (GeoKGs) are highly anticipated for their potential to provide reliable semantic support in geographical knowledge reasoning, training Geographic Large Language Models (Geo-LLMs), enabling geographical recommendation, and facilitating various geospatial knowledge-driven tasks. However, there is a lack of a standardized quality assessment methodology and clearly defined evaluative indicators in the field of GeoKGs research. This research uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to conduct a systematic review of literature and standards in the field of GeoKG in an effort to fill the gap. First, using the lifecycle theory as a guide, we outline and propose five groups including twenty assessment criteria and their accompanying calculation techniques for evaluating GeoKG quality. Then, expanding on this foundation, we present a streamlined evaluation scheme for GeoKGs that relies on just seven key measures, discussing their applicability, utility, and weight scheme in greater detail. After applying the GeoKG quality framework, we stated three key tasks emerge as priorities: the creation of specialized assessment tools, the formation of worldwide standards, and the building of large-scale, high-quality GeoKGs. We believe this thorough and systematic GeoKG quality assessment technique will help construct high-quality GeoKGs and promote GeoKGs as an engine for geo-intelligence applications including Geospatial Artificial Intelligence (GeoAI) systems, Sustainable Development Goals (SDGs) analyzers, and Virtual Geographic Environments (VGEs) models.

Abstract Septic cardiomyopathy is a life-threatening heart dysfunction caused by severe infection. Considering the complexity of pathogenesis and high mortality, the identification of efficient biomarkers are needed to guide clinical practice. Based on multimicroarray analysis, this study aimed to explore the pathogenesis of septic cardiomyopathy and the related immune landscape. The results showed that septic cardiomyopathy resulted in organ dysfunction due to extreme pro- and anti-inflammatory effects. In this process, KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were identified as the hub genes regulating the immune landscape of septic cardiomyopathy. Nine transcription factors regulated the expression of these genes: SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, SMAD3, and STAT3. The hub genes activated the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway. These pathways were mainly involved in regulating the inflammatory response, adaptive immune response, leukocyte-mediated immunity, cytokine-mediated immunity, immune effector processes, myeloid cell differentiation, and T-helper cell differentiation. These nine hub genes could be considered biomarkers for the early prediction of septic cardiomyopathy.

Abstract Background Spinal cord injury (SCI) often leads to a loss of motor and sensory function. Axon regeneration and outgrowth are key events for functional recovery after spinal cord injury. Endogenous growth of axons is associated with a variety of factors. Inspired by the relationship between developing nerves and blood vessels, we believe spinal cord-derived microvascular endothelial cells (SCMECs) play an important role in axon growth. Results We found SCMECs could promote axon growth when co-cultured with neurons in direct and indirect co-culture systems via downregulating the miR-323-5p expression of neurons. In rats with spinal cord injury, neuron-targeting nanoparticles were employed to regulate miR-323-5p expression in residual neurons and promote function recovery. Conclusions Our study suggests that SCMEC can promote axon outgrowth by downregulating miR-323-5p expression within neurons, and miR-323-5p could be selected as a potential target for spinal cord injury repair. Graphical Abstract

Objective To establish the internal quality control standard of leukocyte-depleted suspended red blood cell volume in our center, so as to guide the preparation of components, strengthen the internal quality control and improve the quality of blood preparations. Methods A total of 1 523 bags of whole blood collected using two manufacturers′ leukocyte-depleted blood bags from March to August 2023 at our center were extracted. The blood before and after filtration were weighed, and the volume of whole blood collected, the volume of filtration loss and the product volume based on the formula and measured specific gravity were calculated. According to the data distribution characteristics, the reference range of leukocyte-depleted suspended red blood cell volume was determined, and the differences of whole blood collection volume, filtration loss capacity and product capacity between the two manufacturers were analyzed. The quality control data of leukocyte-depleted red blood cells over the past year with the difference between another 100 bags of these cells and the reference interval were compared, and the effectiveness of reference interval was validated. Results The median whole blood collection volume in the sample size was 402.0 mL, with a median filtration loss of 42.4 mL, and an average volume of leukocyte-depleted suspended red blood cells at 322.5 mL. The whole blood collection volume (A: median 404.4 mL; B: median 397.7 mL, P0.05). Conclusion According to the actual situation of our center, the volume standard of leukocyte-depleted suspended red blood cells in our center is determined to be 284.1-360.9 mL.

Abstract Chimeric antigen receptor T (CAR-T) cell therapy has greatly improved the prognosis of relapsed and refractory patients with large B-cell lymphoma (LBCL). Early identification and intervention of patients who may respond poorly to CAR-T cell therapy will help to improve the efficacy. Ninety patients from a Chinese cohort who received CAR-T cell therapy and underwent 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scans at the screening stage (median time to infusion 53.5 days, range 27–176 days), 1 month and 3 months after CAR-T cell infusion were analyzed, with RNA-sequencing conducted on 47 patients at the screening stage. Patients with maximum diameter of the largest lesion (Dmax)

Abstract Background CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach. Method We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P

This meta-analysis explores training teachers in the use of data, defined as any quantifiable information that helps teachers know more about their students for instructional decision-making. The questions addressed are as follows: (1) What are the features of data literacy training for kindergarten through 12th-grade teachers?; (2) What are the effects of data literacy training on kindergarten through 12th-grade teacher outcomes?; and (3) Do training characteristics moderate the effects of training? A comprehensive search of research conducted between 1975 and 2019 yielded 33 studies with 163 effect sizes that met inclusion criteria. Using a random effects model, the researchers found significant positive effects on knowledge and skills, g = 0.67, 95% confidence interval (CI) = [0.40, 0.93], and beliefs, g = 0.48, 95% CI = [0.17, 0.79]. A collaborative training format significantly and positively moderated effects. Implications for teacher trainings and the design of future research are discussed.

Purpose: The study explored the distinct contributions of domain-general, behavioral, and reading-specific executive function to reading comprehension, and whether reading fluency and vocabulary moderate or mediate the relations between executive function and reading comprehension. Method: Data were collected from 129 4th and 5th graders (35.66%, 28.68%, 6.98%, and 28.68% for White, Hispanic, African American, and others, respectively; 54% are girls) who completed 9 assessments of domain-general and context-specific working memory, inhibition, and switching. Additionally, children completed assessments of reading comprehension, reading fluency, and vocabulary. Results: After controlling for all variables, among executive function variables, only behavioral and reading-specific executive function independently contributed to reading comprehension. Reading fluency and vocabulary partially mediated the relations between behavioral and reading-specific executive function and reading comprehension. Reading-specific executive function appeared to be particularly important for students with weaker vocabulary. Conclusion: These findings align with the context-specific hypothesis of executive function. Context-specific executive function may be more important than domain-general executive function in reading comprehension. Reading-specific executive function may be a distinct construct representing the integration of domain-general executive function and reading knowledge. This integration can enhance the efficiency of domain-general executive function when reading knowledge is limited, playing a compensatory role in reading comprehension.

We conducted a systematic review and meta-analysis to examine if individuals with reading difficulties (RD), mathematics difficulties (MD), or unspecified learning difficulties (ULD) experience internalizing problems (i.e., anxiety, depression, somatic complaints, and social withdrawal) to the same extent, and if the effect sizes are influenced by moderators (age, internalizing problems type, anxiety type, rater type, selection criteria, and attention control). We reviewed 2,806 studies published in English between January 2000 and April 2023. Our final sample consisted of 96 studies that reported effect sizes or data to calculate them (a total of 120 unique samples, 83,260 participants, age range = 7.3-34.8 years). Risk of bias and sensitivity were assessed. A random-effects model analysis revealed a significant and moderate overall effect size (Hedge's g = -0.54), indicating that individuals with RD, MD or ULD experience more internalizing problems than their chronological-age (CA) controls. Follow-up analyses showed that neither learning difficulties type nor age of participants were significant moderators, but selection criteria (diagnosis versus screening) and internalizing problems type were. These findings suggest that individuals with RD do not differ from those with MD or ULD in internalizing problems, and all fare poorly compared to CA controls.

Mayer (2017, 2020) proposed three major design features of computer-assisted instructions (CAI) within the Cognitive Theory of Multimedia Learning: reducing extraneous processing (i.e., excluding irrelevant content), managing essential processing (i.e., focusing on the complex but essential learning materials), and fostering generative processing (i.e., maximizing learner motivation with multimedia features). No study so far has systematically evaluated each design feature, or their combinations for students with reading difficulties. The present study is the first meta-analysis to investigate the optimal design features of CAIs for students with reading difficulties on their decoding/word reading and reading comprehension performance. A total of 49 experimental studies were reviewed with Bayesian network meta-analysis. Results showed that CAI programs with features that reduced extraneous processing were the most effective (g = 0.65) followed by programs that combined reducing extraneous processing, managing essential processing, and fostering generative processing (g = 0.29) as well as programs that combined reducing extraneous processing and managing essential processing (g = 0.27). CAI programs yielded larger effects when they were designed for younger learners with reading difficulties compared to older learners. No significant moderation effects were observed for students' reading difficulty status, reading content, reading outcomes, instruction dosages, control group types, measures, and fidelity checks. These findings suggest that different combinations of design features of CAI programs may generate different effects. Lowering students' cognitive loads by excluding irrelevant content may be the foundation for designing effective computer instructions for students with reading difficulties.

Based on 52 studies with samples mostly from English-speaking countries, the current study used Bayesian network meta-analysis to investigate the intervention effectiveness of different reading comprehension strategy combinations on reading comprehension among students with reading difficulties in 3rd through 12th grade. We focused on commonly researched strategies: main idea, inference, text structure, retell, prediction, self-monitoring, and graphic organizers. Results showed (1) instruction of more strategies did not necessarily have stronger effects on reading comprehension; (2) there was no single reading comprehension strategy that produced the strongest effect; (3) main idea, text structure, and retell, taught together as the primary strategies, seemed the most effective; and (4) the effects of strategies only held when background knowledge instruction was included. These findings suggest strategy instruction among students with reading difficulties follows an ingredient-interaction model--that is, no single strategy works the best. It is not "the more we teach, the better outcomes to expect." Instead, different strategy combinations may produce different effects on reading comprehension. Main idea, text structure, and retell together may best optimize the cognitive load during reading comprehension. Background knowledge instruction should be combined with strategy instruction to facilitate knowledge retrieval as to reduce the cognitive load of using strategies.

The purpose of this meta-analysis was to assess the overall effects of automaticity training of fundamental literacy component skills (i.e., letter names/sounds, individual words) on reading fluency and comprehension. Another purpose was to assess if the effects of automaticity training varied for reading fluency and comprehension. We identified 11 research studies involving students in Grades 1-6 that met the inclusion criteria for this meta-analysis. A total of 83 effect sizes (Hedges g, corrected for sample size bias) were extracted from these studies. These studies were double-coded for specific features (e.g., student age, student grade, type of automaticity training). We meta-analyzed the effect sizes using a multi-level meta-analytic model and examined whether the outcome measure type (comprehension or fluency) moderated the effects of automaticity training. We also analyzed for publication bias. The overall effect size for automaticity training of fundamental literacy component skills on reading fluency and comprehension was 0.28, although it was not statistically significant. Shifting units of analysis approach indicated there was a statistically significant effect found for reading fluency outcomes [g = 0.48 (CI = 0.23, 0.72)] but not for reading comprehension (ES = 0.17 ns). Limitations and recommendations for future research are discussed.

The paper proposes a method based on kernel principal component analysis (KPCA) and multi-scale temporal convolution network (MTCN) for identifying faults in lithium-ion batteries, which is crucial for ensuring the safe and stable operation of energy-storage systems. Lithium-ion batteries are the primary component of energy storage units. The method involves the following steps: First, fault data are normalized, and KPCA is used for dimensionality reduction and single fault detection to reduce computational complexity and improve data reliability. According to the different types of overcharge faults and unknown faults, KPCA is used to reduce the data from the original dimension to 2 or 4 dimensions. The KPCA model is trained using the normal data corresponding to the two groups of fault data, and the fault data are inputted as the test data. The results show that the SPE statistic and the T2 statistic considerably exceed the control limit, verifying the reliability of the data. Then, the data are labeled according to the fault type: the overcharge data are labeled as D0 (normal) and D1 (fault), and the unknown fault data as F0 (normal) and F1 (fault). The labeled data are divided into training and test sets according to a specific proportion. Afterward, the MTCN model is trained with the training dataset, and its hyperparameters are optimized with the frost algorithm to improve model accuracy. Finally, the trained MTCN model is used to classify the test dataset. The method is validated on two groups of data: overcharge fault data and unknown fault data. The results show that the frost algorithm can optimize the hyperparameters after about 20 iterations. Compared with LSTM and CNN, which are also optimized by the frost algorithm, MTCN achieves a higher classification accuracy, reaching 99.265% and 99.688%, on the overcharge fault dataset and unknown fault dataset, respectively, while maintaining comparable performance to Xception and ResNet50. Additionally, to verify the influence of the training data amount, the training and test sets are divided according to different proportions, and the three algorithms are tested. KPCA verifies the reliability of charging fault and unknown fault data, and the results show that MTCN has the highest classification accuracy, especially on the overcharge fault dataset. Owing to the low dimensionality of the original data set, LSTM and CNN exhibit poor classification performance. In contrast, MTCN can extract more temporal information, achieving high classification accuracy. These results demonstrate the effectiveness and superiority of the method in fault diagnosis of lithium-ion batteries.