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The method described in ISO 17497-1 standard has been worldwide adopted to measure the random-incident scattering coefficient of irregular surfaces either in a full-size reverberation room or in a scale model. In practical measurements, the sound energy propagation and decay at high frequencies (over 10 kHz) are easily affected by small operating errors and by tiny changes in the measurement conditions. It is difficult to obtain the same accuracy of results in the scale model as that in a full-size reverberation room while maintaining the same setup in both tests as suggested in the standard. This study performed a series of experiments in a 1:10 scale reverberation room to investigate the influence of several practical aspects on measurement accuracy. A maximum length sequence diffuser, with a design frequency of 1700 Hz (full-size value), was used as the specimen. All results obtained in the scaled reverberation room were compared to the reference values measured in a full-size reverberation room. Five factors were studied, which included different recording methods (double-channel and eight-channel), number of source positions (one and four), number of receiver positions (one, two, four and eight), turntable rotation conditions (full-turn or half-turn rotation during T 3 and T 4 measurements), and numbers of averages (24, 36 and 72). The results show that the first three factors above have greater impacts on the measurement accuracy of the scale model experiments than the remaining factors. When the multichannel recording method and four source positions were used in the scaled tests, the measured scattering coefficient values were more reliable, appearing much closer to the reference data over the whole frequency range. Furthermore, applying a half-turn of the turntable (in reference to the measurements of the axisymmetric diffusers) and a 36-impulse coherent average can greatly reduce the test duration without decreasing the measurement accuracy. [ABSTRACT FROM AUTHOR]

A Mg–8Gd–3Y–0.4Zr (GWK830, G, W, and K refer to Gd, Y and Zr respectively) alloy was free-forged, and the effects of forging temperature and total deformation on forgeability, microstructure and mechanical properties of the alloy were investigated. For forgeability, the suitable temperature is above 380 °C, and a total deformation up to 45% is acceptable at 430 °C. With forging temperature increasing from 380 °C to 430 °C, considerable dynamic recrystalization (DRX) took place at the expense of twinning, resulting in the improvement of mechanical properties. But at 480 °C, complete DRX grains fully consumed the deformation twin and coarsened, leading to the decrease of tensile properties. Holding the forging temperature at 430 °C and raising total deformation, DRX and texture with c -axis of most grains normal to elongation direction (ED) were intensified evidently, accounting for the increasing mechanical properties along ED and the texture also enhanced the anisotropy of tensile properties between ED and forging direction (FD). Furthermore, yield strength (YS) of 276 MPa, ultimate tensile strength (UTS) of 353 MPa and elongation of 9.25% were obtained when the free-forging was carried out under a temperature of 430 °C and a total deformation of 45%. [ABSTRACT FROM AUTHOR]

The traditional literature analysis mainly focuses on the literature metadata such as topics, authors, keywords, references, and rarely pays attention to the main content of papers. However, in many scientific domains such as science, computing, engineering, the methods and datasets involved in the papers published carry important information and are quite useful for domain analysis and recommendation. Method and dataset entities have various forms, which are more difficult to recognize than common entities. In this paper, we propose a novel Method and Dataset Entity Recognition model (MDER), which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model is the first to combine rule embedding, a parallel structure of Convolutional Neural Network (CNN) and a two-layer Bi-directional Long Short-Term Memory (BiLSTM) with the self-attention mechanism. We evaluate the proposed model on datasets constructed from the published papers of different research areas in computer science. Our model performs well in multiple areas and features a good capacity for cross-area learning and recognition. Ablation study indicates that building different modules collectively contributes to the good entity recognition performance as a whole. The data augmentation positively contributes to model training, making our model much more robust. We finally apply the proposed model on PAKDD papers published from 2009–2019 to mine insightful results over a long time span. 1 1 PAKDD is the abbreviation of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Our source code and datasets are available at https://github.com/houlinlinvictoria/MDER. [ABSTRACT FROM AUTHOR]

• An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data. • Includes integrated dynamic distortion and slice-to-volume motion correction. • A robust multimodal registration approach which includes custom neonatal templates. • Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection. • Data analysis of 538 infants imaged at 26–45 weeks post-menstrual age. The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates. [ABSTRACT FROM AUTHOR]

Conductive hydrogels as ideal candidate materials for flexible sensors have exhibited many promising applications. However, complex application environments, such as low temperatures or underwater conditions, have introduced new requirements for hydrogel sensors. Herein, a high-performance conductive hydrogel based on carboxymethyl cellulose-polyaniline (CMC-PANI) submicron spheres, poly (vinyl alcohol) (PVA) and phytic acid (PA) was designed and fabricated via a dual design strategy. CMC-PANI particles were introduced to not only empower the good electromechanical performance to the hydrogels, but also enhance the mechanical properties. The obtained hydrogel exhibited good mechanical property, anti-freezing, anti-swellable behavior and recyclable performance. Resistive-type strain sensors assembled by the prepared hydrogels exhibited high pressure sensitivity (34.17×10 -2  kPa -1 ) and fast response time (100 ms), which can clearly detect the pulse beats. Moreover, the hydrogel sensors can achieve long-term stability, high sensitivity and fatigue resistance as an underwater sensor. Based on these favorable performances, the conductive polymer hydrogels may open up an enticing avenue for functional soft materials in health diagnostic and electronic components., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: To investigate the effect of aripiprazole on prolactin levels in patients with schizophrenia and analyze whether varying baseline prolactin levels affect the effectiveness and safety of aripiprazole, in a real-life diagnostic and therapeutic setting in a post-hoc analysis., Methods: In this post-hoc analysis, patients with schizophrenia in the acute phase were divided into an elevated-prolactin group and a normal-prolactin group. After 8 weeks of aripiprazole treatment, changes in the proportion of patients with an abnormal prolactin level were analyzed in both groups, and the efficacy and safety of aripiprazole were compared between the two groups., Results: The elevated-prolactin group had more women, a longer duration of disease, and lower Positive and Negative Syndrome Scale (PANSS) total and subscale scores at baseline compared with the normal-prolactin group (all P < 0.05), and there was no significant difference in the proportion of patients with prior use of antipsychotic medication between the two groups. Regardless of previous antipsychotic use, patients in both groups developed hyperprolactinemia (23/168 [13.7%] in those who had taken antipsychotics vs. 43/375 [11.4%] in those who had not). After 8 weeks of aripiprazole treatment, the proportion of patients with abnormal prolactin in the elevated-prolactin group significantly decreased with prolonged treatment (P < 0.001), and aripiprazole had no significant effect on the normal-prolactin group (P = 0.250). Normal-prolactin group showed better efficacy than the elevated-prolactin group, and the differences in efficacy between the two groups was observed from week 4 to the endpoint (all p<0.05). In total, 87.2% (68/78) patients experienced mild to moderate adverse events in the elevated-prolactin group, which was significantly more frequent compared with the normal-prolactin group 71.0% (365/514)., Conclusions: In this real-world study, for patients with acute schizophrenia, aripiprazole was effective in lowering the proportion of patients with abnormal prolactin levels, while it had no significant effect on patients with normal baseline prolactin. After adjusting for factors such as sex, age, prior antipsychotic drugs use history and disease severity, effectiveness and safety of aripiprazole in patients with normal baseline prolactin was significantly better than that in patients with elevated baseline prolactin., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Su, Liao, Fang, Gao, Xu, Duan, Yu, Yang, Chen, Liu, Yan, Yao, Li, Wang, Wu, Zhang and Si.)

The prognosis of fracture is directly related to several factors. Due to the limitations of existing treatment strategies, there are still many fractures with poor healing. Bone marrow mesenchymal stem cells (BMSCs) have the potential to differentiate into osteoblasts and chondrocytes. Therefore, BMSC transplantation is promised as an effective method for treating bone fractures. We aim to explore whether silently expressing sclerostin gene (SOST) can promote bone formation through the SOST/Wnt/β-catenin signal pathway. We isolated rat BMSCs and the target gene (SOST shRNA) was transduced into them for osteogenic induction. The results showed that SOST significantly inhibited the proliferation and osteogenic differentiation of BMSCs during osteogenic induction, whereas silently expressing SOST not only increased the number of surviving BMSCs but also promoted the expression of osteogenesis-related proteins RUNX2, osteoprotegerin, Collagen I (COL-I), and bone morphogenetic protein-2 during osteogenic induction. The results of imaging examination in rats show that downregulating the expression of SOST can promote the formation of bony callus and the transformation of cartilage tissue into normal bone tissue, and then accelerate the healing of osteoporotic fracture. In addition, we also found that SOST silencing can activate the Wnt/β-catenin pathway to achieve these effects. In conclusion, SOST silencing can promote the proliferation and osteogenic differentiation of BMSCs in situ, and therefore may enhance the therapeutic efficiency of BMSC transplantation in OPF.

Multi-modal attention mechanisms have been successfully used in multi-modal graph learning for various tasks. However, existing attention-based multi-modal graph learning (AMGL) architectures heavily rely on manual design, requiring huge effort and expert experience. Meanwhile, graph neural architecture search (GNAS) has made great progress toward automatically designing graph-based learning architectures. However, it is challenging to directly adopt existing GNAS methods to search for better AMGL architectures because of the search spaces that only focus on designing graph neural network architectures and the search objective that ignores multi-modal interactive information between modalities and long-term content dependencies within different modalities. To address these issues, we propose an automated attention-based multi-modal graph learning architecture search (AutoAMS) framework, which can automatically design the optimal AMGL architectures for different multi-modal tasks. Specifically, we design an effective attention-based multi-modal (AM) search space consisting of four sub-spaces, which can jointly support the automatic search of multi-modal attention representation and other components of multi-modal graph learning architecture. In addition, a novel search objective based on an unsupervised multi-modal reconstruction loss and task-specific loss is introduced to search and train AMGL architectures. The search objective can extract the global features and capture multi-modal interactions from multiple modalities. The experimental results on multi-modal tasks show strong evidence that AutoAMS is capable of designing high-performance AMGL architectures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Mucins secreted by mucous cells constitute a core part of the defense line against the invasion of pathogens. However, mucins' structure and immunological functions remain largely unknown in teleost fish. In this study, two typical mucins, Muc2 and Muc5ac of flounder (Paralichthys olivaceus), were cloned and their physicochemical properties, structure and conservation were analyzed. Notably, specific antibodies against flounder Muc2 and Muc5ac were developed. It was verified at the gene and protein level that Muc2 was expressed in the hindgut and gills but not in the skin, while Muc5ac was expressed in the skin and gills but not in the hindgut. After flounders were immunized by immersion with inactivated Edwardsiella tarda, Muc2 and Muc5ac were significantly upregulated at both the gene expression and protein levels, and Muc2 + /Muc5ac + mucous cells proliferated and increased secretion of Muc2 and Muc5ac. Moreover, Muc2 and Muc5ac exerted retention and clearance effects on E. tarda in a short period (within 1 dpi). These results revealed the characterization of fish mucins Muc2 and Muc5ac at the protein level and clarified the role of mucins as key guardians to maintain the mucus barrier, which advanced our understanding of teleost mucosal barrier., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Conductive hydrogels as promising candidate materials for flexible strain sensors have gained considerable attentions. However, it is still a great challenge to construct hydrogel with multifunctional performance via natural polymer. Herein, a novel multifunctional conductive hydrogel based on methylcellulose and cellulose nanocrystal was prepared via a facile and low-cost strategy. Methylcellulose (MC) was introduced to not only guarantee the stability of tannic acid coated cellulose nanocrystal (TA@CNCs) in LiCl solution, but also improve anti-freezing ability. The obtained hydrogel exhibited high transparency (98 % at 800 nm), good stretchability (663.1 %), low temperature tolerance (-23.9 °C), superior conductivity (2.89 S/m) and excellent UV shielding behavior. Flexible strain sensor assembled by the prepared hydrogels can be used to detect human body motions include subtle and large motions, and exhibited good sensitivity and stability over a wide temperature range. Multiple flexible hydrogels can also be assembled into a 3D sensor array to detect the distribution and magnitude of spatial pressure. Therefore, the hydrogels prepared via natural polymers will have broad application prospects in wearable devices, electronic skin and multifunctional sensor components., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

The development of environmentally friendly and low-cost adsorbents with high adsorption capacity remains a challenge. Herein, chitin nanofiber-polydopamine composite materials (CNDA) have been obtained by surface modification of chitin nanofiber using dopamine. According to the results of transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectrometer (FTIR), and X-ray photoelectron spectrometer (XPS), polydopamine have been successfully coated on the surface of chitin nanofiber (ChNF). The ability to remove methylene blue (MB) has been analyzed via standard adsorption experiments, indicating that the maximum adsorption capacity (q max ) can reach 196.6 mg/g at MB initial concentration of 50 mg/L. Most importantly, the adsorption kinetics, isotherm, and thermodynamics were used to investigate the MB adsorption mechanism on composites. This indicated that the polydopamine on the surface of chitin nanofiber (ChNF) plays an important role in the MB dye adsorption. Moreover, the removal ability of CNDA to metal ions has also been investigated, indicating high capacities for Fe 3+ , Mn 2+ , Cu 2+, and Ni 2+ . Based on their biodegradability and good adsorption capacity, the CNDA composite material can be considered a promising adsorbent for wastewater treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure's features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics.

Motivation: Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge., Results: In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness., Availability and Implementation: https://github.com/BeObm/AutoCDRP., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Patients who suffer comorbidity of major depressive disorder (MDD) and chronic pain (CP) maintain a complex interplay between maladaptive prospective memory (PM) and retrospective memory (RM) with physical pain, and their complications are still unknown., Aims: We aimed to focus on the full cognitive performance and memory complaints in patients with MDD and CP, patients with depression without CP, and control subjects, considering the possible influence of depressed affect and chronic pain severity., Methods: According to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders and the criteria given by the International Association of Pain, a total of 124 participants were included in this cross-sectional cohort study. Among them, 82 depressed inpatients and outpatients from Anhui Mental Health centre were divided into two groups: a comorbidity group(patients with MDD and CP) (n=40) and a depression group (patients with depression without CP) (n=42). Meanwhile, 42 healthy controls were screened from the hospital's physical examination centre from January 2019 to January 2022. The Hamilton Depression Rating Scale-24 (HAMD-24) and Beck Depression Inventory-II (BDI-II) were used to evaluate the severity of depression. The Pain Intensity Numerical Rating Scale (PI-NRS), Short-Form McGill Pain Questionnaire-2 Chinese version (SF-MPQ-2-CN), Montreal Cognitive Assessment-Basic Section (MoCA-BC), and Prospective and Retrospective Memory Questionnaire (PRMQ) were used to assess pain-related features and the global cognitive functioning of study participants., Results: The impairments in PM and RM differed remarkably among the three groups (F=7.221, p<0.001; F=7.408, p<0.001) and were severe in the comorbidity group. Spearman correlation analysis revealed the PM and RM were positively correlated with continuous pain and neuropathic pain (r=0.431, p<0.001; r=0.253, p=0.022 and r=0.415, p<0.001; r=0.247, p=0.025), respectively. Regression analysis indicated a significant positive relationship between affective descriptors and total BDI-II score (β=0.594, t=6.600, p<0.001). Examining the mediator pathways revealed the indirect role of PM and RM in patients with comorbid MDD and CP., Conclusions: Patients with comorbid MDD and CP presented more PM and RM impairments than patients with MDD without CP. PM and RM are possibly mediating factors that affect the aetiology of comorbid MDD and CP., Trial Registration Number: chiCTR2000029917., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Childhood trauma might be a modifiable risk factor among adults with serious mental illness. However, the correlation of child trauma and suicide is unclear, which were cited most frequently as the biggest challenge to schizophrenia (SCZ) patients in China. We aim to study relationships between child trauma and suicide in SCZ patients of different disease stages., Methods: Ninety-one participants were included and divided into two groups, namely, first-episode group ( n = 46), relapsed group ( n = 45). The Positive and Negative Syndrome Scale was used to evaluate the severity of psychotic symptoms. The Beck's Suicide Intent Scale and The Nurses' Global Assessment of Suicide Risk were conducted by patient self-report to assess suicide symptom. The childhood trauma questionnaire was used to estimate severity of traumatic stress experienced during childhood., Results: Childhood trauma and different dimensions of suicide were significantly higher in the relapsed group than first-episode group ( P < 0.01, respectively). BMI has a significant positive relationship with recent psychosocial stress ( β = 0.473, t = 3.521, P < 0.001) in first-episode group. As in relapsed group, BMI has a positive effect between severe mental illness and suicide ideation ( β = 0.672, t = 5.949, P < 0.001; β = 0.909, t = 2.463, P < 0.001), Furthermore, emotional neglect presented positively related to the suicide risk and proneness to suicidal behavior ( β = 0.618, t = 5.518, P < 0.001; β = 0.809, t = 5.356, P < 0.001)., Conclusion: Relapsed group of patients had significantly more severe childhood trauma, recent psychosocial stress, suicidal risk and proneness to suicidal behavior. BMI and emotional neglect are unique predictors for different dimensions of suicide., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer YH declared a shared affiliation with the author PJ to the handling editor at the time of review., (Copyright © 2022 Cheng, Ju, Xia, Chen, Li, Gao, Zhang, Yan, Cheng, Pei, Chen, Zhu and Zhang.)

Acidizing technology is an important means to increase production in oil-gas reservoirs. In recent years, acidizing technology has been widely used to increase the permeability of coal seams to enhance gas extraction, where acidizing fluid is the key factor to determine the permeability improvement effect by acidizing technology. In order to clarify the influence of mixed acid fluid on the pore structure of high rank coal and seek the optimal mixed acid fluid suitable for acidizing and permeability improvement of high rank coal in the Jiaozuo coal mine area. Taking the Jiulishan Mine in the Jiaozuo mining area as an example, low field nuclear magnetic resonance (LFNMR) test and static dissolution test were conducted to obtain the T 2 spectrum, porosity, movable fluid saturation, pore throat distribution, nuclear magnetic permeability, and dissolution rate of coal samples before and after treatment with distilled water and three mixed acid fluids. On this basis, the influence of mixed acid fluid on the pore structure of high rank coal was analyzed and the optimal mixed acid fluid suitable for high rank coal was selected. The results showed that the pore size, number, and volume of all kinds of pore sizes of coal samples treated with distilled water all decreased, which was manifested by the decrease of effective porosity and nuclear magnetic permeability. After acidification, the proportion of micropore volume in coal decreased significantly, the number and proportion of pore volume of mesopores and macropore-microfractures increased significantly, and the connectivity between mesopores and macropore-microfractures was enhanced, which was characterized by the increase in effective porosity and nuclear magnetic permeability of coal samples. After acidification, the pore-throat ratio of adsorption pores of all coal samples decreased, while the pore-throat ratio of seepage pores increased. By comparatively analyzing the change law of pore structure of coal samples before and after acidizing with three kinds of mixed acid fluids, the optimal mixed acid fluid suitable for acidizing and permeability improvement of high rank coal in the Jiaozuo coal mine area was selected, which was 12%HCL +3%HF., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Background: Although comorbidity of major depressive disorder (MDD) and chronic pain (CP) has been well-studied, their association with pain catastrophizing is largely elusive. This study aimed to investigate the potential effects of pain catastrophizing in patients with a comorbidity., Methods: In total, 140 participants were included in this study and divided into three groups according to the Diagnostic and Statistical Manual of Mental Disorders and the International Association for the study of pain (i.e., the comorbidity group: patients with depression with chronic pain, n = 45; depression group: patients with depression without chronic pain, n = 47; and healthy controls: n = 48). The Hamilton Depression Rating Scale (HAMD)-24 and Hamilton Anxiety Rating Scale (HAMA)-14 were used by professional psychiatrists to evaluate the severity of depression and anxiety. Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) were conducted by patients' self-report to assess the symptom severity. The pain intensity numerical rating scale (PI-NRS) was used to assess the pain intensity. Pain Catastrophizing Scale (PCS) and Pain Anxiety Symptoms Scale (PASS) were used to estimate pain-related negative thinking., Results: The results showed that PASS and PCS scores were significantly different among the three groups. Particularly, the scores in the comorbidity group were the highest. The Pearson correlation analysis revealed a positive correlation between PCS (including the patients' helplessness, magnification, rumination, and total scores) and the severity of depression symptoms, anxiety symptoms, and pain intensity ( P < 0.05). A stepwise regression analysis further demonstrated that the total PCS score, high monthly income level, and BDI score had positive impacts on PASS ( P < 0.05). We also found that the total BDI score, disease course ≥1 year, and pain intensity had positive effects on PCS ( P < 0.05), whereas years of education (≤ 12 years) had a negative effect on PCS ( P = 0.012). In all, we have clearly demonstrated that PCS and PASS could serve as potentially predictive factors in patients suffering from comorbidity of MDD and CP., Conclusion: Our results suggested that the pain-related catastrophic thinking and anxiety were more severe in the comorbidity group than in MDD-only group and healthy group. Pain-related catastrophizing thoughts and anxiety may have potentially effects on the comorbidity of depression and chronic pain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past collaboration with one of the author's, CZ., (Copyright © 2022 Chen, Ju, Xia, Cheng, Gao, Zhang, Gao, Cheng, Yu, Yan, Wang, Zhu and Zhang.)

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Edwards, Rueckert, Smith, Abo Seada, Alansary, Almalbis, Allsop, Andersson, Arichi, Arulkumaran, Bastiani, Batalle, Baxter, Bozek, Braithwaite, Brandon, Carney, Chew, Christiaens, Chung, Colford, Cordero-Grande, Counsell, Cullen, Cupitt, Curtis, Davidson, Deprez, Dillon, Dimitrakopoulou, Dimitrova, Duff, Falconer, Farahibozorg, Fitzgibbon, Gao, Gaspar, Harper, Harrison, Hughes, Hutter, Jenkinson, Jbabdi, Jones, Karolis, Kyriakopoulou, Lenz, Makropoulos, Malik, Mason, Mortari, Nosarti, Nunes, O’Keeffe, O’Muircheartaigh, Patel, Passerat-Palmbach, Pietsch, Price, Robinson, Rutherford, Schuh, Sotiropoulos, Steinweg, Teixeira, Tenev, Tournier, Tusor, Uus, Vecchiato, Williams, Wright, Wurie and Hajnal.)

Background: Cardiovascular disease (CVD) risk factors such as dyslipidemia and systemic aberrant inflammatory processes may occur in patients with psychotic disorders, which may cause increased mortality. The interplay between immune and metabolic markers and its contribution to the clinical symptoms of schizophrenia (SCZ) remain unclear. This study aimed to examine the association of a series of inflammatory factors, plasma biochemical indicators, and SCZ clinical symptomatology with the severity of SCZ symptoms., Methods: A total of 115 participants, including 79 first-episode drug-naïve patients with SCZ and 36 healthy controls, were enrolled in this study. Semi-structured interviews were used to collect sociodemographic data, family history of SCZ, and medical and psychiatric history. The Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS) were administered by a clinical psychiatrist to evaluate the symptom severity of patients with SCZ. Plasma inflammatory cytokines were measured by a fully automated electrochemiluminescent immunoassay (Meso Scale Discovery)., Results: Blood routine, biochemical, and inflammation cytokine test results showed that the levels of white blood cell count, neutrophil count, natrium, CRP, IL-8, IL-6, IL-13, and IL-16 significantly increased in the case group than in the healthy controls ( p < 0.05), whereas levels of red blood cell count, hemoglobin concentration, mean corpuscular hemoglobin concentration, total protein, albumin, total bile acid, high-density lipoprotein (HDL), apolipoprotein A1, blood urea nitrogen, kalium and IL-15 were lower than in the healthy controls ( p < 0.05). Correlation network analysis results shown that the natrium, HDL and red blood cell count were the top 3 factors closely to with BPRS and PANSS related clinical symptoms among of correlation network (degree = 4). ROC curve analysis explored the IL-16, IL-8, IL-13, IL-15, natrium, and HDL had highly sensitivity and specificity to the predictive validity and effectiveness for SCZ symptoms., Conclusion: Our study revealed a complex interactive network correlation among the cardiovascular risk factors, biological immunity profiles, and psychotic symptoms in first-episode patients. Abnormal inflammatory factors and CVD risk factors had high sensitivity and specificity for predicting SCZ symptoms. Generally, our study provided novel information on the immune-related mechanisms involved in early CVD risk in patients with psychotic disorders., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yan, Chen, Ju, Gao, Zhang, Li, Wang, Zhang, Li, Xia, Zhu and Zhang.)

Value-based decision making plays an important role in social interaction. Previous studies have reported that individuals with autism spectrum disorder (ASD) exhibit deficits in terms of decision making. However, it is still unknown clearly whether individuals with high autistic traits within nonclinical populations employ abnormal neural substrates in value-based decision-making. To explore this issue, we investigated value-based decision making and its neural substrates in individuals with high and low autistic traits within a typically developing population who completed the revised Iowa gambling task (IGT) based on measurements of event-related potentials (ERPs). The IGT net scores were significantly lower in the group with high autistic traits than the group with low autistic traits in the fifth and sixth blocks. The ERP results showed that the feedback-related negativity (FRN) amplitude in individuals with high autistic traits allowed slight discrimination between positive and negative feedback in the low-risk option. The event-related spectral perturbations (ERSPs) and inter-trial coherence (ITC) of the theta-band frequency were also lower in the group with high autistic traits than the group with low autistic traits in the loss low-risk option. The results obtained in this study indicate that individuals with high autistic traits exhibit an unusual negative feedback process and relevant neural substrate. The FRN amplitude and theta-band oscillation may comprise a neural index of abnormal decision-making processes in individuals with high autistic traits. This study of a small sample may be considered an important step toward a more comprehensive understanding of the autism "spectrum" within a nonclinical population based on cognitive neuroscience., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Osteoporosis (OP) is a metabolic disease characterized by decreased bone mass and increased risk of fragility fractures, which significantly reduces the quality of life. Stem cell-based therapies, especially using bone marrow mesenchymal stem cells (BMSCs), are a promising strategy for treating OP. Nevertheless, the survival and differentiation rates of the transplanted BMSCs are low, which limits their therapeutic efficiency. Icariin (ICA) is a traditional Chinese medicine formulation that is prescribed for tonifying the kidneys. It also promotes the proliferation and osteogenic differentiation of BMSCs, although the specific mechanism remains unclear. Based on our previous research, we hypothesized that ICA promotes bone formation via the sclerostin/Wnt/ β -catenin signaling pathway. We isolated rat BMSCs and transfected them with sclerostin gene ( SOST ) overexpressing or knockdown constructs and assessed osteogenic induction in the presence or absence of ICA. Sclerostin significantly inhibited BMSC proliferation and osteogenic differentiation, whereas the presence of ICA not only increased the number of viable BMSCs but also enhanced ALP activity and formation of calcium nodules during osteogenic induction. In addition, the osteogenic genes including Runx2, β -catenin, and c-myc as well as antioxidant factors (Prdx1, Cata, and Nqo1) were downregulated by sclerostin and restored by ICA treatment. Mechanistically, ICA exerted these effects by activating the Wnt/ β -catenin pathway. In conclusion, ICA can promote the proliferation and osteogenic differentiation of BMSCs in situ and therefore may enhance the therapeutic efficiency of BMSC transplantation in OP., Competing Interests: All the authors declare they have no competing interests., (Copyright © 2021 Jianliang Gao et al.)

Background: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes., Results: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species., Conclusions: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species.