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Abstract Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, and respiratory difficulties. This study aimed to identify novel markers for infantile-onset Pompe disease by analyzing key genes and immune cells infiltrating affected skeletal muscles, building on existing research linking its progression to the immune cell infiltration. The datasets GSE38680 and GSE159062 were downloaded and differential expression genes (DEGs) were identified. Potential markers were screened using support vector machine recursive feature elimination (SVM-RFE) analysis and least absolute shrinkage and selection operator (LASSO) regression models. To analyze DEGs and immune processes, 22 immune cell types in affected tissues were examined using CIBERSORT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed significant enrichment in the calcium signaling pathway, phosphatidylinositol signaling system, ubiquitin-mediated proteolysis and JAK-STAT signaling pathway. Machine learning identified GPNMB, CALML6 and TRIM7 as key genes. Immune cell infiltration analysis showed that the expression levels of the above three genes were closely related to immune cells in our target population under study, including T cells regulatory (Tregs), monocytes, macrophages M0, resting and activated dendritic cells, naive and memory B cells. Overall, our study results may provide new clues for exploring markers related to Pompe disease by highlighting key genes and their relationship with immune infiltration, offering insights into the disease’s development. However, previous studies have shown limited evidence of significant immune cell infiltration in muscle biopsies from Pompe patients, and this lack of direct evidence necessitates further investigation and experimental validation in laboratory settings to confirm these associations.

Abstract Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.

Abstract Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER’s superior accuracy and robustness over existing methods.

Objective: Panic disorder (PD) is a common disabling condition characterized by recurrent panic attacks. Emotional and behavioral impairments are associated with functional connectivity (FC) and network abnormalities. We used whole-brain FC, modular networks, and graph-theory analysis to investigate extensive network profiles in PD. Methods: Functional magnetic resonance imaging (fMRI) data from 82 subjects with PD and 97 healthy controls were included. Intrinsic FC between each pair of 160 regions, six intra-network, and 15 inter-network FCs were analyzed. Topological properties were explored. Results: PD patients showed altered FCs within the right insula, between frontal cortex-posterior cingulate cortex (PCC), frontal cortex-cerebellum, and PCC-occipital cortex (corrected p < 0.001). Lower connections within the sensorimotor network (SMN) and SMN-occipital network (OCN) were detected (p < 0.05). Various decreased global and local network features were found in PD (p < 0.05). In addition, significant correlations were found between PD symptoms and nodal efficiency (Ne) in the insula (r = -0.273, p = 0.016) and intra-insula FC (r = -0.226, p = 0.041). Conclusion: PD patients present with abnormal functional brain networks, especially decreased FC and Ne within the insula, suggesting that dysfunction of information integration plays an important role in PD.

Abstract Ossification of the Posterior Longitudinal Ligament (OPLL) is a degenerative hyperostosis disease characterized by the transformation of the soft and elastic vertebral ligament into bone, resulting in limited spinal mobility and nerve compression. Employing both bulk and single-cell RNA sequencing, we elucidate the molecular characteristics, cellular components, and their evolution during the OPLL process at a single-cell resolution, and validate these findings in clinical samples. This study also uncovers the capability of ligament stem cells to exhibit endothelial cell-like phenotypes in vitro and in vivo. Notably, our study identifies LOXL2 as a key regulator in this process. Through gain-and loss-of-function studies, we elucidate the role of LOXL2 in the endothelial-like differentiation of ligament cells. It acts via the HIF1A pathway, promoting the secretion of downstream VEGFA and PDGF-BB. This function is not related to the enzymatic activity of LOXL2. Furthermore, we identify sorafenib, a broad-spectrum tyrosine kinase inhibitor, as an effective suppressor of LOXL2-mediated vascular morphogenesis. By disrupting the coupling between vascularization and osteogenesis, sorafenib demonstrates significant inhibition of OPLL progression in both BMP-induced and enpp1 deficiency-induced animal models while having no discernible effect on normal bone mass. These findings underscore the potential of sorafenib as a therapeutic intervention for OPLL.

ObjectiveIn the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning.MethodTo overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification.ResultsThe proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others.ConclusionOur method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Abstract Pyruvate kinase M2 (PKM2), a subtype of pyruvate kinase, plays a crucial role as a key enzyme in the final step of glycolysis. It is involved in regulating the tumor microenvironment and accelerating tumor progression. However, the relationship between PKM2 expression and the prognosis and immune infiltration remains unclear in lung cancer. In this study, we analyzed PKM2 expression in pan-cancer, and investigated its association with prognosis and immune cell infiltration of lung cancer by using multiple online databases, including Gent2, Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, Kaplan–Meier plotter, and The Human Protein Atlas (HPA). The results showed that PKM2 expression is elevated in tumor tissues compared with the adjacent normal tissues of most cancers, including lung cancer. Prognostic analysis indicated that high expression of PKM2 was associated with poorer prognosis in overall lung cancer patients, especially in lung adenocarcinoma (LUAD). Notably, PKM2 exhibited a strong correlation with B cells and CD4+ T cells in LUAD; and with B cells, CD8+ T cells, CD4+ cells, and macrophages in lung squamous cell carcinoma (LUSC). Furthermore, PKM2 expression displayed a significant negative correlation with the expression of immune cell markers in both LUAD and LUSC. These findings suggested that PKM2 could serve as a promising prognostic biomarker for lung cancer and provided insights into its essential role in modulating the immune cell infiltration.

Accurate monitoring of forest aboveground biomass (AGB) is vital for sustainable forest management. Generally, the AGB is estimated by combining satellite images and field measurements. Nevertheless, field plots are inaccessible in complex forest areas. Due to the limitations related to either optical or SAR data alone, combining the two data types is indispensable. Here, we explored the LiDAR-derived biomass as training/testing samples for the combination of yearly time-series of Sentinel-1 and Sentinel-2 images to estimate AGB in north-eastern Conghua, Guangdong province, China. The AGB reference map derived from airborne LiDAR data and field plots could provide more samples for satellite-based AGB estimation with a limited amount of sampling plots. We designed four groups of experiments based on diverse Sentinel-1 and Sentinel-2 variables, including backscatter, backscatter indices, texture features, spectral bands, vegetation indices, and biophysical features. Four different prediction methods (support vector regression, multi-layer perceptron neural network, K-nearest neighbour, and random forest (RF)) were separately used to estimate AGB. Results showed that the RF model achieved the best accuracy for AGB mapping. All Sentinel-1 and Sentinel-2 experiment (R2: 0.72, RMSEr: 17.65%) performed better than the monthly complementary experiment (R2: 0.66, RMSEr: 19.01%). Additionally, the arid period images were observed to be sensitive for estimating AGB. The most contributing Sentinels predictors were determined via a sequential forward selection. Consequently, the proposed methodology has great potential for low-cost, large-scale, and high-precision AGB estimation.

This study investigates methods to enhance the efficiency of CO2 capture using organic amine absorption and compares the performance of traditional and novel amine solvents. It reviews various single-component and mixed amine absorbents, as well as catalysts used in these methods, highlighting the superiority of mixed amine absorbents over single-component amine absorbents in CO2 absorption and desorption. Additionally, the study explores the catalytic mechanisms and effects of catalysts in the CO2 absorption/desorption process with amine solvents and provides an outlook on future research directions. The aim is to promote the widespread adoption of organic amine absorption technology in industrial applications and to contribute to the development of more sustainable and efficient CO2 capture technologies.

Abstract Goethite nanoparticles modified biochar (FBC) could address the weak effectiveness of conventional biochar commonly to process heavy metal(loids) (HMs) co-contamination with different charges. However, few studies have focused on the change of soil mechanical properties after stabilization. In this study, FBC was synthesized to stabilize simultaneously arsenic (As (V)) (anions) and cadmium (Cd (II)) (cations) in co-contaminated soils. Batch adsorption, leaching toxicity, geotechnical properties and micro-spectroscopic tests were comprehensively adopted to investigate the stabilization mechanism. The results showed that FBC could immobilize As (V) mainly through redox and surface precipitation while stabilizing Cd (II) by electrostatic attraction and complexation, causing soil agglomeration and ultimately making rougher surface and stronger sliding friction of contaminated soils. The maximum adsorption capacity of FBC for As (V) and Cd (II) was 31.96 mg g−1 and 129.31 mg g−1, respectively. Besides, the dosages of FBC required in contaminated soils generally were approximately 57% higher than those in contaminated water. FBC promoted the formation of small macroaggregates (0.25–2 mm) and the shear strengths of co-contaminated soils by 21.40% and 8.34%, respectively. Furthermore, the soil reutilization level was significantly improved from 0.14–0.46 to 0.76–0.83 after FBC stabilization according to TOPSIS method (i.e., technique for order preference by similarity to an ideal solution). These findings confirm the potential of FBC in immobilizing As (V) and Cd (II) of co-contaminated soils and provide a useful reference for green stabilization and remediation of HMs co-contaminated sites. Graphical Abstract

High‐grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5‐year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced‐stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high‐quality ovary‐specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan‐human spectral library (DPHL), this spectral library provides 10% more ovary‐specific and 3% more ovary‐enriched proteins. This library was then applied to analyze data‐independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six‐protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log‐rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary‐specific spectral library for targeted proteome analysis, and propose a six‐protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT‐IDS treatment.

Based on Zhaogu No. 1 Mine’s characters that are the overlying thick alluvium, multi-aquifers (groups) and thin bedrock, the water pressure of the gravel aquifer under the alluvial layer reaches 4.0 MPa, defined a high-pressure aquifer. To determine the influence of bedrock properties on roof water inrush and sand bursting, and ensure the normal mining around the thin bedrock area under groups, there were tests, point loading, dry saturated water absorption rate and indoor disintegration, of bedrock samples taken from hydrological survey holes to determine those properties and influence on retaining sand-proof pillars by analyzing the variation curves of various indexes of them with depth. The experiments’ results showed that the weathering depth of bedrock exceeds 20 m; the dry saturated water absorption rate of mudstone in the vertical depth ranging of 0−6.5 m from the bottom interface of the alluvial layer is greater than 15%. The mudstone exposed to water features muddy disintegration, broken rock fragments and mud blocks, which means it is good water-proof performance of effective bridging mining cracks and a protective layer for waterproof coal pillars; as the strength of weathered mudstone below the alluvial layer 0 to 11.4 m is lower than it of the fine gravel aquifer in the lower that of 4.0 MPa, the sand control coal pillar’s protective layer that is greater more than 11.4 m is cannot be entirely composed of weathered mudstone; due to strong resistance to disintegration and lower dry saturated water absorption rate of sandstone, the protective layer cannot be entirely composed of weathered sandstone. The compressive strength of weathered sandstone, when it is higher than 4.0 MPa, can effectively resist the overlying water head pressure.

Abstract Partisan sorting in residential environments is an enduring feature of contemporary American politics, but little research has examined partisan segregation individuals experience in activity spaces through their daily activities. Relying on advances in spatial computation and global positioning system data on everyday mobility flows collected from smartphones, we measure experienced partisan segregation in two ways: place-level partisan segregation based on the partisan composition of its daily visitors and community-level experienced partisan segregation based on the segregation level of places visited by its residents. We find that partisan segregation experienced in places varies across different geographic areas, location types, and time periods. Moreover, partisan segregation is distinct from experienced segregation by race and income. We also find that partisan segregation individuals experience is relatively lower when they visit places beyond their residential areas, but partisan segregation in residential space and activity space is strongly correlated. Residents living in predominantly black, liberal, low-income, non-immigrant, more public transit-dependent, and central city communities tend to experience a higher level of partisan segregation.

Land Surface Temperature (LST) products obtained by thermal infrared (TIR) remote sensing contain considerable blank areas due to the frequent occurrence of cloud coverage. The studies on the all-time reconstruction of the cloud-covered LST of geostationary meteorological satellite LST products are relatively few. To accurately fill the blank area, a hybrid method for reconstructing hourly FY-4A AGRI LST under cloud-covered conditions was proposed using a random forest (RF) regression algorithm and Savitzky-Golay (S-G) filtering. The ERA5-Land surface cumulative net radiation flux (SNR) reanalysis data was first introduced to represent the change in surface energy arising from cloud coverage. The RF regression method was used to estimate the LST correlation model based on clear-sky LST and the corresponding predictor variables, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), surface elevation and slope. The fitted model was then applied to reconstruct the cloud-covered LST. The S–G filtering method was used to smooth the outliers of reconstructed LST in the temporal dimension. The accuracy evaluation was performed using the measured LST of the representative meteorological stations after scale correction. The coefficients of determination derived with the reference LST were all above 0.73 on the three examined days, with a bias of −1.13–0.39 K, mean absolute errors (MAE) of 1.46–2.4 K, and root mean square errors (RMSE) of 1.77–3.2 K. These results indicate that the proposed method has strong potential for accurately restoring the spatial and temporal continuity of LST and can provide a solution for the production and research of gap-free LST products with high temporal resolution.

Abstract Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder, with onset in childhood and a considerable likelihood to persist into adulthood. Our previous work has identified that across adults and adolescents with ADHD, gray matter volume (GMV) alteration in the frontal cortex was consistently associated with working memory underperformance, and GMV alteration in the cerebellum was associated with inattention. Recent knowledge regarding ADHD genetic risk loci makes it feasible to investigate genomic factors underlying these persistent GMV alterations, potentially illuminating the pathology of ADHD persistence. Based on this, we applied a sparsity-constrained multivariate data fusion approach, sparse parallel independent component analysis, to GMV variations in the frontal and cerebellum regions and candidate risk single nucleotide polymorphisms (SNPs) data from 341 unrelated adult participants, including 167 individuals with ADHD, 47 unaffected siblings, and 127 healthy controls. We identified one SNP component significantly associated with one GMV component in superior/middle frontal regions and replicated this association in 317 adolescents from ADHD families. The association was stronger in individuals with ADHD than in controls, and stronger in adults and older adolescents than in younger ones. The SNP component highlights 93 SNPs in long non-coding RNAs mainly in chromosome 5 and 21 protein-coding genes that are significantly enriched in human neuron cells. Eighteen identified SNPs have regulation effects on gene expression, transcript expression, isoform percentage, or methylation level in frontal regions. Identified genes highlight MEF2C, CADM2, and CADPS2, which are relevant for modulating neuronal substrates underlying high-level cognition in ADHD, and their causality effects on ADHD persistence await further investigations. Overall, through a multivariate analysis, we have revealed a genomic pattern underpinning the frontal gray matter variation related to working memory deficit in ADHD.

Abstract Background Although child maltreatment (CM) experiences are recognized risk factors for nonsuicidal self-injury (NSSI), the mechanisms underlying this relationship remain unclear. The purpose of this study was to examine whether difficulty in emotion regulation (DER) and depressive symptoms mediate the relationship between child maltreatment experiences and NSSI severity, adjusting for demographic variables. Methods The participants were 224 adolescent inpatients recruited from a hospital in China (mean age 15.30 years, SD = 1.83; 78.6% females). Study measures included the Clinician-Rated Severity of Nonsuicidal Self-Injury (CRSNSSI), Childhood Trauma Questionnaire (CTQ-SF), Difficulties in Emotion Regulation Scale (DERS), and Patient Health Questionnaire-9 (PHQ-9). The hypothesized chain mediation model was tested using the structural equation model. Results A total of 146 (65.18%) adolescents reported engaging in NSSI during the past 12 months, and 103 (45.98%) participants met the DSM-5 diagnostic criteria for NSSI. Emotional neglect (48.1%) and emotional abuse (46.1%) had the highest prevalence, followed by physical neglect (43.1%) and physical abuse (24.1%), whereas sexual abuse (12.5%) was the least prevalent form of CM. Separately, both DER and depressive symptoms significantly mediated the association between CM and NSSI, with DER being the strongest mediator, with an indirect effect of 49.40% (p = 0.014). At the same time, we also proved a potential chain-mediated pathway of DER and depression in the relationship between CM and NSSI. Conclusion Child maltreatment seems to play a role in the aetiology of NSSI. DER and depressive symptoms both have a mediating role in the relationship between CM and NSSI. Importantly, DER seems to be a mediator with a stronger indirect effect compared to depressive symptoms.

Spatially resolved proteomics is an emerging approach for mapping proteome heterogeneity. Here, the authors report a method based on the combination of hydrogel-based tissue transformation with mass spectrometry-based proteomics, that enables proteome profiling with a lateral resolution of 160 µm.

BackgroundThe amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous brain activity derived from resting-state functional magnetic resonance imaging (rs-fMRI). Previous research has suggested that abnormal ALFF values may be associated with major depressive disorder (MDD) and suicide attempts in adolescents. In this study, our aim was to investigate the differences in ALFF values between adolescent MDD patients with and without a history of suicide attempts, and to explore the potential utility of ALFF as a neuroimaging biomarker for aiding in the diagnosis and prediction of suicide attempts in this population.MethodsThe study included 34 adolescent depression patients with suicide attempts (SU group), 43 depression patients without suicide attempts (NSU group), and 36 healthy controls (HC group). Depression was diagnosed using a threshold score greater than 17 on the Hamilton Depression Rating Scale (HDRS). The rs-fMRI was employed to calculate zALFF values and compare differences among the groups. Associations between zALFF values in specific brain regions and clinical variables such as emotion regulation difficulties were explored using Pearson partial correlation analysis. Receiver-Operating Characteristics (ROC) analysis assessed the ability of mean zALFF values to differentiate between SU and NSU groups.ResultsSignificant differences in zALFF values were observed in the left and right inferior temporal gyrus (l-ITG, r-ITG) and right fusiform gyrus (r-FG) among the three groups (GRF corrected). Both SU and NSU groups exhibited increased zALFF values in the inferior temporal gyrus compared to the HC group. Furthermore, the SU group showed significantly higher zALFF values in the l-ITG and r-FG compared to both the NSU group and the HC group. Partial correlation analysis revealed a negative correlation between zALFF values in the left superior and middle frontal gyrus (l-SFG, l-MFG) and the degree of emotional dysregulation in the SU group (R = −0.496, p = 0.003; R = −0.484, p = 0.005). Combining zALFF values from the l-ITG and r-FG achieved successful discrimination between depressed adolescents with and without suicide attempts (AUC = 0.855) with high sensitivity (86%) and specificity (71%).ConclusionDepressed adolescents with suicidal behavior exhibit unique neural activity patterns in the inferior temporal gyrus and fusiform gyrus. These findings highlight the potential utility of these specific brain regions as biomarkers for identifying suicide risk in depressed adolescents. Furthermore, associations between emotion dysregulation and activity in their frontal gyrus regions were observed. These findings provide preliminary yet pertinent insights into the pathophysiology of suicide in depressed adolescents.

IntroductionMajor Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD.MethodsIn this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC.ResultsThe experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD.DiscussionIn conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.

Photovoltaic (PV) solar energy generation attracts considerable attention to archive carbon neutrality goals worldwide. Geospatial data describing the PV system based on satellite images are critical for PV deployment. However, it remains challenging to obtain relative data in coastal zones due to frequent cloud cover. There have also been relatively few studies on spatial analysis of PV development in terms of solar resources and the energy demands. Therefore, this study took Chinese coastal provinces as example, and proposed a random forest classification method with information on morphology, optics, and SAR to address cloud pollution. The results obtained were analyzed with geographical and socioeconomic factors to determine its development status. The results have shown that (1) by October 2022, the total PV area in Chinese coastal provinces reached 837.3 km2, with an overall accuracy of 96.9 % and a Kappa coefficient of 0.91. Water-based PV represents 33.7 % which tended to be distributed near the shoreline. (2) Bounded by the Huai River, the PV density in the north was 9.0 km2 per 104 km2, which is more than twice that in the south. The development of water-based PV is a key reason for the high PV construction density in coastal areas. (3) PV distribution was slightly mismatched with solar resource and power demand, especially in Liaoning and Guangdong. Liaoning has relatively high potential for PV development. The study findings can play a key role in evaluation of PV power generation, development potential, and ecological environmental impacts.

Abstract Prostaglandin E2 (PGE2), a major cyclooxygenase-2 (COX-2) product, is highly secreted by the osteoblast lineage in the subchondral bone tissue of osteoarthritis (OA) patients. However, NSAIDs, including COX-2 inhibitors, have severe side effects during OA treatment. Therefore, the identification of novel drug targets of PGE2 signaling in OA progression is urgently needed. Osteoclasts play a critical role in subchondral bone homeostasis and OA-related pain. However, the mechanisms by which PGE2 regulates osteoclast function and subsequently subchondral bone homeostasis are largely unknown. Here, we show that PGE2 acts via EP4 receptors on osteoclasts during the progression of OA and OA-related pain. Our data show that while PGE2 mediates migration and osteoclastogenesis via its EP2 and EP4 receptors, tissue-specific knockout of only the EP4 receptor in osteoclasts (EP4 LysM ) reduced disease progression and osteophyte formation in a murine model of OA. Furthermore, OA-related pain was alleviated in the EP4 LysM mice, with reduced Netrin-1 secretion and CGRP-positive sensory innervation of the subchondral bone. The expression of platelet-derived growth factor-BB (PDGF-BB) was also lower in the EP4 LysM mice, which resulted in reduced type H blood vessel formation in subchondral bone. Importantly, we identified a novel potent EP4 antagonist, HL-43, which showed in vitro and in vivo effects consistent with those observed in the EP4 LysM mice. Finally, we showed that the Gαs/PI3K/AKT/MAPK signaling pathway is downstream of EP4 activation via PGE2 in osteoclasts. Together, our data demonstrate that PGE2/EP4 signaling in osteoclasts mediates angiogenesis and sensory neuron innervation in subchondral bone, promoting OA progression and pain, and that inhibition of EP4 with HL-43 has therapeutic potential in OA.

Abstract Articular cartilage repair and regeneration is an unmet clinical need because of the poor self-regeneration capacity of the tissue. In this study, we found that the expression of prostaglandin E receptor 4 (PTGER4 or EP4) was largely increased in the injured articular cartilage in both humans and mice. In microfracture (MF) surgery-induced cartilage defect (CD) and destabilization of the medial meniscus (DMM) surgery-induced CD mouse models, cartilage-specific deletion of EP4 remarkably promoted tissue regeneration by enhancing chondrogenesis and cartilage anabolism, and suppressing cartilage catabolism and hypertrophy. Importantly, knocking out EP4 in cartilage enhanced stable mature articular cartilage formation instead of fibrocartilage, and reduced joint pain. In addition, we identified a novel selective EP4 antagonist HL-43 for promoting chondrocyte differentiation and anabolism with low toxicity and desirable bioavailability. HL-43 enhanced cartilage anabolism, suppressed catabolism, prevented fibrocartilage formation, and reduced joint pain in multiple pre-clinical animal models including the MF surgery-induced CD rat model, the DMM surgery-induced CD mouse model, and an aging-induced CD mouse model. Furthermore, HL-43 promoted chondrocyte differentiation and extracellular matrix (ECM) generation, and inhibited matrix degradation in human articular cartilage explants. At the molecular level, we found that HL-43/EP4 regulated cartilage anabolism through the cAMP/PKA/CREB/Sox9 signaling. Together, our findings demonstrate that EP4 can act as a promising therapeutic target for cartilage regeneration and the novel EP4 antagonist HL-43 has the clinical potential to be used for cartilage repair and regeneration.

Abstract Background Bone fibrous dysplasia is a benign disease of bone tissue dysplasia. Vision impairment is the commonest neurological complication of craniofacial fibrous dysplasia. Most of the vision loss caused by craniofacial fibrous dysplasia is usually a gradual process. Very few present with acute visual impairment as described in our case. Case presentation We report a patient with fibrous dysplasia presenting rapidly progressive visual loss in the left eye secondary to bone cyst formation. Transnasal endoscopic surgery guided by navigation with drainage and curettage of this bone cyst and orbital decompression resulted in progressive improvement in visual acuity that returned to normal 1 month post-operatively. Conclusions In cases with acute visual loss due to fibrous dysplasia, emergency surgical treatment should be considered to preserve vision. In the surgical approach, navigation-guided nasal endoscopic surgery may be preferred because of its advantages.

Mental health is one of the major causes of disability worldwide, and mental health problems such as depression and anxiety are ranked among the top 25 leading causes of disease burden in the world. This burden is considerable over the lifetime of both men and women and in various settings and ages. This study aims to compare the mental health status of people in China and Pakistan and to highlight the mental health laws and policies during COVID-19 and afterwards. According to the literature on mental health, before the COVID-19 pandemic, mental health problems increased gradually, but during and after the COVID-19 pandemic, an abrupt surge occurred in mental health problems. To overcome mental health disorders, most (but not all) countries have mental health laws, but some countries ignore mental health disorders. China is one such country that has mental health laws and policies and, during the COVID-19 pandemic, China made beneficial and robust policies and laws, thereby succeeding in defeating the COVID-19 pandemic. The mortality rate and financial loss were also lower than in other countries. While Pakistan has mental health laws and general health policies, the law is only limited to paperwork and books. When it came to COVID-19, Pakistan did not make any specific laws to overcome the virus. Mental health problems are greater in Pakistan than in China, and China’s mental health laws and policies are more robust and more widely implemented than those in Pakistan. We conclude that there are fewer mental health issues in China than in Pakistan both before and since the COVID-19 pandemic. China has strong mental health laws and these are robustly implemented, while the mental health law in Pakistan is not applied in practice.

BackgroundStructural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.Materials and methodsWe analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.ResultsComponent A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.ConclusionThese multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.

In this study, the governing equation for one-dimensional large strain consolidation of soft soils under electro-osmosis–surcharge preloading is derived with some assumptions, and the general analytical solution is proposed for the first time by using the methods of algebraic transformation and separation variable. The expressions for the specific analytical solutions under several common cases in foundation treatment are developed. Besides, the correctness of the proposed analytical solutions is verified by comparing them with the existing analytical solutions and finite difference solution. Finally, based on the proposed analytical solutions, the effects of effective voltage Vm, the ratio of electro-osmosis (EO) conductivity to hydraulic conductivity ke/kv and volume compressibility coefficient mv1 on the consolidation behaviors are analyzed. It is found that the increase of Vm increases the absolute values of excess pore water pressure (EPWP) and hence the settlement increases. Furthermore, the increasing ratio of ke/kv leads to larger absolute values of EPWP generated in the soil, resulting in an increase of settlement. The consolidation degree Us defined by settlement based on small strain and large strain models decreases with the increase of mv1, and both the differences of Us and settlement between the two consolidation theories increase with the increase of mv1. In conclusion, the proposed analytical solution is simple in form and practical, which can be adopted to analyze the one-dimensional large strain consolidation behaviors under electro-osmosis–surcharge preloading.

Neuroinflammation and anhedonia in major depressive disorder (MDD) are closely connected, though the exact mechanism is unclear. This study aimed to investigate the relationships between cytokines, C-reactive protein (CRP), cortisol, and anhedonia, revealing the potential predictive value in identifying anhedonic MDD. In total, 66 patients with MDD (29 with anhedonia and 37 without anhedonia) and 66 healthy controls (HCs) were included. The severity of depression and anhedonia was evaluated using the Hamilton Rating Scale for Depression-24 (HAMD-24) and Snaith-Hamilton Pleasure Scale (SHAPS), respectively. Serum cytokines were measured using flow cytofluorometric kits, while CRP and cortisol were measured using enzyme-linked immunosorbent assay kits. We found higher serum levels of interleukin-2 (IL-2), IL-6, and cortisol in MDD than in HC where anhedonic MDD was highest. CRP and IL-6 were positively associated with anhedonia, and cortisol levels were related to both anhedonia and depression. A combination of IL-6, CRP, and cortisol had optimal predictive value for distinguishing anhedonic MDD. Anhedonic MDD has unique neuroendocrine-immune characteristics compared with those without anhedonia. The combination of IL-6, CRP, and cortisol might be an early marker to distinguish anhedonic MDD.

Abstract Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neuropsychiatric disorder and may persist into adulthood. Working memory and attention deficits have been reported to persist from childhood to adulthood. How neuronal underpinnings of deficits differ across adolescence and adulthood is not clear. In this study, we investigated gray matter of two cohorts, 486 adults and 508 adolescents, each including participants from ADHD and healthy controls families. Two cohorts both presented significant attention and working memory deficits in individuals with ADHD. Independent component analysis was applied to the gray matter of each cohort, separately, to extract cohort-inherent networks. Then, we identified gray matter networks associated with inattention or working memory in each cohort, and projected them onto the other cohort for comparison. Two components in the inferior, middle/superior frontal regions identified in adults and one component in the insula and inferior frontal region identified in adolescents were significantly associated with working memory in both cohorts. One component in bilateral cerebellar tonsil and culmen identified in adults and one component in left cerebellar region identified in adolescents were significantly associated with inattention in both cohorts. All these components presented a significant or nominal level of gray matter reduction for ADHD participants in adolescents, but only one showed nominal reduction in adults. Our findings suggest although the gray matter reduction of these regions may not be indicative of persistency of ADHD, their persistent associations with inattention or working memory indicate an important role of these regions in the mechanism of persistence or remission of the disorder.

In large smelting plants such as steel plant and coking plant the utilization rate of the byproduct gas is generally low, especially in developing countries. Usually, multiple low-parameter boilers are combined with multiple low-parameter steam turbines, which are built in phases, to drive the smelting accessory machineries. In terms of energy utilization efficiency, this model is definitely not economical. Based on the law of thermodynamics, the direction of efficiency improvement was proposed, and the solution of gas utilization system was put forward, according to the Rankine cycle principle. Finally, with a steel plant as the research object, the proposed scheme was applied in the transformation of this plant, and the economic benefit was estimated. The results show that the proposed scheme in this paper can generate more electricity with the same gas consumption.

Aimed at the limited engineering condition that only gas calorific value but not gas composition can be provided, a new theoretical combustion calculation method based on gas calorific value was proposed, which is suitable for calculating the theoretical dry flue gas volume of metallurgical gas. In the new method, three simplified models were proposed, including blast furnace gas (BFG) model, Linz–Donawitz converter gas (LDG) model and coke oven gas (COG) model. The simplified models were respectively derived from the original model of theoretical combustion calculation, according to the composition characteristics of each metallurgical gas. Finally, the proposed models were verified through large sample of BFG, LDG and COG. The verification results show that the new models for calculating the theoretical dry flue gas volume of metallurgical gas all have high accuracy. The average relative errors of BFG model, LDG model and COG model are only 0.35%, 0.12%, 0.64% respectively. Thus, the new method proposed in this paper is reasonable and feasible. It can meet the accuracy requirement of engineering and can provide a new way for theoretical combustion calculation of metallurgical gas.

In the control of air pollutant emissions, the measured emission concentration should be converted into the standard condition. The converted concentration is obtained in order to determine whether the emissions meet requirements. At present, there are several conversion methods available, and the EAR (excess air ratio) conversion method is one of them. Based on the conversion mechanism of air pollutant concentration, the origin of EAR conversion method was deduced and analyzed in this paper. The results show that the EAR method is a simplified method that is only suitable for the fuels with fuel composition factor close to zero, such as coal, biofuel, solid waste, oil, etc. It is not suitable for the fuels with big fuel composition factor, such as blast furnace gas, converter gas and producer gas, etc.

Abstract Immune checkpoint blockade (ICB) has a limited effect on colorectal cancer, underlining the requirement of co‐targeting the complementary mechanisms. Here, we identified prostaglandin E2 (PGE2) receptor 4 (EP4) as the master regulator of immunosuppressive myeloid cells (IMCs), which are the major driver of resistance to ICB therapy. PGE2‐bound EP4 promotes the differentiation of immunosuppressive M2 macrophages and myeloid‐derived suppressor cells (MDSCs) and reduces the expansion of immunostimulated M1 macrophages. To explore the immunotherapeutic role of EP4 signaling, we developed a novel and selective EP4 antagonist TP‐16. TP‐16 effectively blocked the function of IMCs and enhanced cytotoxic T‐cell‐mediated tumor elimination in vivo. Cell co‐culture experiments revealed that TP‐16 promoted T‐cell proliferation, which was impaired by tumor‐derived CD11b+ myeloid cells. Notably, TP‐16 and anti‐PD‐1 combination therapy significantly impeded tumor progression and prolonged mice survival. We further demonstrated that TP‐16 increased responsiveness to anti‐PD‐1 therapy in an IMC‐related spontaneous colorectal cancer mouse model. In summary, this study demonstrates that inhibition of EP4‐expressing IMCs may offer a potential strategy for enhancing the efficacy of immunotherapy for colorectal cancer.

Abstract Background Several studies have demonstrated autophagy was involved in the process of esophageal adenocarcinoma (EAC). The aim of this study was to explore autophagy-related genes (ARGs) correlated with overall survival (OS) in EAC patients. Methods Expressions of ARGs in EAC and normal samples were downloaded from TCGA database. GO and KEGG enrichment analyses were used to investigate the ARGs bioinformatics functions. Univariate and multivariate cox regressions were performed to identify prognostic ARGs and the independent risk factors. ROC curve was established to evaluate the feasibility to predict the prognosis. Finally, the correlations between ARGs and clinical features were further explored. In addition, significantly different ARGs were verified in EAC specimens and normal esophageal mucosal tissues. Results Thirty significantly different ARGs were selected from EAC and normal tissues. Functional enrichments showed these ARGs were mainly related apoptosis. Multivariate cox regression analyses demonstrated eight ARGs were significantly associated with OS. Among these eight genes, BECN1 (HR = 0.321, P = 0.046), DAPK1 (HR = 0.636, P = 0.025) and CAPN1 (HR = 0.395, P = 0.004) played protective roles in survival. Gender (HR = 0.225, P = 0.032), stage (HR = 5.841, P = 0.008) and risk score (HR = 1.131, P < 0.001) were independent prognostic risk factors. ROC curves showed better efficacy to predict survival using the risk score. Additionally, we found BECN1, DAPK1, VAMP7 and SIRT1 genes were correlated significantly with survival status, gender, primary tumor and tumor stage (all P < 0.05). The experimental results confirmed the BIRC5 was overexpressed and the ITPR1, PRKN were downregulated in the EAC tissues compared with the normal esophageal mucosal tissues (all P < 0.05). Conclusion Our findings suggested that autophagy was involved in the process of EAC. Several ARGs probably could serve as diagnostic and prognostic biomarkers and may help facilitate therapeutic targets in EAC patients.

Therapeutic hypothermia (TH) is potentially an important therapy for central nervous system (CNS) trauma. However, its clinical application remains controversial, hampered by two major factors: (1) Many of the CNS injury sites, such as the optic nerve (ON), are deeply buried, preventing access for local TH. The alternative is to apply TH systemically, which significantly limits the applicable temperature range. (2) Even with possible access for ‘local refrigeration’, cold-induced cellular damage offsets the benefit of TH. Here we present a clinically translatable model of traumatic optic neuropathy (TON) by applying clinical trans-nasal endoscopic surgery to goats and non-human primates. This model faithfully recapitulates clinical features of TON such as the injury site (pre-chiasmatic ON), the spatiotemporal pattern of neural degeneration, and the accessibility of local treatments with large operating space. We also developed a computer program to simplify the endoscopic procedure and expand this model to other large animal species. Moreover, applying a cold-protective treatment, inspired by our previous hibernation research, enables us to deliver deep hypothermia (4 °C) locally to mitigate inflammation and metabolic stress (indicated by the transcriptomic changes after injury) without cold-induced cellular damage, and confers prominent neuroprotection both structurally and functionally. Intriguingly, neither treatment alone was effective, demonstrating that in situ deep hypothermia combined with cold protection constitutes a breakthrough for TH as a therapy for TON and other CNS traumas.

Therapeutics targeting osteoclasts are commonly used treatments for bone metastasis; however, whether and how osteoclasts regulate premetastatic niche and bone tropism are largely unknown. In this study, we report that osteoclast precursors (OPs) can function as a premetastatic niche component that facilitates breast cancer (BCa) bone metastasis at early stages. At the molecular level, unbiased GPCR ligand/agonist screening in BCa cells suggested that R-spondin 2 (RSPO2) and RANKL, through interaction with their receptor LGR4, promoted osteoclastic premetastatic niche formation and enhanced BCa bone metastasis. This was achieved by RSPO2/RANKL-LGR4 signal modulating the WNT inhibitor DKK1 through Gαq and β-catenin signaling. DKK1 directly facilitated OP recruitment through suppression of its receptor LDL receptor–related protein 5 (LRP5) but not LRP6, upregulating Rnasek expression via inhibition of canonical WNT signaling. In clinical samples, RSPO2, LGR4, and DKK1 expression showed a positive correlation with BCa bone metastasis. Furthermore, soluble LGR4 extracellular domain (ECD) protein, acting as a decoy receptor for RSPO2 and RANKL, significantly alleviated bone metastasis and osteolytic lesions in a mouse bone metastasis model. These findings provide unique insights into the functional role of OPs as key components of the premetastatic niche for BCa bone metastasis and identify RSPO2/RANKL-LGR4 signaling as a promising target for inhibiting BCa bone metastasis.

AimsThe purpose of this study is to explore whether pre-treatment features of brain function can discriminate non-responders to antidepressant medication in the early phase.MethodsForty-four treatment-responsive depressed (RD) patients, 36 non-responsive depressed (NRD) patients, and 42 healthy controls (HCs) were recruited. Regional cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) values were calculated for all subjects. Correlation analyses were used to explore the relationship between symptom improvement and CBF/ALFF. Receiver operating characteristics (ROC) and the 10-fold cross-validation support vector machine (SVM) classifier were applied for the discrimination of treatment response.ResultsCompared with the HCs, the RD and NRD groups exhibited lower CBF and ALFF in the right posterior lobe of the cerebellum. Compared with the NRD group, the RD group showed distinct CBF patterns in the left frontal striatal regions and right frontal cerebellar regions, as well as distinct ALFF features in the left frontoparietal striatum and right frontotemporal striatal cerebellar regions. The ROC and SVM classifier revealed the optimal power to distinguish the RD and NRD groups based on the combined measures (i.e., CBF and ALFF).ConclusionDistinct features of CBF and ALFF in the frontal striatal network may serve as promising neuroimaging predictors for identifying patients with blunted responsiveness, which may facilitate personalized antidepressant treatment.

Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.

The resistance transfer coefficient of supports plays an important role in support selection in coal mines, which is the main factor in support crushing accidents. Based on the key layer theory, the formula for calculating the resistance transfer coefficient of supports under the load of a loose layer was deduced. The analysis of four working faces with thick loose layers and the corresponding mining pressure data were used to deduce the load transfer coefficients of the thick loose layers and ultimately illustrate the relationship between thick loose layers and different influencing factors. By using microseismic technology to monitor the process of roof failure in a thin bedrock working face with a thick loose layer during mining, the roof failure characteristics of a large-mining-height working face under the load of a thick loose layer were further verified. The results show that a thicker loose layer and thinner bedrock caused more of the load to transfer to the working face, the roof microseismic events were mainly concentrated in the range of 60 m to 75 m above the coal seam, and the most active events occurred during the square stage (the length of the working face’s goaf is equal to its width). The height of the water-conducting fracture zone was analyzed by microseismic data and then verified with theoretical calculations.

Chronic and excessive alcohol consumption can lead to the development of alcoholic liver disease (ALD), which is characterized by oxidative damage, inflammation, and impaired intestinal barrier function. To evaluate the effect of exopolysaccharides (EPS) from Sporidiobolus pararoseus on ALD, alcohol was used in this study to cause liver damage in mice. Then, the antioxidant and anti-inflammation activities and protective effect of EPS on the intestinal barrier function were further explored in an ALD model. The results show that the histopathological change and liver injury were significantly improved in the EPS group. EPS treatment significantly increased the levels of superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-Px) (P

Abstract Background Cone dystrophy with supernormal rod response (CDSRR) is an autosomal recessive retinal disorder characterized by myopia, dyschromatopsia, nyctalopia, photophobia, and nystagmus. CDSRR is caused by mutations in KCNV2, the gene encoding for an electrically silent Kv subunit (Kvs) named Kv8.2. Methods A Chinese CDSRR family was recruited. Complete ophthalmology clinical examinations were performed to clarify the phenotype. Genetic examination was underwent using whole exome sequencing (WES). In addition, a candidate gene was validated by Sanger sequencing. Expression analysis in vitro including immunoblotting, quantitative real‐time PCR (qRT‐PCR), and co‐immunoprecipitation experiments was performed to investigate the pathogenic mechanism of the identified gene variants. Results WES identified two KCNV2 heterozygous mutations from the proband. Sanger sequencing validated that the patient's parents had, respectively, carried those two mutations. Further in vitro functional experiments indicated that the mutated alleles had led the Kv8.2 proteins to fail in expressing and interacting with the Kv2.1 protein, respectively. Conclusions This study expanded the KCNV2 mutation spectrum. It can also be deduced that CDSRR has a broad heterogeneity. It is further confirmed that the inability expression of Kv8.2 proteins and the failure of Kv8.2 proteins to interact with Kv2.1 may have accounted for the etiology of CDSRR based on previous studies and this study.

In cold regions, freeze-thaw action poses a significant hazard to road engineering. In order to avoid the adverse effects of inorganic materials on soil modification, we applied lignin, which is an environmentally friendly and organic polymer, to improve the silt sand from cold regions. The significance of this study is to facilitate the better application of lignin. The macroscopic engineering properties of the soil showed that, before freeze-thaw, as the lignin content increased, thermal conductivity and permeability decreased, pH first increased rapidly and then stabilized between 10 and 11, and dynamic resilient modulus first increased then decreased; after freeze-thaw, as lignin content increased, thermal conductivity and permeability decreased, and dynamic resilient modulus first increased then decreased. The freeze-thaw action reduced the thermal conductivity and dynamic resilient modulus of silt sand treated with lignin and increased its permeability. The test results of soil microstructure indicated that, before freeze-thaw, the silt sand and silt sand treated with lignin were structurally compact; after freeze-thaw, the silt sand showed numerous cracks and pores and had a loose soil structure, whereas the silt sand treated with lignin showed fewer cracks and pores, and its soil structure was more compact under the encapsulation and filling action of cementitious materials. No new chemical elements, mineral components, or functional groups were produced when lignin was mixed with silt sand. The mechanism by which lignin improved the macroengineering properties of silt sand involved the cementitious material produced by the interaction between lignin and soil minerals, which encapsulated the soil particles and filled the interparticle pores. Research results can provide a theoretical reference for engineering application of lignin in cold regions.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

This study aimed to analyze the intestinal flora of male versus female swamp eels, which have the unique characteristic of sex reversal. Same-aged swamp eels from the same parents, kept under the same conditions in terms of environment, diet, etc., were used as the study materials. After breeding for 1 year, 18 healthy swamp eels (nine males and nine females), weighing 39.4 ± 2.3 g, were selected. The intestinal contents of three swamp eels of the same sex were combined and labeled as follows: FM1-FM3 (n = 3) for females and MM1-MM3 (n = 3) for males. High-throughput sequencing was used to research the v3-v4 region of 16S rDNA in the intestinal flora. The results suggested significant differences in the structure, composition, and diversity of the intestinal flora of male versus female swamp eels. The relative abundances of Actinobacteria and Spirochaetes in female swamp eels were significantly higher (p < 0.05) than in male swamp eels at the phylum level. The relative abundances of Mycobacterium, Bacillus, and Vagococcus in female swamp eels were considerably higher (p < 0.05) than in male swamp eels at the genus level. The Alpha diversity of females was higher than that of males, and the Shannon index of females was also significantly higher (p < 0.05, Tukey’s HSD) than that in males. Investigations of Beta diversity, including NMDS ordination, UPGMA cluster analysis, and PLS-DA analysis, showed that female and male swamp eels could be clearly distinguished based on significant differences in intestinal flora between the FM group and the MM group. LEfSe analysis showed that the dominant bacteria were Cetobacterium in male swamp eels and Clostridium_sp_ND2, Mycobacterium, Alphaproteobacteria, and Bacilli in female swamp eels. The results showed dramatic differences in the intestinal flora between the sexes, which indicates the need for a more in-depth study on sex reversal in the future.

Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ± 11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks.

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 to 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a ADHD/typically developing difference with subthreshold cases being intermediate between ADHD and typically developing controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to individuals with ADHD, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

While gray matter (GM) anomalies have been reported for attention-deficit/hyperactivity disorder (ADHD), investigating their associations with cognitive deficits and individual symptom domains can help pinpoint the neural underpinnings critical for the pathology of ADHD, particularly the persist form of ADHD. In this work, we performed both independent component analysis and voxel-based morphometry analysis on whole brain GM of 486 adults including 214 patients, 96 unaffected siblings, and 176 healthy controls, in relation to cognition and symptoms. Independent component analysis revealed that higher GM volume in inferior semilunar lobule, inferior frontal gyri, and superior and middle frontal gyri was associated with better working memory performance, and lower GM volume in cerebellar tonsil and culmen was associated with more severe inattention symptoms. Consistently, voxel-based morphometry analysis showed that higher GM volume in multiple regions of frontal lobe, cerebellum and temporal lobe was related to better working memory performance. Focusing on the networks derived from ICA, our results integrated prefrontal regions and cerebellar regions through associations with working memory and inattention symptoms, lending support for the theory of ‘cool’-cognition dysfunction being mediated by inferior fronto-striato-cerebellar networks in ADHD. Siblings showed intermediate cognitive impairments between patients and controls but presented GM anomalies in unique focal regions, suggesting they are a separate group potentially affected by the shared genetic and environmental risks with ADHD patients. Keywords: Adult ADHD, Independent component analysis, Prefrontal cortex, Cerebellum, Working memory

An optical see-through two-dimensional (2D)/three-dimensional (3D) compatible display using variable-focus lens and multiplexed holographic optical elements (MHOE) is presented. It mainly consists of a MHOE, a variable-focus lens and a projection display device. The customized MHOE, by using the angular multiplexing technology of volumetric holographic grating, records the scattering wavefront and spherical wavefront array required for 2D/3D compatible display. In particular, we proposed a feasible method to switch the 2D and 3D display modes by using a variable-focus lens in the reconstruction process. The proposed system solves the problem of bulky volume, and makes the MHOE more efficient to use. Based on the requirements of 2D and 3D displays, we calculated the liquid pumping volume of the variable-focus lens under two kinds of diopters.

Conversion reactions of SnO2-based anode materials have recently been confirmed reversible by using well-designed nanostructures, while the detailed mechanism during charging and discharging is still ambiguous. Unwrapping the real mechanism is helpful to design high-performance SnO2-based anode materials. In this work, we designed and fabricated a nanoarchitecture of ultrafine SnO2 (3–5 nm) anchored on the surfaces of carbon nanofibers (denoted as u-TOCNFs). The u-TOCNFs electrode exhibits high reversible capacity of 1006.4 mAh·g−1, high initial Coulombic efficiency of 71.3% and good cycle stability mainly due to good dispersion of ultrafine SnO2 on the conductive carbon nanofibers. The structure evolution is investigated by TEM observations that confirm the presence of amorphous SnO2 on the delithiated nanoanode at 1.28 V after 100 cycles. Charge/discharge induced amorphization of SnO2 nanocrystals is firstly observed that readily explain the capacity increasing around 100 cycles. In particular, we propose the mechanism of reversible conversion reactions of SnO2 and Sn induced by the amorphization of ultrafine SnO2 during lithiation/delithiation. We believe that it is a new direction to study SnO2-based anode materials to improve the electrochemical performance for lithium ion batteries. Keywords: Ultrafine SnO2, Reversible conversion, Amorphization, Lithium ion battery