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Fritillaria cirrhosa, an endangered medicinal plant in the Qinghai-Tibet Plateau, is facing resource scarcity. Artificial cultivation has been employed to address this issue, but problems related to continuous cultivation hinder successful transplantation. Imbalanced microbial communities are considered a potential cause, yet the overall changes in the microbial community under continuous cropping systems remain poorly understood. Here, we investigated the effects of varying durations of continuous cropping on the bacterial and fungal communities, as well as enzymatic activities, in the rhizospheric soil of F. cirrhosa. Our findings revealed that continuous cropping of F. cirrhosa resulted in soil acidification, nutrient imbalances, and increased enzyme activity. Specifically, after 10 years of continuous cropping, there was a notable shift in the abundance and diversity (e.g., Chao1 index) of soil bacteria and fungi. Moreover, microbial composition analyses revealed a significant accumulation of harmful microorganisms associated with soil-borne diseases (e.g., Luteimonas, Parastagonospora, Pseudogymnoascus) in successively cropped soils, in contrast to the significant reduction of beneficial microorganisms (e.g., Sphingomonas, Lysobacter, Cladosporium) that promote plant growth and development and protect against diseases such as Fusarium sp.These changes led to decreased connectivity and stability within the soil microbial community. Structural equation modeling and redundancy analysis revealed that alkaline hydrolytic nitrogen and available phosphorus directly influenced soil pH, which was identified as the primary driver of soil microbial community changes and subsequently contributed to soil health deterioration. Overall, our results highlight that soil acidification and imbalanced rhizosphere microbial communities are the primary challenges associated with continuous cropping of F. cirrhosa. These findings establish a theoretical foundation for standardized cultivation practices of F. cirrhosa and the bioremediation of continuously cultivated soils., (© 2024. The Author(s).)

Intracerebral hemorrhage (ICH) is a common cerebral vascular disease with high incidence, disability, and mortality. Ferroptosis is a regulated type of iron-dependent, non-apoptotic programmed cell death. There is increasing evidence that ferroptosis may lead to neuronal damage mediated by hemorrhagic stroke mediated neuronal damage. Salvianolic acid A (SAA) is a natural bioactive polyphenol compound extracted from salvia miltiorrhiza, which has anti-inflammatory, antioxidant, and antifibrosis activities. SAA is reported to be an iron chelator that inhibits lipid peroxidation and provides neuroprotective effects. However, whether SAA improves neuronal ferroptosis mediated by hemorrhagic stroke remains unclear. The study aims to evaluate the therapeutic effect of SAA on Ferroptosis mediated by Intracerebral hemorrhage and explore its potential mechanisms. We constructed in vivo and in vitro models of intracerebral hemorrhage in rats. Multiple methods were used to analyze the inhibitory effect of SAA on ferroptosis in both in vivo and in vitro models of intracerebral hemorrhage in rats. Then, network pharmacology is used to identify potential targets and mechanisms for SAA treatment of ICH. The SAA target ICH network combines SAA and ICH targets with protein-protein interactions (PPIs). Find the specific mechanism of SAA acting on ferroptosis through molecular docking and functional enrichment analysis. In rats, SAA (10 mg/kg in vivo and 50 μM in vitro, p < 0.05) alleviated dyskinesia and brain injury in the ICH model by inhibiting ferroptosis (p < 0.05). The molecular docking results and functional enrichment analyses suggested that AKT (V-akt murine thymoma viral oncogene homolog) could mediate the effect of SAA. NRF2 (Nuclear factor erythroid 2-related factor 2) was a potential target of SAA. Our further experiments showed that salvianolic acid A enhanced the Akt /GSK-3β/Nrf2 signaling pathway activation in vivo and in vitro. At the same time, SAA significantly expanded the expression of GPX4, XCT proteins, and the nuclear expression of Nrf2, while the AKT inhibitor SH-6 and the Nrf2 inhibitor ML385 could reduce them to some extent. Therefore, SAA effectively ameliorated ICH-mediated neuronal ferroptosis. Meanwhile, one of the critical mechanisms of SAA inhibiting ferroptosis was activating the Akt/GSK-3β/Nrf2 signaling pathway., (© 2024. The Author(s).)

Chest X-ray (CXR) is an extensively utilized radiological modality for supporting the diagnosis of chest diseases. However, existing research approaches suffer from limitations in effectively integrating multi-scale CXR image features and are also hindered by imbalanced datasets. Therefore, there is a pressing need for further advancement in computer-aided diagnosis (CAD) of thoracic diseases. To tackle these challenges, we propose a multi-branch residual attention network (MBRANet) for thoracic disease diagnosis. MBRANet comprises three components. Firstly, to address the issue of inadequate extraction of spatial and positional information by the convolutional layer, a novel residual structure incorporating a coordinate attention (CA) module is proposed to extract features at multiple scales. Next, based on the concept of a Feature Pyramid Network (FPN), we perform multi-scale feature fusion in the following manner. Thirdly, we propose a novel Multi-Branch Feature Classifier (MFC) approach, which leverages the class-specific residual attention (CSRA) module for classification instead of relying solely on the fully connected layer. In addition, the designed BCEWithLabelSmoothing loss function improves the generalization ability and mitigates the problem of class imbalance by introducing a smoothing factor. We evaluated MBRANet on the ChestX-Ray14, CheXpert, MIMIC-CXR, and IU X-Ray datasets and achieved average AUCs of 0.841, 0.895, 0.805, and 0.745, respectively. Our method outperformed state-of-the-art baselines on these benchmark datasets., (© 2024. The Author(s).)

The influence of the aneurysm evolution on the hemodynamic characteristic of the blood flow inside the sac region is comprehensively investigated. By using the computational method, the blood flow through the vessel and aneurysm of the sac region is examined to find the role of aneurysm evolution on the wall shear stress, pressure, and risk of aneurysm rupture. Three different models of ICA aneurysms are chosen for the investigation of the aneurysm evolution at risk of rupture. Obtained data shows that the evolution of the aneurysm decreases the wall shear stress and pressure on the sac surface while an oscillatory index of blood increases on the aneurysm wall., (© 2024. The Author(s).)

With the rapid development of the economy, people have increasingly higher requirements for the comfort of living spaces, and the result is the sharp increase in building energy consumption. Several design parameters influence living space comfort and building energy efficiency. Since the same design standard can include different design parameter combinations, synergic relationships may exist between these criteria for one case. Identifying these synergic relationships requires an inverse problem approach. This paper established a model by combining an improved genetic algorithm (IGA) and numerical calculation to determine the synergic design parameter relationships (e.g. the thermophysical building material properties and energy-saving factors). For [Formula: see text], the shading coefficient significantly influenced the linear function between the thermal conductivity and insulation thickness. In this case, the insulation thickness was exponentially related to the shading coefficient, while the thermal conductivity of the insulation material significantly impacted the synergic relationship. For [Formula: see text], the insulation thickness was a segmented function of the shading coefficient. The results verified that the proposed model was efficient and reliable for identifying the synergic relationships between energy-saving parameters. In engineering applications, designers can select the optimal design parameter combination based on the relationship curve between the parameters in this paper according to the local market conditions and specific design requirements., (© 2024. The Author(s).)

The objective of this research is to examine the impact of eccentric training on hamstring flexibility and strength in young dancers during the concluding stages of their foundational dance training program. A total of 24 female, second-year dance students from Hebei Normal University were selected as participants. They were divided into three distinct groups: Nordic hamstring exercise and single-leg deadlift group (NHE&SLD), forward bending exercises and standing leg lift group (FBE&SLL), and a control group (CG). The study was designed around a 6-week training regimen. An isokinetic dynamometer was used to measure seated knee flexor-extensor strength, while electronic goniometry was employed to measure hamstring flexibility in the supine position. Paired sample t-tests were conducted within each group, and one-way analysis of covariance (ANCOVA) was utilized for comparisons between groups. In the NHE&SLD group, significant disparities were observed in both concentric (T = - 5.687, P = 0.001) and eccentric (T = - 3.626, P = 0.008) hamstring strength pre and post-intervention. The pre-intervention dominant leg concentric strength test values significantly influenced the post-intervention outcomes (F = 5.313, P = 0.001, η 2  = 0.840). Similarly, the pre-intervention dominant leg eccentric strength test values impacted the post-intervention results (F = 4.689, P = 0.043, η 2  = 0.190). Following the intervention, the NHE&SLD group displayed marked changes in the active straight leg raising angle on both left (T = - 4.171, P = 0.004) and right (T = - 6.328, P = 0.001) sides. The FBE&SLL group also revealed significant changes in the active straight leg raising angle on both left (T = - 4.506, P = 0.003) and right (T = - 4.633, P = 0.002) sides following the intervention. The pre-intervention left leg concentric strength test value significantly influenced the post-intervention outcomes (F = 25.067, P = 0.001, η 2  = 0.556). Likewise, the pre-intervention right leg eccentric strength test value significantly influenced the post-intervention results (F = 85.338, P = 0.01, η 2  = 0.810). Eccentric training can better enhance the flexibility and strength of hamstring muscles in dance students. Traditional stretching training significantly improves the flexibility of the hamstring muscles. Eccentric training has more training benefits than traditional stretching training. It is recommended for dance students to use eccentric training when increasing hamstring flexibility and strength., (© 2024. The Author(s).)

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method., (© 2023. Springer Nature Limited.)

The production of semen in boars involves multiple reproductive glands, including the testis (Tes), epididymis (Epi), vesicular gland (VG), prostate gland (PG), and bulbourethral gland (BG). However, previous studies on boar reproduction primarily focused on the testis, with little attention paid to the other glands. Here, we integrated single-molecule long-read sequencing with short-read sequencing to characterize the RNA landscape from five glands of Banna mini-pig inbred line (BMI) and Diannan small-ear pigs (DSE). We identified 110,996 full-length isoforms from 22,298 genes, and classified the alternative splicing (AS) events in these five glands. Transcriptome-wide variation analysis indicated that the number of single nucleotide polymorphisms (SNPs) in five tissues of BMI was significantly lower than that in the non-inbred pig, DSE, revealing the effect of inbreeding on BMI. Additionally, we performed small-RNA sequencing and identified 299 novel miRNAs across all glands. Overall, our findings provide a comprehensive overview of the RNA landscape within these five glands, paving the path for future investigations on reproductive biology and the impact of inbreeding on pig transcriptome., (© 2023. Springer Nature Limited.)

Morchella esculenta is an edible fungus with a uniquely delicious flavor and remarkable benefits for health. Herein, the molecular mechanism underlying the anti-inflammatory effects of Morchella esculenta was elucidated using molecular docking and network pharmacology. NPASS, Super-pred, SEA, Swiss Target Prediction, GeneCards, DisGeNET, Omim database, and STRING platform were used to select anti-inflammatory targets and construct target protein interaction networks using the active ingredients of Morchella esculenta. The OmicShare cloud platform was used to analyze GO functions and KEGG pathways related to the target, and the AutoDock Vina software was used to perform molecular docking and molecular dynamics (MD) simulation on the main target. Based on Cytoscape's "Network Analysis", the degree was used to identify potential key targets, and different inflammatory transcriptome data sets were used to evaluate core targets showing clinical significance. The active ingredient of Morchella esculenta identified from the NPASS database was EOYA, which had 43 anti-inflammatory targets, including NR1I2, PTGS1, PTGS2, CYP4F2, CYP3A4, TLR4, MAPK1, PLA2G4A, and PTPN11, and was mainly implicated in arachidonic acid metabolism, vascular endothelial growth factor signal pathway, and sphingomyelin signal transduction pathway, indicating that the anti-inflammatory effects of EOYA were mainly related to these biological processes. The degree was used to select 9 potential effective targets, namely NR1I2, PTGS1, PTGS2, CYP4F2, CYP3A4, TLR4, MAPK1, PLA2G4A, and PTPN11, among which NR1I2, PTGS1, PTGS2, PLA2G4A, MAPK1, CYP3A4, and TLR4 showed clinical significance. Molecular docking results showed that (E)-Octadec-11-En-9-Ynoic Acid (EOYA) could spontaneously bind to the 9 core targets, and the binding fractions of NR1I2, PTGS1, PTGS2, CYP4F2, and CYP3A4 were the highest. The MD simulation results showed that EYOA did indeed bind well NR1I2 to PTGS2, and the complex has high stability. Morchella esculenta can regulate the activity of prostaglandin endoperoxide synthetase, and affect the biosynthesis of prostaglandins, thereby impacting the metabolic pathway of arachidonic acid., (© 2023. Springer Nature Limited.)

Allicin exhibits various pharmacological activities and has been suggested to be beneficial in the treatment of stroke. However, the underlying mechanisms are largely unknown. Here, we confirmed that allicin protected the brain from cerebral injury, which could be ascribed to its anti‑apoptotic and anti‑inflammatory effects, as well as the regulation of lipid metabolism, using proteomics and metabolomics analysis. Our results suggested that allicin could significantly ameliorate behavioral characteristics, cerebral infarct area, cell apoptosis, inflammatory factors, and lipid metabolic-related factors (arachidonic acid, 15-hydroperoxy-eicosatetraenoic acid (15S-HPETE), palmitoylcarnitine, and acylcarnitine) by recalibrating astrocyte homeostasis in mice with photothrombotic stroke (PT). In astrocytes, allicin significantly increased glutathione peroxidase 1 (GPX1) levels and inhibited the arachidonic acid-related pathway, which was also observed in the brains of mice with PT. Allicin was proven to inhibit hypoxia-induced astrocyte apoptosis by increasing GPX1 expression, activating proto-oncogene tyrosine-protein kinase Src (Src)- protein kinase B (AKT)-extracellular signal-regulated kinase (ERK) phosphorylation, and decreasing lipid peroxidation. Thus, we concluded that allicin significantly prevented and ameliorated ischemic stroke by increasing GPX1 levels to complete the complex physiological process., (© 2023. Cell Death Differentiation Association (ADMC).)

Purpose: Our meta-analysis aimed to evaluate the diagnostic performance of 68 Ga-PSMA-11 PET/CT vs. 68 Ga-PSMA-11 PET/MRI for biochemical recurrence of prostate cancer., Methods: We searched for relevant articles in PubMed and Embase until February 2022. Studies evaluating head-to-head comparison of 68 Ga-PSMA-11 PET/CT and 68 Ga-PSMA-11 PET/MRI in men with prostate cancer biochemical recurrence were included. The quality of each study was assessed using the Quality Assessment of Diagnostic Performance Studies-2 (QUADAS-2) tool., Results: A total of 5 studies with 219 patients were included in the analysis. The pooled overall detection rates of 68 Ga-PSMA-11 PET/CT and 68 Ga-PSMA-11 PET/MRI in detecting recurrent PCa after definitive treatment were 0.89 (95% CI: 0.65-1.00), 0.92 (95% CI: 0.77-1.00), while the detection rates were 0.20 (95% CI: 0.05-0.41) and 0.29 (95% CI: 0.10-0.53) in local recurrence, 0.51 (95% CI: 0.33-0.69) and 0.52 (95% CI: 0.44-0.61) in lymph node metastasis, 0.18 (95% CI: 0.07-0.33) and 0.20 (95% CI: 0.09-0.35) in bone metastasis. There was no significant difference between the two imaging modalities in the overall detection rate (P = 0.82). In addition, detection rates were also not significantly different in local recurrence, lymph node metastasis, or bone metastasis (P = 0.54, 1.00, 0.82)., Conclusions: 68 Ga-PSMA-11 PET/CT and 68 Ga-PSMA-11 PET/MRI seem to have equivalent performance in detecting biochemical recurrence in prostate cancer. However, the results of the meta-analysis were drawn from studies with small samples. Further larger studies in this setting are warranted., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Allium mongolicum Regel is a wild and sandy vegetable with unique flavours. In this study, a complete chloroplast (cp) genome of A. mongolicum was obtained (Genbank accession number: OM630416), and contained 153,609 base pairs with the GC ratio as 36.8%. 130 genes were annotated including 84 protein-coding genes, 38 tRNA, and 8 rRNA genes. The large single-copy (LSC) region was 82,644 bp, and a small single-copy (SSC) region was 18,049 bp, which were separated by two inverted repeats (IRs, including IRa and IRb) of 26,458 bp. Comparative genome analyses of 55 Allium species suggested that genomic structure of genus Allium was conserved, and LSC and SSC regions were outstanding with high variability. Among them, more divergent loci were in the SSC region covering ycf1-rrn4.5 and ndhF-ccsA. Phylogenetic analysis on cp genomes of 55 Allium determined that all members were clustered into 13 clades, and A. mongolicum had close relationship with A. senescens. Corresponding analyses of four protein-coding genes (ycf1, ndhF, rpl32, and ccsA) in aforementioned divergent loci confirmed that ycf1 was finally chosen as the candidate gene for species identification and evolutionary classification of genus Allium. These data provide valuable genetic resources for future research on Allium., (© 2022. The Author(s).)

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs., (© 2022. The Author(s).)

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures., (© 2022. The Author(s).)

Since the proposal of the "Materials Genome Initiative", several material databases have emerged and advanced many materials fields. In this work, we present the Materials Informatics Platform with Three-Dimensional Structures (MIP-3d). More than 80,000 structural entries, mainly from the inorganic crystal structural database, are included in MIP-3d. Density functional theory calculations are carried out for over 30,000 entries in the database, which contain the relaxed crystal structures, density of states, and band structures. The calculation of the equations of state and sound velocities is performed for over 12,000 entries. Notably, for entries with band gap values larger than 0.3 eV, the band degeneracies for the valence band maxima and the conduction band minima are analysed. The electrical transport properties for approximately 4,400 entries are also calculated and presented in MIP-3d under the constant electron-phonon coupling approximation. The calculations of the band degeneracies and electrical transport properties make MIP-3d a database specifically designed for thermoelectric applications., (© 2021. The Author(s).)

To compare the differences between asleep and awake robot-assisted deep brain stimulation (DBS) surgery for Parkinson's Disease (PD), we conducted this retrospective cohort study included 153 PD patients undergoing bilateral robot-assisted DBS from June 2017 to August 2019, of which 58 cases were performed under general anesthesia (GA) and 95 cases under local anesthesia (LA). Procedure duration, stimulation parameters, electrode implantation accuracy, intracranial air, intraoperative electrophysiological signal length, complications, and Unified PD Rating Scale (UPDRS) measurements were recorded and compared. The clinical evaluation was conducted by two raters who were blinded to the choice of anesthesia. Procedure duration was significantly shorter in the GA group, while on stimulation off medication motor scores (UPDRS-III) were significantly improved in both the GA and LA group. ANCOVA covariated for the baseline UPDRS-III and levodopa challenge exhibited no significant differences. In terms of amplitude, frequency, and pulse width, the stimulation parameters used for DBS power-on were similar. There were no significant differences in electrode implantation accuracy, intraoperative electrophysiological signal length, or intracerebral hemorrhage (no occurrences in either group). The pneumocephalus volume was significantly smaller in the GA group. Six patients exhibited transient throat discomfort associated with tracheal intubation in the GA group. The occurrence of surgical incision infection was similar in both groups. Compared with the awake group, the asleep group exhibited a shorter procedure duration with a similar electrode implantation accuracy and short-term motor improvement. Robot-assisted asleep DBS surgery is a promising surgical method for PD., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

Microbial fuel cell (MFC) technology is a simple way to accelerate the treatment of the oily sludge which is a major problem affecting the quality of oil fields and surrounding environment while generating electricity. To investigate the oil removal and the characteristics of changes in the composition of bacteria, sediment microbial fuel cells (SMFCs) supplemented with oily sludge was constructed. The results showed that the degradation efficiency of total petroleum hydrocarbon (TPH) of SMFC treatment was 10.1 times higher than the common anaerobic degradation. In addition, the degradation rate of n-alkanes followed the order of high carbon number > low carbon number > medium carbon number. The odd-even alkane predominance (OEP) increased, indicating that a high contribution of even alkanes whose degradation predominates. The OUT number, Shannon index, AEC index, and Chao1 index of the sludge treated with SMFC (YN2) are greater than those of the original sludge (YN1), showing that the microbial diversity of sludge increased after SMFC treatment. After SMFC treatment the relative abundance of Chloroflexi, Bacteroidia and Pseudomonadales which are essential for the degradation of the organic matter and electricity production increased significantly in YN2. These results will play a crucial role in improving the performance of oily sludge MFC.

The decrease of coal temperature has been confirmed by many tests during methane desorption in coal, including coal and gas outburst, but the thermal-dynamic process for methane desorption has not been quantitatively studied. Therefore, firstly, the coal temperature and gas pressure are measured by temperature and pressure sensors in the process of methane desorption. Secondly, isosteric heats of adsorption are calculated according to the adsorption isotherm. Finally, heat transfer model is established and simulate the temperature evolution during methane desorption in coal under different conditions (initial temperature and gas pressure). The real tests and simulation results show that a lot of heat will be absorbed from coal as methane desorbing, which causes the coal temperature will go down by 5.5 K, and methane desorption is no longer isothermal process. In the initial stage of methane desorption in coal, the coal temperature will decrease sharply to an extremely low value, then slowly rise to the previous ambient temperature. And at the same ambient temperature, the higher the initial methane equilibrium pressure is, the larger the temperature at the coal body center drops in the process of methane desorption. In the coal body, the farther away from the wall of the coal sample canister, the more significant the decrease of the coal body temperature is, and the longer the time is to reach extremely low value, which is mainly due to the different heat transfer coefficients at different positions in the coal body. The total specific power, which is a key index in heat transfer model to simulate the change of coal temperature, sharply decreases during methane desorption, because the methane desorption quantity in unit time decreases gradually. This study has an important practical significance to reveal the evolution mechanism of coal and gas outburst, and predict outburst with temperature change as an index.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

Buffalobur (Solanum rostratum Dunal), which belongs to the Solanaceae family, is a worldwide noxious invasive weed and is listed as one of the top 10 alien invasive species in China. It is harmful to humans and livestock because the entire plant is covered with spines containing toxins. Many studies have analysed the gene expression in this weed species under different stress conditions using quantitative real-time PCR (qPCR). However, until now, there has been no report on suitable reference genes in buffalobur. Herein, 14 candidate reference genes were selected and evaluated for their expression stability in buffalobur in different tissues, at different developmental stages, and in response to several stress conditions using the geNorm, NormFinder, BestKeeper and RefFinder statistical algorithms. The results showed that EF1α, ACT and SAND are suitable reference genes across all samples tested. We recommend the normalization of target gene expression under different experimental conditions using these three genes together. Validation of selected reference genes was achieved by assessing the relative expression levels of P5CS and GI. This work identified the appropriate reference genes for transcript normalization in buffalobur, which will be helpful in future genetic studies of this invasive weed.

Studies indicate that the chemokine receptor is responsible for poor prognosis of hepatocellular carcinoma (HCC) patients. In this study, we initially demonstrated that CCR4 is overexpressed in HCC specimens, and its elevation in HCC tissues positively correlates with tumor capsule breakthrough and vascular invasion. Although overexpression of CCR4 failed to influent proliferation of HCC cells in vitro apparently, the prominent acceleration on HCC tumor growth in vivo was remarkable. The underlying mechanism may be involved in neovascularization. Interestingly, different from effect on proliferation, CCR4 overexpression could trigger HCC metastasis both in vitro and in vivo also induced HCC cell epithelial-mesenchymal transition (EMT) as well. Then we identified matrix metalloproteinase 2 (MMP2) as a direct target of CCR4 which plays an important role in CCR4-mediated HCC cell invasion, which was up-regulated by ERK/AKT signaling. Positive correlation between CCR4 and MMP2 expression was also observed in HCC tissues. In conclusion, our study suggested that chemokine receptor CCR4 promotes HCC malignancy and facilitated HCC cell metastases via ERK/AKT/MMP2 pathway. These findings suggest that CCR4 may be a potential new diagnostic and prognostic marker in HCC, and targeting CCR4 may be a potential therapeutic option for blocking HCC metastasis.

Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.

Millions of people die every year from diseases caused by exposure to outdoor air pollution. Some studies have estimated premature mortality related to local sources of air pollution, but local air quality can also be affected by atmospheric transport of pollution from distant sources. International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region. The effects of international trade on air pollutant emissions, air quality and health have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM 2.5 ) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM 2.5 pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM 2.5 pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM 2.5 pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport.

Photoreceptor degeneration due to retinitis pigmentosa (RP) is a primary cause of inherited retinal blindness. Photoreceptor cell-replacement may hold the potential for repair in a completely degenerate retina by reinstating light sensitive cells to form connections that relay information to downstream retinal layers. This study assessed the therapeutic potential of photoreceptor progenitors derived from human embryonic and induced pluripotent stem cells (ESCs and iPSCs) using a protocol that is suitable for future clinical trials. ESCs and iPSCs were cultured in four specific stages under defined conditions, resulting in generation of a near-homogeneous population of photoreceptor-like progenitors. Following transplantation into mice with end-stage retinal degeneration, these cells differentiated into photoreceptors and formed a cell layer connected with host retinal neurons. Visual function was partially restored in treated animals, as evidenced by two visual behavioral tests. Furthermore, the magnitude of functional improvement was positively correlated with the number of engrafted cells. Similar efficacy was observed using either ESCs or iPSCs as source material. These data validate the potential of human pluripotent stem cells for photoreceptor replacement therapies aimed at photoreceptor regeneration in retinal disease.

Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.

Resistance to β-lactam antibiotics through β-lactamase production by Enterobacteriaceae continues to burden the health-care sector worldwide. Traditional methods for detection of β-lactamases are time-consuming and labor-intensive and newer methods with varying capabilities continue to be developed. The objective of this study was to develop a multiplex PCR (M-PCR) system for the detection of blaSHV, blaTEM, blaCTX-M-1, blaCTX-M-9 and blaOXA-1 group genes and to apply it in clinical Klebsiella pneumoniae and Escherichia coli strains. To do this, we used group-specific PCR primers in singleplex reactions followed by optimization into multiplex reactions. Specificity and sensitivity of the M-PCR were then evaluated using 58 reference strains before its application to detect bla group genes in 203 clinical Enterobacteriaceae strains. PCR amplicons were sequenced to determine the β-lactamase subtypes. The M-PCR system exhibited 100% specificity and sensitivity. In all, 83.7% of K. pneumoniae and 89.8% of E. coli clinical strains harbored bla group genes with 46.9%, 40.1%, 15.0%, 21.1% and 6.1% of K. pneumoniae having blaSHV, blaTEM, blaCTX-M-1, blaCTX-M-9 and blaOXA-1 group genes, respectively, whereas 12.2%, 77.6%, 22.4%, 36.7% and 8.2% of E. coli had blaSHV, blaTEM, blaCTX-M-1, blaCTX-M-9 and blaOXA-1 group genes, respectively. BlaSHV-1, blaSHV-11, blaSHV-27, blaSHV-33, blaSHV-144, blaTEM-1, blaTEM-135, blaOXA-1, blaCTX-M-3, blaCTX-M-9, blaCTX-M-14, blaCTX-M-15, blaCTX-M-27, blaCTX-M-55, blaCTX-M-65 and blaCTX-M-104 were detected. In conclusion, the M-PCR system was efficient and versatile with an advantage of simultaneously detecting all the targeted bla group genes. Hence, it is a potential candidate for developing M-PCR kits for the screening of these genes for clinical or epidemiological purposes.

Asymmetric catalysis is seen as one of the most economical strategies to satisfy the growing demand for enantiomerically pure small molecules in the fine chemical and pharmaceutical industries. And visible light has been recognized as an environmentally friendly and sustainable form of energy for triggering chemical transformations and catalytic chemical processes. For these reasons, visible-light-driven catalytic asymmetric chemistry is a subject of enormous current interest. Photoredox catalysis provides the opportunity to generate highly reactive radical ion intermediates with often unusual or unconventional reactivities under surprisingly mild reaction conditions. In such systems, photoactivated sensitizers initiate a single electron transfer from (or to) a closed-shell organic molecule to produce radical cations or radical anions whose reactivities are then exploited for interesting or unusual chemical transformations. However, the high reactivity of photoexcited substrates, intermediate radical ions or radicals, and the low activation barriers for follow-up reactions provide significant hurdles for the development of efficient catalytic photochemical processes that work under stereochemical control and provide chiral molecules in an asymmetric fashion. Here we report a highly efficient asymmetric catalyst that uses visible light for the necessary molecular activation, thereby combining asymmetric catalysis and photocatalysis. We show that a chiral iridium complex can serve as a sensitizer for photoredox catalysis and at the same time provide very effective asymmetric induction for the enantioselective alkylation of 2-acyl imidazoles. This new asymmetric photoredox catalyst, in which the metal centre simultaneously serves as the exclusive source of chirality, the catalytically active Lewis acid centre, and the photoredox centre, offers new opportunities for the 'green' synthesis of non-racemic chiral molecules.

Patients with dyskeratosis congenita (DC), a disorder of telomere maintenance, suffer degeneration of multiple tissues. Patient-specific induced pluripotent stem (iPS) cells represent invaluable in vitro models for human degenerative disorders like DC. A cardinal feature of iPS cells is acquisition of indefinite self-renewal capacity, which is accompanied by induction of the telomerase reverse transcriptase gene (TERT). We investigated whether defects in telomerase function would limit derivation and maintenance of iPS cells from patients with DC. Here we show that reprogrammed DC cells overcome a critical limitation in telomerase RNA component (TERC) levels to restore telomere maintenance and self-renewal. We discovered that TERC upregulation is a feature of the pluripotent state, that several telomerase components are targeted by pluripotency-associated transcription factors, and that in autosomal dominant DC, transcriptional silencing accompanies a 3' deletion at the TERC locus. Our results demonstrate that reprogramming restores telomere elongation in DC cells despite genetic lesions affecting telomerase, and show that strategies to increase TERC expression may be therapeutically beneficial in DC patients.

Pluripotency pertains to the cells of early embryos that can generate all of the tissues in the organism. Embryonic stem cells are embryo-derived cell lines that retain pluripotency and represent invaluable tools for research into the mechanisms of tissue formation. Recently, murine fibroblasts have been reprogrammed directly to pluripotency by ectopic expression of four transcription factors (Oct4, Sox2, Klf4 and Myc) to yield induced pluripotent stem (iPS) cells. Using these same factors, we have derived iPS cells from fetal, neonatal and adult human primary cells, including dermal fibroblasts isolated from a skin biopsy of a healthy research subject. Human iPS cells resemble embryonic stem cells in morphology and gene expression and in the capacity to form teratomas in immune-deficient mice. These data demonstrate that defined factors can reprogramme human cells to pluripotency, and establish a method whereby patient-specific cells might be established in culture.

Previously, we have found that lipid rafts/caveolae were essential for insulin-like growth factor-1 (IGF-1) receptor signaling during 3T3-L1 preadipocytes differentiation induction. However, it was not identified as to which of the membrane lipid-ordered microdomains mediates the receptor signal. Using small double-stranded RNA-mediated interference (RNAi), we successfully suppressed the caveolin-1 protein expression. In cells stably transfected with vector expressing small interfering RNA (siRNA) fragment, no caveolin-1 protein or caveola was detected. On the other hand, removal of caveolin-1 did not affect the caveolinless lipid rafts or the localization of IGF-1 receptor in lipid rafts on plasma membrane. IGF-1 receptor signal transduction and induced cellular differentiation were normal in RNAi cells with only lipid rafts. Furthermore, these IGF-1 receptor signaling events were still sensitive to the cholesterol-binding reagents. Thus, our results suggest that lipid rafts are sufficient for IGF-1 receptor signaling and the recruitment of signal molecules by caveolin-1 is not essential for IGF-1 receptor signaling.