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This study utilizes panel data from 30 provinces in mainland China from 2011 to 2020 to investigate the impact of carbon-neutral development on economic high-quality development by constructing an economic high-quality development index and a carbon-neutral development index. Firstly, the study examines the effects of carbon-neutral development on economic high-quality development using baseline regression and spatial Durbin regression. The results indicate that carbon-neutral development has a positive direct effect on economic high-quality growth, but there are negative spatial spillover effects. Secondly, this study employs total factor productivity (TFP) as an intermediate variable in the mediation model regression. The findings demonstrate that carbon-neutral development significantly improves TFP, and the significant improvement in TFP promotes high-quality economic growth. Lastly, the study conducts regional heterogeneity analysis and finds a significant promoting effect of carbon-neutral development on economic high-quality development in the eastern and central regions of China, while it is not significant in the western region. Therefore, it is recommended that China, in the process of achieving carbon-neutral growth, consider the geographical connections between different regions to prevent negative spillover effects. Additionally, regional heterogeneity should be taken into account when formulating relevant policies to promote economic high-quality development. [ABSTRACT FROM AUTHOR]

Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan–Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes. [ABSTRACT FROM AUTHOR]

Motivation The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. Results We introduce a statistical s patial transcri p tomics cell a ssig n ment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. Availability and implementation https://github.com/ChengZ352/SPAN. [ABSTRACT FROM AUTHOR]

This work elucidates the potential impact of intramolecular H‐bonds within the pore walls of covalent organic frameworks (COFs) on proton conductivity. Employing DaTta and TaTta as representative hosts, it was observed that their innate proton conductivities (σ) are both unsatisfactory and σ(DaTta)<σ(TaTta). Intriguingly, the performance of both imidazole‐loaded products, Im@DaTta and Im@TaTta is greatly improved, and the σ of Im@DaTta (0.91×10−2 S cm−1) even surpasses that of Im@TaTta (3.73×10−3 S cm−1) under 100 °C and 98 % relative humidity. The structural analysis, gas adsorption tests, and activation energy calculations forecast the influence of imidazole on the H‐bonded system within the framework, leading to observed changes in proton conductivity. It is hypothesized that intramolecular H‐bonds within the COF framework impede efficient proton transmission. Nevertheless, the inclusion of an imidazole group disrupts these intramolecular bonds, leading to the formation of an abundance of intermolecular H‐bonds within the pore channels, thus contributing to a dramatic increase in proton conductivity. The related calculation of Density Functional Theory (DFT) provides further evidence for this inference. [ABSTRACT FROM AUTHOR]

The structural stability of γ-boron is investigated using Raman spectra and DFT calculations under high pressures, up to 126 GPa, at ambient temperature. The pressure dependence of all the Raman-active modes of the γ-boron is reported. We also observe amusing changes within the B3g and B1g Raman-active vibrational modes, which result in the phenomenon of first merging and then separating the Raman peaks. In addition to the Raman measurements, the changes in crystal structure and force constants are calculated to reasonably explain the discrepancy between the two Raman modes in response to pressure. The results of the continuous shifts for all Raman modes and the unit-cell parameters, as well as volume with increasing pressure, indicate that there is no structural transformation of γ-boron below this pressure value, with no changes in either symmetry or structure. [ABSTRACT FROM AUTHOR]

Mannitol is a readily accessible component of seaweed with higher energy content than glucose, making it a promising feedstock for biomanufacturing. Microorganisms have been engineered for converting mannitol into various bioproducts. Microbial strain discovery and synthetic biology approach will advance biomanufacturing using mannitol and other complex components of marine biomass. [ABSTRACT FROM AUTHOR]

The rolling bearing fault diagnosis is affected by industrial environmental noise and other factors, leading to the existence of some redundant components after signal decomposition. At the same time, the existence of the modal aliasing phenomenon in empirical mode decomposition (EMD) and the relevant improved algorithms also leads to the existence of many invalid features in the components. These phenomena have great influence on the bearing fault diagnosis. So a rolling bearing bidirectional-long short term memory (Bi-LSTM) fault diagnosis method was proposed based on segmented interception auto regressive (SIAR) spectrum analysis and information fusion. The ensemble empirical mode decomposition (EEMD), the complementary ensemble empirical mode decomposition (CEEMD) and the robust EMD (REMD) algorithms decompose the rolling bearing fault signals, and AR spectrum analysis is performed on the obtained components respectively. By comparing the AR spectra of the components corresponding to different fault locations, the effective AR spectral values are intercepted as the eigenvalues of the data, and finally all the eigenvalues are fused to achieve the purpose of screening effective features more efficiently so as to reduce the impact of feature redundancy caused by mode aliasing on neural network training. Then the Bi-LSTM neural network was used as a rolling bearing fault diagnosis classifier, and the simulation experiments were conducted based on the rolling bearing fault signal data from Case Western Reserve University to verify the effectiveness of the proposed feature extraction and fault diagnosis method. [ABSTRACT FROM AUTHOR]

Gingiva or gum is a part of the periodontium that surrounds the tooth. Its main function is to provide an effective barrier to both mechanical trauma and bacterial invasion. Gingiva is the target site for some topical drugs. The most common disease in gingiva is periodontal diseases (gum infections). Understanding the gingiva barrier properties could provide insights into approaches to effective drug delivery for the gingiva. Porcine gingiva was chosen as the model in the present membrane transport study. The permeability coefficients of gingiva were determined using a modified Franz diffusion cell with small diffusional area (0.03 cm2) and 12 model permeants with different physicochemical properties. The influences of edge effect and aqueous boundary layers were not observed in the modified diffusion cell setup for the small pieces of gingiva tissue samples. Lipophilic permeants exhibit higher permeability coefficients than hydrophilic permeants. A correlation was observed between the Log permeability coefficient (Log P) and Log octanol-water distribution coefficient (Log D ow) in the analysis. The permeant molecular weight (MW) was also a factor in the Log P vs. Log D ow relationship. The coefficient of Log D ow in this three-factor relationship (0.42) suggested that the gingiva barrier was less lipophilic than octanol. [ABSTRACT FROM AUTHOR]

Background: Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants. Results: In this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV. Conclusions: The proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage. [ABSTRACT FROM AUTHOR]

Objective: To examine the effects of prenatal maternal depression, anxiety and stress, and postnatal depression on infant early neurodevelopment, and the sex dimorphism.Study Design: We used data from 3379 mother-infant pairs from the Shanghai Birth Cohort. Maternal mental health was assessed using the Center for Epidemiological Studies-Depression Scale, Zung Self-Rating Anxiety Scale, Perceived Stress Scale at mid-pregnancy, and the Edinburgh Postnatal Depression Scale at postpartum. Infant neurodevelopment was evaluated using the Ages & Stages Questionnaires and Bayley Scales at ages 6, 12, and 24 months, respectively. Linear mixed models and linear regression models were used.Results: Among 3379 mothers, 11.07 %, 5.42 %, and 34.85 % of women experienced depression, anxiety, and elevated stress, separately. As maternal prenatal mental scores increased per 1SD, infant social-emotional scores decreased -2.82 (-3.86, -1.79) vs -2.86 (-3.94, -1.79) for depression, -2.34 (-3.38, -1.31) vs -2.72 (-3.81, -1.64) for anxiety, and -2.55 (-3.60, -1.50) vs -3.41 (-4.48, -2.35) for stress among boys and girls at age 24 months, respectively. Associations were also observed on social-emotional and communication scores in boys and girls, and fine motor in girls at age 6 and 12 months. These associations were not observed for postpartum depression.Limitation: Generalizability of the results to other population remains to be determined.Conclusions: Prenatal maternal depression, anxiety, and stress were negatively associated with infant early neurodevelopment, which were not observed for postpartum depression. We underscore the importance of maternal prenatal mental health in optimizing infant neuropsychiatric development. [ABSTRACT FROM AUTHOR]

In this paper, we consider the d-boundedness of the Bergman metric and a vanishing theorem of L2-cohomology on a class of Hartogs domain, whose base domain is the production of two irreducible bounded symmetric domains of the first type, by using the Bergman kernel function, invariant function, holomorphic automorphism group and so on. [ABSTRACT FROM AUTHOR]

In order to obtain a high photoelectrochemical performance, co-catalysts loading is the most commonly used way, which can facilitate reactions and suppress the charge recombination. In this paper, a novel composite of ZnO/Ti3C2TX photoanode was fabricated by a facile spin coating of precipitating Ti3C2TX (MXene) flakes onto the surface of ZnO, as co-catalyst for enhanced photoelectrochemical (PEC) water splitting. Under simulated sunlight, the optimum composite of ZnO/Ti3C2TX photoanode showed the photocurrent density as 1.2 mA cm−2 at 1.23 VRHE, which is 1.4 times higher than that of pristine ZnO without Ti3C2TX co-catalyst (0.83 mA cm−2 at 1.23 VRHE). The ZnO/Ti3C2TX photoanode showed a photoconversion efficiency of 0.32% and maintained a stable photocurrent over 2000s. The Ti3C2TX (MXene) flakes as co-catalyst to promote the charge transfer and accelerates the reaction kinetics in ZnO/Ti3C2TX photoanode. This work delivers a two-dimensional (2D) material Ti3C2TX (MXene) as co-catalyst for enhanced ZnO photoanode PEC water splitting. [ABSTRACT FROM AUTHOR]

Argostemma ehuangzhangense, a new Rubiaceae species from E'huangzhang Nature Reserve, Guangdong Province, China, is here described and illustrated. A morphological comparison between the new species and its putative relatives, A. lamxayanum, A. laotica and A. verticillatum, is presented. The new species is mostly similar to A. laotica, but they can be distinguished from each other since Argostemma ehuangzhangense presents solitary flower (vs. 2-flowered inflorescences), flower lobes 4 (vs. 5) and anthers opening by longitudinal slits (vs. apical pores). In a preliminary IUCN Red List status of Argostemma ehuangzhangense this species is assigned as Vulnerable (VU). [ABSTRACT FROM AUTHOR]

The ever-increasing demand for flexible and portable electronics has stimulated research and development in building advanced electrochemical energy devices which are lightweight, ultrathin, small in size, bendable, foldable, knittable, wearable, and/or stretchable. In such flexible and portable devices, semi-solid/solid electrolytes besides anodes and cathodes are the necessary components determining the energy/power performances. By serving as the ion transport channels, such semi-solid/solid electrolytes may be beneficial to resolving the issues of leakage, electrode corrosion, and metal electrode dendrite growth. In this paper, the fundamentals of semi-solid/solid electrolytes (e.g., chemical composition, ionic conductivity, electrochemical window, mechanical strength, thermal stability, and other attractive features), the electrode–electrolyte interfacial properties, and their relationships with the performance of various energy devices (e.g., supercapacitors, secondary ion batteries, metal–sulfur batteries, and metal–air batteries) are comprehensively reviewed in terms of materials synthesis and/or characterization, functional mechanisms, and device assembling for performance validation. The most recent advancements in improving the performance of electrochemical energy devices are summarized with focuses on analyzing the existing technical challenges (e.g., solid electrolyte interphase formation, metal electrode dendrite growth, polysulfide shuttle issue, electrolyte instability in half-open battery structure) and the strategies for overcoming these challenges through modification of semi-solid/solid electrolyte materials. Several possible directions for future research and development are proposed for going beyond existing technological bottlenecks and achieving desirable flexible and portable electrochemical energy devices to fulfill their practical applications. It is expected that this review may provide the readers with a comprehensive cross-technology understanding of the semi-solid/solid electrolytes for facilitating their current and future researches on the flexible and portable electrochemical energy devices. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, online learning has experienced increasing utilization and poses new challenges for schoolteachers to engage students. Project-based Learning (PBL) is widely acknowledged as an effective pedagogy for motivating and involving students. However, few studies have examined scaffolds that facilitate student engagement in the context of distance PBL. This mixed-method study was conducted with grade 7 teachers and students in a junior middle school in East China from March 2020 to April 2020. Qualitative analysis was employed in interviews with 2 teachers and 21 students. Quantitative analysis was used to visualize the self-reflection reports of 39 students. The findings suggest that the scaffolds of teacher direction, technology support, peer collaboration, and parental assistance play a significant role. In addition, specific scaffolding within the above categories was revealed. The results highlight the problem-oriented, methodological, and synthesized application of various scaffold(ing)s in engaging students and emphasize that scaffolding students emotionally is the core issue to support engagement for remote learning. [ABSTRACT FROM AUTHOR]

Wire bonding is widely used in various radio frequency (RF) devices and is one of the common forms of signal interconnection. In this article, a new geometric model with related physical parameters is used to describe various actual types of the bondwire, and the corresponding calculation method for the total length of the bondwire is obtained. Consequently, a new equivalent circuit modeling method of the single‐bondwire can be developed and the accuracy of model has been improved. On this basis, using the concept of partial inductance, the modeling method of double‐bondwire is proposed, which can be applied to asymmetric double‐bondwire modeling. The model of bondwire obtained by this modeling method is applied to the design of PA, and the special electrical properties of the bondwire are used to verify a novel harmonic control method, which second and third harmonics could be controlled. The results show that in the Sub‐6G, the proposed bondwire model has good accuracy, and its performance is close to the measured results. The designed PA combined with harmonic control network has good performance, achieving drain efficiency of 62.6%–70% and output power of 40–41.5 dBm at 3.2–3.8 GHz. It is proved that the proposed bonding line modeling method can be applied to the design of high‐efficient PA. [ABSTRACT FROM AUTHOR]

The most frequent internal modification in eukaryotic mRNA is N6-methyladenosine (m6A). However, what we know about the m6A regulators in Ankylosing spondylitis (AS) is still limited. In our study, eight distinct m6A regulators were selected utilizing Differentially Expressed Gene (DEG) analysis of the Gene Expression Omnibus GSE73754 dataset for making comparisons between AS (Ankylosing spondylitis) and non-AS patients. The random forest model and the nomogram model were used to screen the eight candidate m6A regulators and evaluate their prediction accuracy for the occurrence of AS. Furthermore, based on the selected m6A regulators, the AS patients were divided into two subgroups, and we applied principal component analysis algorithms to calculate their m6A score and evaluate the m6A patterns. Our findings revealed that patients in cluster A were linked to activated CD4 T cell immunity and activated CD8 T cell immunity. With its major contributions in the area of immunology, our research in m6A patterns may benefit the future diagnosis and treatment strategies of AS. [ABSTRACT FROM AUTHOR]

Summary: The consequences of soils exposed to hydraulic fracturing (HF) return fluid, often collectively termed flowback and produced water (FPW), are poorly understood, even though soils are a common receptor of FPW spills. Here, we investigate the impacts on soil microbiota exposed to FPW collected from the Montney Formation of western Canada. We measured soil respiration, microbial community structure and functional potentials under FPW exposure across a range of concentrations, exposure time and soil types (luvisol and chernozem). We find that soil type governs microbial community response upon FPW exposure. Within each soil, FPW exposure led to reduced biotic soil respiration, and shifted microbial community structure and functional potentials. We detect substantially higher species richness and more unique functional genes in FPW‐exposed soils than in FPW‐unexposed soils, with metagenome‐assembled genomes (e.g. Marinobacter persicus) from luvisol soil exposed to concentrated FPW being most similar to genomes from HF/FPW sites. Our data demonstrate the complex impacts of microbial communities following FPW exposure and highlight the site‐specific effects in evaluation of spills and agricultural reuse of FPW on the normal soil functions. [ABSTRACT FROM AUTHOR]

Motivation Disease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in a simple scenario when the action space is small; however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialog system for policy learning. The high-level policy consists of a master model that is responsible for triggering a low-level model, the low-level policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms. Results Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches. Availability and implementation The code and data are available from https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In the future, various types and parameters of display devices and lenslet arrays will be commercially available. Different display devices can be matched by cutting and expanding elemental images, while different lenses require changing the pixel mapping structure individually for each pixel, which involves complex calculations. A simple pseudoscopic-to-orthoscopic conversion for lenslet arrays with different apertures in real and virtual fields has meaningful practical implications. To this end, orthoscopic integral imaging for different lenslet arrays in real and virtual fields is proposed herein. The tilt-shift photography technique is used to generate a wider view image array, which is cropped to form the orthoscopic elemental image arrays required for lenslet arrays of various shapes, considering their specific structures and shapes. The approach expands the viewing angle without the need to change the system parameters or recalculate the elemental image relative to the viewer's position, enabling multiple individuals to view the three-dimensional (3D) images from different angles simultaneously. It does not require additional devices or depth information. The repositioning of the original camera array enables various display modes, such as real images, virtual images, and co-displays, which enhance the 3D display effect. The experimental results demonstrate that the proposed method can generate 3D images with correct depth relationships and large viewing angles, through subjective and quantitative comparison of the parallax. • The pseudoscopic problem in viewing 3D images effectively addressed. • A flexible method for generating orthoscopic integral images proposed. • Method applicable for different lenslet arrays in real and virtual fields. • Real, virtual, and co-displayed 3D images obtained. • Simulations and optical experiments confirmed method efficacy. [ABSTRACT FROM AUTHOR]

Pansharpening involves the spatial super-resolution of a low-resolution multispectral (LR-MS) image by leveraging a simultaneously acquired panchromatic (PAN) image, aiming to generate a high-resolution multispectral (HR-MS) image. Such an inverse problem mainly requires more accurately establishing the relation between the underlying HR-MS image and the PAN image. Because of the high redundancy of framelet transform, the framelet-based sparse error reconstruction has recently been well-investigated and achieved promising results. Nevertheless, previous works ignore the negative impact of the low-pass filter within the framelet, which experimentally distinguishes the coefficient similarity and reduces the error sparsity, thereby leading to limited numerical performance and high hyperparameter sensitivity. In this paper, we propose an improved pansharpening model via semi-framelet-guided sparse reconstruction, called SemiFGSR. This model only considers the partial rather than the whole framelet transform, which avoids the interference of low-frequency information, thus facilitating sparse reconstruction. To solve the proposed ℓ 0 norm-based model, we develop an efficient proximal alternating minimization (PAM)-based algorithm and theoretically prove its convergence. Numerical experiments conducted on various datasets demonstrate the superiority of the SemiFGSR, revealing the effectiveness of such semi-framelet-guided improvement. • We reveal the potential of the semi-framelet-guided transform. • We develop a semi-framelet-guided sparse reconstruction for pansharpening. • We design a PAM-based algorithm and theoretically prove the convergence. [ABSTRACT FROM AUTHOR]

[Display omitted] Neovascularization contributes to various posterior eye segment diseases such as age-related macular degeneration and diabetic retinopathy. RNA nanoparticles were demonstrated previously to enter the corneal and retinal cells after subconjunctival injection for ocular delivery. In the present study, antiangiogenic aptamers (anti-vascular endothelial growth factor (VEGF) and anti-angiopoietin-2 (Ang2) aptamers) were conjugated to RNA nanoparticles. The objectives were to investigate the clearance and distribution of these angiogenesis-inhibiting RNA nanoparticles after subconjunctival injection in vivo and their antiangiogenic effects for inhibiting ocular neovascularization in vitro. The results in the whole-body fluorescence imaging study showed that the clearance of RNA nanoparticles was size-dependent with no significant differences between RNA nanoparticles with and without the aptamers except for pRNA-3WJ. The distribution study of RNA nanoparticles by confocal microscopy of the dissected eye tissues in vivo indicated cell internalization of the larger RNA nanoparticles in the retina and retinal pigment epithelium after subconjunctival injection, and the larger nanoparticles with aptamers showed higher levels of cell internalization than those without. In the cell proliferation assay in vitro , RNA nanoparticles with multiple aptamers had higher antiangiogenic effects. With both longer retention time and high antiangiogenic effect, SQR-VEGF-Ang2 could be a promising RNA nanoparticle for posterior eye delivery. [ABSTRACT FROM AUTHOR]

It is essential to reveal the phase transformation and element diffusion mechanism at the interface between the Inconel 625 coating prepared by high-speed laser cladding and the Q245R substrate to meet the requirement of high-temperature service environment. A banded area formed at the bottom of the laser cladding layer after being heated at 700 °C. The banded area thickened as the duration of high-temperature treatment increased. The cellular and columnar crystals in the banded area were not visible. Laves phase at the banded area dissolved, and M 23 C 6 precipitated and accumulated significantly. The main reasons for phase transformation in the banded area could be attributed to C diffusion from the substrate to the laser cladding layer. Additionally, the lamellar structure contained α-Fe and Fe 3 C, which formed in the substrate beneath the interface due to the C enrichment at the interface. Simultaneously, element diffusion and phase transformation led to the increase of lattice misfit, dislocations, and vacancies at the interface. Ultimately, element diffusion and phase transformation led to an increase in the microhardness at the bottom of the laser cladding layer and a decrease in the microhardness at the heat affect zone. • A banded area without cellular and columnar crystals formed in the laser cladding layer after high temperature duration. • The C diffusion from the substrate into the cladding layer is the main reason for phase transformation in banded area. • Element diffusion and phase transformation led to the increase of lattice misfit and dislocations at the interface. • The microhardness of banded area in laser cladding layer increased significantly after high temperature duration. [ABSTRACT FROM AUTHOR]

Background: The pathogen of banana Fusarium oxysporum f. sp. cubense race 4(Foc4) infects almost all banana species, and it is the most destructive. The molecular mechanism of the interactions between Fusarium oxysporum and banana still needs to be further investigated. Methods: We use both the interolog and domain-domain method to predict the protein–protein interactions (PPIs) between banana and Foc4. The predicted protein interaction sequences are encoded by the conjoint triad and autocovariance method respectively to obtain continuous and discontinuous information of protein sequences. This information is used as the input data of the neural network model. The Long Short-Term Memory (LSTM) neural network five-fold cross-validation and independent test methods are used to verify the predicted protein interaction sequences. To further confirm the PPIs between banana and Foc4, the GO (Gene Ontology) and KEGG (Kyoto Encylopedia of Genes and Genomics) functional annotation and interaction network analysis are carried out. Results: The experimental results show that the PPIs for banana and foc4 predicted by our proposed method may interact with each other in terms of sequence structure, GO and KEGG functional annotation, and Foc4 protein plays a more active role in the process of Foc4 infecting banana. Conclusions: This study obtained the PPIs between banana and Foc4 by using computing means for the first time, which will provide data support for molecular biology experiments. [ABSTRACT FROM AUTHOR]

Modal regression is as efficient as mean regression when the random error follows normal distribution, and is robust to the presence of outliers or skewed distributions. Due to such advantages as efficiency and robustness, it has been widely applied in different fields, such as medicine, economics, environment and so on. However, most existing literature based on modal regression are assumed that the covariates are continuous, but discrete variable is very common in practice. In this paper, inspired by the idea of modal regression, an adaptive estimation procedure is proposed for nonparametric models with mixed discrete and continuous regressors. Under some mild assumptions, the limiting behavior of the proposed approach can be established, and a simply feasible estimation algorithm is provided in practice. To illustrate the performance of our proposed method, some numerical results are presented in simulation part. Finally, the proposed method is applied to analyze the GDP data of OECD and non-OECD countries. [ABSTRACT FROM AUTHOR]

• Modeling unknown dynamics of microgrids using Gaussian processes. • The model of the MPC controller consists of a physical model and a data model based on Gaussian processes. • Frequency control of a microgrid consisting of photovoltaics, energy storage and electric vehicles is considered. • The proposed method overcomes the stochasticity of each generating unit and load and the parameter uncertainty of the physical models. Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method. [ABSTRACT FROM AUTHOR]

The randomness and variation of material composition at the meso-scale lead to some limitations in the application of asphalt mixture. When all features are taken into account, the computational effort increases significantly. Therefore, the relationship can be established using the meso-element equivalence method (MEEM). In this paper, the effects of aggregate-asphalt interface, initial void defects and material damage were introduced and MEEM of asphalt mixture was established based on industrial computed tomography (ICT) and finite element method (FEM). The rationality of the model was verified by laboratory performance tests, and the effects of aggregate type, mixture gradation and mesostructural parameters on the mechanical response of MEEM were compared and analyzed. The results showed that the MEEM model captures the mechanical behavior of asphalt mixtures well, the input of visco-elastic material parameters is an important step of MEEM simulation accuracy. The aggregate type, the discrete degree of the skeleton structure and the distribution characteristics of mesostructural parameters are important factors affecting the mechanical properties of asphalt mixture in MEEM. • The model of the material homogenization of the asphalt mixture is realized by using the meso-unit equivalence method at the meso scale • The effects of the aggregate-asphalt interface, initial void defects, and material damage are introduced in the equivalence process • The validity of the meso-unit model is verified by digital image correlation based on indirect tensile test • Different factors on the mechanical response of asphalt mixtures in MEEM are compared at the meso scale. [ABSTRACT FROM AUTHOR]

The porous structures and Ov generated from photo-driven electrochemically etched ZnO electrodes promote the efficiency of light trapping and charge separation, which also facilitate electrode/electrolyte interface charge transfer. [Display omitted] • Photo-driven electrochemical etched electrodes with porous structures and O V. • Promoted electrode/electrolyte interface charge transfer. • Improved light trapping and charge separation efficiency. • Tailored surface pore structures by illumination. Fast charge separation and oxidation kinetics are the key issues for photoelectrochemical (PEC) water splitting. Herein, photo-driven electrochemical etching (PEE) was applied to modify the morphology and surface properties of ZnO photoanodes to improve PEC performance. By regulating the applied potential and treatment time, the PEE generated a lotus root like porous structure with a pore diameter of 20–30 nm and depth of about 300 nm, and produced a large amount (30.1 %) of oxygen vacancies (Ov) on ZnO photoanode. PEE treated-ZnO array demonstrated a charge separation efficiency of 87 %, and achieved 1.8 times larger photocurrent (1.26 mAcm−2 at 1.23 V RHE) with a cathodic shift of onset potential of 0.12 V RHE than pristine ZnO. The introduced Ov and the shortened charge transport distance of porous microstructures by PEE treatment improved the electrode/electrolyte interface charge transfer and water oxidation kinetics. DFT calculations indicate that the construction of oxygen defects on the catalyst surface can reduce the overpotential of oxygen evolution reaction (OER) and further promote the catalytic activity of OER. Moreover, electrode surface pore structure can be tailored by adjusting illumination parameters and electrolytes. This work illustrates a novel and effective approach to tunable photo-corrodible electrode materials for solar water splitting. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute to diagnose, treatment, and prognosis of diseases. However, the identification of LDAs by the biological experiments is time-consuming, costly and inefficient. Therefore, the development of efficient and high-accuracy computational methods for predicting LDAs is of great significance. Results: In this paper, we propose a novel computational method (gGATLDA) to predict LDAs based on graph-level graph attention network. Firstly, we extract the enclosing subgraphs of each lncRNA-disease pair. Secondly, we construct the feature vectors by integrating lncRNA similarity and disease similarity as node attributes in subgraphs. Finally, we train a graph neural network (GNN) model by feeding the subgraphs and feature vectors to it, and use the trained GNN model to predict lncRNA-disease potential association scores. The experimental results show that our method can achieve higher area under the receiver operation characteristic curve (AUC), area under the precision recall curve (AUPR), accuracy and F1-Score than the state-of-the-art methods in five fold cross-validation. Case studies show that our method can effectively identify lncRNAs associated with breast cancer, gastric cancer, prostate cancer, and renal cancer. Conclusion: The experimental results indicate that our method is a useful approach for predicting potential LDAs. [ABSTRACT FROM AUTHOR]

Pansharpening refers to a spatio-spectral fusion of a lower spatial resolution multispectral (MS) image with a high spatial resolution panchromatic image, aiming at obtaining an image with a corresponding high resolution both in the domains. In this article, we propose a generic fusion framework that is able to weightedly combine variational optimization (VO) with deep learning (DL) for the task of pansharpening, where these crucial weights directly determining the relative contribution of DL to each pixel are estimated adaptively. This framework can benefit from both VO and DL approaches, e.g., the good modeling explanation and data generalization of a VO approach with the high accuracy of a DL technique thanks to massive data training. The proposed method can be divided into three parts: 1) for the VO modeling, a general details injection term inspired by the classical multiresolution analysis is proposed as a spatial fidelity term and a spectral fidelity employing the MS sensor’s modulation transfer functions is also incorporated; 2) for the DL injection, a weighted regularization term is designed to introduce deep learning into the variational model; and 3) the final convex optimization problem is efficiently solved by the designed alternating direction method of multipliers. Extensive experiments both at reduced and full-resolution demonstrate that the proposed method outperforms recent state-of-the-art pansharpening methods, especially showing a higher accuracy and a significant generalization ability. [ABSTRACT FROM AUTHOR]

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model. [ABSTRACT FROM AUTHOR]

Neck dissection is the standard surgical management strategy to determine the pathologic status of cervical lymph nodes in patients with OSCC, but the immunological relationship between the primary tumor and metastatic lymph nodes is insufficiently understood. References 1 Kawaguchi S, Kawahara K, Fujiwara Y. Naringenin potentiates anti-tumor immunity against oral cancer by inducing lymph node CD169-positive macrophage activation and cytotoxic T cell infiltration. [Extracted from the article]

A practical and efficient approach for the synthesis of fluorescent 2,3-naphthalimide derivatives has been developed from readily available starting materials via an intramolecular didehydro-Diels–Alder reaction, which proceeded well under room temperature, exhibiting a wide substrate scope and good functional group tolerance. The practicability of this methodology has been verified by one-step synthesis of the environmentally sensitive fluorophore 6-DMN on a gram scale with a shorter time, fewer steps and less waste disposal, and without the utilization of toxic transition metals. The present experimental and computational studies support the crucial role of the propiolimide moiety in the transformation. [ABSTRACT FROM AUTHOR]

Poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) receives increasing attention in membrane separation field based on its advantages such as high mechanical strength, thermal and chemical stability. However, controlling the microporous structure is still challenging. In this work, we attempted to tailor the morphology of PVDF-HFP membrane via a one-step reactive vapor induced phase separation method. Namely, PVDF-HFP was dissolved in a volatile solvent and then was cast in an ammonia water vapor atmosphere. After complete evaporation of solvent, membranes with adjustable porous structure were prepared, and the microstructures of the membranes were analyzed by scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and X-ray diffraction characterizations. Based on the results, a mechanism of dehydrofluorination induced cross-linking of PVDF-HFP has been suggested to understand the morphology tailoring. To our knowledge, this is the first report of one-step reactive vapor induced phase separation strategy to tailor morphology of PVDF-HFP membrane. In addition, the membranes prepared in the ammonia water vapor exhibited enhanced mechanical strength and achieved satisfactory separation efficiency for water-in-oil emulsions, suggesting promising potential. [ABSTRACT FROM AUTHOR]

Background: Hyperuricemia, pulmonary hypertension, renal failure, and alkaline intoxication syndrome (HUPRA syndrome) is a rare autosomal recessive mitochondrial disease. SARS2 gene encoding seryl‐tRNA synthetase is the only pathogenic gene of HUPRA syndrome. All the previously reported cases with HUPRA syndrome were detected for homozygous mutation. Methods: We identified compound heterozygous mutations causing HUPRA syndrome using whole‐exome sequencing, and verifed pathogenicity with ACMG standards. All the previously published cases with SARS2 mutations were reviewed. Results: SARS2 gene compound heterozygotes variants were detected in this Chinese patient (c.667G>A/c.1205G>A). Bioinformatics studies and protein models predict that a new variant (c.667G>A) is likely to be pathogenic. A total of six patients, five of whom were previously reported with HUPRA syndrome, were analyzed. All of the six had typical clinical manifestations of HUPRA syndrome, except the Chinese girl who had no pulmonary hypertension or alkaline intoxication. The shrunken kidney was more prominent in our proband. The average survival time for previously reported patients was 17 months, and the Chinese girl was 70 months. Three mutation variants were found, including five homozygous mutants, three of which were Palestinian (c.1169A > G), two of which were from a Spanish family (c.1205G> A), and one was a new variant (c.667G>A/c.1205G>A). Conclusion: We found a new pathogenic form (compound heterozygous mutation) causing HUPRA syndrome, and identified a novel pathogenic site (c.667G>A) of the SARS2 gene, expanding the spectrum of SARS2 pathogenic variants. The mild phenotype in complex heterozygous mutations is described. [ABSTRACT FROM AUTHOR]

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma. [ABSTRACT FROM AUTHOR]

Background: Acute pharyngitis and tonsillitis are common respiratory diseases for which children seek medical care. Their main clinical manifestation is sore throat which interferes with patients' quality of life. However, there is no proven effective or safe method to treat it. It is necessary to find an excellent strategy to reduce sore throat and reduce the burden of acute illness. We designed the randomized controlled trial with the characteristics of traditional Chinese medicine (TCM) to determine the clinical positioning of Kai-Hou-Jian spray (children's type) (KHJS) through evidence-based research. This trial aims to evaluate the immediate analgesic efficacy of KHJS on sore throat caused by acute pharyngitis and tonsillitis (wind-heat syndrome/heat exuberance in lung and stomach syndrome) in children and to observe its safety.Methods/design: This is a prospective, multicenter, randomized, double-blind, parallel-group, placebo-controlled trial. It will include 240 children with acute pharyngitis/tonsillitis from 7 study sites across China. All participants are randomly assigned to two parallel treatment groups, one with KHJS and the other with placebo sprays, for 5 consecutive days. The primary outcome is the time of analgesic onset. Secondary outcomes include duration of analgesic effect, area under time curve of 0-3 h Wong-Baker FACES Pain Rating Scale (WBS) score (AUC0-3 h), rate of analgesic onset, rate of disappearance of sore throat, changes of WBS score (in days), effective rate of pharyngeal signs, and effective rate of TCM syndrome. The incidence of adverse events during the trial is the primary safety outcome. In addition, vital signs and laboratory tests before and after medication are monitored.Discussion: To our knowledge, this will be the first clinical trial to explore the immediate analgesic efficacy of a Chinese patent medicine spray for acute pharyngitis/tonsillitis induced sore throat in children in a multicenter, randomized, double-blinded, parallel-group, placebo-controlled manner. Not only might it prove the efficacy and safety of KHJS in the treatment of sore throat caused by acute pharyngitis/tonsillitis in children, but it might also provide evidence for the treatment of acute sore throat with Chinese herbal medicine.Trial Registration: A multicenter, randomized, double-blind, very low-dose, parallel controlled trial for the immediate analgesic effect and safety of Kai-Hou- Jian spray (children's type) in the treatment of sore throat caused by acute pharyngitis and tonsillitis in children. Chinese Clinical Trial Registry ChiCTR2000031599 . Registered on 5 April 2020. [ABSTRACT FROM AUTHOR]

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma. [ABSTRACT FROM AUTHOR]

[Display omitted] • Non-selective single extraction was achieved through polarity gradient solvents. • A D-optimal design was used to obtain the best pretreatment conditions. • A novel integrated non-target data acquisition workflow was developed. • Based on FBMN in GNPS to mine potential metabolites and structure–related products. Identifying contaminants in agricultural plant food products (APFPs) is a major problem. In this study, we developed a single-step extraction and integrated non-target data acquisition (INDA) workflow for increasing hazardous substances coverage. D-optimal experimental designs were applied to optimize filter plate extraction (FPE) for one-single extraction of multipolar hazardous substances. The vDIA mode was used to collect all precursor ion fragments within the range to supplement data loss caused by DDA mode. The underlying principle of vDIA is to increase the utilization rate of MS2 spectra that are likely to identify a maximum number and minimum amounts of hazardous substances. Compared with traditional DDA mode alone, a combination of the two modes increased the rate of identification of hazardous substances by 18.5%. The molecular network of hazardous substance provided by GNPS could enable some metabolites and structure-related products to discover potentially hazardous substance. [ABSTRACT FROM AUTHOR]

• The wind farm reduces fatigue load without significant impact on power tracking. • Incorporating load equalization in optimization reduces wind turbine load differences. • Wind farm optimizes load equalization, with slight increase in dynamic fatigue load. With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and the load equalization. The controller is evaluated in a medium-fidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor. [ABSTRACT FROM AUTHOR]

• We present Chinese migrant parents' dynamic perceptions of their roles, abilities, and contributions in and to HSI. • Parents initially self-select to participate in HSI and then self-disqualify. • We challenge the stereotypes that migrant parents are indifferent and incompetent in educational involvement. • We unravel the symbolic biases and exclusionary practices that marginalize migrant parents in HSI. In the past decades, the expansion of parentocracy in China's official and civil rhetorics has bolstered the home-school interaction (HSI hereafter) paradigm where parents actively participate in children's schooling. In previous studies, migrant parents are usually simply described as cohorts who are unwilling and incompetent to HSI. This paper introduces the concept of "involvement imaginaries" to capture migrant parents' dynamic perceptions of their roles, abilities, and contributions in and to HSI. The findings suggest a process wherein migrant parents self-select to integrate themselves into the urban HSI field and acquire urban disposition, and then come to self-disqualify in encountering structural and institutional barriers. Through this, the study challenges the stereotypes about migrant parents and contributes to understanding the symbolic biases and violence that exclude them from HSI. [ABSTRACT FROM AUTHOR]

Objective: To determine whether pre-eclampsia is associated with polymorphisms in superoxide dismutase (SOD) genes among mother-father-infant triads.Methods: We did this follow-up cohort study at 17 urban hospitals in Canada between October 1, 2008, and September 30, 2010. We recruited Canadian participants who had participated in the International Trial of Antioxidant Supplementation for the Prevention of Pre-eclampsia. Saliva specimens were collected for DNA extraction. The SOD1 +35A/C (rs2234694) and SOD2 Ala16Val C/T (rs4880) single-nucleotide polymorphisms (SNPs) were genotyped.Results: Dual presence of the SOD2 Ala16Val TT variant among mother-father pairs (n=657) was associated with an increased risk of pre-eclampsia when compared with the absence of the TT variant among the mother-father pairs (7/48 [14.6%] vs 11/339 [3.2%]; adjusted odds ratio 6.80, 95% confidence interval 2.32-19.95; P<0.001). By contrast, presence of a single T variant in mother-father pairs (16/270 [5.9%]) or mother-infant pairs (8/179 [4.5%]) was not associated with pre-eclampsia. The SOD1 +35A/CSNP was not associated with pre-eclampsia.Conclusion: The SOD2 Ala16Val SNP might be involved in paternal influence on the maternal predisposition to pre-eclampsia. Genotyping of mother-father pairs could be a promising strategy to identify pre-eclampsia genes. [ABSTRACT FROM AUTHOR]

The chaotic motions were shown by the vibrations of a bulk permanent magnet (PM) supported by a cylindrical high-Tc superconductor (HTSC) under external disturbance in very early experiments. There are few theoretical studies on the nonlinear behaviors of the HTSC levitation system as the complex interaction between the HTSC and PM. In this paper, we developed a numerical program to analyze the nonlinear vibrations of the levitated PM coupled with the electromagnetic force, taking into account the heat diffusion on HTSC. When the levitation system displayed chaotic oscillations by vertical excitation, the electromagnetic and thermal characteristics of the HTSC will change. The dynamic responses are shown by comparing the calculation results in which the thermal effects are taken into account and not. The numerical results display the displacements of PM, magnetic levitation forces, the acceleration, and the maximum Lyapunov exponent. The results show that the thermal effect will influence the stability of the levitation system. [ABSTRACT FROM AUTHOR]

Background: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. Results: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. Conclusions: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods. [ABSTRACT FROM AUTHOR]

Background: Drugs achieve pharmacological functions by acting on target proteins. Identifying interactions between drugs and target proteins is an essential task in old drug repositioning and new drug discovery. To recommend new drug candidates and reposition existing drugs, computational approaches are commonly adopted. Compared with the wet-lab experiments, the computational approaches have lower cost for drug discovery and provides effective guidance in the subsequent experimental verification. How to integrate different types of biological data and handle the sparsity of drug-target interaction data are still great challenges. Results: In this paper, we propose a novel drug-target interactions (DTIs) prediction method incorporating marginalized denoising model on heterogeneous networks with association index kernel matrix and latent global association. The experimental results on benchmark datasets and new compiled datasets indicate that compared to other existing methods, our method achieves higher scores of AUC (area under curve of receiver operating characteristic) and larger values of AUPR (area under precision-recall curve). Conclusions: The performance improvement in our method depends on the association index kernel matrix and the latent global association. The association index kernel matrix calculates the sharing relationship between drugs and targets. The latent global associations address the false positive issue caused by network link sparsity. Our method can provide a useful approach to recommend new drug candidates and reposition existing drugs. [ABSTRACT FROM AUTHOR]

In natural and engineered anaerobic ecosystems, syntrophic oxidation of short-chain fatty acid is one of the key factors and interspecies electron transfer mechanisms between bacteria and archaea play a pivotal role. Growing evidence suggests that microorganisms have the capability to promote interspecies electrons transfer in a more direct manner. In the current study, three different types of iron oxides were used to thermophilic anaerobic reactors (at 50 °C), quantitative polymerase chain reaction and 16S rRNA sequencing technique were applied to investigate the relation between iron reduction bacteria and syntrophic acetate oxidation bacteria. Experiment results showed that goethite and hematite promoted the methane production and generation rate and partially relieved the negative effects of H 2. The syntrophic relation between Syntrophaceticus and Methanobacterium worked with a direct interspecies electron transfer model rather than interspecies hydrogen transfer in goethite and hematite dosed reactors. The gene copies of Syntrophaceticus predominated over that of Geobacter. Theoretical calculations revealed that the rates of iron oxide mediated interspecies electron transfer among syntrophic partners were 105-106 times higher than those attainable via interspecies H 2 transfer. • Iron oxide promoted the growth of Syntrophaceticu s rather than Geobacter. • DIET happened between acetate oxidation bacteria and methanogenic archaea. • Theoretical iron oxides-mediated electron transfer rate was higher than H 2. • Maximum electron carrier flux was 9.0 × 10−6 A in goethite reactor. [ABSTRACT FROM AUTHOR]

For a long time, the development model of traditional rice planting industry has been single, linear, and focusing on production first and then market. However, with the low price of rice, such problems as slow technological upgrading, meager profits, and unstable rice quality in the primary industry planting units and farmers have become increasingly prominent. The integration and development of the rural primary, secondary, and tertiary industries and industrial integration have the same root. They are integrated industrial development based on technological or model innovation, vertical and horizontal integration. Three agricultural leading companies (Nanjing Tianwei Agricultural Technology Co., Ltd., Nanjing CHYKINGYOUNG Biological Technology Co., Ltd., and Jiangsu Jingshan Eco-organic Agriculture Co., Ltd.) established an industrialization complex, and used their respective technical, operational, and market advantages to integrate and form a rice contract farming 1 + 3 + X model. This model effectively integrates scattered rice planting, rice processing, and rice sales in Nanjing City of Jiangsu Province, and Chuzhou City of Anhui Province, and unifies planting regulations and supply of production materials, safeguards the supply of green and high-quality rice and realizes high quality and high price. In addition, it integrates farmers into the entire industrial chain of contract farming, summons up the production enthusiasm of farmers and greatly increases their income from planting production. [ABSTRACT FROM AUTHOR]

Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide. [ABSTRACT FROM AUTHOR]