Search

Abstract Current stress and soft switching range are important performance indexes of dual active bridge DC–DC converter. Based on three‐phase shift control, Karush–Kuhn–Tucker (KKT) algorithm is used to solve the optimal control strategy of current stress under soft switching conditions, and the interval construction method that KKT cannot solve is given. Aiming at the problem that all or most of the switching devices lose the zero voltage switching (ZVS) soft switching characteristics when the current dual active bridge (DAB) modulation strategy is seriously mismatched in voltage gain or when the output power is relatively small, an optimized modulation strategy is proposed here, which not only realizes the minimum current stress within the full power range, but also ensures that at least six switching tubes can achieve ZVS on–off. The on‐state and switching losses of the system are further reduced, and the transmission efficiency of the converter is improved.

Modular Multilevel Converters (MMCs) play a crucial role in high-voltage DC (HVDC) systems due to their adaptable control and swift response capabilities. However, AC side faults could introduce asymmetric double-line frequency components into the MMC's circulating current, jeopardizing DC bus stability. Although strategies to mitigate DC side fluctuations are well-established, a detailed comparison of their operational impacts remains unexplored. The operational effects of MMCs under AC side faults are firstly examined in this study, including issues related to grid-connected current asymmetry and DC bus current fluctuations. Subsequently, two targeted control schemes are devised based on the goals of suppressing the double-line frequency components and only the zero-sequence components of circulating current. Through simulation, these approaches are contrasted, delineating their advantages and disadvantages in terms of circulating current suppression, submodules(SMs) capacitance voltage fluctuation, and other performance metrics, which could provide a foundational reference for designing and selecting MMC control strategies under AC side faults.

For the situation that coal is still capable of oxidative spontaneous combustion under different oxygen conditions, we choose three coals with different metamorphic degrees: lignite, gas coal and fatty coal, selected as experimental samples to carry out programmed temperature gas chromatography(TPGC) experiments and molecular dynamics simulations based on ReaxFF force fields at oxygen concentrations of 3%, 5%, 10%, 14% and 18%, to investigate the characteristics of changes in the oxygen influence of groups under oxygen-poor conditions in coals with different degrees of metamorphosis according to the production patterns and pathways of CO and CO2, etc. The results show that coal is easily affected by temperature and oxygen to produce indicator gases such as CO and CO2, and when the indicator gas tends to increase exponentially, the lower the degree of coal deterioration and the lower the temperature at which the indicator gas is produced at the same oxygen concentration. The simulations show that the reaction of coal with oxygen under oxygen-poor conditions is likely to be an active intermediate fragment reaction between oxygen and coal affected by temperature, rather than a direct attack on the main structure of the coal. The coal structural groups are not directly altered when the coal is subjected to temperature, but undergo structural adjustments for bond twisting, breaking and atomic transformations to produce reactive intermediate fragments suitable for reaction with oxygen. The higher the degree of metamorphosis, the more stable the molecular structure of the coal and the less likely it is to be affected by temperature to produce reactive intermediate fragments, and when oxygen is introduced, the rate of reaction of oxygen with the reactive intermediate is lower than the rate at which the coal is affected by temperature to produce the reactive intermediate, therefore, oxygen under depleted conditions is generally reacted with reactive intermediates to produce indicator gases such as water, CO, rather than with the main structure of the coal among them. A combination of experimental and simulation analysis reveals that the amount of water and oxygen containing gases increases significantly at 3%-5% oxygen concentration for lignite, 10%-14% oxygen concentration for gas coal and 14%-18% oxygen concentration for fat coal compared to the other stages, indicating that these three coals with different degrees of deterioration are more affected by oxygen in this oxygen-poor concentration range and accelerated the process of coal oxidation reaction.

Hepatocellular carcinoma (HCC), commonly known as primary liver cancer, is a major cause of malignant tumors and cancer-related deaths in China, accounting for approximately 85% of all cancer cases in the country. Several guidelines have been used to diagnose and treat liver cancer. However, these guidelines provide a broad definition for classifying advanced liver cancer, with an emphasis on a singular approach, without considering treatment options for individual patients. Therefore, it is necessary to establish a comprehensive and practical expert consensus, specifically for China, to enhance the diagnosis and treatment of HCC using the Delphi method. The classification criteria were refined for Chinese patients with HCC, and the corresponding optimal treatment regimen recommendations were developed. These recommendations took into account various factors, including tumor characteristics, vascular tumor thrombus grade, distant metastasis, liver function status, portal hypertension, and the hepatitis B virus replication status of patients with primary HCC, along with treatment prognosis. The findings and recommendations provide detailed, scientific, and reasonable individualized diagnosis and treatment strategies for clinicians.

In order to study the effect of typical transition metal ions on coal spontaneous combustion under low temperature oxidation conditions, the transition metal ions of lignite (HM), gas coal (QM) and fertilizer coal (FM) were determined, and Fe (Ⅲ), Mn (Ⅲ) and Cu (Ⅱ), which are abundant in transition metal ions and have strong oxidation, were selected as the research objects. The charge distribution, frontier orbitals and Laplacian bond levels were analyzed by the quantum chemistry calculation method, and the existence of active sites was proved. Two reaction processes of Ar—CH2—CH3 to Ar—CO—CH3 with or without transition metal ions were investigated, and the thermodynamic parameters were obtained by model optimization, transition state calculation and IRC verification. The calculation results show that the activation energy barrier is 164.18 kJ/mol without transition metal ion and 158.72 kJ/mol with transition metal ion. The effect of transition metal ion on the total reaction rate is not significant. With the participation of Fe (Ⅲ), Mn (Ⅲ) and Cu (Ⅱ), the total heat releases of the reaction are 1535.52, 1834.97 and 365.93 kJ/mol, respectively, which are higher than the heat release from the oxidation of aliphatic hydrocarbons by oxygen molecules (319.93 kJ/mol). The free energy barriers of C—H in oxidized aliphatic hydrocarbons are 42.79, 4.30 and 117.29 kJ/mol, respectively, which are all lower than the activation energies of C—H oxidation by oxygen molecules (146.38 kJ/mol). The order of C—H oxidation capacity of transition metal ions from high to low is Mn (Ⅲ), Fe (Ⅲ) and Cu (Ⅱ). During the reaction process, the formation of ·OH and H+ is accompanied by further reaction with the coal structure to accelerate the spontaneous combustion process of coal. In order to verify the accuracy of the simulation results, the time and concentration of CO gas products in four different coal samples were measured by temperature programmed gas chromatography. It was found that the temperature points of CO gas products in four coal samples were similar. The coal samples with manganese ion added produced CO gas products at 90 ℃, while the other three coal samples produced CO gas products at 100 ℃. The concentration of CO gas products from large to small is Mn (Ⅲ), Fe (Ⅲ), Cu (Ⅱ) and raw coal sample, which is consistent with the calculated results. With the increase of temperature, the effect of Mn (Ⅲ) on increasing the rate of CO production gradually weakens, and the effect of iron and copper ions on catalytic coal spontaneous combustion is gradually significant. The experimental results have a certain correlation with the simulation results. Compared with oxygen molecules, transition metal ions in coal are more likely to oxidize C—H, which not only causes the free radical chain reaction to occur earlier, but also increases the heat release of the reaction and the production of ·OH and H+, thus promoting spontaneous combustion of coal. This has reference significance for the mechanism of spontaneous combustion of transition metal catalyzed coal.

Abstract Background Acute pancreatitis (AP) is a prevalent exocrine inflammatory disorder of the pancreas characterized by pancreatic inflammation and injury to acinar cells. Vitamin B6 (VB6) is a vital nutrient that plays a significant role in preserving human health and has anti-inflammatory and anti-apoptotic effects. Methods This study aimed to explore the potential pancreatic protective effects of VB6 in mitigating pancreatic inflammation and apoptosis induced by taurocholate sodium (TLCS) in an AP model and to assess the underlying mechanism of action. AP was induced in Sprague‒Dawley (SD) rats through TLCS administration and lipopolysaccharide (LPS)-treated AR42J cells, followed by treatment with VB6. Results Various parameters associated with AP were assessed in both plasma and pancreatic tissues. VB6 has been shown to ameliorate the severity of AP through various mechanisms. It effectively reduces the levels of serum amylase, lipase, and inflammatory factors, thereby mitigating histological injury to the pancreas. Moreover, VB6 inhibited pancreatic apoptosis by downregulating bax expression and up-regulating Bcl2 expression in TLCS-treated rats. Additionally, VB6 suppressed the expression of caspase3. The anti-inflammatory and anti-apoptotic effects of VB6 observed in LPS-treated AR42J cells are consistent with those observed in a rat model of AP. Conclusions These results suggest that VB6 exerts anti-inflammatory and anti-apoptotic effects through inhibition of the caspase3 signaling pathway and has a protective effect against AP.

This paper addresses the issues of short impulse response signal duration and susceptibility to noise interference by using the Leaky Integrate-and-Fire (LIF) neuron model to simulate the charging and discharging process for impulse signal generation. In addition, an Impulse Response Signal-Convolutional Spiking Neural Network (IRS-CSNN) is developed to exploit the time-frequency characteristics of the target impulse response signal for target identification. First, the signal sequence of pitch and azimuth angle changes is subjected to time-frequency analysis, from which the time-frequency features are extracted and fed into the IRS-CSNN with Time-Frequency Spatial Attention Mechanism (TF-SAM) to facilitate target classification and identification. The experimental results show that this approach effectively utilizes the feature information in the impulse response signal and exploits the feature extraction capabilities of the CSNN. The average recognition accuracy for targets at different angles under noise-added conditions is 89.93%, an improvement of 1.87%-4.34% over that of a similarly structured CNN and other machine learning models. These results confirm the effectiveness of the proposed method.

The root of Angelica sinensis is utilized in Traditional Chinese medicine to enhance blood replenishment and facilitate blood circulation. The early bolting and flowering (EBF) of A. sinensis, however, compromises the quality of the roots and restricts the yield of medicinal substances. The study was conducted to compare the transcriptomic and metabolomic profiles between EBF plants and normal plants of two cultivars of A. sinensis, followed by validation of the transcriptome results using qRT-PCR. There were 3677 DEGs in EBF plants compared to normal plants of cultivar 2 (Mingui No.2), and cultivar 4 (Mingui No.4) was 3354. The main differential metabolites in the EBF and normal plants were phenolic acids, flavonoids, lignans, and coumarins. The analysis of 5 EBF-related pathways revealed 28 genes exhibiting differential expression and 5 metabolites showing differential accumulation. The expression of the Lhcb5, Lhcb2, Lhcb6, Lhcb1, Lhca4, ATPG1, EGLC, CELB, AMY, glgA, CYCD3, SnRK2, PYL, AHK2, AUX1, BSK, FabI/K, ACACA and FabV decreased and the expression of the PsbR, PsbA, LHY, FT, CO, malQ, HK, GPI and DELLA increased in EBF plants. In addition, the Abscisic acid, d-Glucose-6P, α-d-Glucose-1P, NADP+, and ADP were more significantly enriched in EBF plants. The findings offer novel perspectives on the EBF mechanisms in A. sinensis and other medicinal plants of the Apiaceae family.

Abstract Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite booming techniques in molecular representation learning, key elements underlying molecular property prediction remain largely unexplored, which impedes further advancements in this field. Herein, we conduct an extensive evaluation of representative models using various representations on the MoleculeNet datasets, a suite of opioids-related datasets and two additional activity datasets from the literature. To investigate the predictive power in low-data and high-data space, a series of descriptors datasets of varying sizes are also assembled to evaluate the models. In total, we have trained 62,820 models, including 50,220 models on fixed representations, 4200 models on SMILES sequences and 8400 models on molecular graphs. Based on extensive experimentation and rigorous comparison, we show that representation learning models exhibit limited performance in molecular property prediction in most datasets. Besides, multiple key elements underlying molecular property prediction can affect the evaluation results. Furthermore, we show that activity cliffs can significantly impact model prediction. Finally, we explore into potential causes why representation learning models can fail and show that dataset size is essential for representation learning models to excel.

Transposable elements (TEs) significantly contribute to the evolution and diversity of plant genomes. In this study, we explored the roles of TEs in the genomes of Citrus and Citrus-related genera by constructing a pan-genome TE library from 20 published genomes of Citrus and Citrus-related accessions. Our results revealed an increase in TE content and the number of TE types compared to the original annotations, as well as a decrease in the content of unclassified TEs. The average length of TEs per assembly was approximately 194.23 Mb, representing 41.76% (Murraya paniculata) to 64.76% (Citrus gilletiana) of the genomes, with a mean value of 56.95%. A significant positive correlation was found between genome size and both the number of TE types and TE content. Consistent with the difference in mean whole-genome size (39.83 Mb) between Citrus and Citrus-related genera, Citrus genomes contained an average of 34.36 Mb more TE sequences than Citrus-related genomes. Analysis of the estimated insertion time and half-life of long terminal repeat retrotransposons (LTR-RTs) suggested that TE removal was not the primary factor contributing to the differences among genomes. These findings collectively indicate that TEs are the primary determinants of genome size and play a major role in shaping genome structures. Principal coordinate analysis (PCoA) of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) identifiers revealed that the fragmented TEs were predominantly derived from ancestral genomes, while intact TEs were crucial in the recent evolutionary diversification of Citrus. Moreover, the presence or absence of intact TEs near the AdhE superfamily was closely associated with the bitterness trait in the Citrus species. Overall, this study enhances TE annotation in Citrus and Citrus-related genomes and provides valuable data for future genetic breeding and agronomic trait research in Citrus.

Concrete is widely used in large-scale hydraulic structures, which often need to undergo multiple pouring operations due to construction demands, temperature-induced shrinkage phenomena, and structural reinforcement and repair, which in turn creates the bonding surface of old and new concrete. Therefore, it is of great significance to study the strength of the bond between old and new concrete. We designed and completed a split tension test to investigate the evolution of the tensile strength of concrete joint surfaces with age at early ages. The test groups included three sets of matured concrete aged 3 days, 5 days, and 10 days, respectively. Within each group, multiple test specimens were prepared with different ages of the interface, ranging from 1 day to 15 days. The test utilized ready-mixed concrete materials from a commercial batching plant. To ensure uniform and standard roughness of the interface between new and matured concrete, a method employing non-destructive surface roughening tapes was employed. During the test, each specimen was subjected to tensile failure at its corresponding age. The maximum load applied by the testing machine at the point of tensile failure was recorded for each age group. Based on the fundamental principles of material mechanics and relevant formulas, the tensile strength of the interface at different ages was determined for each test group. The obtained data were then used to fit a curve representing the relationship between the early-age tensile strength of concrete and its age, using MATLAB R2020b software. The results show that there is a small increase in the tensile strength of the bonding surface as the age of the old test blocks is increased. This experiment revealed the changing pattern of early-age tensile strength of concrete at the interface with age, providing a basis for accurately simulating the mechanical properties of the interface during numerical simulations. Then, based on the existing temperature-controlled simulation program, a simplified simulation and calculation method of concrete cracking is proposed to make it possible to determine the tensile cracking (vertical cracking) when the stress exceeds the standard. The validity is verified by simulation calculations of a simplified model, using the tensile strength curves obtained from the tests.

Abstract 3D orthogonal woven composites are receiving increasing attention with the ever-growing market of composites. A current challenge for these materials’ development is how to improve their damage tolerance in orthogonal and layer-to-layer structures under extreme loads. In this paper, a damage reduction strategy is proposed by combining structural and electromagnetic properties. An integrated experimental platform is designed combining a power system, a drop-testing machine, and data acquisition devices to investigate the effects of pulse current and impact force on woven composites. Experimental results demonstrate that pulse current can effectively reduce delamination damage and residual deformation. A multi-field coupled damage model is developed to analyze the evolutions of temperature, current and damage. Parallel current-carrying carbon fibers that cause yarns to be transversely compressed enhance the mechanical properties. Moreover, the microcrack formation and extrusion deformation in yarns cause the redistribution of local current among carbon fibers, and its interaction with the self-field produces an obvious anti-impact effect. The obtained results reveal the mechanism of damage reduction and provide a potential approach for improving damage tolerance of these composites.

This study investigates the fatigue behavior of off-axis carbon fiber reinforced polymer (CFRP) composites under varying stress levels, with a focus on both tensile-tensile and compressive-compressive loading modes. We conduct a comprehensive analysis of energy dissipation and stiffness across various loading conditions, highlighting the critical role of fiber deflection effects in the recovery of tensile-tensile fatigue properties. Utilizing digital image correlation (DIC) technology, we identify both commonalities and distinctions in crack propagation pathways and failure mechanisms between these two fatigue scenarios. In the case of tensile-tensile fatigue, the predominant damage mechanism involves the development of multiple interlaminar shear cracks. These cracks initiate debonding at the fiber/resin interface, propagating from macrocracks at the edges to microcracks at the center, ultimately culminating in fiber pull-out failure. Conversely, in compressive fatigue, damage occurs centrally in the form of intralaminar shear cracks. As damage accumulates, these cracks progressively propagate along the fibers towards the edges, accompanied by localized fiber buckling, ultimately resulting in compressive failure. Furthermore, we determine a critical compressive strain threshold, which serves as a pivotal indicator of failure in compressive-compressive fatigue testing for off-axis CFRP composites.

In order to clarify the adsorption pattern between coal and CO, CO2 and O2, the competition between CO and CO2 and O2 in coal is studied. Qianjiaying bituminous coal is used as the research object, and the molecular unit parameters are calculated by quantitative analysis method based on the experimental results of Fourier transform infrared spectroscopy (FTIR). The molecular cell structure of Qianjiaying bituminous coal is constructed (C1160H860O80N20). To verify the accuracy of the model, the infrared spectrum of molecules is simulated by quantum chemical calculation, and the calculated results are basically consistent with the experimental results. On this basis, the effects of pressure(0−16 MPa) and temperature(20−60 ℃) on the adsorption of CO, CO2 and O2 by coal are investigated by using the Grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) methods. From the experimental results, it can be concluded that the fitted isothermal adsorption curves conformed to the Langmuir equation. Under the same pressure, the adsorption capacity of CO, CO2 and O2 is weaker as the temperature increases. At the same temperature, there is a positive correlation trend between the burial pressure of coal seam and the adsorption amount. The magnitude of adsorption of single gases CO, CO2 and O2 is CO2 > O2 > CO, and CO2 can reach saturation adsorption state in the first. The competitive adsorption results of binary gases show that the adsorption selectivity of CO2/CO has obvious advantages in low-pressure or shallow buried coal seams. However, the adsorption selectivity of O2/CO did not change significantly with the change of pressure. The competitive adsorption capacity of CO2 is greater than that of CO, and the adsorption capacity of CO2 decreases with the increase of CO concentration; The competitive adsorption of O2 is greater than CO when the ratio of CO to O2 molar concentration is ≤ 1, but the adsorption of CO is greater than O2 when the molar concentration of CO is much greater than O2. Therefore, the molar concentration of CO is high, which inhibits the adsorption capacity of CO2 and O2. In other words, in bituminous coal seams with high abnormal CO concentration, the effect of using CO2 injection to control fire extinguishing is not significant, so the amount of air leakage from the working face should also be controlled to prevent CO from desorption to the coal body and to ensure that the CO concentration in the well is within the permissible range.

Abstract Scutellaria baicalensis has been one of the most commonly used traditional Chinese medicinal plants in China for more than 2000 years. The three new varieties cultivated could not be distinguished by morphology before flowering. It will hinder the promotion of later varieties. Chloroplast DNA has been widely used in species identification. Moreover, previous studies have shown that complete chloroplast genome sequences have been suggested as super barcodes for identifying plants. Therefore, we sequenced and annotated the complete chloroplast genomes of three cultivated varieties. The chloroplast genomes of SBW, SBR, and SBP were 151,702 bp, 151,799 bp, and 151,876 bp, which contained 85 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. The analysis of the repeat sequences, codon usage, and comparison of chloroplast genomes shared a high degree of conservation. However, the sliding window results show significant differences among the three cultivated varieties in matK-rps16 and petA-psbJ. And we found that the matK-rps16 sequence can be used as a barcode for the identification of three varieties. In addition, the complete chloroplast genome contains more variations and can be used as a super-barcode to identify these three cultivated varieties. Based on the protein-coding genes, the phylogenetic tree demonstrated that SBP was more closely related to SBW, in the three cultivated varieties. Interestingly, we found that S. baicalensis and S. rehderiana are closely related, which provides new ideas for the development of S. baicalensis. The divergence time analysis showed that the three cultivated varieties diverged at about 0.10 Mya. Overall, this study showed that the complete chloroplast genome could be used as a super-barcode to identify three cultivated varieties of S. baicalensis and provide biological information, and it also contributes to bioprospecting.

Accurate forecasting of ocean surface currents is crucial for the planning of marine activities, including fisheries, shipping, and pollution control. Previous studies have often neglected the consideration of spatiotemporal correlations and interdependencies among ocean elements, leading to suboptimal accuracy in medium to long-term forecasts, especially in regions characterized by intricate ocean currents. This paper proposes an adaptive spatiotemporal and multi-element fusion network for ocean surface currents forecasting (ASTMEN). Specifically, we use an improved Swin Transformer (Swin-T) to perform self-attention computation at any given moment, enabling the adaptive generation of multi-element time series with spatial dependencies. Then, we utilize a Long Short-Term Memory network (LSTM) to encode and decode these series in the dimensions of temporal and multi-element features, resulting in accurate forecasts of ocean surface currents. This study takes the Kuroshio region in the northwest Pacific Ocean as the study area with data from the ocean reanalysis dataset. The experimental results show that ASTMEN significantly outperforms the baseline model and the climate state method, and is the only model whose correlation coefficient is still higher than 0.8 at day 12. In the experiments during the summer, when the currents are most variable, ASTMEN provides better forecasts at the sea-land interface and at the junction of different currents, which has the potential to fill the gap of poor forecast performance of previous methods for complex current fields.

Abstract The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts who develop standard terminologies that they link to 3D reference objects, describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference object models in 3D editing tools. This paper introduces the Common Coordinate Framework (CCF) Ontology v2.0.1 that interlinks specimen, biological structure, and spatial data, together with the CCF API that makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCF Ontology design and implementation, present CCF Ontology classes and properties together with exemplary usage, and report on validation methods. The CCF Ontology graph database and API are used in the HuBMAP portal, HRA Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.

Highlights 1. The epidemiology of A. baumannii in Jilin Province showed distinctive local characteristic. 2. Newly discovered sequence types (STs), the results of genetic relatedness and geographical distribution of all STs showed epidemiological research by multi-locus sequence typing (MLST) in single hospital is helpful to monitor and control nosocomial infection of A. baumannii. 3. The rate of carbapenems resistance among A. baumannii is about 94% in Jilin province (northeast China). Carbapenems resistance is a helpful factor for predominant STs spreading.

Abstract Introduction With the spread of the epidemic worldwide, an increasing number of doctors abroad have observed the following atypical symptoms of coronavirus disease 2019 (COVID‐19): olfactory or taste disorders. Therefore, clarifying the incidence and clinical characteristics of olfactory and taste disorders in Chinese COVID‐19 patients is of great significance and urgency. Materials and Methods A retrospective study was conducted, which included 229 severe acute respiratory syndrome coronavirus 2 confirmed patients, through face‐to‐face interviews and telephone follow‐up. Following the completion of questionnaires, the patients participating in the study, were categorized according to the degree of olfactory and taste disorders experienced, and the proportion of each clinical type of patient with olfactory and taste disorders and the time when symptoms appeared were recorded. Results Among the 229 patients, 31 (13.54%) had olfactory dysfunction, and 44 (19.21%) had gustatory dysfunction. For the patients with olfactory dysfunction, 6 (19.35%) developed severe disease and became critically ill. Olfactory dysfunction appeared before the other symptoms in 21.43% of cases. The proportion of females with olfactory and gustatory dysfunction was higher than that of males (P

A numerical simulation method combining the detailed chemical reaction mechanism of methane deflagration with an approximate real tunnel structure was proposed to confirm whether the unevenness of the tunnel wall during a coal mine gas explosion can be ignored. The approximate real tunnel model and smooth wall tunnel model were developed using 3D modeling methods. The propagation and attenuation processes of shock waves in the two tunnel models, as well as the different dynamic responses of the two tunnel walls, were compared and analyzed. Research results show that the non-uniformity of the tunnel wall decreases the shock wave overpressure and propagation velocity. The peak overpressure reduction value of the shock wave reaches 81.91 kPa, and the shock wave overpressure reaches its peak at an extended maximum time of 7.4 ms. The stress distribution on the approximate real tunnel wall is discontinuous, the propagation speed of stress waves in the bend tunnel is slower, and the duration of high load is relatively low. The displacement of the approximate real tunnel after gas explosion is lower than that of tunnels with smooth walls, and the displacement of most measuring points on the tunnel on the right is only 1/3–1/2 that of the smooth tunnel.

Purpose: This study aimed to evaluate the efficacy of sivelestat sodium on mortality, oxygenation index, and serum markers in patients with acute respiratory distress syndrome (ARDS) associated with Coronavirus Disease 2019 (COVID-19). Methods: A retrospective analysis was conducted on adult inpatients admitted to the Intensive Care Unit (ICU). The study compared clinical characteristics, laboratory indices, and mortality rates between patients treated with and without sivelestat sodium. Cox regression analysis was employed to assess the effect of sivelestat sodium on the risk of death, oxygenation index, and improvement of serum markers in patients with COVID-19-associated ARDS. Results: A total of 110 patients with COVID-19-associated ARDS were included, with 45 patients in the sivelestat group and 65 patients in the control group. The overall patient mortality rate was 69.1%, with 62.2% in the sivelestat group and 73.8% in the control group. After five days of treatment, the median change from baseline in the oxygenation index was 21 mmHg in the medicated group and −31 mmHg in the control group (p < 0.05). Analysis of the oxygenation index as a clinical endpoint event showed a significantly higher rate of improvement in the sivelestat group compared to the control group (57.8% vs. 38.5%, p < 0.05), and the odds of raising the oxygenation index after treatment were 2.05 times higher in the sivelestat group than in the control group (HR = 2.05, 95%CI: 1.02–4.15, p < 0.05). Among patients with a baseline oxygenation index < 200 mmHg, patients in the sivelestat group had an 86% lower risk of death compared to the control group (HR = 0.14, 95%CI: 0.02–0.81, p < 0.05). Conclusions: Sivelestat sodium demonstrated a significant improvement in the oxygenation index of patients with COVID-19-associated ARDS and was found to considerably reduce the risk of death in patients with a baseline oxygenation index of

Response regulator (RR) is an important component of the cytokinin (CK) signal transduction system associated with root development and stress resistance in model plants. However, the function of RR gene and the molecular mechanism on regulating the root development in woody plants such as citrus remain unclear. Here, we demonstrate that CcRR5, a member of the type A RR, regulates the morphogenesis of root through interacting with CcRR14 and CcSnRK2s in citrus. CcRR5 is mainly expressed in root tips and young leaves. The activity of CcRR5 promoter enhanced by CcRR14 was proved with transient expression assay. Seven SnRK2 family members with highly conserved domains were identified in citrus. Among them, CcSnRK2.3, CcSnRK2.6, CcSnRK2.7, and CcSnRK2.8 can interact with CcRR5 and CcRR14. Phenotypic analysis of CcRR5 overexpressed transgenic citrus plants indicated that the transcription level of CcRR5 was associated with root length and lateral root numbers. This was also correlated to the expression of root-related genes and thus confirmed that CcRR5 is involved in the root development. Taken together, the results of this study indicate that CcRR5 is a positive regulator of root growth and CcRR14 directly regulates the expression of CcRR5. Both CcRR5 and CcRR14 can interact with CcSnRK2s.

Abstract The modular multilevel photovoltaic (PV) converter can achieve independent power control and help to increase the efficiency of the system. However, the output power among PV modules is unbalanced, which will result in the power loss of system if not properly controlled. This paper first introduces a power control scheme to adjust the power distribution among dc grid, ac grid and PV modules, and presents the mathematical bases of the scheme and the unbalanced power control of the strategy. However, the voltage ripple of MMC sub module (SM) capacitor is also an important parameter. In this system, the SM voltage ripple has not been analysed in relevant articles. Because there are many variables, it is not easy to analyse. Here, the voltage ripple law of the SM capacitor is analysed, by simplifying variables and presenting a fast analytical method, which has reference significance for the selection of SM capacitor. Matlab simulation and the hardware in the loop simulation experiment built by StarSim of Modelling Tech verify the effectiveness of the introduced control strategy and the correctness of the SM capacitor ripple analysis.

The dried roots of the perennial herb Angelica sinensis (Oliv.) Diels (AS) are commonly used as medicinal and edible resources. In commercial planting, early bolting and flowering (EB) of ca. 60% in the medicine formation period reduces root yield and quality, becoming a significant bottleneck in agricultural production. In the cultivation process, summer bolting (SB) occurs from June to July, and autumn bolting (AB) occurs in September. The AB root is often mistaken for the AS root due to its similar morphological characteristics. Few studies have involved whether the root of AB could be used as herbal medicine. This study explored and compared the accumulation dynamics of primary and secondary metabolites in AS and EB roots during the vegetative growth stage (from May to September) by light microscopy, ultraviolet spectrometry, and HPLC methods. Under a microscope, the amount of free starch granules and oil chamber in the AS root increased. On the contrary, they decreased further from EB-Jul to EB-Sep. By comparison, the wall of the xylem vessel was slightly thickened and stacked, and the cell walls of parenchyma and root cortex tissue were thickened in the EB root. Early underground bolting reduces soluble sugar, soluble protein, free amino acids, total C element, total N element, ferulic acid, and ligustilide accumulation, accompanied by the lignification of the root during the vegetative growth stage. Furthermore, a total of 55 root samples from different bolting types of AS root (29 samples), SB root (14 samples), and AB root (12 samples) were collected from Gansu Province during the harvesting period (October). The later the bolting occurred, the less difference there was between unbolted and bolted roots in terms of morphological appearance and efficacy components. Fourier transform infrared spectroscopy with the attenuated total reflection mode (ATR-FTIR) provides a “holistic” spectroscopic fingerprinting of all compositions in the tested sample. The ATR-FTIR spectrum of the AB root was similar to that of the AS root. However, the number and location of absorption peaks in the spectra of SB were different, and only one strong absorption peak at 1021 cm−1 was regarded as the characteristic peak of C-O stretching vibration in lignin. The ATR-FTIR spectra can be effectively differentiated based on their various characteristics using orthogonal partial least squares discrimination analysis (OPLS-DA). Results were assessed using multiple statistical techniques, including Spearman’s correlation, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and OPLS-DA. Among these methods, the ATR-FTIR data demonstrated the most effective outcomes in differentiating between viable and non-viable roots for their application in herbal medicine. Essential substances are ferulic acid and flavonoid, which are much more abundant in the AB root. It provides a material basis for the pharmacological action of the AB roots and a theoretical basis for improving their availability.

Breast cancer is considered the most prevalent malignancy due to its high incidence rate, recurrence, and metastasis in women that makes it one of the deadliest cancers. The current study aimed to predict the genes associated with the recurrence and metastasis of breast cancer and to validate their effect on MDA-MB-231 cells. Through the bioinformatics analysis, the transcription factor 7 cofactor (MLLT11) as the target gene was obtained. MLLT11-specific siRNA was synthesized and transfected into MDA-MB-231 cells. The results demonstrated that the siRNA significantly reduced the MLLT11 mRNA levels. Moreover, cell migration and invasion, as well as the protein levels of phosphatidylinositol 3-kinase (PI3K), AKT, matrix metalloproteinase (MMP) 2, and MMP9, were significantly lower in the groups treated with siRNA while the apoptosis was augmented. Collectively, MLLT11 siRNA elicited ameliorative properties on breast cancer cells, possibly via the inhibition of the PI3K/AKT signaling pathway.

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. The goal of this study is to register serial sections from multi-stain histopathology whole-slide image blocks. We propose a novel translation-based deep learning registration network CGNReg that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with 1 H&E and 2 IHC serial WSIs for each patient, CGNReg presents promising performance as compared with multiple state-of-the-art systems in our evaluation. Our results suggest that CGNReg can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.

Morphogenesis of root is a vital factor to determine the root system architecture. Cytokinin response regulators (RRs) are the key transcription factors in cytokinin signaling, which play important roles in regulating the root morphogenesis. In this study, 29 RR proteins, including 21 RRs and 8 pseudo RRs, were identified from the genome of citrus, and termed as CcRR1-21 and CcPRR1-8, respectively. Phylogenetic analysis revealed that the 29 CcRRs could be classified into four types according to their representative domains. Analysis of cis-elements of CcRRs indicated that they were possibly involved in the regulation of growth and abiotic stress resistance in citrus. Within the type A and type B CcRRs, CcRR4, CcRR5, CcRR6 and CcRR16 highly expressed in roots and leaves, and dramatically responded to the treatments of hormones and abiotic stresses. CcRR2, CcRR10, CcRR14 and CcRR19 also highly expressed in roots under different treatments. Characteristic analysis revealed that the above 8 CcRRs significantly and differentially expressed in the three zones of root, suggesting their functional differences in regulating root growth and development. Further investigation of the 3 highly and differentially expressed CcRRs, CcRR5, CcRR10 and CcRR14, in 9 citrus rootstocks showed that the expression of CcRR5, CcRR10 and CcRR14 was significantly correlated to the length of primary root, the number of lateral roots, and both primary root and the number of lateral roots, respectively. Results of this study indicated that CcRRs were involved in regulating the growth and development of the root in citrus with different functions among the members.

In order to solve the defects of traditional coal spontaneous combustion prevention technology in a closed goaf, a strain of aerobic endogenous bacteria was isolated from coal and used as a blocking raw material. Based on the metabolic and reproductive characteristics of microorganisms, the experimental study on the inhibition of coal spontaneous combustion by microorganisms was carried out. The colonies were isolated and purified by the dilution concentration plate method and the scribing plate method. The growth morphology of microorganisms was analyzed, and the growth curve was determined. The strains were identified by seamless cloning technology for high-throughput sequencing. The surface morphology of coal was analyzed by SEM, the differences of oxidation characteristic temperature points were analyzed by TG–DTG–DSC images, a programmed heating experiment was used to analyze the concentration of the indicator gas CO, and the changes in microscopic groups before and after microbial action were analyzed by FTIR and XPS spectra. Therefore, the inhibition of coal oxidation by endogenous bacteria was verified from macroscopic and microscopic perspectives. The results show that the coal bacteria isolated from the coal is Lysinibacilus sp. After the culture of Lysinibacilus sp., the surface of the coal demonstrated less detritus, and was relatively smooth. In the early stage of low temperature oxidation of coal spontaneous combustion, the characteristic temperature point of coal oxidation and the reaction between coal and O2 could be delayed by Lysinibacilus sp., and the total heat release was reduced in the combustion process. Not only that, Lysinibacilus sp. could also reduce the CO concentration during coal heating. After the coal was decomposed by Lysinibacilus sp., the C=C thick ring skeleton structure had little effect; however, the aromatic substitution pattern changed. This bacterium had an effect on the C-O bond, reducing the percentage of -CH2- and increasing the percentage of -CH3. It might also use the crystalline water in coal for life activities. The carboxyl carbon in coal changed the most, with a decrease of 12.03%, so it might become the carbon source required for microbial growth. The reproductive metabolism of microorganisms also affected the form of nitrogen, and the percentage of pyridine nitrogen in coal was reduced. The ratio of single-bond carbon to double-bond carbon in raw coal was about 3:2, but after this bacterial action, the ratio of the two was about 1:1. The analytical conclusions of XPS and FTIR spectra were consistent, and the results supported each other.

In view of the problem that the measurement accuracy of screen gaps is affected by the R region of the transition rounded corner of the large-size flat back cover, a mobile vision system with dual linear motor synchronous drive is designed to realize precise positioning of monocular vision in 16 local image acquisition positions set in the R region. Full-size measurement of the R region is achieved using local information fusion to accurately measure the screen gap. First, to solve the problem of edge breaking and the blurring of local images, a region extraction and segmentation method based on block statistics is proposed to quickly obtain accurate outer edges. Then, an adaptive Hough line detection method based on dichotomy is proposed to accurately locate the outer edges. The experimental results show that the accuracy of region extraction and segmentation is up to 99.68% within 60 ms; the angle error and position error of line detection are less than 0.01° and 0.2 pixels, respectively. With optimal anti-interference performance, the proposed algorithm realizes the adaptive and accurate positioning of the outer edge within 200 ms; the measurement time and the measurement error of the measurement system are less than 6.0 s and 0.03 mm, respectively.

Abstract Opioid overdose related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP). To discover trends and patterns of opioid poisoning and the demographic and regional disparities, we analyzed large scale patient visits data in New York State (NYS). Demographic, spatial, temporal and correlation analyses were performed for all OP patients extracted from the claims data in the New York Statewide Planning and Research Cooperative System (SPARCS) from 2010 to 2016, along with Decennial US Census and American Community Survey zip code level data. 58,481 patients with at least one OP diagnosis and a valid NYS zip code address were included. Main outcome and measures include OP patient counts and rates per 100,000 population, patient level factors (gender, age, race and ethnicity, residential zip code), and zip code level social demographic factors. The results showed that the OP rate increased by 364.6%, and by 741.5% for the age group > 65 years. There were wide disparities among groups by race and ethnicity on rates and age distributions of OP. Heroin and non-heroin based OP rates demonstrated distinct temporal trends as well as major geospatial variation. The findings highlighted strong demographic disparity of OP patients, evolving patterns and substantial geospatial variation.

Abstract Liver cancer is one of the leading causes of cancer deaths in Asia and Africa. It is caused by the Hepatocellular carcinoma (HCC) in almost 90% of all cases. HCC is a malignant tumor and the most common histological type of the primary liver cancers. The detection and evaluation of viable tumor regions in HCC present an important clinical significance since it is a key step to assess response of chemoradiotherapy and tumor cell proportion in genetic tests. Recent advances in computer vision, digital pathology and microscopy imaging enable automatic histopathology image analysis for cancer diagnosis. In this paper, we present a multi-resolution deep learning model HistoCAE for viable tumor segmentation in whole-slide liver histopathology images. We propose convolutional autoencoder (CAE) based framework with a customized reconstruction loss function for image reconstruction, followed by a classification module to classify each image patch as tumor versus non-tumor. The resulting patch-based prediction results are spatially combined to generate the final segmentation result for each WSI. Additionally, the spatially organized encoded feature map derived from small image patches is used to compress the gigapixel whole-slide images. Our proposed model presents superior performance to other benchmark models with extensive experiments, suggesting its efficacy for viable tumor area segmentation with liver whole-slide images.

BackgroundOncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.MethodsWe retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.ResultsThe Pearson’s correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10–13) and 0.5041 (p-value = 1.15 × 10–12) for the validation sets 1 and 2, respectively. The adjusted R2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.ConclusionOur results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Background: The most common type of primary liver cancer is hepatocellular carcinoma (HCC), and hepatitis B virus (HBV)-related HCC accounts for many HCC cases and has a high mortality rate. The goal of our study was to investigate the efficacy and safety of lenvatinib plus sintilimab therapy in real-world practice and identify factors affecting long-term prognosis. Methods: A retrospective study was conducted with 139 consecutive patients with unresectable HCC treated with lenvatinib or lenvatinib plus sintilimab at the Fifth Medical Center of PLA General Hospital from June 2018 to June 2021. The 139 patients were divided into the control group (85 patients) and the combined treatment group (54 patients) according to the antitumour drugs used for treatment. Efficacy was determined using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and the HCC-specific modified RECIST (mRECIST) for 139 patients who completed the 1st and second tumour assessments. Safety was evaluated in 60 patients in the combined treatment group and 90 patients in the control group using the Common Terminology Criteria for Adverse Events version 5.0. Results: A total of 139 male Chinese patients (49.6% ≥ 55 years old) were included in the efficacy analysis. The median overall survival in the combined treatment group was 21.7 months, and the median progression-free survival was 11.3 months. According to the mRECIST criteria, the objective response rate was 38.9%, and the disease control rate was 92.6%. The median overall survival (mOS), median progression-free survival (mPFS), overall response rate (ORR) and disease control rate (DCR) in the lenvatinib monotherapy group were 12.8 months, 6.6 months, 24.7%, and 74.1%, respectively. Hypertension was the most common adverse event in both groups. Some immune-related adverse events, such as hypothyroidism (n = 5), elevated blood creatinine (n = 3), elevated cardiac enzymes (n = 1), elevated amylase (n = 1) and increased fasting glucose (n = 1), occurred only in the combined therapy group. Five patients in the lenvatinib monotherapy group and six patients in the lenvatinib plus sintilimab group discontinued therapy due to severe adverse events (AEs) (grade 3). No ≥ 4-grade AEs occurred in any patients. Conclusion: The TKI lenvatinib combined with PD-1-targeted immunotherapy sintilimab is efficacious and safe in real-world practice and may lead to better long-term outcomes than lenvatinib alone.

BackgroundOpioid addiction and overdose have a large burden of disease and mortality in New York State (NYS). The medication naloxone can reverse an overdose, and buprenorphine can treat opioid use disorder. Efforts to increase the accessibility of both medications include a naloxone standing order and a waiver program for prescribing buprenorphine outside a licensed drug treatment program. However, only a slim majority of NYS pharmacies are listed as participating in the naloxone standing order, and less than 7% of prescribers in NYS have a buprenorphine waiver. Therefore, there is a significant opportunity to increase access. ObjectiveIdentifying the geographic regions of NYS that are farthest from resources can help target interventions to improve access to naloxone and buprenorphine. To maximize the efficiency of such efforts, we also sought to determine where these underserved regions overlap with the largest numbers of actual patients who have experienced opioid overdose. MethodsWe used address data to assess the spatial distribution of naloxone pharmacies and buprenorphine prescribers. Using the home addresses of patients who had an opioid overdose, we identified geographic locations of resource deficits. We report findings at the high spatial granularity of census tracts, with some neighboring census tracts merged to preserve privacy. ResultsWe identified several hot spots, where many patients live far from the nearest resource of each type. The highest density of patients in areas far from naloxone pharmacies was found in eastern Broome county. For areas far from buprenorphine prescribers, we identified subregions of Oswego county and Wayne county as having a high number of potentially underserved patients. ConclusionsAlthough NYS is home to thousands of naloxone pharmacies and potential buprenorphine prescribers, access is not uniform. Spatial analysis revealed census tract areas that are far from resources, yet contain the residences of many patients who have experienced opioid overdose. Our findings have implications for public health decision support in NYS. Our methods for privacy can also be applied to other spatial supply-demand problems involving sensitive data.

Abstract Background Limonoids are major bioactive compounds that are produced by the triterpenoid metabolic pathway. The detailed biochemical process of limonoid biosynthesis and the mechanism of its molecular regulation remain elusive. The identification of transcription factors that regulate limonoid biosynthetic pathways is very important for understanding the underlying regulatory mechanisms. This information could also provide tools for manipulating biosynthesis genes to modulate limonoid production. Results In this study, the CiMYB42 transcription factor was isolated to identify its role in limonoid biosynthesis. Multiple alignment analysis and phylogenetic analysis demonstrated that CiMYB42 is a typical R2R3MYB transcription factor that shares high similarity of its amino acid sequence with AtMYB42. Limonoids contents were higher in Citrus sinensis and Citrus grandis than in other species. Limonoid accumulation during leaf development also showed diverse trends in different genotypes. The expression of CiMYB42 was significantly related to the limonoid content and the expression of CiOSC in some citrus accessions. The overexpression of CiMYB42 in sweet orange resulted in significant accumulation of limonin, whereas the downregulation of CiMYB42 by RNAi resulted in a dwarf phenotype and less nomilin accumulation. Furthermore, the results of a yeast one-hybrid assay and EMSA indicated that CiMYB42 binds exclusively to the TTGTTG sequence (type II MYB core) in the promoter of CiOSC. Together, these results suggest that CiMYB42 positively regulates limonoid biosynthesis by regulating the expression of CiOSC by binding to the TTGTTG sequence (type II MYB core) of its promoter. Conclusions CiMYB42 is an important transcription activator involved in limonoid biosynthesis that regulates the expression of CiOSC by binding to the TTGTTG sequence (type II MYB core).

In this study, we explored the adaptive mechanism of two varieties of Angelica sinensis exposed to enhanced Ultraviolet-B (UV-B) radiation. The radiation had different effects on the biomass, photosynthetic performance, oxidative damage, antioxidant defense system, and levels of bioactive compounds of Mingui 1 (C1) and Mingui 2 (C2). C2 outperformed C1 under enhanced UV-B radiation, compared to natural light. Using the Illumina RNA-seq, we obtained 6,326 and 2,583 DEGs in C1 and C2, respectively. Under enhanced UV-B radiation, the mRNA levels of genes involved in photosynthesis, antennae protein synthesis, carbon fixation, chlorophyll synthesis, and carotenoid synthesis were decreased in C1 but stable in C2, involving few DEGs. TFs were widely involved in the response of C1 to enhanced UV-B radiation; almost all bHLH and MYB coding genes were downregulated whereas almost all genes encoded WRKY22, WRKY50, WRKY72, NCF, and HSF were upregulated. These results indicate that enhanced UV-B radiation was not conducive to the synthesis of flavonoids, while disease resistance was enhanced. Regarding the ROS scavenging system, upregulated DEGs were mainly found in the AsA-GSH cycle and PrxR/Trx pathways. Remarkably, DEGs that those encoding biosynthetic key enzymes, including ferulic acid (CHS, CHI, DFR, and ANS) and flavonoid (CHS, CHI, DFR, and ANS), most upregulation in C2, leading to increased accumulation of ferulic acid and flavonoids and adversely affecting C1. Genes encoding key enzymes involved in the synthesis of lactone components (ACX, PXG) were mostly up-regulated in C1, increasing the content of lactone components. Our results reveal the DEGs present between C1 and C2 under enhanced UV-B radiation and are consistent with the observed differences in physiological and biochemical indexes. C1 was more sensitive to enhanced UV-B radiation, and C2 was more tolerant to it under moderate enhanced UV-B radiation stress. In addition, the large amount of A. sinensis transcriptome data generated here will serve as a source for finding effective ways to mitigate UV-B enhancement, and also contribute to the well-established lack of genetic information for non-model plant species.

Coal spontaneous combustion is a great threat to mine safety, and gas is the key index to describe coal spontaneous combustion. Taking the coal samples of different kinds of coal as research object, the temperature programmed oxidation experiment was carried out, and the gases produced by coal samples at different temperatures were collected and analyzed by gas chromatography. This research studied the variation characteristics of gas species and gas concentrations in different coal samples during heating oxidation. The experimental results show that different coal samples produce different kinds of gases in the process of heating and oxidation. The order of gas production is CO, C2H6, C2H4, C3H8, and the relationship between gas production and temperature is approximately exponential. With the increase of coal metamorphic degree, the turning point temperature of sharp rise in coal sample gas production rate become higher, the oxidation ability of coal sample decreases, and the quantity of gas production decreases during the same time period. Keywords: Heating and oxidation, Metamorphic degree of coal, Oxidizing ability, Gas chromatography

Abstract Background Preterm birth and feeding are the most important pathogenic factors of neonatal necrotizing enterocolitis (NEC). Metabonomic has been widely used in the diagnosis and treatment of other diseases, but there is no research on the related diseases of premature infants. Compared with full-term infants, the metabolism of preterm infants has its own specificity, so it can easily lead to NEC and other digestive tract inflammatory diseases. Metabonomic may be applied to the diagnosis of preterm related diseases, such as NEC. Methods The model was established with premature infant serum samples from 19 premature infants in our hospital, which was compared with the full-term infant control group. Serum was analyzed by gas chromatography-mass spectrometry (GC-MS), coupled with the analysis of serum metabolic characteristics. The variable important in projection, P value and Pearson correlation coefficient of samples were analyzed by using SIMCA, SPSS and other multivariate statistical analysis software. Results Compared to the term infants, premature infants had significantly higher levels of luteolin, and lower levels of xylose, O-succinyl-L-homoserine and lauric acid in the serum. There were some correlations among several different metabolites and clinically related indices (albumin, total bilirubin) for premature birth related diseases. Conclusions There are metabolic alterations in the serum of premature infants, which make contribution to the diagnosis of NEC.

Light, as a primary source of energy, directly or indirectly influences virtually all morphological modifications occurring in both shoots and roots. A pot experiment was conducted to assess the growth patterns of one-year-old Pleioblastus pygmaeus plants’ rhizome-root systems and their responses to different light intensities from 11 March to 26 December 2016. The experiment design scheme was 3.87% (L1), 11.25% (L2), 20.25% (L3), 38.76% (L4), 60.70% (L5), and 100% full sunlight (control CK). The results indicated that along the growing period from March to December, eight of the eleven studied parameters of the rhizome-root system showed significant variability and diverse growth patterns. In addition, light intensity is a key factor for determining P. pygmaeus plants’ rhizome and root growth. Specifically, the light intensity had a significant, positive, and linear/or almost linear impact on the number of old and new rhizomes, old rhizome length, new rhizome diameter, as well as the culm root diameter. A nonlinear and positive relationship was found between light intensity and the listed three parameters, i.e., new rhizome length, new rhizome internode length, and rhizome root length. The value of the above-mentioned three parameters significantly increased when affected from 0% to 40–60% of full sunlight and then gradually increased until 100% of full sunlight. The ratio of aboveground dry weight to underground dry weight (A/U ratio) showed a single peak curve with increasing light intensity and presented the highest value under ca. 55% full sunlight. Furthermore, 40% full sunlight (equal to an average light of 2232 lux) might be the threshold for P. pygmaeus rhizome-root system growth. When the light intensity was below 40%, the generalized additive models (GAMs) predicted value of most studied parameters decreased to lower than zero. In conclusion, current study provides a solid basis for understanding the dynamic growth and development of P. pygmaeus rhizome-root system, and its responses to different light conditions, which could be used as inputs to P. pygmaeus plant cultivation.

ObjectiveThis study explored gender differences in anxiety, depression, and nursing needs among isolated Coronavirus Disease 2019 (COVID-19) patients, with a particular focus on the influencing factors. The main goal was to elucidate breakthrough points and intervention targets for psychological counseling aimed at the promotion of overall health during isolation treatment.MethodsA survey was conducted to obtain information about the nursing needs of COVID-19 patients, with mental health assessed via the Hospital Anxiety and Depression Scale (HADS). Participants included 219 isolated COVID-19 patients at a Wuhan module hospital in Hubei province, China.ResultsA total of 216 valid questionnaires were collected (98.63% retrieval rate). Of these participants, 21.76% had anxiety symptoms, while 17.59% had depression symptoms. Colleagues infected with COVID-19 (OR = 3.896, 95%CI: 1.555–9.764, P = 0.004) were the main influencing factors for anxiety symptoms, while marital status (OR = 2.700, 95% CI: 1.033–7.055, P = 0.043) and family members infected with COVID-19 (OR = 2.969, 95% CI: 1.243–7.095, P = 0.014) were the main influencing factors for depression symptoms. As for gender, male patients were generally more prone to depression and anxiety than female patients, especially those who were infected with colleagues. On the other hand, female patients reported greater concerns about safe treatment environments and communication with medical staff.ConclusionThis study found gender-based differences regarding the factors influencing anxiety and depression in isolated COVID-19 patients, with males reporting a greater general tendency for symptoms. On the other hand, female patients reported greater overall psychological nursing needs than males. Targeted nursing should thus be implemented to address specific psychological characteristics and nursing needs.

To pursue higher compensation, the agent’s earnings management behavior may damage the principal’s interests. Can managers whose compensation reaches the expected level seek benefits for the company through earnings management? This study takes China’s A-share listed companies from 2014 to 2018 as samples. The conclusions show that managers with higher compensation levels will carry out earnings management in favor of the company while taking their own interests into consideration. For companies with stronger profitability, the higher the managers’ compensation is, the more they are inclined to reduce accrued earnings in the current period to further reduce taxes and fees. For companies with weaker profitability, managers with higher compensation tend to choose to increase real earnings to further optimize financial indicators. It has been found through further research that high pressure generated by media attention can make well-paid executives restrain the above earnings management behavior, which serves as an effective method to protect investors’ rights and interests.

This paper presents a new method of removing mismatches of redundant points based on oriented fast and rotated brief (ORB) in vision simultaneous localization and mapping (SLAM) systems. On the one hand, the grid-based motion statistics (GMS) algorithm reduces the processing time of key frames with more feature points and greatly increases the robustness of the original algorithm in a complex environment. On the other hand, aiming at the situation that the GMS algorithm is prone to false matching when there are few symmetry feature point pairs, the random sample consensus (RANSAC) algorithm is used to optimize and correct it. Experiments show that the method we propose has an average error correction rate of 28.81% for individual GMS while the time consumed at the same accuracy threshold is reduced by 72.18% on average. At the same time, we compared it to locality preserving matching (LPM) and progressive sample consensus (PROSAC), and it performed the best. Finally, we integrated GMS-RANSAC into the ORB-SLAM2 system for monocular initialization, which results in a significant improvement.

Spatial cross-matching operation over geospatial polygonal datasets is a highly compute-intensive yet an essential task to a wide array of real-world applications. At the same time, modern computing systems are typically equipped with multiple processing units capable of task parallelization and optimization at various levels. This mandates for the exploration of novel strategies in the geospatial domain focusing on efficient utilization of computing resources, such as CPUs and GPUs. In this paper, we present a CPU-GPU hybrid platform to accelerate the cross-matching operation of geospatial datasets. We propose a pipeline of geospatial subtasks that are dynamically scheduled to be executed on either CPU or GPU. To accommodate geospatial datasets processing on GPU using pixelization approach, we convert the floating point-valued vertices into integer-valued vertices with an adaptive scaling factor as a function of the area of minimum bounding box. We present a comparative analysis of GPU enabled cross-matching algorithm implementation in CUDA and OpenACC accelerated C++. We test our implementations over Natural Earth Data and our results indicate that although CUDA based implementations provide better performance, OpenACC accelerated implementations are more portable and extendable while still providing considerable performance gain as compared to CPU. We also investigate the effects of input data size on the IO / computation ratio and note that a larger dataset compensates for IO overheads associated with GPU computations. Finally, we demonstrate that an efficient cross-matching comparison can be achieved with a cost-effective GPU.

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

An amendment to this paper has been published and can be accessed via the original article.

Coal spontaneous combustion fire seriously threatens the safety of mine production. How to improve the prediction accuracy of coal spontaneous combustion is a hot topic in current research. The critical temperature point of coal spontaneous combustion and the temperature point of C2H4 occurrence were determined by the temperature-programmed oxidation simulation experiment of coal spontaneous combustion. Taking the reliability of each prediction index as the weight and introducing the error weakening factor, the multi-index fusion prediction model of coal spontaneous combustion was constructed, and the multi-index fusion prediction method of coal spontaneous combustion was put forward. The multi-index fusion prediction method can significantly improve the accuracy of coal spontaneous combustion prediction. The research method can provide theoretical guidance for the prevention of mine fire.

Audiovisual integration significantly changes over the lifespan, but age-related functional connectivity in audiovisual temporal asynchrony integration tasks remains underexplored. In the present study, electroencephalograms (EEGs) of 27 young adults (22–25 years) and 25 old adults (61–76 years) were recorded during an audiovisual temporal asynchrony integration task with seven conditions [auditory (A), visual (V), AV, A50V, A100V, V50A and V100A]. We calculated the phase lag index (PLI)-weighted connectivity networks modulated by the audiovisual tasks and found that the PLI connections showed obvious dynamic changes after stimulus onset. In the theta (4–7 Hz) and alpha (8–13 Hz) bands, the AV and V50A conditions induced stronger functional connections and higher global and local efficiencies, reflecting a stronger audiovisual integration effect, which was attributed to the auditory information arriving at the primary auditory cortex earlier than the visual information reaching the primary visual cortex. Importantly, the functional connectivity and network efficiencies of old adults revealed higher global and local efficiencies and higher degree in both the theta and alpha bands. These larger network efficiencies indicated that old adults might experience more difficulties in attention and cognitive control during the audiovisual integration task with temporal asynchrony than young adults. There were significant associations between network efficiencies and peak time of integration only in young adults. We propose that an audiovisual task with multiple conditions might arouse the appropriate attention in young adults but would lead to a ceiling effect in old adults. Our findings provide new insights into the network topography of old adults during audiovisual integration and highlight higher functional connectivity and network efficiencies due to greater cognitive demand.

The goal of this study is to discover disease co-occurrence and sequence patterns from large scale cancer diagnosis histories in New York State. In particular, we want to identify disparities among different patient groups. Our study will provide essential knowledge for clinical researchers to further investigate comorbidities and disease progression for improving the management of multiple diseases. We used inpatient discharge and outpatient visit records from the New York State Statewide Planning and Research Cooperative System (SPARCS) from 2011-2015. We grouped each patient's visit history to generate diagnosis sequences for seven most popular cancer types. We performed frequent disease co-occurrence mining using the Apriori algorithm, and frequent disease sequence patterns discovery using the cSPADE algorithm. Different types of cancer demonstrated distinct patterns. Disparities of both disease co-occurrence and sequence patterns were observed from patients within different age groups. There were also considerable disparities in disease co-occurrence patterns with respect to different claim types (i.e., inpatient, outpatient, emergency department and ambulatory surgery). Disparities regarding genders were mostly found where the cancer types were gender specific. Supports of most patterns were usually higher for males than for females. Compared with secondary diagnosis codes, primary diagnosis codes can convey more stable results. Two disease sequences consisting of the same diagnoses but in different orders were usually with different supports. Our results suggest that the methods adopted can generate potentially interesting and clinically meaningful disease co-occurrence and sequence patterns, and identify disparities among various patient groups. These patterns could imply comorbidities and disease progressions.