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Abstract Background Although beta blockers, such as metoprolol, have been widely used in the management of vasovagal syncope (VVS), their efficacy remains debated, with larger studies showing limited benefit. Identifying patient-specific characteristics that predict a positive response to metoprolol could optimize its use. This study aims to investigate the key factors that may determine which VVS patients are suitable candidates for metoprolol treatment. Methods This retrospective study was conducted at a single center. Patients diagnosed with VVS and treated with metoprolol for a minimum of three months were included. A 50% reduction in syncope symptom score (SS) after three months of metoprolol treatment was used to define metoprolol responders, while those who did not achieve this reduction were classified as non-responders. After a three-month follow-up, patients were divided into two groups: responders and non-responders. Baseline data—including age, gender, comorbidities, laboratory tests, and the type of VVS—were analyzed for each patient. Changes in heart rate (HR) and blood pressure during the head-up tilt test (HUTT) were also evaluated between the two groups. Logistic regression analysis was performed to identify potential predictors of metoprolol response, and receiver operating characteristic (ROC) curve analysis was used to assess the value of these predictors in selecting potential responders. Results A total of 168 VVS patients were enrolled between January 2016 and December 2020. Of these, 100 patients responded positively to metoprolol, while 68 did not. No significant differences in baseline characteristics or HUTT results were observed between the two groups (p > 0.05). However, responders exhibited a significantly higher peak HR compared to non-responders during the HUTT (120.13 ± 19.33 vs. 106.60 ± 22.32, p

Chitosan is a naturally-occurring alkaline polysaccharide with excellent biocompatibility, biodegradability, low toxicity, and easy preparation. It has been widely used in the construction of delivery systems for various nutrients and bioactive substances. The chitosan-based nutrient delivery systems are mainly constructed by cross-linking, drying, layer-by-layer self-assembly, coalescence, covalent grafting, microfluidics and biosynthesis. Chitosan can be combined with carbohydrates, proteins and liposomes to form a composite carrier that can obtain more excellent performance and broaden its application in food packaging materials, slow release of bioactive substances and probiotic embedding. In addition, this paper further analyzes the obstacles and limitations of the actual application of chitosan-based nutrient delivery systems in the food industry, to provide important references for the application of chitosan-based delivery carriers in imparting new functional properties to foods, developing new food ingredients, and nutrient fortification.

Abstract Realizing transformation from transparent insulating to transparent conducting, is a pursuing goal in designing and fabricating novel optoelectronic materials and devices. Here, a pronounced insulating to metal transition in anatase TiO2 thin films is achieved through ionic liquid gating, and interestingly the material maintains an invariable high optical transparency. It is revealed that the emergent metallic state can be attributed to the electron doping associated with the hydrogen ion intercalation. Importantly, the hydrogenation leads to the almost rigid shift of the Fermi energy and therefore maintains nicely the transparency at the visible light region. This result is in strong contrast with the case of oxygen vacancy doping, in which the optical bandgap is suppressed due to improved orbitals hybridization and intraband transition. Moreover, through synergistic ion‐electron doping, the selective control of the gating area and pattern is realized in the micrometer or even nanometer scale with extremely distinct physical properties, which can be employed to fabricate novel optical and electronic devices. The result greatly deepens the understanding of the underlying physics and formation mechanism of transparent conducting oxide (TCO) materials. It is envisioned that this work would provide a new pathway to design other potential optoelectronic materials with novel functionalities.

BackgroundsCurrent observational investigations hint at a potential linkage between ankylosing spondylitis and cardiovascular wellness. However, the nature of this causality remains to be elucidated. Consequently, this study is designed to evaluate the causal interconnection between ankylosing spondylitis and cardiovascular-related conditions utilizing a bidirectional two-sample Mendelian Randomization (MR) methodology.MethodsIn this study, we conducted Mendelian randomization (MR) analyses using genome-wide association study (GWAS) data. The fixed-effects inverse variance weighted (IVW) model was used as the primary analysis method, and MR-Egger regression and the weighted median method were employed as supplementary approaches. Horizontal pleiotropy and heterogeneity were evaluated using various statistical tests, including MR-PRESSO global test, MR-Egger intercept, and Cochran’s Q test.ResultsThe MR result demonstrated an increased risk of heart failure in individuals with ankylosing spondylitis (OR: 1.0132, 95% CI = 1.0043-1.0221, p = 0.003). The MR analysis results did not demonstrate a causal relationship between ankylosing spondylitis and other cardiovascular diseases, such as atrial fibrillation, coronary artery disease, ischemic stroke, myocardial infarction, and valvular heart disease (all p > 0.05). No evidence of reverse causality was found between ankylosing spondylitis and mentioned cardiovascular diseases in reverse MR analyses. Sensitivity analysis verified the reliability of the results.ConclusionOur MR study indicates a relationship between ankylosing spondylitis and an increased risk of heart failure. Further research is needed to confirm these findings and elucidate the underlying mechanisms involved.

Abstract Solution‐processed 2D materials hold promise for their scalable applications. However, the random, fragmented nature of the solution‐processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, conduction modulation of the solution‐processed 2D materials is reported via Stark effect. Using liquid‐phase exfoliated molybdenum disulfide (MoS2) as an example, nonlinear conduction switching with a ratio of >105 is demonstrated by the local fields from the interfacial ferroelectric P(VDF‐TrFE). Through density‐functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, the modulation is understood to arise from a charge redistribution in the solution‐processed MoS2. Beyond MoS2, the modulation may be shown effective for the other solution‐processed 2D materials and low‐dimensional materials. The modulation can open their electronic device applications, for instance, thin‐film nonlinear electronics and non‐volatile memories.

This study aimed to valorize pawpaw (Carica papaya L.) leaves as a rich source of polyphenols through the application of ionic liquid-based microwave-assisted extraction (ILMAE). Initially, the ILMAE process was optimized using response surface methodology (RSM), resulting in a total polyphenols yield of 27.84 ± 0.33 mg GAE/g DW under the optimal conditions: [BMIM]Br concentration of 0.57 mol/L, extraction time of 14 min, microwave power of 460 W, extraction temperature of 77 °C, solvent-to-material ratio of 30 mL/g, and three extraction cycles. Compared to conventional methods such as maceration extraction (ME), heat reflux extraction (HRE), and microwave-assisted extraction (MAE), the ILMAE method exhibited a significantly higher PLTP yield. Furthermore, the PLTP extracts demonstrated strong antioxidant activity against DPPH• and ABTS+• radicals, as well as a significant inhibitory effect on α-glucosidase activity. This work demonstrates that ILMAE is a green and efficient strategy for the valorization of pawpaw leaves.

Abstract The copper (Cu)-catalyzed electrochemical CO2 reduction provides a route for the synthesis of multicarbon (C2+) products. However, the thermodynamically favorable Cu surface (i.e. Cu(111)) energetically favors single-carbon production, leading to low energy efficiency and low production rates for C2+ products. Here we introduce in situ copper faceting from electrochemical reduction to enable preferential exposure of Cu(100) facets. During the precatalyst evolution, a phosphate ligand slows the reduction of Cu and assists the generation and co-adsorption of CO and hydroxide ions, steering the surface reconstruction to Cu (100). The resulting Cu catalyst enables current densities of > 500 mA cm−2 and Faradaic efficiencies of >83% towards C2+ products from both CO2 reduction and CO reduction. When run at 500 mA cm−2 for 150 hours, the catalyst maintains a 37% full-cell energy efficiency and a 95% single-pass carbon efficiency throughout.

Given the limitations of traditional methods in automatic feature extraction, the study aims to improve the accuracy of posture recognition in adolescent running training through deep learning techniques. This study first constructed a human pose motion model to capture complex pose changes during the motion process. Furthermore, an algorithm framework combining convolutional neural networks and long short-term memory networks was proposed, and optimized by integrating attention modules to enhance the model’s ability to capture key action details and improve recognition accuracy. The outcomes indicated that the recognition accuracy of the raw data processed using the convolutional neural network algorithm is 0.93, which is improved to 0.96 after preprocessing, while the accuracy of the long-short-term memory network algorithm is 0.91 on the raw data and 0.95 after preprocessing. The Inception combined with long-short-term memory network algorithm optimised by incorporating the attention module has the highest accuracy of 0.98 on the preprocessed data, while that on the 0.95 on the raw data, and 1.0 on specific actions such as running and riding. It can be found that the attention module significantly improves the recognition accuracy of the algorithm and its ability to recognise complex actions. This study not only improves the way that teenagers train to run, but it also creates new opportunities for the use of deep learning methods in the context of sports-assisted training.

Abstract Objective Meta-analysis of the comparative efficacy of Oxford unicompartmental knee arthroplasty (OUKA) for the treatment of spontaneous osteonecrosis of the knee (SONK) and medial knee osteoarthritis (MKOA). Methods A computerized search was conducted for literature related to OUKA treatments of SONK and MKOA across various databases, including the China National Knowledge Infrastructure, WAN FANG, VIP, SinoMed, Cochrane Library, PubMed, Embase, and Web of Science, covering the period from each database's inception to September 2023. Literature screening, quality assessment and data extraction were performed according to the inclusion and exclusion criteria. After extracting the literature data, RevMan 5.4 software was applied to analyse the postoperative knee function score, postoperative knee mobility, postoperative pain, bearing dislocation rate, aseptic loosening, postoperative progression of posterolateral arthritis, and revision rate. Result A total of 9 studies were included, including 6 cohort studies and 3 matched case‒control studies. A total of 1544 knees were included, including 183 in the SONK group and 1361 in the MKOA group. The meta-analysis results showed that the SONK and MKOA groups showed a significant difference in postoperative knee function scores [MD = 0.16, 95% CI (− 1.20, 1.51), P = 0.82], postoperative knee mobility [MD = − 0.05, 95% CI (− 1.99. 1.89), P = 0.96], postoperative pain [OR = 0.89, 95% CI (0.23, 3.45), P = 0.87], rate of bearing dislocation [OR = 1.28, 95% CI (0.34, 4.81), P = 0.71], aseptic loosening [OR = 2.22, 95% CI (0.56, 8.82), P = 0.26], postoperative posterolateral arthritis progression [OR = 2.14, 95% CI (0.47, 9.86), P = 0.33], and revision rate [OR = 1.28, 95% CI (0.53, 3.04), P = 0.58] were not statistically significant. Conclusion OUKA treatment with SONK and MKOA can achieve similar satisfactory clinical results.

Abstract Immune inhibitory receptors (IRs) play a critical role in the regulation of immune responses to various respiratory viral infections. However, in coronavirus disease 2019 (COVID-19), the roles of these IRs in immune modulation, metabolic reprogramming, and clinical characterization remain to be determined. Through consensus clustering analysis of IR transcription in the peripheral blood of patients with COVID-19, we identified two distinct IR patterns in patients with COVID-19, which were named IR_cluster1 and IR_cluster2. Compared to IR_cluster1 patients, IR_cluster2 patients with lower expressions of immune inhibitory receptors presented with a suppressed immune response, lower nutrient metabolism, and worse clinical manifestations or prognosis. Considering the critical influence of the integrated regulation of multiple IRs on disease severity, we established a scoring system named IRscore, which was based on principal component analysis, to evaluate the combined effect of multiple IRs on the disease status of individual patients with COVID-19. Similar to IR_cluster2 patients, patients with high IRscores had longer hospital-free days at day 45, required ICU admission and mechanical ventilatory support, and presented higher Charlson comorbidity index and SOFA scores. A high IRscore was also linked to acute infection phase and absence of drug intervention. Our investigation comprehensively elucidates the potential role of IR patterns in regulating the immune response, modulating metabolic processes, and shaping clinical manifestations of COVID-19. All of this evidence suggests the essential role of prognostic stratification and biomarker screening based on IR patterns in the clinical management and drug development of future emerging infectious diseases such as COVID-19.

Abstract Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5-related (both MERS related viruses), and Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-CoV-2 and all viral sequences released after the SARS-CoV-2 was published. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.

Abstract Implementing and integrating spiking neurons for neuromorphic hardware realization conforming to spiking neural networks holds great promise in enabling efficient learning and decision‐making. The spiking neurons, however, may lack the spiking dynamics to encode the dynamical information in complex real‐world problems. Herein, using filamentary memristors from solution‐processed hexagonal boron nitride, this study assembles leaky integrate‐and‐fire spiking neurons and, particularly, harnesses the common switching stochasticity feature in the memristors to allow key neural dynamics, including Poisson‐like spiking and adaptation. The neurons, with the dynamics fitted via hardware‐algorithm codesign, suggest a potential in realizing spike‐based neuromorphic hardware capable of handling complex problems. Simulation of an autoencoder for anomaly detection of time‐series real analog and digital data from physical systems is demonstrated, underscoring its promising prospect in applications, especially, at the edges with limited computation resources, for instance, auto‐pilot, manufacturing, wearables, and Internet of things.

The unprecedented medium-resolution ( R _λ ∼ 1500–3500) near- and mid-infrared (1–18 μ m) spectrum provided by JWST for the young (140 ± 20 Myr) low-mass (12–20 M _Jup ) L–T transition (L7) companion VHS 1256 b gives access to a catalog of molecular absorptions. In this study, we present a comprehensive analysis of this data set utilizing a forward-modeling approach applying our Bayesian framework, ForMoSA . We explore five distinct atmospheric models to assess their performance in estimating key atmospheric parameters: T _eff , log( g ), [M/H], C/O, γ , f _sed , and R . Our findings reveal that each parameter’s estimate is significantly influenced by factors such as the wavelength range considered and the model chosen for the fit. This is attributed to systematic errors in the models and their challenges in accurately replicating the complex atmospheric structure of VHS 1256 b, notably the complexity of its clouds and dust distribution. To propagate the impact of these systematic uncertainties on our atmospheric property estimates, we introduce innovative fitting methodologies based on independent fits performed on different spectral windows. We finally derived a T _eff consistent with the spectral type of the target, considering its young age, which is confirmed by our estimate of log( g ). Despite the exceptional data quality, attaining robust estimates for chemical abundances [M/H] and C/O, often employed as indicators of formation history, remains challenging. Nevertheless, the pioneering case of JWST’s data for VHS 1256 b has paved the way for future acquisitions of substellar spectra that will be systematically analyzed to directly compare the properties of these objects and correct the systematics in the models.

We present a performance analysis for the aperture masking interferometry (AMI) mode on board the James Webb Space Telescope Near Infrared Imager and Slitless Spectrograph (JWST/NIRISS). Thanks to self-calibrating observables, AMI accesses inner working angles down to and even within the classical diffraction limit. The scientific potential of this mode has recently been demonstrated by the Early Release Science (ERS) 1386 program with a deep search for close-in companions in the HIP 65426 exoplanetary system. As part of ERS 1386, we use the same data set to explore the random, static, and calibration errors of NIRISS AMI observables. We compare the observed noise properties and achievable contrast to theoretical predictions. We explore possible sources of calibration errors and show that differences in charge migration between the observations of HIP 65426 and point-spread function calibration stars can account for the achieved contrast curves. Lastly, we use self-calibration tests to demonstrate that with adequate calibration NIRISS F380M AMI can reach contrast levels of ∼9–10 mag at ≳ λ / D . These tests lead us to observation planning recommendations and strongly motivate future studies aimed at producing sophisticated calibration strategies taking these systematic effects into account. This will unlock the unprecedented capabilities of JWST/NIRISS AMI, with sensitivity to significantly colder, lower-mass exoplanets than lower-contrast ground-based AMI setups, at orbital separations inaccessible to JWST coronagraphy.

Individual-level interactions shape societal or economic processes, such as infectious diseases spreading, stock prices fluctuating and public opinion shifting. Understanding how the interaction of different individuals affects collective outcomes is more important than ever, as the internet and social media develop. Social networks representing individuals' influence relations play a key role in understanding the connections between individual-level interactions and societal or economic outcomes. Recent research has revealed how the topology of a social network affects collective decision-making in a community. Furthermore, the traits of individuals that determine how they process received information for making decisions also change a community's collective decisions. In this work, we develop stochastic processes to generate networks of individuals with two simple traits: Being a conformist and being an anticonformist. We introduce a novel deterministic voter model for a trait-attributed network, where the individuals make binary choices following simple deterministic rules based on their traits. We show that the simple deterministic rules can drive unpredictable fluctuations of collective decisions which eventually become periodic. We study the effects of network topology and trait distribution on the first passage time for a sequence of collective decisions showing periodicity.

Liver diseases constitute a major healthcare burden globally, including acute hepatic injury resulted from acetaminophen overdose, ischemia–reperfusion or hepatotropic viral infection and chronic hepatitis, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Attainable treatment strategies for most liver diseases remain inadequate, highlighting the importance of substantial pathogenesis. The transient receptor potential (TRP) channels represent a versatile signalling mechanism regulating fundamental physiological processes in the liver. It is not surprising that liver diseases become a newly explored field to enrich our knowledge of TRP channels. Here, we discuss recent findings revealing TRP functions across the fundamental pathological course from early hepatocellular injury caused by various insults, to inflammation, subsequent fibrosis and hepatoma. We also explore expression levels of TRPs in liver tissues of ALD, NAFLD and HCC patients from Gene Expression Omnibus (GEO) or The Cancer Genome Atlas (TCGA) database and survival analysis estimated by Kaplan–Meier Plotter. At last, we address the therapeutical potential and challenges by pharmacologically targeting TRPs to treat liver diseases. The aim is to provide a better understanding of the implications of TRP channels in liver diseases, contributing to the discovery of novel therapeutic targets and efficient drugs.

Abstract Background Current diagnosis tools for prostate cancer (PCa) such as serum PSA detection and prostate biopsy cannot distinguish dormant tumors from invasive malignancies, either be used as prognosis marker for castration resistant prostate cancer (CRPC), the lethal stage of PCa patients. Exosomes have been widely investigated as promising biomarkers for various diseases. We aim to characterize the proteomic and metabolomic profile of exosomes and to evaluate their potential value for the diagnosis of PCa, especially CRPC. We also investigate the functions of some specific exosome biomarkers in the progression of CRPC. Methods Integrated proteomics and metabolomics analysis were performed for plasma-derived exosomes collected from tumor-free controls (TFC), PCa and CRPC patients. Expression of specific exosomal proteins were further validated by targeted 4D-parallel reaction monitoring (PRM) mass spectrometry among the three cohorts. Tissue distribution and functional role of exosomal protein LRG1 was studied in clinical PCa tissue samples and cell line models. Results Three potential exosomal protein markers were identified. The apolipoprotein E level in PCa samples was 1.7-fold higher than that in TFC (receiver operating characteristic value, 0.74). Similarly, the levels of exosome-derived leucine-rich alpha2-glycoprotein 1 (LRG1) and inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) in the CRPC group were 1.7 and 2.04 times, respectively, higher than those in the PCa group (ROC values, 0.84 and 0.85, respectively), indicating that LRG1 and ITIH3 could serve as predictive markers for CRPC. For metabolomic evaluation of exosomes, a series of differentially expressed metabolites were identified, and a combined metabolite panel showed ROC value of 0.94 for distinguishing PCa from TFC and 0.97 for distinguishing CRPC from PCa. Immunohistochemistry of tissue microarray showed that LRG1 protein was significantly upregulated in advanced prostate cancer and functional assay revealed that ectopic expression of LRG1 can significantly enhance the malignant phenotype of prostate cancer cells. More importantly, PCa cell derived LRG1-overexpressed exosomes remarkably promoted angiogenesis. Conclusion Integration of proteomics and metabolomics data generated proteomic and metabolic signatures of plasma exosomes that may facilitate discrimination of CRPC from PCa and TFC patients, suggesting the potential of exosomal proteins and metabolites as CRPC markers. The study also confirmed the important role of exosomal protein LRG1 in PCa malignant progression.

Abstract The Three‐Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two‐dimensional video coding standard HEVC and used to encode multi‐view plus depth format video. This paper proposes an algorithm based on eXtreme Gradient Boosting to solve the problem of high inter‐frame coding complexity in 3D‐HEVC. Firstly, explore the correlation between the division depth of the inter‐frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter‐frame coding tree unit based on the eXtreme Gradient Boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM‐16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.

Lying along the south of Mongol–Okhotsk Suture Belt (MOSB) in Horqin Right Middle Banner of Inner Mongolia, the Chuhuertu granite is exposed in the Duerji tectonic melange. Based on systematic field geological characteristics, petrology, geochemistry, and zircon U-Pb chronology, we determined the petrogenesis of pluton and the tectonic setting, and provide evidence for the dominant factors of granite magmatism of the study area. LA-ICP-MS zircon U-Pb dating showed an age of 155.6 ± 1.6 Ma, indicating that the formation age of the Chuhuertu granite is Late Jurassic. The granite is geochemically characterized by high SiO2 (73.04–76.01 wt.%) and alkali (Na2O + K2O = 7.81–9.50 wt.%) but low CaO (0.20–0.98 wt.%) and MgO (0.10–0.22 wt.%). The granite has obvious negative Eu anomaly (Eu = 0.30–0.75), relative enrichment of Rb, Th, U, K, and Ga, depletion of Ba, Sr, P, and Ti, as well as high 10000Ga/Al (2.52–2.90), and TFeO/MgO (5.76–14.90). The differentiation index (DI) of the samples is 92.05–96.36. The petrological, mineralogical, and geochemical characteristics of the granite show the characteristics of highly differentiated A-type granite. In accordance with the region, Mesozoic A-type granites have positive correlation εNd(t) values, which suggest that the granite may have formed through partial melting of intermediate basic crust in neoaccretion under low pressure and high temperature, and subsequent crystallization differentiation. Therefore, Chuhuertu granite is the product of extension of the closure of the Mongol–Okhotsk Ocean (MOO), and, together with other A-type granites distributed in mid-eastern Inner Mongolia, indicates extensive middle and lower crust extension in the Late Jurassic.

The primary constraints hindering the widespread adoption of laser powder bed fusion (LPBF) are expensive raw materials, internal defects, difficulty in controlling the quality and stability, and lack of consistency in the powder feedstock. The spatters induced by the violent interaction between the laser and powder can be deposited on the unmelted powder bed, and if not effectively separated by sieving, they would pose a significant detriment to the characteristics and consistency of the recycled powders. In this work, a systematic investigation of the evolution mechanism of 316L powders after 10 and 30 successive recycling in the LPBF process was presented. Firstly, the variation mechanism of particle size distribution and morphology of 316L powder and the generation mechanism of heterogeneous particles were studied. Then, changes in microstructure and hardness trends were characterized. Finally, we emphasized the changes of chemical composition, phase composition, magnetic properties, and the formation mechanism of oxide spots. The results show that the circulation had significant effects on the physical properties, phase transformation, mechanical properties, and magnetic properties of the 316L powders, but had minor effects on the chemical composition, surface and cross-sectional microstructure. In addition, a large number of oddly shaped, large-diameter spatters were identified in the recycled powders, which could not be separated effectively by sieving, and the formation mechanism of the spatters were elaborated. Based on the comprehensive high-temperature oxidation thermodynamics and experimental results, it was concluded that the circular oxide spots on the spatter surface were a composite of multiple oxides of Mn and Si. Further, it was also shown that the ferrite content, XRD peak characteristics, and magnetic properties in the recycled powders were governed by the droplet solidification modes, which in turn is determined by the chemical composition and cooling rates.

We present JWST Early Release Science coronagraphic observations of the super-Jupiter exoplanet, HIP 65426b, with the Near-Infrared Camera (NIRCam) from 2 to 5 μ m, and with the Mid-Infrared Instrument (MIRI) from 11 to 16 μ m. At a separation of ∼0.″82 (87 ${}_{-31}^{+108}$ au), HIP 65426b is clearly detected in all seven of our observational filters, representing the first images of an exoplanet to be obtained by JWST, and the first-ever direct detection of an exoplanet beyond 5 μ m. These observations demonstrate that JWST is exceeding its nominal predicted performance by up to a factor of 10, depending on separation and subtraction method, with measured 5 σ contrast limits of ∼1 × 10 ^−5 and ∼2 × 10 ^−4 at 1″ for NIRCam at 4.4 μ m and MIRI at 11.3 μ m, respectively. These contrast limits provide sensitivity to sub-Jupiter companions with masses as low as 0.3 M _Jup beyond separations of ∼100 au. Together with existing ground-based near-infrared data, the JWST photometry are fit well by a BT-SETTL atmospheric model from 1 to 16 μ m, and they span ∼97% of HIP 65426b's luminous range. Independent of the choice of model atmosphere, we measure an empirical bolometric luminosity that is tightly constrained between $\mathrm{log}\left({L}_{\mathrm{bol}}/{L}_{\odot }\right)$ = −4.31 and −4.14, which in turn provides a robust mass constraint of 7.1 ± 1.2 M _Jup . In totality, these observations confirm that JWST presents a powerful and exciting opportunity to characterize the population of exoplanets amenable to high-contrast imaging in greater detail.

We present the highest fidelity spectrum to date of a planetary-mass object. VHS 1256 b is a

Ulcerative colitis (UC) is one form of inflammatory bowel disease (IBD), characterized by chronic relapsing intestinal inflammation. As increasing morbidity of UC and deficiency of conventional therapies, there is an urgent need for attractive treatment. Cassane diterpenoids, the characteristic chemical constituents of Caesalpinia genus plants, have been studied extensively owing to various and prominent biological activities. This study attempted to investigate the bioactivity of caesaldekarin e (CA), a cassane diterpenoid isolated from C. bonduc in our previous work, on dextran sulfate sodium (DSS)-induced experimental colitis and clarify the function mechanism. The results indicated that CA ameliorated mice colitis by relieving disease symptoms, suppressing inflammatory infiltration and maintaining intestinal barrier integrity. Furthermore, 16S rRNA gene sequencing analysis indicated that CA could improve the gut microbiota imbalance disrupted by DSS and especially restored abundance of Lactobacillus. In addition, untargeted metabolomics analysis suggested that CA regulated metabolism and particularly the tryptophan metabolism by inhibiting the upregulation of indoleamine 2,3-dioxygenase 1 (IDO-1). It also been proved in IFN-γ induced RAW264.7 cells. Overall, this study suggests that CA exhibits anti-UC effect through restoring gut microbiota and regulating tryptophan metabolism and has the potential to be a treatment option for UC.

Gravel is one of the main factors affecting the mechanical properties of conglomerates, which plays a decisive role in crack propagation. In this paper, taking the conglomerate of the Baikouquan Formation in Mahu Sag of Xinjiang as the research object, a three-dimensional model of the conglomerate is constructed by the discrete element numerical simulation method, and the triaxial compression experiment under different confining pressures is simulated. The mechanical properties and fracture morphology of conglomerate are analyzed with gravel content as a variable and verified by laboratory tests. In this simulation, with the increase of gravel content, the compressive strength of the conglomerate decreases, angle of internal friction decreases, and the fractures show different forms. The results show that the gravel morphology, spatial location, and gravel content have an impact on the mechanical properties of the conglomerate. The gravel content affects the formation process of the dominant fracture surface by controlling the distance between gravels so as to control the internal friction angle and it is the main controlling factor for the mechanical properties of the conglomerate. Gravel cracks initiate at the edge of gravel. Stress controls the formation of main cracks under low gravel content, and the influence of gravels under high gravel content makes cracks more discrete and complex.

BackgroundProstate cancer (PCa) is among the leading causes of cancer death worldwide. Ferroptosis refers to an iron-dependent form of regulated cell death and is involved in prostate tumorigenesis. A few ferroptosis-related gene signatures have been developed to predict the prognosis for PCa patients. However, previous signatures were typically established based on biochemical recurrence-free survival, which has proven not to be a good surrogate for overall survival (OS). This study aimed to construct a novel ferroptosis-related gene prognostic index (FRGPI) to predict disease-free survival (DFS) and response to immunotherapy for PCa patients after radical prostatectomy.MethodsGene expression and clinicopathological data on PCa patients were obtained from the TCGA database. Ferroptosis-related hub genes associated with DFS of PCa patients were identified by an in-depth bioinformatics analysis using a novel and comprehensive algorithm based on functional enrichment, consensus clustering, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. The FRGPI was established on the basis of the genes selected using multivariate cox regression analysis and further validated in two additional PCa cohorts. Next, the clinicopathological, molecular, and immune profiles were characterized and compared between FRGPI-high and FRGPI-low subgroups. Finally, the predictive role of the FRGPI in response to immunotherapy was estimated using a metastatic urothelial cancer cohort treated with an anti-PD-L1 agent.ResultsThe FRGPI was constructed based on four genes (E2F1, CDC20, TYMS, and NUP85), and FRGPI-high patients had worse DFS than FRGPI-low patients. Multivariate cox regression analysis revealed that FRGPI could act as an independent prognostic factor for PCa patients after radical prostatectomy. A prognostic nomogram comprising the FRGPI and other clinicopathological parameters was established to predict the DFS for PCa patients quantitatively. In addition, comprehensive results demonstrated that high FRGPI scores showed a significantly positive correlation with worse clinicopathological features, higher mutation counts, increased frequency of copy number variations (CNVs), higher homologous recombination deficiency (HRD) and immune scores, higher mRNAsi, and more importantly, enhanced sensitivity to immunotherapy.ConclusionsFRGPI is not only a promising and robust prognostic biomarker, but also a potential indicator of immunotherapeutic outcomes for PCa patients after radical prostatectomy.

Abstract AXIN1 mutations are observed in 8–10% of hepatocellular carcinomas (HCCs) and originally were considered to support tumor growth by aberrantly enhancing β-catenin signaling. This view has however been challenged by reports showing neither a clear nuclear β-catenin accumulation nor clearly enhanced expression of β-catenin target genes. Here, using nine HCC lines, we show that AXIN1 mutation or siRNA mediated knockdown contributes to enhanced β-catenin signaling in all AXIN1-mutant and non-mutant lines, also confirmed by reduced signaling in AXIN1-repaired SNU449 cells. Both AXIN1 and AXIN2 work synergistically to control β-catenin signaling. While in the AXIN1-mutant lines, AXIN2 is solely responsible for keeping signaling in check, in the non-mutant lines both AXIN proteins contribute to β-catenin regulation to varying levels. The AXIN proteins have gained substantial interest in cancer research for a second reason. Their activity in the β-catenin destruction complex can be increased by tankyrase inhibitors, which thus may serve as a therapeutic option to reduce the growth of β-catenin-dependent cancers. At concentrations that inhibit tankyrase activity, some lines (e.g. HepG2, SNU398) were clearly affected in colony formation, but in most cases apparently independent from effects on β-catenin signaling. Overall, our analyses show that AXIN1 inactivation leads to enhanced β-catenin signaling in HCC cell lines, questioning the strong statements that have been made in this regard. Enhancing AXIN activity by tankyrase monotherapy provides however no effective treatment to affect their growth exclusively through reducing β-catenin signaling.

Abstract Background Human dicer is an enzyme that cleaves pre-miRNAs into miRNAs. Several models have been developed to predict human dicer cleavage sites, including PHDCleav and LBSizeCleav. Given an input sequence, these models can predict whether the sequence contains a cleavage site. However, these models only consider each sequence independently and lack interpretability. Therefore, it is necessary to develop an accurate and explainable predictor, which employs relations between different sequences, to enhance the understanding of the mechanism by which human dicer cleaves pre-miRNA. Results In this study, we develop an accurate and explainable predictor for human dicer cleavage site – ReCGBM. We design relational features and class features as inputs to a lightGBM model. Computational experiments show that ReCGBM achieves the best performance compared to the existing methods. Further, we find that features in close proximity to the center of pre-miRNA are more important and make a significant contribution to the performance improvement of the developed method. Conclusions The results of this study show that ReCGBM is an interpretable and accurate predictor. Besides, the analyses of feature importance show that it might be of particular interest to consider more informative features close to the center of the pre-miRNA in future predictors.

Genomic epidemiology plays an ever-increasing role in our understanding of and response to the spread of infectious pathogens. Phylogeography, the reconstruction of the historical location and movement of pathogens from the evolutionary relationships among sampled pathogen sequences, can inform policy decisions related to viral movement among jurisdictions. However, phylogeographic reconstruction is impacted by the fact that the sampling and virus sequencing policies differ among jurisdictions, and these differences can cause bias in phylogeographic reconstructions. Here we assess the potential impacts of geographic-based sampling bias on estimated viral locations in the past, and on whether key viral movements can be detected. We quantify the effect of bias using simulated phylogenies with known geographic histories, and determine the impact of the biased sampling and of the underlying migration rate on the accuracy of estimated past viral locations. We find that overall, the accuracy of phylogeographic reconstruction is high, particularly when the migration rate is low. However, results depend on sampling, and sampling bias can have a large impact on the numbers and nature of estimated migration events. We apply these insights to the geographic spread of Ebolavirus in the 2014-2016 West Africa epidemic. This work highlights how sampling policy can both impact geographic inference and be optimized to best ensure the accuracy of specific features of geographic spread.

Surface ozone (O3) pollution is an emerging environmental abiotic stress that poses substantial risks to crop yield losses and food security worldwide, and especially in China. However, the O3-induced detrimental effects on double-season rice have rarely been investigated at large scales and over relatively long temporal spans. In this study, we estimated the crop production reductions and associated economic losses for double-season rice across southern China during 2013–2019, using a high spatial resolution surface ozone reanalysis dataset and rice distribution maps, and county-level production data, in combination with a locally derived exposure-response function for rice. Results show that AOT40 (cumulative hourly O3 exposure above 40 ppb) presented generally increasing trends over growing seasons in 2013–2019, spanning from 4.0 to 7.1 ppm h and 6.1 to 10.5 ppm h for double-early rice and double-late rice, respectively. Moreover, O3-induced relative yield losses ranged from 4.0% to 6.6% for double-early rice and 6.3% to 11.1% for double-late rice. Over the seven years, ambient O3 exposure resulted in crop production losses of 1951.5 × 104 tons and economic losses of 8,081.03 million USD in total. To combat the O3-induced agricultural risks, measures such as stringent precursors emission reductions and breeding O3-resistant cultivars should be continuously implemented in the future.

The relationship between the source and sink of atmospheric CO2 has always been a widely discussed issue in global climate change research. Recent studies revealed that the chemical weathering of carbonate rocks contributed to 1/3 (~0.5 Pg C/yr) of the missing carbon sinks (MCS) globally, and there are still 2/3 of MCS (~0.5 Pg C/yr) that need to be explored. As one of the main overburdened parts of the earth, loess is one of the important driving factors for atmospheric CO2 consumption. Here, we elaborated on the dissolution process and the carbon sink effect from carbonate and silicate minerals in loess. The relationship between carbonate dissolution and carbon source/sink is elucidated, and the mechanism of carbon sink formation from secondary carbonates in loess is clarified. Additionally, the commonly used methods for the identification of primary and secondary carbonates are summarized, and the methods for the study of loess carbon sinks and the influencing factors of loess carbon sinks are also revealed. Based on the research results and progress interpretations, the prospects of loess carbon sinks are discussed to provide a scientific basis for further research on loess carbon sinks.

Abstract Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2, was first identified in Wuhan, China, in December 2019. As the number of COVID‐19 infections and deaths worldwide continues to increase rapidly, the prevention and control of COVID‐19 remains urgent. This article aims to analyze COVID‐19 from a geographical perspective, and this information can provide useful insights for rapid visualization of spatial‐temporal epidemic information and identification of the factors important to the spread of COVID‐19. A new type of vitalization method, called the point grid map, is integrated with calendar‐based visualization to show the spatial‐temporal variations in COVID‐19. The combination of mixed geographically weighted regression (mixed GWR) and extreme gradient boosting (XGBoost) is used to identify the potential factors and the corresponding importance. The visualization results clearly reflect the spatial‐temporal patterns of COVID‐19. The quantified results reveal that the impact of population outflow from Wuhan is the most important factor and indicate statistically significant spatial heterogeneity. Our results provide insights into how multisource big geodata can be employed within the framework of integrating visualization and analytical methods to characterize COVID‐19 trends. In addition, this work can help understand the influential factors for controlling and preventing epidemics, which is important for policy design and effective decision‐making for controlling COVID‐19. The results reveal that one of the most effective ways to control COVID‐19 include controlling the source of infection, cutting off the transmission route, and protecting vulnerable groups.

The 46, XX male sex reversal syndrome (SRS) is a rare disease with a gender dysplasia phenotype. Scientists concur that SRS is associated with translocation of the sex-determining region Y gene (SRY). We obtained peripheral blood mononuclear cells (PBMCs)from an 18-year-old male with SRS to generate an induced pluripotent stem cell (iPSC) line (SMUSHi001-A). Karyotyping analysis of the patient PBMCs revealed a normal 46, XX karyotype carrying the SRY gene. Pluripotent markers were successfully detected in SMUSHi001-A which can be differentiated into three germ layers in vitro. This cell line will provide a cell model for exploring the pathogenesis and potential therapeutic methods of SRS.

The conglomerate reservoir is rich in oil and gas reserves; however, the gravel's mechanical properties and laws are difficult to gain through laboratory experiments, which furthermore constrain the hydraulic fracturing design. To analyse the failure law of conglomerate, we simulated the uniaxial compression test based on discrete element software PFC2D and analysed the effect of different cementation strength, gravel content and gravel geometry on the rock deformation and failure characteristics. Results show that (i) as the cementation strength decreases, the compressive strength and elasticity modulus both reduce clearly, while the crack shapes get more complex and the critical value is 0.3; (ii) as the gravel content increases, the conglomerate strength first decreases then increases under the influences of cracks bypassing gravels; cementation strength and gravel content of the conglomerate both contribute to the increase in local additional stress, which leads to a series of changes in crack shapes and mechanical properties of the conglomerate. Based on the above research, the conglomerate strength and crack shapes after failure are relatively complex due to the common influence of cementation strength and gravel content. The gravel edge crack caused by stress concentration is the micro-mechanism that affects the conglomerate mechanical properties.

Abstract A tunable terahertz (THz) chain-link metamaterial (CLM) is presented, which is composed of a tailored Au layer fabricated on Si substrate. CLM exhibits bidirectional polarization-dependent characteristic by applying a direct-current (dc) bias voltage on device. This CLM device can be heated up the surrounding temperature to tune the corresponding resonance. The tuning range is 0.027 THz from 0.318 THz to 0.291 THz on the bias of 0.60 V to 1.32 V. By reconfiguring the gap between CLM, there are single-resonance with red-shift at TE mode, and multi-resonance with blue-shift and red-shift at TM mode, respectively. These characterizations of CLM are polarization-dependence and bidirectional tunability. These results show the electromagnetic responses of proposed CLM device is suitable for the uses for resonator, filter, switch, and sensor in the THz frequency range.

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Neurodevelopmental disorder with spastic diplegia and visual defects (NEDSDV) is a rare disease. Patients with NEDSDV are usually accompanied by microcephaly, severe mental retardation, spasticity, and global developmental delay. Recent studies showed that mutations in CTNNB1 are responsible for the phenotype. Here, we generated an induced pluripotent stem cell (iPSC) line (SBWCHi001-A) from an 18-month-old patient with NEDSDV, who harbored a de novo heterozygous mutation in CTNNB1. The transduced iPSCs expressed pluripotency markers, and could differentiate into three germ layers in vitro. This cell line will be a cell model to explore the pathogenesis of NEDSDV and discover potential therapies.

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

Coding tree unit (CTU) partition technique is one of the most advanced techniques, which devotes to the excellent performance of High Efficiency Video Coding (HEVC). However, the enhancement of coding performance is at the expense of increased coding complexity. To reduce the complexity of HEVC intracoding, a fast CTU depth decision algorithm based on texture features and convolutional neural network (CNN) classification technique is proposed herein. First, the relationship between texture complexity and coding unit depth is explored. Based on this, CTUs are divided into simple CTUs and complex CTUs in line with their texture complexity, which are limited to different depth ranges. Then, the CNN for HEVC intradepth range (HIDR-CNN) decision-making is proposed, which is used for CTU classification and depth range restriction. Finally, the optimal CTU partition is achieved by recursive rate-distortion cost calculation in the depth range. Experimental results show that the proposed algorithm can yield average 27.54% encoding time reduction with 0.99% BDBR gain or 0.05 dB BDPSNR loss compared with HM 16.9. The proposed algorithm contributes to promote HEVC coding efficiency under real-time environments.

A next-generation video coding standard High Efficiency Video Coding (HEVC) provides higher video quality and lower compression bit rate but leads to very high encoding complexity, especially in the quad-tree-based coding tree unit partitioning process. To reduce the computational complexity of HEVC, in this paper, we propose an adaptive quad-tree depth range prediction mechanism. First, the proposed mechanism defines the similar region flag to distinguish between the similar region and the non-similar region. Then, two algorithms, the similar region depth range prediction algorithm and the non-similar region depth range prediction algorithm, are proposed. The similar region depth range prediction algorithm estimates the features of the similar region based on the coding unit depth of this region. The optimal depth of this region can be predicted. The non-similar region depth range prediction algorithm can skip low probability tree nodes based on the depth correlation coefficient, which is calculated based on scene content change. Both the similar region depth range prediction algorithm and the non-similar region depth range prediction algorithm show more than 90% predictive accuracy. Experimental results show that under random access configuration and low delay configuration, the proposed mechanism can yield 28.17% and 32.99% computational complexity reduction with negligible rate distortion performance loss, respectively, compared with HM16.9. The results show that the proposed mechanism is expected to be applied in real-time environments.

In recent years, battery-swapping heavy-duty trucks have seen rapid growth in China. Summarizing from the practical experiences gained in this development, and starting from market research and analysis of the most typical city of application case, Beijing, we aim to achieve the following: (ⅰ) Establish an operating cost model for battery-swapping heavy-duty trucks throughout a full operation cycle from the perspective of a heavy-duty truck freight transport capacity operator, based on four key cost dimensions, including transportation equipment, operation and maintenance, environmental protection compensation, and battery recycling compensation. (ⅱ) Calculate and compare the operating cost economy of battery-swapping heavy-duty trucks and other types of heavy-duty truck under different energy supplement modes, including charging, hydrogenation, and diesel. (ⅲ) Propose suggestions for faster and more successful heavy-duty truck electrification. The results indicate that battery-swapping heavy-duty trucks have good cost economy in a full operation cycle under specific scenario, and their economy will be improved with the popularization of battery-swapping stations.

River system is critical for the future sustainability of our planet but is always under the pressure of food, water and energy demands. Recent advances in machine learning bring a great potential for automatic river mapping using satellite imagery. Surface river mapping can provide accurate and timely water extent information that is highly valuable for solid policy and management decisions. However, accurate large-scale river mapping remains challenging given limited labels, spatial heterogeneity and noise in satellite imagery (e.g., clouds and aerosols). In this paper, we propose a new multi-source data-driven method for large-scale river mapping by combining multi-spectral imagery and synthetic aperture radar data. In particular, we build a multi-source data segmentation model, which uses contrastive learning to extract the common information between multiple data sources while also preserving distinct knowledge from each data source. Moreover, we create the first large-scale multi-source river imagery dataset based on Sentinel-1 and Sentinel-2 satellite data, along with 1013 handmade accurate river segmentation mask (which will be released to the public). In this dataset, our method has been shown to produce superior performance (F1-score is 91.53%) over multiple state-of-the-art segmentation algorithms. We also demonstrate the effectiveness of the proposed contrastive learning model in mapping river extent when we have limited and noisy data.

Abstract High-quality WS2 film with the single domain size up to 400 μm was grown on Si/SiO2 wafer by atmospheric pressure chemical vapor deposition. The effects of some important fabrication parameters on the controlled growth of WS2 film have been investigated in detail, including the choice of precursors, tube pressure, growing temperature, holding time, the amount of sulfur powder, and gas flow rate. By optimizing the growth conditions at one atmospheric pressure, we obtained tungsten disulfide single domains with an average size over 100 μm. Raman spectra, atomic force microscopy, and transmission electron microscopy provided direct evidence that the WS2 film had an atomic layer thickness and a single-domain hexagonal structure with a high crystal quality. And the photoluminescence spectra indicated that the tungsten disulfide films showed an evident layer-number-dependent fluorescence efficiency, depending on their energy band structure. Our study provides an important experimental basis for large-area, controllable preparation of atom-thick tungsten disulfide thin film and can also expedite the development of scalable high-performance optoelectronic devices based on WS2 film.

In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

Glycopeptides such as vancomycin and telavancin are essential for treating infections caused by Gram-positive bacteria. But the dwindling availability of new antibiotics and the emergence of resistant bacteria are making effective antibiotic treatment increasingly difficult. Ramoplanin, an inhibitor of bacterial cell wall biosynthesis, is a highly effective antibiotic against a wide range of Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus, vancomycin-intermediate resistant Clostridium difficile and vancomycin-resistant Enterococcus sp. Here, two tailoring enzyme genes in the biosynthesis of ramoplanin were deleted by double in-frame gene knockouts to produce new ramoplanin derivatives. The deschlororamoplanin A2 aglycone was purified and its structure was identified with LC-MS/MS. Deschlororamoplanin A2 aglycone and ramoplanin aglycone showed similar activity to ramoplanin A2. The results showed that α-1,2-dimannosyl disaccharide at Hpg11 and chlorination at Chp17 in the ramoplanin structure are not essential for its antimicrobial activity. This work provides new precursor compounds for the semisynthetic modification of ramoplanin.

A karst system, formed by the dissolution of carbonate rocks, is usually susceptible to contamination. Little is known about the composition of natural dissolved organic matter (DOM) in groundwater systems, especially in karstic groundwater. To reveal the characteristics of DOM in a karst aquifer, the Yufuhe River Basin, a typical karst watershed in northern China, was selected. DOM fluorescence (FDOM) was measured with the excitation-emission matrices (EEMs) spectroscopy technique. Parallel factor analysis (PARAFAC) was used to analyze the karst hydrogeological factors that affect FDOM biogeochemical behavior. Three fluorescent components, i.e., tyrosine-like, tryptophan-like, and ultraviolet fulvic acid were found. Their fluorescence properties were closely related to human activity and subterranean hydrology. Fluorescence properties suggested that FDOM in the Yufuhe River karst aquifer was predominant from anthropogenic activity. In addition, due to the effect of karstic heterogeneous hydrological conditions, FDOM showed obvious differentiation in the recharge, flow path, and discharge systems. The FDOM fluorescence intensity (FI) was weak in surface water and groundwater at the upper reaches (recharge area). In the middle of the flow path area, the percentage of tyrosine-like and tryptophan-like substances degraded and fulvic acid rose gradually. However, after infiltrating into the lower reaches (discharge area) of the deep karst aquifer system, the fulvic acid matter was consumed and protein-like matter accumulated.

The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.