Search

Human adenovirus (HAdV) is one of the most common viruses causing respiratory infections among young children. Most adenovirus infections are mild and self-limited; however, these infections may occasionally cause severe pneumonia and even death. The mortality risk factors for severe adenovirus pneumonia are not completely clear. This study aimed to evaluate the mortality risk factors in children with severe adenovirus pneumonia.A retrospective study of children with severe adenovirus pneumonia hospitalized in Guangzhou Women and Children's Hospital between July 2018 and January 2020 was performed. Binary logistic regression analysis was used to identify independent mortality risk factors for severe adenovirus pneumonia after univariate analysis.Our study included 189 patients (123 males and 66 females). Among them, 13 patients did not survive with a mortality of 6.88%. In multivariate analysis, the independent mortality risk factors in children with severe adenovirus pneumonia were age less than 1 year (OR = 18.513, 95% CI: 2.157-158.883, p = 0.008), hypoxia (OR = 62.335, 95% CI: 2.385-1629.433, p = 0.013), and thrombocytopenia (platelet100∗10ˆ9/L) (OR = 13.324, 95% CI: 1.232-144.075, p = 0.033).In children with severe adenovirus pneumonia who are younger than one year old, hypoxia and platelet counts less than 100∗10ˆ9/L represent mortality risk factors.

Purpose: This study investigated to what extent residual hearing and rehabilitation options (e.g., hearing aids [HAs]) affect the auditory cortex in children with hearing loss. Method: Twenty-one children with bilateral congenital sensorineural hearing loss who were candidates for cochlear implantation were recruited. Voxel-based morphometry analysis was conducted to assess the gray matter (GM) volume in the auditory cortex. Children's residual hearing was measured by pure-tone audiometry at different frequencies. Multiple linear regression models were conducted to examine the effects of residual hearing and the use of HAs on GM volume in the auditory cortex with the control of age and gender. Results: Children with more residual hearing at high frequencies had larger GM volume ratio (corrected by total intracranial volume) in the left Heschl's gyrus ( r = −.545, p = .013). An interaction effect between residual hearing and the use of HAs suggested that the effect of residual hearing on GM ratio was moderated by the use of HAs (β = −.791, p = .020). Compared with children with less residual hearing, children who had more residual hearing benefited more from longer use of HAs in terms of a larger GM ratio. Conclusions: Our preliminary findings highlight the impact of residual hearing on the neuroanatomy of the auditory cortex in children with hearing loss. Moreover, our results call for more auditory input via HAs for children with more residual hearing to preserve the auditory cortex before cochlear implantation. For children with less residual hearing who might receive limited benefit from HAs, an early cochlear implant would be necessary.

Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This feature brings a wide range of application prospects for TIR object-tracking methods. However, this field lacks a unified and large-scale training and evaluation benchmark, which has severely hindered its development. To this end, we present a large-scale and high-diversity unified TIR single object tracking benchmark, called LSOTB-TIR, which consists of a tracking evaluation dataset and a general training dataset with a total of 1416 TIR sequences and more than 643 K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 770 K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. We spilt the evaluation dataset into a short-term tracking subset and a long-term tracking subset to evaluate trackers using different paradigms. What’s more, to evaluate a tracker on different attributes, we also define four scenario attributes and 12 challenge attributes in the short-term tracking evaluation subset. By releasing LSOTB-TIR, we encourage the community to develop deep learning-based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze 40 trackers on LSOTB-TIR to provide a series of baselines and give some insights and future research directions in TIR object tracking. Furthermore, we retrain several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.

HER activity of defected MoS2 is not only influenced by the ΔGH* as previously thought, but also by formation energies, charge state and Fermi level's position of defects.

When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task. Specifically, our AMFT tracking method adaptively integrates hand-crafted features and deep convolutional neural network (CNN) features. In order to accurately locate the target position, it takes advantage of the complementarity between different features. Additionally, the model is updated using a simple but effective model update strategy to adapt to changes in the target during tracking. In addition, a simple but effective model update strategy is adopted to adapt the model to the changes of the target during the tracking process. We have shown through ablation studies that the adaptively multi-feature fusion model in our AMFT tracking method is very effective. Our AMFT tracker performs favorably on PTB-TIR and LSOTB-TIR benchmarks compared with state-of-the-art trackers.

Design of chemical probes with high specificity and responses are particularly intriguing. In this work, a fluorescent probe (

The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache for making updates. Each content is associated with a utility function that depends on the AoI and the time duration of absence from the cache. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results of problem tractability. Whereas the problem is NP-hard, we prove solution tractability in polynomial time for a special case with uniform content size, by a reformulation using network flows. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.

Biochar is important for soil improvement, fertilizer innovation, and greenhouse gas reduction. In this paper, Mg-modified biochar was prepared from rice and corn straw and mixed with soil at a 1% (w/w) addition in an indoor soil simulation experiment to study the effect of Mg-modified biochar on the chemical properties of acidic soil. The results showed that the addition of Mg-modified biochar reduced soil acidity and improved soil fertility. Compared with the control group, the Mg-modified biochar treatment significantly increased the concentrations of available potassium, available phosphorus, total phosphorus, organic carbon and exchangeable calcium and magnesium in the soil, and effectively increased the concentration of total nitrogen. Rice straw Mg-modified biochar treatment was more effective in increasing the soil-available potassium, available phosphorus, total phosphorus and exchangeable magnesium concentration, while corn straw Mg-modified biochar was more effective in increasing the soil organic carbon and exchangeable calcium concentration. In addition, the high pyrolysis temperature of Mg-modified biochar was more effective in promoting the soil-available potassium, available phosphorus and total nitrogen concentration, while the low pyrolysis temperature of Mg-modified biochar was more effective in promoting soil alkaline nitrogen, exchangeable calcium and magnesium.

Human languages are based on syntax, a set of rules which allow an infinite number of meaningful sentences to be constructed from a finite set of words. A theory associated with Chomsky and others holds that syntax is a mind-internal, universal structure independent of semantics. This theory, however, has been challenged by studies of the Chinese language showing that syntax is processed under the semantic umbrella, and is secondary and not independent. Here, using intracranial high-density electrocorticography, we find distinct spatiotemporal patterns of neural activity in the left inferior frontal gyrus that are specifically associated with syntactic and semantic processing of Chinese sentences. These results suggest that syntactic processing may occur before semantic processing. Our findings are consistent with the view that the human brain implements syntactic structures in a manner that is independent of semantics.

ConspectusFor the last two decades, materials scientists have contributed to a growing library of porous crystalline materials. These synthetic materials are typically extended networks, including metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), or discrete materials like metal-organic cages (MOCs) and porous organic cages (POCs). Advanced porous materials have shown promise for various applications due to their modular nature and structural tunability. MOCs have recently garnered attention because of their molecularity that bestows them with many unique possibilities (e.g., solution-processability, structural diversity, and postsynthetic processability).MOCs are discrete molecular assemblies of organic ligands coordinated with either metal cations or metal oxide clusters of different nuclearities, resulting in architectures with inherent porosity. Notably, the molecular nature of MOCs endows them with easy solution-processability unattainable with traditional framework materials. To date, a number of stable MOCs have been reported, such as those based on Rh (Rh-O bond energy: 405 ± 42 kJ/mol), Fe (Fe-O bond energy: 407.0 ± 1.0 kJ/mol), Cr (Cr-O bond energy: 461 ± 8.7 kJ/mol), Ti (Ti-O bond energy: 666.5 ± 5.6 kJ/mol), and Zr (Zr-O bond energy: 766.1 ± 10.6 kJ/mol). Paddle-wheel MOCs have also shown great stability in aqueous environments due to their rigid backbones. The zirconium MOC (Zr-MOCs) family emerges as a class of very robust cages for which their high bond energy endows them with high hydrothermal stability.In 2013, we reported the first four zirconocene tetrahedrons assembled from trinuclear zirconium oxide clusters with ditopic or tritopic organic ligands. Since then, significant progress in the rational design of Zr-MOC has led to an assortment of structures dedicated to meaningful applications.In this Account, we highlight the recent progress in synthesizing Zr-MOCs and Zr-MOC-based higher dimensional frameworks and their applications dedicated in our laboratories and beyond. The general Zr-MOC synthetic strategy involves assembling Zr trinuclear clusters with organic ligands (rigid or flexible) containing various functional groups. This chemistry has afforded cages with structural versatility and active sites, e.g., amino groups, for postsynthetic modifications (PSMs). Since the extrinsic porosity of cage-based frameworks is relatively weak, the resulting frameworks are susceptible to structural rearrangement after solvent removal. To circumvent this limitation, increasing the hydrogen bond ratio and strength between interlinked cages and conducting in situ catalytic polymerizations have been reported to afford permanently porous structures amenable to host-guest reactions.To expand their potential applications, multifunctional Zr-MOCs are highly desired. Such multivariate MOCs can be attained by either employing the isoreticular expansion strategy to create MOCs with high surface areas or using mixed-ligand approaches to afford heterogeneous MOCs. In addition, amorphous MOCs, flexible organic ligands, new functionalities, and MOC-based extended networks are exciting new approaches to developing materials with structural versatility and enhanced characteristics. Thereby, we believe the stability and versatility of the Zr-MOC family hold great potential in expanding and addressing challenging applications.

RGB-D images can be used to build 3D models with high precision and efficiency. This paper introduces the method and principle of 3D modeling and the 3D reconstruction process of RGB-D images and analyzes KinectFusion and BundleFusion methods. Based on BundleFusion, this paper constructs a set of 3D reconstruction methods for stroke patients’ food and carries out 3D reconstruction experiments on watermelon, banana, orange, corn, and dish cold bitter gourd. With the actual measurement data validation, the banana’s average error rate of 3.95%; for watermelon, the average error rate is 3.89%; for grapes, the average error rate is 4.28%; for corn, apple, banana mixture composition, the average error rate is 2.61%, the average error rate of caramel treats is 3.48%, and the average error rate of pork floss bread is 2.85%. The average error rate of the hot and dry noodles is 1.85%, and the average error rate of the breakfast mix composition is 2.91%. The error rate in subsequent food volume calculation, quality estimation and calculation application is within the effective range of nutrients. It provides a technical basis and scientific basis for dietary intervention of stroke patients.

In this paper, we investigate the joint task assignment and power control problems for Device-to-Device (D2D) offloading communications with energy harvesting. Exploiting the D2D links for data offloading allows reducing the traffic load of the cellular base stations. The energy consumed by the D2D transmitters for data offloading can be compensated by energy harvesting. The main objective is to maximize the energy efficiency (EE) under energy causality and delay constraints, assuming a harvest-transmit model. Hence, the proposed model results in a non-convex problem. We first derive an equivalent and more tractable optimization problem by exploiting nonlinear fractional programming, also known as the Dinkelbach method. We propose a layered optimization method by decoupling the energy efficiency maximization problem into power allocation and offloading assignment. The first step consists of computing the optimal power values by applying the conjugate gradient method. In the second step, the problem of the D2D pair formation for data offloading amounts to the bipartite graph matching. It can be solved to optimality using the Hungarian algorithm. Extensive simulations were performed on various network scenarios. Numerical results show that the proposed resource allocation scheme achieves remarkable improvements in terms of network EE.

Human neuroimaging studies have demonstrated that exercise influences the cortical structural plasticity as indexed by gray or white matter volume. It remains elusive, however, whether exercise affects cortical changes at the finer-grained myelination structure level. To answer this question, we scanned 28 elite golf players in comparison with control participants, using a novel neuroimaging technique-quantitative magnetic resonance imaging (qMRI). The data showed myeloarchitectonic plasticity in the left temporal pole of the golf players: the microstructure of this brain region of the golf players was better proliferated than that of control participants. In addition, this myeloarchitectonic plasticity was positively related to golfing proficiency. Our study has manifested that myeloarchitectonic plasticity could be induced by exercise, and thus, shed light on the potential benefits of exercise on brain health and cognitive enhancement.

Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of target-tracking tasks. Different from the target-tracking task in the general scenarios, the target-tracking task in the UAV scenarios is very challenging because of factors such as small scale and aerial view. Although the discriminative correlation filter (DCF)-based tracker has achieved good results in tracking tasks in general scenarios, the boundary effect caused by the dense sampling method will reduce the tracking accuracy, especially in UAV-tracking scenarios. In this work, we propose learning an adaptive spatial-temporal context-aware (ASTCA) model in the DCF-based tracking framework to improve the tracking accuracy and reduce the influence of boundary effect, thereby enabling our tracker to more appropriately handle UAV-tracking tasks. Specifically, our ASTCA model can learn a spatial-temporal context weight, which can precisely distinguish the target and background in the UAV-tracking scenarios. Besides, considering the small target scale and the aerial view in UAV-tracking scenarios, our ASTCA model incorporates spatial context information within the DCF-based tracker, which could effectively alleviate background interference. Extensive experiments demonstrate that our ASTCA method performs favorably against state-of-the-art tracking methods on some standard UAV datasets.

Porous organic cages (POCs) have many advantages, including superior microenvironments, good monodispersity, and shape homogeneity, excellent molecular solubility, high chemical stability, and intriguing host-guest chemistry. These properties enable POCs to overcome the limitations of extended porous networks such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs). However, the applications of POCs in bioimaging remain limited due to the problems associated with their rigid and hydrophobic structures, thus leading to strong aggregation-caused quenching (ACQ) in aqueous biological media. To address this challenge, we report the preparation of aggregation-induced emission (AIE)-active POCs capable of stimuli responsiveness for enhanced bioimaging. We rationally design a hydrophilic, structurally flexible tetraphenylethylene (TPE)-based POC that is almost entirely soluble in aqueous solutions. This POC's conformationally flexible superstructure allows the dynamic rotation of the TPE-based phenyl rings, thus endowing impressive AIE characteristics for responses to environmental changes such as temperature and viscosity. We employ these notable features in the bioimaging of living cells and obtain good performance, demonstrating that the present AIE-active POCs are suitable candidates for further biological applications.

The effect of oxygen coordination on the electrocatalytic nitrogen reduction conversion into NH3 on a VO4/G single-atom catalyst was theoretically investigated.

Improving the robustness and efficiency of flash calculations in phase equilibrium is crucial for reservoir simulation. DL-KF (Deep Learning for K-values and Fugacity Calculation) modeling is proposed to accelerate phase equilibrium calculation using deep learning methods, in which the three-steps neural networks are included: ANN-STAB (Artificial Neural Network for Stability Test) model, ANN-KV (Artificial Neural Network for K-values Calculation) model and ANN-FUG (Artificial Neural Network for Fugacity Calculation) model respectively. The ANN-STAB model is generated to test phase stability. When temperature, pressure and feed composition are given, the phase classification is obtained directly with very low computation cost. In the ANN-KV model, initial K-values are determined by trained networks instead of employing Wilson equation employed in traditional flash calculation. Its initial estimations of K-values significantly reduce the number of iterations and avoid converging to incorrect or unphysical solutions. The ANN-FUG model is built to replace the fugacity coefficient calculation in traditional flash calculation algorithms, and simplifies the nonlinear calculation of internal iterative calculation. These three artificial neural network models are embedded into the traditional algorithms to accelerate flash calculations. The framework considers the complete physical process of the algorithms of flash calculations in phase equilibrium calculations using deep learning methods, and it can also guarantee the conservation of component mass, which is crucial for phase equilibrium calculations and reservoir simulation. The proposed DL-KF modeling is validated and compared with the original equation of state modeling and three other deep learning methods using two typical hydrocarbon fluid cases. A sample of C3H8-CO2-heavy oil systems from Huabei oilfield and a PVT experiment in Tahe oilfield are used to examine the DL-KF modeling. The physical properties of oil sample of Bakken reservoir with CO2 injection are also investigated. These results reveal that the DL-KF methoding is accurate and efficient for accelerating phase equilibrium calculations of reservoir fluids.

Since the end of 2019, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. The RNA genome of SARS-CoV-2, which is highly infectious and prone to rapid mutation, encodes both structural and nonstructural proteins. Vaccination is currently the only effective method to prevent COVID-19, and structural proteins are critical targets for vaccine development. Currently, many vaccines are in clinical trials or are already on the market. This review highlights ongoing advances in the design of prophylactic or therapeutic vaccines against COVID-19, including viral vector vaccines, DNA vaccines, RNA vaccines, live-attenuated vaccines, inactivated virus vaccines, recombinant protein vaccines and bionic nanoparticle vaccines. In addition to traditional inactivated virus vaccines, some novel vaccines based on viral vectors, nanoscience and synthetic biology also play important roles in combating COVID-19. However, many challenges persist in ongoing clinical trials.

We consider leveraging the joint potential of non‐orthogonal multiple access (NOMA) and traffic offloading to benefit cell‐edge users in multi‐cell scenarios. We formulate the edge‐driven throughput maximisation problem where both the offloading decision and resource allocation are to be optimised, accounting for the impact of the inter‐cell interference on the decoding order in NOMA. The inter‐cell interference is not given constants as it is influenced by resource allocation. Therefore the non‐orthogonal multiple access (NOMA) decoding order is not known a priori. We explore the problem by decomposing it into two subproblems. By analysis of structural properties, we derive algorithm modules which interact iteratively for reciprocity, forming the overall algorithm. Numerical results show that the proposed scheme considerably improves the cell‐edge throughput, compared with the conventional scheme of orthogonal multiple access without traffic offloading.

Importance: Children with bilateral sensorineural hearing loss (SNHL) benefit from binaural amplification provided by hearing aids (HA) to develop auditory skills, language and social abilities. However, few studies have examined the effect of amplification on brain structure in children with SNHL. This study evaluates a group of children with bilateral SNHL who were cochlear implant candidates.Objective/Design/Setting: To investigate whether the extent of HA use before cochlear implantation (CI) modulates neuroanatomical structural changes in the brain. A cross-sectional, population-based study was conducted.Participants: One hundred and three children with bilateral SNHL who were candidates for CI. Seventy-eight children with normal hearing were selected from the NIH MRI Study of Normal Brain Development.Exposures: The length of HA use was obtained. Moreover, the unaided speech-frequency pure-tone average (PTA) was measured to represent residual hearing.Main Outcomes and Measures: Multivoxel pattern similarity analysis was used to examine the similarity of gray matter (GM) density in the local spatial morphological pattern of the auditory cortex for children with SNHL in comparison with age expectations (children with normal hearing).Results: One hundred and three children with hearing loss were included (55 females and 48 males). The mean (SD) age was 15.84 (11.53) months. Children’s GM preservation in the left Heschl’s gyrus was nonlinearly affected by their experience of HA use (β = -0.380, p = 0.03). During the first year of HA use, children who had longer experience of HA use showed higher GM preservation, while further use of HA attenuated their GM preservation. Such effect was moderated by the degree of HL (β = -0.347, p = 0.03). Children with worse residual hearing benefited more from HA use compared to those with better residual hearing. Conclusions and Relevance: Amplification may prevent auditory structural changes which result from a lack of auditory input in children with HL, especially those whose loss is in the profound range. Although HA does not provide adequate audibility of spoken language for children who are cochlear implant candidates, pre-CI use of HA serves to preserve the auditory cortex during the first year of HA use. After the first year, this effect disappears, which reinforces the need for early cochlear implantation as the main treatment modality for hearing loss.

Halide double perovskite Cs2BMX6 possesses excellent photoelectronic properties. The material properties would have been markedly influenced by the variant electronic structures if the chemical elements of B, M and X had been changed. In this paper, we have a systematic investigation on the mechanical, electronic structure and optical properties of Cs2BSbBr6 (B[Formula: see text][Formula: see text][Formula: see text]Li, Na, K, Rb) using first-principles method. The results show that Cs2LiSbBr6 and Cs2NaSbBr6 are indirect band gap semiconductors, while Cs2KSbBr6 and Cs2RbSbBr6 are direct band gap semiconductors. The band gap values of Cs2BSbBr6 (B = Li, Na, K, Rb) are 2.81, 2.96, 3.37 and 3.36[Formula: see text]eV, respectively. It is worth noting that the band gap value of Cs2BSbBr6 increases with the increased atomic number of B (Li–Na–K–Rb) atoms. Due to the inter-band transition of electrons, the strong absorption spectrum occurs in the visible region. These physical and electronic properties of Cs2BSbBr6 provide references for the potential applications.

Membrane technology is attractive for natural gas separation (removing CO2, H2O, and hydrocarbons from CH4) because of membranes' low energy consumption and small environmental footprint. Compared to polymeric membranes, microporous inorganic membranes such as silicoaluminophosphate-34 (SAPO-34) membrane can retain their separation performance under conditions close to industrial requirements. However, moisture and hydrocarbons in natural gas can be strongly adsorbed in the pores of those membranes, thereby reducing the membrane separation performance. Herein, we report the fabrication of a polycrystalline MIL-160 membrane on an Al2O3 substrate by in situ hydrothermal synthesis. The MIL-160 membrane with a thickness of ca. 3 μm shows a remarkable molecular sieving effect in gas separation. Besides, the pore size and environment of the MIL-160 membrane can be precisely controlled using reticular chemistry by regulating the size and functionality of the ligand. Interestingly, the more polar fluorine-functionalized multivariate MIL-160/CAU-10-F membrane exhibits a 10.7% increase in selectivity for CO2/CH4 separation and a 31.2% increase in CO2 permeance compared to those of the MIL-160 membrane. In addition, hydrophobic MIL-160 membranes and MIL-160/CAU-10-F membranes are more resistant to water vapor and hydrocarbons than the hydrophilic SAPO-34 membranes.

Salt-induced renal metabolism dysfunction is an important mechanism of salt-sensitive hypertension. Given that the gut-liver axis is the first hit of a high-salt diet (HSD), we aimed to identify the extra-renal mechanism from hepatic metabolism and gut microbiota, and attempted to relieve the salt-induced metabolic dysfunctions by curcumin. Untargeted metabolomics analysis was performed to identify the changes in hepatic metabolic pathways, and integrated analysis was employed to reveal the relationship between hepatic metabolic dysfunction and gut microbial composition. HSD induced significant increase in fumaric acid, l-lactic acid, creatinine, l-alanine, glycine, and l-cysteine levels, and amino acids metabolism pathways associated with glycolysis were significantly altered, including alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism, which were involved in the regulation of blood pressure. Integrated multi-omics analysis revealed that changes in Paraprevotella, Erysipelotrichaceae, and genera from Clostridiales are associated with metabolic disorders. Gene functional predication analysis based on 16S Ribosomal RNA sequences showed that the dysfunction in hepatic metabolism were correlated with enhanced lipopolysaccharide (LPS) biosynthesis and apoptosis in gut microbes. Curcumin (50 mg/kg/d) might reduce gut microbes-associated LPS biosynthesis and apoptosis, partially reverse metabolic dysfunction, ameliorate renal oxidative stress, and protect against salt-sensitive hypertension.

Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it hard for intelligent CAD systems to deploy quickly; (ii) due to the personnel and information barriers, it is difficult for medical institutions to conduct the same pre-processing operations, making a deep model that performs well on a specific medical institution difficult to achieve similar performances on the same task in other medical institutions. To overcome these problems, we propose a deep-reinforcement-learning-based task-oriented homogenized automatic pre-processing (DRL-HAPre) framework to overcome these two problems. This framework utilizes deep reinforcement learning techniques to learn a policy network to automatically and adaptively select the optimal pre-processing operations for the input medical images according to different analysis tasks, thus helping the intelligent CAD system to achieve a rapid deployment (i.e., painless) and maintain a satisfactory performance (i.e., accurate) among different medical institutes. To verify the effectiveness and advantages of the proposed DRL-HAPre framework, we further develop a homogenized automatic pre-processing model based on the DRL-HAPre framework to realize the automatic pre-processing of key region selection (called HAPre-KRS) in the pneumonia image classification task. Extensive experimental studies are conducted on three pediatric pneumonia classification datasets with different image qualities, and the results show that: (i) There does exist a hard-to-reproduce problem in clinical practices and the fact that having different medical image qualities in different medical institutes is an important reason for the existing of hard-to-reproduce problem, so it is compelling to propose homogenized automatic pre-processing method. (ii) The proposed HAPre-KRS model and DRL-HAPre framework greatly outperform three kinds of state-of-the-art baselines (i.e., pre-processing, attention and pneumonia baseline), and the lower the medical image quality, the greater the improvements of using our HAPre-KRS model and DRL-HAPre framework. (iii) With the help of homogenized pre-processing, HAPre-KRS (and DRL-HAPre framework) can greatly avoid performance degradation in real-world cross-source applications (i.e., thus overcoming the hard-to-reproduce problem).

This paper analyzes the correlation and causality between the European Union carbon emission allowance market and international fossil energy markets. Asymmetric correlation is examined using local Gaussian correlation and asymmetric causality is assessed using a quantile Granger causality test. Empirical results show that, after the COVID-19 outbreak, the EU carbon emission market is more correlated to the crude oil market, but it is less linked to the coal futures market. In contrast to the UK natural gas futures market, the EU carbon emission market shows contagion with the US natural gas market. During the COVID-19 pandemic, unidirectional causality from the EU carbon emission market to the fossil energy market has been becoming bidirectional.JEL classification: C22, I18, O13, O52, Q43

This paper investigates interdependence and contagion between oil and BRIC stock markets before and after COVID-19. We used a local Gaussian correlation approach to identify the asymmetric relation...

We designed a novel ratiometric fluorescent probe P1-Leu with a donor-acceptor-donor fluorophore for the detection of leucine aminopeptidase in lysosomes. P1-Leu exhibits a lower detection limit than the ratiometric donor-π-acceptor probe, due to the low ratiometric background. Besides, P1-Leu has good lysosome-targeting ability and realizes the distinction of LAP levels in different cells.

We report the antifouling properties of thin-film nanocomposite (TFN) membranes containing two water-stable metal-organic cages (MOCs). The MOC-containing TFN membranes possess excellent antifouling properties against positively-charged foulants and protein (BSA, up to 99.7% water permeability retention) and achieve up to 100% water permeability recovery.

In this paper we discuss the rational of the Multi-model Information based Speech Processing (MISP) Challenge, and provide a detailed description of the data recorded, the two evaluation tasks and the corresponding baselines, followed by a summary of submitted systems and evaluation results. The MISP Challenge aims at tack-ling speech processing tasks in different scenarios by introducing information about an additional modality (e.g., video, or text), which will hopefully lead to better environmental and speaker robustness in realistic applications. In the first MISP challenge, two bench-mark datasets recorded in a real-home TV room with two reproducible open-source baseline systems have been released to promote research in audio-visual wake word spotting (AVWWS) and audio-visual speech recognition (AVSR). To our knowledge, MISP is the first open evaluation challenge to tackle real-world issues of AVWWS and AVSR in the home TV scenario.

The initial immune response to HIV is critical in determining transmission. However, due to technical limitations we still do not have a comparative map of early mucosal transmission events. We combined RNAscope, cyclic-immunofluorescence and novel image analysis tools to quantify HIV transmission dynamics in intact human colorectal tissue. We mapped HIV enrichment to mucosal dendritic cells (DC) and submucosal macrophages, but not CD4+ T-cells, the primary targets of downstream infection. DCs appeared to funnel virus to lymphoid aggregates which acted as early sanctuaries of high viral titres whilst facilitating HIV passage to the submucosa. Finally, HIV entry induced rapid recruitment and clustering of target cells, facilitating DC and macrophage mediated HIV transfer and enhanced infection of CD4+ T-cells. These data demonstrate a rapid response to HIV structured to maximise the likelihood of mucosal infection, and provide a framework for in situ studies of host pathogen interactions and immune mediated pathologies.Highlights-in situ quantification of host cellular microenvironment response to pathogen invasion in human colorectal tissue.-HIV first localises to mucosal DCs and submucosal macrophages, but not CD4+ T cells.-Viral enrichment first occurs in lymphoid aggregates which is associated with passage into the submucosa.-Early localisation of HIV to CD4+ T cells is associated with interactions with DCs and macrophages.Graphical Abstract

Background Liver metastasis of colorectal cancer (CRC) is an important cause of death from CRC, but its molecular mechanism is still unclear. In recent years, whole-exome sequencing has played an increasingly important role in the study of the occurrence and development of diseases, especially malignant tumors. Its high throughput and low cost advantages enable researchers to explore the pathogenic genes of diseases, and screen potential molecular markers and therapeutic targets from the level of genomics. Methods This study collected the primary tumor tissues, matched paracancerous, normal tissues, and liver metastases of 4 CRC patients admitted to the Department of General Surgery of the First Affiliated Hospital of Soochow University, and performed high-depth whole-exome sequencing, with the sequencing depth of each sample reaching 123× on average, then filtered the sequencing data, compared them, and analyzed the bioinformatics data. Results we found 8,565 single nucleotide variants (SNV) and 429 insertions/deletions (InDel) in the primary and hepatic lesion tissues, and the genes with the highest mutation frequency were titin (TTN), obscurin (OBSCN), and homeodomain-interacting protein kinase 2 (HIPK2). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the mutant genes was conducted, and it was found that the mutant genes were mainly concentrated in the cells, cell parts, and cellular process of GO. The results of KEGG pathway analysis showed that mutations were mainly distributed in circadian entrainment, insulin secretion, and glutamatergic synapse. Further, we identified 723 SNV and Indel genes with high frequency mutations including TTN, OBSCN, and hydrocephalus-inducing protein homolog (HYDIN) across all tissues of liver metastases. The GO analysis showed that the mutated genes in liver metastatic tissues were mainly concentrated in cell, cell part, and cellular process. The KEGG pathway analysis showed that high frequency mutation genes were focused on gastric acid secretion, bile secretion, and melanogenesis. Conclusions This study found some candidate genes related to the occurrence of CRC and liver metastasis through whole-exome sequencing of relevant tissues in CRC patients with liver metastasis, which is expected to provide new markers and therapeutic targets for such patients.