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Gas chromatography – tandem mass spectrometry (GC-MS/MS) using formic acid as a protective agent is reported for the determination of acrylic resin monomers in food contact packaging paper. The analytes were extracted by ethanol mixed with formic acid with 2-phenylethyl propionate as the internal standard. The extract was separated by a 6% cyanopropylbenzene-94% dimethylsiloxane ultra-inert chromatographic column and analyzed by GC-MS/MS in multiple reaction monitoring (MRM) mode. 13 acrylic resin monomers including acrylic acid, styrene, and acrylic ester were separated. The procedure was successfully applied for the analysis of real samples. The procedure provided a linear calibration range with detection limits from 0.16 to 0.46 mg kg−1. The mean recoveries were from 88.1% to 100.9% with relative standard deviations less than 5.4%. The residual acrylic resin monomers in packaging paper were primarily from printing base paper and varnish. Of the acrylic resin monomers, the monomers except for butyl acrylate and 2-ethylhexyl acrylate showed low migration rates. The method provides high sensitivity and good reproducibility with simple pretreatment, demonstrating application for monitoring acrylic resin monomers in food contact packaging paper.

The COVID-19 pandemic crisis and the associated lockdown measures have exerted significantly adverse effects on corporate sectors globally. Archanskaia et al. (2023) provide a novel empirical strategy to timely assess corporate financial distress in the EU. The contribution is two-fold. First, this paper's notion of financial distress considers both the equity position and corporate indebtedness. Second, the methodology proposed in this paper allows the authors to estimate corporate financial distress in the EU at a highly granular level and link micro-level simulations to sectoral macroeconomic outcomes. The methodology employed by Archanskaia et al. (2023) consists of three steps. First, the authors apply a nowcasting model to acquire monthly industrial turnover data. Second, they feed the obtained monthly industrial turnover into a profit-generating process via an accounting identity to estimate monthly firm profits at the firm level. Third, the authors use the estimated firm profits with a snapshot of information on pre-existing liquid assets to deduce the firm-level liquidity needs and the depletion of equity through the focus period during COVID-19. These estimated results on firm equity position and indebtedness enable the authors to quantity corporate financial distress in the EU via various angles (e.g., country-level heterogeneity, industry heterogeneity, and the targeting of COVID support policies). The primary advantage of this approach is that it deals with large datasets at the granular level and produces firm-level results almost in real-time. Therefore, it can help policymaking track the effects of crises over time. However, one can quickly critique this three-step approach for its susceptibility to the usual Lucas critique. That said, since the objective here is to estimate firm-level financial distress, a large structural model being more or less aggregate in nature, though able to mitigate the Lucas critique concern, will encounter significant challenges in estimating firm-level results with the requisite level of granularity offered by the available data. Therefore, I broadly concur with the authors’ position that ‘the specific contribution of this paper consists in striking a better balance between the need to carry out a multi-country evaluation of the pandemic's effects on industrial activity in a strongly integrated region like the EU and the difficulty of capturing time, industry, and country variation in turnover with sufficient granularity.’

As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) frameworks. Nevertheless, testing these frameworks is a daunting task due to their intricate implementation. Previous research on fuzzing ML frameworks has struggled to effectively extract input constraints and generate valid inputs, leading to extended fuzzing durations for deep execution or revealing the target crash. In this paper, we propose ConFL, a constraint-guided fuzzer for ML frameworks. ConFL automatically extracting constraints from kernel codes without the need for any prior knowledge. Guided by the constraints, ConFL is able to generate valid inputs that can pass the verification and explore deeper paths of kernel codes. In addition, we design a grouping technique to boost the fuzzing efficiency. To demonstrate the effectiveness of ConFL, we evaluated its performance mainly on Tensorflow. We find that ConFL is able to cover more code lines, and generate more valid inputs than state-of-the-art (SOTA) fuzzers. More importantly, ConFL found 84 previously unknown vulnerabilities in different versions of Tensorflow, all of which were assigned with new CVE ids, of which 3 were critical-severity and 13 were high-severity. We also extended ConFL to test PyTorch and Paddle, 7 vulnerabilities are found to date., 13 pages, 15 figures

3D facial avatar reconstruction has been a significant research topic in computer graphics and computer vision, where photo-realistic rendering and flexible controls over poses and expressions are necessary for many related applications. Recently, its performance has been greatly improved with the development of neural radiance fields (NeRF). However, most existing NeRF-based facial avatars focus on subject-specific reconstruction and reenactment, requiring multi-shot images containing different views of the specific subject for training, and the learned model cannot generalize to new identities, limiting its further applications. In this work, we propose a one-shot 3D facial avatar reconstruction framework that only requires a single source image to reconstruct a high-fidelity 3D facial avatar. For the challenges of lacking generalization ability and missing multi-view information, we leverage the generative prior of 3D GAN and develop an efficient encoder-decoder network to reconstruct the canonical neural volume of the source image, and further propose a compensation network to complement facial details. To enable fine-grained control over facial dynamics, we propose a deformation field to warp the canonical volume into driven expressions. Through extensive experimental comparisons, we achieve superior synthesis results compared to several state-of-the-art methods.

Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importance of extracting structured reactions from scientific literature, data annotation for this purpose is cost-prohibitive due to the significant labor required from domain experts. Consequently, the scarcity of sufficient training data poses an obstacle to the progress of related models in this domain. In this paper, we propose ReactIE, which combines two weakly supervised approaches for pre-training. Our method utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions. Additionally, we adopt synthetic data from patent records as distant supervision to incorporate domain knowledge into the model. Experiments demonstrate that ReactIE achieves substantial improvements and outperforms all existing baselines., Findings of ACL 2023, Short Paper

Figure Data in 'Single-scattering Properties of Encapsuled Fractal Black Carbon Particles Computed Using the invariant imbedding T-matrix method and Deep Learning Approaches'

Purpose Targeted biopsy (TB) combined with systematic biopsy (SB) is an optimized mode of prostate biopsy but can often lead to oversampling and overdiagnosis accompanied by potential biopsy-related complications and patient discomfort. Here, we attempted to reasonably stratify the patient population based on multi-parameter indicators with the aim of avoiding unnecessary SB. Methods In total, 340 biopsy-naïve men with suspected lesions, prostate-specific antigen (PSA) < 20 ng/mL and prostate imaging-reporting and data system (PI-RADS) ≥ 3 enrolled for study underwent both TB and SB. The primary outcome was to determine independent predictors for a valid diagnosis, assuming that only TB was performed and SB omitted (defined as mono-TB), taking TB + SB as the reference standard. The secondary outcomes were exploration of the predictive factors of mono-TB and TB + SB in detection of prostate cancer (PCa) and clinically significant PCa (csPCa). Results The mean PSA density (PSAD) of patient group was 0.27 ng/mL/mL. Multiparametric MRI PI-RADS scores were 3-5 in 146 (42.94%), 105 (30.88%), and 89 (26.18%) cases, respectively. PCa and csPCa were detected in 178/340 (52.35%) and 162/340 (47.65%) patients, respectively. Overall, 116/178 (65.17%) patients diagnosed with PCa displayed pathological consistencies between mono-TB and TB + SB modes. PSAD and PI-RADS were independent predictors of valid diagnosis using mono-TB. Conclusions PSAD combined with PI-RADS showed utility in guiding optimization of the prostate biopsy mode. Higher PSAD and PI-RADS values were associated with greater confidence in implementing mono-TB and safely omitting SB, thus effectively balancing the benefits and risks.

Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. A supervised FET method, which typically relies on human-annotated corpora for training, is costly and difficult to scale. Recent studies leverage pre-trained language models (PLMs) to generate rich and context-aware weak supervision for FET. However, a PLM may still generate a mixture of rough and fine-grained types, or tokens unsuitable for typing. In this study, we vision that an ontology provides a semantics-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel zero-shot, ontology-guided FET method, OntoType, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing.

Introduction: Lipomas are benign tumors that occur in the back, shoulder, neck, and extremities. Giant lipomas of the inguinal-perineal area are extremely rare. Case report: We present a case of a 63-year-old man with a giant lipoma in the inguinal-perineal area. Ultrasound examination showed a heterogeneous hyperechoic mass (14.6 × 8.3 cm) located in the inguinal area, presumably an inguinal hernia. Computed tomography (CT) results showed fat tissue radiographic patterns, and lack of enhancement of the left inguinal area extending to the lateral scrotum. The patient underwent an operation and a radical resection was performed. Histology results revealed a lipoma. At the 1-month follow-up, the patient did not show any evidence of recurrence. Conclusion: Giant lipomas occurring in the inguinal-perineal area are extremely rare and are easily confused with other lesions in the groin area. We recommend a detailed preoperative adjunctive examination such as CT. Open surgical complete excision is the ideal treatment.

The basic helix-loop-helix (bHLH) transcription factors (TFs) gene family is an important gene family in plants, and participates in regulation of plant apical meristem growth, metabolic regulation and stress resistance. However, its characteristics and potential functions have not been studied in chestnut (Castanea mollissima), an important nut with high ecological and economic value. In the present study, 94 CmbHLHs were identified in chestnut genome, of which 88 were unevenly distributed on chromosomes, and other six were located on five unanchored scaffolds. Almost all CmbHLH proteins were predicted in the nucleus, and subcellular localization demonstrated the correctness of the above predictions. Based on the phylogenetic analysis, all of the CmbHLH genes were divided into 19 subgroups with distinct features. Abundant cis-acting regulatory elements related to endosperm expression, meristem expression, and responses to gibberellin (GA) and auxin were identified in the upstream sequences of CmbHLH genes. This indicates that these genes may have potential functions in the morphogenesis of chestnut. Comparative genome analysis showed that dispersed duplication was the main driving force for the expansion of the CmbHLH gene family inferred to have evolved through purifying selection. Transcriptome analysis and qRT-PCR experiments showed that the expression patterns of CmbHLHs were different in different chestnut tissues, and revealed some members may have potential functions in chestnut buds, nuts, fertile/abortive ovules development. The results from this study will be helpful to understand the characteristics and potential functions of the bHLH gene family in chestnut.

We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views capture scene content with different resolutions. To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates and the features of conical frustums as inputs and predicts a local volumetric radiance field. Since the coordinates are continuous, LIRF renders high-quality novel views at a continuously-valued scale via volume rendering. Besides, we predict the visible weights for each input view via transformer-based feature matching to improve the performance in occluded areas. Experimental results on real-world scenes validate that our method outperforms state-of-the-art methods on novel view rendering of unseen scenes at arbitrary scales., Accepted to CVPR 2023. Project page: https://xhuangcv.github.io/lirf/

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/., Project page: https://yiyuzhuang.github.io/NeAI/

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy in the past decade and amplify T-cell-mediated immune responses by disrupting immunoinhibitory signals. The augmented T-cell immune response has led to a range of immune-related adverse effects (irAEs). Immune-related cardiotoxicity has been reported in case series but has been underappreciated due to difficulties in diagnosis. This article describes epidemiological, clinical presentation, subtype, and treatment data and a new systematic framework for the clinical management of cardiotoxicity.MethodsData were extracted for cancer patients who received ICIs in a single center between January 1, 2020, and February 28, 2022. ICI-associated cardiotoxicity was clinically diagnosed based on clinical presentations, biochemical biomarkers, and imaging features.ResultsWe identified a total of 12 (2.46%) cases of ICI-related cardiotoxicity from 487 patients who received PD-1 or PD-L1 inhibitors. All patients were diagnosed with advanced or metastatic solid tumors. The severity of ICI-related cardiotoxicity ranged from subclinical cardiac abnormalities (subclinical type) with only asymptomatic troponin-I (TnI) elevations (25.0%) to symptomatic cardiac abnormalities (clinical type) (75.0%). Patients with symptomatic cardiac abnormalities had several manifestations, including tachyarrhythmia (16.7%), bradyarrhythmia (41.7%), or cardiac failure (8.3%). The median immunotherapy exposure time was 1.5 doses (range: 1 to 5), and the median time from the initial immunotherapy to the onset of ICI-related cardiotoxicity was 33.5 days (IQR: 20.3 to 46.8). Most patients, including those with subclinical cardiac abnormalities, were administered systemic corticosteroids (58.3%). One (8.3%) patient was put on mechanical ventilation, one (8.3%) received plasma exchange therapy, one (8.3%) was implanted with a pacemaker, and one (8.3%) was admitted to the ICU. Three patients with symptomatic cardiac abnormalities (25.0%) died, and other patients presented with significant clinical improvement with good outcomes.ConclusionICI-related cardiotoxicity is uncommon but critical with a high mortality rate and poor prognosis, especially for a small group of patients with symptomatic cardiac abnormalities. More attention should be given to cardiotoxicity associated with ICIs, and these patients should be given baseline examinations and biochemical analyses before and after the initiation of immunotherapy, intensive cardiac assessments, an accurate and rapid diagnosis, and timely multidisciplinary management with immunosuppressive agents and other necessary clinical interventions.

Text classification typically requires a substantial amount of human-annotated data to serve as supervision, which is costly to obtain in dynamic emerging domains. Certain methods seek to address this problem by solely relying on the surface text of class names to serve as extremely weak supervision. However, existing methods fail to account for single-class documents discussing multiple topics. Both topic diversity and vague sentences may introduce noise into the document's underlying representation and consequently the precision of the predicted class. Furthermore, current work focuses on text granularities (documents, sentences, or words) independently, which limits the degree of coarse- or fine-grained context that we can jointly extract from all three to identify significant subtext for classification. In order to address this problem, we propose MEGClass, an extremely weakly-supervised text classification method to exploit Mutually-Enhancing Text Granularities. Specifically, MEGClass constructs class-oriented sentence and class representations based on keywords for performing a sentence-level confidence-weighted label ensemble in order to estimate a document's initial class distribution. This serves as the target distribution for a multi-head attention network with a class-weighted contrastive loss. This network learns contextualized sentence representations and weights to form document representations that reflect its original document and sentence-level topic diversity. Retaining this heterogeneity allows MEGClass to select the most class-indicative documents to serve as iterative feedback for enhancing the class representations. Finally, these top documents are used to fine-tune a pre-trained text classifier. As demonstrated through extensive experiments on six benchmark datasets, MEGClass outperforms other weakly and extremely weakly supervised methods., Code: https://github.com/pkargupta/MEGClass/

This paper studies a team coordination problem in a graph environment. Specifically, we incorporate "support" action which an agent can take to reduce the cost for its teammate to traverse some edges that have higher costs otherwise. Due to this added feature, the graph traversal is no longer a standard multi-agent path planning problem. To solve this new problem, we propose a novel formulation by posing it as a planning problem in the joint state space: the joint state graph (JSG). Since the edges of JSG implicitly incorporate the support actions taken by the agents, we are able to now optimize the joint actions by solving a standard single-agent path planning problem in JSG. One main drawback of this approach is the curse of dimensionality in both the number of agents and the size of the graph. To improve scalability in graph size, we further propose a hierarchical decomposition method to perform path planning in two levels. We provide complexity analysis as well as a statistical analysis to demonstrate the efficiency of our algorithm.

We develop a general universality technique for establishing metric scaling limits of critical random discrete structures exhibiting mean-field behavior that requires four ingredients: (i) from the barely subcritical regime to the critical window, components merge approximately like the multiplicative coalescent, (ii) asymptotics of the susceptibility functions are the same as that of the Erdos-Renyi random graph, (iii) asymptotic negligibility of the maximal component size and the diameter in the barely subcritical regime, and (iv) macroscopic averaging of distances between vertices in the barely subcritical regime. As an application of the general universality theorem, we establish, under some regularity conditions, the critical percolation scaling limit of graphs that converge, in a suitable topology, to an $L^3$ graphon. In particular, we define a notion of the critical window in this setting. The $L^3$ assumption ensures that the model is in the Erdos-Renyi universality class and that the scaling limit is Brownian. Our results do not assume any specific functional form for the graphon. As a consequence of our results on graphons, we obtain the metric scaling limit for Aldous-Pittel's RGIV model [9] inside the critical window. Our universality principle has applications in a number of other problems including in the study of noise sensitivity of critical random graphs [52]. In [10], we use our universality theorem to establish the metric scaling limit of critical bounded size rules. Our method should yield the critical metric scaling limit of Rucinski and Wormald's random graph process with degree restrictions [56] provided an additional technical condition about the barely subcritical behavior of this model can be proved., 65 pages, 1 figure, the universality principle (Theorem 3.4) from arXiv:1411.3417 has now been included in this paper

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

The combination of immunotherapy and lenvatinib has shown a good response for inoperable hepatocellular carcinoma (HCC) patients. However, a specific marker to predict the response, overall survival (OS) and progression-free survival (PFS) of this combination treatment is lacking. The present work focused on investigating whether subcutaneous adipose tissue (SAT) characteristics on CT could predict the response and survival for HCC patients who receive the combination treatment. This study retrospectively enrolled 100 patients with inoperable HCC who received lenvatinib combined with anti-PD-1 antibody treatment from 2018 to 2022. Fifty-six patients were finally included. The area and density of SAT were measured using unenhanced cross-sectional CT images. The SAT volume index was calculated as the SAT area divided by height squared in meters (cm(2)/m(2)). We classified these patients into two groups according to SAT volume index and density. Twenty-one patients (37.5%) with a low SAT volume index and high density were divided into the high risk group. High risk patients showed a markedly decreased objective response rate (ORR) compared with low risk patients (19.0% versus 54.3%, P = 0.021). The median PFS times were 6.00 and 12.03 months for the high risk and low risk groups, respectively (hazard ratio (HR) = 2.296, P = 0.035). High risk patients with Barcelona Clinic Liver Cancer (BCLC) stage-C had a markedly decreased OS of compared to low risk patients (HR = 4.272, P = 0.01). Patients with low SAT volume index and high density were found to have less opportunity to benefit from this combination therapy.

A new method integrating techniques of copula and interval estimation to estimate multi-source water supply was proposed. Using the copula theory, joint probability distributions of multiple water sources were constructed for the estimation of water supply. In addition, the interval estimation was used to obtain the interval of water supply under uncertainty for the formulation of water-diversion strategies and the efficient allocation of water resources. This method can give an in-depth investigation on correlations and synchronous–asynchronous characteristics of runoff variations for multiple water sources, thus solving the uncertainty problem of water supply. To demonstrate its applicability, the method was applied to a case study in the Xiong’an New Area, a future metropolis in North China. The results showed that log-normal distributions for the marginal distributions of source 2 (i.e., the Water Diversion Project from the Yellow River to Baiyangdian Lake) and source 3 (i.e., the South-to-North Water Diversion Project) were feasible. The combined channel source, composed of source 2 and source 3, provided [5.20, 12.10] × 108 m3, and reservoir source provided [0.76, 3.60] × 108 m³ of water resources to the Xiong’an New Area per year. Furthermore, without the implementation of multi-source combined water supply pattern in the Xiong’an New Area, there would be a large water supply deficit. This research can provide effective practical suggestions and guidance on water-resource planning and management.

Battery storage is essential to enhance the flexibility and reliability of electric power systems by providing auxiliary services and load shifting. Storage owners typically gains incentives from quick responses to auxiliary service prices, but frequent charging and discharging also reduce its lifetime. Therefore, this paper embeds the battery degradation cost into the operation simulation to avoid overestimated profits caused by an aggressive bidding strategy. Based on an operation simulation model, this paper conducts the economic viability analysis of whole life cycle using the internal rate of return(IRR). A clustering method and a typical day method are developed to reduce the huge computational burdens in the life-cycle simulation of battery storage. Our models and algorithms are validated by the case study of two mainstream technology routes currently: lithium nickel cobalt manganese oxide (NCM) batteries and lithium iron phosphate (LFP) batteries. Then a sensitivity analysis is presented to identify the critical factors that boost battery storage in the future. We evaluate the IRR results of different types of battery storage to provide guidance for investment portfolio., Comment: 17 pages, accepted by JPS

The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, although a CNNs branch can capture the local image features using vanilla convolution, it cannot achieve adaptive feature learning. Second, although a Transformer branch can capture the global features, it ignores the channel and cross-dimensional self-attention, resulting in a low segmentation accuracy on complex-content images. To address these challenges, we propose a novel hybrid architecture of convolutional neural networks hand in hand with vision Transformers (CiT-Net) for medical image segmentation. Our network has two advantages. First, we design a dynamic deformable convolution and apply it to the CNNs branch, which overcomes the weak feature extraction ability due to fixed-size convolution kernels and the stiff design of sharing kernel parameters among different inputs. Second, we design a shifted-window adaptive complementary attention module and a compact convolutional projection. We apply them to the Transformer branch to learn the cross-dimensional long-term dependency for medical images. Experimental results show that our CiT-Net provides better medical image segmentation results than popular SOTA methods. Besides, our CiT-Net requires lower parameters and less computational costs and does not rely on pre-training. The code is publicly available at https://github.com/SR0920/CiT-Net.

Aim of the Review. This study aims to summarize the therapeutic effect of luteolin on the pathogenesis of viral pneumonia, explore its absorption and metabolism in the human body, evaluate the possibility of luteolin as a drug to treat viral pneumonia, and provide a reference for future research. Materials and Methods. We searched MEDLINE/PubMed, Web of Science, China National Knowledge Infrastructure, and Google Scholar and collected research on luteolin in the treatment of viral pneumonia and related diseases since 2003. Then, we summarized the efficacy and potential of luteolin in directly inhibiting viral activity, limiting inflammatory storms, reducing pulmonary inflammation, and treating pneumonia complications. Results and Conclusion. Luteolin has the potential to treat viral pneumonia in multiple ways. Luteolin has a direct inhibitory effect on coronavirus, influenza virus, and respiratory syncytial virus. Luteolin can alleviate the inflammatory factor storm induced by multiple factors by inhibiting the function of macrophages or mast cells. Luteolin can reduce pulmonary inflammation, pulmonary edema, or pulmonary fibrosis induced by multiple factors. In addition, viral pneumonia may cause multisystem complications, while luteolin has extensive protective effects on the gastrointestinal system, cardiovascular system, and nervous system. However, due to the first-pass metabolism mediated by phase II enzymes, the bioavailability of oral luteolin is low. The bioavailability of luteolin can be improved, and its potential value can be further developed by changing the dosage form or route of administration.

PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Recent experimental studies have led to the suggestion that short-range correlations may be a major contributor to the nuclear EMC effect. This hypothesis requires that the structure function for nucleons involved in short-range correlations should be heavily suppressed compared to that of a free nucleon. Based on calculations performed within an AdS/QCD motivated, light-front quark-diquark model, we find that this large suppression of the nucleon structure function leads to a strong suppression of the nucleon elastic form factors., Comment: 6 pages, 6 figures, 2 tables

We propose a new method called decoupling representation to represent Pauli operators as vectors over GF(2), based on which we propose partially decoupled belief propagation and fully decoupled belief propagation decoding algorithm for quantum low density parity-check codes. Under the assumption that there is no measurement error, compared with traditional belief propagation algorithm in symplectic representation over GF(2), within the same number of iterations, the decoding accuracy of partially decoupled belief propagation and fully decoupled belief propagation algorithm is significantly improved in pure Y noise channel and depolarizing noise channel, which supports that decoding algorithms of quantum error correcting codes might have better performance in decoupling representation than in symplectic representation. The impressive performance of fully decoupled belief propagation algorithm might promote the realization of quantum error correcting codes in engineering.

Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popular in cooperative multi-agent reinforcement learning (MARL). However, value-decomposition methods still have the problems of tremendous sample consumption for training and lack of active exploration. In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay. The scalable training mechanism asynchronously decouples strategy learning with environmental interaction, so as to accelerate sample generation in a MapReduce manner. For the problem of lack of exploration, an intrinsic reward design and explorative experience replay are proposed, so as to enhance exploration to produce diverse samples and filter non-novel samples, respectively. Empirically, our method achieves the best performance on almost all maps compared to other popular algorithms in a set of StarCraft II micromanagement games. A data-efficiency experiment also shows the acceleration of SVDE for sample collection and policy convergence, and we demonstrate the effectiveness of factors in SVDE through a set of ablation experiments., Comment: 13 pages, 9 figures

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence o f the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes.

Side-Channel Analysis (SCA) aims to extract secrets from cryptographic systems by exploiting the physical leakage acquired from implementations of cryptographic algorithms. With the development of Deep Learning (DL), a new type of SCA called Deep Learning Side-Channel Analysis (DLSCA) utilizes the advantages of DL techniques in data features processing to break cryptographic systems more efficiently. Recent works show that DLSCA could be applied not only to extract the secrets from implementations of cryptographic algorithms but also to reverse the architecture of neural networks from physical devices. In short, reverse engineering of neural networks aims to recover the parameters and architecture of neural networks by accessing only the input and output of the target model. However, most of previous works have focused on recovering the ratio between weights and biases, which is not threatening enough. This thesis aims to explore to which extent the DLSCA can make reverse engineering on neural networks more efficient. To achieve this goal, we first implement perceptron networks as the target model on an 8-bit Atmel ATXmega128D4 microcontroller. Then, we use Chipwhisperer Lite to capture power traces from the victim device during the execution of the neural network. Our experimental results first show that it is feasible to distinguish different activation functions directly from the captured traces. Afterward, we select the multiplication of the weights and inputs as the attack point, and use Test Vector Leakage Assessment (TVLA) method to detect the location of leakage intervals. Next, we train a DL classifier by using the captured traces and use the trained model to classify the actual weight of the target neural network. We show that it is feasible to reverse a weight by using less than 1000 traces on average. For some specific weights, our DL classifier is able to recover them by using only one trace. Sidokanalanalys (SCA) syftar till att extrahera hemligheter från kryptografiska system genom att utnyttja det fysiska läckaget från implementeringar av kryptografiska algoritmer. Med utvecklingen av Djup lärning (DL), använder en ny typ av SCA kallad DLSCA fördelarna med DL-tekniker i datafunktionsbehandling för att bryta kryptografiska system mer effektivt. Nya arbeten visar att DLSCA inte bara kan användas för att extrahera hemligheterna från implementeringar av kryptografiska algoritmer utan också för att vända arkitekturen för neurala nätverk från fysiska enheter. Kort sagt, omvänd konstruktion av neurala nätverk syftar till att återställa parametrarna och arkitekturen för neurala nätverk genom att endast komma åt ingången och utdata från målmodellen. Men de flesta tidigare arbeten har fokuserat på att återställa förhållandet mellan vikter och fördomar, vilket inte är tillräckligt hotfullt. Denna avhandling syftar till att undersöka i vilken utsträckning DLSCA kan göra reverse engineering på neurala nätverk mer effektiv. För att uppnå detta mål implementerar vi först perceptronnätverk som målmodell på en 8-bitars Atmel ATXmega128D4 mikrokontroller. Sedan använder vi Chipwhisperer Lite för att fånga kraftspår från offrets enhet under exekveringen av det neurala nätverket. Våra experimentella resultat visar först att det är möjligt att särskilja olika aktiveringsfunktioner direkt från de fångade spåren. Efteråt väljer vi multiplikationen av vikterna och ingångarna som attackpunkt och använder TVLA-metoden för att upptäcka var läckageintervallen finns. Därefter tränar vi en DL-klassificerare genom att använda de fångade spåren och använder den tränade modellen för att klassificera den faktiska vikten av det neurala målnätverket. Vi visar att det är möjligt att vända en vikt genom att använda mindre än 1000 spår i genomsnitt. För vissa specifika vikter kan vår klassificerare DL återställa dem genom att bara använda ett spår.

Many recent practical and theoretical breakthroughs focus on adversarial team multi-player games (ATMGs) in ex ante correlation scenarios. In this setting, team members are allowed to coordinate their strategies only before the game starts. Although there existing algorithms for solving extensive-form ATMGs, the size of the game tree generated by the previous algorithms grows exponentially with the number of players. Therefore, how to deal with large-scale zero-sum extensive-form ATMGs problems close to the real world is still a significant challenge. In this paper, we propose a generic multi-player transformation algorithm, which can transform any multi-player game tree satisfying the definition of AMTGs into a 2-player game tree, such that finding a team-maxmin equilibrium with correlation (TMECor) in large-scale ATMGs can be transformed into solving NE in 2-player games. To achieve this goal, we first introduce a new structure named private information pre-branch, which consists of a temporary chance node and coordinator nodes and aims to make decisions for all potential private information on behalf of the team members. We also show theoretically that NE in the transformed 2-player game is equivalent TMECor in the original multi-player game. This work significantly reduces the growth of action space and nodes from exponential to constant level. This enables our work to outperform all the previous state-of-the-art algorithms in finding a TMECor, with 182.89, 168.47, 694.44, and 233.98 significant improvements in the different Kuhn Poker and Leduc Poker cases (21K3, 21K4, 21K6 and 21L33). In addition, this work first practically solves the ATMGs in a 5-player case which cannot be conducted by existing algorithms., Comment: 9 pages, 5 figures, NIPS 2023

Additional file 1: Figure S1. a-b Venn diagram of target genes related to pyrotinib-induced diarrhea treated with SBP(a). Intertarget interaction network of SBP in the treatment of pyrotinib-induced diarrhea(b). Figure S2. (a, b) GO enrichment analysis was performed on potential targets of main active ingredients from SBP against diarrhea. BP, biological processes; CC, cellular component.

The supercritical carbon dioxide (S-CO2) power cycle is widely applicable to the utilization of multigrade heat sources due to its high efficiency and compact structure, in which the turbine is critical to the performance of the system. In this study, the operation pattern and output performance of an S-CO2 axial turbine with a design power scale of 10 kW power are investigated by experiments and numerical simulations. The loss mechanisms and distributions are analyzed using numerical simulations and loss models. The results show that the turbine output power and efficiency are at 9.66 kW and 36.6% at the designed condition and the output performance depends on the pressure ratio and rational speed. In addition, the flow field distribution reveals that the flow separation phenomenon occurs leading to loss, with the tip clearance loss being the most significant. Furthermore, the loss model validated in this paper is proven to be competent enough to fulfill the prediction of the loss distribution under different operational conditions, which could be very promising in the design of an S-CO2 axial turbine.

Supplemental material, sj-docx-1-taj-10.1177_20406223231181490 for Predicting cardiovascular risk in a Chinese primary Sjögren’s syndrome population: development and assessment of a predictive nomogram by Jincheng Pu, Jiamin Song, Shengnan Pan, Shuqi Zhuang, Ronglin Gao, Yuanyuan Liang, Zhenzhen Wu, Yanqing Wang, Youwei Zhang, Lufei Yang, Fang Han, Huihong Wu, Jianping Tang and Xuan Wang in Therapeutic Advances in Chronic Disease

Additional file 1.

Supplemental material, sj-docx-1-taj-10.1177_20406223231181490 for Predicting cardiovascular risk in a Chinese primary Sjögren’s syndrome population: development and assessment of a predictive nomogram by Jincheng Pu, Jiamin Song, Shengnan Pan, Shuqi Zhuang, Ronglin Gao, Yuanyuan Liang, Zhenzhen Wu, Yanqing Wang, Youwei Zhang, Lufei Yang, Fang Han, Huihong Wu, Jianping Tang and Xuan Wang in Therapeutic Advances in Chronic Disease

In this study, barium calcium zirconate titanate nanoparticles and nanofibers (denoted as BZT-BCT NPs and BZT-BCT NFs, respectively) were prepared by the sol–gel method and electrospinning, respectively. Under different temperatures and frequencies, the dielectric spectra of polyvinylidene fluoride (PVDF), BZT-BCT NPs, and BZT-BCT NFs composites were measured. On the basis of the experimental data, the polarisation activation energies of the polymer matrix interfacial polarisation and the dipole turn polarisation were calculated, and the basic polarisation characteristic parameters of the polymer matrix materials and fillers were obtained. Moreover, the effects of the filling phase and filling ratio on the dielectric properties of the composites were studied through applying BZT-BCT NPs and BZT-BCT NFs as the filling phases of the PVDF matrix and PVDF matrix composites. Furthermore, the double-layer low-density polyethylene (LDPE)/PVDF composites as well as the LDPE/PVDF composites uniformly mixed at a volume fraction of 1:1 were prepared, and the interfacial polarisation behaviours of the two materials were studied by dielectric spectroscopy to establish an effective analytical method so as to characterize interfacial polarisation established. The experimental results revealed as follows: interfacial polarisation was a significant mechanism of the polarisation behaviour of the composite materials; the fillers with different shape factors had varying effects on the dielectric constant of composites; meanwhile, the dielectric constant of the composite conformed to the predictions of the effective medium theory model.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Este trabalho tem como objetivo identificar e analisar a presença de metáforas no discurso jornalístico português, especialmente no domínio da Economia, pois os jornais económicos acompanham de perto a situação e as políticas de desenvolvimento económico mundiais, comunicam e interpretam atempadamente informações e dados económicos. Tendo como base investigação existente, esta pesquisa baseia-se numa combinação de análise qualitativa e análise quantitativa, usando o quadro conceptual de metáforas de Lakoff e Johnson, para explorar o uso de metáfora em textos jornalísticos, especificamente nos títulos de textos publicados em Portugal no Jornal Económico, entre abril e dezembro de 2020. Através da recolha e análise de dados, percebeu-se que as metáforas orientacionais e as metáforas estruturais têm um maior proporção no corpus, uma parte significativa das entradas metafóricas relaciona-se com a pandemia COVID-19. O estudo permitiu confirmar que o uso de metáfora é frequente nos títulos de textos da área da Economia, contribuindo para tornar menos complexas algumas das mensagens veiculadas. This work aims to identify and analyze the presence of metaphors in Portuguese journalistic discourse, especially in the field of economics, as economic newspapers closely monitor the world economic development situation and policies, communicate and interpret timely economic information and data. Following existing bibliography, this research is based on a combination of qualitative analysis and quantitative analysis, using Lakoff and Johnson's (1980) conceptual framework of metaphors, to explore the use of metaphor in journalistic texts, specifically in the titles of texts published in Portugal in Jornal Económico between April and December 2020. Through data collection and analysis, it was perceived that orientational metaphors and structural metaphors have a higher proportion in the corpus, a significant part of the metaphorical entries relates to the pandemic COVID-19. The study allowed confirming that the use of metaphor is frequent in the titles of texts in the Economics area, contributing to make some of the messages conveyed less complex. Mestrado em Português Língua Estrangeira/Língua Segunda

Gastric, liver, and colorectal cancers belong to gastrointestinal (GI) cancers, one of the most threatening diseases in the world. The tonics class in Chinese medicines plays a critical role in antigastrointestinal cancer as adjuvants. However, it is a challenge to study the effects and underlying mechanisms of tonics due to their multiple components and multiple targets; OMICs were introduced to facilitate the investigation of the complex mixture of tonics. In this review, the online databases PubMed, ProQuest, Web of Knowledge, China National Knowledge Infrastructure (CNKI), Chongqing VIP, and Wanfang were retrieved from 1 January 2011 to 31 May 2022, in an aim to summarize and discuss the research progress of the effects and, especially, the underlying mechanisms of tonics for antigastrointestinal cancers via OMICs. The results showed that through the combination of OMICs and other technologies, tonics have been used for gastrointestinal cancer by targeting cancer hallmarks, enhancing body resistance to carcinogenesis, enhancing therapeutic effects, and/or decreasing side effects. In conclusion, tonics may play a promising role in gastric, liver, and colorectal cancers as adjuvants and can be well investigated via the combination of OMICs and other technologies, which deserves further study.

Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis; however, the requirement of accurate camera poses limits its application. Despite analysis-by-synthesis extensions for jointly learning neural 3D representations and registering camera frames exist, they are susceptible to suboptimal solutions if poorly initialized. We propose L2G-NeRF, a Local-to-Global registration method for bundle-adjusting Neural Radiance Fields: first, a pixel-wise flexible alignment, followed by a frame-wise constrained parametric alignment. Pixel-wise local alignment is learned in an unsupervised way via a deep network which optimizes photometric reconstruction errors. Frame-wise global alignment is performed using differentiable parameter estimation solvers on the pixel-wise correspondences to find a global transformation. Experiments on synthetic and real-world data show that our method outperforms the current state-of-the-art in terms of high-fidelity reconstruction and resolving large camera pose misalignment. Our module is an easy-to-use plugin that can be applied to NeRF variants and other neural field applications. The Code and supplementary materials are available at https://rover-xingyu.github.io/L2G-NeRF/., Accepted to CVPR 2023