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Correction to: Nature Human Behaviour https://doi.org/10.1038/s41562-021-01173-x, published online 2 August 2021.In the version of this article initially published, the following authors were omitted from the author list and the Author contributionssection for “investigation” and “writing and editing”: Nandor Hajdu (Institute of Psychology, ELTE Eötvös Loránd University, Budapest,Hungary), Jordane Boudesseul (Facultad de Psicología, Instituto de Investigación Científica, Universidad de Lima, Lima, Perú), RafałMuda (Faculty of Economics, Maria Curie-Sklodowska University, Lublin, Poland) and Sandersan Onie (Black Dog Institute, UNSWSydney, Sydney, Australia & Emotional Health for All Foundation, Jakarta, Indonesia). In addition, Saeideh FatahModares’ name wasoriginally misspelled as Saiedeh FatahModarres in the author list. Further, affiliations have been corrected for Maria Terskova (NationalResearch University Higher School of Economics, Moscow, Russia), Susana Ruiz Fernandez (FOM University of Applied Sciences,Essen; Leibniz-Institut fur Wissensmedien, Tubingen, and LEAD Research Network, Eberhard Karls University, Tubingen, Germany),Hendrik Godbersen (FOM University of Applied Sciences, Essen, Germany), Gulnaz Anjum (Department of Psychology, Simon FraserUniversity, Burnaby, Canada, and Department of Economics & Social Sciences, Institute of Business Administration, Karachi, Pakistan).

The advantages and disadvantages of the lithium-rich cathode material are outstanding. The advantages are large specific capacity, and the disadvantages are low conductivity and fast capacity attenuation. After more than a decade of research, it should be said that these two problems have been better resolved. But a trickier problem is that, as the cycle progresses, the discharge potential of the material gradually decreases. In many papers, this question has been deliberately or unintentionally avoided. The problem of potential attenuation is ultimately the problem of material structure stability. Herein, we develop a Ce and Sn co-doped cathode material with a stable structure by simple co-lithiation method. The electrochemical test results show that the Li1.2[Mn0.525Ce0.012Sn0.003Ni0.13Co0.13]O2 (LMCSO-2) has good cycle stability, and after 100 cycles, the capacity retention rate is 90.21%, while the original material is only 71.25%. Moreover, the voltage decay is reduced from 0.5246 to 0.3078 V. This result is attributed to the synergistic effect of Ce and Sn is beneficial to the stabilization of the lattice structure, thereby reducing the Li activation energy and reducing the polarization during the redox reaction. In addition, a small amount of Ce and Sn can diffuse into the bulk phase during the co-lithiation process, thereby increasing the interplanar spacing and promoting Li+ migration, significantly increasing the Li+ diffusion rate.

Sodium-ion batteries (SIBs) have been considered to be an appealing alternative as alternatives for Lithium-ion batteries (LIBs) on account of abundant resources and low prices. Different cathode materials have been extensively studied, especially layered oxides (P2 or O3). However, it is crucial to design an appropriate cathode material for SIBs with excellent comprehensive performance. Herein, a biphasic cathode material with P2 and O3 structures is successfully synthesized by Sn substitution in Na0.67Ni0.33Mn0.67O2, which is clearly proved by the XRD and HRTEM results. And Fullprof program refinement is used to determine the optimal phase ratio of P2 and O3. Besides, the XRD tests at different voltages illustrate that the cathode material has good reversibility and structural stability during charge and discharge. Due to the synergistic effect of P2 and O3 phase, when the phase ratio is about 1:1, the biphasic material P2/O3–Na0.67Ni0.33Mn0.57Sn0.1O2 exhibits the most outstanding electrochemical performance. The P2/O3–Na0.67Ni0.33Mn0.57Sn0.1O2 cathode delivers a high reversible initial capacity of 155.2 mA h g−1 during the voltage range from 2.0V up to 4.3V with an excellent initial Coulombic efficiency of 98.95%, as well as the satisfactory rate performances and cycling performance. GITT testing also demonstrates the DNa+ of biphasic material is more stable and conducive to the diffusion of sodium ions. These results will pave a significant way for designing cathode material for SIBs with different structure by the substitution of transition metal elements.

Room temperature sodium-sulfur battery has special research value due to the low cost of sulfur resource and its high specific capacity. However, the cathode material of the room temperature sodium-sulfur battery mainly combines carbon materials and elemental sulfur now, and the utilization of the sulfur is low. Herein, a two-dimensional layered material Ti3C2Tx was prepared. The cathode material of Ti3C2Tx@S was prepared by a simple melting method at 155 °C, and the mass percentage of the sublimed sulfur was 55%. Then, the valence states of Ti and C were characterized by XPS after loading the sublimed sulfur. The results indicated that a part of Ti3+ turned into the Ti4+, and some C–S bonds were formed. Additionally, the electrochemical properties of Ti3C2Tx@S were studied in sodium-ion battery under ambient conditions. The battery exhibited excellent rates performance, the first discharge specific capacity was 447.0 mAh g−1, and a specific capacity of 120.0 mAh g−1 was maintained at the current density of 1000 mA g−1. In addition, Ti3C2Tx@S demonstrated excellent cycle stability in room temperature sodium-sulfur battery. In the case of a current density of 100 mA g−1, the discharge specific capacity was maintained at 150.0 mAh g−1 after 300 cycles, and the capacity retention rate was 80% from the second cycle. For the electrode, the specific capacity still remained 107.7 mAh g−1 after 150 cycles.

This paper is concerned with the distributed optimization problem for a class of minimum-phase nonlinear multiagent systems with second relative degree. The target is to minimize a cost function composed of a group of convex local functions of the outputs in a cooperative manner. Novel state feedback control laws are first proposed and the consensus subject to the optimization constraint is achieved when the communication graph is directed. By replacing the derivatives of the outputs in the state feedback control laws with their estimations, the output feedback control laws are constructed and proved to achieve exponential consensus under the undirected graph. The effectiveness of the proposed control laws is validated by some simulations.

Wind energy is attracting increasing attention with its sustainability and cleanliness. However, owing to the volatility and intermittency of wind speed, it is challenging to establish a scientific and reliable forecasting system. Most research has mainly been based on simple data preprocessing, single objective optimization, and point prediction, which may lead to poor forecasting performance. Hence, in this study, an innovative wind speed forecasting system is developed that incorporates effective data preprocessing and a novel algorithm. In order to alleviate the complexity and chaos of a wind speed series, a fuzzy data preprocessing scheme is designed based on “decomposition and ensemble” and a fuzzy time series. Following this, a multi-objective imperialist competitive algorithm (MOICA) is proposed and applied for optimizing an extreme learning machine (ELM), and a corresponding hybrid predictor MOICA-ELM is conducted for wind speed forecasting. For further investigation the uncertainty of wind speed, both point and interval forecasting are employed in this system. Simulation results on four wind speed datasets collected from two wind farms in China are in good accordance with the empirical data with multiple criterion and scientific evaluation; these results and show a good performance of the proposed system in terms of accuracy and stability.

A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.

Brain-derived neurotrophic factor (BDNF) may be related to the pathophysiology of schizophrenia. This study aims to examine the relation between plasma BDNF levels and the cognition of patients with schizophrenia. We recruited 31 patients with chronic schizophrenia, 34 first-episode patients, and 35 healthy control subjects. We examined the MATRICS Consensus Cognitive Battery (MCCB) and the plasma BDNF levels in all groups. The schizophrenic symptoms were assessed using the positive and negative syndrome scale. The BDNF levels of schizophrenic patients were remarkably lower than those of the controls. The cognitive MCCB global composite scores and part index scores of schizophrenic patients were remarkably lower than those of the controls. Moreover, remarkable correlations were observed between BDNF levels and partial cognitive dimensions, such as visual learning, memory, and processing speed. Therefore, BDNF may be involved in the pathophysiology and cognitive impairment of schizophrenia.

Aiming at the problem of low diagnostic accuracy of fault diagnosis models due to changes in actual operating conditions, a novel fault diagnosis method based on transfer learning considering nearest neighbor feature constraints is proposed. First, nearest neighbor samples are considered to measure data features. In addition, a nearest neighbor feature constraint strategy is designed to improve the feature extraction performance of the network. Second, a multiple-alignment strategy of nearest neighbor samples is proposed to enhance the domain adaptation performance of the network model utilizing multiple alignments. Then, a loss function dynamic weight strategy is used to improve the convergence of the loss function during model training. Finally, the experimental verification is carried out on the public data set of the Western Reserve University and the private data set. The experimental results show that the proposed method exhibits superior transfer performance with reliability and stability compared to the existing methods.

Metastasis is the major cause of cancer-related mortalities. A tumor-supportive microenvironment, also known as the premetastatic niche at secondary tumor sites, plays a crucial role in metastasis. Remodeling of the extracellular matrix (ECM) is essential for premetastatic niche formation, especially for circulating tumor cell colonization. However, the underlying molecular mechanism that contributes to this effect remains unclear. Here, we developed a lung metastasis model with 4T1 breast cancer cells and found that the metastasis critically depended on the early recruitment of macrophages to the lung. Disruption of macrophage recruitment reduced fibroblast activation and lung metastasis. Furthermore, we identified the secreted protein S100A4, which is produced by M2 macrophages and participates in fibroblast activation and ECM protein deposition via the ERK signaling pathway. Collectively, these results indicate that recruiting S100A4-expressing inflammatory macrophages plays a vital role in ECM remodeling in the premetastatic niche and may act as a potential therapeutic target for breast cancer lung metastasis.

Non-alcoholic fatty liver disease (NAFLD) is rapidly being recognized as the leading cause of chronic liver disease worldwide. Men1, encoding protein of menin, is a key causative gene of multiple endocrine neoplasia type 1 syndrome including pancreatic tumor. It is known that insulin that secretes by endocrine tissue pancreatic islets plays a critical role in hepatic metabolism. Mouse model of hemizygous deletion of Men1 was shown to have severe hepatic metabolism disorders. However, the molecular function of menin on lipid deposition in hepatocytes needs to be further studied. Transcriptome sequencing does show that expression suppression of Men1 in mouse hepatocytes widely affect signaling pathways involved in hepatic metabolism, such as fatty acid metabolism, insulin response, glucose metabolism and inflammation. Further molecular studies indicates that menin overexpression inhibits expressions of the fat synthesis genes Srebp-1c, Fas, and Acc1, the fat differentiation genes Pparγ1 and Pparγ2, and the fat transport gene Cd36, thereby inhibiting the fat accumulation in hepatocytes. The biological process of menin regulating hepatic lipid metabolism was accomplished by interacting with the transcription factor FoxO1, which is also found to be critical for lipid metabolism. Moreover, menin responds to insulin in hepatocytes and mediates its regulatory effect on hepatic metabolism. Our findings suggest that menin is a crucial mediation factor in regulating the hepatic fat deposition, suggesting it could be a potential important therapeutic target for NAFLD.

Background Preeclampsia (PE) is a complex pregnancy-related disease that endangers the safety of maternal and fetal. The purpose of this study is to reveal the pathogenesis of preeclampsia and discover new predictors from the perspective of peptidomics. The umbilical cord blood of PE and control group was analyzed by peptidomics. A peptide named Regulation of Proliferation Process in Preeclampsia (ROPPIP) was screened out to explore its role in the proliferation, migration and apoptosis of trophoblast cells in preeclampsia. Methods We compared and analyzed the umbilical cord blood of patients with PE and normal pregnant women using liquid chromatography-tandem mass spectrometry (LC-MS). hTR-8/Svneo cells cultured in vitro were divided into ROPPIP group and a disordered peptide group as control. Cell Counting Kit-8 (CCK-8) assay, flow cytometry, Transwell chamber assays and western blot analysis were performed to detect cell proliferation, invasion, migration and apoptosis, in addition to the expression of Matrix metalloproteinase-2 (MMP2), nuclear associated antigen Ki67, B-cell lymphoma-2 (Bcl2), Caspase 3, and β-actin protein. Results We identified 133 differential peptides. Of these, 51 were up-regulated while 82 were down-regulated. the polypeptide SFGVRMATASPTDGNV with low differential expression in the serum of PE patients was selected for the study, we named the polypeptide as Regulation of Proliferation Process in PE (ROPPIP). The experiment shows that ROPPIP can up-regulate the expression levels of MMP2, Ki67, and Bcl2 in HTR-8/Svneo cells, down-regulate the expression of caspase-3, promote the proliferation and migration of HTR-8/Svneo cells and inhibit the apoptosis induced by cisplatin, the activation of the phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) signaling pathway may be associated with the function of ROPPIP. Conclusions ROPPIP promotes HTR-8/Svneo cells migration and proliferation, and inhibits apoptosis, by regulating the activation of the PI3K/AKT/mTOR signaling pathway.

ObjectiveWe aimed to develop an early warning system for real-time sepsis prediction in the ICU by equipping with interpretation analysis and transfer learning tools to improve the feasibility to deploy the sepsis prediction system, particularly to target cohorts.DesignRetrospective and observational study.SettingMedical Information Mart for Intensive Care (MIMIC) dataset, the private Historical Database of local Ruijin Hospital (HDRJH), and data collected from Ruijin real-world study.Patients6891 patients from MIMIC dataset and 453 patients from HDRJH for model development and 67 cases from Ruijin real-world data for model evaluation.InterventionsNone.Measurements and Main ResultsLight Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on MIMIC dataset and then finetuned on HDRJH using transfer learning technique. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC were 0.98–0.98 and 0.95–0.96 respectively on MIMIC dataset, and, in comparison, 0.82–0.86 and 0.84–0.87 respectively on HDRJH, from 1–5h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94–0.94 on HDRJH and to 0.86–0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the Shapley additive explanation (SHAP) analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable.ConclusionsOur machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, effectively ameliorating the model performance for external validation. SHAP analysis may illuminate the importance of optimizing antibiotic use and restricting fluid management.Trial registrationNCT05088850 (retrospectively registered).Key PointsQuestionWe aimed to develop an early warning system for real-time sepsis prediction in the ICU and to improve the feasibility to deploy the system to target cohorts.FindingsTransfer learning technique effectively enhanced the AUCs for LightGBM and MLP models on the target cohort, HDRJH, from 0.82–0.86 and 0.84–0.87 to 0.93-0.94 and 0.92-0.93 for 1-5 hour preceding. Additionally, SHAP analysis illuminated the importance of optimizing antibiotic use and restricting fluid management.MeaningTransfer learning can improve the feasibility to deploy the prediction model to the target cohort, and SHAP analysis made the prediction model interpretable.

Organizations often try to stimulate creative problem-solving by inducing competition among group members. This facilitative effect of intragroup competition lies in group members’ enhanced motivation to outperform the others. Previous research, however, has mainly focused on idea generation, thereby overlooking the subsequent idea selection stage. In fact, groups are poor at selecting both original and feasible ideas (i.e., creative), which may be further exacerbated when group members compete against each other. Furthermore, while originality and feasibility are two key factors of creativity, people seem to have difficulty taking both into account. The current research investigated how intragroup competition, through incorporating a rewarding scheme, influences group idea generation and selection, and whether explicit instructions of focusing on either originality or feasibility (performance criteria) could steer groups’ focus in the intended direction. An experimental study was conducted with 78 three-person groups. Results showed that overall, neither intragroup competition nor performance criteria influenced groups’ generation and selection performance. Nevertheless, some interesting findings emerged - Originality and feasibility were indeed inversely correlated; idea quality at the generation stage predicted the idea quality at the selection stage; furthermore, certain group processes during the group task might have indirectly linked the competition-selection performance relationship. Study limitations, theoretical and practical implications were discussed.

The measurement of creativity remains unsettled. While our understanding of creativity's current operationalization is limited, the present research addresses this issue via two novel computerized techniques. We first applied bibliometric mapping to creativity measurement literature and identified those major measurement approaches. Results were aligned with previous narrative reviews: there are indeed four major creativity measurement approaches, namely, person-, process-, product-, and press-focused approaches. Moreover, mapping and clustering displayed a trend from the traditional process-focused approach toward the product-based approach in recent years, pointing to a shift in creativity’s conceptualization. Secondly, we applied text analysis to a pool of empirical creativity articles published in the past decade (also gathered via a systematic search protocol) to see how creativity was operationalized. Results showed that product-based was the most common approach, followed by the process- and person-centered approaches. Furthermore, a notable amount of work combined multiple approaches to measuring creativity and incorporated both subjective and objective measures, suggesting that the measurement of creativity is becoming multi-faced. Besides contributing to research on creativity, we also demonstrated that computerized techniques could elaborate on the scientific value of narrative reviews.

Antipsychotic medication is the primary treatment for schizophrenia, which is effective on ameliorating positive symptoms and can reduce the risk of recurrence, but it has limited efficacy for negative symptoms and cognitive dysfunction. The negative symptoms and cognitive dysfunction seriously affects the life quality and social function for the patients with schizophrenia. Currently, there is plenty evidence that antipsychotic drugs combined with adjuvant therapy drugs can effectively improve the negative symptoms and cognitive dysfunction. These drugs include anti-oxidants, nicotinic acetylcholine receptors and neuro-inflammatory drugs (anti-inflammatory drugs, minocycline), which show potential clinical effects.抗精神病药物是治疗精神分裂症的主要方法，可有效缓解阳性症状和减少复发风险，但对阴性症状和认知功能障碍的临床疗效有限，而阴性症状和认知功能障碍严重影响精神分裂症患者的社会功能和生活质量。目前有较多的研究支持抗精神病药物联合辅助治疗药物可有效改善阴性症状和认知功能障碍。其中，作用于氧化应激、烟碱能乙酰胆碱受体和神经炎症的药物(抗炎药、米诺环素)有潜在的临床疗效。.

International audience; Objectives: Intravenous immunoglobulin (IVIG) is commonly used to treat severe COVID-19, although the clinical outcome of such treatment remains unclear. This study evaluated the effectiveness of IVIG treatment in severe COVID-19 patients. Methods: This retrospective multicentre study evaluated 28-day mortality in severe COVID-19 patients with or without IVIG treatment. Each patient treated with IVIG was matched with one untreated patient. Logistic regression and inverse probability weighting (IPW) were used to control confounding factors. Results: The study included 850 patients (421 IVIG-treated patients and 429 non-IVIG-treated patients). After matching, 406 patients per group remained. No significant difference in 28-day mortality was observed after IPW analysis (average treatment effect (ATE) = 0.008, 95% CI –0.081 to 0.097, p 0.863). There were no significant differences between the IVIG group and non-IVIG group for acute respiratory distress syndrome, diffuse intravascular coagulation, myocardial injury, acute hepatic injury, shock, acute kidney injury, non-invasive mechanical ventilation, invasive mechanical ventilation, continuous renal replacement therapy and extracorporeal membrane oxygenation except for prone position ventilation (ATE = –0.022, 95% CI –0.041 to –0.002, p 0.028). Discussion: IVIG treatment was not associated with significant changes in 28-day mortality in severe COVID-19 patients. The effectiveness of IVIG in treating patients with severe COVID-19 needs to be further investigated through future studies.

In this article, we consider the regulator design problem for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with arbitrary relative degree. The objective is to regulate the output of the nonlinear system to an optimal steady state that solves a constrained optimization problem, without computing the optimal solution in advance. By embedding saddle-point dynamics, both state and output-feedback-based regulators are proposed and the resulting closed-loop systems are modeled in standard singularly perturbed forms. By invoking the singular perturbation analysis, exponential stability is established under some regularity condition. Compared with the existing methods, the proposed regulators can deal with a class of nonlinear systems with uncertainties and arbitrary relative degree. Furthermore, the current results can include some recent works on the distributed optimization problem as special cases. Finally, the effectiveness of the proposed methods is validated through numerical simulations.

This article pays close attention to a distributed optimization problem for multiagent systems subject to exogenous disturbances. A novel distributed model reference adaptive control (D-MRAC) scheme is proposed that no explicit disturbance observer or internal model unit is involved, which not only enhances robustness but also improves transient performance. In contrast to the continuous communication that is often assumed in the existing distributed optimization works, the new method allows for more realistic scenarios in which the agents communicate with each other at discrete-time instants. It is shown by Lyapunov analysis that the concerned distributed optimization problem can be solved by the proposed D-MRAC scheme as long as the communication interval is smaller than a given threshold, which can be calculated by following the steps given in this article. Numerical simulations have shown the effectiveness of the presented method.

Severe cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) strongly hampered the broad clinical applicability of chimeric antigen receptor T cell (CAR-T) therapy. Vascular endothelial activation has been suggested to contribute to the development of CRS and ICANS after CAR-T therapy. However, therapeutic strategies targeting endothelial dysfunction during CAR-T therapy have not been well studied yet. Here, we found that tumor necrosis factor α (TNFα) produced by CAR-T cells upon tumor recognition and interleukin 1β (IL1β) secreted by activated myeloid cells were the main cytokines in inducing endothelial activation. Therefore, we investigated the potential effectiveness of TNFα and IL1β signaling blockade on endothelial activation in CAR-T therapy. The blockade of TNFα and IL1β with adalimumab and anti-IL1β antibody respectively, as well as the application of focal adhesion kinase (FAK) inhibitor, effectively ameliorated endothelial activation induced by CAR-T, tumor cells, and myeloid cells. Moreover, adalimumab and anti-IL1β antibody exerted synergistic effect on the prevention of endothelial activation induced by CAR-T, tumor cells, and myeloid cells. Our results indicate that TNFα and IL1β blockade might have therapeutic potential for the treatment of CAR-T therapy-associated CRS and neurotoxicity.

From the point of view of drug efficacy and safety, pharmacokinetic profiles of both In this work, a sensitive and reliable liquid chromatographic-tandem mass spectrometric method was established for simultaneous determination of sutetinib and N-oxide metabolite (SNO) in human plasma and further applied to a pharmacokinetic study. Analytes were extracted from plasma samples (100 μl) via acetonitrile-induced protein precipitation and separated on a C18 column using ammonium acetate with ammonium hydroxide and acetonitrile as the mobile phase. Positive electrospray ionization was carried out through multiple reaction monitoring with transitions of m/z 440.2 → 367.1 and 446.2 → 367.1 for sutetinib and SNO, respectively. The method was linear within the concentration range of 0.5-100 ng/ml for both analytes. The precision, accuracy, selectivity, recovery and matrix effect of this method all met the requirements of bioanalytical guidance. In addition, a plasma stability assessment demonstrated unexpected results. Sutetinib was prone to form covalent conjugates with plasma albumin in vitro. The degree of covalent binding increased with increasing temperature, resulting in a significant decrease in its plasma concentrations. However, SNO could not easily bind with albumin owing to steric hindrance or electronegativity. Furthermore, sutetinib and SNO remained stable when blood and plasma samples were kept on wet ice. The validated method was successfully employed for the pharmacokinetic evaluation of sutetinib in patients with advanced malignant solid tumors.

Background: Intravenous immunoglobulin (IVIG) is commonly used to treat severe COVID-19, although the clinical outcomes remain unclear. This study evaluated the effectiveness of IVIG treatment for severe COVID-19.Methods: This retrospective multi-center study evaluated 28-day mortality and time for SARS-CoV-2 RNA clearance in severe COVID-19 patients with or without IVIG treatment. Propensity score matching was used to control confounding factors. Logistic regression and competing risk analyses were performed.Results: The study included 850 patients (421 patients received IVIG). No significant differences in 28-day mortality or time for SARS-CoV-2 RNA clearance were observed (p=0.357 and p=0.123, respectively). High-dose of IVIG treatment (>10 g/day) (n=27) was associated with decreased 28-day mortality (OR: 0.33, 95% CI: 0.14–0.77; p=0.011). The IVIG group had prolonged median hospitalization, less shock, and higher incidences of acute respiratory distress syndrome, myocardial injury. Furthermore, IVIG-treated patients were more likely to require non-invasive mechanical ventilation and less likely to require invasive mechanical ventilation.Conclusions: IVIG treatment for severe COVID-19 patients was not associated with significant improvements in 28-day mortality or time for SARS-CoV-2 RNA clearance. However, some improvements in 28-day survival were observed for high-dose IVIG treatment (>10 g/day).

Organizations often try to stimulate creative problem-solving by inducing competition among group members. This facilitative effect of intragroup competition lies in group members’ enhanced motivation to outperform the others. Previous research, however, has mainly focused on idea generation, thereby overlooking the subsequent idea selection stage. In fact, groups are poor at selecting both original and feasible ideas (i.e., creative), which may be further exacerbated when group members compete against each other. Furthermore, while originality and feasibility are two key factors of creativity, people seem to have difficulty taking both into account. The current research investigated how intragroup competition, through incorporating a rewarding scheme, influences group idea generation and selection, and whether explicit instructions of focusing on either originality or feasibility (performance criteria) could steer groups’ focus in the intended direction. An experimental study was conducted with 78 three-person groups. Results showed that overall, neither intragroup competition nor performance criteria influenced groups’ generation and selection performance. Nevertheless, some interesting findings emerged - Originality and feasibility were indeed inversely correlated; idea quality at the generation stage predicted the idea quality at the selection stage; furthermore, certain group processes during the group task might have indirectly linked the competition-selection performance relationship. Study limitations, theoretical and practical implications were discussed.

The paper is concerned with the distributed optimization problem for a class of higher order linear multi-agent systems. The target is to cooperatively optimize a performance function formed by a sum of convex local cost functions. To deal with the higher order dynamic, the integrator backstepping idea is introduced to break the integrator chains. Moreover, the adaptive gain technique is proposed such that the control law design can be performed in a fully distributed way, i.e., without utilizing the information of Laplacian matrix. Furthermore, the proposed scheme is applied to the control law design for the consensus of networked uncertain Euler-Lagrangian systems under optimization constraint. The effectiveness of the proposed methods is validated through some numerical simulations of a group of networked uncertain robot manipulators.

Purpose To compare preplanning and intraoperative planning methods of lung tumor brachytherapy based on postimplant CT images and dosimetric outcomes. Methods and Materials Detailed postimplant evaluations of iodine-125 seed implants were performed in 15 patients using a preplanning technique and in 15 patients treated with an intraoperative planning technique. The implant details, dosimetric parameters, and implant quality indices were compared. Furthermore, the dose to the lung and the incidence of complications were compared. Results The planning target volume was similar in both groups (p = 0.496). The median V100%, V150%, and V200% values (the percent of the planning target volume receiving 100%, 150%, and 200% of the prescription dose) for the intraoperative planning technique were 95.65%, 76.47%, and 59.80%, respectively. The median V100%, V150%, and V200% values for the preplanning methodology were 88.86%, 69.23%, and 28.30%, respectively (p Conclusion These data showed that the intraoperative planning method was superior to the preplanning method for the treatment of lung tumors.

Accurate real-time prediction of urban air quality is one of the most important problems in control and improve ambient air condition globally. Therefore, the modeling and applications of air pollutant forecasting and evaluation has attracted the attention of researchers in recent years. Based on the method of fuzzy mathematical synthetic evaluation, this paper built a dynamic evaluation model for the purpose of mastering the future air quality immediately. A newly proposed computational intelligence optimization algorithm is improved to optimize the least square support vector machine, which can generate rolling forecasts of six air pollutants concentration. The information of future air quality status is built by the fuzzy synthetic assessment model based on entropy weighing method. The results and analysis of air quality monitoring show that accurate and reliable forecast of urban air pollutants concentration are possible and the air quality conditions can be evaluated objectively. Through the simulation design, it proves that the proposed dynamic evaluation model can provide a practical tool for ambient air ambient quality evaluation.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion regulation strategy which modifies how one thinks about a situation. Participants from 87 countries/regions (N = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vs. both control conditions) had consistent effects in reducing negative emotions and increasing positive emotions across different measures. Reconstrual and repurposing had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world to build resilience during the pandemic and beyond.

Primary tumors can secrete many cytokines, inducing tissue damage or microstructural changes in distant organs. The purpose of this study was to investigate changes in texture features in the cerebral tissue of patients with lung cancer without brain metastasis. In this study, 50 patients with lung cancers underwent 3.0-T magnetic resonance imaging (MRI) within 2 weeks of being diagnosed with lung cancer. Texture analysis (TA) was carried out in 8 gray matter areas, including bilateral frontal cortices, parietal cortices, occipital cortices and temporal cortices, as well as 2 areas of bilateral frontoparietal white matter. The same procedure was performed for 57 healthy controls. A total of 32 texture parameters were separately compared between the patients and controls in the different cerebral tissue sites. Texture features among patients based on histological type and clinical stage were also compared. Of the 32 texture parameters, 27 showed significant differences between patients with lung cancer and healthy controls. There were significant differences in cerebral tissue, both gray matter and white matter between patients and controls, especially in several wavelet-based parameters. However, there were no significant differences between tissue at homologous sites in bilateral hemispheres, either in patients or controls. TA detected overt changes in the texture features of cerebral tissue in patients with lung cancer without brain metastasis compared with those of healthy controls. TA may be considered as a novel and adjunctive approach to conventional brain MRI to reveal cerebral tissue changes invisible on MRI alone in patients with lung cancer.

Accurate forecast of wind speed is the first prerequisite to supply high quality power energy to customer in a secure and economic manner. However, traditional point forecast may not be sufficiently reliable and accurate for decision-makers to perform operational strategies purely when the uncertainty level increases. For the sake of quantifying the uncertainty associated with point predictions, it is necessary to conduct interval prediction to provide reliable and accurate wind speed information. In this work, a hybrid model framework based on combinatorial modules was proposed and successfully adopted to construct the prediction intervals of the future wind speed. Feature selection methods are developed to determine the most suitable modes of original time series and the optimal input form of the model, while the optimization forecasting module is applied to model the wind speed series based on the machine learning method and the multi-objective optimization algorithm, then the compromise solution of Pareto front is chosen by “Min-max” method. Finally, the proposed combined model was investigated via the hourly wind speed data from two different periods in Penglai, China. Besides, the study’s experimental results indicated that the prediction intervals generated perform well and are satisfactory in both criterion functions of high coverage and small width through discussion among single-objective models and other multi-objective models (signal pre-processing method comparison included).

Due to the strong electron-withdrawing capability, the rhodanine unit is widely used as the acceptor of the D-π-A organic sensitizer. However, in this unit, the carboxyl group is shut out from the whole conjugation system, which affects the injection of the excited electrons, leading to poor photovoltaic performance. Herein, with the introduction of cyanoacetic acid into the rhodanine segment, two novel indoline sensitizers, CS-22 and CS-24, based on D-π-A construction are designed and synthesized. With this modification, the carboxyl group is conjugated with the whole molecule, leading to sharp enhance of light-harvesting capability of the dye and more important, quite smooth electron injection process. Moreover, the introduced long alkyl chain can also prevent the π-aggregation on TiO2 surface and restrain the charge recombination effectively. Accordingly, using the modified acceptor, the photovoltaic performance is significantly improved: an extremly high PCE of 8.48% is obtained by CS-22 sensitized DSSC, which is the highest value among rhodanine dyes without any co-adsorbent. Our work demonstrates that the introduction of conjugated carboxyl group is greatly benefit for the improvement of rhodanine dyes in photovoltaic performance, providing a powerful strategy for the development of rhodanine based highly efficient D-π-A organic sensitizer in the future.

In the current paper the decentralized output regulation problem of a new class of interconnected uncertain nonlinear systems is considered. A novel decentralized high-gain input driven filter is proposed such that the output feedback based control law can be designed. Moreover, a robust multi-input changing supply function technique is presented such that the stability analysis can be performed by the non-quadratic Lyapunov functions. Therefore, the assumptions on the interconnection terms can be removed. Finally the proposed decentralized control laws are applied to the interconnected mass-spring systems immersed in the liquid and the simulation results illustrate the effectiveness of the proposed control scheme.

Aims Evidence of altered structural and functional connectivity in the frontal-occipital network is associated with cognitive deficits in patients with schizophrenia. However, the altered patterns of functional connectivity strength (FCS) in individuals with ultra-high risk (UHR) for psychosis remain unknown. In this study, whole-brain FCS was assessed to examine the altered patterns of FCS in UHR subjects. Methods A total of 34 UHR subjects and 37 age- and sex-matched healthy controls were enrolled to undergo resting-state functional magnetic resonance imaging. The imaging data were analyzed using the graph theory method. Results Compared with healthy controls, UHR subjects showed significantly decreased FCS in the left middle frontal gyrus and significantly increased FCS in the left calcarine cortex. The FCS values in the left middle frontal gyrus were positively correlated to the scores of the Brief Assessments of Cognitionin Schizophrenia Symbol Coding Test (r = 0.366, P = 0.033) in the UHR subjects. A negative correlation was found between the FCS values in the left calcarine cortex and the scores of the Stroop color-naming test (r = -0.475, P = 0.016) in the UHR subjects. A combination of the FCS values in the 2 brain areas showed an accuracy of 87.32%, a sensitivity of 73.53%, and a specificity of 100% for distinguishing UHR subjects from healthy controls. Conclusions Significantly altered FCS in the frontal-occipital network is observed in the UHR subjects. Furthermore, decreased FCS in the left middle frontal gyrus and increased FCS in the left calcarine have significant correlations with the cognitive measures of the UHR subjects and thus improve our understanding of the underlying pathophysiological mechanisms of schizophrenia. Moreover, a combination of the FCS values in the 2 brain areas can serve as a potential image marker to distinguish UHR subjects from healthy controls.