A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection.

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

Human airway and nasal organoids reveal escalating replicative fitness of SARS-CoV-2 emerging variants.

The high transmissibility of SARS-CoV-2 Omicron subvariants was generally ascribed to immune escape. It remained unclear whether the emerging variants have gradually acquired replicative fitness in human respiratory epithelial cells. We sought to evaluate the replicative fitness of BA.5 and earlier variants in physiologically active respiratory organoids. BA.5 exhibited a dramatically increased replicative capacity and infectivity than B.1.1.529 and an ancestral strain wildtype (WT) in human nasal and airway organoids. BA.5 spike pseudovirus showed a significantly higher entry efficiency than that carrying WT or B.1.1.529 spike. Notably, we observed prominent syncytium formation in BA.5-infected nasal and airway organoids, albeit elusive in WT- and B.1.1.529-infected organoids. BA.5 spike-triggered syncytium formation was verified by lentiviral overexpression of spike in nasal organoids. Moreover, BA.5 replicated modestly in alveolar organoids, with a significantly lower titer than B.1.1.529 and WT. Collectively, the higher entry efficiency and fusogenic activity of BA.5 spike potentiated viral spread through syncytium formation in the human airway epithelium, leading to enhanced replicative fitness and immune evasion, whereas the attenuated replicative capacity of BA.5 in the alveolar organoids may account for its benign clinical manifestation.

Visible-Light-Activated Molecular Machines Kill Fungi by Necrosis Following Mitochondrial Dysfunction and Calcium Overload.

Invasive fungal infections are a growing public health threat. As fungi become increasingly resistant to existing drugs, new antifungals are urgently needed. Here, it is reported that 405-nm-visible-light-activated synthetic molecular machines (MMs) eliminate planktonic and biofilm fungal populations more effectively than conventional antifungals without resistance development. Mechanism-of-action studies show that MMs bind to fungal mitochondrial phospholipids. Upon visible light activation, rapid unidirectional drilling of MMs at ≈3 million cycles per second (MHz) results in mitochondrial dysfunction, calcium overload, and ultimately necrosis. Besides their direct antifungal effect, MMs synergize with conventional antifungals by impairing the activity of energy-dependent efflux pumps. Finally, MMs potentiate standard antifungals both in vivo and in an ex vivo porcine model of onychomycosis, reducing the fungal burden associated with infection.

The Oral Inactivated Porcine Epidemic Diarrhea Virus Presenting in the Intestine Induces Mucosal Immunity in Mice with Alginate-Chitosan Microcapsules.

The porcine epidemic diarrhea virus, PEDV, which causes diarrhea, vomiting and death in piglets, causes huge economic losses. Therefore, understanding how to induce mucosal immune responses in piglets is essential in the mechanism and application against PEDV infection with mucosal immunity. A method of treatment in our research was used to make an oral vaccine that packaged the inactive PEDV with microencapsulation, which consisted of sodium alginate and chitosan, and adapted the condition of the gut in mice. The in vitro release experiment of microcapsules showed that inactive PEDV was not only easily released in saline and acid solutions but also had an excellent storage tolerance, and was suitable for use as an oral vaccine. Interestingly, both experimental groups with different doses of inactive virus enhanced the secretion of specific antibodies in the serum and intestinal mucus, which caused the effective neutralization against PEDV in the Vero cell by both IgG and IgA, respectively. Moreover, the microencapsulation could stimulate the differentiation of CD11b+ and CD11c+ dendritic cells, which means that the microencapsulation was also identified as an oral adjuvant to help phagocytosis of dendritic cells in mice. Flow cytometry revealed that the B220+ and CD23+ of the B cells could significantly increase antibody production with the stimulation from the antigens' PEDV groups, and the microencapsulation could also increase the cell viability of B cells, stimulating the secretion of antibodies such as IgG and IgA in mice. In addition, the microencapsulation promoted the expression of anti-inflammatory cytokines, such as IL-10 and TGF-ß. Moreover, proinflammatory cytokines, such as IL-1, TNF-α, and IL-17, were inhibited by alginate and chitosan in the microencapsulation groups compared with the inactivated PEDV group. Taken together, our results demonstrate that the microparticle could play the role of mucosal adjuvant, and release inactivated PEDV in the gut, which can effectively stimulate mucosal and systemic immune responses in mice.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection.

Introduction: As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. Methods: The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Results: Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Discussion: Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

A community-based cross-sectional study of anxiety and metabolic syndrome.

BACKGROUND: The prevalence of anxiety and other psychological disorders has increased during the COVID-19 pandemic, especially among the elderly. Anxiety and metabolic syndrome (MetS) may aggravate each other. This study further clarified the correlation between the two. METHODS: Adopting a convenience sampling method, this study investigated 162 elderly people over 65 years of age in Fangzhuang Community, Beijing. All participants provided baseline data on sex, age, lifestyle, and health status. The Hamilton Anxiety Scale (HAMA) was used to assess anxiety. Blood samples, abdominal circumference, and blood pressure were used to diagnose MetS. The elderly were divided into MetS and control groups according to the diagnosis of MetS. Differences in anxiety between the two groups were analysed and further stratified by age and gender. Multivariate logistic regression analysis was used to analyse the possible risk factors for MetS. RESULTS: Compared with the control group, anxiety scores of the MetS group were statistically higher (Z = 4.78, P < 0.001). There was a significant correlation between anxiety levels and MetS (r = 0.353, P < 0.001). Multivariate logistic regression revealed that anxiety (possible anxiety vs no anxiety: odds ratio [OR] = 2.982, 95% confidence interval [CI] 1.295-6.969; definite anxiety vs no anxiety: OR = 14.573, 95%CI 3.675-57.788; P < 0.001) and BMI (OR = 1.504, 95% CI 1.275-1.774; P < 0.001) were possible risk factors for MetS. CONCLUSION: The elderly with MetS had higher anxiety scores. Anxiety may be a potential risk factor for MetS, which provides a new perspective on anxiety and MetS.

Spatial analysis and optimization of self-pickup points of a new retail model in the Post-Epidemic Era: the case of Community-Group-Buying in Xi'an City.

The Community-Group-Buying Points (CGBPs) flourished during COVID-19, safeguarding the daily lives of community residents in community lockdowns, and continuing to serve as a popular daily shopping channel in the Post-Epidemic Era with its advantages of low price, convenience and neighborhood trust. These CGBPs are allocated on location preferences however spatial distribution is not equal. Therefore, in this study, we used point of interest (POI) data of 2,433 CGBPs to analyze spatial distribution, operation mode and accessibility of CGBPs in Xi'an city, China as well as proposed the location optimization model. The results showed that the CGBPs were spatially distributed as clusters at α = 0.01 (Moran's I = 0.44). The CGBPs operation mode was divided into preparation, marketing, transportation, and self-pickup. Further CGBPs were mainly operating in the form of joint ventures, and the relying targets presented the characteristic of 'convenience store-based and multi-type coexistence'. Influenced by urban planning, land use, and cultural relics protection regulations, they showed an elliptic distribution pattern with a small oblateness, and the density showed a low-high-low circular distribution pattern from the Palace of Tang Dynasty outwards. Furthermore, the number of communities, population density, GDP, and housing type were important driving factors of the spatial pattern of CGBPs. Finally, to maximize attendance, it was suggested to add 248 new CGBPs, retain 394 existing CGBPs, and replace the remaining CGBPs with farmers' markets, mobile vendors, and supermarkets. The findings of this study would be beneficial to CGB companies in increasing the efficiency of self-pick-up facilities, to city planners in improving urban community-life cycle planning, and to policymakers in formulating relevant policies to balance the interests of stakeholders: CGB enterprises, residents, and vendors.

Establishment of hospital resilience framework in urban China: insight from Wuhan City.

Since the Corona Virus Disease 2019 (COVID-19) swept the world, many countries face a problem that is a shortage of medical resources. The role of emergency medical facilities in response to the epidemic is beginning to arouse public attention, and the construction of the urban resilient emergency response framework has become the critical way to resist the epidemic. Today, China has controlled the domestically transmitted COVID-19 cases through multiple emergency medical facilities and inclusive patient admission criteria. Most of the existing literature focuses on case studies or characterizations of individual facilities. This paper constructs an evaluation system to measure urban hospital resilience from the spatial perspective and deciphered the layout patterns and regularities of emergency medical facilities in Wuhan, the city most affected by the epidemic in China. Findings indicate that the pattern of one center and two circles are a more compelling layout structure for urban emergency medical facilities in terms of accessibility and service coverage for residents. Meanwhile, the Fangcang shelter hospital has an extraordinary performance in terms of emergency response time, and it is a sustainable facility utilization approach in the post-epidemic era. This study bolsters areas of the research on the urban resilient emergency response framework. Moreover, the paper summarizes new medical facilities' planning and location characteristics and hopes to provide policy-makers and urban planners with valuable empirical evidence.

Pre-Clinical Development of a Potent Neutralizing Antibody MW3321 With Extensive SARS-CoV-2 Variants Coverage.

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, several variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged and have consistently replaced the previous dominant variant. Therapeutics against variants of SARS-CoV-2 are urgently needed. Ideal SARS-CoV-2 therapeutic antibodies would have high potency in viral neutralization against several emerging variants. Neutralization antibodies targeting SARS-CoV-2 could provide immediate protection after SARS-CoV-2 infection, especially for the most vulnerable populations. In this work, we comprehensively characterize the breadth and efficacy of SARS-CoV-2 RBD-targeting fully human monoclonal antibody (mAb) MW3321. MW3321 retains full neutralization activity to all tested 12 variants that have arisen in the human population, which are assigned as VOC (Variants of Concern) and VOI (Variants of Interest) due to their impacts on public health. Escape mutation experiments using replicating SARS-CoV-2 pseudovirus show that escape mutants were not generated until passage 6 for MW3321, which is much more resistant to escape mutation compared with another clinical staged SARS-CoV-2 neutralizing mAb MW3311. MW3321 could effectively reduce viral burden in hACE2-transgenic mice challenged with either wild-type or Delta SARS-CoV-2 strains through viral neutralization and Fc-mediated effector functions. Moreover, MW3321 exhibits a typical hIgG1 pharmacokinetic and safety profile in cynomolgus monkeys. These data support the development of MW3321 as a monotherapy or cocktail against SARS-CoV-2-related diseases.

Human Cardiac Pericytes are Susceptible to SARS-CoV-2 Infection.

Coronavirus disease 2019 (COVID-19) is associated with serious cardiovascular complications, with incompletely understood mechanism(s). Pericytes have key functions in supporting endothelial cells and maintaining vascular integrity. We demonstrate that human cardiac pericytes are permissive to SARS-CoV-2 infection in organotypic slice and primary cell cultures. Viral entry into pericytes is mediated by endosomal proteases, and infection leads to upregulation of inflammatory markers, vasoactive mediators, and NF-κB-dependent cell death. Furthermore, we present evidence of cardiac pericyte infection in COVID-19 myocarditis patients. These data demonstrate that human cardiac pericytes are susceptible to SARS-CoV-2 infection and suggest a role for pericyte infection in COVID-19.

Modeling of emergency support capacity and optimization of delivery service system for urban living materials under uncertain situations: a case study of Xi'an City during COVID-19 epidemic.

The severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has brought a heavy burden and severe challenges to the global economy and society, forcing different countries and regions to take various preventive and control measures ranging from normal operations to partial or complete lockdowns. Taking Xi'an city as an example, based on multisource POI data for the government's vegetable storage delivery points, logistics terminal outlets, designated medical institutions, communities, etc., this paper uses the Gaussian two-step floating catchment area method (2SFCA) and other spatial analysis methods to analyze the spatial pattern of emergency support points (ESPs) and express logistics terminals in different situations. It then discusses construction and optimization strategies for urban emergency support and delivery service systems. The conclusions are as follows. (1) The ESPs are supported by large-scale chain supermarkets and fresh supermarkets, which are positively related to the population distribution.The spatial distribution of express logistics terminals is imbalanced, dense in the middle while sparse at the edges. 90% of express terminals are located within a 500 m distance of communities, however, some terminals are shared, which restrict their ability to provide emergency support to surrounding residents. (2) In general, accessibility increases as the number of ESPs increases; under normal traffic, as the distance threshold increases, the available ESPs increase but accessibility slightly decreases; with a traffic lockdown, the travel distance of residents is limited, and as ESPs increase, accessibility and the number of POIs covered significantly increase. (3) The spatial accessibility of the ESPs has a "dumbbell-shaped" distribution, with highest accessibility in the north and south, higher around the second ring road, slightly lower in the center, and lowest near the third ring road at east and west. (4) With the goal of "opening up the logistics artery and unblocking the distribution microcirculation", based on "ESPs + couriers + express logistics terminals + residents", this paper proposes to build and optimize the urban emergency support and delivery service system to improve the comprehensive ability of the city to cope with uncertain risks.

Association of COVID-19 patient's condition with fasting blood glucose and body mass index: A retrospective study.

BACKGROUND: The COVID-19 pandemic broke out in 2019 and rapidly spread across the globe. Most of the severe and dead cases are middle-aged and elderly patients with chronic systemic diseases. OBJECTIVE: This study aimed to assess the association between fasting blood glucose (FPG) and body mass index (BMI) levels in patients with coronavirus disease 2019 (COVID-19) under different conditions. METHODS: Experimental-related information (age, gender, BMI, and FPG on the second day of admission) from 86 COVID-19 cases (47 males and 39 females) with an average age of (39 ± 17) years was collected in April and November 2020. These cases were divided into three groups according to the most severe classification of each case determined by the clinical early warning indicators of severe-critically illness, the degree of progression, and the treatment plan shown in the diagnosis and treatment plan of COVID-19 pneumonia. Statistical models were used to analyze the differences in the levels of FPG and BMI, age, and gender among the three groups. RESULTS: 1. Experimental group: 21 patients with asymptomatic or and mild symptoms (group A), 45 patients with common non-progression (group B), and 20 patients with common progression and severe symptoms (group C). 2. The age differences among the three groups were statistically significant and elderly patients had a higher risk of severe disease (t= 4.1404, 3.3933, 9.2123, P= 0.0001, 0.0012, 0.0000). There was a higher proportion of females than males in the normal progression and severe disease cases (χ2= 5.512, P= 0.019). 3. The level of FPG was significantly higher in group C than in group A (t= 3.1655, P= 0.0030) and B (t= 2.0212, P= 0.0475). The number of diabetes or IFG in group C was significantly higher than in group A (χ2= 5.979, P= 0.014) and group B (χ2= 6.088, P= 0.014). 4. BMI was significantly higher in group C than in groups A (t= 3.8839, P= 0.0004) and B (t= 3.8188, P= 0.0003). The number of overweight or obese patients in group C was significantly higher than in groups A (χ2= 8.838, P= 0.003) and B (χ2= 10.794, P= 0.001). 5. Patients' age, gender, and FPG were independent risk factors for COVID-19 disease progression (ß= 0.380, 0.191, 0.186; P= 0.000, 0.034, 0.045). CONCLUSION: The levels of FPG and BMI were significantly increased in the population with common progressive and severe COVID-19. FPG and age are independent risk factors for the progression of COVID-19.

A non-contact oxygen saturation detection method based on dynamic spectrum.

Blood oxygen saturation (SpO2) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. Therefore, we propose a method for non-contact SpO2 measurement based on the principle of DS (dynamic spectrum) in this paper. A multispectral camera with 24 wavelengths (range in 660 nm-950 nm) is used to capture video of the people's cheek region, and then the two-dimensional images are converted into a one-dimensional temporal PPG signal. After pre-processing the PPG signal, the 24 wavelengths DS values are extracted. The optimal wavelength combination is obtained by wavelength screening using the one-by-one elimination method, and a PLS (partial least squares) model is established using the SpO2 values measured simultaneously by pulse oximetry as the modeled true values. The facial videos of eight healthy subjects were collected, and a total of 140 valid samples were obtained. By analyzing the modeling results, the regression coefficient (R) and root mean square error (RMSE) of the modeled set were 0.6366 and 0.9906, respectively. This method can significantly respond to the variation of SpO2, and the prediction results are approaching to the prediction accuracy (±2%) of most pulse oximeters in the market. Using DS theory in this method eliminates in principle the interference of static tissue, individual differences, and environment. It fully meets the strong demand for non-contact oximetry and provides a new measurement idea.

Non-structure protein ORF1ab (NSP8) in SARS-CoV-2 contains potential γδT cell epitopes.

Upon activation by the pathogen through T-cell receptors (TCRs), γδT cells suppress the pathogenic replication and thus play important roles against viral infections. Targeting SARS-CoV-2 via γδT cells provides alternative therapeutic strategies. However, little is known about the recognition of SARS-CoV-2 antigens by γδT cells. We discovered a specific Vγ9/δ2 CDR3 by analyzing γδT cells derived from the patients infected by SARS-CoV-2. Using a cell model exogenously expressing γδ-TCR established, we further screened the structural motifs within the CDR3 responsible for binding to γδ-TCR. Importantly, these sequences were mapped to NSP8, a non-structural protein in SARS-CoV-2. Our results suggest that NSP8 mediates the recognition by γδT cells and thus could serve as a potential target for vaccines.