Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.

In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.

Glycolytic PFKFB3 and Glycogenic UGP2 Axis Regulates Perfusion Recovery in Experimental Hind Limb Ischemia.

BACKGROUND: Despite being in an oxygen-rich environment, endothelial cells (ECs) use anaerobic glycolysis (Warburg effect) as the primary metabolic pathway for cellular energy needs. PFKFB (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase)-3 regulates a critical enzymatic checkpoint in glycolysis and has been shown to induce angiogenesis. This study builds on our efforts to determine the metabolic regulation of ischemic angiogenesis and perfusion recovery in the ischemic muscle. METHODS: Hypoxia serum starvation (HSS) was used as an in vitro peripheral artery disease (PAD) model, and hind limb ischemia by femoral artery ligation and resection was used as a preclinical PAD model. RESULTS: Despite increasing PFKFB3-dependent glycolysis, HSS significantly decreased the angiogenic capacity of ischemic ECs. Interestingly, inhibiting PFKFB3 significantly induced the angiogenic capacity of HSS-ECs. Since ischemia induced a significant in PFKFB3 levels in hind limb ischemia muscle versus nonischemic, we wanted to determine whether glucose bioavailability (rather than PFKFB3 expression) in the ischemic muscle is a limiting factor behind impaired angiogenesis. However, treating the ischemic muscle with intramuscular delivery of D-glucose or L-glucose (osmolar control) showed no significant differences in the perfusion recovery, indicating that glucose bioavailability is not a limiting factor to induce ischemic angiogenesis in experimental PAD. Unexpectedly, we found that shRNA-mediated PFKFB3 inhibition in the ischemic muscle resulted in an increased perfusion recovery and higher vascular density compared with control shRNA (consistent with the increased angiogenic capacity of PFKFB3 silenced HSS-ECs). Based on these data, we hypothesized that inhibiting HSS-induced PFKFB3 expression/levels in ischemic ECs activates alternative metabolic pathways that revascularize the ischemic muscle in experimental PAD. A comprehensive glucose metabolic gene qPCR arrays in PFKFB3 silenced HSS-ECs, and PFKFB3-knock-down ischemic muscle versus respective controls identified UGP2 (uridine diphosphate-glucose pyrophosphorylase 2), a regulator of protein glycosylation and glycogen synthesis, is induced upon PFKFB3 inhibition in vitro and in vivo. Antibody-mediated inhibition of UGP2 in the ischemic muscle significantly impaired perfusion recovery versus IgG control. Mechanistically, supplementing uridine diphosphate-glucose, a metabolite of UGP2 activity, significantly induced HSS-EC angiogenic capacity in vitro and enhanced perfusion recovery in vivo by increasing protein glycosylation (but not glycogen synthesis). CONCLUSIONS: Our data present that inhibition of maladaptive PFKFB3-driven glycolysis in HSS-ECs is necessary to promote the UGP2-uridine diphosphate-glucose axis that enhances ischemic angiogenesis and perfusion recovery in experimental PAD.

The impact of spiritual care on the psychological health and quality of life of adults with heart failure: a systematic review of randomized trials.

Background: Heart failure (HF) brings not only physical pain but also psychological distress. This systematic review investigated the influence of spiritual care on the psychological well-being and quality of life in adults with HF. Methods: We conducted a systematic literature review following PRISMA guidelines, searching seven electronic databases for relevant randomized controlled studies without language or temporal restrictions. The studies were assessed for quality using the Cochrane Bias Risk tool. Results: A total of 13 studies (882 participants) were reviewed, investigating interventions such as religion, meditation, mental health, cognitive interventions, and spiritual support. Key factors influencing the effectiveness of spiritual care implementation included integration into routine care, respect for diversity, patient engagement, intervention quality, and alignment with patient beliefs. The majority of the studies indicated that spiritual care has a potentially beneficial impact on the mental health and quality of life of patients with HF. Conclusion: The findings provide valuable insights for healthcare professionals, highlighting the importance of adopting a spiritual care approach to healthcare for this population.

A density map-based method for counting wheat ears.

Introduction: Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field. Methods: There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios. Results: Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information. Discussion: The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer.

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.

A nomogram for predicting choledochal cyst with perforation.

BACKGROUND: Choledochal cyst with perforation (CC with perforation) rarely occurs, early diagnosis and timely treatment plan are crucial for the treatment of CC with perforation. This study aims to forecast the occurrence of CC with perforation. METHODS: All 1111 patients were conducted, who underwent surgery for choledochal cyst at our hospital from January 2011 to October 2022. We conducted univariate and multivariate logistic regression analysis to screen for independent predictive factors for predicting CC with perforation, upon which established a nomogram. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) curves. RESULTS: The age of children with choledochal cyst perforation is mainly concentrated between 1 and 3 years old. Logistic regression analysis indicates that age, alanine aminotransferase, glutamyl transpeptidase, C-reactive protein, vomiting, jaundice, abdominal distension, and diarrhea are associated with predicting the occurrence of choledochal cyst perforation. ROC curves, calibration plots, and DCA curve analysis curves demonstrate that the nomogram has great discriminative ability and calibration, as well as significant clinical utility. CONCLUSION: The age of CC with perforation is mainly concentrated between 1 and 3 years old. A nomogram for predicting the perforation of choledochal cyst was established.

Effect of miR-98/IL-6/STAT3 on Autophagy and Apoptosis of Cardiac Stem Cells Under Hypoxic Conditions In vitro.

BACKGROUND: The heavy burden of cardiovascular diseases demands innovative therapeutic strategies dealing with cardiomyocyte loss. Cardiac Stem Cells (CSCs) are renewable cells in the myocardium with differentiation and endocrine functions. However, their functions are significantly inhibited in conditions of severe hypoxia or inflammation. The mechanism of hypoxia affecting CSCs is not clear. Interleukin-6 (IL-6) appears active in both hypoxic and inflammatory microenvironments. The aim of this study was to explore whether IL-6 is related to CSC apoptosis and autophagy under severe hypoxia. METHODS: In this study, rat CSCs were extracted by alternate digestion. The interaction of miR-98 and IL-6 mRNA was detected by the dual luciferase method, and qPCR was applied to confirm the effect of miR-98 on IL-6 expression. The effect of IL-6 on CSC apoptosis was measured by flow cytometry and the effect of IL-6 on CSC autophagy by transmission electron microscopy. The western blot method was applied to detect the effect of IL-6 on the expressions of proteins related to apoptosis and autophagy. ANOVA and Dunnett T3's test were employed in the statistical analysis. When p < 0.05, the difference was significant. RESULTS: Under severe hypoxia conditions, IL-6 increased CSC apoptosis and decreased p-STAT3 expression significantly. CSC apoptosis increased significantly after inhibition of the STAT3 signaling pathway under severe hypoxia. IL-6 could also significantly inhibit CSCs' autophagy and block their autophagy flow under severe hypoxic conditions. Meanwhile, it was confirmed that miR-98 had a binding site on IL-6 mRNA and miR-98 significantly inhibited IL-6 mRNA expression in CSCs under severe hypoxic conditions. CONCLUSION: miR-98/IL-6/STAT3 has been found to be involved in the regulation of CSCs' apoptosis and autophagy under severe hypoxic conditions and there might be a mutual linkage between CSCs' apoptosis and their autophagy.

An Aerial Image Detection Algorithm Based on Improved YOLOv5.

To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection accuracy. Secondly, we integrate the constructed hybrid attention module into the backbone network to enhance the model's capability in extracting pertinent feature information. Subsequently, we incorporate feature fusion layer 7 and P2 fusion into the neck network, leading to a notable enhancement in the model's capability to detect small targets. Finally, we replace the original PAN + FPN network structure with the optimized BiFPN (Bidirectional Feature Pyramid Network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. Experimental results indicate a substantial improvement in both the detection accuracy and speed of the enhanced algorithm compared to its original version. It is noteworthy that the enhanced algorithm exhibits a markedly improved detection performance for aerial images, particularly under real-time conditions.

Fault-scale crustal structure across the Dunhua-Mishan fault (Tanlu northern segment) constrained from teleseismic P-wave receiver functions.

The Dunhua-Mishan fault, located in the northern segment of the Tanlu fault zone, experienced multiple tectonic processes associated with the effects of the Pacific Plate subduction and the Indo-Asia collision. The high-resolution fault-scale structure is critical for understanding the fault evolution and potential fault damage. However, the well-defined deep structure of the Dunhua-Mishan fault is still unclear due to the lack of the dense seismic array. In this study, we construct a high-resolution P-wave receiver function imaging based on linear dense seismic array across the fault. Our results reveal the strong Moho depth variation across the Dunhua-Mishan fault zone. The slightly higher Vp/Vs ratio values within the fault zone indicate the presence of a small amount of mafic crust composition. Interestingly, the significant double positive Ps converted phases are observed within the fault zone, which may represent double Moho discontinuities. The double Moho structure may be related to multiple significant tectonic activities in the Tanlu northern segment. These newly observed structures provide new seismic constraints on the formation and evolution of the Tanlu fault zone and probably reflect that the lithospheric structure of the Dunhua-Mishan fault has been modified by a series of tectonic processes.

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.

BACKGROUND: A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS: PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS: In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION: AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.

A Comparative Study of the Effects of Electronic Cigarette and Traditional Cigarette on the Pulmonary Functions of C57BL/6 Male Mice.

INTRODUCTION: Electronic cigarettes (E-cigs) are in a controversial state. Although E-cig aerosol generally contains fewer harmful substances than smoke from burned traditional cigarettes, aerosol along with other compounds of the E-cigs may also affect lung functions and promote the development of lung-related diseases. We investigated the effects of E-cig on the pulmonary functions of male C57BL/6 mice and reveal the potential underlying mechanisms. METHODS: A total of 60 male C57BL/6 mice were randomly divided into four groups. They were exposed to fresh-air, traditional cigarette smoke, E-cig vapor with 12 mg/mL of nicotine, and E-cig with no nicotine for 8 weeks. Lung functions were evaluated by using quantitative analysis of the whole body plethysmograph, FlexiVent system, lung tissue histological and morphometric analysis, and RT-PCR analysis of mRNA expression of inflammation-related genes. In addition, the effects of nicotine and acrolein on the survival rate and DNA damage were investigated using cultured human alveolar basal epithelial cells. RESULTS: Exposure to E-cig vapor led to significant changes in lung functions and structures including the rupture of the alveolar cavity and enlarged alveolar space. The pathological changes were also accompanied by increased expression of interleukin-6 and tumor necrosis factor-α. CONCLUSIONS: The findings of the present study indicate that the safety of E-cig should be further evaluated. IMPLICATIONS: Some people currently believe that using nicotine-free E-cigs is a safe way to smoke. However, our research shows that E-cigs can cause lung damage regardless of whether they contain nicotine.

Correlation between maximum heart distance and thoracic diameter changes and diaphragmatic descent in left-sided breast cancer patients during deep inspiration breath-hold (DIBH).

BACKGROUND: Cardioprotection is valued in radiotherapy for patients with left-sided breast cancer. Deep inspiration breath-hold (DIBH) technique can achieve cardioprotection well. However, during DIBH, the extent to which the heart enters the radiation field is affected by the movement of the thorax and diaphragm. The aim of this study was to analyze the correlation between the maximum distance of the heart entering the field (maximum heart distance, MHD) and thoracic diameter changes and diaphragmatic descent in left-sided breast cancer patients during DIBH. PATIENTS AND METHODS: Ninety-eight patients with left-sided breast cancer were included in this retrospective study. They performed simulation in Sentinel-guided DIBH, and two sets of CT images were collected under both free breathing (FB) and DIBH, and diaphragm positions, anteroposterior thoracic diameter (ATD), transverse thoracic diameter (TTD), gating window level (GWL), and MHD were measured, and the change (Δ) of each parameter in DIBH relative to that in FB were calculated. Pearson or Spearman test were used to analyze the correlation between ΔMHD and the changes in other parameters. RESULTS: For all patients with DIBH, the average of ΔMHD was -8.3 mm, and the average of ΔATD and ΔTTD were 11.0 and 8.6 mm, and the median of both left diaphragmatic descent (LDD) and right diaphragmatic descent (RDD) were 35.0 mm, and the median of GWL was 11.1 mm. The correlation coefficients between MHD decrease (ΔMHD) and LDD, RDD, and ΔTTD were -0.430 (p = 0.000), -0.592 (p = 0.000) and 0.208 (p = 0.040), respectively, but not significantly correlated with ΔATD or GWL. CONCLUSIONS: The MHD decrease showed a moderate correlation with diaphragmatic descent In Sentinel-guided DIBH for patients with left-sided breast cancer, while there was a weak or no correlation with thoracic diameter changes or GWL. Abdominal breathing can lower diaphragm more and may be more beneficial to the heart stay away from tangential field.

Enhancement and contextual modulation of visuospatial processing by thalamocollicular projections from ventral lateral geniculate nucleus.

In the mammalian visual system, the ventral lateral geniculate nucleus (vLGN) of the thalamus receives salient visual input from the retina and sends prominent GABAergic axons to the superior colliculus (SC). However, whether and how vLGN contributes to fundamental visual information processing remains largely unclear. Here, we report in mice that vLGN facilitates visually-guided approaching behavior mediated by the lateral SC and enhances the sensitivity of visual object detection. This can be attributed to the extremely broad spatial integration of vLGN neurons, as reflected in their much lower preferred spatial frequencies and broader spatial receptive fields than SC neurons. Through GABAergic thalamocollicular projections, vLGN specifically exerts prominent surround suppression of visuospatial processing in SC, leading to a fine tuning of SC preferences to higher spatial frequencies and smaller objects in a context-dependent manner. Thus, as an essential component of the central visual processing pathway, vLGN serves to refine and contextually modulate visuospatial processing in SC-mediated visuomotor behaviors via visually-driven long-range feedforward inhibition.

The "Trinity" smart hospital construction policy promotes the development of hospitals and health management in China.

Recently, in order to comprehensively promote the development of medical institutions and solve the nationwide problems in the healthcare fields, the government of China developed an innovative national policy of "Trinity" smart hospital construction, which includes "smart medicine," "smart services," and "smart management". The prototype of the evaluation system has been established, and a large number of construction achievements have emerged in many hospitals. In this article, the summary of this field was performed to provide a reference for medical workers, managers of hospitals, and policymakers.

Sensory processing deficits and related cortical pathological changes in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily affecting cognitive functions. However, sensory deficits in AD start to draw attention due to their high prevalence and early onsets which suggest that they could potentially serve as diagnostic biomarkers and even contribute to the disease progression. This literature review examines the sensory deficits and cortical pathological changes observed in visual, auditory, olfactory, and somatosensory systems in AD patients, as well as in various AD animal models. Sensory deficits may emerge at the early stages of AD, or even precede the cognitive decline, which is accompanied by cortical pathological changes including amyloid-beta deposition, tauopathy, gliosis, and alterations in neuronal excitability, synaptic inputs, and functional plasticity. Notably, these changes are more pronounced in sensory association areas and superficial cortical layers, which may explain the relative preservation of basic sensory functions but early display of deficits of higher sensory functions. We propose that sensory impairment and the progression of AD may establish a cyclical relationship that mutually perpetuates each condition. This review highlights the significance of sensory deficits with or without cortical pathological changes in AD and emphasizes the need for further research to develop reliable early detection and intervention through sensory systems.

Space Charge Characteristics and Breakdown Properties of Nanostructured SiO2/PP Composites.

Polypropylene (PP) has gained attention in the industry as an environmentally friendly material. However, its electrical properties are compromised due to space charge accumulation during operation, limiting its application in high-voltage DC cable insulation. This study investigates the effect and mechanism of SiO2 with a DDS surface hydrophobic treatment on space charge suppression and the electrical properties of PP composites. The PP matrix was doped with SiO2 nanostructures, both with a DDS surface hydrophobic treatment and untreated as a control group. The functional group structure and dispersion of nanostructured SiO2 in the matrix were characterized. The findings reveal that the incorporation of SiO2 nanostructures effectively mitigates charge accumulation in PP composites. However, a high concentration of unsurfaced nanostructures tends to agglomerate, resulting in inadequate space charge suppression and a diminished DC breakdown field strength. Nonetheless, surface treatment improves the dispersion of SiO2 within the matrix. Notably, the composite containing 1.0 wt% of surface hydrophobic SiO2 exhibits the least space charge accumulation. Compared to the base material PP, the average charge density is reduced by 83.9% after the 1800 s short-circuit discharges. Moreover, its DC breakdown field strength reaches 3.45 × 108 V/m, surpassing pure PP by 19.4% and untreated SiO2/PP composites of the same proportion by 24.0%.

The medial preoptic area mediates depressive-like behaviors induced by ovarian hormone withdrawal through distinct GABAergic projections.

Fluctuations in reproductive hormone levels are associated with mood disruptions in women, such as in postpartum and perimenopausal depression. However, the neural circuit mechanisms remain unclear. Here we report that medial preoptic area (MPOA) GABAergic neurons mediate multifaceted depressive-like behaviors in female mice after ovarian hormone withdrawal (HW), which can be attributed to downregulation of activity in Esr1 (estrogen receptor-1)-expressing GABAergic neurons. Enhancing activity of these neurons ameliorates depressive-like behaviors in HW-treated mice, whereas reducing their activity results in expression of these behaviors. Two separate subpopulations mediate different symptoms: a subpopulation projecting to the ventral tegmental area (VTA) mediates anhedonia and another projecting to the periaqueductal gray mediates immobility. These projections enhance activity of dopaminergic neurons in the VTA and serotonergic neurons in the dorsal raphe, respectively, with increased release of dopamine and serotonin, possibly through disinhibition mechanisms. Thus, the MPOA is a hub that mediates depressive-like behaviors resulting from transitions in reproductive hormone levels.

1-D and 2-D Direction of Arrival Estimation in a Conical Conformal Array: Design and Implementation.

Direction of arrival (DOA) estimation for conformal arrays is challenging due to non-omnidirectional element patterns and shadow effects. Conical conformal array (CCA) can avoid the shadow effect at small elevation angles. So CCA is suitable for DOA estimation on both azimuth and elevation angles at small elevation angles. However, the element pattern in CCA cannot be obtained by conventional directional element coordinate transformation. Its local element pattern also has connection with the cone angle. The paper establishes the CCA radiation pattern in local coordinate system using 2-D coordinate transformation. In addition, in the case of large elevation angle, only half elements of the CCA can receive signal due to the shadow effect. The array degrees of freedom (DOF) are reduced by halves. We introduce the difference coarray method, which increases the DOF. Moreover, we propose a more accurate propagator method for 2-D cases. This method constructs a new propagation matrix and reduces the estimation error. In addition, this method reduces computational complexity by using linear computations instead of eigenvalue decomposition (EVD) and avoids spectral search. Simulation and experiment verify the estimation performance of the CCA. Both demonstrate the CCA model established in this paper is corresponding to the designed CCA antenna, and the proposed algorithms meet the needs of CCA angle detection. When the number of array elements is 12, the estimation accuracy is about 5 degrees.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Glutamatergic and GABAergic neurons in pontine central gray mediate opposing valence-specific behaviors through a global network.

Extracting the valence of environmental cues is critical for animals' survival. How valence in sensory signals is encoded and transformed to produce distinct behavioral responses remains not well understood. Here, we report that the mouse pontine central gray (PCG) contributes to encoding both negative and positive valences. PCG glutamatergic neurons were activated selectively by aversive, but not reward, stimuli, whereas its GABAergic neurons were preferentially activated by reward signals. The optogenetic activation of these two populations resulted in avoidance and preference behavior, respectively, and was sufficient to induce conditioned place aversion/preference. Suppression of them reduced sensory-induced aversive and appetitive behaviors, respectively. These two functionally opponent populations, receiving a broad range of inputs from overlapping yet distinct sources, broadcast valence-specific information to a distributed brain network with distinguishable downstream effectors. Thus, PCG serves as a critical hub to process positive and negative valences of incoming sensory signals and drive valence-specific behaviors with distinct circuits.