A YOLOv7-based method for crown extraction from UAV imagery.

In response to the challenges of complex background interference, inadequate feature utilization, and model redundancy in multispectral crown extraction, this paper proposes a dual-channel crown detection and segmentation approach based on an improved YOLOv7 architecture, named Dual-YOLOv7. First, a dual-branch feature extraction network is designed, integrating visible light and infrared spectral information and dynamically weights key features through an attention mechanism. Second, the D-SimSPPF module is introduced, which employs depthwise separable convolution to optimize spatial pyramid pooling, thereby enhancing the capability to capture fine details while reducing the number of parameters.

Enhancing stock timing predictions based on multimodal architecture: Leveraging large language models (LLMs) for text quality improvement.

This study aims to enhance stock timing predictions by leveraging large language models (LLMs), specifically GPT-4, to filter and analyze online investor comment data. Recognizing challenges such as variable comment quality, redundancy, and authenticity issues, we propose a multimodal architecture that integrates filtered comment data with stock price dynamics and technical indicators. Using data from nine Chinese banks, we compare four filtering models and demonstrate that employing GPT-4 significantly improves financial metrics like profit-loss ratio, win rate, and excess return rate.

Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT.

Pig counting is an essential activity in the administration of pig farming. Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. However, research on dynamic pig counting encounters challenges, including inadequate detection accuracy stemming from crowding, occlusion, deformation, and low-light conditions.

An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

Introduction: Machine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and testing sets from different periods. The purpose of this study is to investigate the impact of temporality, i.e.

Self-Assembled Subwavelength Nanophotonic Structures for Spatial Object Localization and Tracking.

Subwavelength resonant nanostructures have facilitated strong light-matter interactions and tunable degrees of freedom of light, such as spectrum, polarization, and direction, thus boosting photonic applications toward light emission, manipulation, and detection. For photodetection, resonant nanostructures have enabled emerging technologies, such as light detection and ranging, spectrometers, and polarimeters, within an ultracompact footprint. However, resonant nanophotonics usually relies on nanofabrication technology, which suffers from the trade-offs between precision and scalability.

Dual immunotherapy alternating with anti-PD-1 antibody plus liposomal doxorubicin show good efficacy in prostate epithelioid hemangioendothelioma: a case report.

Epithelioid hemangioendothelioma is a rare vascular malignancy, and currently, there is no standard treatment regimen for this disease and existing treatment options have limited efficacy. In this case report, we present a patient with lung and lymph node metastases from prostate epithelioid hemangioendothelioma who achieved a significant partial response. This was accomplished through alternating nivolumab therapy with ipilimumab and liposomal doxorubicin, resulting in a progression-free-survival more than 6 months to date.

Association between nutritional status assessed by a digital self-administered tool (R+ dietitian) and clinicopathologic factors in cancer patients: A comprehensive analysis.

Objective: Malnutrition is prevalent among cancer patients, smartphone-based self-administered nutritional assessment tools offer a promising solution for effective nutritional screening. This study aims to retrospectively analyze the relationships between nutritional status evaluated by the digital tool (R+ Dietitian) and clinicopathologic factors of cancer patients.

Methods: Cancer patients who met the inclusion criteria were divided into two subgroups based on age, Nutritional Risk Screening-2002, Patient-Generated Subjective Global Assessment Short Form, body mass index, and hospital stays for comparison.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US.

Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System.

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a genomic feature-based classifier that refines the identification of m6A sites, distinguishing those genuinely present from those that can be detected in in-vitro transcribed (IVT) control experiments. We find that m6ACali effectively identifies non-specific binding peaks reported by exomePeak2 and MACS2 in novel MeRIP-Seq datasets without the need for paired IVT controls.

Characteristics of exopolysaccharides from Paecilomyces hepiali and their simulated digestion and fermentation in vitro by human intestinal microbiota.

The metabolic process of polysaccharides in gastrointestinal digestions and the effects of the resulting carbohydrates on the composition of gut microbes are important to explore their prebiotic properties. Therefore, the purpose of this study was to investigate the simulated digestion and fecal fermentation in vitro of three fractions (PHEPSs-1, PHEPSs-2 and PHEPSs-3) purified from the crude exopolysaccharides of Paecilomyces hepiali HN1 (PHEPSs) and to explore the potential prebiotic mechanisms. The three purified fractions were characterized by HPLC, UV, FT-IR, SEM and AFM, and they were all of galactoglucomannan family with molecular weight of 178, 232 and 119 kDa, respectively.

Joint extremes in precipitation and infectious disease in the USA: A bivariate POT study.

Mounting heavy precipitation events (HPEs) caused by the climate change have drawn wide attention. Increased incidences of infectious diseases are known as the common following health impact, while little has been studied about the extremal relationship in between. Therefore, this study aims to investigate the joint extremes of precipitation and infectious disease mortality rate in the USA, using publicly accessible data from the National Centers for Environmental Information and the Centers for Disease Control and Prevention.

Mixed-valence gold-porphyrin two-dimensional coordination networks for repurposing of chrysotherapy.

Catalytic gold nanomaterials typically exhibit antibacterial properties, albeit significantly weaker than ionic gold in chrysotherapy. The inherent stability of gold nanoparticles prevents the release of gold ions, limiting their ability to achieve efficient antibacterial therapy. To address this limitation, we propose a novel sustained ionic gold release strategy through the construction of a mixed-valence gold-porphyrin coordination network (Au-Por).

A Generalized Bootstrap Procedure of the Standard Error and Confidence Interval Estimation for Inverse Probability of Treatment Weighting.

The inverse probability of treatment weighting (IPTW) approach is commonly used in propensity score analysis to infer causal effects in regression models. Due to oversized IPTW weights and errors associated with propensity score estimation, the IPTW approach can underestimate the standard error of causal effect. To remediate this, bootstrap standard errors have been recommended to replace the IPTW standard error, but the ordinary bootstrap (OB) procedure might still result in underestimation of the standard error because of its inefficient resampling scheme and untreated oversized weights.

Nonlinear spectral compression in high-power narrow-linewidth polarization maintaining fiber amplifiers for SBS suppression.

In this paper, we propose a method for narrowing the spectrum in high-power narrow-linewidth polarization-maintaining (PM) fiber amplifiers and investigate its potential for suppressing the stimulated Brillouin scattering (SBS). In this method, in addition to common phase modulation to suppress SBS, precisely designed amplitude modulation is induced to generate self-phase modulation in a high-power PM fiber amplifier. In this co-modulation way, the spectrum can be gradually compressed along the fiber.

Investigating the Marginal and Herd Effects of COVID-19 Vaccination for Reducing Case Fatality Rate: Evidence from the United States between March 2021 to January 2022.

Vaccination campaigns have been rolled out in most countries to increase vaccination coverage and protect against case mortality during the ongoing pandemic. To evaluate the effectiveness of COVID-19 vaccination, it is vital to disentangle the herd effect from the marginal effect and parameterize them separately in a model. To demonstrate this, we study the relationship between the COVID-19 vaccination coverage and case fatality rate (CFR) based on U.

Human amniotic mesenchymal stem cell-conditioned medium inhibited LPS-induced cell injury and inflammation by modulating CD14/TLR4-mediated signaling pathway in monocytes.

Human amniotic mesenchymal stem cells (hAMSCs) have attracted considerable attention as a promising regenerative therapy. Many studies reported that the conditioned medium of hAMSCs (AM-CM) exerted anti-inflammatory and immunomodulatory functions, while its underlying mechanism is poorly understood. In this study, we first confirmed that AM-CM (25%, 50%, 100%) was optimal for anti-inflammation at 24 h.

[Experimental study of improved sclerotherapy injection in rat model of vertebral arteriocervical spondylopathy].

Objective: To improve the rat model of cervical spondylosis of vertebral artery type (CSA) induced by injecting sclerosing agent. To evaluate the efficacy of injecting sclerosing agent to induce CSA.

Methods: Forty Health SPF SD rats(20 males and 20 females), were randomly divided into two groups:the model group (20) and the blank group (20).

Evaluation of toxicological effects of bisphenol S with an in vitro human bone marrow mesenchymal stem cell: Implications for bone health.

As the use of bisphenol A (BPA) has been restricted in consumer products, bisphenol S (BPS) is one major alternative to BPA for various materials, leading to growing concerns about its health risks in human beings. However, little is known about the toxic effects of BPS on bone health. We employed human bone marrow mesenchymal stem cells (hBMSCs) for the in vitro assessment of BPS on cell proliferation, differentiation, and self-renewal.

Factors Associated with the Uptake of Genetic Testing for Cancer Risks: A Pathway Analysis Using the Health Information National Trends Survey Data.

Our study aimed to identify pathways from the source of information to the uptake of cancer genetic testing, with consideration of intermediate variables including perceptional, attitudinal and psychosocial factors. We used the Health Information National Trends Survey (2020 database) and constructed a structural equation model for pathway analysis (using SPSS version 24). Variables for socio-demographic, lifestyle and health information were also collected and used for confounding adjustment.

Amplified effect of social vulnerability on health inequality regarding COVID-19 mortality in the USA: the mediating role of vaccination allocation.

Background: Vaccination reduces the overall burden of COVID-19, while its allocation procedure may introduce additional health inequality, since populations characterized with certain social vulnerabilities have received less vaccination and been affected more by COVID-19. We used structural equation modeling to quantitatively evaluate the extent to which vaccination disparity would amplify health inequality, where it functioned as a mediator in the effect pathways from social vulnerabilities to COVID-19 mortality.

Methods: We used USA nationwide county (n = 3112, 99% of the total) level data during 2021 in an ecological study design.

Edaravone Ameliorates Cerebral Ischemia-Reperfusion Injury by Downregulating Ferroptosis via the Nrf2/FPN Pathway in Rats.

Edaravone, an antioxidant protective agent, has anti-cerebral ischemic reperfusion injury (CIRI) effects, but its anti-CIRI mechanism is unclear. The aim of this study is to investigate the anti-CIRI mechanism of edaravone based on the nuclear factor-E2-related factor 2 (Nrf2)/ferroportin (FPN) pathway that regulates ferroptosis-mediated cerebral ischemia-reperfusion injury. We evaluated the brain injury by constructing a middle cerebral artery occlusion and reperfusion (MCAO/R) model in rats.

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference.

Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction).

Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic.

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays.