Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.

BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database. METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram. RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001). CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis

Assessing Eco-Efficiency with Emphasis on Carbon Emissions from Fertilizers and Plastic Film Inputs

In the context of growing environmental challenges and the push for sustainable agriculture, this study delves into the eco-efficiency of three-season indica rice across 16 key provinces in China from 2004 to 2021. Utilizing the super-efficiency Slacks-Based Measure (SBM) model coupled with the Malmquist index, our approach uniquely incorporates undesirable outputs, focusing on carbon emissions from chemical and plastic inputs. Findings indicate that while the overall efficiency hinged around a modest mean, periods like 2005–2006 and 2017–2018 spotlighted the pivotal role of technological advancements and judicious resource use. The Malmquist Index revealed an intricate interplay between technological change and efficiency, notably when accounting for environmental impact. Diverse provincial efficiencies spotlighted the need for bespoke strategies harmonizing efficiency objectives with ecological sustainability. This study emphasizes the indispensable role of technological innovation in advancing eco-efficiency and fostering sustainable agricultural practices, urging for policy changes that prioritize both technology adoption and ecological awareness

Prediction of Precipitation in the Western Mountainous Regions of China Using a Statistical Model

During the summer in the western mountainous regions of China (WMR), the disasters such as mountain floods, landslides, and debris flows caused by heavy rain occur frequently, which often result in huge economic losses and many casualties. Therefore, it is of great significance to predict the precipitation accurately in these regions. In this paper, a statistical model is established to predict the precipitation in the WMR using the linear regression statistical method, in which the summer area-averaged precipitation anomaly in WMR is taken as the predictand and the prewinter Niño3 SST is taken as the predictor. The results of the return cross test for the historical years from 1979 to 2008 and independent sample return test from 2009 to 2018 show that this statistical model has a good performance in predicting the summer precipitation in the WMR, especially in the flood years. It has better skill in the prediction of WMR precipitation than the dynamical model SINTEX-F

Correlation Analysis between the Viral Load and the Progression of COVID-19

Objectives. This study is aimed at exploring the relationship of the viral load of coronavirus disease 2019 (COVID-19) with lymphocyte count, neutrophil count, and C-reactive protein (CRP) and investigating the dynamic change of patients’ viral load during the conversion from mild COVID-19 to severe COVID-19, so as to clarify the correlation between the viral load and progression of COVID-19. Methods. This paper included 38 COVID-19 patients admitted to the First Hospital of Jiaxing from January 28, 2020, to March 6, 2020, and they were clinically classified according to the Guidelines on the Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment. According to the instructions of the Nucleic Acid Detection Kit for the 2019 novel coronavirus (SARS-CoV-2), respiratory tract specimens (throat swabs) were collected from patients for nucleic acid testing. Patients’ lymphocyte count and neutrophil count were determined by blood routine examination, and CRP was measured by biochemical test. Results. The results of our study suggested that the cycle threshold (Ct) value of Nucleocapsid protein (N) gene examined by nucleic acid test was markedly positively correlated with lymphocyte count (p=0.0445, R2=0.1203), but negatively correlated with neutrophil count (p=0.0446, R2=0.1167) and CRP (p=0.0393, R2=0.1261), which indicated that patients with a higher viral load tended to have lower lymphocyte count but higher neutrophil count and CRP. Additionally, we detected the dynamic change of Ct value in patients who developed into a severe case, finding that viral load of 3 patients increased before disease progression, whereas this phenomenon was not found in 2 patients with underlying diseases. Conclusion. The results of this study demonstrated that viral load of SARS-CoV-2 is significantly negatively correlated with lymphocyte count, but markedly positively correlated with neutrophil count and CRP. The rise of viral load is very likely to be the key factor leading to the overloading of the body’s immune response and resulting in the disease progression into severe disease

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies

Effect of fission products on the thermal conductivity of ThO2-A molecular dynamics study

Thermal conductivity (k), as an important thermal property of nuclear fuels, would be deteriorated due to fission products. Therefore, to investigate the effect of fission products on the thermal conductivity of nuclear fuels is essential. Two typical fission products: Xe and Kr with 0–2 % concentration are considered in this work. The lattice constants (L) of ThO2 increase due to fission products at all testing temperatures. The extent of increase in L due to Xe interstitials is the maximum. The fission products significantly reduce the thermal conductivity of ThO2. The extent of reduction in thermal conductivity of ThO2 by the defects follows the trend Xe (interstitials) > Xe (substitutional defects) > Kr (substitutional defects) > Kr (interstitials). Finally, the full-filled Xe/Kr bubble has a nearly identical thermal conductivity as an empty void or half-filled Xe/Kr bubble. The underlying reason may be that the thorium atoms have a lower mobility than uranium atoms. These calculated values can be used to predict the thermal properties of the irradiated ThO2

Dependence of thermal conductivity on radiation defects in ThO2 investigated by molecular dynamics method

Nuclear fuel performance would be deteriorated due to radiation defects. Therefore, investigating the effect of irradiation-induced defects on nuclear fuel properties is essential. Thermal conductivity is an important property of nuclear fuel. In this work, the influence of radiation defects on the thermal conductivity of ThO2 within 600–1500 K has been studied using molecular dynamics (MD) method. Three types of point defects have been investigated in the present work: Frenkel pairs, substitutional Xe and vacancies with concentrations from 0 to 1 %. The results indicate that these irradiation-induced point defects increase the lattice parameter (L) at all studied temperatures. The strength of the dependence of Xe atoms on L is the highest. The analytical models for pure ThO2 and defected ThO2 are developed and there is a good agreement between the MD derived results and the model. The thermal conductivity of ThO2 systems is decreased due to Frenkel pairs, substitutional Xe and vacancies. The thermal resistance to thermal conductivity due to three types of defects are different. The degree of reduction in thermal conductivity by Xe is the largest. The dependence of vacancy cluster size on thermal conductivity is analyzed. For the ThO2 system with 0.5 % porosity, the effect of vacancy cluster size on thermal conductivity is weak. For the ThO2 system with a fixed porosity of 2 %, there exists a critical cluster radius of about 0.6 nm, below which the thermal conductivity increases with the cluster size and above which the thermal conductivity almost remains unchanged. Finally, the thermal conductivity of the amorphous ThO2 is calculated and effects of the amorphous structure and Frenkel pairs on thermal conductivity are compared. The result shows that the thermal conductivity of ThO2 systems can be further degraded by the amorphous structure. All these results indicate irradiation-induced defects could degrade the thermal properties of ThO2 systems and should be considered seriously for estimation of radiation damages in nuclear fuels used in nuclear reactors

Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics

Abstract Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation

Toll-Like Receptor 4 Promotes NO Synthesis by Upregulating GCHI Expression under Oxidative Stress Conditions in Sheep Monocytes/Macrophages

Many groups of Gram-negative bacteria cause diseases that are harmful to sheep. Toll-like receptor 4 (TLR4), which is critical for detecting Gram-negative bacteria by the innate immune system, is activated by lipopolysaccharide (LPS) to initiate inflammatory responses and oxidative stress. Oxidation intermediates are essential activators of oxidative stress, as low levels of free radicals form a stressful oxidative environment that can clear invading pathogens. NO is an oxidation intermediate and its generation is regulated by nitric oxide synthase (iNOS). Guanosine triphosphate cyclohydrolase (GCHI) is the rate-limiting enzyme for tetrahydrobiopterin (BH4) synthesis, which is essential for the production of inducible iNOS. Previously, we made vectors to overexpress the sheep TLR4 gene. Herein, first generation (G1) of transgenic sheep was stimulated with LPS in vivo and in vitro, and oxidative stress and GCHI expression were investigated. Oxidative injury caused by TLR4 overexpression was tightly regulated in tissues. However, the transgenic (Tg) group still secreted nitric oxide (NO) when an iNOS inhibitor was added. Furthermore, GCHI expression remained upregulated in both serum and monocytes/macrophages. Thus, overexpression of TLR4 in transgenic sheep might accelerate the clearance of invading microbes through NO generation following LPS stimulation. Additionally, TLR4 overexpression also enhances GCHI activation

Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control