Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of the Influence of Winter Snow Depth over the Tibetan Plateau on Summer Rainfall in China

The influence of snow depth over the Tibetan Plateau (TP) in the preceding winter on rainfall in the following summer in China is simulated by the regional climate model (NCC_RegCM1.0). The simulation results are consistent with the relation of observed snow depth and rainfall, namely, more rainfall occurs in the Yangtze River valley, less rainfall in South China and North China. The results accord with the pattern of "southern flood and northern drought" in China in recent 20 years. So more snow in the TP in winter is one important reason causing "southern flood and northern drought" in summer in China. The paper reveals the possible physical mechanism of the impact of winter snow over TP on summer rainfall in China. The more the snow over the TP, the weaker the sensible heat source is, which results in stronger winter monsoon in winter, and its effect can reach South China, Southwest China and the Bay of Bengal. At the same time, weaker heat source of the TP can last to summer, which is a reason for the weakening of East Asian and South Asian summer monsoon. In the early stage of snow, the increasing of surface reflected flux is dominant; after snow has melted, "Moist Soil" contributes to prolonging the influence process of snow on weather and climate. The reduction of absorbed solar radiation due to the increased reflected flux, the increase of absorbed heat as snow melts, and the long-term interaction between moist soil and atmosphere weaken the heat source of the TP, which are the main impacting mechanism of winter snow over the TP on monsoon and rainfall distribution in China. The simulation results show that the distinct effect time of snow over the TP can persist to June.
Limits and consequences of the large-scale deployment of renewable energy technologies

A message presently heralded in the scientific literature is that by installing the appropriate number of renewable energy devices (i.e. wind turbines, photovoltaic panels, etc.), the atmosphere can return to pre-industrial conditions — this is impossible. Renewable energy technologies alter the Earth System during the energy extraction process. How these changes are manifested depends on more complex factors such as the technology, geographic location, and scale, but climatic differences are unavoidable. For example, although about 450 terawatts (TW) of wind power is normally dissipated near the Earth surface, through increasingly complex methods, we estimate a maximum electricity production rate of near-surface wind energy extraction over the global non-glaciated land surface of 18-68 TWe. The general circulation model (GCM) simulation representing the maximum energy extraction rate shows that some climatic effects are similar in magnitude to a doubling of CO2, with large-scale differences in temperature and precipitation. The influence of the large-scale wind turbines also causes atmospheric dynamics that result in a 2-3% decrease in the generation rate of total atmospheric wind energy which will also influence subsequent atmospheric dynamics. Exploring jet stream wind power from the same top-down perspective, we estimate a maximum electricity production rate of 4.5 TWe, about 200-times less than the previous estimate and quantified with a GCM to result in upper-atmospheric temperature increases of > 20 ◦C at both poles and substantial differences in climate. With the clear relevance of the energy transformation hierarchy to estimate both power potentials and climatic impacts, we model near-surface wind power deployed in the ’windiest’ areas or photovoltaic power deployed in the ’sunniest’ areas. GCM simulations suggest that photovoltaics are able to fulfill the current global human energy of 17 TW demand with less than 1% coverage of the prescribed ’sunniest’ areas and without any clearly discernible climatic differences compared to the control simulation. In strong contrast, near-surface wind power requires 100% of the ’windiest’ areas to produce the same power, with this maximum extraction scenario associated with substantial climatic differences compared to the control simulation. Numerous other renewable technologies, geographic configurations, and complex model frameworks are presently available and fully worthy of similar fundamental limit–climatic consequence related studies. Ultimately, this work emphasizes the relevance of a ’top-down’ thermodynamic Earth System based approach to large-scale renewable energy estimates. The actual quantified power estimates and environmental consequences included here may change slightly depending on the model or the assumptions, but the more core-realization that renewables have finite extraction limits and must be associated with climatic consequences seems certain.
Differentially Expressed Genes Involved in Primary Resistance to Immunotherapy in Patients with Advanced-Stage Pulmonary Cancer

In the last few years, nivolumab has become the standard of care for advanced-stage lung cancer patients. Unfortunately, up to 60% of patients do not respond to this treatment. In our study, we identified variations in gene expression related to primary resistance to immunotherapy. Bronchoscopy biopsies were obtained from advanced non-small cell lung cancer (NSCLC) patients previously characterized as responders or non-responders after nivolumab treatment. Ten tumor biopsies (from three responders and seven non-responders) were analyzed by the differential expression of 760 genes using the NanoString nCounter platform. These genes are known to be involved in the response to anti-PD1/PD-L1 therapy. All the patients were treated with nivolumab. Examining the dysregulated expression of 24 genes made it possible to predict the response to nivolumab treatment. Supervised analysis of the gene expression profile (GEP) revealed that responder patients had significantly higher levels of expression of CXCL11, NT5E, KLRK1, CD3G, GZMA, IDO1, LCK, CXCL9, GNLY, ITGAL, HLA-DRB1, CXCR6, IFNG, CD8A, ITK, B2M, HLA-B, and HLA-A than did non-responder patients. In contrast, PNOC, CD19, TP73, ARG1, FCRL2, and PTGER1 genes had significantly lower expression levels than non-responder patients. These findings were validated as predictive biomarkers in an independent series of 201 patients treated with nivolumab (22 hepatocellular carcinomas, 14 non-squamous cell lung carcinomas, 5 head and neck squamous cell carcinomas, 1 ureter/renal pelvis carcinoma, 120 melanomas, 4 bladder carcinomas, 31 renal cell carcinomas, and 4 squamous cell lung carcinomas). ROC curve analysis showed that the expression levels of ITK, NT5E, ITGAL, and CD8A were the best predictors of response to nivolumab. Further, 13/24 genes showed an adverse impact on overall survival (OS) in an independent, large series of patients with NSCLC (2166 cases). In summary, we found a strong association between the global GEP of advanced NSCLC and the response to nivolumab. The classification of NSCLC patients based on GEP enabled us to identify those patients who genuinely benefited from treatment with immune checkpoint inhibitors (ICIs). We also demonstrated that abnormal expression of most of the markers comprising the genomic signature has an adverse influence on OS, making them significant markers for therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these biomarkers.
Active-Learning-Based Sparse Near-Field Scanning With Time-Domain Current Measurement for Conductive Coupling Path Visualization

This article presents a novel and efficient near-field scanning method for coupling path visualization of conductive electromagnetic interference (EMI) using sparse scanning samples based on active machine learning. A high-voltage discharge system with a false triggering problem is studied as the device under test (DUT). Near-field scanning is adopted to measure the time-domain waveform of the conductive current and investigate the coupling path of the conductive EMI that causes the false triggering issue. To facilitate the time-consuming near-field scanning process, an adaptive sampling approach based on active learning is proposed to seek the most informative locations and minimize the number of scanning samples. Through the developed active learning method, the scanning process is more efficient and intelligent than random sampling and a uniform-like sampling strategy called greedy sampling (GSx). Moreover, the dominant EMI coupling path in a key area is clearly visualized. By cutting off the identified coupling path, the false triggering phenomenon is effectively eliminated. The proposed active learning method is much more efficient than the conventional full scanning and can be applied to expedite various near-field scanning scenarios.
Miniaturized High-Selectivity High-Resistivity-Silicon IPD Bandpass Filter Based on Multiple Transmission Paths

In this letter, a miniaturized bandpass filter chip is designed and implemented on high-resistivity-silicon (HRS) integrated passive device (IPD) technology. The filter is designed into a three-path topology, which can generate multiple transmission zeros to achieve a high roll-off rate at the passband edge and good rejection in a very wide stopband. For validation, the filter chip operating at 5G N77 frequency band is fabricated with a compact size of <inline-formula> <tex-math notation="LaTeX">$1.1\times0.7$ </tex-math></inline-formula> mm2 on IPD technology. Measured results show an in-band insertion loss of smaller than 1.7 dB and a wide stopband from 5.39 to 31.5 GHz.
Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN

Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe’s action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient’s upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method’s recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient’s rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.
Confidence intervals for time averages in the presence of long‐range correlations, a case study on Earth surface temperature anomalies

Time averages, a standard tool in the analysis of environmental data, suffer severely from long‐range correlations. The sample size needed to obtain a desired small confidence interval can be dramatically larger than for uncorrelated data. We present quantitative results for short‐ and long‐range correlated Gaussian stochastic processes. Using these, we calculate confidence intervals for time averages of surface temperature measurements. Temperature time series are well known to be long‐range correlated with Hurst exponents larger than 1/2. Multidecadal time averages are routinely used in the study of climate change. Our analysis shows that uncertainties of such averages are as large as for a single year of uncorrelated data.
Differential Photosensitivity of Fibroblasts Obtained from Normal Skin and Hypertrophic Scar Tissues

It is unclear whether normal human skin tissue or abnormal scarring are photoreceptive. Therefore, this study investigated photosensitivity in normal skin tissue and hypertrophic scars. The expression of opsins, which are photoreceptor proteins, in normal dermal fibroblasts (NDFs) and hypertrophic scar fibroblasts (HSFs) was examined. After exposure to blue light (BL), changes in the expression levels of αSMA and clock-related genes, specifically PER2 and BMAL1, were examined in both fibroblast types. Opsins were expressed in both fibroblast types, with OPN3 exhibiting the highest expression levels. After peripheral circadian rhythm disruption, BL induced rhythm formation in NDFs. In contrast, although HSFs showed changes in clock-related gene expression levels, no distinct rhythm formation was observed. The expression level of αSMA was significantly higher in HSFs and decreased to the same level as that in NDFs upon BL exposure. When OPN3 knocked-down HSFs were exposed to BL, the reduction in αSMA expression was inhibited. This study showed that BL exposure directly triggers peripheral circadian synchronization in NDFs but not in HSFs. OPN3-mediated BL exposure inhibited HSFs. Although the current results did not elucidate the relationship between peripheral circadian rhythms and hypertrophic scars, they show that BL can be applied for the prevention and treatment of hypertrophic scars and keloids.
An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Background Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust. Methods 420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed. Results 64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes. Conclusion Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.
Inhibitory Effect of Zingerone on Secretory Group IIA Phospholipase A2

The expression of secretory group IIA phospholipase A2 (sPLA2-IIA) has been shown to be elevated in various inflammatory diseases, and lipopolysaccharide (LPS) up-regulates the expression of sPLA2-IIA in human umbilical vein endothelial cells (HUVECs). Zingerone (ZGR), a phenolic alkanone isolated from ginger, has been reported to have various pharmacological activities. Here, we examined the effects of ZRG on the expression and activity of sPLA2-IIA in LPS-activated HUVECs and in mouse models of endotoxemia and sepsis. Treatment of cells or mice with ZRG inhibited LPS-induced expression and activity of sPLA2-IIA. In addition, ZRG suppressed LPS-mediated activation of cytosolic phospholipase A2 (cPLA2) and extracellular signal-regulated kinase (ERK) 1/2. These results suggest that ZRG inhibits LPS-mediated activation of sPLA2-IIA expression by suppressing cPLA2 and ERK 1/2.
Effects of gamma radiation on the stability and degradation kinetics of phenolic compounds and antioxidant activity during storage of (Oryza sativa L.) black rice flour

Luiz Gustavo Lacerda https://orcid.org/0000-0002-4689-5431 1 Graduate Program in Food Science and Technology – State University of Ponta Grossa (UEPG), Av. Carlos Cavalcanti 4748 Uvaranas Campus, CEP 84.030-900 – Ponta Grossa – PR – Brazil. 2 Department of Chemical Engineering and Food Engineering Federal University of Santa Catarina (UFSC), Florianópolis, SC, 88010-970, Brazil 3 Centre for Nuclear Energy in Agriculture, University of São Paulo (USP). Av. Centenário, 303, Luiz de Queiroz Campus, 13900-470, Piracicaba, Brazil.
‘Am I fixed, am I better now?’: undergoing MR-guided focused ultrasound for essential tremor: an interpretative phenomenological analysis

Introduction Essential tremor (ET) is characterised by postural and intentional tremor typically affecting the upper limbs, which can negatively impact functionality and quality of life. Magnetic Resonance-guided Focused Ultrasound (MRgFUS) is a novel and promising non-invasive treatment for ET which offers instantaneous results. Methods Using interpretative phenomenological analysis we explored the experience of undergoing MRgFUS in six ET patients as well as their experiences pre- and post-procedure. Results One-time, retrospective semi-structured interviews were conducted and six themes emerged: Life pre-treatment: “It’s everyday tasks that get you down” and “Most people who understand, they are okay. Some people aren’t”; MRgFUS: Treatment day: “Going into the unknown” and “There’s no way I was going to press that button”; and Life post-treatment: “One is good. Two is better” and “Am I fixed, am I better now? ” Discussion The findings point to a significant period of adjustment associated with living with ET and the effects of undergoing ET MRgFUS treatment. As ET progressed, participants struggled to cope with increasing symptoms and had to develop coping strategies to manage life with ET. The procedure itself was perceived as strange and extraordinary and despite some immediate adverse effects participants were determined to go through with it. Post procedure, all participants reported tremor suppression which was life changing. While some participants still felt burdened by ET, others expressed it took them a while to psychologically adjust to what essentially was their new body. This study has highlighted the need for patients to be supported at all stages of their ET journey.
The Analysis of Spatial Conflict Measurement in Fast Urbanization Region from the Perspective of Geography—A Case Study of Changsha-Zhuzhou-Xiangtan Urban Agglomeration

Spatial conflict is a kind of objective geographical phenomena, which is due to the scarcity of spatial resources and spillover of spatial function. It is an opposite phenomena associated with the resource allocation and competition in the process of human-land interaction. Geographic space is the basic object of research on geography, spatial pattern and process has been the core content of geographical research, the spatial conflict is the result as well as an influencing factor of spatial process, and it is also reflected in the spatial pattern. From the perspective of geography the basic point of space conflict is the disharmony of proportion structure, spatial pattern and mutual transformation process between different types of geographic space. Based on this, referring to landscape ecology index theory and method, identifying the connotation and calculating methods, and choosing Changsha-Zhuzhou- Xiangtan(Chang- Zhu- Tan) urban agglomeration to take the verification, the results showed that: 1) The spatial conflict index is the quantized index used to measure space conflict intensity, speed of changes and direction. It is an important basis of evaluation for spatial conflict risk, revealing the mechanism of the formation and evolution of spatial conflict.Based on the direction of geography, the types of spatial index can be divided into spatial type conflict index(STC), spatial structure conflict index(SSC) and spatial process conflict index(SPC). 2) It has been concluded from the test of spatial conflict index on Chang-ZhuTan urban agglomeration that the total spatial structure is barely reasonable. The spatial conflict has not happened yet, but the speed of transformation from un-constructional space to constructional space is too fast. Because the limited agriculture space, and irrational distribution of some districts especially industrial districts, conflict of spatial type and process has been emerged. Even in some units the space conflict has exceeded the upper limit threshold,where there is a large possibility for the happening of spatial conflict risk. Additionally, patterns of spatial conflicts present urban-rural gradient changes, and the spatial type conflict in city area is prominent, while the districts with serious spatial pattern and process conflict mainly are distributed in the suburbs or mutual connection of Chang-Zhu-Tan. After verification, the conclusions of empirical analysis are basically consistent with the actual situation,which further explains that the construction of spatial conflict index is scientific and has geographical significance.