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Abstract With the rapid global warming in recent decades, the Tibetan Plateau (TP) has suffered severe impacts, such as glacier retreat, glacial lake expansion, and permafrost degradation, which threaten the lives and properties of the local and downstream populations. Regional Reanalysis (RR) is vital for TP due to the limitations of observations. In this work, a 62-year (1961–2022) long atmospheric regional reanalysis with spatial resolution of 9 km (convective gray-zone scale) and temporal resolution of 1 hour over the TP (TPRR) was developed using the Weather Research and Forecasting (WRF) model, combined with re-initialization method, spectral nudging (SN), and several optimizations. TPRR is forced by ERA5 at hourly intervals. TPRR outperforms ERA5, realistically capturing climatological characteristics and seasonal variations of precipitation and T2m (air temperature at 2m above ground level). Moreover, TPRR better reproduces the frequency and intensity of precipitation, as well as the diurnal cycle of precipitation. This study also quantifies the wetting trend of 0.0071 mm/year over the TP amid global warming using TPRR.

Abstract The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds.

Abstract Distant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval. Low expression of VWA5A gene in METABRIC cohort was associated with poor survival in BCs, especially in hormone receptor (HR)-positive BCs. In-vitro experiments confirmed tumor suppressive effect of VWA5A on BCs in HR+ and triple-negative BC cell lines. We found that expression of VWA5A can be assessed by immunohistochemistry (IHC) on archival tissue samples. Decreasing nuclear expression of VWA5A was significantly associated with advanced T stage and lymphatic invasion in consecutive BCs of all subtypes. We discovered lower expression of VWA5A as the potential biomarker for metastasis-prone BCs, and our results support the clinical utility of VWA5A IHC, as an adjunctive tools for prognostication of BCs.

We investigate the effects of incorporating single-walled carbon nanotubes (CNTs) into sol–gel-derived ZnO thin films to enhance their optoelectronic properties for photodetector applications. ZnO thin films were fabricated on c-plane sapphire substrates with varying CNT concentrations ranging from 0 to 2.0 wt%. Characterization techniques, including high-resolution X-ray diffraction, photoluminescence, and atomic force microscopy, demonstrated the preferential growth of the ZnO (002) facet and improved optical properties with the increase in the CNT content. Electrical measurements revealed that the optimal CNT concentration of 1.5 wt% resulted in a significant increase in the dark current (from 0.34 mA to 1.7 mA) and peak photocurrent (502.9 µA), along with enhanced photoresponsivity. The rising and falling times of the photocurrent were notably reduced at this concentration, indicating improved charge dynamics due to the formation of a p-CNT/n-ZnO heterojunction. The findings suggest that the incorporation of CNTs not only modifies the structural and optical characteristics of ZnO thin films but also significantly enhances their electrical performance, positioning CNT-ZnO composites as promising candidates for advanced photodetector technologies in optoelectronic applications.

Under extended-phase-shift (ESP) control, the current stress of the dual active bridge converter (DAB) is relatively high, which reduces the efficiency of the converter. To solve this problem, a particle swarm optimization (PSO) algorithm based on minimizing the current stress is proposed in this paper. The optimal phase-shift ratio of the DAB converter with ESP control is obtained by using the algorithm’s optimization characteristic. This approach ensures that the converter achieves minimal current stress, thereby enhancing the steady-state performance of the DAB converter. Moreover, in terms of dynamic performance, traditional PI control has poor dynamic response ability when there are sudden changes in load and input voltage. To solve this problem, the voltage dynamic matrix control (DMC) algorithm is introduced to combine with the PSO algorithm to minimize the current stress of the DAB converter under EPS control while enhancing the dynamic response capability of the DAB converter. A simulation model was constructed for comparative validation on MATLAB/Simulink 2019, demonstrating the correctness and effectiveness of the improved control method.

This study investigates the impact of spin-coating speed on the performance of carbon nanotube (CNT)-based optoelectronic synaptic devices, focusing on their long-term memory properties. CNT films fabricated at lower spin speeds exhibited a greater thickness and density compared to those at higher speeds. These denser films showed enhanced persistent photoconductivity, resulting in higher excitatory postsynaptic currents (EPSCs) and the prolonged retention of memory states after UV stimulation. Devices coated at a lower spin-coating speed of 2000 RPM maintained EPSCs above 70% for 3600 s, outperforming their higher-speed counterparts in long-term memory retention. Additionally, the study demonstrated that the learning efficiency improved with repeated UV stimulation, with fewer pulses needed to achieve the maximum EPSC in successive learning cycles. These findings highlight that optimizing spin-coating speeds can significantly enhance the performance of CNT-based synaptic devices, making them suitable for applications in neuromorphic computing and artificial neural networks requiring robust memory retention and efficient learning.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.

Abstract To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance. In particular, we inferred gene regulatory network (GRN) and operons of radiation-resistance bacterium Spirosoma montaniterrae DY10 $$^T$$ T and identified the major regulators for radiation-resistance. Our results showed that DNA repair and reactive oxygen species (ROS) scavenging mechanisms are key processes and Crp/Fnr family transcriptional regulator works as a master regulatory TF in early response to radiation.

Real-time monitoring of rainfall areas based on satellite remote sensing is of vital importance for extreme rainfall research and disaster prediction. In this study, a new rainfall area identification algorithm was developed for the new generation of geostationary satellites with high spatial and temporal resolution and rich bands. As the main drivers of the rainfall process, the macro and micro physical properties of clouds play an important role in the formation and development of rainfall. We considered differences in the absorption capacity of the water vapor absorption channels in the infrared band and introduced a sensitivity difference of rainfall area in water vapor channels to construct a sensitive detection of the water vapor region. The results of this algorithm were evaluated using Global Precipitation Measurement (GPM) satellite products and CloudSat measurements in various scenarios, with hit rates of 70.03% and 81.39% and false alarm rates of 2.05% and 21.34%, respectively. Spatiotemporal analysis revealed that the types of upper clouds in the rainfall areas mainly consisted of deep convection, cirrostratus, and nimbostratus clouds. Our study provides supporting data for weather research and disaster prediction, as well as an efficient and reliable method for capturing temporal and spatial features.

Measurement(s) land surface temperature Technology Type(s) satellite imaging Sample Characteristic - Environment planetary surface Sample Characteristic - Location global

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties.

Abstract In this study, we aimed to investigate the recovery after traumatic spinal cord injury (SCI) by inducing cellular differentiation of transplanted neural stem cells (NSCs) into neurons. We dissociated NSCs from the spinal cords of Fisher 344 rat embryos. An injectable gel crosslinked with glycol chitosan and oxidized hyaluronate was used as a vehicle for NSC transplantation. The gel graft containing the NSC and positively charged gold nanoparticles (pGNP) was implanted into spinal cord lesions in Sprague–Dawley rats (NSC‐pGNP gel group). Cellular differentiation of grafted NSCs into neurons (stained with β‐tubulin III [also called Tuj1]) was significantly increased in the NSC‐pGNP gel group (***p

Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5’s horizontal resolution and MERRA-2’s vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data’s RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research.

Total precipitable water (TPW) is a key parameter in global hydrological cycle and plays an important role in climate change as a kind of green house. Especially, a long-term TPW dataset will help us to better understand its impact on hydrological cycle and our living environment under the background of warming climate. Thus, a long-term global TPW product from 2002 to 2017 was created for the first time at the resolution of 0.25° × 0.25° based on the observations from passive microwave radiometers AMSR-E and AMSR2 and TPW product from AIRS2RET in this study. Based on the newly created TPW product, a long-term trend analysis was carried out on both global and regional scale. According to the long-term trend analysis, the linear trend of global average TPW anomaly is 0.34 mm/decade. Especially, there is a faster increasing speed for TPW in recent decade from 2008 to 2017. For the trend analysis on regional scale, the most noteworthy thing is that there is a negative linear trend of TPW anomaly in the atmosphere over rainforest distributed in the Amazon and central Africa, which means the atmosphere is becoming dryer in these areas. The drying atmosphere will cause a decrease in precipitation and will further cause a great damage to the rainforests in the Amazon and central Africa if the dry trend continues.

For the timely retrieval of global positioning system based precipitable water vapor (GPS-PWV) over complex topography when there is no in situ pressure-derived zenith hydrostatic delays (ZHDs), this article evaluates three types of ready-made ZHD products, including two newly released products from the Vienna University of Technology (TU Wien, TUW-VMF3) and GeoForschungsZentrum Potsdam (GFZ-VMF1) and a legacy product TUW-VMF1. We implement them for the first time for GPS-PWV retrieval in regions with highly variable topography, such as the Tibetan plateau (TP). First, we present a refined method [model-assisted ZHD height adjustment, model-assisted ZHD height adjustment (MAZHA)] for implementing the above VMF1/VMF3-like ZHD products. The results reveal that the MAZHA method improves the implementation accuracy over the TP by 70% compared to the conventional method. Then, the above multisource VMF1/VMF3-like ZHD products are evaluated using the in situ pressure-derived ZHDs across the TP. The results show that the GFZ-VMF1 outperforms the TUW-VMF1 mainly because a more advanced underlying numerical weather model (ERA5) are adopted. TUW-VMF3 achieves the best performance mainly because it is provided with improved horizontal resolution. Finally, we find that implementing the state-of-the-art TUW-VMF3 ZHD product for GPS-PWV retrieval yields no statistically significant errors, even under extreme weather conditions, and the retrieved GPS-PWVs can well characterize the PWV diurnal variations in all seasons except winter. Additionally, considering the fact that the operational and forecasted TUW-VMF3 ZHDs present almost equivalent performance, this article is also beneficial to real-time GPS-PWV retrieval using forecasted TUW-VMF3 ZHDs, thus allowing for the ease of real-time GPS-PWV retrieval.

Abstract Background In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis. Methods First, we investigated the effect of mutations in DNA methylation modification genes on genome-wide methylation profiles. We collected 3,644 samples that have both of mRNA and methylation data from 12 major cancer types in The Cancer Genome Atlas (TCGA). The samples were divided into two groups according to the mutational signature. Differentially methylated regions (DMR) that overlapped with the promoter region were selected using minfi and differentially expressed genes (DEG) were identified using EBSeq. By integrating the DMR and DEG results, we constructed a comprehensive DNA methylome profiles on a pan-cancer scale. Second, we investigated the effect of DNA methylations in the promoter regions on downstream genes by comparing the two groups of samples in 11 cancer types. To investigate the effects of promoter methylation on downstream gene activations, we performed clustering analysis of DEGs. Among the DEGs, we selected highly correlated gene set that had differentially methylated promoter regions using graph based sub-network clustering methods. Results We chose an up-regulated DEGs cluster where had hypomethylated promoter in acute myeloid leukemia (LAML) and another down-regulated DEGs cluster where had hypermethylated promoter in colon adenocarcinoma (COAD). To rule out effects of gene regulation by transcription factor (TF), if differentially expressed TFs bound to the promoter of DEGs, that DEGs did not included to the gene set that effected by DNA methylation modifiers. Consequently, we identified 54 hypomethylated promoter DMR up-regulated DEGs in LAML and 45 hypermethylated promoter DMR down-regulated DEGs in COAD. Conclusions Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.

The use of ionising radiation (IR) for medical diagnosis and treatment procedures has had a major impact on the survival of paediatric patients. Although the benefits of these techniques lead to efficient health care, evaluation of potential associated long-term health effects is required. HARMONIC aims to better understand the increased risk of cancer and non-cancer effects after exposure to medical IR in children with cancer treated with modern external beam radiotherapy (EBRT) – radiation energy in MeV range – and in children with cardiac defects diagnosed and treated with cardiac fluoroscopy procedures (CFP) – radiation energy in keV range. The project investigates, among survivors of paediatric cancer, potential endocrine dysfunction, cardiovascular and neurovascular damage, health-related quality of life and second (and subsequent) primary cancer (SPC). The cardiac component builds a pooled cohort of approximately 90 000 paediatric patients who underwent CFP during childhood and adolescence to investigate cancer risk following exposure to IR and explore the potential effects of conditions predisposing to cancer. HARMONIC develops software tools to allow dose reconstruction in both EBRT and CFP to enable epidemiological investigations and future optimisation of treatments. With the creation of a biobank of blood and saliva samples, HARMONIC aims to provide a mechanistic understanding of radiation-induced adverse health effects and identify potential biomarkers that can predict these effects.