Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining the function of macrophage oxidative stress response and immune system in glioblastoma multiforme through analysis of single-cell transcriptomics

Background Glioblastoma (GBM), a prevalent malignant neoplasm within the neuro-oncological domain, has been a subject of considerable scrutiny. Macrophages, serving as the principal immunological constituents, profoundly infiltrate the microenvironment of GBM. However, investigations elucidating the intricate immunological mechanisms governing macrophage involvement in GBM at the single-cell level remain notably limited. Methods We conducted a comprehensive investigation employing single-cell analysis, aiming to redefine the intricate cellular landscape within both the core and peripheral regions of GBM tumors. Our analytical focus extended to the profound study of macrophages, elucidating their roles within the context of oxidative stress, intercellular information exchange, and cellular trajectories concerning GBM and its assorted subpopulations. We pursued the identification of GBM prognostic genes intricately associated with macrophages. Utilizing experimental research to investigate the relevance of MANBA in the context of GBM. Results Our investigations have illuminated the central role of macrophages in the intricate interplay among various subpopulations within the GBM microenvironment. Notably, we observed a pronounced intensity of oxidative stress responses within macrophages when compared to their GBM counterparts in other subpopulations. Moreover, macrophages orchestrated intricate cellular communication networks, facilitated by the SPP1-CD44 axis, both internally and with neighboring subpopulations. These findings collectively suggest the potential for macrophage polarization from an M1 to an M2 phenotype, contributing to immune suppression within the tumor microenvironment. Furthermore, our exploration unearthed GBM prognostic genes closely associated with macrophages, most notably MANBA and TCF12. Remarkably, MANBA appears to participate in the modulation of neuroimmune functionality by exerting inhibitory effects on M1-polarized macrophages, thereby fostering tumor progression. To bolster these assertions, experimental validations unequivocally affirmed the promotional impact of MANBA on GBM, elucidated through its capacity to curb cell proliferation, invasiveness, and metastatic potential. Conclusion These revelations represent a pivotal step towards unraveling the intricate immunological mechanisms governing the interactions between macrophages and diverse subpopulations within the GBM milieu. Furthermore, they lay the foundation for the development of an innovative GBM prognostic model, with MANBA at its epicenter, and underscore the potential for novel immunotherapeutic targets in the ongoing pursuit of enhanced treatment modalities for this formidable malignancy.
Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data

Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usually based solely on the production data with no systematic link to the geological and geophysical data, and therefore, the obtained permeability distribution may be not geologically consistent. Several other reparameterization techniques have attempted to preserve geological consistency by incorporating 4D seismic data; however, these techniques cannot be applied to our fractured basement reservoirs (FBRs) as they do not have 4D seismic data. Taking into account these challenges, in this study, an AHM methodology and workflow have been developed using a new reparameterization technique. This approach attempts to minimize the potential for geological nonconsistency of the calibrated results by linking the permeability to geophysical data. The proposed methodology can be applied to fields with only traditional geophysical data (3D seismic and conventional well logs). In the proposed workflow, the spatial distributions of seismic attributes and geomechanical properties were calculated and estimated from 3D seismic data and well logs, respectively. After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. Then, the calibration of the permeability distribution is performed by adjustment of the ANN model. Modification of the ANN model is performed using the simultaneous perturbation stochastic approximation (SPSA) algorithm to calibrate transmission coefficients in the ANN model to minimize the discrepancy between the simulated results and observed data. The developed methodology is applied to calibrate the permeability distribution of a simulation model of Bach Ho FBR in Vietnam. The effectiveness of the methodology is evident by comparing the historical matches with an available manually history-matched simulation model. The application shows that the proposed methodology could be considered as a suitable practical approach for adjusting the permeability distribution for FBR reservoir simulation models.
Role of CD1d and iNKT cells in regulating intestinal inflammation

Invariant natural killer T (iNKT) cells, a subset of unconventional T cells that recognize glycolipid antigens in a CD1d-dependent manner, are crucial in regulating diverse immune responses such as autoimmunity. By engaging with CD1d-expressing non-immune cells (such as intestinal epithelial cells and enterochromaffin cells) and immune cells (such as type 3 innate lymphoid cells, B cells, monocytes and macrophages), iNKT cells contribute to the maintenance of immune homeostasis in the intestine. In this review, we discuss the impact of iNKT cells and CD1d in the regulation of intestinal inflammation, examining both cellular and molecular factors with the potential to influence the functions of iNKT cells in inflammatory bowel diseases such as Crohn’s disease and ulcerative colitis.
Rare earth elements application in agriculture

Rare earth elements (REEs) are a group of chemical elements that include lanthanides as well as scandium and yttrium. Today REEs are used in various industries, such as agriculture where they are used as micro fertilizers and feed additives, the latter being used in medicine as well. There is no indication that REEs might be essential for any form of life. At lower concentrations, they can favorably influence certain physiological processes of plants (enzyme activity, hormone content, photosynthesis, seed germination, plant growth, etc.). They may induce an increase in some antioxidant systems and thereby increase the tolerance of plants to environmental stressors caused by high concentrations of heavy metals, herbicides, lack of water and essential nutrients, UV radiation and oxidative stress. Thus, their favorable effect was documented regarding the yield of cultivated species as well as the effect of their chemical composition on the content of vitamin C, soluble sugars and essential elements, reduction of the concentration of toxic heavy metals, improvement of the quality of wheat kernel for different uses. REEs have been commonly used as feed additives in organic and inorganic forms in livestock production. The available literature on the use of REEs as feed additives in livestock suggests positive outcomes (affected various physiological processes, increase in milk, egg and meat production, promoted growth and reproductive performance), but further investigation and results are needed before extending their use to zootechnical purposes.
A hybrid spectral-spatial fusion technique for hyperspectral object classification

In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the feature fusion of both spectral and spatial have been done. The current feature fusion method fails to retain semantic object intrinsic feature; further, current classification technique induces higher misclassification. In addressing the research issues this paper introduces a hybrid spectral-spatial fusion (HSSF) technique to reduce feature size and retains object intrinsic properties. Finally, in reducing misclassification a soft-margins kernel is introduced in support vector machine (SVM). Experiment is conducted on standard Indian Pines dataset; the result shows the HSSF-SVM model attain much higher accuracy and Kappa coefficient performance.
Genome analysis l (cid:2) PBWT: a lightweight r-indexing of the PBWT for storing and querying UK Biobank data

Motivation: The Positional Burrows–Wheeler Transform ( PBWT ) is a data structure that indexes haplotype sequences in a manner that enables ﬁnding maximal haplotype matches in h sequences containing w variation sites in Oð hw Þ time. This represents a signiﬁcant improvement over classical quadratic-time approaches. However, the original PBWT data structure does not allow for queries over Biobank panels that consist of several millions of haplotypes, if an index of the haplotypes must be kept entirely in memory. Results: In this article, we leverage the notion of r-index proposed for the BWT to present a memory-efﬁcient method for constructing and storing the run-length encoded PBWT, and computing set maximal matches (SMEMs) queries in haplotype sequences. We implement our method, which we refer to as l (cid:2) PBWT , and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the l (cid:2) PBWT reduces the memory usage up to a factor of 20% compared to the best current PBWT-based indexing. In particular, l (cid:2) PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in about a third of the space of its BCF ﬁle. l (cid:2) PBWT is an adaptation of techniques for the run-length compressed BWT for the PBWT (RLPBWT) and it is based on keeping in memory only a succinct representation of the RLPBWT that still allows the efﬁcient computation of set maximal matches (SMEMs) over the original panel.
Sibling earthquakes generated within a persistent rupture barrier on the Sunda megathrust under Simeulue Island

A section of the Sunda megathrust underneath Simeulue is known to persistently halt rupture propagation of great earthquakes, including those in 2004 (Mw 9.2) and 2005 (Mw 8.6). Yet the same section generated large earthquakes in 2002 (Mw 7.3) and 2008 (Mw 7.4). To date, few studies have investigated the 2002 and 2008 events, and none have satisfactorily located or explained them. Using near‐field InSAR, GPS, and coral geodetic data, we find that the slip distributions of the two events are not identical but do show a close resemblance and largely overlap. We thus consider these earthquakes “siblings” that were generated by an anomalous “parent” feature of the megathrust. We suggest that this parent feature is a locked asperity surrounded by the otherwise partially creeping Simeulue section, perhaps structurally controlled by a broad morphological high on the megathrust.
A Quad-Band Highly Selective Frequency Selective Surface with Ultra-Wideband Rejection

In this paper, a highly selective quad-band frequency selective surface (FSS) with ultra-wideband rejection is presented. The proposed FSS structure was developed by cascading five metallic layers by three thin dielectric substrates. The five metallic layers are composed of two bent slot layers, two metallic square rings, and a metal patch. The dimensions of the unit cell are 0. 13λ0× 0. 13λ0× 0. 18λ0 (λ0 is the free-space wavelength at the first operating frequency). The proposed structure achieves four transmission bands and has two wide stop-bands located at 1 to 5.5 GHz and 14 to 40 GHz, with a suppressed transmission coefficient below −20 dB. In order to verify the simulation results, an FSS prototype was fabricated and measured. It can be observed that the measured results are in favorable agreement with the simulation results. Its multiple narrow passbands and highly selective and ultra-wideband rejection properties ensure that our design can play a significant role in narrowband antennas, spatial filters, and many other fields.
Impact assessment of climate change on rice productivity in the Indochinese Peninsula using a regional‐scale crop model

In order to assess the impact of climate change on rice yields for the Indochina peninsula region, a regional rice model was forced with climate variables from CORDEX‐East Asia climate models. The future climate's impact on rice yield varies between countries, and it is dependent on both current climate conditions and the projected future climate. Climate change alone would lead to a 3.5–23.2% and 5.8–27.2% reduction in rice yield for the 2020s and 2040s, respectively, in the Indochinese peninsula, with the largest decreases in Cambodia. The negative impacts of climate change on rice can be partly offset by the positive effect of CO2 fertilization. For the Indochinese peninsula regions, changes in rice yield are quite sensitive to increases in temperature, and each one‐degree increase in temperature can cause about a 10.26% reduction in rice yields. The uncertainty in the predicted temperature from the regional climate models used contributed to approximately 59.7% of the uncertainty in rice yield projections. This result implies that the uncertainty in the projected temperatures from climate models could be one of the major sources of uncertainty in impact assessments. In this study, adaptation strategies, including changes in planting dates and use of irrigation and heat tolerant varieties, were tested. The use of tolerant varieties and shifting planting date are simple management responses that may offset a small part of the negative impact of climate change. For all the countries in the Indochinese peninsula, the CO2 fertilization effect and irrigation can offset most of the negative impacts caused by climate change by 2050, and irrigation is an effective adaptation method.
BTC: A Binary and Triangle Combined Descriptor for 3-D Place Recognition

Accurate and robust place recognition is essential for robot navigation, yet achieving full pose invariance and high performance across diverse scenes remains challenging. In this work, we propose a novel global and local combined descriptor named binary triangle combined (BTC) descriptor. We first extract the keypoints of a point cloud by projecting the points to planes extracted therein. Any three keypoints form a unique triangle, with the lengths of its sides constituting a triangle descriptor that captures the global appearance of the point cloud. Thanks to the distinct shape of a triangle given three side lengths, the similarity between two triangles and their vertices (i.e., keypoints) correspondence can be naturally determined from the side lengths of the triangle descriptors. The matched triangle pairs evaluate the appearance similarity between two point clouds, while the vertices’ correspondence enables accurate estimation of their relative pose; both are crucial for the place recognition task. To enhance the accuracy of triangle matching, BTC introduces a binary descriptor, which describes the point distribution neighboring each keypoint. The local geometry information encoded by the binary descriptor augments descriptiveness and discriminativeness to the triangle descriptor. Collectively, the two descriptors achieve both global and local descriptions of the environment with high accuracy, efficiency, and robustness. We extensively compare the proposed BTC descriptor against state-of-the-art methods (e.g., Scan Context, LCD-Net) on a wide range of datasets collected using different types of LiDAR sensors (spinning LiDARs and nonrepetitive scanning LiDARs) in various environments (urban, campus, forest, park, and mountain). The quantitative results demonstrate that BTC exhibits greater adaptability and significant improvement in precision compared to its counterparts, especially in challenging cases with large viewpoint variations (e.g., reverse direction, large translation, and/or rotation).
Characteristics of elderly diabetes patients: focus on clinical manifestation, pathogenic mechanism, and the role of traditional Chinese medicine

Diabetes mellitus has become a major public health issue globally, putting an enormous burden on global health systems and people. Among all diseased groups, a considerable part of patients are elderly, while their clinical features, pathogenic processes, and medication regimens are different from patients of other ages. Despite the availability of multiple therapies and techniques, there are still numerous elderly diabetes patients suffering from poor blood glucose control, severe complications, and drug adverse effects, which negatively affect the quality of life in their golden years. Traditional Chinese Medicine (TCM) has been widely used in the treatment of diabetes for several decades, and its relevant clinical practice has confirmed that it has a satisfactory effect on alleviating clinical symptoms and mitigating the progression of complications. Chinese herbal medicine and its active components were used widely with obvious clinical advantages by multiple targets and signaling pathways. However, due to the particular features of elderly diabetes, few studies were conducted to explore Traditional Chinese Medicine intervention on elderly diabetic patients. This study reviews the research on clinical features, pathogenic processes, treatment principles, and TCM treatments, hoping to provide fresh perspectives on the prevention and management strategies for elderly diabetes.
Cutting the Frame: An In-Depth Look at the Hitchcock Computer Vision Dataset

This paper presents a comprehensive dataset comprising annotations generated by the Google Vision API for approximately 105,000 frames extracted from 15 Alfred Hitchcock films. These annotations include information about object detection, facial recognition, web-entity analysis, and explicit content filtering. With potential applications in the digital humanities and film studies, this dataset enables researchers to not only explore and evaluate cinematic content but also the ways that it resurfaces in various cultural contexts online. The paper provides a detailed account of the dataset creation process, which involved the decryption of the DVD, frame extraction, and costs of annotations. Additionally, the paper outlines future research possibilities based on the dataset. These include statistical analysis of frame content and labels to identify patterns and trends, comparisons of different computer vision algorithms to assess their accuracy and effectiveness, and the utilization of bipartite networks to explore mise-en-scene in films.
Economic costs of reservoir sedimentation: A regional approach to estimating cropland erosion damage

ABSTRACT: A regional approach to estimating sediment damages in lakes and reservoirs indicated that 0.22% of the nation9s water storage capacity is lost annually. Of this, an average of 24% is due to soil erosion on cropland. The greatest water storage capacity losses from deposited sediment originating on cropland occurred in the central United States. Annual national damages to storage capacity ranged from $597 million to $819 million, with cropland9s contribution being $144 million to $197 million.
WiFi-Aided Ultra Wideband Localization in Indoor NLoS Environment

The Non-Line-of-Sight (NLoS) base station is the main factor making Ultra Wideband (UWB) localization accuracy decrease. To handle this issue, we propose a <inline-formula> <tex-math notation="LaTeX">${W}$ </tex-math></inline-formula>iFi-<inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>ided <inline-formula> <tex-math notation="LaTeX">${U}$ </tex-math></inline-formula>WB <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>ocalization (WAUL) system in this letter, including 1) NLoS base station identification algorithm, 2) fusion localization algorithm combining WiFi fingerprint-based and UWB range-based localization results. Specifically, the proposed identification algorithm first divides the ranging results of different UWB base stations into a few of subsets, and obtains different location estimated results by these subsets, then identifies NLoS base stations and filters them out before a location decision is made, hence maintaining localization accuracy under NLoS environment. Furthermore, WiFi fingerprint-based localization technology is leveraged in WAUL to calibrate the localization results obtained from UWB for providing a accurate result. Experimental results in a real scenario show that WAUL can effectively identify the NLoS base stations and enable to enhance localization accuracy.
Risk Factors for Bleeding Events in Japanese Patients with Advanced Lung Cancer: Data from the Rising-VTE/NEJ037 Study

Simple Summary Cancer increases the risk of venous thrombosis (VTE) and anticoagulant therapy is often used to treat this condition. Therefore, it is essential to identify risk factors for bleeding events in these patients. In our study, we used data from the Rising-VTE/NEJ037 study, which involved Japanese patients with advanced lung cancer, to calculate these risk factors. We found that complications with VTE were the most significant risk factor. This finding underscores the importance of carefully assessing the risks and benefits before starting anticoagulant therapy in patients with cancer. Our research aims to inform safer treatment strategies for patients with advanced lung cancer, which could have meaningful implications for their care and treatment approaches in the research community. Abstract Despite the occurrence of various hemorrhagic events during advanced lung cancer treatment, few researchers have reported on their risk factors. Moreover, the development of cancer-related thromboembolism indicates anticoagulant use. However, adverse events such as bleeding should be monitored. In this study, we aimed to identify factors that influence the onset of hemorrhagic events in patients with lung cancer. The Rising-VTE/NEJ037 study was a multicenter, prospective, observational study. A total of 1008 patients with lung cancer who were unsuitable for radical resection or radiation were enrolled and followed up for 2 years. Multivariate analysis using a Cox proportional hazard model was performed to compare the outcomes of the time to the onset of hemorrhagic events for 2 years after registration. Hemorrhagic events occurred in 115 patients (11.4%), with 35 (30.4%) experiencing major bleeding. Significant risk factors included venous thromboembolism (VTE) (hazard ratio [HR]: 4.003, p < 0.001) and an Eastern Cooperative Oncology Group Performance Status score of 1 (HR: 2.476, p < 0.001). Factors that significantly reduced hemorrhagic event risk were female sex (HR: 0.454, p = 0.002) and M1a status (HR: 0.542, p = 0.038). VTE is a risk factor for hemorrhagic events in patients with advanced lung cancer, and risks associated with anticoagulant therapy should be considered.