Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the oncogenic roles of FAP in human cancers based on systematic analysis

Background: Fibroblast activation protein-α (FAP) is a specific marker of cancer-associated fibroblasts (CAFs) and plays a crucial role in tumor development. However, the biological processes underlying FAP expression in tumor progression and tumor immunity have not been fully elucidated. Methods: We utilized RNA-seq data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to perform differential analysis of FAP expression in tumor tissues and matched-normal tissues. The relationship between FAP expression and clinical prognosis, DNA methylation, and tumor-infiltrating immune cells in pan-cancer was assessed using R Studio (version 4.2.1). Additionally, we employed gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to investigate the biological functions and pathways associated with FAP expression. Results: FAP exhibits high expression in most malignancies, albeit to a lesser extent in CESC, KICH, UCEC, SKCM, THCA, and UCS. Furthermore, FAP is either positively or negatively associated with the prognosis of several malignancies. In seven types of cancer, FAP expression is positively correlated with DNA methylation. CIBERSORT analysis revealed an inverse correlation between FAP expression and T cells, B cells, monocytes, and NK cells, while it exhibited a positive correlation with M0, M1, and M2 macrophages. Enrichment analysis further demonstrated that FAP modulates the cell cycle, epithelial-mesenchymal transition (EMT) process, angiogenesis, and immune-related functions and pathways. Conclusion: Our findings indicate a close relationship between FAP expression and tumorigenesis as well as tumor immunity. FAP has the potential to serve as a diagnostic, prognostic, and immunotherapy marker.
Maize responses to reduced tillage, different plant residue mulch and nitrogen fertiliser on granitic sandy soils of Zimbabwe

Mulching in smallholder conservation agriculture (CA) systems is constrained by lack of adequate crop residues. A three-year study assessed the effects of reduced tillage systems combined with different plant residue mulch and nitrogen (N) fertiliser on nitrogen uptake using the normalised difference vegetation index (NDVI), maize growth and yield, and agronomic efficiency. A split plot design with three or four replicates was used. Maize, Hyparrhenia grass, leaf litter, sunnhemp and Tephrosia residue mulch in CA and a conventional control were the main treatments. The N subtreatments were 0, 60 and 120 kg N ha−1. The main treatments had a similar effect on NDVI, and N increased NDVI across the treatments. The conventional practice had taller plants compared with CA. The conventional and CA treatments had a similar effect on maize yields. The different plant residues in CA had a similar effect on yields. Nitrogen increased yield, and 60 and 120 kg N ha−1 had a similar effect. The 60 kg N ha−1 subtreatment had a higher agronomic N efficiency than 120 kg N ha−1. The study demonstrated that CA with different plant residues and the conventional practice give similar yields in the short term.
Recombinant Search at Temporary Gatherings

Temporary gatherings—such as conferences, trade shows, and festivals—create a forum where strangers meet one another, thereby helping people establish new social connections. The infusion of new social ties may permit knowledge transfers that facilitate recombinant search. However, knowledge transfer is not assured: ties newly formed at a temporary gathering may have low information carrying capacity. In this research project, we examine under what conditions temporary gatherings catalyze the creation of novel combinations. We study a large sample of novel technological combinations created at software development hackathons. We find that both the software developers’ prior knowledge and several attributes of hackathon context strongly influence the rate and direction of inventive activity at a hackathon.
Resultative and goal phrases in Polish and English: Interaction with aspect

Abstract The article examines the interaction of resultative and goal phrases with aspect in Polish and English. The first research problem concerns the ability of resultative and goal phrases to aspectually delimit (telicize) an atelic predicate. Data from English shows that resultative and goal phrases systematically make an atelic predicate telic in non-progressive sentences, but they fail to do so in progressive constructions. In Polish, imperfective (atelic) constructions can never be aspectually delimited by such phrases. It is argued that resultative and goal phrases lose their telicizing potential when in the scope of an aspectual functional head Asp specified as [-telic]. This is the case in English progressive and Polish imperfective sentences. The Asp head is able to override the telicity specification established compositionally within VP. The Asp head in Polish is obligatory and the value of its telicity feature ([+telic] for perfective and [-telic] for imperfective) is responsible for the interpretation of the VP selected by Asp as a complement. In English such projection is optional. When it is absent, the telicity of a predicate can be computed from the default aspectual type of the lexical verb combined with other elements inside VP, including resultative and goal phrases. A related problem also addressed in the article concerns the meaning of progressive / imperfective resultative and goal constructions. The problem is presented as part of a larger task of finding a proper analysis for accomplishment predicates combined with a progressive / imperfective operator. The proposed solution is based on the notion of directionality. It is suggested that resultative and goal constructions denote a process of some entity changing in the ‘direction’ of a new state. When the process is understood as having a culmination, the ‘direction’ of the transition indicates a new state actually holding of the entity undergoing transition. When a progressive / imperfective operator is introduced, it can remove the culmination leaving just the directed-transition process. A formalization of this analysis is provided.
THE EFFECT OF MANAGEMENT COMPENSATION, THIN CAPITALIZATION AND SALES GROWTH ON TAX AVOIDANCE WITH INSTITUTIONAL OWNERSHIP AS MODERATION

This study aims to examine the effect of management compensation, thin capitalization and sales growth with institutional ownership as a moderation on tax avoidance. The population used in this study is manufacturing goods sector manufacturing companies listed on the Indonesia Stock Exchange (IDX) in 2017-2020. The dependent variable is tax avoidance and the independent variable is management compensation, thin capitalization and sales growth. The moderating variable is institutional ownership. The control variables used are leverage, firm size. The sampling technique used was purposive sampling. The results of this study indicate that management compensation and thin capitalization have an effect on tax avoidance. Meanwhile, sales growth has no effect on tax avoidance. Institutional ownership can weaken the positive effect of management compensation, thin capitalization and sales growth on tax avoidance. This study also reveals that profitability and firm size control variables have an effect on tax avoidance.
Precoder Based Transceivers Design for Non-Orthogonal Multiple Access Mixed Numerologies

Orthogonal Frequency Division Multiplexing (OFDM) based mixed numerology scheme and Non-Orthogonal Multiple Access (NOMA) are the two important key features of 5G radio standard capable of handling diversified service requirements. However, the mixed numerology scheme suffers from an inherent problem called Inter Numerology Interference (INI) that arises due to non-orthogonality issue between any two numerologies, whereas NOMA suffers from potential Bit Error Rate (BER). In this research work, transmitter architecture based on windowing and pre-coding techniques to mitigate the INI and two receiver architectures based on the principle of Successive Interference Cancellation (SIC) and Combined Maximum Likelihood (CDML) decoding are proposed for optimal decoding of the information. The proposed system model is designed to operate under mixed numerology NOMA scenario. The BER performance analysis is done for both the receivers and compared with each other and the conclusive remarks are drawn.
Does reading about fictional minds make us more curious about real ones?

Although there is a large body of research assessing whether exposure to narratives boosts social cognition immediately afterward, not much research has investigated the underlying mechanism of this putative effect. This experiment investigates the possibility that reading a narrative increases social curiosity directly afterward, which might explain the short-term boosts in social cognition reported by some others. We developed a novel measure of state social curiosity and collected data from participants (N = 222) who were randomly assigned to read an excerpt of narrative fiction or expository nonfiction. Contrary to our expectations, we found that those who read a narrative exhibited less social curiosity afterward than those who read an expository text. This result was not moderated by trait social curiosity. An exploratory analysis uncovered that the degree to which texts present readers with social targets predicted less social curiosity. Our experiment demonstrates that reading narratives, or possibly texts with social content in general, may engage and fatigue social-cognitive abilities, causing a temporary decrease in social curiosity. Such texts might also temporarily satisfy the need for social connection, temporarily reducing social curiosity. Both accounts are in line with theories describing how narratives result in better social cognition over the long term.
A Hardware-Based Approach to Determine the Frequently Accessed DRAM Pages for Multi-Core Systems

It is likely that processor performance improvements will continue to outpace the improvements in memory latency. As processor architectures have been evolved, memory latency has become increasingly an obstacle in achieving optimal application performance. In this paper, a new Hardware-based approach is introduced to determine at run-time the DRAM pages that are frequently accessed (hot DRAM pages). This approach would be an effective and low-cost solution designed primarily to be used with other DRAM memory latency reduction mechanisms. Our experimental results reveal that the prediction accuracy of the hot DRAM pages at run-time obtained by our proposed approach is 88.1 % using a 256-entry history table.
Double-Layer Detection Model of Malicious PDF Documents Based on Entropy Method with Multiple Features

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.) and 82 dangerous features (such as suspicious and encoding function, etc.), which can effectively resist obfuscation and encryption. Second, we generate the best set of features (a total of 153) by creatively applying an entropy method based on RReliefF and MIC (EMBORAM) to PDF samples with 37 typical document vulnerabilities, which can effectively resist anti-detection methods, such as filling data and imitation attacks. Finally, we build a double-layer processing framework to detect samples efficiently through the AdaBoost-optimized random forest algorithm and the robustness-optimized support vector machine algorithm. Compared to the traditional static detection method, this model performs better for various evaluation criteria. The average time of document detection is 1.3 ms, while the accuracy rate reaches 95.9%.
Design method for industrial product using clothoid curve, industrial product designed by the design method, and method and device for numerical control using the clothoid curve

A design method for industrial products using a clothoid curve, wherein (A). the route of the motion of a mechanical element is designed by using a three-dimensional curve (called the three-dimensional clothoid curve) in which each of a pitch angle and a yaw angle in tangential direction is given by the quadratic of a curve length or curve length variables and (B). the route of a tool or the profile of a work is expressed by using a three-dimensional curve (called the three-dimensional clothoid curve) in which each of a pitch angle and a yaw angle in tangential direction is given by the quadratic of a curve length or curve length variables, and the motion of the tool is controlled by the three-dimensional curve.
Fault diagnosis of automaton based on EMD and close degree

Targeting the non-stationary characteristics of short and transient impulse vibration signals of high speed automaton, a new method based on empirical mode decomposition (EMD) and close degree to diagnose fault for high speed automaton is proposed in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), and then the EMD energy of each IMF is calculated. The correlation analysis is applied and the results show that the first three IMFs contained the most dominant fault information; therefore, the energy feature extracted from the first three IMFs that could serve as a feature vector for fault patterns recognition of automaton. Finally close degree evaluation method was used to diagnose the automaton fault. The experimental results indicate that the proposed approach put forward in this paper can effectively identify automaton fault patterns and it has a great application potential in condition monitoring and fault diagnosis of automaton. Copyright © 2013 IFSA.
A Review of Multi-Access Edge Devices Utilizing Software System Design for System Sectioning

Mobile cellular systems of the 5th generation (5G), which support highly-reliable & low-discontinuation systeming, actual time & adaptive data computation, heavy processing, as well as widespread device connection, offer a promising foundation for new, creative, & varied Iot systems. End-to-End (E2E) system slicing options offer a viable method for allocating & distributing resources that enables carriers to adaptably deliver configurable virtualization & devoted logic systeming over shared infrastructural facilities. A Multi-Access Edge Computation (MEC) technology must be integrated into 5G systems in order to support many of systeming slicing's application scenarios, including autonomous vehicles as well as Google Stadia. Edge computing is expected to be one of the driving forces behind 5G & 6G mobile cellular systems; however its function in system slicing is yet to explore.We look at MEC &system slicing based on application instances that are specific to the delivery of 5G services. With MEC application cases offering system scalability, adaptability, versatility, & automation, enhancements to the cloud-native 5G base have recently come into focus. A cloud-native micro-services framework is envisaged, along with some of its possible use applications for 5G system slicing. In addition, this paper goes into detail on current standardization initiatives, supporting advancements, & applications for E2E system slicing. This report concludes by outlining unresolved study problems & issues while offering potential fixes & suggestions.
When Machine Comes to Town: Fund Analysts’ Performance with Artificial Intelligence

: How does the introduction of artificial intelligence (AI) technology affect the performance of mutual fund analysts? Using a unique setting in which AI was introduced to generate ratings for previously uncovered mutual funds in a large financial research company, we find that the analyst ratings’ predictive power for future fund performance increases, suggesting higher rating quality. Further difference-in-differences analyses suggest two possible mechanisms through which AI adoption involving a separate set of funds affects the quality of human ratings: the reduction of analyst favoritism towards socially connected fund managers, and the availability of ratings for a large set of previously uncovered benchmark funds. The effects of AI adoption on fund analysts are more pronounced for analysts with stronger social ties with fund managers and higher past performance. This paper contributes to the growing literature on the interaction between AI and humans and provides unique insights on the mechanisms through which AI affects human performance.
Similarity Analysis of Klebsiella pneumoniae Producing Carbapenemases Isolated from UTI and Other Infections

Klebsiella pneumoniae is an important opportunistic pathogen responsible for severe infections, mainly urinary tract infections (UTIs) and pneumonia. Hospital epidemic infections caused by multiresistant strains of carbapenemase-producing K. pneumoniae are the most concerning. NDM-producing strains are resistant to a wide range of antibiotics and have become the most significant threat. Determining the natural reservoirs and routes of infections is essential to end hospital outbreaks. Understanding the relatedness of K. pneumoniae strains is essential to determine the range and nature of the infection. The study compared phylogenetic relatedness between multiresistant K. pneumoniae strains isolated from hospitalized patients. Susceptibility to drugs and mechanisms of resistance were confirmed using phenotypic methods. PFGE was used to analyze the relatedness between strains. We analyzed 69 K. pneumoniae strains from various healthcare units. The isolates were mainly identified from urine. Strains were resistant to β-lactam antibiotics with β-lactamase inhibitors, cephalosporins, and quinolones. Their susceptibility to aminoglycosides and carbapenem antibiotics was diverse. Most of the isolated strains produced New Delhi metallo-ß-lactamase (NDM). Although K. pneumoniae strains were classified into several genotype clusters, closely related isolates were confirmed in the same hospital’s wards, and in two hospitals in the same province.
The genotype–phenotype correlations of the CACNA1A-related neurodevelopmental disorders: a small case series and literature reviews

Background Genotype–phenotype correlations of the CACNA1A-related neurodevelopmental disorders such as global developmental delay (GDD)/intellectual disability (ID), epileptic encephalopathy (EE), and autism spectrum disorder (ASD) are unknown. We aimed to summarize genotype–phenotype correlations and potential treatment for CACNA1A-related neurodevelopmental disorders. Methods Six children diagnosed with CACNA1A-related neurodevelopmental disorders at Xiangya Hospital, Central South University from April 2018 to July 2021 were enrolled. The PubMed database was systematically searched for all reported patients with CACNA1A-related neurodevelopmental disorders until February 2023. Thereafter, we divided patients into several groups for comparison. Results Six patients were recruited from our hospital. Three cases presented with epilepsy, five with GDD/ID, five with ataxia, and two with ASD. The variants included p.G701R, p.R279C, p.D1644N, p.Y62C, p.L1422Sfs*8, and p. R1664Q [two gain-of-function (GOF) and four loss-of-function (LOF) variants]. About 187 individuals with GDD/ID harboring 123 variants were found (case series plus data from literature). Of those 123 variants, p.A713T and p.R1664* were recurrent, 37 were LOF, and 7 were GOF. GOF variants were linked with severe-profound GDD/ID while LOF variants were associated with mild–moderate GDD/ID (p = 0.001). The p.A713T variant correlated with severe-profound GDD/ID (p = 0.003). A total of 130 epileptic patients harboring 83 variants were identified. The epileptic manifestations included status epilepticus (n = 64), provoked seizures (n = 49), focal seizures (n = 37), EE (n = 29), absence seizures (n = 26), and myoclonic seizures (n = 10). About 49 (42.20%) patients had controlled seizures while 67 (57.80%) individuals remained with refractory seizures. Status epilepticus correlated with variants located on S4, S5, and S6 (p = 0.000). Among the 83 epilepsy-related variants, 23 were recurrent, 32 were LOF, and 11 were GOF. Status epilepticus was linked with GOF variants (p = 0.000). LOF variants were associated with absence seizures (p = 0.000). Six patients died at an early age (3 months to ≤5 years). We found 18 children with ASD. Thirteen variants including recurrent ones were identified in those 18 cases. GOF changes were more linked to ASD. Conclusion The p.A713T variant is linked with severe-profound GDD/ID. More than half of CACNA1A-related epilepsy is refractory. The most common epileptic manifestation is status epilepticus, which correlates with variants located on S4, S5, and S6.
Nmr-VSM: Non-Touch Motion-Robust Vital Sign Monitoring via UWB Radar Based on Deep Learning

In recent years, biometric radar has gained increasing attention in the field of non-touch vital sign monitoring due to its high accuracy and strong ability to detect fine-grained movements. However, most current research on biometric radar can only achieve heart rate or respiration rate monitoring in static environments, which have strict monitoring requirements and single monitoring parameters. Moreover, most studies have not applied the collected data despite their significant potential for applications. In this paper, we introduce a non-touch motion-robust vital sign monitoring system via ultra-wideband (UWB) radar based on deep learning. Nmr-VSM not only enables multi-dimensional vital sign monitoring under human motion environments but also implements cardiac anomaly detection. The design of Nmr-VSM includes three key components. Firstly, we design a UWB radar that can perform multi-dimensional vital sign monitoring, including heart rate, respiratory rate, distance, and motion status. Secondly, we collect real experimental data and analyze the impact of eight factors, such as motion status and distance, on heart rate monitoring. We then propose a deep neural network (DNN)-based heart rate data correction model that achieves high robustness in motion environments. Finally, we model the heart rate variability (HRV) of the human body and propose a convolutional neural network (CNN)-based anomaly detection model that achieves low-latency detection of heart diseases, such as ventricular tachycardia and ventricular fibrillation. Experimental results in a real environment demonstrate that Nmr-VSM can not only accurately monitor heart rate but also achieve anomaly detection with low latency.