Miracle-dz/UniFAR-FTUs huggingface dataset:
Nuclear translocation of thioredoxin-1 promotes colorectal cancer development via modulation of the IL-6/STAT3 signaling axis through interaction with STAT3

Background: Thioredoxin 1 (Trx-1) is a small redox protein predominantly localized in the cytoplasm. Its expression is increased in several cancers, including colorectal cancer (CRC). However, the function of Trx-1 translocation to the nucleus in cancer is not clear. In this study, we investigated the role of Trx-1 nuclear translocation in development of CRC. Methods: Expression of Trx-1 and STAT3 was analyzed by Western blot and immunofluorescence. Endogenous interaction of Trx-1, STAT3, and karyopherin α1 in CRC cells was analyzed by co-immunoprecipitation. Trx-1 and pSTAT3 nuclear staining in human CRC tissues was analyzed by immunohistochemistry. A mouse model of AOM/DSS induced colitis-associated cancer (CAC) was utilized to investigate the antitumor effect of PX-12, a Trx-1 inhibitor. A knockin mouse with the Txn1(KK81-82EE) mutation was generated via CRISPR/Cas9, and CAC was induced in knockin and wild-type mice. Results: Nuclear translocation of Trx-1 was induced by IL-6, and inhibition of this translocation reversed IL-6-induced epithelial-to-mesenchymal transition, invasion and metastasis. Karyopherin α1 was found to specifically mediate IL-6-induced translocation of the Trx-1-pSTAT3 complex into the nucleus. Nuclear Trx-1 expression was closely correlated with lymph node metastasis and distant metastasis in human CRC. In addition, nuclear staining of Trx-1 showed significant positive correlation with nuclear staining of pSTAT3 in human CRC tissues. PX-12, an inhibitor of Trx-1, significantly impaired the activation of STAT3 and suppressed the development of AOM/DSS-induced CAC in mice. Moreover, AOM/DSS-induced nuclear Trx-1 expression was suppressed in Txn1(KK81-82EE) mice, which inhibited STAT3 activation and cancer progression. Conclusions: These results provide new insights into the mechanisms of STAT3 activation triggered by IL-6 and identify nuclear translocation of Trx-1 as a potential therapeutic target for the treatment of CRC and CAC.
Location-Based Social Networks: Latent Topics Mining and Hybrid Trust-Based Recommendation

The rapid advances of the 4th generation mobile networks, social media and the ubiquity of the advanced mobile devices in which GPS modules are embedded have enabled the location-based services, especially the Location-Based Social Networks (LBSNs) such as Foursquare and Facebook Places. LBSNs have been attracting more and more users by providing services that integrate social activities with geographic information. In LBSNs, a user can explore places of interests around his current location, check in at these venues and also selectively share his check-ins with the public or his friends. LBSNs have accumulated large amounts of information related to personal or social activities along with their associated location information. Analyzing and mining LBSN information are important to understand human preferences related to locations and their mobility patterns. Therefore, in this thesis, we aim to understand the human mobility behavior and patterns based on huge amounts of information available on LBSNs and provide a hybrid trust-based POI recommendation for LBSN users. In this dissertation, we first carry out a comprehensive and quantitative analysis about venue popularity based on a cumulative dataset collected from greater Pittsburgh area in Foursquare. It provides a general understanding of the online population's preferences on locations. Then, we employ a probabilistic graphical model to mine the check-in dataset to discover the local geographic topics that capture the potential and intrinsic relations among the locations in accordance with users' check-in histories. We also investigate the local geographic topics with different temporal aspects. Moreover, we explore the geographic topics based on travelers' check-ins. The proposed approach for mining the latent geographic topics successfully addresses the challenges of understanding location preferences of groups of users. Lastly, we focus on individual user's preferences of locations and propose a hybrid trust-based POI recommendation algorithm in this thesis. The proposed approach integrates the trust based on both users' social relationship and users' check-in behavior to provide POI recommendations. We implement the proposed hybrid trust-based recommendation algorithm and evaluate it based on the Foursquare dataset and the experimental results show good performances of our proposed algorithm.
Optimization of DNA isolation for RAPD-PCR analysis of selected (Echinaceae purpurea L. Moench) medicinal plants of conservation concern from Turkey.

Genetic analysis of plants relies on high yields of pure DNA samples. Here we present the optimization of DNA isolation and PCR conditions for RAPD analysis of selected medicinal plants of conservation concern from Turkey, containing high levels of polysaccharides, polyphenols and secondary metabolites. The method involves a modified CTAB extraction employing polyvinyl pyrrolidone (PVP) while grinding, successive long-term chloroform-isoamylalcohol extractions, EZ1 nucleic acid isolation protocols. The yield of DNA ranged from 1 - 2 μg/μl per gram of the leaf tissue and the purity (ratio) was between 1.7 - 1.8 indicating minimal levels of contaminating metabolites. EZ1 nucleic acid isolation technique is ideal for isolation of DNA from different plant species and the DNA isolated was used for randomly amplified polymorphic DNA (RAPD) analysis. RAPD protocol was optimized based on the use of higher concentration of MgCl2 (3 mM), lower concentrations of primer (0.5 μM) and Taq polymerase (0.2 units), 50 ng of template DNA and an annealing temperature of 37°C, resulted optimal amplification. Reproducible amplifiable products were observed in all PCR reactions. Thus, the results indicate that the optimized protocol for DNA isolation and PCR was amenable to plant species belonging to different genera which is suitable for further work on diversity analysis. Furthermore, here we used suitable DNA isolation protocol for RAPD analysis to study the genetic variation in the future in Echinaceae sp. grown in Turkey. Key words: Echinaceae purpurea, random amplified polymorphic DNA, polymerase chain reaction, medicinal plant.
3-D Self-Aligned Stacked Ge Nanowire pGAAFET on Si nFinFET of Single Gate CFET

In this study, we propose a self-aligned stacked Ge nanowire (NW) p-type gate-all-around field-effect transistor (pGAAFET) on Si nFinFET of single gate complementary FET (CFET). The self-aligned stacked Ge NW pGAAFET on Si nFinFET of single gate CFET device is fabricated on a SOI wafer. The CFET device is fully compatible with current Si technology platform using alternating anisotropic and isotropic dry etching process. The Ge NW pGAAFET presents an on-state current (ION) of <inline-formula> <tex-math notation="LaTeX">$166 ~\mu \text{A}/\mu \text{m}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${\mathrm{ V}}_{\mathrm{ D}}\,\,=$ </tex-math></inline-formula> VG-VTH <inline-formula> <tex-math notation="LaTeX">$=\,\,-0.5$ </tex-math></inline-formula> V and shows minimum subthreshold swing (SSmin) of 79, 91 mV/dec, and ION/IOFF of <inline-formula> <tex-math notation="LaTeX">$3.03\times10\,\,^{\mathrm{ 5}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$3.4\times 10^{4}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${\mathrm{ V}}_{\mathrm{ D}}\,\,=\,\,-0.05$ </tex-math></inline-formula> V and −0.5 V, respectively. The Si nFinFET presents an <inline-formula> <tex-math notation="LaTeX">${\mathrm{ I}}_{\mathrm{ ON}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$60.4 ~\mu \text{A}/\mu \text{m}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${\mathrm{ V}}_{\mathrm{ D}}\,\,=$ </tex-math></inline-formula> VG-VTH = 0.5 V and shows <inline-formula> <tex-math notation="LaTeX">${\mathrm{ SS}}_{\min }$ </tex-math></inline-formula> of 91, 101 mV/dec, and ION/IOFF of <inline-formula> <tex-math notation="LaTeX">$9.01\times10\,\,^{\mathrm{ 4}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5.62\times 10^{5}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${\mathrm{ V}}_{\mathrm{ D}}\,\,=$ </tex-math></inline-formula> 0.05 V and 0.5 V, respectively. The proposed CFET can simplify the process and shows promising potential for extending scaling beyond the technology node.
Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study

Background While numerous prognostic factors have been reported for large vessel occlusion (LVO)-acute ischemic stroke (AIS) patients, the same cannot be said for distal medium vessel occlusions (DMVOs). We used machine learning (ML) algorithms to develop a model predicting the short-term outcome of AIS patients with DMVOs using demographic, clinical, and laboratory variables and baseline computed tomography (CT) perfusion (CTP) postprocessing quantitative parameters. Methods In this retrospective cohort study, consecutive patients with AIS admitted to two comprehensive stroke centers between January 1, 2017, and September 1, 2022, were screened. Demographic, clinical, and radiological data were extracted from electronic medical records. The clinical outcome was divided into two categories, with a cut-off defined by the median National Institutes of Health Stroke Scale (NIHSS) shift score. Data preprocessing involved addressing missing values through imputation, scaling with a robust scaler, normalization using min-max normalization, and encoding of categorical variables. The data were split into training and test sets (70:30), and recursive feature elimination (RFE) was employed for feature selection. For ML analyses, XGBoost, LightGBM, CatBoost, multi-layer perceptron, random forest, and logistic regression algorithms were utilized. Performance evaluation involved the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), the area under these curves, accuracy, precision, recall, and Matthews correlation coefficient (MCC). The relative weights of predictor variables were examined using Shapley additive explanations (SHAP). Results Sixty-nine patients were included and divided into two groups: 35 patients with favorable outcomes and 34 patients with unfavorable outcomes. Utilizing ten selected features, the XGBoost algorithm achieved the best performance in predicting unfavorable outcomes, with an area under the ROC curve (AUROC) of 0.894 and an area under the PRC curve (AUPRC) of 0.756. The SHAP analysis ranked the following features in order of importance for the XGBoost model: mismatch volume, time-to-maximum of the tissue residue function (Tmax) >6 s, diffusion-weighted imaging (DWI) volume, neutrophil-to-platelet ratio (NPR), mean corpuscular volume (MCV), Tmax >10 s, hemoglobin, potassium, hypoperfusion index (HI), and Tmax >8 s. Conclusions Our ML models, trained on baseline quantitative laboratory and CT parameters, accurately predicted the short-term outcome in patients with DMVOs. These findings may aid clinicians in predicting prognosis and may be helpful for future research.
Difficulty and Severity-Oriented Metrics for Test Prioritization in Deep Learning Systems

Recently, there has been a growing trend in AI testing toward developing new test prioritization algorithms for deep learning systems. These algorithms aim to reduce the cost and time needed to annotate test datasets by prioritizing instances with a higher chance of exposing faults. Various metrics have been used to evaluate the effectiveness of these algorithms, e.g., APFD, RAUC, and ATRC. However, there is a lack of research to confirm their validity. The results indicate that the existing metrics have severe limitations. For example, some metrics ignore the labeling budget and prioritize the fault detection rate instead of the fault detection ratio. Moreover, others overlook the prioritization difficulty in the evaluation.As a solution, we develop a new metric (WFDR), which solves the deficiencies of previous metrics. We also draw attention to a new research area, known as severity prioritization, which emphasizes the importance of prioritizing misclassified instances according to the severity level, particularly in critical situations. Our experiments reveal that instances with high severity make up more than 20% of all misclassified instances. Thus, these instances should be prioritized when it comes to labeling. Consequently, we proposed a new metric known as (SFDR) that evaluates the effectiveness of algorithms in prioritizing high-severity instances. Our evaluations show that our proposed metrics are more effective than other existing metrics. Besides, our two metrics re-evaluate some recent algorithms and indicate that these algorithms perform poorly.
Unlocking the Potential of Dynamic Languages: An Exploration of Automated Unit Test Generation Techniques

This paper addresses the software quality evaluation through unit testing in dynamic programming languages, with a specific emphasis on Python. The use of automated testing methods, including generation frameworks such as Pynguin, is thoroughly examined. The Pynguin framework exhibited certain limitations in its ability to generate unit tests for modules, and as such, represents an important topic of discussion within the scope of this work. An important challenge presented by dynamic languages, particularly the lack of strict type constraints, is discussed in relation to the automatic generation of unit tests. The main objective of this study is to benchmark and present algorithms, e.g., genetic algorithms, and techniques for generating unit tests in dynamic languages through automation, in order to improve the efficiency and effectiveness of software testing. This research is particularly important as it addresses a current gap in the field of automated testing for dynamic languages and has the potential to greatly enhance the overall quality of software development in this context.
Enabling Age-Aware Big Data Analytics in Serverless Edge Clouds

With the fast development of artificial intelligence applications, large-volume big data generated in the edge of networks are waiting for real-time analysis, such that the valuable information is unveiled. Analytic developers for big data applications usually face the burden of managing the underlying cloud resources, which greatly drags the speed of analytic development. Serverless Computing is envisioned as an enabling technology to release the management burden of developers and to enable agile big data analytics. That is, big data analytics can be implemented in short-lived functions via the Function-as-a-Service (FaaS) programming paradigm. In this paper, we aim to fill the gap between serverless computing and mobile edge computing, via enabling query evaluations for big data analytics in short-lived functions of a serverless edge cloud (SEC). Specifically, we formulate novel age-aware big data query evaluation problems in an SEC so that the age of data is minimized, where the age of data is defined as the time difference between the current time and the generation time of the dataset. We propose approximation algorithms for the age-aware big data query evaluation problem with a single query, by proposing a novel parameterized virtualization technique that strives for a fine trade-off between short-lived functions and large resource demands of big data queries. We also devise an online learning algorithm with a bounded regret for the problem with multiple queries arriving dynamically and without prior knowledge of resource demands of the queries. We finally evaluate the performance of the proposed algorithms by extensive simulations. Simulation results show that the performance of our algorithms is promising.
A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip

DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases. Here we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation. We evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis.
Artemisinins Target GABA Receptor Signaling and A Impair a Cell Identity Graphical Abstract Highlights

Graphical Abstract Highlights d Artemisinins inhibit ARX function and impair a cell identity d Compounds act by stabilizing gephyrin, thus enhancing GABA A receptor signaling d Artemisinins increase b cell mass in zebrafish and rodent models d Functional and transcriptional data indicate a conserved phenotype in human islets In Brief The anti-malarial drug Artemisinin can drive the in vivo conversion of pancreatic a cells into functional b-like cells through enhanced GABA signaling and may have potential as a therapeutic for diabetes. SUMMARY Type 1 diabetes is characterized by the destruction of pancreatic b cells, and generating new insulin-producing cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types, including glucagon-producing a cells. In a genetic model, loss of the master regulatory transcription factor Arx is sufficient to induce the conversion of a cells to functional b-like cells. Here, we identify artemisinins as small molecules that functionally repress Arx by causing its translocation to the cytoplasm. We show that the protein gephyrin is the mammalian target of these antimalarial drugs and that the mechanism of action of these molecules depends on the enhancement of GABA A receptor signaling. Our results in zebrafish, rodents, and primary human pancreatic islets identify gephyrin as a druggable target for the regeneration of pancreatic b cell mass from a cells.
Methodology to Match Distributions of Both Household and Person Attributes in Generation of Synthetic Populations

The advent of microsimulation approaches in travel demand modeling, wherein activity-travel patterns of individual travelers are simulated in time and space, has motivated the development of synthetic population generators. These generators typically use census-based marginal distributions on household attributes to generate joint distributions on variables of interest using standard iterative proportional fitting (IPF) procedures. Households are then randomly drawn from an available sample in accordance with the joint distribution such that household-level attributes are matched perfectly. However, these traditional procedures do not control for person-level attributes and joint distributions of personal characteristics. In this paper, a heuristic approach, called the Iterative Proportional Updating (IPU) algorithm, is presented to generate synthetic populations whereby both household-level and person-level characteristics of interest can be matched in a computationally efficient manner. The algorithm involves iteratively adjusting and reallocating weights among households of a certain type (cell in the joint distribution) until both household and person-level attributes are matched. The algorithm is illustrated with a small example, and then demonstrated in the context of a real-world application using small geographies (blockgroups) in the Maricopa County of Arizona in the United States. The algorithm is found to perform very well, both from the standpoint of matching household and person-level distributions and computation time. It appears that the proposed algorithm holds promise to serve as a practical population synthesis procedure in the context of activity-based microsimulation modeling.
Risk Factors of Health-Related Quality of Life among Gastrointestinal Cancer Survivors in the U.S.: With a Focus on Social and Behavioral Determinants of Health (SBDH)

Background: Increasing numbers of long-term gastrointestinal (GI) cancer survivors highlight the importance of understanding the factors contributing to their health-related quality of life (HRQoL). We investigated the risk factors of HRQoL, including demographics, clinical characteristics, and social and behavioral determinants of health (SBDH). Methods: Data on adult GI cancer survivors (n = 3201) from the Behavioral Risk Factors Surveillance System (BRFSS) surveys from 2014–2021 (except for 2015) were analyzed. Unadjusted/adjusted logistic regression was used. Results: The majority were women (54%) and white (78%), with a median age of 67. Survivors who were 65 years or older, diagnosed with colorectal cancer, or who had fewer comorbidities were more likely to report significantly better HRQoL. Significant social factors of poor HRQoL included unmarried, racial and ethnic minorities, poor socioeconomic status, and poor healthcare access. Significant behavioral factors of poor HRQoL were lack of physical activity, heavy alcohol consumption, and current smoking, with lack of physical activity being the most significant factor. Conclusions: The SBDH has a critical role in HRQoL. Future studies are warranted to develop a tailored survivorship intervention, such as physical rehabilitation, and to explore machine learning/artificial intelligence-based predictive models to identify cancer survivors at a high risk of developing poor HRQoL.
SKEMA Business School and NBER

We study the effect of environmental, social and governance (ESG) scores on the portfolio allocations of institutional investors. Using a unique data set, we find that institutional investor holdings (as measured by 13F filings) are strongly driven by the ESG quality of companies. While investors are driven to add high quality ESG companies to their portfolios, there is a negative relationship to ESG when it comes to taking large ownership stakes. Blockholders appear much less motivated by ESG scores. Evaluating individual ESG scores, we find that the individual ESG governance score has the highest impact on institutional investor holdings.