Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Interleukin 28B genotype and hepatocellular carcinoma recurrence in patients with chronic hepatitis C Authors:

Purpose: Several single nucleotide polymorphisms (SNPs) in the interleukin 28B (IL28B) locus have recently been shown to be associated with antiviral treatment efficacy for chronic hepatitis C (CH-C). However, such an association with hepatocellular carcinoma (HCC) is unknown. Here, we investigated the association between the IL28B genotype and the biology and clinical outcome of HCC patients receiving curative treatment. Experimental Design: Genotyping of 183 HCC patients with CH-C who were treated with hepatic resection or radiofrequency ablation (RFA) was carried out, and the results were analyzed to determine the association between the IL28B genotype (rs8099917) and clinical outcome. Gene expression profiles of 20 HCC patients and another series of 91 CH-C patients were analyzed using microarray analysis and gene set enrichment analysis. Histological and immunohistochemical analyses were also performed. Results: The TT, TG and GG proportions of the rs8099917 genotype were 67.8% (124/183), 30.6% (56/183) and 1.6% (3/183), respectively. Multivariate Cox proportional hazard analysis demonstrated that the IL28B TT genotype was significantly associated with HCC recurrence (p = 0.007; hazard ratio, 2.674; 95% CI, 1.16-2.63). Microarray analysis showed high expression levels of interferon-stimulated genes in background liver samples and immune-related genes in tumor tissues of the IL28B TG/GG genotype. Histological findings showed that more lymphocytes infiltrated into tumor tissues in the TG/GG genotype. Conclusions: The IL28B genotype is associated with HCC recurrence, gene expression and histological findings in patients with CH-C. Research. on November 1, 2017. © 2013 American Association for Cancer clincancerres.aacrjournals. org Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on February 20, 2013; DOI: 10.1158/1078-0432.CCR-12-1641
Multi-agent Coordination Under Temporal Logic Tasks and Team-Wise Intermittent Communication

Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.
Weak $(1-\epsilon)$-nets for polynomial superlevel sets

We prove that for any Borel probability measure $\mu$ on $\mathbb R^n$ there exists a set $X\subset \mathbb R^n$ of $n+1$ points such that any $n$-variate quadratic polynomial $P$ that is nonnegative on $X$ (i.e. $P(x)\geq 0$, for every $x \in X$) satisfies $\mu\{P\geq 0\}\geq \frac{2}{(n+1)(n+2)}$. We also prove that given an absolutely continuous probability measure $\mu$ on $\mathbb R^n$ and $D\leq 2k$, for every $\delta>0$ there exists a set $X\subset \mathbb R^n$ with $|X|\leq \binom{n+2k}{n}-n-1$ such that any $n$-variate polynomial $P$ of degree $D$ that is nonnegative on $X$ satisfies $\mu\{P\geq 0\}\geq \frac{1}{\binom{n+2k}{n}+1} - \delta$. These statements are analogues of the celebrated centerpoint theorem, which corresponds to the case of linear polynomials. Our results follow from new estimates on the Carath\'eodory numbers of real Veronese varieties, or alternatively, from bounds on the nonnegative symmetric rank of real symmetric tensors.
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying

Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. For example, by observing a partial stack of cubes, LLMs can predict the correct sequence in which the remaining cubes should be stacked by extrapolating the observed patterns (e.g., cube sizes, colors or other attributes) in the partial stack. In this work, we introduce LaGR (Language-Guided Reinforcement learning), which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent, in order to subsequently guide the latter's training. However, as RL training is generally not sample-efficient, deploying this approach would inherently imply that the LLM be repeatedly queried for solutions; a process that can be expensive and infeasible. To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions. Specifically, we use the quality of the solutions emanating from the LLM as the reward to train this agent. We show that our proposed framework LaGR-SEQ enables more efficient primary RL training, while simultaneously minimizing the number of queries to the LLM. We demonstrate our approach on a series of tasks and highlight the advantages of our approach, along with its limitations and potential future research directions.
Molecular and morphological data reveal paraphyly and speciation in imperiled freshwater mussels ( Anodontoides and Strophitus )

Accurate taxonomic placement is vital to conservation efforts considering many intrinsic biological characteristics of understudied species are inferred from closely related taxa. The rayed creekshell, Anodontoides radiatus (Conrad, 1834), exists in the Gulf of Mexico drainages from western Florida to Louisiana and has been petitioned for listing under the Endangered Species Act. We set out to resolve the evolutionary history of A. radiatus, primarily generic placement and species boundaries, using phylogenetic, morphometric, and geographic information. Our molecular matrix contained 3 loci:
Visualizing the Invisible: The CONNECT Approach for Teaching Science

The CONNECT project proposes a pedagogical and technological approach that crosscuts the boundaries between schools and science museums and involves both students and teachers in extended episodes of playful learning. It develops tests and evaluates learning pathways to facilitate in situ learning that will be implemented in ambient, always available educational environments developed with emerging technology. This paper focuses on the first results from the project's implementation in Greece
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction

Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems.
Interactive exploratory soccer data analytics

Abstract Spatiotemporal soccer data enables in-depth analysis of a soccer game. However, the amount and the nature of the data makes it challenging for analysts to easily uncover insights from the data. In this article, we introduce an interactive visualization tool that uses novel data mining and machine learning methods to enable coaches and analysts to work on large amounts of data by moving most of the complicated models to the backend and presenting interactive visualization that can be manipulated in real-time. A unique interactive replay and modification feature enables creation of what-if scenarios on existing game data to explore alternative situations, such as a defensive player taking a different position or an offensive player choosing another pass, while making the experience seamless to the users. • Information systems ➝Information system applications • Applied computing ➝ Computers in other domains.
Using Mean Shift for Iranian License Plate Detection

In this paper, Mean Shift (MS) as a clustering algorithm is used to localize the Iranian license plate. In this procedure after clustering, based on the optimized MS method, we applied the geometrical features and edge density in order to remove those parts in every cluster, which cannot be the license plate. The main advantages of MS are no need to know the number of clusters and it is completely independent of the Iranian license plate characters colors or background colors. However, for MS implementation, we should only predetermine a parameter named bandwidth. The experimental results show that our proposed method achieves appropriate performance. We should mention that our system accuracy for optical (OP) with 300 images is 94.6% and for infrared (IR) with 80 images is 98.3%.
DETDet: Dual Ensemble Teeth Detection

The field of dentistry is in the era of digital transformation. Particularly, artificial intelligence is anticipated to play a significant role in digital dentistry. AI holds the potential to significantly assist dental practitioners and elevate diagnostic accuracy. In alignment with this vision, the 2023 MICCAI DENTEX challenge aims to enhance the performance of dental panoramic X-ray diagnosis and enumeration through technological advancement. In response, we introduce DETDet, a Dual Ensemble Teeth Detection network. DETDet encompasses two distinct modules dedicated to enumeration and diagnosis. Leveraging the advantages of teeth mask data, we employ Mask-RCNN for the enumeration module. For the diagnosis module, we adopt an ensemble model comprising DiffusionDet and DINO. To further enhance precision scores, we integrate a complementary module to harness the potential of unlabeled data. The code for our approach will be made accessible at https://github.com/Bestever-choi/Evident
loud computing for education : A new dawn ?

Educational establishments continue to seek opportunities to rationalize the way they manage their resources. The economic crisis that befell the world following the near collapse of the global financial system and the subsequent bailouts of local banks with billions of tax payers’ money will continue to affect educational establishments that are likely to discover that governments will have less money than before to invest in them. It is argued in this article that cloud computing is likely to be one of those opportunities sought by the cash-strapped educational establishments in these difficult times and could
Antiangiogenic and Neurogenic Activities of Sleeping Beauty-Mediated PEDF-Transfected RPE Cells In Vitro and In Vivo JOHNEN

Pigment epithelium-derived factor (PEDF) is a potent multifunctional protein that inhibits angiogenesis and has neurogenic and neuroprotective properties. Since thewet formof age-related macular degeneration is characterized by choroidal neovascularization (CNV), PEDF would be an ideal candidate to inhibit CNV and support retinal pigment epithelial (RPE) cells. However, its short half-life has precluded its clinical use. To deliver PEDF to the subretinal space, we transfected RPE cells with the PEDF gene using the Sleeping Beauty transposon system. Transfected cells expressed and secreted biologically active recombinant PEDF (rPEDF). In cultures of human umbilical vein endothelial cells, rPEDF reduced VEGF-induced cumulative sprouting by ≥47%, decreased migration by 77%, and increased rate of apoptosis at least 3.4 times. rPEDF induced neurite outgrowth in neuroblastoma cells and protected ganglion and photoreceptor cells in organotypic retinal cultures. In a rat model of CNV, subretinal transplantation of PEDF-transfected cells led to a reduction of the CNV area by 48% 14 days after transplantation and decreased [. ] JOHNEN, Sandra, et al. Antiangiogenic and Neurogenic Activities of Sleeping Beauty -Mediated PEDF-Transfected RPE Cells In Vitro and In Vivo. BioMed Research International, 2015, vol. 2015 DOI : 10.1155/2015/863845
An electronic surgical order, undertaking patient education, and obtaining informed consent for regional analgesia before the day of surgery reduce block-related delays

Background Obtaining patient informed consent for a regional analgesia block on the day of surgery can result in surgical case delays. We hypothesized that implementing a preoperative electronic surgical order, undertaking patient education, and obtaining informed consent for a regional block in our preoperative assessment clinic prior to the day of surgery would reduce surgical case delays attributed to our regional anesthesia pain service and increase the percentage of patients for whom our regional anesthesia pain service was requested to provide a block. Methods A prospective two-group time-series design, with a nonrandomized, pre- and post-intervention data collection strategy, was applied. Based upon the surgeons’ newly implemented preoperative electronic outpatient orders, patients were identified by our preoperative assessment clinic staff to receive educational materials. The attending anesthesiologist in the preoperative assessment clinic then obtained written informed consent. Block-related delay and utilization data were analyzed with conventional inferential statistics. Results We observed a 14.8% (95% CI: 9.4%, 20.1%; P<0.001) decrease in surgical case delays, attributed to the regional nerve block, in the post- vs pre-intervention group. In addition, there was a 9.9% (95% CI: 4.7%, 15.1%); P<0.001) increase in the proportion of patients for whom a regional nerve block was ordered by our three high-volume orthopedic surgeons in the post- vs pre-intervention time periods. Conclusion When performed before the day of surgery, a surgeon’s electronic order, patient education, and informed consent for regional postoperative analgesia can improve patient throughput, thereby reducing block-related operating room delays. The preoperative assessment clinic can serve as a venue to achieve this goal, thereby adding value by decreasing downstream delays on the day of surgery.
Predictors of sleep disturbance in heroin users receiving methadone maintenance therapy: a naturalistic study in Taiwan

Introduction: Sleep disturbance is a major health concern for heroin users receiving methadone maintenance treatment (MMT). The present study was aimed to investigate the predictors for new-onset clinically predominant sleep disturbance (CPSD) among heroin users receiving MMT. Methods: This 2-year retrospective study included 152 individuals (127 males and 25 females) with heroin use disorder who visited our MMT clinics for the first time. A univariate Cox proportional hazards regression model (Cox model) was used to estimate the potential factors of subsequent CPSD, followed by a multivariate Cox model to identify significant predictors of CPSD after adjusting for other covariates. Results: Twenty-nine (19.1%) participants developed CPSD during the 2-year period. After forward selection in the Cox model, earlier age at onset of heroin exposure (OR = 0.95; P = 0.044), lower attendance rate (OR = 0.04; P = 0.03), greater maximum dose of methadone (OR = 1.01; P = 0.022), and shorter time to maximum methadone dose (OR = 0.98; P = 0.007) were significantly associated with new-onset CPSD. Conclusion: We identified predictors that were significantly associated with new-onset CPSD, and clinicians should be aware of sleep disturbance in heroin users receiving MMT with these risk factors. Future studies are necessary to verify our findings and extend the applicability.
Clinical characteristics of patients newly diagnosed with chronic obstructive pulmonary disease by the fixed ratio and lower limit of normal criteria

Background: Consensus on the definition of airflow obstruction to diagnose COPD remains unresolved. Methods: We undertook systematic case finding for COPD in primary care using the fixed ratio (FR) criterion (forced expiratory volume in 1 s/forced vital capacity [FEV 1 /FVC]  0.7) for defining airflow obstruction and also using the lower limit of normal (LLN). We then compared the clinical characteristics of those identified by the 2 criteria. Results: A total of 3,721 individuals reporting respiratory symptoms were invited for spirometry. A total of 2,607 attended (mean age 60.4 years, 52.8% male, 29.8% current smokers) and 32.6% had airflow obstruction by FR (“FR + ”) and 20.2% by LLN (“LLN + ”). Compared with the LLN + /FR + group, the LLN − /FR + group (12.4%) was significantly older, had higher FEV 1 and FEV 1 /FVC, lower COPD assessment test scores, and less cough, sputum, and wheeze, but was significantly more likely to report a diagnosis of heart disease (14.2% versus 6.9%, p  0.001). Compared with the LLN + /FR + group, the LLN − /FR − group was younger, had a higher body mass index, fewer pack-years, a lower prevalence of respiratory symptoms except for dyspnea, and lower FVC and higher FEV 1 . The probability of known heart disease was significantly lower in the LLN + /FR + group compared with those with preserved lung function (LLN − /FR − ) (adjusted odds ratio 0.62, 95% CI: 0.43–0.90) but this was not seen in the LLN − / FR + group (adjusted odds ratio 0.90, 95% CI: 0.63–1.29). Conclusion: In symptomatic individuals, defining airflow obstruction by FR instead of LLN identifies a significant number of individuals who have less respiratory and more cardiac clinical characteristics.
Topology analysis of time-dependent multi-fluid data using the Reeb graph

Liquid-liquid extraction is a typical multi-fluid problem in chemical engineering where two types of immiscible fluids are mixed together. Mixing of two-phase fluids results in a timevarying fluid density distribution, quantitatively indicating the presence of liquid phases. For engineers who design extraction devices, it is crucial to understand the density distribution of each fluid, particularly flow regions that have a high concentration of the dispersed phase. The propagation of regions of high density can be studied by examining the topology of isosurfaces of the density data. We present a topology-based approach to track the splitting and merging events of these regions using the Reeb graphs. Time is used as the third dimension in addition to two-dimensional (2D) point-based simulation data. Due to low time resolution of the input dataset, a physics-based interpolation scheme is required in order to improve the accuracy of the proposed topology tracking method. The model used for interpolation produces a smooth timedependent density field by applying Lagrangian-based advection to the given simulated point cloud data, conforming to the physical laws of flow evolution. Using the Reeb graph, the spatial and temporal locations of bifurcation and merging events can be readily identified supporting in-depth analysis of the extraction process.
Loss of a-Type Lamin Expression Compromises Nuclear Envelope Integrity Leading to Muscular Dystrophy

The nuclear lamina is a protein meshwork lining the nucleoplasmic face of the inner nuclear membrane and represents an important determinant of interphase nuclear architecture. Its major components are the A- and B-type lamins. Whereas B-type lamins are found in all mammalian cells, A-type lamin expression is developmentally regulated. In the mouse, A-type lamins do not appear until midway through embryonic development, suggesting that these proteins may be involved in the regulation of terminal differentiation. Here we show that mice lacking A-type lamins develop to term with no overt abnormalities. However, their postnatal growth is severely retarded and is characterized by the appearance of muscular dystrophy. This phenotype is associated with ultrastructural perturbations to the nuclear envelope. These include the mislocalization of emerin, an inner nuclear membrane protein, defects in which are implicated in Emery-Dreifuss muscular dystrophy (EDMD), one of the three major X-linked dystrophies. Mice lacking the A-type lamins exhibit tissue-specific alterations to their nuclear envelope integrity and emerin distribution. In skeletal and cardiac muscles, this is manifest as a dystrophic condition related to EDMD.
Stay Rates of Foreign Doctorate Recipients from U.S. Universities, 2005

This is the latest in a series of studies conducted for the National Science Foundation to estimate the proportion of foreign science / engineering doctorate recipients from U.S. universities who stayed in the United States after graduation. Sixty-six percent of those who graduated in 2003 were still in the United States in 2005, a 5 percent decline since the last report. But the five-year stay rate continues to climb, now at 68 percent.
Development of a food frequency questionnaire for children from 7 to 10 years old

AbstrAct: Objective: To present and discuss the process of the development of a Quantitative Food Frequency Questionnaire (QFFQ) aimed at 7-10 year-old children. Methods: A methodological study was performed based on the application of a Food Diary (FD) fulfilled by 85 children. Based on registered food, the diet composition in relation to energy and macronutrients was calculated. Food was grouped into items and the percentage of the contribution of each item to the diet in terms of calories and macronutrients was calculated. Results: The QFFQ consisted of 92 food items, 76 were selected by Block’s method and 16 were identified from other instruments from literature developed for children and adolescents. The food items were grouped in 17 food groups with 3 serving sizes and 7 categories to measure food frequency. Conclusion: Among the food items, rice was the one that showed the biggest percentage of contribution for energy and carbohydrates. For other nutrients, meat and milk were the ones that contributed most to the consumption of proteins and lipids, respectively.