Miracle-dz/UniFAR-FTUs huggingface dataset:
Readmission Rates and Medication Selection for Patients with Heart Failure Preserved Ejection Fraction

Purpose Heart failure with preserved ejection fraction (HFpEF) has less guideline driven treatment options due to a lack of trials demonstrating medications with improved clinical outcomes for this patient population. The primary objective of this study is to determine which medications and dosages are related to high readmission rates for HFpEF patients. Methods A retrospective, single center, chart review was performed on patients with HFpEF at an academic medical center. Heart failure patients ages between 18-89 with an ejection fraction [≥]45% from a transthoracic echocardiogram (TTE) were included. Primary outcomes include 30-day all cause readmission rates, prescribing patterns, and avoidance of potentially harmful medications. Descriptive statistics and multivariate logistic regression were used to assess potential risk factors. Results This study analyzed 455 patient admissions. Univariate analysis shows patients who were not readmitted were more likely to be on furosemide (54% vs 42%; p = 0.019). Conversely, readmitted patients were more likely to be taking bumetanide (4% vs 1%; p = 0.039). Lisinopril was the only angiotensin converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) associated with lower readmission rates (p = 0.036). Multivariate logistic regression showed bumetanide on admission (OR 14.6, p = 0.001), discharged on rosuvastatin (OR 6.29, p = 0.003) and meloxicam therapy (OR 6.33, p = 0.003) to be independent predictors of hospital readmission. Conclusion Three independent pharmacologic predictors for 30-day readmissions for patients with HFpEF were therapy with bumetanide, meloxicam, or rosuvastatin. Further research is needed to clarify the significance of these results.
Classification of Products in Retail using Partially Abbreviated Product Names Only

: The management of product data in ERP systems is a big challenge for most retail companies. The reason lies in the large amount of data and its complexity. There are companies having millions of product data records. Sometimes more than one thousand data records are created daily. Because data entry and maintenance processes are linked with considerable manual effort, costs - both in time and money - for data management are high. In many systems, the product name and product category must be specified before the product data can be entered manually. Based on the product category many default values are proposed to simplify the manual data entry process. Consequently, classification is essential for error-free and efficient data entry. In this paper, we show how to classify products automatically and compare different machine learning approaches to this end. In order to minimize the effort for the manual data entry and due to the severely limited length of the product name field the classification algorithms are based on shortened names of the products. In particular, we analyse the benefits of different pre-processing strategies and compare the quality of classification models on different hierarchy levels. Our results show that, even in this special case, machine learning can considerably simplify the process of data input.
Government Digital Service Co-design: Concepts to Collaboration Tools

The inception of digital services is succeeded with the myriad advancements in ICT that aim to support the growing needs of users and internet-enabled devices. The diversities of these functionalities and digital services are motivated by the presence of dynamic technologies, delivery channels, and diverse user needs in an internet-enabled environment. What appropriate instruments could help to support the collaborative design and create an environment characterized by active knowledge between providers of service and end-users of the service? The co-design is a well-known approach within the design community that utilizes advanced ideas and varied instruments of co-design. This study aimed is how to identify unmet requirements for Government to Citizen (G2C) e-service design and significant ways of achieving the requisite design needs using a suitable design process? Detailed interviews were undertaken with three groups of stakeholders in regards to the service design. This study analyses the data collected using an inductive thematic analysis to analyze qualitative data collected from study participants. This study also explores the patterns resulting from the G2C e-service design process and further establishes the interconnection between the processes in regards to fostering the effectiveness and efficiency of the services.
Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

Providing reliable connectivity to cellular-connected Unmanned Aerial Vehicles (UAVs) can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground Base Stations (BSs). On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a Reinforcement Learning (RL) algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum efficiency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAV Quality of Service (QoS) by 6% to 41%, depending on the scenario. We also conclude that, when flying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.
The safety and clinical effects of administering a multiantigen-targeted T cell therapy to patients with multiple myeloma

Infusions of autologous T cells specific for multiple tumor antigens were safe and induced responses in some patients with multiple myeloma. Fighting multiple myeloma with multitargeted T cells Adoptive cellular therapies are being developed to treat many types of cancer, including multiple myeloma (MM). Lulla et al. performed a clinical study using T cells expanded in response to MM target antigens. The cells were used as adjuvant therapy or in high-risk patients. Therapy was well tolerated and induced clinical responses in a subset of patients. Examination of T cell clonal expansion and antigen loss in MM cells aided understanding of how the multiantigen-targeted T cells were behaving in patients. This approach, which relies on native T cells and does not require expensive or time-consuming gene editing required for some other types of cellular therapy, warrants further investigation for fighting MM. Multiple myeloma (MM) is an almost always incurable malignancy of plasma cells. Despite the advent of new therapies, most patients eventually relapse or become treatment-refractory. Consequently, therapies with nonoverlapping mechanisms of action that are nontoxic and provide long-term benefit to patients with MM are greatly needed. To this end, we clinically tested an autologous multitumor-associated antigen (mTAA)–specific T cell product for the treatment of patients with high-risk, relapsed or refractory MM. In this study, we expanded polyclonal T cells from 23 patients with MM. T cells whose native T cell receptors were reactive toward five myeloma-expressed target TAAs (PRAME, SSX2, MAGEA4, Survivin, and NY-ESO-1) were enriched ex vivo. To date, we have administered escalating doses of these nonengineered mTAA-specific T cells (0.5 × 107 to 2 × 107 cells/m2) to 21 patients with MM, 9 of whom were at high risk of relapse after a median of 3 lines of prior therapy and 12 with active, relapsed or refractory disease after a median of 3.5 prior lines. The cells were well tolerated, with only two transient, grade III infusion-related adverse events. Furthermore, patients with active relapsed or refractory myeloma enjoyed a longer than expected progression-free survival and responders included three patients who achieved objective responses concomitant with detection of functional TAA-reactive T cell clonotypes derived from the infused mTAA product.
Efficient and Secure Statistical DDoS Detection Scheme

One of the hardest challenges in cybersecurity is the detection and prevention of Distributed Denial of Service (DDoS) attacks. In this paper, a lightweight statistical approach for DDoS detection is presented, in addition to preventive and corrective countermeasures. The proposed solution is designed to be applied at the Internet Service Provider (ISP) level. Based on aggregated NetFlow statistics, the proposed solution relies on the Zscore and co-variance measures to detect DDoS traffic as a deviation from normal traffic. The implementation results show a high detection rate (up to 100%) for 30 seconds time slot.
Exploring User Behavior in Email Re-Finding Tasks

Email continues to be one of the most commonly used forms of online communication. As inboxes grow larger, users rely more heavily on email search to effectively find what they are looking for. However, previous studies on email have been exclusive to enterprises with access to large user logs, or limited to small-scale qualitative surveys and analyses on limited public datasets such as Enron1 and Avocado2. In this work, we propose a novel framework that allows for experimentation with real email data. In particular, our approach provides a realistic way of simulating email re-finding tasks in a crowdsourcing environment using the workers' personal email data. We use our approach to experiment with various ranking functions and quality degradation to measure how users behave under different conditions, and conduct analysis across various email types and attributes. Our results show that user behavior can be significantly impacted as a result of the quality of the search ranker, but only when differences in quality are very pronounced. Our analysis confirms that time-based ranking begins to fail as email age increases, suggesting that hybrid approaches may help bridge the gap between relevance-based rankers and the traditional time-based ranking approach. Finally, we also found that users typically reformulate search queries by either entirely re-writing the query, or simply appending terms to the query, which may have implications for email query suggestion facilities.
Activation of NRF2 ameliorates oxidative stress and cystogenesis in autosomal dominant polycystic kidney disease

Pharmacological induction of NRF2 restores redox homeostasis and slows cystogenesis in mouse models of polycystic kidney disease. Antioxidants dominate ADPKD Autosomal dominant polycystic kidney disease (ADPKD) is a relatively common genetic disorder whose pathogenesis is only partially understood. By studying both human patients and mouse models, Lu et al. identified inactivating mutations of the antioxidant protein NRF2 as playing a key role in the pathogenesis of this disorder. In addition to clarifying the mechanism of disease development, the authors demonstrated that pharmacologic induction of the NRF2 pathway slows the progression of disease in mouse models, suggesting a potential intervention for human patients. Oxidative stress is emerging as a crucial contributor to the pathogenesis of autosomal dominant polycystic kidney disease (ADPKD), but the molecular mechanisms underlying the disturbed redox homeostasis in cystic cells remain elusive. Here, we identified the impaired activity of the NRF2 (nuclear factor erythroid 2–related factor 2) antioxidant pathway as a driver of oxidative damage and ADPKD progression. Using a quantitative proteomic approach, together with biochemical analyses, we found that increased degradation of NRF2 protein suppressed the NRF2 antioxidant pathway in ADPKD mouse kidneys. In a cohort of patients with ADPKD, reactive oxygen species (ROS) frequently accumulated, and their production correlated negatively with NRF2 abundance and positively with disease severity. In an orthologous ADPKD mouse model, genetic deletion of Nrf2 further increased ROS generation and promoted cyst growth, whereas pharmacological induction of NRF2 reduced ROS production and slowed cystogenesis and disease progression. Mechanistically, pharmacological induction of NRF2 remodeled enhancer landscapes and activated NRF2-bound enhancer-associated genes in ADPKD cells. The activation domain of NRF2 formed phase-separated condensates with MEDIATOR complex subunit MED16 in vitro, and optimal Mediator recruitment to genomic loci depended on NRF2 in vivo. Together, these findings indicate that NRF2 remodels enhancer landscapes and activates its target genes through a phase separation mechanism and that activation of NRF2 represents a promising strategy for restoring redox homeostasis and combatting ADPKD.
Harnessing the Microbiomes of Suppressive Composts for Plant Protection: From Metagenomes to Beneficial Microorganisms and Reliable Diagnostics

Soil-borne diseases cause significant yield losses worldwide, are difficult to treat and often only limited options for disease management are available. It has long been known that compost amendments, which are routinely applied in organic and integrated farming as a part of good agricultural practice to close nutrient cycles, can convey a protective effect. Yet, the targeted use of composts against soil-borne diseases is hampered by the unpredictability of the efficacy. Several studies have identified and/or isolated beneficial microorganisms (i.e., bacteria, oomycetes, and fungi) from disease suppressive composts capable of suppressing pathogens (e.g., Pythium and Fusarium) in various crops (e.g., tomato, lettuce, and cucumber), and some of them have been developed into commercial products. Yet, there is growing evidence that synthetic or complex microbial consortia can be more effective in controlling diseases than single strains, but the underlying molecular mechanisms are poorly understood. Currently, a major bottleneck concerns the lack of functional assays to identify the most potent beneficial microorganisms and/or key microbial consortia from complex soil and compost microbiomes, which can harbor tens of thousands of species. This focused review describes microorganisms, which have been isolated from, amended to or found to be abundant in disease-suppressive composts and for which a beneficial effect has been documented. We point out opportunities to increasingly harness compost microbiomes for plant protection through an integrated systems approach that combines the power of functional assays to isolate biocontrol and plant growth promoting strains and further prioritize them, with functional genomics approaches that have been successfully applied in other fields of microbiome research. These include detailed metagenomics studies (i.e., amplicon and shotgun sequencing) to achieve a better understanding of the complex system compost and to identify members of taxa enriched in suppressive composts. Whole-genome sequencing and complete assembly of key isolates and their subsequent functional profiling can elucidate the mechanisms of action of biocontrol strains. Integrating the benefits of these approaches will bring the long-term goals of employing microorganisms for a sustainable control of plant pathogens and developing reliable diagnostic assays to assess the suppressiveness of composts within reach.
Sustainable Printed Electrochemical Platforms for Greener Analytics

The development of miniaturized electrochemical platforms holds considerable importance for the in situ analytical monitoring of clinical, environmental, food, and forensic samples. However, it is crucial to pay attention to the sustainability of materials chosen to fabricate these devices, in order to decrease the amount and the impact of waste coming from their production and use. In the framework of a circular economy and an environmental footprint reduction, the electrochemical sensor production technology must discover the potentiality of innovative approaches based on techniques and materials that can satisfy the needs of environmental-friendly and greener analytics. The aim of this review is to describe some of the printing technologies most used for sensor production, including screen-printing, inkjet-printing, and 3D-printing, and the low-impact materials that are recently proposed for these techniques, such as polylactic acid, cellulose, silk proteins, biochar.
Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification

Weed spotting through image classification is one of the methods applied in precision agriculture to increase efficiency in crop damage reduction. These classifications are nowadays typically based on deep machine learning with convolutional neural networks (CNN), where a main difficulty is gathering large amounts of labeled data required for the training of these networks. Thus, synthetic dataset sources have been developed including simulations based on graphic engines; however, some data inputs that can improve the performance of CNNs like the near infrared (NIR) have not been considered in these simulations. This paper presents a simulation in the Unity game engine that builds fields of sugar beets with weeds. Images are generated to create datasets that are ready to train CNNs for semantic segmentation. The dataset is tested by comparing classification results from the bonnet CNN network trained with synthetic images and trained with real images, both with RGB and RGBN (RGB+near infrared) as inputs. The preliminary results suggest that the addition of the NIR channel to the simulation for plant-weed segmentation can be effectively exploited. These show a difference of 5.75% for the global mean IoU over 820 classified images by including the NIR data in the unity
Interleukin-1β impairment of transforming growth factor β1 signaling by DOWN-REGULATION of transforming growth factor β receptor type II and up-regulation of Smad7 in human articular chondrocytes

OBJECTIVE Extracellular matrix deposition is tightly controlled by a network of regulatory cytokines. Among them, interleukin-1beta (IL-1beta) and transforming growth factor beta1 (TGFbeta1) have been shown to play antagonistic roles in tissue homeostasis. The purpose of this study was to determine the influence of IL-1beta on TGFbeta receptor type II (TGFbetaRII) regulation and TGFbeta1 responsiveness in human articular chondrocytes. METHODS TGFbeta1-induced gene expression was analyzed through plasminogen activator inhibitor 1 and p3TP-Lux induction. Receptor-activated Smad (R-Smad) phosphorylation, TGFbeta receptors, and Smad expression were determined by Western blotting and real-time reverse transcription-polymerase chain reaction techniques. Signaling pathways were investigated using specific inhibitors, messenger RNA (mRNA) silencing, and expression vectors. RESULTS IL-1beta down-regulated TGFbetaRII expression at both the protein and mRNA levels and led to inhibition of the TGFbeta1-induced gene expression and Smad2/3 phosphorylation. Moreover, IL-1beta strongly stimulated the expression of inhibitory Smad7. TGFbetaRII overexpression abolished the loss of TGFbeta1 responsiveness induced by IL-1beta. The decrease in TGFbetaRII required de novo protein synthesis and involved both the NF-kappaB and JNK pathways. CONCLUSION We demonstrate that IL-1beta impairs TGFbeta1 signaling through down-regulation of TGFbetaRII, which is mediated by the p65/NF-kappaB and activator protein 1/JNK pathways, and secondarily through the up-regulation of Smad7. These findings show that there is cross-talk in the signaling of 2 regulatory cytokines involved in inflammation.
Integrating Lightweight Compression Capabilities into Apache Arrow

With the ongoing shift to a data-driven world in almost all application domains, the management and in particular the analytics of large amounts of data gain in importance. For that reason, a variety of new big data systems has been developed in recent years. Aside from that, a revision of the data organization and formats has been initiated as a foundation for these big data systems. In this context, Apache Arrow is a novel cross-language development platform for in-memory data with a standardized language-independent columnar memory format. The data is organized for efficient analytic operations on modern hardware, whereby Apache Arrow only supports dictionary encoding as a specific compression approach. However, there exists a large corpus of lightweight compression algorithms for columnar data which helps to reduce the necessary memory space as well as to increase the processing performance. Thus, we present a flexible and language-independent approach integrating lightweight compression algorithms into the Apache Arrow framework in this paper. With our so-called ArrowComp approach, we preserve the unique properties of Apache Arrow, but enhance the platform with a large variety of lightweight compression capabilities.
Evaluation of the Potential for Soil Organic Carbon Content Monitoring With Farmers

Increasing soil organic carbon (SOC) content is crucial for soil quality and climate change mitigation. SOC monitoring is indispensable to the corresponding policies and should provide results at farm scale to allow for incentives. In Switzerland, farmers perform mandatory analyses of the SOC content of the 0–20 cm topsoil of every field, based on a composite sample, at least every 10 years. The corresponding results are stored in a database in canton of Geneva. These data may be relevant for topsoil SOC monitoring, in particular for carbon sequestration policies, provided that they show appropriate quality, which is analyzed in this study. The minimum detectable change (MDC) of past results calculated based on the observed SOC changes was 0.013% g g–1 at canton scale (2,700 fields). Based on extended sampling of three representative fields, different sampling strategies were simulated to determine the best future sampling guidelines for farmers. Collecting 20 aliquots with a gouge on the field diagonals was considered the best sampling compromise with field MDC of ∼0.1% g g–1 and a sampling duration of 20 min. Compared to this procedure, former farmers’ sampling was not biased in average but showed a variance of 0.22% g g–1 due to smaller number of aliquots and varying sampling depths. Based on the best sampling results and assumptions on farm-scale SOC variance or SOC differences, the MDCs at farm scale ranged from 0.21 to 0.12% g g–1 (5 fields) and 0.09 to 0.05% g g–1 (30 fields), respectively. These MDCs are small compared to published monitoring networks MDCs and allow determining SOC change rates at farm scale, thus offering perspectives for inexpensive and efficient monitoring in the frame of soil quality or climate mitigation incentives. For the latter, however, additional information with equivalent soil mass and deeper-layer carbon content would be necessary.