Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Linkage of RKIP to the Epithelial to Mesenchymal Transition and Autophagy during the Development of Prostate Cancer

Raf kinase inhibitor protein (RKIP) plays a critical role in many signaling pathways as a multi-functional adapter protein. In particular, the loss of RKIP’s function in certain types of cancer cells results in epithelial to mesenchymal transition (EMT) and the promotion of cancer metastasis. In addition, RKIP inhibits autophagy by modulating LC3-lipidation and mTORC1. How the RKIP-dependent inhibition of autophagy is linked to EMT and cancer progression is still under investigation. In this study, we investigated the ways by which RKIP interacts with key gene products in EMT and autophagy during the progression of prostate cancer. We first identified the gene products of interest using the corresponding gene ontology terms. The weighted-gene co-expression network analysis (WGCNA) was applied on a gene expression dataset from three groups of prostate tissues; benign prostate hyperplasia, primary and metastatic cancer. We found two modules of highly co-expressed genes, which were preserved in other independent datasets of prostate cancer tissues. RKIP showed potentially novel interactions with one EMT and seven autophagy gene products (TGFBR1; PIK3C3, PIK3CB, TBC1D25, TBC1D5, TOLLIP, WDR45 and WIPI1). In addition, we identified several upstream transcription modulators that could regulate the expression of these gene products. Finally, we verified some RKIP novel interactions by co-localization using the confocal microscopy analysis in a prostate cancer cell line. To summarize, RKIP interacts with EMT and autophagy as part of the same functional unit in developing prostate cancer.
Building Blocks for Communication Over Noisy Quantum Nerworks

Capacity of a quantum channel characterizes the limits of reliable communication through a noisy quantum channel. This fundamental information theoretic question is very well studied specially in the setting of many independent uses of the channel. An important scenario, both from practical and conceptual point of view, is when the channel can be used only once. This is known as the one-shot channel coding problem. We provide a tight characterization of the one-shot entanglement assisted classical capacity of a quantum channel. We arrive at our result by introducing a simple decoding technique which we refer to as position-based decoding. We also consider two other important quantum network scenarios: quantum channel with a jammer and quantum broadcast channel. For these problems, we use the recently introduced convex split technique [1] in addition to position based decoding. Our approach exhibits that the simultaneous use of these two techniques provides a uniform and conceptually simple framework for designing communication protocols for quantum networks.
A Novel User Pairing in Downlink Non-Orthogonal Multiple Access

In this paper, we study the user pairing in downlink non-orthogonal multiple access (NOMA) networks. Users are divided into multiple 2-user clusters. In each cluster, the two users share the same transmit source, like time slot and spectrum, and the base station (BS) distributes the total transmit power to them by following certain rules. The performance of NOMA depends greatly on how we group them and how the power is allocated. In this paper, we propose two user pairing algorithms based on neighbor search methods, specifically the hill climbing and simulated annealing. Different from most existing methods, they don't have to work jointly with a certain power allocation scheme. Moreover, simulation results show that they significantly reduce the complexity compared with exhaustive search and can achieve a near-optimal solution.
Cold storage of platelets in additive solution: the impact of residual plasma in apheresis platelet concentrates

Platelet transfusion has become essential therapy in modern medicine. Although the clinical advantage of platelet transfusion has been well established, adverse reactions upon transfusion, especially transmission of bacterial infection, still represent a major challenge. While bacterial contamination is favored by the storage of platelets at room temperature, cold storage may represent a solution for this important clinical issue. In this study, we aimed to clarify whether plasma has protective or detrimental effects on cold-stored platelets. We investigated the impact of different residual plasma contents in apheresis-derived platelet concentrates, stored at 4°C or room temperature, on platelet function and survival. We found that platelets stored at 4°C have higher expression of apoptosis marker compared to platelets stored at room temperature, leading to accelerated clearance from the circulation in a humanized animal model. While cold-induced apoptosis was independent of the residual plasma concentration, cold storage was associated with better adhesive properties and higher response to activators. Interestingly, delta (δ) granule-related functions, such as ADP-mediated aggregation and CD63 release, were better preserved at 4°C, especially in 100% plasma. An extended study to assess cold-stored platelet concentrates produced under standard care Good Manufacturing Practice conditions showed that platelet function, metabolism and integrity were better compared to those stored at room temperature. Taken together, our results show that residual plasma concentration does not have a cardinal impact on the cold storage lesions of apheresis-derived platelet concentrates and indicate that the current generation of additive solutions represent suitable substitutes for plasma to store platelets at 4°C.
Coronary Artery Disease Severity Is Associated With Abdominal Aortic Intima–Media Thickness in Patients With Non-ST-Segment Elevation Myocardial Infarction

The aim of this study was to evaluate the relationship between aortic intima–media thickness (aIMT) and coronary artery disease (CAD) severity in patients with non-ST-segment elevation myocardial infarction (NSTEMI). SYNTAX score (SS) was calculated. Patients were categorized into 2 main groups according to the SS: patient with high risk (SS ≥ 13) and low risk (SS < 13). Common carotid artery IMT (cIMT), common femoral artery IMT, and aIMT were measured with a high-resolution ultrasound Doppler system. We had 147 (52.9%) patients in the high-risk group and 132 patients in the low-risk group. Both cIMT and aIMT were significantly increased, and left ventricular ejection fraction (LVEF) was significantly lower in the high-risk group; aIMT (odds ratio [OR]: 1.272, P < .001), cIMT (OR: 1.239, P = .009), and LVEF (OR: 0.931, P = .002) were determined as independent predictors for the high-risk group. When the cutoff value was accepted as 1.25 mm for the aIMT, patients with high SS identification had 74.1% sensitivity and 89.4% specificity (area under the curve: 0.764, P < .001). In conclusion, aIMT detected by abdominal B-mode ultrasonography is closely associated with CAD severity in patients with NSTEMI.
An Analytical Tuning Approach for Adaptive MPC Parameters Applied to LTV SISO Systems

A successful implementation of model predictive control (MPC) requires parameters appropriately tuned. Such a goal is more difficult to reach with Linear-Time-Variant (LTV) systems. This is the reason why an analytical method is proposed here and especially detailed in the case of controllable single-input-single-output (SISO) systems. As an advantage, it can be applied online to indirect-adaptive MPC (IA-MPC) while guarantying the closed-loop stability and limiting the tuning computational load. Simulation results shown emphasize its effectiveness when compared to existing methods.
Thrown under the bus and outrunning it! The logic of Didi and taxi drivers’ labour and activism in the on-demand economy

This article examines how taxi drivers adapt to, manipulate and fight against the rise of ride-hailing platforms like Didi Chuxing in China (which purchased Uber China). Chinese taxi drivers entered the on-demand labour platforms before private car drivers. Based on a nationwide data survey, the article argues that the technological power of Didi took shape by reinforcing inequalities facing informally employed taxi drivers prior to the emergence of ride-hailing apps. Drivers, far from being passive app users, have counteracted the changes in the work environment that resulted from platformisation in new and evolving ways, from strikes to algorithmic activism. This study suggests that online platforms are contested spaces where digital labour politics penetrate beyond the purported algorithmic power of the technology. The article enriches researches on on-demand labour by deconstructing the distinction between taxi drivers and private gig drivers and by pointing to the unfolding new grounds for digital labour activism.
Beyond Academics: A Model for Simultaneously Advancing Campus-Based Supports for Learning Disabilities, STEM Students’ Skills for Self-Regulation, and Mentors’ Knowledge for Co-regulating and Guiding

Learning disabilities are highly prevalent on college campuses, yet students with learning disabilities graduate at lower rates than those without disabilities. Academic and psychosocial supports are essential for overcoming challenges and for improving postsecondary educational opportunities for students with learning disabilities. A holistic, multi-level model of campus-based supports was established to facilitate culture and practice changes at the institutional level, while concurrently bolstering mentors’ abilities to provide learning disability-knowledgeable support, and simultaneously creating opportunities for students’ personal and interpersonal development. Mixed methods were used to investigate implementation of coordinated personal, interpersonal, and institutional level supports for undergraduate science, technology, engineering, and math (STEM) students with learning disabilities. A one-group pre-test post-test strategy was used to examine undergraduate outcomes. Participants included 52 STEM undergraduates with learning disabilities, 57 STEM graduate student mentors, 34 STEM faculty mentors, and 34 university administrators and personnel as members of a university-wide council. Enrolled for 2 years, undergraduates were engaged in group meetings involving psychoeducation and reflective discussions, development of self-advocacy projects, and individual mentorship. Undergraduates reported improved self-efficacy (p = 0.001), campus connection (p < 0.001), professional development (p ≤ 0.002), and self-advocacy (p < 0.001) after two academic years. Graduate student mentors increased their understanding about learning disabilities and used their understanding to support both their mentees and other students they worked with. Council members identified and created opportunities for delivering learning disability-related trainings to faculty, mentors and advisors on campus, and for enhancing coordination of student services related to learning and related disorders. Disability-focused activities became integrated in broader campus activities regarding diversity. This research explicates a role that college campuses can play in fostering the wellbeing and the academic and career development of its students with developmental learning and related disorders. It offers an empirically tested campus-based model that is multilevel, holistic, and strengths-based for supporting positive outcomes of young people with learning disabilities in STEM. Moreover, findings advance the knowledge of supports and skills that are important for self-regulating and navigating complex and multi-faceted disability-related challenges within both the postsecondary educational environment and the young adults’ sociocultural context.
Outcome of allogeneic hematopoietic cell transplantation in patients with adult T‐cell leukemia

Adult T‐cell leukemia/lymphoma (ATL) is an aggressive peripheral T‐cell neoplasm, and the outcome of patients with ATL after chemotherapy is poor. Allogeneic hematopoietic stem‐cell transplantation (allo‐HSCT) is a curative treatment modality for ATL, and four factors, namely, age > 50 years, male recipient, lack of complete remission at transplantation, and transplantation of cord blood, were previously shown to be associated with poor survival. We retrospectively analyzed the outcome of 21 patients with ATL who had undergone allo‐HSCT at our hospital during a 3‐year period. Of 21 patients, all had at least one of the above risk factors, and 18 had two or more. With a median follow‐up of 19.7 months for living patients, the 1‐ and 2‐year overall survival (OS) rates after transplantation were 34% and 27%, respectively. All relapse/progression events occurred within 1 year after allo‐HSCT, and the cumulative incidence of relapse/progression at 1 year after allo‐HSCT was 46.9%. The 100‐day and 1‐year nonrelapse mortality (NRM) rates were 19% and 42%, respectively. No significant difference in OS was observed between myeloablative and reduced‐intensity conditioning regimens. The 3‐year OS (27%) of ATL patients who received allo‐HSCT and who had at least one adverse factor was somewhat poorer than the 3‐year OS of 33% identified in a nationwide study of allo‐HSCT in ATL patients in Japan. The high relapse/progression and NRM rates are major problems to be solved to achieve better outcome.
Plant Disease Detection Using Machine Learning

Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non attendance of the important foundation. Emergence of accurate techniques in the field of leaf-based image classification has shown impressive results. This paper makes use of Random Forest in identifying between healthy and diseased leaf from the data sets created. Our proposed paper includes various phases of implementation namely dataset creation, feature extraction, training the classifier and classification. The created datasets of diseased and healthy leaves are collectively trained under Random Forest to classify the diseased and healthy images. For extracting features of an image we use Histogram of an Oriented Gradient (HOG). Overall, using machine learning to train the large data sets available publicly gives us a clear way to detect the disease present in plants in a colossal scale.
Predicting resting-state functional connectivity with efficient structural connectivity

The complex relationship between structural connectivity ( SC ) and functional connectivity ( FC ) of human brain networks is still a critical problem in neuroscience. In order to investigate the role of SC in shaping resting-state FC, numerous models have been proposed. Here, we use a simple dynamic model based on the susceptible-infected-susceptible ( SIS ) model along the shortest paths to predict FC from SC. Unlike the previous dynamic model based on SIS theory, we focus on the shortest paths as the principal routes to transmit signals rather than the empirical structural brain network. We first simplify the structurally connected network into an efficient propagation network according to the shortest paths and then combine SIS infection theory with the efficient network to simulate the dynamic process of human brain activity. Finally, we perform an extensive comparison study between the dynamic models embedded in the efficient network, the dynamic model embedded in the structurally connected network and dynamic mean field ( DMF ) model predicting FC from SC. Extensive experiments on two different resolution datasets indicate that i: the dynamic model simulated on the shortest paths can predict FC among both structurally connected and unconnected node pairs; ii: though there are fewer links in the efficient propagation network, the predictive power of FC derived from the efficient propagation network is better than the dynamic model simulated on a structural brain network; iii: 9 in comparison with the DMF model, the dynamic model embedded in the shortest paths is found to perform better to predict FC.
Coupling Traditional Monitoring and Citizen Science to Disentangle the Invasion of Halyomorpha halys

The brown marmorated stink bug, Halyomorpha halys Stal (Hemiptera: Pentatomidae), is an invasive pest that has expanded its range outside of its original confinements in Eastern Asia, spreading through the United States, Canada and most of the European and Eurasian countries. The invasiveness of this agricultural and public nuisance pest is facilitated by the availability of an array of suitable hosts, an r-selected life history and the release from natural enemies in the invaded zones. Traditional monitoring methods are usually impeded by the lack of time and resources to sufficiently cover large geographical ranges. Therefore, the citizen science initiative “BugMap” was conceived to complement and assist researchers in breaking down the behavior of this invasive pest via a user-friendly, freely available mobile application. The collected data were employed to forecast its predicted distribution and to identify the areas at risk in Trentino, Northern Italy. Moreover, they permitted the uncovering of the seasonal invasion dynamics of this insect, besides providing insight into its phenological patterns, life cycle and potential management methods. Hence, the outcomes of this work emphasize the need to further integrate citizens in scientific endeavors to resolve ecological complications and reduce the gap between the public and science.
Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.
Browsing Historical Pompeian Watercolours Through a Google Earth-based Meta Interface: Luigi Bazzani's Exhibition

A Google-Earth-based meta interface provides the traditional text-based access to cultural repo DB with an easy and quick way for gaining insight into the full available data. Developed at Cineca for the Department of History and Hu man Cultures - Bologna University – the interface is proving its flexibility with this case of re-use as a support to an upcoming exhibition about watercolors realized at Pompeii at the turn of XIX century.
Systematic assessment of decision-analytic models evaluating diagnostic tests for acute myocardial infarction based on cardiac troponin assays

ABSTRACT Introduction: Despite the emphasis on the clinical importance of cardiac troponin assays (cTn), there are insufficient cost-effectiveness comparisons of various troponin test protocols for the diagnosis of myocardial infarction (MI). Therefore, the purpose of the review was to identify and systematically assess published economic evaluations using decision-analytic models for diagnostic testing strategies based on cTn and to make recommendations for the development of future models. Areas covered: MEDLINE, Science Direct, Cochrane Database, CRD Database, and gray literature were screened for full economic evaluation studies with relevant clinical outcomes over a defined time horizon addressing a population with suspected MI and comparison of different diagnostic test strategies. Standardized forms for data extraction and evidence tables were used for the summary of study design, methodological framework and data sources. Studies were assessed for quality using the CHEERS and the BMJ checklists. Expert commentary: Although there are 11 identified studies and several well-designed models, there remains a need for decision-analytic models including differential diagnosis for acute MI, different health facility configurations, clinician preferences, and behavioral components, and in the top of the subgroup analyses additional important personalized medicine aspects.
Assessing inference of the basic reproduction number in an SIR model incorporating a growth‐scaling parameter

The standard mass action, which assumes that infectious disease transmission occurs in well‐mixed populations, is popular for formulating compartmental epidemic models. Compartmental epidemic models often follow standard mass action for simplicity and to gain insight into transmission dynamics as it often performs well at reproducing disease dynamics in large populations. In this work, we formulate discrete time stochastic susceptible‐infected‐removed models with linear (standard) and nonlinear mass action structures to mimic varying mixing levels. Using simulations and real epidemic data, we demonstrate the sensitivity of the basic reproduction number to these mathematical structures of the force of infection. Our results suggest the need to consider nonlinear mass action in order to generate more accurate estimates of the basic reproduction number although its uncertainty increases due to the addition of one growth scaling parameter.