Miracle-dz/UniFAR-FTUs huggingface dataset:
Deregulated microRNAs Are Associated with Patient Survival and Predicted to Target Genes That Modulate Lung Cancer Signaling Pathways

Simple Summary Lung cancer is the leading cause of cancer death, worldwide. The low survival rates are mainly due to disease diagnosis in advanced stages, and the lack of effective treatments. In this study, we analyzed molecules known as microRNAs, which regulate the expression of a large proportion of the human genes. microRNAs are involved in processes related to the development and progression of cancer. In lung cancer, many microRNAs can drive disease. This study showed that some microRNAs have aberrant levels in tumor cells of the two most common types of lung cancer: lung adenocarcinoma and squamous cell carcinoma. In addition, we found that one microRNA, named miR-25-3p, had aberrantly increased levels in tumor cells from patients who died of lung cancer. These results are useful to better understand the biology of lung cancer, and can contribute as an additional tool to predict patient outcome/survival. Abstract (1) Background: Although the advances in diagnostic and treatment strategies, lung cancer remains the leading cause of cancer-related deaths, worldwide, with survival rates as low as 16% in developed countries. Low survival rates are mainly due to late diagnosis and the lack of effective treatment. Therefore, the identification of novel, clinically useful biomarkers is still needed for patients with advanced disease stage and poor survival. Micro(mi)RNAs are non-coding RNAs and potent regulators of gene expression with a possible role as diagnostic, prognostic and predictive biomarkers in cancer. (2) Methods: We applied global miRNA expression profiling analysis using TaqMan® arrays in paired tumor and normal lung tissues (n = 38) from treatment-naïve patients with lung adenocarcinoma (AD; n = 23) and lung squamous cell carcinoma (SCC; n = 15). miRNA target genes were validated using The Cancer Genome Atlas (TCGA) lung AD (n = 561) and lung SCC (n = 523) RNA-Seq datasets. (3) Results: We identified 33 significantly deregulated miRNAs (fold change, FC ≥ 2.0 and p < 0.05) in tumors relative to normal lung tissues, regardless of tumor histology. Enrichment analysis confirmed that genes targeted by the 33 miRNAs are aberrantly expressed in lung AD and SCC, and modulate known pathways in lung cancer. Additionally, high expression of miR-25-3p was significantly associated (p < 0.05) with poor patient survival, when considering both tumor histologies. (4) Conclusions: miR-25-3p may be a potential prognostic biomarker in non-small cell lung cancer. Genes targeted by miRNAs regulate EGFR and TGFβ signaling, among other known pathways relevant to lung tumorigenesis.
Inhibitory Activity of Quercetin 3-O-Arabinofuranoside and 2-Oxopomolic Acid Derived from Malus domestica on Soluble Epoxide Hydrolase

Flavonoids and triterpenoids were revealed to be the potential inhibitors on soluble epoxide hydrolase (sEH). The aim of this study is to reveal sEH inhibitors from Fuji apples. A flavonoid and three triterpenoids derived from the fruit of Malus domestica were identified as quercetin-3-O-arabinoside (1), ursolic acid (2), corosolic acid (3), and 2-oxopomolic acid (4). They had half-maximal inhibitory concentration of the inhibitors (IC50) values of 39.3 ± 3.4, 84.5 ± 9.5, 51.3 ± 4.9, and 11.4 ± 2.7 μM, respectively, on sEH. The inhibitors bound to allosteric sites of enzymes in mixed (1) and noncompetitive modes (2–4). Molecular simulations were carried out for inhibitors 1 and 4 to calculate the binding force of ligands to receptors. The inhibitors bound to the left (1) and right (4) pockets next to the enzyme’s active site. Based on analyses of their molecular docking and dynamics, it was shown that inhibitors 1 and 4 can stably bind sEH at 1 bar and 300 K. Finally, inhibitors 1 and 4 are promising candidates for further studies using cell-based assays and in vivo cardiovascular tests.
Colistin dry powder inhalation with the Twincer™: An effective and more patient friendly alternative to nebulization

Background Nebulization of antimicrobial drugs such as tobramycin and colistin is a cornerstone in the treatment of patients with cystic fibrosis (CF) infected with Pseudomonas aeruginosa. However, nebulization has a high treatment burden. The Twincer™ is a dry powder inhaler specifically developed for the inhalation of antibiotics such as colistin. The aim of this study was to compare patient outcomes and experience with colistin dry powder by the Twincer with nebulization of colistin or tobramycin in adult CF patients in a real-life setting. Methods This was a retrospective study from 01-01-2015 until 01-07-2018. Effectiveness was evaluated by comparing FEV1 decline and exacerbation rate during a mean of 4.1 years of nebulization therapy prior to the initiation of the Twincer against the same values during a mean of 1.7 years of treatment with the Twincer. Results Twenty-one patients were evaluated, of whom twelve could be included in the effectiveness analysis, with a total of twenty patient years. Of all patients 71.4% preferred therapy with the Twincer over nebulization. Twincer use resulted in high treatment adherence with an average adherence rate of 92.5%. There was no significant difference in annual decline in FEV1%pred prior to and after start changing from nebulization to the use of the Twincer powder inhaler (median decline -1.56 [-5.57–5.31] and 1.35 [-8.45–6.36]) respectively, p = 0.45 (linear mixed effect model)). No significant difference was found in the number of intravenous or combined total intravenous and oral antibiotic courses during Twincer therapy compared to when using nebulization (1.68 and 2.49 courses during Twincer therapy versus 1.51 and 2.94 courses during nebulization, p = 0.88 and p = 0.63). Conclusion Colistin dry powder inhalation with the Twincer is a more patient friendly alternative to nebulization, and we did not observe significant differences in the clinical outcome, regarding lung function and exacerbation rates.
Genetic diversity and population structure of Indian melon (Cucumis melo L.) landraces with special reference to disease and insect resistance loci

This study was aimed to examine the genetic diversity and population structure of Indian melon landraces with special reference to disease and insect resistance loci. Thirty-six simple sequence repeat (SSR) markers along with seven markers at disease and insect resistance loci were used for this purpose on a panel of 91 accessions available at Indian Institute of Horticultural Research, Bengaluru, India. Model-based structure analysis revealed the presence of four groups that were consistent with the results of principal coordinate analysis (PCoA). The delineation of populations was mostly based on geography with improved varieties as a separate group. Ten accessions have been identified to possess beneficial alleles at all the selected disease resistance loci and shall be useful for incorporating multiple disease resistance after phenotypic validation. The results obtained in the current study demonstrate the importance of the Indian melon group as a valuable genetic reservoir and the need to plan strategies for its conservation and utilization in breeding programmes.
Si Nanowire With Integrated Space-Charge- Limited Conducted Schottky Junction for Enhancing Field Electron Emission and Its Gated Devices

Integrated field emission electron sources with low driving voltage are desired for micro/nano vacuum electronics. Herein, we demonstrate the enhanced and stable electron emission from crystalline gold–silver (Au–Ag) alloy nanoparticle capped Si nanowires (SiNWs) and the achieved gated electron sources with low driving voltage (<50 V). Thermally induced self-assembly hemispherical (50–80 nm) crystalline Au–Ag nanoparticles were prepared and used as masks in plasma etching to form vertically aligned high aspect ratio (~14) SiNWs. The nanoparticles were preserved after the etching, forming in situ integrated Au–Ag/Si junctions with each individual SiNWs. The SiNWs with capped nanoparticles possess superior field emission current density (45.80 mA/cm2) and current stability (fluctuation ~6%). A self-aligned process was developed to fabricate gated field emission devices, using the Au–Ag nanoparticle capped SiNWs as cathode. The gated devices showed field emission at low gate voltage (69.52- $\mu \text{A}$ /cm2 at 42.1 V). Experimental and numerical simulation studies demonstrated the Au–Ag/Si contact behaved as space-charge-limited (SCL) Schottky conduction. The idea behind the enhancing and stable emission is that the SCL–Schottky junction undergoes a process of negative feedback and thus suppressed the current and shot-noise from the outstanding SiNWs. Accordingly, more SiNWs were activated and contributed stable and higher field emission current.
UNIFORM: Automatic Alignment of Open Learning Datasets

Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed bechmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners' outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher that those obtained on the original data.
Prediction of Crime Hotspots based on Spatial Factors of Random Forest

Crime has always been one of the important social issues that people care about. In the problem of urban security, preventing and reducing crime is one of the primary tasks of the police. Crime hotspot prediction can use historical crime data to infer geographic areas where crime may occur in the future. Machine learning is the mainstream method of current crime prediction method.But in the era of big data, more and more data information appears in the eyes of people, it is far from enough to use historical crime data to infer crime hotspots. Therefore, this paper is based on the random forest algorithm, first of all,divides the study areas into four categories according to the hot spot distribution based on the historical crime data: frequent hot areas, common hot areas, occasional hot areas and non-hot areas,and then, representative covariates from the non-historical crime data are added to the prediction model to explore the changes in the result accuracy of crime prediction based on the historical crime data by integrating different covariates. The data is based on real data, and the experimental results show that compared with the inference method based only on historical crime data, the accuracy of the model with covariates is improved compared with that without covariates.
DGLA from the Microalga Lobosphaera Incsa P127 Modulates Inflammatory Response, Inhibits iNOS Expression and Alleviates NO Secretion in RAW264.7 Murine Macrophages

Microalgae have been considered as a renewable source of nutritional, cosmetic and pharmaceutical compounds. The ability to produce health-beneficial long-chain polyunsaturated fatty acids (LC-PUFA) is of high interest. LC-PUFA and their metabolic lipid mediators, modulate key inflammatory pathways in numerous models. In particular, the metabolism of arachidonic acid under inflammatory challenge influences the immune reactivity of macrophages. However, less is known about another omega-6 LC-PUFA, dihomo-γ-linolenic acid (DGLA), which exhibits potent anti-inflammatory activities, which contrast with its delta-5 desaturase product, arachidonic acid (ARA). In this work, we examined whether administrating DGLA would modulate the inflammatory response in the RAW264.7 murine macrophage cell line. DGLA was applied for 24 h in the forms of carboxylic (free) acid, ethyl ester, and ethyl esters obtained from the DGLA-accumulating delta-5 desaturase mutant strain P127 of the green microalga Lobosphaera incisa. DGLA induced a dose-dependent increase in the RAW264.7 cells’ basal secretion of the prostaglandin PGE1. Upon bacterial lipopolysaccharide (LPS) stimuli, the enhanced production of pro-inflammatory cytokines, tumor necrosis factor alpha (TNFα) and interleukin 1β (IL-1β), was affected little by DGLA, while interleukin 6 (IL-6), nitric oxide, and total reactive oxygen species (ROS) decreased significantly. DGLA administered at 100 µM in all forms attenuated the LPS-induced expression of the key inflammatory genes in a concerted manner, in particular iNOS, IL-6, and LxR, in the form of free acid. PGE1 was the major prostaglandin detected in DGLA-supplemented culture supernatants, whose production prevailed over ARA-derived PGE2 and PGD2, which were less affected by LPS-stimulation compared with the vehicle control. An overall pattern of change indicated DGLA’s induced alleviation of the inflammatory state. Finally, our results indicate that microalgae-derived, DGLA-enriched ethyl esters (30%) exhibited similar activities to DGLA ethyl esters, strengthening the potential of this microalga as a potent source of this rare anti-inflammatory fatty acid.
Investigating m-health gamification rewards elements for adults 50+

Most of the m-health available on the market today aims to promote a healthy lifestyle and to increase the well-being of adults 50+, but few are specifically designed for them. Thus, keeping users engaged for long periods of usage is a constant challenge, especially in cases of diseases that cannot be cured, such as diabetes and cardiac insufficiency, which require long-term care. Some applications adopt gamification as a strategy to keep their users motivated to follow the medical recommendations repeatedly. As a reward, they usually make use of virtual currencies, points, and badges. However, in the case of adults 50+, this engagement can be even more particular, where research points out to a different perception on what motivates them to achieve well-defined and more significant purposes than pure entertainment in games. This paper investigates how to project reward elements on gamified health platforms for this audience, suggesting what can satisfy their intrinsic motivations to engage them for a future continued use of an m-health app. We have found that excess reward elements can be harmful and have the opposite effect of what usually m-health applications are designed for. Besides, the first boost coming from a close friend or family can be determinant to motivate them intrinsically or even to change their routine, more than coming from a health agent. Finally, we conclude through an m-health gamification framework focused on adults 50+.
Tracking a human being via the gray local dissimilarity map

Tracking human being from real scenes has attracted great interest in the computer vision community. We aim in this paper to provide a visual tracking system that is based on a dissimilarity measure. The proposed method includes the gray Local Dissimilarity Map and the Kalman Filter. Experimental results on several image sequences illustrate that the proposed method performs well in several challenging aspects of real world scenes.
Paralogs and Off-Target Sequences Improve Phylogenetic Resolution in a Densely Sampled Study of the Breadfruit Genus (Artocarpus, Moraceae)

Abstract We present a 517-gene phylogenetic framework for the breadfruit genus Artocarpus (ca. 70 spp., Moraceae), making use of silica-dried leaves from recent fieldwork and herbarium specimens (some up to 106 years old) to achieve 96% taxon sampling. We explore issues relating to assembly, paralogous loci, partitions, and analysis method to reconstruct a phylogeny that is robust to variation in data and available tools. Although codon partitioning did not result in any substantial topological differences, the inclusion of flanking noncoding sequence in analyses significantly increased the resolution of gene trees. We also found that increasing the size of data sets increased convergence between analysis methods but did not reduce gene-tree conflict. We optimized the HybPiper targeted-enrichment sequence assembly pipeline for short sequences derived from degraded DNA extracted from museum specimens. Although the subgenera of Artocarpus were monophyletic, revision is required at finer scales, particularly with respect to widespread species. We expect our results to provide a basis for further studies in Artocarpus and provide guidelines for future analyses of data sets based on target enrichment data, particularly those using sequences from both fresh and museum material, counseling careful attention to the potential of off-target sequences to improve resolution. [Artocarpus; Moraceae; noncoding sequences; phylogenomics; target enrichment. ]
Magnetic resonance assessment of sinusoidal obstruction syndrome after neoadjuvant chemotherapy for colorectal liver metastases is not reproducible

Background Sinusoidal obstruction syndrome (SOS) due to chemotherapy can cause severe hepatotoxicity, leading to impaired outcome in patients with colorectal cancer. A previous study introduced gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI) to diagnose SOS. Purpose To assess the reproducibility of Gd-EOB-MRI-based SOS diagnosis and its relationship with response to chemotherapy and long-term outcome. Material and Methods Twenty-six Gd-EOB-MRI scans of patients undergoing chemotherapy for colorectal liver metastases (CRLM) were retrospectively analyzed. Three radiologists, blinded to clinical data, independently scored presence and severity of SOS on a 5-point scale (0, definitely not present to 4, definitely present). Patients with a score ≥3 were considered SOS+. Inter-observer agreement between readers was assessed with kappa statistics. Response (RECIST 1.1.), occurrence of new CRLM during follow-up (hepatic progression) and overall survival (OS) were compared between patients with and without SOS. Results The inter-observer agreement of SOS scores was poor, with quadratic kappas of 0.17–0.40. For the binary outcome of SOS+ (confidence level [CL] 3–4) vs. SOS– (CL 0–2) agreement was poor, with kappas of 0.03–0.37. Median follow-up was 24 months (range 4–44 months). Response and OS between patients with and without SOS did not differ significantly for any of the readers. Conclusion Inter-observer agreement for the diagnosis of SOS on Gd-EOB-MRI is poor. No significant correlation with relevant outcomes was found for any of the readers. Therefore, MRI for SOS diagnosis might be less useful than previously reported. Other techniques should be explored to accurately diagnose SOS in absence of histological confirmation.
Prior sleep problems and adverse post-traumatic neuropsychiatric sequelae (APNS) of motor vehicle collision in the AURORA study.

STUDY OBJECTIVES Many patients in Emergency Departments (ED) after motor vehicle collisions (MVC) develop posttraumatic stress disorder (PTSD) or major depressive episodes (MDE). This report from the AURORA study focuses on associations of pre-MVC sleep problems with these outcomes 8 weeks after MVC mediated through peritraumatic distress and dissociation and 2-week outcomes. METHODS 666 AURORA patients completed self-report assessments in the ED and at 2 and 8 weeks after MVC. Peritraumatic distress, peritraumatic dissociation and pre-MVC sleep characteristics (insomnia, nightmares, daytime sleepiness and sleep duration in the 30 days before the MVC, trait sleep stress reactivity) were assessed retrospectively in the ED. The survey assessed acute stress disorder (ASD) and MDE at 2 weeks and at 8 weeks assessed PTSD and MDE (past 30 days). Control variables included demographics, MVC characteristics, and retrospective reports about PTSD and MDE in the 30 days before the MVC. RESULTS Prevalence estimates were 41.0% for 2-week ASD, 42.0% for 8-week PTSD, 30.5% for 2-week MDE, and 27.2% for 8-week MDE. Pre-MVC nightmares and sleep stress reactivity predicted 8-week PTSD (mediated through 2-week ASD) and MDE (mediated through the transition between 2-week and 8-week MDE). Pre-MVC insomnia predicted 8-week PTSD (mediated through 2-week ASD). Estimates of population attributable risk suggest that blocking effects of sleep disturbance might reduce prevalence of 8-week PTSD and MDE by as much as one-third. CONCLUSIONS Targeting disturbed sleep in the immediate aftermath of MVC might be one effective way of reducing MVC-related PTSD and MDE.
Supplier Selection for High Temperature Die Attach by hybrid Entropy-Range of Value MCDM Technique: A Semiconductor Industry

Demands of High temperature die attach material (HTDM) are growing to increase the efficiency of Microelectronic packaging. However, Appropriate HTDM supplier selection is one of the major concerns for semiconductors industries due to various conflicting criteria. This study will employ a multicriteria decision making modelling (MCDM) to select the suitable HTDM supplier. Seven different international HTDM supplier (S1, S2, S3, S4, S5,S6,S7) are ranked based on eight conflicting criteria (electrical resistivity, thermal conductivity, impression, management system, production facility, geographical location, response speed and use of technology) by employing hybrid entropy-range of value (E-ROV) method. The result of this study showed that S7 is the most suitable supplier as it occupies rank one in the modelling and S5 being a worst. Sensitivity analysis illustrated that the results of E-ROV are consistent and reliable. Based on these results, E-ROV method can also be applied for supplier selection of various production complex parts in semiconductor industry.
Sparse Index OFDM Modulation for IoT Communications

One of the greatest challenges facing the physical layer design of the internet of things (IoT) resides in the imposed constraint of very low power consumption. Recently, new modulation scheme termed OFDM with sparse index modulation (OFDM-SIM) has been introduced as an energy efficient multicarrier scheme (MCS). Although of its high energy efficiency (EE) and spectral efficiency (SE), OFDM-SIM cannot fulfill the IoT energy requirements owing to its high PAPR. In this regard, an enhanced OFDM-SIM is proposed in this paper as an energy efficient MCS for IoT communications. In particular, a novel clipping-compressive sensing (CS) based PAPR reduction technique for OFDM-SIM is proposed. In the transmitter (TX) side, considering the complexity constraints for IoT devices, the simple and low complex clipping method is exploited to deal with the PAPR issue. On the receiver (RX) side, a robust CS signal recovery scheme is proposed to deal with tough resulting clipping noise. Unlike high complex conventional CS-based schemes, the proposed scheme exploits the inherent sparsity of the received enhanced OFDM-SIM signal rather than clipping noise sparsity to achieve a low complex CS signal detection. Moreover, in this paper, the information-theoretic limits on sparsity recovery are exploited to derive an upper bound measure of the bit error rate (BER). The simulation results demonstrate the superiority of the proposed scheme, as it significantly enhances the overall system performance in terms of EE and PAPR reduction compared to the conventional clipped coded-OFDM.