Miracle-dz/UniFAR-FTUs huggingface dataset:
A Qualitative Application of Temporal Self-Regulation Theory to Understand Adherence to Simple and Complex Medication Regimens

Medication adherence is a global health concern, and variables of temporal self-regulation theory (TST) have been shown to be important in improving adherence. This qualitative study aims to explore how TST can help explain medication adherence in people’s daily lives, and whether there are differences in the adherence to simple and complex medication regimens. Twenty-nine participants from Australia engaged in semi-structured interviews based on TST (intention, behavioural prepotency, self-regulation), and other variables important to adherence. Interviews were analysed using thematic analysis. Six themes were identified (Routines, External Supports, Cost, Sense of Agency, Adverse Outcomes, and Weighing Up Pros and Cons), with partial support for TST (specifically intention, past behaviour, cues and planning). Four themes not related to TST were also identified. Individuals with more complex medication regimens spoke of the importance of routines, planning, and knowledge-seeking, whereas those with simpler regimens spoke of the importance of visual cues. TST may be useful for identifying some variables important in medication adherence, however, additional factors were also identified. For simple regimens, future research should focus on the manipulation of visual cues. For complex regimens, health professionals should consider supporting the use of medication management apps to assist in planning and ensuring a consistent routine.
Fundamental Concepts of Hydrogels: Synthesis, Properties, and Their Applications

In the present review, we focused on the fundamental concepts of hydrogels—classification, the polymers involved, synthesis methods, types of hydrogels, properties, and applications of the hydrogel. Hydrogels can be synthesized from natural polymers, synthetic polymers, polymerizable synthetic monomers, and a combination of natural and synthetic polymers. Synthesis of hydrogels involves physical, chemical, and hybrid bonding. The bonding is formed via different routes, such as solution casting, solution mixing, bulk polymerization, free radical mechanism, radiation method, and interpenetrating network formation. The synthesized hydrogels have significant properties, such as mechanical strength, biocompatibility, biodegradability, swellability, and stimuli sensitivity. These properties are substantial for electrochemical and biomedical applications. Furthermore, this review emphasizes flexible and self-healable hydrogels as electrolytes for energy storage and energy conversion applications. Insufficient adhesiveness (less interfacial interaction) between electrodes and electrolytes and mechanical strength pose serious challenges, such as delamination of the supercapacitors, batteries, and solar cells. Owing to smart and aqueous hydrogels, robust mechanical strength, adhesiveness, stretchability, strain sensitivity, and self-healability are the critical factors that can identify the reliability and robustness of the energy storage and conversion devices. These devices are highly efficient and convenient for smart, light-weight, foldable electronics and modern pollution-free transportation in the current decade.
Dudomycins: New Secondary Metabolites Produced after Heterologous Expression of an Nrps Cluster from Streptomyces albus ssp. Chlorinus Nrrl B-24108

Since the 1950s, natural products of bacterial origin were systematically developed to be used as drugs with a wide range of medical applications. The available treatment options for many diseases are still not satisfying, wherefore, the discovery of new structures has not lost any of its importance. Beyond the great variety of already isolated and characterized metabolites, Streptomycetes still harbor uninvestigated gene clusters whose products can be accessed using heterologous expression in host organisms. This works presents the discovery of a set of structurally novel secondary metabolites, dudomycins A to D, through the expression of a cryptic NRPS cluster from Streptomyces albus ssp. Chlorinus NRRL B-24108 in the heterologous host strain Streptomyces albus Del14. A minimal set of genes, required for the production of dudomycins, was defined through gene inactivation experiments. This paper also proposes a model for dudomycin biosynthesis.
Myosin II isoforms play distinct roles in adherens junction biogenesis

Adherens junction (AJ) assembly under force is essential for many biological processes like epithelial monolayer bending, collective cell migration, cell extrusion and wound healing. The acto-myosin cytoskeleton acts as a major force-generator during the de novo formation and remodelling of AJ. Here, we investigated the role of myosinII isoforms in epithelial junction assembly. Myosin IIA (NMIIA) and Myosin IIB (NMIIB) differentially regulate biogenesis of adherens junction through association with distinct actin networks. Analysis of junction dynamics, actin organization, and mechanical forces of control and knockdown cells for myosins revealed that NMIIA provides the mechanical tugging force necessary for cell-cell junction reinforcement and maintenance. NMIIB is involved in E-cadherin clustering, maintenance of a branched actin layer connecting E-cadherin complexes and perijunctional actin fibres leading to the building-up of anisotropic stress. These data reveal unanticipated complementary functions of NMIIA and NMIIB in the biogenesis and integrity of AJ.
Reducing EEG (Electroencephalogram) Electrode-induced Skin Injury among Ambulatory EEG Monitored Patients: A Non-randomized Interventional Study of Two Commonly Used Cream-based Products for Electrode Application

ABSTRACT Ambulatory electroencephalography (AEEG) seeks to capture inter-ictal epileptiform activity or paroxysmal events when patients are not in the clinic. Skin inflammation is a common complication of prolonged EEG monitoring. This non-randomized study aimed to investigate the performance of two commonly used cream-based methods of electrode application in reducing electrode-induced skin injury among patients undergoing AEEG monitoring. A non-randomized interventional study was conducted from July to December 2019 in the Neurosciences Ambulatory Care Unit at Royal Prince Alfred Hospital, Australia. Patients were enrolled into two groups: i) Group T, which received Ten20® Conductive Paste with Tensive® Conductive Adhesive Gel as the primary approach to electrode application; ii). Group E, which received EC2⁺® Conductive Cream as the primary approach to electrode application. Patients in Group T were enrolled in the 1st and 3rd week of the month, and patients in Group E were enrolled in the 2nd and 4th week for each month of the study. A total of 152 patients participated in this study. Two sub-groups were established: those who were monitored for two days (Group T; n = 36, Group E; n = 30) and those who were monitored for four days (Group T; n = 43, Group E; n = 43). Significant (p < 0.05) differences indicating greater inflammation in the Group E were noted for both Day 2 and Day 4 participants. Skin injury/inflammation was significantly less using the standard method (Group T: Ten20® with Tensive® gel) when compared to EC2⁺® (Group E) as the conductive material at the electrode site.
8-Hydroxydaidzein, an Isoflavone from Fermented Soybean, Induces Autophagy, Apoptosis, Differentiation, and Degradation of Oncoprotein BCR-ABL in K562 Cells

8-Hydroxydaidzein (8-OHD, 7,8,4′-trihydoxyisoflavone) is a hydroxylated derivative of daidzein isolated from fermented soybean products. The aim of this study is to investigate the anti-proliferative effects and the underlying mechanisms of 8-OHD in K562 human chronic myeloid leukemia (CML) cells. We found that 8-OHD induced reactive oxygen species (ROS) overproduction and cell cycle arrest at the S phase by upregulating p21Cip1 and downregulating cyclin D2 (CCND2) and cyclin-dependent kinase 6 (CDK6) expression. 8-OHD also induced autophagy, caspase-7-dependent apoptosis, and the degradation of BCR-ABL oncoprotein. 8-OHD promoted Early Growth Response 1 (EGR1)-mediated megakaryocytic differentiation as an increased expression of marker genes, CD61 and CD42b, and the formation of multi-lobulated nuclei in enlarged K562 cells. A microarray-based transcriptome analysis revealed a total of 3174 differentially expressed genes (DEGs) after 8-OHD (100 μM) treatment for 48 h. Bioinformatics analysis of DEGs showed that hemopoiesis, cell cycle regulation, nuclear factor-κB (NF-κB), and mitogen-activated protein kinase (MAPK) and Janus kinase/signal transducers and activators of transcription (JAK-STAT)-mediated apoptosis/anti-apoptosis networks were significantly regulated by 8-OHD. Western blot analysis confirmed that 8-OHD significantly induced the activation of MAPK and NF-κB signaling pathways, both of which may be responsible, at least in part, for the stimulation of apoptosis, autophagy, and differentiation in K562 cells. This is the first report on the anti-CML effects of 8-OHD and the combination of experimental and in silico analyses could provide a better understanding for the development of 8-OHD on CML therapy.
Corrosion Behavior of CoCrFeNiTax Alloys in 1 M Sodium Chloride Aqueous Solution

This paper investigates the effects of Ta content on the microstructures, hardness and corrosion behavior of as-cast CoCrFeNiTax alloys. The results indicate that the addition of Ta can change the microstructures of these alloys to dual-phased structures (FCC + HCP), as well as increasing their hardness. This study uses constant galvanostatic/potentiometric methods to measure the polarization curves of CoCrFeNiTa0.1, CoCrFeNiTa0.3 and CoCrFeNiTa0.5 alloys in deaerated 1 M sodium chloride solution at different temperatures. Electrochemical impedance spectroscopy is also used to analyze these alloys in sodium chloride solution. The results indicate that the CoCrFeNiTa0.5 alloy has a eutectic structure and the highest hardness. Furthermore, although the CoCrFeNiTa0.5 alloy has the best corrosion resistance, the CoCrFeNiTa0.3 alloy has the best pitting resistance among these alloys.
Methods for correcting inference based on outcomes predicted by machine learning

Significance Machine learning is now being used across the entire scientific enterprise. Researchers commonly use the predictions from random forests or deep neural networks in downstream statistical analysis as if they were observed data. We show that this approach can lead to extreme bias and uncontrolled variance in downstream statistical models. We propose a statistical adjustment to correct biased inference in regression models using predicted outcomes—regardless of the machine-learning model used to make those predictions. Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with arbitrarily complicated machine-learning models including random forests and deep neural nets. Rather than trying to derive the correction from first principles for each machine-learning algorithm, we observe that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for postprediction inference that naturally fits into the standard machine-learning framework where the data are divided into training, testing, and validation sets. We train the prediction model in the training set, estimate the relationship between the observed and predicted outcomes in the testing set, and use that relationship to correct subsequent inference in the validation set. We show our postprediction inference (postpi) approach can correct bias and improve variance estimation and subsequent statistical inference with predicted outcomes. To show the broad range of applicability of our approach, we show postpi can improve inference in two distinct fields: modeling predicted phenotypes in repurposed gene expression data and modeling predicted causes of death in verbal autopsy data. Our method is available through an open-source R package: https://github.com/leekgroup/postpi.
Robo functions as an attractive cue for glial migration through SYG-1/Neph

As one of the most-studied receptors, Robo plays functions in many biological processes, and its functions highly depend on Slit, the ligand of Robo. Here we uncover a Slit-independent role of Robo in glial migration and show that neurons can release an extracellular fragment of Robo upon cleavage to attract glia during migration in Caenorhabditis elegans. Furthermore, we identified the conserved cell adhesion molecule SYG-1/Neph as a receptor for the cleaved extracellular Robo fragment to mediate glial migration and SYG-1/Neph functions through regulation of the WAVE complex. Our studies reveal a previously unknown Slit-independent function and regulatory mechanism of Robo and show that the cleaved extracellular fragment of Robo can function as a ligand for SYG-1/Neph to guide glial migration. As Robo, the cleaved region of Robo, and SYG-1/Neph are all highly conserved across the animal kingdom, our findings may present a conserved Slit-independent Robo mechanism during brain development.
Is there a dose-response relationship between musical instrument playing and later-life cognition? A cohort study using EPIC-Norfolk data.

INTRODUCTION Musical instrument playing provides intellectual stimulation, which is hypothesised to generate cognitive reserve that protects against cognitive impairment. Studies to date have classified musicianship as a binary entity. This investigation draws on the dataset of the European Prospective Investigation of Cancer Norfolk study to examine the effect of frequency of playing on later-life cognition. METHODS We compared three categorisations of self-reported musical playing frequency in late mid-life (12-month period) against cognitive performance measured after a 4-11 year delay, adjusted for relevant health and social confounders. Logistic regression models estimated the adjusted association between frequency of musical playing and the likelihood of being in the top and bottom cognitive deciles. RESULTS A total of 5,693 participants (745 musicians) provided data on music playing, cognition and all co-variables. Classification of musicianship by frequency of playing demonstrated key differences in socio-demographic factors. Musicians outperformed non-musicians in cognition generally. Compared with non-musicians, frequent musicians had 80% higher odds of being in the top cognitive decile (OR 1.80 [95% CI 1.19-2.73]), whereas musicians playing at any frequency had 29% higher odds (95% CI 1.03-1.62). There was evidence of a threshold effect, rather than a linear dose-response relationship. DISCUSSION This study supports a positive association between late mid-life musical instrument playing and later-life cognition, although causation cannot be assumed. Musicians playing frequently demonstrated the best cognition. 'Musicians' are a heterogeneous group and frequency of music playing seems a more informative measure than binary classification. Ideally, this more nuanced measure would be collected for different life course phases.
Classifying Firms’ Performance using Data Mining Approaches

— Superior prediction and classification in determining company’s performance are major concern for practitioners and academic research in providing useful or important information to the shareholders and potential investors for investment decision. Generally, the normal practice to analysed firm’s performance are based on financial indicators reported in the company’s annual report including the balance sheet, income and cash flow statements. In this work, a few popular and important benchmarking machine learning techniques for the data mining including neural networks, support vector machine, rough set theory, discriminant analysis, logistic regression, decision table, sequential minimal optimization and decision tree have been tested as to classify firm’s performance. The data mining techniques produce high classification rate that is more than 92%. This work also has reduced total number of ratios to be evaluated due to long processing time and large processing resources. Finally, the CA/TA, S/TA, E/TA, GM, FC, PBT/TA, and EPS have been considered for of the final reduced financial ratios. The results show that the 7 reduced ratios are comparable as the common 24 ratios. And to the still produce high classification rate and able classify the firm’s performance.
Parenchymatous hematoma in patients with atraumatic subarachnoid hemorrhage: Characteristics, treatment, and clinical outcomes

Background Data regarding the influence of concomitant parenchymatous hematoma (PH) on long-term outcomes in patients with atraumatic subarachnoid hemorrhage (SAH) are scarce. Further, it is not established if these patients benefit from surgical intervention. Aim The aim of this study was to determine the influence of concomitant PH in SAH patients on functional long-term outcome, and whether these patients may benefit from surgical hematoma evacuation. Methods Over a 5-year period, all consecutive patients with SAH treated at the Departments of Neurology, Neuroradiology, and Neurosurgery, at the University Hospital Erlangen (Germany) were recorded. In addition to the clinical and imaging characteristics of SAH, we documented the presence, location, and volume of PH as well as treatment parameters. Outcome assessment at 12 months included functional outcome (modified Rankin scale (mRS), favorable = 0–2), health-related quality of life, and long-term complications. For outcome analysis, a propensity score matching (ratio 1:1, caliper 0.1) was performed to compare SAH patients with and without PH. Sub-analyses were performed regarding PH treatment (surgical evacuation vs. conservative). Results A total of 494 patients with atraumatic SAH were available. Eighty-five (17.2%) had PH on initial imaging. SAH patients with PH had a worse clinical condition on admission and had a greater extent of subarachnoid/intraventricular hemorrhage. Median PH volume was 11.0 ml (5.4–31.8) with largest volumes observed in patients with ruptured middle cerebral artery (MCA)-aneurysm (31.7 ml (16.3–43.2)). After propensity-score matching (PSM), patients with PH had worse functional outcomes at 12 months (modified Rankin scale (mRS) 0–2: PH 31.8% vs. ØPH57. 7% p < 0.001), and a lower rate of self-reported health compared to patients without PH (EQ-5D VAS: PH 50(30–70) vs. ØPH 80(65–95); p < 0.001). In PH patients, surgical evacuation was associated with a higher rate of favorable outcome at 12 months compared to those treated conservatively (surgery 14/28 (50.0%) vs. conservative 14/57 (24.6%); adjusted odds-ratio (OR; 95%CI): 1.34 (1.08–1.66); p = 0.001), irrespective of aneurysm location. Subgroup-analysis revealed positive associations of surgical hematoma evacuation with outcome in subgroups with larger PH volumes (>10 ml; OR (95%CI): 1.39 (1.09–1.79)), frontal PH location (OR 1.59 (1.14–2.23)), and early surgery (within 600 min after onset; OR 1.42 (1.03–1.94)). Conclusions Concomitant PH occurs frequently in patients with SAH and is associated with functional impairment after 1 year. Surgical evacuation of PH may improve outcomes in these patients, irrespective of aneurysm-location.
3D Polyaniline Nanofibers Anchored on Carbon Paper for High-Performance and Light-Weight Supercapacitors

In the field of advanced energy storage, nanostructured Polyaniline (PANI) based materials hold a special place. Extensive studies have been done on the application of PANI in supercapacitors, however, the structure–property relationship of these materials is still not understood. This paper presents a detailed characterization of the novel sodium phytate doped 3D PANI nanofibers anchored on different types of carbon paper for application in supercapacitors. An excellent relationship between the structures and properties of the synthesized samples was found. Remarkable energy storage characteristics with low values of solution, charge transfer and polarization resistance and a specific capacitance of 1106.9 ± 1.5 F g−1 and 779 ± 2.6 F g−1 at current density 0.5 and 10 Ag−1, respectively, was achieved at optimized conditions. The symmetric supercapacitor assembly showed significant enhancement in both energy density and power density. It delivered an energy density of 95 Wh kg−1 at a power of 846 W kg−1. At a high-power density of 16.9 kW kg−1, the energy density can still be kept at 13 Wh kg−1. Cyclic stability was also checked for 1000 cycles at a current density of 10 Ag−1 having excellent retention, i.e., 96%.
Concurrent Metabolic Profiling and Quantification of Aromatic Amino Acids and Phytohormones in Solanum lycopersicum Plants Responding to Phytophthora capsici

Pathogenic microorganisms account for large production losses in the agricultural sector. Phytophthora capsici is an oomycete that causes blight and fruit rot in important crops, especially those in the Solanaceae family. P. capsici infection is difficult to control due to genetic diversity, arising from sexual reproduction, and resistant spores that remain dormant in soil. In this study, the metabolomics of tomato plants responding to infection by P. capsici were investigated. Non-targeted metabolomics, based on liquid chromatography coupled to mass spectrometry (LC-MS), were used with multivariate data analyses to investigate time-dependent metabolic reprogramming in the roots, stems, and leaves of stem-infected plants, over an 8 day period. In addition, phytohormones and amino acids were determined using quantitative LC-MS. Methyl salicylate and 1-aminocyclopropane-1-carboxylate were detected as major signalling molecules in the defensive response to P. capsici. As aromatic amino acid precursors of secondary metabolic pathways, both phenylalanine and tryptophan showed a continuous increase over time in all tissues, whereas tyrosine peaked at day 4. Non-targeted metabolomic analysis revealed phenylpropanoids, benzoic acids, glycoalkaloids, flavonoids, amino acids, organic acids, and fatty acids as the major classes of reprogrammed metabolites. Correlation analysis showed that metabolites derived from the same pathway, or synthesised by different pathways, could either have a positive or negative correlation. Furthermore, roots, stems, and leaves showed contrasting time-dependent metabolic reprogramming, possibly related to the biotrophic vs. necrotrophic life-stages of the pathogen, and overlapping biotic and abiotic stress signaling. As such, the targeted and untargeted approaches complemented each other, to provide a detailed view of key time-dependent metabolic changes, occurring in both the asymptomatic and symptomatic stages of infection.