Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Extracellular Vesicles as a New Class of Medicines

Extracellular vesicles (EVs) are nanovesicles that are naturally released from cells in a lipid bilayer-bound form. A subset population with a size of 200 nm, small EVs (sEVs), is enticing in many ways. Initially perceived as mere waste receptacles, sEVs have revealed other biological functions, such as cell-to-cell signal transduction and communication. Besides their notable biological functions, sEVs have profound advantages as future drug modalities: (i) excellent biocompatibility, (ii) high stability, and (iii) the potential to carry undruggable macromolecules as cargo. Indeed, many biopharmaceutical companies are utilizing sEVs, not only as diagnostic biomarkers but as therapeutic drugs. However, as all inchoate fields are challenging, there are limitations and hindrances in the clinical translation of sEV therapeutics. In this review, we summarize different types of sEV therapeutics, future improvements, and current strategies in large-scale production.
Examining the Relationship Between Word Reading and Nonword Reading Development Within an Orthographic Learning Framework

Perfetti’s representational quality hypothesis suggests that orthographic learning in developing readers is associated with two important changes to the orthographic lexicon that drive continued word reading development, namely, addition of word-specific representations and acquisition of complex context-dependent decoding skills. Perfetti further hypothesizes that these two changes are mutually facilitative suggesting a bidirectional relationship. To test this hypothesis, cross-lagged panel models were used to model the relationship between word reading and nonword reading longitudinally across Grades 1–4 in a diverse sample (N = 433) of developing readers. Overall results revealed the codevelopmental pattern between word and nonword reading to be bidirectional in nature such that word reading impacted later nonword reading and vice versa. These bidirectional relations did not differ significantly between those who qualify for free and reduced lunch and those who do not. However, differences were observed between those at-risk for dyslexia and those not at-risk.
Pancancer survival prediction using a deep learning architecture with multimodal representation and integration

Abstract Motivation Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data. Results In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics data. We first developed an unsupervised learning part to extract high-level feature representations from omics data of different modalities. Then, we used an attention-based method to integrate feature representations, produced by the unsupervised learning part, into a single compact vector and finally we fed the vector into fully connected layers for survival prediction. We used multimodal data to train the model and predict pancancer survival, and the results show that using multimodal data can lead to higher prediction accuracy compared to using single modal data. Furthermore, we used the concordance index and the 5-fold cross-validation method for comparing our proposed method with current state-of-the-art methods and our results show that our model achieves better performance on the majority of cancer types in our testing datasets. Availability and implementation https://github.com/ZhangqiJiang07/MultimodalSurvivalPrediction. Supplementary information Supplementary data are available at Bioinformatics online.
“I’m not Just Some Criminal, I’m Actually a Person to Them Now”: The Importance of Child-Staff Therapeutic Relationships in the Children and Young People Secure Estate

Therapeutic relationships between children and staff across the Children and Young People Secure Estate (CYPSE) is an under-researched area. The aim of this research is to explore the research question, ‘ what are the key elements of the development and maintenance of effective therapeutic relationships? ’ Qualitative semi-structured interviews and a focus group were conducted with children in Young Offender Institutions, Secure Training Centers and Secure Children ’ s Homes in England between February – November 2019 (N ¼ 28 children; 25 interviews, 1 focus group). Framework analysis and grounded theory methodology were applied to the data, and 13 themes were generated. The facilitating properties of good communication, understanding, reciprocal respect, trust, and a sense of fairness in these relationships are highlighted. Positive relationships may be facilitated through the formulation process. The varied experiences of children are considered, highlighting the need for knowledge sharing and training staff in effective helping skills.
Electron density and temperature measurement method by using emission spectroscopy in atmospheric pressure nonequilibrium nitrogen plasmas

A novel spectroscopic method is proposed for the measurement of electron density and temperature in atmospheric pressure dielectric barrier discharges using nitrogen gas. Simplified collisional-radiative models for the electronic and the vibrational states yield two separate continuity equations as a function of the electron density and the temperature with the coefficients expressed in terms of rotational temperature, vibrational temperature, and emission intensity ratio between the first positive system and the second positive system of nitrogen molecules. The electron density and the temperature in nonequilibrium atmospheric pressure plasmas can be determined by solving the continuity equations with the coefficients estimated from the spectroscopic measurements. It was confirmed by applying to a high power dielectric barrier discharge, where the measured plasma parameters were in good agreement with the estimation by using the electron conductivity of the discharge.
Increased Methylation of Brain-Derived Neurotrophic Factor (BDNF) Is Related to Emotionally Unstable Personality Disorder and Severity of Suicide Attempt in Women

Brain-derived neurotrophic factor (BDNF) has previously been associated with the pathogenesis of both emotionally unstable personality disorder (EUPD) and suicidal behavior. No study has yet investigated BDNF-associated epigenetic alterations in a group of severely impaired EUPD and suicidal patients. The discovery cohort consisted of 97 women with emotionally unstable personality disorder (EUPD) with at least two serious suicide attempts (SAs) and 32 healthy female controls. The genome-wide methylation pattern was measured by the Illumina EPIC BeadChip and analyzed by robust linear regression models to investigate mean BDNF methylation levels in a targeted analysis conditioned upon severity of suicide attempt. The validation cohort encompassed 60 female suicide attempters, stratified into low- (n = 45) and high-risk groups (n = 15) based on degree of intent-to-die and lethality of SA method, and occurrence of death-by-suicide at follow-up. Mean BDNF methylation levels exhibited increased methylation in relation to EUPD (p = 0.0159, percentage mean group difference ~3.8%). Similarly, this locus was confirmed as higher-methylated in an independent cohort of females with severe suicidal behavior (p = 0.0300). Results were independent of age and BMI. This is the first study to reveal emerging evidence of epigenetic dysregulation of BDNF with dependence on features known to confer increased risk of suicide deaths (lethality of suicide-attempt method and presence of EUPD diagnosis with history of recent SAs). Further studies investigating epigenetic and genetic effects of BDNF on severe suicidal behavior and EUPD are needed to further elucidate the role of epigenetic regulatory mechanisms and neurotrophic factors in relation to suicide and EUPD, and hold potential to result in novel treatment methods.
Mechanism for Utilization of the Populus-Derived Metabolite Salicin by a Pseudomonas—Rahnella Co-Culture

Pseudomonas fluorescens GM16 associates with Populus, a model plant in biofuel production. Populus releases abundant phenolic glycosides such as salicin, but P. fluorescens GM16 cannot utilize salicin, whereas Pseudomonas strains are known to utilize compounds similar to the aglycone moiety of salicin–salicyl alcohol. We propose that the association of Pseudomonas to Populus is mediated by another organism (such as Rahnella aquatilis OV744) that degrades the glucosyl group of salicin. In this study, we demonstrate that in the Rahnella–Pseudomonas salicin co-culture model, Rahnella grows by degrading salicin to glucose 6-phosphate and salicyl alcohol which is secreted out and is subsequently utilized by P. fluorescens GM16 for its growth. Using various quantitative approaches, we elucidate the individual pathways for salicin and salicyl alcohol metabolism present in Rahnella and Pseudomonas, respectively. Furthermore, we were able to establish that the salicyl alcohol cross-feeding interaction between the two strains on salicin medium is carried out through the combination of their respective individual pathways. The research presents one of the potential advantages of salicyl alcohol release by strains such as Rahnella, and how phenolic glycosides could be involved in attracting multiple types of bacteria into the Populus microbiome.
IRAS 08572+3915: constraining the aromatic versus aliphatic content of interstellar HACs

We analyze dust features present in the mid-infrared (Spitzer) and recently published L-band (UKIRT) spectra of the infrared galaxy IRAS 08572+3915. The line of sight toward the AGN nucleus crosses a high column density of carbonaceous dust whose characteristic absorption features appear clearly. They provide a real insight into the chemical environment of the diffuse interstellar medium. Thanks to the moderate redshift of IRAS 08572+3915, the wavelength of the aromatic CH stretching mode is free of major telluric lines, and a strong observational constraint of H$_{\rm sp2} $/H$_{\rm sp3} \le 0.08$ has been determined. This limit clearly shows that the bonding of hydrogen atoms in interstellar hydrogenated amorphous carbon is highly aliphatic. The presence of a broad absorption feature centered at 6.2 μ m, probably arising from olefinic/aromatic structures, corresponds to the backbone of this carbonaceous material, which is the major carbon-containing component of the interstellar medium along this line of sight.
Optimization and Technical Considerations for the Dye-Exclusion Protocol Used to Assess Blood–Brain Barrier Integrity in Adult Drosophila melanogaster

The blood–brain barrier (BBB) is a multicellular construct that regulates the diffusion and transport of metabolites, ions, toxins, and inflammatory mediators into and out of the central nervous system (CNS). Its integrity is essential for proper brain physiology, and its breakdown has been shown to contribute to neurological dysfunction. The BBB in vertebrates exists primarily through the coordination between endothelial cells, pericytes, and astrocytes, while invertebrates, which lack a vascularized circulatory system, typically have a barrier composed of glial cells that separate the CNS from humoral fluids. Notably, the invertebrate barrier is molecularly and functionally analogous to the vertebrate BBB, and the fruit fly, Drosophila melanogaster, is increasingly recognized as a useful model system in which to investigate barrier function. The most widely used technique to assess barrier function in the fly is the dye-exclusion assay, which involves monitoring the infiltration of a fluorescent-coupled dextran into the brain. In this study, we explore analytical and technical considerations of this procedure that yield a more reliable assessment of barrier function, and we validate our findings using a traumatic injury model. Together, we have identified parameters that optimize the dye-exclusion assay and provide an alternative framework for future studies examining barrier function in Drosophila.
The Dynamics of Detonation in Explosive Systems

This article reviews advances in modeling condensed-phase explosive detonation waves and their interaction with inerts for precision applications. We describe how constitutive data are obtained for a basic, predictive hydrodynamic model for explosives that subsequently can be studied numerically and analytically. Theory for multidimensional, time-dependent detonation dynamics is reviewed with a focus on freely propagating detonation and the asymptotic theory for quasi-one-dimensional, quasi-steady, detonation shock evolution (detonation shock dynamics). We discuss verification of these theories by direct numerical simulation (DNS) and validation by experiment. We describe a subscale model of detonation that uses an evolution equation to predict detonation dynamics and front states in complex engineering geometries that otherwise could not be computed by DNS. Four areas for future research are identified.
Measuring The Responsiveness of Staffs in using an Organizations Electronic Internal Communication Channels

The effectiveness and efficiency of communication is very crucial in ensuring the accomplishment of an organization’s goals. Communication channels play an important role in achieving these goals. The adaptation of the latest and appropriate technology will be of significant value to enhance the effectiveness of internal communication. Due to issues of internal communication efficacy, a preliminary study will be conducted in an organization to find major problems and opportunities. An adaptation of the new Marketing Technology Acceptance Model (MTAM) is used in order to understand and determine the effectiveness of electronic communication channels. This model is applied in developing a set of questionnaires where the questions are adapted or adopted from previous studies. Some of the questions are also customised to suit the requirements of the organization under the current study. There will be more than 80% of 265 respondents for this study. The findings will be used as bases in formulating and developing a mobile application for more effective internal communication within the organization. The findings of this research will also contribute towards the theory and practical refinement of MTAM and communication through mobile applications.
DIVERSITY TECHNIQUES WITH PARALLEL DIPOLE ANTENNAS: RADIATION PATTERN ANALYSIS

Two parallel dipoles are assessed for antenna diversity. The three-dimensional radiation pattern is considered for signals correlation coefficient. The pattern analysis reveals that, depending on dipole spacing, three types of diversity techniques are generated: space, amplitude-pattern and phase-pattern diversity. The weighting of each technique in signals correlation coefficient mitigation is investigated. The results show that for closely spaced dipoles, the generated phase- pattern diversity is the most dominant factor which greatly reduces the signals correlation coefficient. The diversity configuration is measured in a rich scattering environment. Results include signals correlation coefficient, diversity gain for selection combining and maximum ratio combining, effective diversity gain and antenna radiation efficiency can be demonstrated. We show that in rich multipath channel the minimum spatial distance, for effective diversity gain performance, is reduced from 0. 5λ for uncoupled dipoles to 0. 15λ for coupled dipoles.
Anion Exchange Resin and Inorganic Anion Parameter Determination for Model Validation and Evaluation of Unintended Consequences during PFAS Treatment.

When implementing anion exchange (AEX) for per- and polyfluoroalkyl substances treatment, temporal drinking water quality changes from concurrent inorganic anion (IA) removal can create unintended consequences (e.g., corrosion control impacts). To understand potential effects, four drinking water-relevant IAs (bicarbonate, chloride, sulfate, and nitrate) and three gel-type, strong-base AEX resins were evaluated. Batch binary isotherm experiments provided estimates of IA selectivity with respect to chloride ( K x ∕ C ) for IA/resin combinations where bicarbonate < sulfate ≤ nitrate at studied conditions. A multi-IA batch experiment demonstrated that binary isotherm-determined K x ∕ C values predicted competitive behavior. Subsequent column experiments with and without natural organic matter (NOM) allowed for the validation of a new ion exchange column model (IEX-CM; https://github.com/USEPA/Water_Treatment_Models). IA breakthrough was well-simulated using binary isotherm-determined K x ∕ C values and was minimally impacted by NOM. Initial AEX effluent water quality changes with corrosion implications included increased chloride and decreased sulfate and bicarbonate concentrations, resulting in elevated chloride-to-sulfate mass ratios (CSMRs) and Larson ratios (LRs) and depressed pH until the complete breakthrough of the relevant IA(s). IEX-CM utility was further illustrated by simulating the treatment of low-IA source water and a change in the source water to understand the resulting duration of changes in IAs and water quality parameters.
Antimicrobial Polymeric Surfaces Using Embedded Silver Nanoparticles

Pathogens (disease-causing microorganisms) can survive up to a few days on surfaces and can propagate through surfaces in high percentages, and thus, these surfaces turn into a primary source of pathogen transmission. To prevent and mitigate pathogen transmission, antimicrobial surfaces seem to be a promising option that can be prepared by using resilient, mass-produced polymers with partly embedded antimicrobial nanoparticles (NPs) with controlled size. In the present study, a 6 nm thick Ag nanolayer was sputter deposited on polycarbonate (PC) substrate and then thermally annealed, in a first step at 120 °C (temperature below Tg) for two hours, for promoting NP diffusion and growth, and in a second step at 180 °C (temperature above Tg) for 22 h, for promoting thermal embedding of the NPs into the polymer surface. The variation in the height of NPs on the polymer surface with thermal annealing confirms the embedding of NPs. It was shown that the incorporation of silver nanoparticles (Ag NPs) had a great impact on the antibacterial capacity, as the Ag NP-embedded polymer surface presented an inhibition effect on the growth of Gram-positive and Gram-negative bacteria. The tested surface-engineering process of incorporating antimicrobial Ag NPs in a polymer surface is both cost-effective and highly scalable.
Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.
Peer shadowing graduate assistantships: an autoethnographic study

ABSTRACT The unique nature of the COVID-19 pandemic prevented many typical graduate assistantships from occurring due to school-building closures, virtual classes, and stay-at-home orders. As such, the authors address the increase of ‘shadowing’ graduate assistantships at a large land-grant institution. To uphold the governmental stay-at-home regulations per COVID-19, shadowing provided first-year students with exposure and access to more experienced graduate student instructors (‘the shadowed’) who taught undergraduate and graduate coursework. In this autoethnographic study, the authors investigate the following questions: (1) In what ways did our experiences align with Mentoring Enactment Theory and Social Exchange Theory ? (2) How might we better align our conception of shadowing to both theories? The article finishes with modifications to the theories in light of the shadowing experiences.
Goodness-of-fit tests for dependent data

We establish some properties for a class of functional tests of goodness-of-fit for correlated observations generated by α-mixing discrete time stochastic processes. These tests are associated with projection density estimators. This class of functional tests contains in particular Pearson's χ2 test and the ‘smooth test’ of Neyman, J. (1937). Smooth test for goodness of fit. Scand. Aktuar. 20, 119–128. Results in the case of independent observations, in a general framework, can be found in Bosq, D. (2002). Functional tests of fit. Goodness-of-Fit Tests and Model Validity, Statistics for Industry and Technology, Birkhäuser, Boston, pp. 341–356. We analyze the asymptotic behavior of the test statistics, both under the null hypothesis and under the alternative, establishing the rate of convergence to their limit distributions. We determine the necessary and sufficient conditions for consistency of the test. Indications for implementing the test are provided.