Miracle-dz/UniFAR-FTUs huggingface dataset:
Online synchronous chat counselling for young people aged 12–25: a mixed methods systematic review protocol

Introduction Youth mental health is a global issue, with 75% of many serious mental health difficulties emerging before the age of 25. An increase in the popularity of online counselling for young people’s mental health has been seen in recent years, due to their accessibility, cost-effectiveness and reduced stigmatising effects. Online synchronous chat counselling consists of real-time, text-based, one-to-one chats with a mental health professional and/or trained volunteer. Literature to date examining the effectiveness of these interventions has been limited, and little is known about their design features, their acceptance, effectiveness and the therapeutic processes that contribute to their working. Methods and analysis A mixed-methods systematic review of the literature will be conducted. PsycINFO MEDLINE, CINAHL, Web of Science and relevant grey literature will be searched for peer-reviewed, English language studies between January 1995 and June 2021. Backward and forward reference checking will be conducted. Quality of included articles will be examined using the Mixed Methods Appraisal Tool and a combination of the TIDieR checklist and a prepopulated data table will be used for extraction. A mixed methods review adopting a convergent-integrated design will be employed. Quantitative data will be transformed and analysed simultaneously alongside qualitative data using narrative synthesis. Ethics and dissemination The research does not require ethical approval. Findings will be disseminated through peer-reviewed publications, academic conference presentations, academic social media and invited workshops, webinars and seminars. PROSPERO registration number CRD42021256016.
Wonders & Worries: A randomized clinical trial of a psychosocial intervention for children who have a parent with cancer

The aim of this study was to test the effectiveness of Wonders & Worries, a psychosocial intervention for children who have a parent with cancer. Primary goals were to improve family quality of life, functioning and communication skills as reported by parent and child, enhance children's emotional/behavioral adjustment and parenting efficacy, while decreasing parenting concerns and ill parents' depression and anxiety.
Design and analysis of partially randomized preference trials with propensity score stratification

While the two-stage randomized design allows us to unbiasedly evaluate the impact of patients’ treatment preference on the outcome of interest, it may not always be practical to implement in clinical practice; patients with a strong preference may not be willing to be randomized. The more pragmatic, partially randomized preference design (PRPD) allows patients who are unwilling to be randomized, but willing to state their preference, to receive their preferred treatment in lieu of the first-stage randomization in the two-stage design, at the cost of potentially introducing bias in estimating the effects of interest. In this article, we consider the application of propensity score stratification (PSS) in a PRPD to recreate a conditional first-stage randomization based on observed covariates, enabling the estimation and inference of the overall treatment, selection and preference effects with minimum bias. We additionally derive a set of closed-form sample size formulas for detecting all three effects of interest in a PSS-PRPD. Simulation studies demonstrate the bias reduction properties of the PSS-PRPD, and validate the accuracy of the proposed sample size formulas. Our results show that 5 to 10 propensity score strata may be needed to correct for biases in effect estimates, and the exact number of strata needed to achieve the best match between the empirical power and formula prediction may depend on the degree of effect heterogeneity. Finally, we demonstrate our proposed formulas by estimating the required sample sizes to detect treatment, selection and preference effects in the context of the Harapan Study.
Thermal Alteration in Adsorption Sites over SAPO‐34 Zeolite

Abstract Zeolites have found tremendous applications in the chemical industry. However, the dynamic nature of their active sites under the flow of adsorbate molecules for adsorption and catalysis is unclear, especially in operando conditions, which could be different from the as‐synthesized structures. In the present study, we report a structural transformation of the adsorptive active sites in SAPO‐34 zeolite by using acetone as a probe molecule under various temperatures. The combination of solid‐state nuclear magnetic resonance, in situ variable‐temperature synchrotron X‐ray diffraction, and in situ diffuse‐reflectance infrared Fourier‐transform spectroscopy allow a clear identification and quantification that the chemisorption of acetone can convert the classical Brønsted acid site adsorption mode to an induced Frustrated Lewis Pairs adsorption mode at increasing temperatures. Such facile conversion is also supported by the calculations of ab‐initio molecular‐dynamics simulations. This work sheds new light on the importance of the dynamic structural alteration of active sites in zeolites with adsorbates at elevated temperatures.
An improved clothing size recommendation approach based on subdivision of female body types

Abstract With the rapid development of clothing online shopping, customers have a higher demand for more accurate clothing size recommendation systems. The work this paper presented is compatible with the four body types specified in the Chinese standard. Then, the subdivision was performed where each of the major body types corresponded to four characteristic indices. In addition, each characteristic index had three attributes. Therefore, the female body can be subdivided into 81 types for each major type. Subsequently, this paper suggests an improved clothing size recommendation system that considers subdivided body types. The recommendation criteria in the approach were refined by a pressure distribution analysis through a virtual fitting simulation. Finally, case studies were conducted on customers of body type A (accounting for 52.73% of bodies) to demonstrate the procedures of the proposed approach. Practitioner summary: This study proposes an improved clothing size recommendation approach based on the subdivision of code-specified female body types. Compared with existing clothing size recommendations, the proposed approach can help customers find formfitting sizes more accurately.
The matricellular protein decorin delivered intradermally with coacervate improves wound resolution in the CXCR3‐deficient mouse model of hypertrophic scarring

Cutaneous wound healing is an intricate orchestration of three overlapping phases of repair that encompass numerous cell types, signalling cascades, and microenvironment modifications to reach a successful resolution. Disruption of any of these steps will create an abnormal healing response resulting in either ulceration or excessive scarring. It has become evident that the extracellular matrix and its associated components are key orchestrators during this process. One of these essential matrix proteins is decorin, a small leucine‐rich proteoglycan (SLRP) that acts as a regulator of collagen fibrillogenesis and a non‐competitive inhibitor of multiple growth factors signalling cascades. Decorin is a necessary shut‐off switch for the pro‐reparative mechanism of the tissue replacement phase and limits the occurrence of hypertrophic scarring by preventing excessive repair. We investigated the use of decorin as a therapeutic by administering the matrix protein anchored in a slow‐release coacervate in a hypertrophic scarring mouse model. The results show that early wound healing phase measurements exhibit little difference in performance compared to our coacervate‐only baseline or HB‐EGF‐treated control mice. However, during the resolution phase of wound healing, the decorin‐treatment significantly reduces cutaneous thickness, enhances collagen alignment, and improves overall wound scoring in the mice. Thus, mice treated with decorin display better healing outcomes and could limit the hypertrophic scarring phenotype in the coacervate only, and HB‐EGF controls. These results suggest that decorin may be a promising tool and alternative therapy for patients who suffer from over‐exuberant matrix deposition during wound healing.
Preparation and potential applications of alginate oligosaccharides

Abstract Alginate, a linear polymer consisting of β-D-mannuronic acid (M) and α-L-guluronic acid (G) with 1,4-glycosidic linkages and comprising 40% of the dry weight of algae, possesses various applications in the food and nutraceutical industries. However, the potential applications of alginate are restricted in some fields because of its low water solubility and high solution viscosity. Alginate oligosaccharides (AOS) on the other hand, have low molecular weight which result in better water solubility. Hence, it becomes a more popular target to be researched in recent years for its use in foods and nutraceuticals. AOS can be obtained by multiple degradation methods, including enzymatic degradation, from alginate or alginate-derived poly G and poly M. AOS have unique bioactivity and can bring human health benefits, which render them potentials to be developed/incorporated into functional food. This review comprehensively covers methods of the preparation and analysis of AOS, and discussed the potential applications of AOS in foods and nutraceuticals.
Cohesin-dependence of neuronal gene expression relates to chromatin loop length

Cohesin and CTCF are major drivers of 3D genome organization, but their role in neurons is still emerging. Here, we show a prominent role for cohesin in the expression of genes that facilitate neuronal maturation and homeostasis. Unexpectedly, we observed two major classes of activity-regulated genes with distinct reliance on cohesin in mouse primary cortical neurons. Immediate early genes (IEGs) remained fully inducible by KCl and BDNF, and short-range enhancer-promoter contacts at the IEGs Fos formed robustly in the absence of cohesin. In contrast, cohesin was required for full expression of a subset of secondary response genes characterized by long-range chromatin contacts. Cohesin-dependence of constitutive neuronal genes with key functions in synaptic transmission and neurotransmitter signaling also scaled with chromatin loop length. Our data demonstrate that key genes required for the maturation and activation of primary cortical neurons depend on cohesin for their full expression, and that the degree to which these genes rely on cohesin scales with the genomic distance traversed by their chromatin contacts.
Non-linear modelling and stability analysis of the PTGS at pump mode

For the sake of studying the stability of the pumped storage power station, the authors focused on the mathematical modelling of the pump-turbine governing system (PTGS) at pump mode. First of all, six transfer coefficients were introduced into the model of the pump-turbine. Furthermore, utilising the internal characteristics formulas, they obtained six analytical expressions of the transfer coefficients at pump mode. Considering the elastic water-hammer model of the penstock with hydraulic friction and the second-order model of the generator, a non-linear model of the PTGS was proposed. On the basis of a real case, they obtained the curve of bifurcation points of the system by using the stability theorem, and verified the correctness of the theoretical analysis results from numerical experiments. Moreover, the stable domains of the proportional–integral–derivative parameters k p and k d were identified and the non-linear dynamic behaviours of the governing system were studied. Finally, these methods and analytic results could help them improve the safety operation and performance of the pumped storage power station.
TranscriptionMacrophage IL-10 Promoter during Dynamic and Transient Remodeling of the

To gain insight into the molecular mechanism(s) whereby macrophages produce large amounts of IL-10, we analyzed IL-10 gene expression and temporally correlated it with modiﬁcations to chromatin associated with the IL-10 promoter. In resting cells, which make essentially no cytokines, the IL-10 promoter is associated with histones containing little or no detectable modiﬁcations. Macrophages stimulated in the presence of immune complexes begin to produce high levels of IL-10 pre-mRNA transcripts within minutes of stimulation. Coincident with this transcription was a rapid and dynamic phosphorylation of histone H3 at speciﬁc sites in the IL-10 promoter. Histone phosphorylation was closely followed by the binding of transcription factors to the IL-10 promoter. Blocking the activation of ERK prevented histone phosphorylation and transcription factor binding to the IL-10 promoter. In contrast to histone phosphorylation, the peak of histone acetylation at this promoter did not occur until after transcription had peaked. Inhibition of histone deactylase did not alter IL-10 gene expression, suggesting that phosphorylation but not acetylation was the proximal event responsible for IL-10 transcription. Our ﬁndings reveal a rapid and well-orchestrated series of events in which ERK activation causes a rapid and transient phosphorylation of histone H3 at speciﬁc regions of the IL-10 promoter, resulting in a transient exposure of the IL-10 promoter to the transcription factors that bind there. This exposure is essential for the efﬁcient induction of IL-10 gene expression in macrophages. To our knowledge, this represents a unique way in which the expression of a cytokine gene is regulated in macrophages. The Journal of Immunology, 2006, 177: 1282–1288. 0.15 1% Triton X-100, and protease inhibitor and phosphatase inhib- itor mixtures). After sonication, of whole-cell lysate subjected to SDS-PAGE analysis. Western blotting assay then formed to detect histone H3 acetylation using rabbit polyclonal anti-acetylyl-histone Ab and mouse monoclonal anti-histone H3 Ab (Upstate Biotechnology).
A Novel Open Set Energy-based Flow Classifier for Network Intrusion Detection

Network intrusion detection systems (NIDS) are one of many solutions that make up a computer security system. Several machine learning-based NIDS have been proposed in recent years, but most of them were developed and evaluated under the assumption that the training context is similar to the test context. In real networks, this assumption is false, given the emergence of new attacks and variants of known attacks. To deal with this reality, the open set recognition field, which is the most general task of recognizing classes not seen during training in any domain, began to gain importance in NIDS research. Yet, existing solutions are often bounded to high temporal complexities and performance bottlenecks. In this work, we propose an algorithm to be used in NIDS that performs open set recognition. Our proposal is an adaptation of the single-class Energy-based Flow Classifier (EFC), which proved to be an algorithm with strong generalization capability and low computational cost. The new version of EFC correctly classifies not only known attacks, but also unknown ones, and differs from other proposals from the literature by presenting a single layer with low temporal complexity. Our proposal was evaluated against well-established multi-class algorithms and as an open set classifier. It proved to be an accurate classifier in both evaluations, similar to the state of the art. As a conclusion of our work, we consider EFC a promising algorithm to be used in NIDS for its high performance and applicability in real networks.
On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations

Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model’s design space and informed decision making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four machine learning (ML) approaches in the modelling of surrogates of sound transmission loss (STL). Feature importance and feature engineering are used to improve the models’ accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed. Experiments show that neural network surrogates with physics-guided features have better accuracy than other ML models across different STL models. Furthermore, sensitivity analysis methods are used to assess how physically coherent the analyzed surrogates are.
Deep Simultaneous Limits on Optical Emission from FRB 20190520B by 24.4 fps Observations with Tomo-e Gozen

We conduct 24.4 fps optical observations of repeating fast radio burst (FRB) 20190520B using Tomo-e Gozen, a high-speed CMOS camera mounted on the Kiso 105 cm Schmidt telescope, simultaneously with radio observations carried out using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). We succeeded in the simultaneous optical observations of 11 radio bursts that FAST detected. However, no corresponding optical emission was found. The optical fluence limits as deep as 0.068 Jy ms are obtained for the individual bursts (0.029 Jy ms on the stacked data) corrected for the dust extinction in the Milky Way. The fluence limit is deeper than those obtained in the previous simultaneous observations for an optical emission with a duration ≳0.1 ms. Although the current limits on radio-optical spectral energy distribution (SED) of FRBs are not constraining, we show that SED models based on observed SEDs of radio variable objects such as optically detected pulsars, and a part of parameter spaces of theoretical models in which FRB optical emission is produced by inverse Compton scattering in a pulsar magnetosphere or a strike of a magnetar blastwave into a hot wind bubble, can be ruled out once a similar fluence limit as in our observation is obtained for a bright FRB with a radio fluence ≳5 Jy ms.
Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.
Generating and Visualizing Trace Link Explanations

Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in the domain can find it difficult to understand the underlying semantics of the link, making it hard for them to evaluate the link's correctness or suitability for a specific software engineering task. In this paper we present a novel NLP pipeline for generating and visualizing trace link explanations. Our approach identifies domain-specific concepts, retrieves a corpus of concept-related sentences, mines concept definitions and usage examples, and identifies relations between cross-artifact concepts in order to explain the links. It applies a post-processing step to prioritize the most likely acronyms and definitions and to eliminate non-relevant ones. We evaluate our approach using project artifacts from three different domains of interstellar telescopes, positive train control, and electronic health-care systems, and then report coverage, correctness, and potential utility of the generated definitions. We design and utilize an explanation interface which leverages concept definitions and relations to visualize and explain trace link rationales, and we report results from a user study that was conducted to evaluate the effectiveness of the explanation interface. Results show that the explanations presented in the interface helped non-experts to understand the underlying semantics of a trace link and improved their ability to vet the correctness of the link.
Period tripling due to Josephson parametric down-conversion beyond the rotating-wave approximation

Parametrically driven oscillators can display period-tripling in response to a drive at thrice the resonance frequency. In contrast to the parametric instability for period doubling, the symmetric fixed-point corresponding to the state of rest remains stable at arbitrary strong driving for the tripling transition. Previously, it has been shown that fluctuations can circumvent this and induce a period-tripling instability. In this article, we explore an alternative way of inducing a period-tripling transition by investigating properties of period-tripling due to parametric down-conversion beyond the rotating-wave approximation. We show that despite the absence of an instability threshold, off-resonant frequency contributions can induce a period-tripling transition by activating the parametric down-conversion. Moreover, we study the subsequent period-tripled states of the Josephson potential and discuss the asymmetry between the clockwise and counter-clockwise rotating fixed-points that only arises beyond the rotating-wave approximation.
Learning First-Order Symbolic Planning Representations That Are Grounded

Two main approaches have been developed for learning first-order planning (action) models from unstructured data: combinatorial approaches that yield crisp action schemas from the structure of the state space, and deep learning approaches that produce action schemas from states represented by images. A benefit of the former approach is that the learned action schemas are similar to those that can be written by hand; a benefit of the latter is that the learned representations (predicates) are grounded on the images, and as a result, new instances can be given in terms of images. In this work, we develop a new formulation for learning crisp first-order planning models that are grounded on parsed images, a step to combine the benefits of the two approaches. Parsed images are assumed to be given in a simple O2D language (objects in 2D) that involves a small number of unary and binary predicates like"left","above","shape", etc. After learning, new planning instances can be given in terms of pairs of parsed images, one for the initial situation and the other for the goal. Learning and planning experiments are reported for several domains including Blocks, Sokoban, IPC Grid, and Hanoi.