Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Multiple Regression Analyses to Uncover Patterns of Correlates of Grief Problems, Depression and Suicidal Ideation Among Suicide Bereaved Individuals

This analysis of a convenience sample survey of 195 suicide bereaved adults focuses on predictors of three important highly interrelated experiences among the suicide bereaved: grief problems, depression and suicidal thinking. Although each of these three experiences can be explained by a unique set of predictors, they share many commonalities. Several predictors stood out especially in either aggravating or alleviating these experiences: personal (or post-traumatic) growth, perceived social support, feelings of blameworthiness, perceived suicide stigma, years since loss and a respondent’s mental health difficulties experienced prior to their suicide loss. We also review the clinical implications of these results.
Posttranslational modifications of the cytoskeleton

The cytoskeleton plays important roles in many essential processes at the cellular and organismal levels, including cell migration and motility, cell division, and the establishment and maintenance of cell and tissue architecture. In order to facilitate these varied functions, the main cytoskeletal components—microtubules, actin filaments, and intermediate filaments—must form highly diverse intracellular arrays in different subcellular areas and cell types. The question of how this diversity is conferred has been the focus of research for decades. One key mechanism is the addition of posttranslational modifications (PTMs) to the major cytoskeletal proteins. This posttranslational addition of various chemical groups dramatically increases the complexity of the cytoskeletal proteome and helps facilitate major global and local cytoskeletal functions. Cytoskeletal proteins undergo many PTMs, most of which are not well understood. Recent technological advances in proteomics and cell biology have allowed for the in‐depth study of individual PTMs and their functions in the cytoskeleton. Here, we provide an overview of the major PTMs that occur on the main structural components of the three cytoskeletal systems—tubulin, actin, and intermediate filament proteins—and highlight the cellular function of these modifications.
The Mediating Role of Temperamental Traits on the Relationship between Age of Puberty and Eating Disorders: A Mediating Analysis through Structural Equation Modelling of Australian Eating Disorder Outpatients

Abstract The current study had the aim to assess whether temperamental traits mediate the relationship between time of puberty and eating disorder (ED) severity using a sample of 292 outpatients with EDs [68 with Anorexia Nervosa Restrictive Type (AN-R), 101 with Anorexia Nervosa Binge Purging Type (AN-BP), 72 with Bulimia Nervosa (BN) and 51 with Other Specified Feeding and Eating Disorder (OSFED)]. Age of puberty, the severity of EDs, and temperamental traits were assessed through Demographic and Medical History Form, Eating Disorder Examination 17.0d (EDE-17.0d) and Temperament and Character Inventory-Revised (TCI-R), with a focus on the temperament scales: novelty seeking (TCI-NS), harm avoidance (TCI-HA), reward dependence (TCI-RD) and persistence (TCI-P). One-way ANOVA, correlation, and mediating analyses through structural equation modeling were performed to test the relationship between variables under investigation and assess if the four temperamental traits act as mediators in the relationship between time of puberty and ED severity. The results show a full mediating effect of the temperamental sub-scales on the relationship between puberty and EDE-17. In particular, TCI-R HA showed a complementary mediation on the relationship between age of puberty and EDE-17.0d, meaning that age of puberty increases the level of TCI-R HA, which in turn increases the severity of ED, confirming that this temperamental trait plays an important role in the development of ED after puberty. To conclude, temperamental traits seem to play a full mediating role in the relationship between puberty and ED severity, but more research is needed.
Laser spectroscopy of light muonic atoms and the nuclear charge radii

The energy levels of hydrogen-like atomic systems are shifted slightly by the complex structure of the nucleus, in particular by the finite size of the nucleus. These energy shifts are vastly magnified in muonic atoms and ions, i.e. the hydrogen-like systems formed by a negative muon and a nucleus. By measuring the 2S-2P energy splitting in muonic hydrogen, muonic deuterium and muonic helium, we have been able to deduce the p, d, ^33He and ^44He nuclear charge radii to an unprecedented accuracy. These radii provide benchmarks for hadron and nuclear theories, lead to precision tests of bound-state QED in regular atoms and to a better determination of the Rydberg constant.
qu an tph ] 10 O ct 2 01 1 Quantum Communication in Rindler Spacetime

A state that an inertial observer in Minkowski space perceiv es to be the vacuum will appear to an accelerating observer to be a thermal ba th of radiation. We study the impact of this Davies-Fulling-Unruh noise on comm unication, particularly quantum communication from an inertial sender to an ac celerating observer and private communication between two inertial observers i n the presence of an accelerating eavesdropper. In both cases, we establish com pact, tractable formulas for the associated communication capacities assuming enco dings that allow a single excitation in one of a fixed number of modes per use of the co mmunications channel. Our contributions include a rigorous presentatio n of the general theory of the private quantum capacity as well as a detailed analysis o f the structure of these channels, including their group-theoretic properties and proof that they are conjugate degradable. Connections between the Unruh channel a d optical amplifiers are also discussed.
Review on High Valent FeVI (Ferrate): A Sustainable Green Oxidant in Organic Chemistry and Transformation of Pharmaceuticals

Iron is the most common metal by mass on earth and represents the basic element of industrial society. The usage of iron to synthesize consumer products and to remediate environment is an attractive approach. This perspective presents applications of the high-valent iron oxo compound FeVIO42– (ferrate) in sustainable organic synthesis and treatment technology. In synthesizing organic molecules, C–H bond activation and selectivity are two basic ingredients of efficiency, which are attainable by ferrate. Examples of hydroxylation of hydrocarbons and conversion of alcohols to aldehyde/ketone are presented. In addition, many other organic molecules of amines, aniline, phenolic, and thiol functionalities can be oxidized by ferrate. This oxidation chemistry of ferrate is expedient in transforming pharmaceuticals, micropollutants in bodies of water with implications for human and ecological health. A wide range of micropollutants, which are commonly found in drinking water resources and wastewater effluents, can.
Fact versus fiction: bridging contrasting medicinal cannabis information needs

Regulatory changes now permit the availability and prescribing of medicinal cannabis in Australia. Public awareness and patient interest are high. Using a co‐design approach involving cancer patients, carers and oncology and palliative care clinicians, two information resources were developed to provide readily available sources of important information for cancer patients and clinicians who are considering the use of medicinal cannabis. Focus groups and evaluation interviews revealed some areas of dissonance between consumers and clinicians concerning aspects of medicinal cannabis.
Prediction of type 2 diabetes mellitus based on nutrition data

Abstract Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013–14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to build our predictive model and select among 193 food intake variables. After selecting the significant predictor variables, we built a logistic regression model with these variables as predictors and T2DM status as the outcome. The values of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of our predictive model were calculated. Eleven out of the 193 food intake variables were selected for inclusion in our model, which yielded a value of area under the ROC curve of 0⋅79 and a maximum PPV, NPV and accuracy of 0⋅37, 0⋅98 and 0⋅91, respectively. The present results suggest that nutrition data should be implemented in predictive models to predict the risk of T2DM, since they improve their performance and they are easy to assess.
Towards more accurate preclinical glioblastoma modelling: reverse translation of clinical standard of care in a glioblastoma mouse model

Introduction Contrarily to clinical trials, where glioblastoma (GBM) patients are usually heavily pre-treated (surgery, radiotherapy (RT) and temozolomide (TMZ)) before receiving the experimental therapies, in preclinical GBM studies experimental treatments have mostly been administered as monotherapy or combined with a very limited part of the clinical standard of care. This discrepancy can explain the failed clinical translation of preclinically successful treatments. This study is aimed at evaluating the feasibility and survival impact of the clinical standard of care in glioma-bearing mice. Methods. Neurospheres CT-2A tumor-bearing mice were treated with fluorescence-guided tumor resection, focal RT and oral TMZ. Results The implementation of postoperative intensive care treatment reduced surgical mortality by approximately 50% and increased experimental cost-effectiveness. Partial and total tumor resection significantly prolonged survival, the latter showing the strongest effect. Mice treated with surgery combined with RT or with RT and TMZ survived significantly longer than untreated controls. Conclusions Implementing the current GBM clinical standard of care in preclinical research is feasible and it leads to results in line with those obtained in patients. Systematic preclinical evaluation of the efficacy of experimental therapies in combination with standard of care treatments could improve the translational impact of next generation GBM animal studies.
A rigorous runtime analysis of the 2-MMASib on jump functions: ant colony optimizers can cope well with local optima

Ant colony optimizers have been successfully used as general-purpose optimization heuristics. Due to the complicated nature of the random processes that describe the runs of ACO algorithms, the mathematical understanding of these algorithms is much less developed than that of other nature-inspired heuristics. In this first runtime analysis of a basic ACO algorithm on a classic multimodal benchmark, we analyze the runtime of the 2-MMASib on jump functions. For moderate jump sizes k ≤ α0 ln n, α0 > 0 a constant, we prove a runtime of order O(√n/ρ), when the evaporation factor ρ satisfies ρ ≤ Cn-1/2 ln(n)-1 for a sufficiently small constant C. For ρ = Θ(n-1/2 ln(n)-1), we thus obtain a runtime of O(n ln(n)). This result shows that simple ACO algorithms can cope much better with local optima than many evolutionary algorithms, which need Ω(nk) time.
Continuously running genetic algorithm for real-time networking device optimization

Networking devices deployed in ultra-scale data centers must run perfectly and in real-time. The networking device performance is tuned using the device configuration registers. The optimal configuration is derived from the network topology and traffic patterns. As a result, it is not possible to specify a single configuration that fits all scenarios, and manual tuning is required in order to optimize the devices' performance. Such tuning slows down data center deployments and consumes massive resources. Moreover, as traffic patterns change, the original tuning becomes obsolete and causes degraded performance. This necessitates expensive retuning, which in some cases is infeasible. In this work, we present ZTT: a continuously running Genetic Algorithm that can be used for online, automatic tuning of the networking device parameters. ZTT is adaptive, fast to respond and have low computational costs required for running on a networking device. We test ZTT in a diversity of real-world traffic scenarios and show that it is able to obtain a significant performance boost over static configurations suggested by experts. We also demonstrate that ZTT is able to outperform alternative search algorithms like Simulated Annealing and Recursive Random Search even when those are adapted to better match the task at hand.
Towards a User Integration Framework for Personal Health Decision Support and Recommender Systems

Supporting personal health with Decision Support Systems (DSS) and, specifically, recommender systems (RS) is a promising and growing area of research. Integrating the user in the loop is vital in such health systems due to the complexity of recommendations, gravity of the decisions and the reliance on user autonomy. However, for such a purpose, to the best of our knowledge there exists no profound or comprehensive framework nor model to guide system designers, to exploit the full potential of integrating users in the system’s reasoning process by design. In this paper, we present a multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components, has been derived from an iterative mixed-methods development and evaluation procedure, including expert workshops and extensive multidisciplinary literature reviews. Users are accordingly integrated into the whole process from system reasoning until decision making through the following actionable design strategies: (1) Empower: Enabling them to understand the result generation and implications, (2) Encourage: encouraging them to question and reflect system outcomes and to get involved in the generation process and (3) Engage: enabling them to take an active role by facilitating and providing opportunities for user control. The framework offers support to designers of personal health-related DSS and RS in properly integrating users into their systems.
Assessing Cognitive Test Performance Using Automatic Digital Pen Features Analysis

Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The clinician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the clinician in real-time, thereby reducing manual scoring effort and producing objective reports. The presented research describes the architecture of our cognitive assessment tool and examines how accurately different machine learning (ML) models can automatically score cognitive tests, without a semantic content analysis. Our system uses a set of more than 170 pen features, calculated directly from the raw digital pen signal. We evaluate our system with 40 subjects from a geriatrics daycare clinic. Using standard ML techniques our feature set outperforms previous approaches on the cognitive tests we consider, i.e., the Clock Drawing, the Rey-Osterrieth Complex Figure, and the Trail Making Test, by automatically scoring tests with up to 82% accuracy in a binary classification task.
Right Way, Right Time: Towards a Better Comprehension of Young Students’ Needs when Looking for Relevant Search Results

We report on the exploration we conducted to understand better children’s needs for the design of Search Engine Result Pages (SERP) that can help them notice relevant resources when performing online inquiry tasks in a classroom context. We analyse children’s interactions with traditional and emoji-enriched SERP and look for trends linking children’s engagement with SERP and search success (based on experts’ assessments). We also identify areas remaining to be interpreted and considered in future studies. On this mixed ground, we discuss the complexity of this design space and the need to bypass the one-size-fits-all approach in favor of adaptive SERP to cater to children’s different and ever-evolving skills in searching and recognising useful results if we aim to support learning.
Evolving soft robotic jamming grippers

Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacuum pump and function by pressing the un-jammed gripper against a target object and evacuating the air to transition the material to a jammed (solid) state, gripping the target object. However, due to the complex interactions between grain morphology and target object shape, much uncertainty still remains regarding optimal constituent grain shapes for specific gripping applications. We address this challenge by utilising a modern Evolutionary Algorithm, NSGA-III, combined with a Discrete Element Method soft robot model to perform a multi-objective optimisation of grain morphology for use in jamming grippers for a range of target object sizes. Our approach optimises the microscopic properties of the system to elicit bespoke functional granular material performance driven by the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Results establish the important contribution of grain morphology to gripper performance and the critical role of local surface curvature and the length scale governed by the relative sizes of the grains and target object.
Factors Influencing Privacy Concern for Explanations of Group Recommendation

Explanations can help users to better understand why items have been recommended. Additionally, explanations for group recommender systems need to consider further goals than single-user recommender systems. For example, we need to balance group members’ need for privacy with their need for transparency, since a transparent explanation might pose a privacy hazard. In an online experiment with real groups (n=114 participants: 38 groups of size 3), we seek to understand which factors influence people’s privacy concerns when a single explanation is presented to a group in the tourism domain. In particular, we study the direct effects of three factors on privacy concern: a) group members’ personality (using the ‘Big Five’ personality traits), b) specific preference scenarios (i.e., having minority or majority preferences compared to two other group members), c) the type of relationship they have in the group (i.e., loosely coupled heterogeneous, versus tightly coupled homogeneous). We find that for personality two traits, Extroversion, and Agreeableness, each significantly affects the privacy concern. Moreover, having the minority or majority preferences in the group, as well as the type of relationship people have in the group, have a strong and significant influence on participants’ privacy concern. These results suggest that explanations presented to groups need to be adapted to all three factors (personality, type of relationship, and preference scenario) when considering the privacy concern of users.
Inaudible Manipulation of Voice-Enabled Devices Through BackDoor Using Robust Adversarial Audio Attacks: Invited Paper

The BackDoor system provides a method for inaudibly transmitting messages that are recorded by unmodified receiver microphones as if they were transmitted audibly. Adversarial Audio attacks allow for an audio sample to sound like one message but be transcribed by a speech processing neural network as a different message. This study investigates the potential applications of Adversarial Audio through the BackDoor system to manipulate voice-enabled devices, or VEDs, without detection by humans or other nearby microphones. We discreetly transmit voice commands by applying robust, noise-resistant adversarial audio perturbations through BackDoor on top of a predetermined speech or music base sample to achieve a desired target transcription. Our analysis compares differing base carriers, target phrases, and perturbation strengths for maximal effectiveness through BackDoor. We determined that such an attack is feasible and that the desired adversarial properties of the audio sample are maintained even when transmitted through BackDoor.
An Effective Method of Wood Crack Trace and Quantity Detection Based on Digital Image Processing Technology

With the rapid development of digital image processing technology, wood quality detection technology has become more convenient and more accurate. This paper uses the digital image processing technology of MATLAB to detect wood cross-section cracks. However, in actual wood crack detection, because the gray level contrast between crack target and background is not obvious, some small cracks cannot be identified. In view of this phenomenon, this paper puts forward the method of crack detection by the ratio of long and short axis of connected domain. Firstly, the binary image is obtained by double threshold segmentation. Then, the minimum ratio of long and short axis of the connected domain of cracks is set, and the ratio of long and short axis of the connected domain in the crack image is discriminated from it to extract cracks; finally, the extracted cracks are marked, counted and measured. The feasibility of the method is verified by detecting cracks of different types and degrees in various experiments.
An evolutionary multi-objective feature selection approach for detecting music segment boundaries of specific types

The goal of music segmentation is to identify boundaries between parts of music pieces which are perceived as entities. Segment boundaries often go along with a change in musical properties including instrumentation, key, and tempo (or a combination thereof). One can consider different types (or classes) of boundaries according to these musical properties. In contrast to existing datasets with missing specifications of changing properties for annotated boundaries, we have created a set of artificial music tracks with precise annotations for boundaries of different types. This allows for a profound analysis and interpretation of annotated and predicted boundaries and a more exhaustive comparison of different segmentation algorithms. For this scenario, we formulate a novel multi-objective optimisation task that identifies boundaries of only a specific type. The optimisation is conducted by means of evolutionary multi-objective feature selection and a novelty-based segmentation approach. Furthermore, we provide lists of audio features from non-dominated fronts which most significantly contribute to the estimation of given boundaries (the first objective) and most significantly reduce the performance of the prediction of other boundaries (the second objective).