Miracle-dz/UniFAR-FTUs huggingface dataset:
Joining the dots: theorising professionalism in the English Further Education Sector

ABSTRACT Professionalism is an important issue for policymakers in post-16 education because of its established links to competence, morale and staff continuity. Arguably, it is the pursuit of professionalism that ensures high quality teaching and learning, satisfied students and stakeholders, and the ongoing esteem of the general public. However, despite its alleged importance, professionalism has been largely missing as a topic from policy narratives. This lacuna may be linked to common perceptions that professionalism is complex, opaque and difficult to operationalise. Redressing this issue, this article examines what professionalism means to practitioners who work in the English Further Education (FE) sector. 461 practitioners working in teaching, management and curriculum settings completed an online survey, responding to the question: ‘What does being professional mean to you in the context of your working role and duties’? Perceptions of professionalism were content analysed and reported thematically. Using two-way correspondence analysis, the results were theorised as a tripartite model comprising three intersecting professionalism schemas: expertise, service and compliance. This model provides the basis for understanding and exploring the contested properties of professionalism expressed across the FE literature. Uniquely, the model emphasises the role that recognition plays in respondents’ constructions. The article concludes by suggesting a number of ways professionalism in FE might be supported.
Mapping Networks via Parallel kth-Hop Traceroute Queries

For a source node, v , and target node, w , the traceroute command iteratively issues “ k th-hop” queries, for k = 1 , 2 , . , δ ( v, w ), which return the name of the k th vertex on a shortest path from v to w , where δ ( v, w ) is the distance between v and w , that is, the number of edges in a shortest-path from v to w . The traceroute command is often used for network mapping applications, the study of the connectivity of networks, and it has been studied theoretically with respect to biases it introduces for network mapping when only a subset of nodes in the network can be the source of traceroute queries. In this paper, we provide efficient network mapping algorithms, that are based on k th-hop traceroute queries. Our results include an algorithm that runs in a constant number of parallel rounds with a subquadratic number of queries under reasonable assumptions about the sampling coverage of the nodes that may issue kth-hop traceroute queries. In addition, we introduce a number of new algorithmic techniques, including a high-probability parametric parallelization of a graph clustering technique of Thorup and Zwick, which may be of independent interest.
µBert: Mutation Testing using Pre-Trained Language Models

We introduce µBert, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it. Thus, the mutants are generated by replacing the masked tokens with the predicted ones. We evaluate µBert on 40 real faults from Defects4J and show that it can detect 27 out of the 40 faults, while the baseline (PiTest) detects 26 of them. We also show that µBert can be 2 times more cost-effective than PiTest, when the same number of mutants are analysed. Additionally, we evaluate the impact of µBert’s mutants when used by program assertion inference techniques, and show that they can help in producing better specifications. Finally, we discuss about the quality and naturalness of some interesting mutants produced by µBert during our experimental evaluation.
Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

Recent works have demonstrated the viability of convolutional neural networks (CNN) for capturing the highly non-linear microstructure-property linkages in high contrast composite material systems. In this work, we develop a new CNN architecture that utilizes a drastically reduced number of trainable parameters for building these linkages, compared to the benchmarks in current literature. This is accomplished by creating CNN architectures that completely avoid the use of fully connected layers, while using the 2-point spatial correlations of the microstructure as the input to the CNN. In addition to increased robustness (because of the much smaller number of trainable parameters), the CNN models developed in this work facilitate the construction of property closures at very low computational cost. This is because it allows for easy exploration of the space of valid 2-point spatial correlations, which is known to be a convex hull. Consequently, one can generate new sets of valid 2-point spatial correlations from previously available valid sets of 2-point spatial correlations, simply as convex combinations. This work demonstrates the significant benefits of utilizing 2-point spatial correlations as the input to the CNN, in place of the voxelated discrete microstructures used in current benchmarks.
Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes

Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions.
Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid

Nutrient shifts from glycolytic-to-gluconeogenic carbon sources can create large sub-populations of extremely antibiotic tolerant bacteria, called persisters. Positive feedback in Escherichia coli central metabolism was believed to play a key role in the formation of persister cells. To examine whether positive feedback in nutrient transport can also support high persistence to β-lactams, we performed nutrient shifts for E. coli from gluconeogenic carbon sources to fatty acid (FA). We observed tri-phasic antibiotic killing kinetics characterized by a transient period of high antibiotic tolerance, followed by rapid killing then a slower persister-killing phase. The duration of transient tolerance (3–44 h) varies with pre-shift carbon source and correlates strongly with the time needed to accumulate the FA degradation enzyme FadD after the shift. Additionally, FadD accumulation time and thus transient tolerance time can be reduced by induction of the glyoxylate bypass prior to switching, highlighting that two interacting feedback loops simultaneously control the length of transient tolerance. Our results demonstrate that nutrient switches along with positive feedback are not sufficient to trigger persistence in a majority of the population but instead triggers only a temporary tolerance. Additionally, our results demonstrate that the pre-shift metabolic state determines the duration of transient tolerance and that supplying glyoxylate can facilitate antibiotic killing of bacteria.
Sympathetic Neurons Regulate Cardiomyocyte Maturation in Culture

Embryos devoid of autonomic innervation suffer sudden cardiac death. However, whether autonomic neurons have a role in heart development is poorly understood. To investigate if sympathetic neurons impact cardiomyocyte maturation, we co-cultured phenotypically immature cardiomyocytes derived from human induced pluripotent stem cells with mouse sympathetic ganglion neurons. We found that 1) multiple cardiac structure and ion channel genes related to cardiomyocyte maturation were up-regulated when co-cultured with sympathetic neurons; 2) sarcomere organization and connexin-43 gap junctions increased; 3) calcium imaging showed greater transient amplitudes. However, sarcomere spacing, relaxation time, and level of sarcoplasmic reticulum calcium did not show matured phenotypes. We further found that addition of endothelial and epicardial support cells did not enhance maturation to a greater extent beyond sympathetic neurons, while administration of isoproterenol alone was insufficient to induce changes in gene expression. These results demonstrate that sympathetic neurons have a significant and complex role in regulating cardiomyocyte development.
Vision-based position computation from in-vehicle video log images for road sign inventory

Computing the positions of road signs from in-vehicle video log images is one of the most fundamental tasks for intelligent sign inventory. This study analysed vision-based positioning methods from video log images and proposed an analytic model of road sign positioning (AM-RSP) based on error propagation and first-order approximation. Both image noises and camera calibration errors were modelled as two error sources. The AM-RSP model can evaluate and quantify the positioning uncertainties for both single view and stereo vision-based methods. Moreover, it can quantitatively establish the relationship between the positioning uncertainties and the various parameters of the vehicle and the camera,such as camera focal length, view angle, baseline width, height, image acquisition interval, vehicle distance to the road edge etc. Hence, the proposed AM-RSP model can be applied to configure the parameters of the camera and the vehicle to meet some specific computation requirement such as RSP accuracy. The proposed AM-RSP model has been validated with real video log image data collected in the field. These results show that the AM-RSP model is accurate and reliable to compute sign positioning uncertainties.
Exploring the Structure and Interrelations of Time-Stable Psychological Resilience, Psychological Vulnerability, and Social Cohesion

The current study explores the relationship between three constructs of high relevance in the context of adversities which have, however, not yet been systematically linked on the level of psychological dispositions: psychological vulnerability, psychological resilience, and social cohesion. Based on previous theoretical and empirical frameworks, a collection of trait questionnaires was assessed in a Berlin sample of 3,522 subjects between 18 and 65 years of age. Using a confirmatory factor analytical approach, we found no support for a simple three-factor structure. Results from exploratory structural analyses suggest that instead of psychological resilience and psychological vulnerability constituting two separate factors, respective indicators load on one bipolar latent factor. Interestingly, some psychological resilience indicators contributed to an additional specific latent factor, which may be interpreted as adaptive capacities, that is, abilities to adapt to changes or adjust to consequences of adversities. Furthermore, instead of evidence for one single social cohesion factor on the psychological level, indicators of perceived social support and loneliness formed another specific factor of social belonging, while indicators of prosocial competencies were found to form yet another distinct factor, which was positively associated to the other social factors, adaptive capacities and social belonging. Our results suggest that social cohesion is composed of different independent psychological components, such as trust, social belonging, and social skills. Furthermore, our findings highlight the importance of social capacities and belonging for psychological resilience and suggest that decreasing loneliness and increasing social skills should therefore represent a valuable intervention strategy to foster adaptive capacities.
Hypericum lanceolatum Lam. Medicinal Plant: Potential Toxicity and Therapeutic Effects Based on a Zebrafish Model

Hypericum lanceolatum Lam. (H. lanceolatum) is a traditional medicinal plant from Reunion Island used for its pleiotropic effects mainly related to its antioxidant activity. The present work aimed to 1) determine the potential toxicity of the plant aqueous extract in vivo and 2) investigate its putative biological properties using several zebrafish models of oxidative stress, regeneration, estrogenicity, neurogenesis and metabolic disorders. First, we characterized the polyphenolic composition by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and identified chlorogenic acid isomers, quercetin and kaempferol derivatives as the major compounds. We then evaluated for the first time the toxicity of an aqueous extract of H. lanceolatum and determined a maximum non-toxic concentration (MNTC) in zebrafish eleutheroembryos from 0 to 96 hpf following OECD (Organization for Economic Cooperation and Development) guidelines. This MNTC test was also determined on hatched eleutheroembryos after 2 days of treatment (from 3 to 5 dpf). In our study, the anti-estrogenic effects of H. lanceolatum are supported by the data from the EASZY assay. In a tail amputation model, we showed that H. lanceolatum at its MNTC displays antioxidant properties, favors immune cell recruitment and tissue regeneration. Our results also highlighted its beneficial effects in metabolic disorders. Indeed, H. lanceolatum efficiently reduces lipid accumulation and body mass index in overfed larva- and adult-models, respectively. In addition, we show that H. lanceolatum did not improve fasting blood glucose levels in a hyperglycemic zebrafish model but surprisingly inhibited neurogenesis impairment observed in diabetic conditions. In conclusion, our study highlights the antioxidant, pro-regenerative, anti-lipid accumulation and pro-neurogenic effects of H. lanceolatum in vivo and supports the use of this traditional medicinal plant as a potential alternative in the prevention and/or treatment of metabolic disorders.
Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study

Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8–12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement. Clinical Trial Registration: [http://www.chictr. org.cn], identifier [ChiCTR2000041119].
Molecular Mechanisms Underlying Bacterial Uranium Resistance

Environmental uranium pollution due to industries producing naturally occurring radioactive material or nuclear accidents and releases is a global concern. Uranium is hazardous for ecosystems as well as for humans when accumulated through the food chain, through contaminated groundwater and potable water sources, or through inhalation. In particular, uranium pollution pressures microbial communities, which are essential for healthy ecosystems. In turn, microorganisms can influence the mobility and toxicity of uranium through processes like biosorption, bioreduction, biomineralization, and bioaccumulation. These processes were characterized by studying the interaction of different bacteria with uranium. However, most studies unraveling the underlying molecular mechanisms originate from the last decade. Molecular mechanisms help to understand how bacteria interact with radionuclides in the environment. Furthermore, knowledge on these underlying mechanisms could be exploited to improve bioremediation technologies. Here, we review the current knowledge on bacterial uranium resistance and how this could be used for bioremediation applications.
Brief Maternal Separation Inoculates Against the Effects of Social Stress on Depression-Like Behavior and Cocaine Reward in Mice

Exposure to intermittent repeated social defeat (IRSD) increases the vulnerability of mice to the rewarding effects of cocaine in the conditioned place preference (CPP) paradigm. According to the “inoculation of stress” hypothesis, a brief period of maternal separation (MS) can provide protection against the negative effects of IRSD. The aim of the present study was to assess whether exposure to a brief episode of MS prevents the subsequent short-term effects of IRSD on depression- and anxiety-like behaviors and to explore its long-term effects on cocaine CPP in mice. Four groups of male C57BL/6 mice were employed; two groups were separated from their mother [6 h on postnatal day (PND) 9], while the other two groups were not (controls). On PND 47, 50, 53 and 56, mice that had experienced MS were exposed to social defeat in the cage of an aggressive resident mouse (MS + IRSD group) or were allowed to explore an empty cage (MS + EXPL group). The same procedure was performed with control mice that had not experienced MS (CONTROL + IRSD and CONTROL + EXPL groups). On PND57-58, all the mice performed the elevated plus maze and the hole-board, social interaction and splash tests. Three weeks after the last episode of defeat, all the mice underwent the CPP procedure with cocaine (1 mg/kg). Irrespective of whether or not MS had taken place, a reduction in open arms measures, dips, and social interaction was observed in mice that experienced IRSD. A higher latency of grooming and acquisition of cocaine-induced CPP were observed only in mice exposed to IRSD alone (CONTROL + IRSD). These results suggest that exposure to a brief episode of stress early in life increases the subsequent resilience of animals to the effects of social stress on vulnerability to cocaine.
The Daily Rhythmic Changes of Undergraduate Students’ Emotions: An Analysis Based on Tencent Tweets

Emotional stability is of great importance for undergraduates and has significant predictive power for mental health. Emotions are associated with individuals’ daily lives and routines. Undergraduates commonly post their opinions and feelings on social networks, providing a huge amount of data for studying their emotional states and rhythms. Based on the construction of the emotion dictionary of undergraduates’ Tencent tweets (TTs)—a social network for users to share their life situations and express emotions and feelings to friends—we used big data text analysis technology to analyze the emotion words in 45,996 Tencent tweets published by 894 undergraduates. Then, we used hierarchical linear modeling to further analyze the daily rhythms of undergraduate students’ emotions and how demographic variables are associated with the daily rhythmic changes. The results were as follows: (1) Undergraduates tweeted about more positive emotions than negative emotions (love was most common and fear was the least common); (2) The emotions in undergraduates’ tweets changed considerably from 1 a.m. to 6 a.m., but were fairly stable during the day; (3) There was a rising trend in the frequency of using emotion words in Tencent tweets during the day as each hour progressed, and there was a higher increase in positive emotion than negative emotion; and (4) The word frequencies and daily rhythms of emotions varied depending on demographic variables. Gender was correlated with the frequencies of gratitude and the daily rhythms of anger. As the grade increased, the frequency of emotion words in most subcategories in TTs decreased and the fluctuation in daily rhythms became smaller. There was no significant difference in the frequency and daily rhythm of emotion words used in TTs based on having had a left-behind experience. The results of the present study provided emotion expression in social networks in Chinese collectivist culture. This study added new evidence to support the notion that positive and negative emotions are independent dimensions.
Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference

Although still not fully understood, sleep is known to play an important role in learning and in pruning synaptic connections. From the active inference perspective, this can be cast as learning parameters of a generative model and Bayesian model reduction, respectively. In this article, we show how to reduce dimensionality of the latent space of such a generative model, and hence model complexity, in deep active inference during training through a similar process. While deep active inference uses deep neural networks for state space construction, an issue remains in that the dimensionality of the latent space must be specified beforehand. We investigate two methods that are able to prune the latent space of deep active inference models. The first approach functions similar to sleep and performs model reduction post hoc. The second approach is a novel method which is more similar to reflection, operates during training and displays “aha” moments when the model is able to reduce latent space dimensionality. We show for two well-known simulated environments that model performance is retained in the first approach and only diminishes slightly in the second approach. We also show that reconstructions from a real world example are indistinguishable before and after reduction. We conclude that the most important difference constitutes a trade-off between training time and model performance in terms of accuracy and the ability to generalize, via minimization of model complexity.