Miracle-dz/UniFAR-FTUs huggingface dataset:
Alcohol-induced tubulin post-translational modifications directly alter hepatic protein trafficking

Background: Chronic ethanol exposure leads to enhanced protein acetylation and acetaldehyde adduction. Of the multitude of proteins that are modified on ethanol administration, tubulin is among the best studied. However, an open question is whether these modifications are observed in patient samples. Both modifications have also been implicated in promoting alcohol-induced defects in protein trafficking, but whether they do so directly is also unanswered. Methods and Results: We first confirmed that tubulin was hyperacetylated and acetaldehyde-adducted in the livers from ethanol-exposed individuals to a similar extent as observed in the livers from ethanol-fed animals and hepatic cells. Livers from individuals with nonalcohol-associated fatty liver showed modest increases in tubulin acetylation, whereas nonalcohol-associated fibrotic human and mouse livers showed virtually no tubulin modifications. We also asked whether tubulin acetylation or acetaldehyde adduction can directly explain the known alcohol-induced defects in protein trafficking. Acetylation was induced by overexpressing the α-tubulin–specific acetyltransferase, αTAT1, whereas adduction was induced by directly adding acetaldehyde to cells. Both αTAT1 overexpression and acetaldehyde treatment significantly impaired plus-end (secretion) and minus-end (transcytosis)–directed microtubule-dependent trafficking and clathrin-mediated endocytosis. Each modification led to similar levels of impairment as observed in ethanol-treated cells. The levels of impairment by either modification showed no dose dependence or no additive effects suggesting that substoichiometric tubulin modifications lead to altered protein trafficking and that lysines are not selectively modified. Conclusions: These results not only confirm that enhanced tubulin acetylation is observed in human livers but that it is most relevant to alcohol-induced injury. Because these tubulin modifications are associated with altered protein trafficking that alters proper hepatic function, we propose that changing the cellular acetylation levels or scavenging free aldehydes are feasible strategies for treating alcohol-associated liver disease.
A simple procedure to identify children with B‐lineage acute lymphoblastic leukemia who can be successfully treated with low or moderate intensity: Sequential versus single‐point minimal residual disease measurement

Sequential monitoring of minimal residual disease (MRD) by molecular techniques or multicolor flow cytometry (MFC) has emerged over the past two decades as the primary tool to optimize treatment in pediatric B‐cell precursor acute lymphoblastic leukemia (BCP‐ALL). The aim of our study was to compare the prognostic power of repeated MFC–MRD measurement with single‐point MRD assessment in children with BCP‐ALL treated with the reduced‐intensity protocol ALL‐MB 2008. Data from consecutive MFC–MRD at day 15 and day 36 (end of induction, EOI) were available for 507 children with Philadelphia‐negative BCP‐ALL. They were stratified into standard risk (SR, n = 265), intermediate risk (ImR, n = 211), and high risk (HR, n = 31) according to the initial clinical characteristics defined in the ALL‐MB 2008 protocol. Quantitative (relative to quantitative thresholds) and kinetic (logarithmic reduction) assessments of MFC–MRD at both time points effectively separated patients into three groups with different risk of recurrence. On the other hand, starting with low (for the SR group) and moderate (for the ImR group) induction therapy, a single MFC–MRD measurement at EOI proved sufficient to unequivocally identify patients in whom this therapy is highly effective and distinguish them from those who cannot be successfully treated with such therapy. Therefore, initiating treatment with low or moderate treatment from the start, together with careful consideration of initial clinical risk factors and just one EOI–MFC–MRD measurement is simple, inexpensive, and entirely sufficient for treatment optimization. Furthermore, for a large proportion of patients, this approach allows better adjustment, in particular also reduction of therapy intensity than sequential MRD measurements.
Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model

Background: Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: “Lasso model”). Methods: We evaluated the Lasso model’s ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia. Results: For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden’s Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model’s significant improvement in AUC. Conclusions: The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model’s advantage.
Associations Between Pornography Consumption Patterns, Pornography Consumption Motives, and Social Wellbeing among U.S. College Students: A Latent Profile Analysis with a Primarily Female Sample

Abstract This study identified profiles of pornography motivations and outcomes and assessed differences between profiles on three measures of social well-being: social support, fear of intimacy, and loneliness. Latent profile analysis and group comparisons were conducted using cross-sectional data from college students (N = 389). Results indicated four profiles: low motivation/average distress, porn for enjoyment, high motivation/average guilt, low motivation/high distress. Those in the high motivation/average guilt profile reported more social well-being difficulties relative to the other profiles and non-pornography consumers. Results suggest that individuals who report varying pornography use motivations and negative outcomes may report difficulties with social well-being, with implications for intimate relationships.
Bias analysis in entropy estimation

We consider the problem of finite sample corrections for entropy estimation. New estimates of the Shannon entropy are proposed and their systematic error (the bias) is computed analytically. We find that our results cover correction formulae of current entropy estimates recently discussed in the literature. The trade-off between bias reduction and the increase of the corresponding statistical error is analysed.
Erratum: A 22-yr southern sky survey for transient and variable radio sources using the Molonglo Observatory Synthesis Telescope: Erratum: Transient and variable radio sources with MOST

The paper ‘A 22-yr southern sky survey for transient and variable radio sources using the Molonglo Observatory Synthesis Telescope’ was published in Mon. Not. R. Astron. Soc. 412, 634–664 (2011). We wish to correct a number of typographical and data processing errors in our original paper. First, the header in table 2 of Bannister et al. (2011) should read ‘Semi-minor axis (a0/2)’. Secondly, the NVSS (Condon et al. 1998) identifications for four transient sources were incorrectly specified as ‘not detected’ (ND) in the original paper, as an incorrect matching radius was used. Also, the quoted NVSS measurements for all sources were not bias-corrected. The bias correction is small, and not significant to the overall result. The correct NVSS flux densities for the transient sources are shown in Table 1. The radio light curves of sources with the previously ‘not detected’ NVSS fluxes are shown in Fig. 1 and comments on the same are shown in Table 2. The change in NVSS identifications does not impact on our formal definition of a transient source, but it does imply a change in the blind survey transients rate. As described in section 3.6 of the original paper, we define a transient source as one that is not detected by MOST at least once. As described in section 4.3, there are 15 such transient sources, implying a completeness-corrected rate of 1.1 × 10−2 deg−2 in two epochs. If we now add the requirement that a transient source was also not detected in NVSS, there are only five such transients, with an implied rate 1/3 of the original rate, i.e. 3.7×10−3 deg−2 in two epochs. Of these five transients, three were initially detected at other wavelengths (SN1987A, GRO 1655−40, Nova Muscae 1991), so only two were detected in a truly blind survey, implying a rate of 2/15 the original, i.e. 1.5 × 10−3 deg−2 in two epochs.
KERNEL BINARY REGRESSION WITH MULTIPLE COVARIATES

In this paper, we consider kernel-based estimators in the nonparametric binary regression problem with multidimensional covariates. We propose a local linear type estimator of the response probability function with kernel weighted at each observed covariate. In addition, we discuss the rule of thumb bandwidth selector and the plug-in bandwidth selector. The efficiency of the weighted local linear estimator is determined from results of asymptotic properties and our simulation study.
High taxonomic diversity and miniaturization in benthic communities under persistent natural CO2 disturbances

Metabarcoding techniques have revolutionized ecological research in recent years, facilitating the differentiation of cryptic species and revealing previously hidden diversity. In the current scenario of climate change and ocean acidification, biodiversity loss is one of the main threats to marine ecosystems. Here, we explored the effects of ocean acidification on marine benthic communities using DNA metabarcoding to assess the diversity of algae and metazoans. Specifically, we examined the natural pH gradient generated by the Fuencaliente CO2 vent system, located near La Palma Island (Canary Islands). High-resolution COI metabarcoding analyses revealed high levels of taxonomic diversity in an acidified natural area for the first time. This high number of species arises from the detection of small and cryptic species that were previously undetectable by other techniques. Such species are apparently tolerant to the acidification levels expected in future oceans. Hence and following our results, future subtropical communities are expected to keep high biodiversity values under an acidification scenario, although they will tend toward overall miniaturization due to the dominance of small algal and invertebrate species, leading to changes in ecosystem functions.
Organic or junk food? Microplastic contamination in Antarctic krill and salps

Microplastics (MP) have been reported in Southern Ocean (SO), where they are likely to encounter Antarctic zooplankton and enter pelagic food webs. Here we assess the presence of MP within Antarctic krill (Euphausia superba) and salps (Salpa thompsoni) and quantify their abundance and type by micro-Fourier transform infrared microscopy. MP were found in both species, with fibres being more abundant than fragments (krill: 56.25% and salps: 22.32% of the total MP). Polymer identification indicated MP originated from both local and distant sources. Our findings prove how in situ MP ingestion from these organisms is a real and ongoing process in the SO. MP amount was higher in krill (2.13 ± 0.26 MP ind−1) than salps (1.38 ± 0.42 MP ind−1), while MP size extracted from krill (130 ± 30 µm) was significantly lower than MP size from salps (330 ± 50 µm). We suggest that differences between abundance and size of MP ingested by these two species may be related to their food strategies, their ability to fragment MP as well as different human pressures within the collection areas of the study region. First comparative field-based evidence of MP in both krill and salps, two emblematic zooplankton species of the SO marine ecosystems, underlines that Antarctic marine ecosystems may be particularly sensitive to plastic pollution.
Individualized Tracking of Neurocognitive-State-Dependent Eye-Movement Features Using Mobile Devices

With current clinical techniques, it is difficult to assess a patient's neurodegenerative disease (e.g., Alzheimer's) state accurately and frequently. The most widely used tests are qualitative or only performed intermittently, motivating the need for quantitative, accurate, and unobtrusive metrics to track disease progression. Clinical studies have shown that saccade latency (an eye movement measure of reaction time) and error rate (the proportion of eye movements in the wrong direction) may be significantly affected by neurocognitive diseases. Nevertheless, how these features change over time as a disease progresses is underdeveloped due to the constrained recording setup. In this work, our goal is to first understand how these features change over time in healthy individuals. To do so, we used a mobile app to frequently and accurately measure these features outside of the clinical environment from 80 healthy participants. We analyzed their longitudinal characteristics and designed an individualized longitudinal model using a Gaussian process. With a system that can measure eye-movement features on a much finer timescale in a broader population, we acquired a better understanding of eye-movement features from healthy individuals and provided research directions in understanding whether eye-movement features can be used to track neurocognitive states.
The Tale of a Complicated Relationship

Smartphones have gotten under public scrutiny due to their ostensible negative impact on users' well-being. Nonetheless, users and related work report positive aspects of smartphones, too. We investigated this discrepancy through the prism of the emotional user-smartphone relationship by having people write love/breakup letters to their smartphones. We gathered 82 letters - 42 before and 40 during the COVID-19 pandemic. We found a mixed nature regarding the distribution of love and breakup letters and associated emotions based on the revisited OCC-model of emotions - with a slight shift towards the negative emotional spectrum during the COVID-19 pandemic. Furthermore, we performed an extensive qualitative analysis of 819 user statements extracted from the letters, resulting in a connection of emotions to 17 smartphone features and eight themes of real-life consequences of smartphone use. We then identified eight common patterns of this connection, classified as smartphone roles. The collected letters mostly model a complex user-smartphone relationship, comprising different roles depending on users' inner and outer context. We discuss how HCI could help in shaping the complex user-smartphone relationship in future research and suggest supporting a healthy balance between users' daily life and smartphone use.
Mood Measurement on Smartphones

Mood, often studied using smartphones, influences human perception, judgment, thought, and behavior. Mood measurements on smartphones face challenges concerning the selection of a proper mood measure and its transfer, or translation, into a digital application (app) that is user-engaging. Addressing these challenges, researchers sometimes end up developing a new interaction design and modifying the classic mood measure for an app. However, the extent to which such design alterations can impact user compliance, user experience, and the accuracy of mood measurements throughout a mood self-tracking study is unclear. In this paper, we explore and investigate how the selection of a mood measure (from two widely used measures) and its design alteration (from three options of classic, chatbot, and interactive designs) impact the (i) validity, (ii) user compliance, and (iii) user experience of mood measurement apps. For this purpose, we conducted a hybrid study with a mixed design in three parts. The first part suggests that a measure's validity can be susceptible to design modifications and introduces the concept of measure's resilience which can be essential when modifying the interaction design of a measurement tool. The second part discovers that both the type and design of the chosen measure can impact user compliance. This part also portrays a more complete picture of user compliance by demonstrating the use of several variables to investigate compliance. This investigation reveals that user compliance is not just about the response duration or length of a measurement tool. The final part finds that a measure or its design does not significantly influence the user experience for a well-designed app. In this part, we also discover which user experience criteria are more impactful for improving user compliance when designing mood tracking (or mood self-tracking) tools. Our results further suggest that, for a resilient measure, the interactive design is more likely to attract users and have higher user compliance and satisfaction as a whole. Ultimately, choosing a measure or design alternative would be a three-way trade-off between the measure's validity (or accuracy), user compliance, and user satisfaction, which researchers have to prioritize. A successful mood measurement with a smartphone needs to balance both concepts of app quality and assessment quality.
Behavior Modeling Approach for Forecasting Physical Functioning of People with Multiple Sclerosis

Forecasting physical functioning of people with Multiple Sclerosis (MS) can inform timely clinical interventions and accurate "day planning" to improve their well-being. However, people's physical functioning often remains unchecked in between infrequent clinical visits, leading to numerous negative healthcare outcomes. Existing Machine Learning (ML) models trained on in-situ data collected outside of clinical settings (e.g., in people's homes) predict which people are currently experiencing low functioning. However, they do not forecast if and when people's symptoms and behaviors will negatively impact their functioning in the future. Here, we present a computational behavior model that formalizes clinical knowledge about MS to forecast people's end-of-day physical functioning in advance to support timely interventions. Our model outperformed existing ML baselines in a series of quantitative validation experiments. We showed that our model captured clinical knowledge about MS using qualitative visual model exploration in different "what-if" scenarios. Our work enables future behavior-aware interfaces that deliver just-in-time clinical interventions and aid in "day planning" and "activity pacing".
A Data-Driven Context-Aware Health Inference System for Children during School Closures

Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures.
Skills as stepping stones for employability: Perception research into the skills of Humanities students

OECD reports a mismatch between field of study and job content for Humanities students, while at the same time signalling labour market shortages for skills that seem clearly linked with the Humanities, e.g. delivering information. In order to investigate which skills Humanities students associate with their university studies, we conducted a survey among 1306 European Humanities and 231 non-Humanities students. The survey asked these students whether they felt they acquired skills during their Humanities university education which were identified in a pilot phase. The students’ answers were analysed quantitatively, arriving at a Humanities self-perception skills profile of 70 detailed descriptions of skills that fall under six clusters. We argue that this profile could be a crucial step to improve the employability of Humanities students, as students’ perceptions of their skills greatly determine the quality of transition to the labour market.
A mismatch: Why non-tenured teachers are ill-prepared to deal with the perceived job insecurity of students in the humanities

Most of the instructional workforce within the humanities in countries like the United States, United Kingdom, Australia, and The Netherlands comprises non-tenure track appointments. This commentary is a starting point in thinking about what the meaning and consequences are of far-reaching casualization for humanities education. Based on my experience as a supervisor of non-tenured early-career teachers in the humanities and on an international exploration of the position of the so-called precariat, I describe the competing, and perhaps irreconcilable, discourses on the importance of the humanities for society and the labour market which these non-tenured teachers must navigate. These discourses put especially non-tenured academics, who are themselves in a very precarious, in an even more disempowered space that is not only detrimental to these non-tenured teachers but also to the students who must learn to deal with perceived job insecurity.