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Guidelines for conducting surveys by mobile phone calls in low- and middle-income countries suggest keeping interviews short (<20 minutes). The evidence supporting this recommendation is scant, even though limiting interview duration might reduce the amount of data generated by such surveys. We recruited nearly 2,500 mobile phone users in Malawi and randomly allocated them to 10-, 20-, or 30-minute phone interviews, all ending with questions on parental survival. Cooperation was high in all groups, and differences in completion rates were minimal. The extent of item nonresponse, age heaping, and temporal displacement of deaths in data on parental survival generally did not vary between study groups, but reports of maternal age at death were more reliable in longer interviews. Recommendations about the duration of mobile phone interviews might be too restrictive. They should not preclude additional modules, including ones on mortality, in mobile phone surveys conducted in LMICs.

Intraocular lenses (IOLs) based on a transparent hydrophilic acrylic polymer have been irradiated by a 365 nm UV lamp at a 200 mJ/cm2 fluence and at different exposure times, from 1 h up to 19 h, in air and at room temperature. The macromolecular modifications induced in the lens have been investigated by attenuated total reflectance coupled to Fourier transform infrared spectroscopy and optical spectroscopy. Particular attention was devoted to the study of chemical modifications of the IOL by UV irradiation, which induced chain scissions, radical formation, and cross-links in the more superficial polymer layers. The experimental results at long exposures demonstrate that the IOL transmission decreases in the UV and NIR ranges, remaining nearly constant in the visible range.

Academics at Griffith university envisioned a complementary learning and supportive relationship could be developed between Bachelor of Information Technology (BAIT) students and Master of Social Work (MSW) students. Discussions between discipline specific staff highlighted that each discipline had strengths and expertise that could assist students to overcome challenges brought about by systemic changes in tertiary education, gaps in skillsets and curriculum, and workforce expectations. Pressures included students attending university from culturally and linguistically diverse backgrounds, the increasing importance of information technology (IT) in the classroom and workplace, as well as the requirement to communicate effectively across a range of disciplines. Working, interacting, and communicating effectively in cross-discipline and culturally diverse environments is a requisite for all graduates, however academics found there were few opportunities for students to partner across disciplinary silos. Staff from both disciplines collaborated to develop a conversational pedagogical framework to underpin a pilot program to support peer learning, using active problem-based learning with IT and social work students. The pilot program evidenced a complementary transdisciplinary, learning partnership and enabled students from IT to help students from social work develop IT skills, while social work students assisted IT students to become more confident in their interpersonal communication skills.

Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.

Abstract Glioblastoma (GBM) represents an aggressive brain tumor, characterized by intra- and inter-tumoral heterogeneity and therapy resistance, leading to unfavourable prognosis. An increasing number of studies pays attention on the regulation of ferroptosis, an iron-dependent cell death, as a strategy to reverse drug resistance in cancer. However, the debate on whether this strategy may have important implications for the treatment of GBM is still ongoing. In the present study, we used ferric ammonium citrate and erastin to evaluate ferroptosis induction effects on two human GBM cell lines, U-251 MG, with proneural characteristics, and T98-G, with a mesenchymal profile. The response to ferroptosis induction was markedly different between cell lines, indeed T98-G cells showed an enhanced antioxidant defence, with increased glutathione levels, as compared to U-251 MG cells. Moreover, using bioinformatic approaches and analysing publicly available datasets from patients’ biopsies, we found that GBM with a mesenchymal phenotype showed an up-regulation of several genes involved in antioxidant mechanisms as compared to proneural subtype. Thus, our results suggest that GBM subtypes differently respond to ferroptosis induction, emphasizing the significance of further molecular studies on GBM to better discriminate between various tumor subtypes and progressively move towards personalized therapy.

Abstract Marital quality is an important determinant of well-being and is related to how unions are formed. Both processes of union formation and marital relationships may be influenced by stressful conditions, including insecurity and displacement. We leverage unique representative data on young Syrian refugees in Jordan to learn more about the interplay between displacement, union formation characteristics and marital quality. Through comparisons with youth from the Jordanian host population, we assess the role of key contextual predictors of marital quality, such as consanguinity and young people’s role in choosing their spouse, with a focus on gender differences. We measure marital quality with six separate items capturing aspects of equality, respect and interpersonal communication and a unidimensional scale identified through exploratory factor analysis. Results show that consanguinity, marital duration and number of children are generally unrelated to marital quality in both populations. In contrast, involvement in spouse choice emerges as the strongest predictor, with both Syrians and Jordanians in family-arranged unions experiencing lower-quality marriages. Among Syrians, women suffer more from lower marital quality, especially when arranged unions happen at a young age, and marriages formed after displacement to Jordan appear more fragile. The study is one of the first to explore predictors of marital quality in a non-Western, conflict-affected population. The findings emphasise the importance of the demographic study of family dynamics in situations of insecurity and displacement moving beyond a narrow focus on marriage timing to cover a wider range of marriage outcomes related to family well-being.

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

IntroductionChronic low back pain (cLBP) poses significant challenges, often addressed through avoidance or distraction. Emerging evidence suggests that mind-body interventions, like our novel Mind Your Pain (MyP) smartphone mobile application, may offer relief. We conducted a single-arm, mixed-methods neuroimaging study to assess the degree to which treatment response to our 8-week intervention, as measured by the reduction in the Pain, Enjoyment of Life and General Activity Scale (PEG), was associated with enhanced pain-related insula activation over time.MethodsTwenty-nine individuals with cLBP completed patient-reported assessments, qualitative sensory testing (QST) measures, and neuroimaging pre- and post-MyP. Functional MRI data during experimental heat pain on the left forearm were collected and analyzed, comparing responders (≥50% reduction in PEG scores) and non-responders.ResultsMyP led to significant decreases in PEG scores overall. Furthermore, MyP responders exhibited increased pain-related activation in key brain regions, including the contralateral posterior insula, bilateral ventral anterior insula, ventral anterior cingulate, dorsolateral prefrontal cortex, and nucleus accumbens. Although baseline behavioral and sensory measures did not differ between the two responder groups, baseline neural differences related to the impact of the endogenous back pain were observed.DiscussionMyP appears to modify pain response and underlying neural circuitry, suggesting neural changes in interoception may serve as biomarkers for mind-body interventions in cLBP. This study highlights the potential of MyP as a novel approach for cLBP management, warranting further investigation.

The sudden and rapid spread of the COVID_19 pandemic with its terrible consequences has put the management of governments and the various world institutions into a crisis. They have been subjected to a considerable economic effort to be taken to combat the spread of the pandemic. The economic investment for the research and purchase of vaccines intended for populations is subject to cost–benefit analyses in various situations in different cases. In this review work, several recent models are analyzed where the appearance of the components is coupled with the economic aspect. The analysis of these models is detailed and the results discussed from different points of view.

Objectives: Moderate daily mocha pot coffee intake has been associated with better mood and cognition in patients with mild vascular cognitive impairment (VCI). Similarly, moderate red wine consumption has shown protective effects on cognitive disorders, including Alzheimer's disease and vascular dementia. The aim of this study was to explore the synergistic relation between red wine and coffee intake on mood and cognitive status in mild VCI patients at risk for dementia. Methods: A total of 300 non-demented older patients with mild VCI were asked for coffee and red wine consumption and administered with the 17-items Hamilton Depression Rating Scale (HDRS), the Mini Mental State Examination (MMSE), and the Stroop Color-Word Interference Test (Stroop T), as well as the Activities of Daily Living (ADL) and the Instrumental ADL to measure their mood status, cognitive performance, and functional independence. Linear regression models were used to test the association between variables. Results: Moderate wine drinkers tended to show the best Stroop T score at any level of coffee consumption; conversely, heavy wine consumers performed worse at the Stroop T, especially in patients reporting high coffee intake. Moderate drinkers of both coffee and wine showed the lowest HDRS scores. Finally, a progressive increase in MMSE score was evident with increasing coffee consumption, which peaks when combined with a moderate wine consumption. Conclusions: Daily mocha pot coffee and red wine intake seem to be synergistically associated with global cognition, executive functioning, and mood status in patients with mild VCI; the association was not linear, resulting in a protective direction for moderate intake and detrimental for heavy consumption. Future studies are needed to further corroborate the present findings and the potential long-term protective effects of these dietary compounds over time.

Abstract In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers’ decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions.

There is no single agreed-upon definition of data literacy because expectations of what it means to be data literate varies across contexts. The lack of agreement on a definition of data literacy across contexts is therefore necessary. However, definitions are important. Definitions embody our understanding of concepts and are the foundation for operationalization of concepts. The work reported in the chapter is motivated by the observation that despite no shortage of university graduates, organizations are struggling to find data-literate talent. There is an apparent disconnect between data literacy as taught in the academic domain and data literacy as expected by businesses in the public domain. An exploration of definitions of data literacy in academic and public domains is undertaken to gain insight in why the disconnect exists. A thematic analysis and comparison of definitions in the two domains were conducted. The differences identified provide some broad directions for developing data literacy capabilities in students that better fulfil the needs of business organizations.

In higher education, the role of assessment has evolved from serving only the purpose of evaluation of the outcomes of the learning process to assessment as part of the learning purposes. Summative or formative, the potential of assessment to facilitate learning is now recognised. Feedback is one of the most influential factors on learning but is also one of the least satisfactory elements of the student experience. How has interest in, understanding of, and implementation of feedback changed over time? What are the general characteristics of feedback implementation found in literature? To what productive directions might future research feedback on assessment take? To gain these insights, a systematic quantitative review of literature on feedback on written assessment in higher education was undertaken, revealing that feedback as a field of inquiry, despite burgeoning interest, remains nascent, mostly 'stuck in old ways'.

Vitamin D3 is a crucial fat-soluble pro-hormone essential for bolstering bone health and fortifying immune responses within the human body. Orodispersible films (ODFs) serve as a noteworthy formulation strategically designed to enhance the rapid dissolution of vitamin D, thereby facilitating efficient absorption in patients. This innovative approach not only streamlines the assimilation process but also plays a pivotal role in optimizing patient compliance and therapeutic outcomes. The judicious utilization of such advancements underscores a paradigm shift in clinical strategies aimed at harnessing the full potential of vitamin D for improved patient well-being. This study aims to examine the vitamin D3 ODF structure using spectroscopic techniques to analyze interactions with excipients like mannitol. Fourier-transform infrared spectroscopy (FTIR) and ultraviolet–visible (UV–Vis) spectroscopy were utilized to assess molecular composition, intermolecular bonding, and vitamin D3 stability. Understanding these interactions is essential for optimizing ODF formulation, ensuring stability, enhancing bioavailability, and facilitating efficient production. Furthermore, this study involves a translational approach to interpreting chemical properties to develop an administration protocol for ODFs, aiming to maximize absorption and minimize waste. In conclusion, understanding the characterized chemical properties is pivotal for translating them into effective self-administration modalities for Vitamin D films.

Abstract Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe2. Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/α-RuCl3 by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed online.

This case shows the use of ultrasound guidance to optimize non-invasive mechanical ventilation for a 62-year-old patient with a complex medical history. Point-of-care ultrasound (POCUS) was used to assess diaphragmatic function and hemodynamics, leading to adjustments in ventilator setting. The approach improved gas exchange, resolved respiratory acidosis, and enhanced hemodynamics, providing a promising strategy for ventilator management in complex clinical cases.

Abstract Background Follicular thyroid cancer (FTC) is a prevalent form of differentiated thyroid cancer, whereas anaplastic thyroid cancer (ATC) represents a rare, fast-growing, undifferentiated, and highly aggressive tumor, posing significant challenges for eradication. Ferroptosis, an iron-dependent cell death mechanism driven by the excessive production of reactive oxygen species and subsequent lipid peroxidation, emerges as a promising therapeutic strategy for cancer. It has been observed that many cancer cells exhibit sensitivity to ferroptosis, while some other histotypes appear to be resistant, by counteracting the metabolic changes and oxidative stress induced by iron overload. Methods Here we used human biopsies and in vitro approaches to analyse the effects of iron-dependent cell death. We assessed cell proliferation and viability through MTT turnover, clonogenic assays, and cytofluorimetric-assisted analysis. Lipid peroxidation assay and western blot were used to analyse molecular mechanisms underlying ferroptosis modulation. Two distinct thyroid cancer cell lines, FTC-133 (follicular) and 8505C (anaplastic), were utilized. These cell lines were exposed to ferroptosis inducers, Erastin and RSL3, while simulating an iron overload condition using ferric ammonium citrate. Results Our evidence suggests that FTC-133 cell line, exposed to iron overload, reduced their viability and showed increased ferroptosis. In contrast, the 8505C cell line seems to better tolerate ferroptosis, responding by modulating CD71, which is involved in iron internalization and seems to have a role in resistance to iron overload and consequently in maintaining cell viability. Conclusions The differential tolerance to ferroptosis observed in our study may hold clinical implications, particularly in addressing the unmet therapeutic needs associated with ATC treatment, where resistance to ferroptosis appears more pronounced compared to FTC.

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity.