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Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

The efficacy of vocational teachers' utilisation of digital technology in the context of the Industrial Revolution 4.0 remains a pressing concern that necessitates pragmatic resolutions. Social support and infrastructure are the leading causes of the limited technological self-efficacy of digital technology practices. This research seeks to examine the impact of infrastructure and social support through technological self-efficacy on vocational teachers' digital technology practices during the Industrial Revolution 4.0. The research employed a quantitative approach and ex-post-facto methods, involving 207 vocational teacher respondents. The data was analysed using structural equation modelling techniques, namely path analysis and bootstrapping approaches. The inquiry results indicate that technological self-efficacy acts as a mediator of digital technology practices, with a statistically significant p-value of less than 0.05. The relationship between social support for digital technology practices and technology self-efficacy is mediated by an estimate of 0.089 with a p-value of 0.019. Similarly, the relationship between infrastructure for digital technology practices and technology self-efficacy is mediated by an estimate of 0.250 with a p-value of 0.000. A comprehensive analysis of variables, including their direct, indirect, and total effects, revealed a considerable influence of the variables included in the study. After analysing the features of each respondent, a p-value < 0.05 was achieved, indicating a significant influence between the variables evaluated. It suggests that the relationship between the variables can be broadly applied to the characteristics of each respondent. Enhancing digital technology practices in vocational education requires increased efforts from educational institutions and the government.

We study algorithms for drawing planar graphs and 1-planar graphs using cubic Bézier curves with bounded curvature. We show that any n-vertex 1-planar graph has a 1-planar RAC drawing using a single cubic Bézier curve per edge, and this drawing can be computed in O(n) time given a combinatorial 1-planar drawing. We also show that any n-vertex planar graph G can be drawn in O(n) time with a single cubic Bézier curve per edge, in an O(n) × O(n) bounding box, such that the edges have Θ(1/degree(v)) angular resolution, for each v ∈ G, and O(√ n) curvature.

Tree growth and longevity trade-offs fundamentally shape the terrestrial carbon balance. Yet, we lack a unified understanding of how such trade-offs vary across the world's forests. By mapping life history traits for a wide range of species across the Americas, we reveal considerable variation in life expectancies from 10 centimeters in diameter (ranging from 1.3 to 3195 years) and show that the pace of life for trees can be accurately classified into four demographic functional types. We found emergent patterns in the strength of trade-offs between growth and longevity across a temperature gradient. Furthermore, we show that the diversity of life history traits varies predictably across forest biomes, giving rise to a positive relationship between trait diversity and productivity. Our pan-latitudinal assessment provides new insights into the demographic mechanisms that govern the carbon turnover rate across forest biomes.

The extraordinary properties of the Kitaev model have motivated an intense search for new physics in materials that combine geometrical and bond frustration. In this Letter, we employ inelastic neutron scattering, spin wave theory, and exact diagonalization to study the spin dynamics in the perfect triangular-lattice antiferromagnet (TLAF) CsCeSe2. This material orders into a stripe phase, which is demonstrated to arise as a consequence of the off-diagonal bond-dependent terms in the spin Hamiltonian. By studying the spin dynamics at intermediate fields, we identify an interaction between the single-magnon state and the two-magnon continuum that causes decay of coherent magnon excitations, level repulsion, and transfer of spectral weight to the continuum that are controlled by the strength of the magnetic field. Our results provide a microscopic mechanism for the stabilization of the stripe phase in TLAF and show how complex many-body physics can be present in the spin dynamics in a magnet with strong Kitaev coupling even in an ordered ground state.

PurposeThe purpose of this study was to evaluate RB1 expression and survival across ovarian carcinoma histotypes and how co-occurrence of BRCA1 or BRCA2 (BRCA) alterations and RB1 loss influences survival in tubo-ovarian high-grade serous carcinoma (HGSC).Experimental designRB1 protein expression was classified by immunohistochemistry in ovarian carcinomas of 7,436 patients from the Ovarian Tumor Tissue Analysis consortium. We examined RB1 expression and germline BRCA status in a subset of 1,134 HGSC, and related genotype to overall survival (OS), tumor-infiltrating CD8+ lymphocytes, and transcriptomic subtypes. Using CRISPR-Cas9, we deleted RB1 in HGSC cells with and without BRCA1 alterations to model co-loss with treatment response. We performed whole-genome and transcriptome data analyses on 126 patients with primary HGSC to characterize tumors with concurrent BRCA deficiency and RB1 loss.ResultsRB1 loss was associated with longer OS in HGSC but with poorer prognosis in endometrioid ovarian carcinoma. Patients with HGSC harboring both RB1 loss and pathogenic germline BRCA variants had superior OS compared with patients with either alteration alone, and their median OS was three times longer than those without pathogenic BRCA variants and retained RB1 expression (9.3 vs. 3.1 years). Enhanced sensitivity to cisplatin and paclitaxel was seen in BRCA1-altered cells with RB1 knockout. Combined RB1 loss and BRCA deficiency correlated with transcriptional markers of enhanced IFN response, cell-cycle deregulation, and reduced epithelial-mesenchymal transition. CD8+ lymphocytes were most prevalent in BRCA-deficient HGSC with co-loss of RB1.ConclusionsCo-occurrence of RB1 loss and BRCA deficiency was associated with exceptionally long survival in patients with HGSC, potentially due to better treatment response and immune stimulation.

The two-dimensional triangular-lattice antiferromagnet (TLAF) is a textbook example of frustrated magnetic systems. Despite its simplicity, the TLAF model exhibits a highly rich and complex magnetic phase diagram, featuring numerous distinct ground states that can be stabilized through frustrated next-nearest-neighbor couplings or anisotropy. In this paper, we report low-temperature magnetic properties of the TLAF material CsCeSe2. The inelastic neutron scattering (INS) together with specific heat measurements and density functional theory calculations of crystalline electric field suggest that the ground state of Ce ions is a Kramers doublet with strong easy-plane anisotropy. Elastic neutron scattering measurements demonstrate the presence of stripe-yz magnetic order that develops below TN=0.35K, with the zero-field ordered moment of mCe≈0.65μB. Application of magnetic field first increases the ordering temperature by about 20% at the intermediate field region and eventually suppresses the stripe order in favor of the field-polarized ferromagnetic state via a continuous quantum phase transition (QPT). The field-induced response demonstrates sizable anisotropy for different in-plane directions, Ba and Ba, which indicates the presence of bond-dependent coupling in the spin Hamiltonian. We further show theoretically that the presence of anisotropic bond-dependent interactions can change the universality class of QPT for Ba and Ba.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

We present results from a 3.25 kg-day target exposure of two silicon charge-coupled devices (CCDs), each with 24 megapixels and skipper readout, deployed in the DAMIC setup at SNOLAB. With a reduction in pixel readout noise of a factor of 10 relative to the previous detector, we investigate the excess population of low-energy events in the CCD bulk previously observed above expected backgrounds. We address the dominant systematic uncertainty of the previous analysis through a depth fiducialization designed to reject surface backgrounds on the CCDs. The measured bulk ionization spectrum confirms the presence of an excess population of low-energy events in the CCD target with characteristic rate of ∼7 events per kg-day and electron-equivalent energies of ∼80 eV, whose origin remains unknown.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Differentiated instruction is an important teaching approach that adapts educational experiences to fit the varied needs and learning styles of students in the classroom. This study examines the trend of research on differentiated instruction, using a bibliometric analysis to understand the current state of research in this area. The analysis was performed using online databases such as Scopus to locate relevant research articles from 1961 to 2023. The results of the analysis revealed that 746 articles were examined, focusing on factors such as publication year, language, journal, authors, and keywords. The findings indicate a gradual increase in research on differentiated instruction, with a significant rise in the number of articles published in recent years. The majority of the research articles were written in English and authored by scholars from universities in the USA and Europe. The keywords used most frequently in the articles included differentiated instruction, inclusive education and differentiation. This study provides a comprehensive view of the research trends in differentiated instruction, underscoring the need for further research to enhance our understanding of the approach's effectiveness and its impact on student learning outcomes. Recommendations for future research in the field of differentiated instruction include the need for research on themes and concepts that are less studied in the field, such as the application of differentiated learning in online learning, and the use of technology in differentiated instruction.

In an era of increasingly diverse education, the need to understand the effectiveness of differential instruction on learning outcomes is critical in creating inclusive and student-centered learning experiences. This study aims to comprehensively review the effectiveness of differentiated instruction on student learning outcomes and investigate what moderator variables contribute significantly to the effect of implementing differentiated instruction on student learning outcomes. The study was a systematic review and meta-analysis that utilized 49 primary studies, from which 63 effect sizes were extracted. It was found that there was a significant (gRE = 1.109, p < 0.01) of the application of differential instruction on student learning outcomes compared to learning in the control group. When measured from the aspect of the measured ability, subjects, grade level, sample size, and index type, there was no significant difference in effect size. However, there was a significant difference in effect size when viewed from the country's perspective. It was concluded that differential instruction is an appropriate learner strategy used to improve the quality of learning at all levels. However, it is prudent to acknowledge that the effectiveness of implementing differentiated instruction is intrinsically intertwined with the specific context and educational milieu of a given country. This study provides a nuanced understanding that while differentiated instruction holds universal promise, its optimal outcomes are sensitive to contextual influences.