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The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available. Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in oxides.

Semi-local DFT methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on the DFT+U method can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. We develop a systematic methodology to determine the parameters and to statistically assess the results by considering thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-U correction, and (3) DFT/DFT+U compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach for 3d transition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1,710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross-validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the SCAN density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimize DFT+U performance for other target physical properties.

Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexity with respect to system sizes leads to prohibitive computational costs when the simulation contains over a few hundred atoms in practice. We present an In-Distribution substructure Embedding Active Learner (IDEAL) to enable efficient simulation of large complex systems with quantum accuracy by maintaining a machine learning force field (MLFF) as an accurate surrogate to the first principles methods. By extracting high-uncertainty substructures into low-uncertainty atom environments, the active learner is allowed to concentrate on and learn from small substructures of interest rather than carrying out intractable quantum chemical computations on large structures. IDEAL is benchmarked on various systems and shows sub-linear complexity, accelerating the simulation thousands of times compared with conventional active learning and millions of times compared with pure first principles simulations. To demonstrate the capability of IDEAL in practical applications, we simulated a polycrystalline lithium system composed of one million atoms and the full ammonia formation process in a Haber-Bosch reaction on a 3-nm Iridium nanoparticle catalyst on a computing node comprising one single A100 GPU and 24 CPU cores.

Mediterranean and low-fat diets are effective in the primary prevention of cardiovascular disease. We did a long-term randomised trial to compare the effects of these two diets in secondary prevention of cardiovascular disease. The CORDIOPREV study was a single-centre, randomised clinical trial done at the Reina Sofia University Hospital in Córdoba, Spain. Patients with established coronary heart disease (aged 20–75 years) were randomly assigned in a 1:1 ratio by the Andalusian School of Public Health to receive a Mediterranean diet or a low-fat diet intervention, with a follow-up of 7 years. Clinical investigators (physicians, investigators, and clinical endpoint committee members) were masked to treatment assignment; participants were not. A team of dietitians did the dietary interventions. The primary outcome (assessed by intention to treat) was a composite of major cardiovascular events, including myocardial infarction, revascularisation, ischaemic stroke, peripheral artery disease, and cardiovascular death. This study is registered with ClinicalTrials.gov, NCT00924937. From Oct 1, 2009, to Feb 28, 2012, a total of 1002 patients were enrolled, 500 (49·9%) in the low-fat diet group and 502 (50·1%) in the Mediterranean diet group. The mean age was 59·5 years (SD 8·7) and 827 (82·5%) of 1002 patients were men. The primary endpoint occurred in 198 participants: 87 in the Mediterranean diet group and 111 in the low-fat group (crude rate per 1000 person-years: 28·1 [95% CI 27·9–28·3] in the Mediterranean diet group vs 37·7 [37·5–37·9] in the low-fat group, log-rank p=0·039). Multivariable-adjusted hazard ratios (HRs) of the different models ranged from 0·719 (95% CI 0·541–0·957) to 0·753 (0·568–0·998) in favour of the Mediterranean diet. These effects were more evident in men, with primary endpoints occurring in 67 (16·2%) of 414 men in the Mediterranean diet group versus 94 (22·8%) of 413 men in the low-fat diet group (multiadjusted HR 0·669 [95% CI 0·489–0·915], log-rank p=0·013), than in 175 women for whom no difference was found between groups. In secondary prevention, the Mediterranean diet was superior to the low-fat diet in preventing major cardiovascular events. Our results are relevant to clinical practice, supporting the use of the Mediterranean diet in secondary prevention. Fundacion Patrimonio Comunal Olivarero; Fundacion Centro para la Excelencia en Investigacion sobre Aceite de Oliva y Salud; local, regional, and national Spanish Governments; European Union.

Abstract Background Large Language Models (LLMs), as in the case of OpenAI TM ChatGPT-4 TM Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalised through customization process to meet the student demands on every particular subject, like statistics. Recently, OpenAI launched the possibility of customizing their model with a natural language web interface, enabling the creation of customised GPT versions deliberately conditioned to meet the demands of a specific task. Methods This preliminary research aims to assess the potential of the customised GPTs. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behaviour was evaluated and compared with that of ChatGPT-4 Turbo. Firstly, each professor collected 15-30 genuine student questions from \"Statistics and Probability\" and \"Business Statistics\" courses across seven degrees, primarily from second-year courses. These questions, often ambiguous and imprecise, were posed to ChatGPT-4 Turbo and BSVP, with their initial responses recorded without follow-ups. In the third stage, professors blindly evaluated the responses on a 0-10 scale, considering quality, depth, and personalization. Finally, a statistical comparison of the systems' performance was conducted. Results The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP responded in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, when explicitly asked for something like, \"I would like to practice a programming exercise similar to those in R practice 4,\" BSVP could provide a far superior response. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. Conclusions It appears that customised assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.

A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.

Major histocompatibility complex class I chain-related gene A (MICA) is a non-classical MHC-I molecule essential for immune surveillance, yet its intracellular maturation remains poorly understood. We show that MICA is predominantly retained intracellularly in melanoma cells and colocalizes with the endoplasmic reticulum chaperone calreticulin (CRT). Notably, MICA also colocalizes with CRT in healthy skin. Immunoprecipitation assays reveal that CRT preferentially associates with a low-molecular-weight form of MICA. Recombinant protein assays and in silico analyses support direct interaction between CRT and non-glycosylated MICA, but not with fully glycosylated eukaryotic MICA. These findings identify CRT-dependent retention of MICA as a physiological checkpoint that may be dysregulated in melanoma to promote immune evasion.

This study was partially funded by the LIFE programme (LIFE Ricotí LIFE15-NAT-ES000802 and LIFE Connect Ricotí LIFE20-NAT-ES-000133), by the European Commission, Levantina y Asociado de Minerales, S.A., with the project “Estudios de investigación aplicado a la conservación de las poblaciones de alondra ricotí (Chersophilus duponti) en el entorno del municipio de Vallanca”, by the Dirección General de Política Forestal y Espacios Naturales de la Junta de Comunidades de Castilla La Mancha with the project “SSCC/046/2017 Censo de Alondra ricotí en la provincia de Guadalajara. Año 2017” and by the Junta de Castilla y León with the project “Estudio para la Mejora del Conocimiento de las Poblaciones de Alondra Ricotí Chersophilus duponti en Castilla y León”. Censuses in Catalonia were carried out with the support of the Generalitat of Catalonia. CPG acknowledges the support from the Ministerio de Educación y Formación Profesional through the Beatriz Galindo Fellowship (Beatriz Galindo—Convocatoria 2020). JGC is funded by a Margarita Salas postdoctoral fellowship (CA4/RSUE/2022-00205) provided by the Spanish Ministry of Universities and Universidad Autónoma de Madrid (Spain).

Background Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants. Results Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ . Conclusion SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Abstract* Background Large Language Models (LLMs), as in the case of OpenAI TM ChatGPT-4 TM Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalised through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI launched the possibility of fine-tuning their model with a natural language web interface, enabling the creation of customised GPT versions deliberately conditioned to meet the demands of a specific task. Methods This preliminary research aims to assess the potential of the customised GPTs. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behaviour was evaluated and compared with that of ChatGPT-4 Turbo. Firstly, each professor collected 15-30 genuine student questions from \"Statistics and Probability\" and \"Business Statistics\" courses across seven degrees, primarily from second-year courses. These questions, often ambiguous and imprecise, were posed to ChatGPT-4 Turbo and BSVP, with their initial responses recorded without follow-ups. In the third stage, professors blindly evaluated the responses on a 0-10 scale, considering quality, depth, and personalization. Finally, a statistical comparison of the systems' performance was conducted. Results The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP responded in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, when explicitly asked for something like, \"I would like to practice a programming exercise similar to those in R practice 4,\" BSVP could provide a far superior response. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. Conclusions It appears that customised assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.