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Background: A marked increase in physical activity (PA) publications has occurred since 1985. Increased publications reflect a globalization of publications and increase the difficulty of reviewing evidence and identifying effective PA strategies. Methods: Review of Scopus database publications with the title or keyword "physical activity" between 1985 and 2022, examining 193,335 PA publications. The choice of search strategy was standardized across the period and maximized relevant PA papers (high sensitivity). Results: The results showed 3 clear periods for increases in PA publications; an initial increase in period 1 between 1985 and 2000, a 9-fold increase in period 2 between 2001 and 2016 (from 645/y to 5876/y), and a further increase to 15,812 publications per year in period 3 between 2017 and 2022. The primary driver of this increase was the transition from publications in traditional subscription-model journals to open-access business model journals, which comprised 80% of PA publications by period 3. Although this trend was global, some countries showed greater increases through open-access publication. Discussion: These data identify a relatively unrecognized and urgent threat to the field: An exponential increase in PA publications may influence the quality of published work and increases the difficulty of finding and applying important research findings. The shift in publication patterns is contributed to by higher acceptance rates and rapid time to publication offered in open-access journals. This represents a "commercial determinant" of publications in the PA field, influencing the type and volume of publications without consideration of their contribution to evidence or impact. [ABSTRACT FROM AUTHOR]

Absolute line strength measurements of hydroperoxyl (HO2) radical in the OO-stretching (ν3) fundamental band have been performed by means of mid-infrared time-resolved dual-comb spectroscopy. By employing two sets of dual-comb spectrometers, high-resolution time-resolved spectra of HO2 and HCl, formed in the photolysis reaction system of Cl2/CH3OH/O2, could be, respectively, measured near 1123 and 3059 cm−1. With kinetic simulations, spectral analysis of both HO2 and HCl, as well as the accurate line strength of the HCl R(9) transition at 3059.316 cm−1, an absolute line strength of the ν3 131,13 ← 121,12 F1,2 transitions in HO2 at 1122.983 cm−1 was first determined to be 1.80 × 10−20 cm molecule−1 with a small uncertainty down to 4% under the conditions with low initial concentrations of Cl radical (1.63–1.81 × 1013 molecule cm−3). Furthermore, broadband high-resolution spectra of the ν3 fundamental band of HO2 were recorded in the range of 1088–1124 cm−1 with an average spectral resolution of 0.002 cm−1. By contour fitting the measured broadband spectra with PGOPHER, the line strengths of hundreds of rovibrational transitions were obtained relative to the well-determined HO2 lines at 1122.983 cm−1, and those values were observed to be higher than those tabulated in the HITRAN database by a factor of ∼2.8. Moreover, the absolute band strength of the ν3 fundamental band in HO2 was derived to be 22.3 km mol−1 with an uncertainty of 5%. This work providing precise and detailed spectral data would be crucial in revisiting the theoretical modeling of HO2 geometry and updating the database of the HO2 radical. [ABSTRACT FROM AUTHOR]

Background: Chronic musculoskeletal (MSK) pain significantly impacts individuals' quality of life. Regular physical activity is an important key to therapy. However, adherence to exercise is often below the intended levels. This systematic review aims to assess the effectiveness of adherence support strategies in physical activity randomized controlled trials (RCTs) for patients with chronic MSK pain, to address the challenge of motivation and adherence in this population. Methods: Ovid and CINAHL databases were searched for MSK "chronic pain," "adherence" and "physical activity" (in various synonyms) for RCTs with a follow-up of at least 6 months. After a screening process that included several team members, a narrative synthesis and a meta-analysis on adherence and effect sizes of different outcomes were performed. Results: An initial database output of 5816 articles resulted in 76 RCTs that remained for analysis. These RCTs showed high heterogeneity of intervention design and outcome data. Adherence to exercise varied widely, with 21 RCTs achieving rates of 80% or higher using strategies like education, goal setting, self-monitoring, and personal feedback. The meta-analysis, based on 29 out of 76 RCTs, indicated a nonsignificant correlation between adherence levels and an improved outcome (eg, pain or quality of life). No individual strategy emerged as superior, emphasizing the complexity and diversity of intervention designs. Conclusions: This review offers strategies, such as information/education and individual goal setting/feedback that may be beneficial for enhancing physical activity in patients with chronic MSK pain. Future research should focus on including promising adherence support strategies and providing data for adherence calculations. [ABSTRACT FROM AUTHOR]

Background: Ubiquitous car ownership may affect children's activity and health. We assessed the cross-sectional and longitudinal associations between household car ownership and children's daily time spent sedentary (SED) and in moderate to vigorous physical activity (MVPA). Methods: Pooled cohort data were from the International Children's Accelerometry Database. Outcome measures were average daily accelerometer-measured SED and MVPA (in minutes per day). Exposures were household car ownership (none, 1, and ≥2) and change in car ownership. Associations were examined using multivariable mixed-effects linear regression. Results: Mean age of participants (N = 4193) was 10.4 years (SD = 2.0), 53.4% were girls, and mean follow-up duration (N = 1333) was 3.3 years (SD = 1.1). Cross-sectionally, household car ownership was associated with higher SED (vs none: 1 car: β = 14.1 min/d, 95% CI, 6.7–21.5; ≥2 cars: 12.8, 95% CI, 5.3–20.4) and lower MVPA (vs none: 1 car: β = –8.8, 95% CI, −11.9 to −5.7; ≥2 cars: β = –8.8, 95% CI, −12.0 to −5.7). Associations were stronger in boys than girls and in children from lower socioeconomic backgrounds (SED only). Prospectively, there were no associations with increased car ownership. Reductions in household car ownership (of which 93.1% had ≥ 2 cars at baseline) were associated with a greater reduction in MVPA (vs no change: β = −8.4, 95% CI, −13.9 to −3.0) but not SED. Conclusions: Children in households with car access were more SED and less active than those without. Losing access to a second car was associated with greater decreases in MVPA, potentially related to losing access to activity-enabling environments. Reducing car access and use are important public health targets (eg, reducing air pollution), but their potential impact on children's activity opportunities should be mitigated. [ABSTRACT FROM AUTHOR]

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works. [ABSTRACT FROM AUTHOR]

Increasing physical activity (PA) and/or decreasing sedentary behaviors is important in the delay and prevention of long-term conditions. PA can help maintain function and independence and decrease the need for hospitalization/institutionalization. Activity rates often decline in later life resulting in a need for interventions that encourage uptake and adherence through the use of Behavior Change Techniques (BCTs). We conducted a systematic review of the evidence for interventions that included BCTs in community-dwelling adults with a mean age of 50–70. The review followed PRISMA guidelines. The interventions were psychosocial, nonpharmacological, and noninvasive interventions utilizing components based on BCTs that evaluated change in PA and/or sedentary behavior. Intervention Component Analysis (ICA) was used to synthesize effectiveness of intervention components. Twelve randomized controlled trials were included in this review. The mean sample age was 50–64. Thirteen BCTs were used across all studies, and the most commonly used techniques were goals and planning, feedback and monitoring, and natural consequences. Seven intervention components linked with BCTs were found: personalized goal setting, tailored feedback from facilitators, on-site and postintervention support, education materials and resources, reinforcing change on behavior and attitudes, self-reported monitoring, and social connectedness. All components, except for social connectedness, were associated with improved health behavior and PA levels. The interventions that use BCTs have incorporated strategies that reinforce change in behavior and attitudes toward PA. [ABSTRACT FROM AUTHOR]

Successful synthesis of novel high entropy ceramic (HEC) for ultra-high temperature application classes, namely, borides, carbides, and nitrides, has been experiencing a bottleneck in having a suitable design and successful synthesis strategy. Producing high-entropy ultra-high-temperature ceramics from their oxides offers a major processing benefit, while employing a design approach using machine learning enhances the efficiency of the formation of single-phase HECs. In this regard, we propose a generalized strategy to generate a semi-synthetic database for each of these classes using literature data and atomic environment mapping-based structure plots, which can further be used to build machine learning models. The imbalance of the dataset was addressed using adaptive synthetic sampling and the edited nearest neighbors technique. The trained models are able to accurately predict over 90% of the single-phase chemistry for each of the classes. Furthermore, a few compositions representing these classes were successfully synthesized from the corresponding oxide mixture to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations. [ABSTRACT FROM AUTHOR]

We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example. [ABSTRACT FROM AUTHOR]

In recent papers, Becke et al. [J. Chem. Phys. 158, 151103 (2023)] and then Becke [J. Chem. Phys. 159, 241101 (2023)] have developed a novel double hybrid density functional, "DH23," whose terms are based on good local physics. Its 12 coefficients are trained on the GMTKN55 (general main-group thermochemistry, kinetics, and noncovalent interactions) chemical database of Goerigk et al. [Phys. Chem. Chem. Phys. 19, 32184 (2017)]. The lowest GMTKN55 "WTMAD2" error to date for any hybrid or double hybrid density functional was obtained (1.73 kcal/mol for the revDH23 variant). Here, we simplify DH23 by introducing a dispersion damping scheme involving atomic numbers only and one global parameter. The resulting new functional, "DH24," performs as well as its predecessors. [ABSTRACT FROM AUTHOR]

4H Silicon carbide (SiC) is widely recognized as one of the most advanced wide bandgap semiconductors used in the production of high-efficiency power electronic devices. Impurities play a crucial role in achieving the desired electrical properties in 4H-SiC, yet the understanding of impurities in this material remains limited. In this study, first-principles formation-energy calculations were employed to establish a comprehensive database of formation-energy diagrams for impurities in 4H-SiC. This database includes valuable information on site preference, lattice distortion, solubility, and charge transition levels (CTLs) of the impurities. The site preference for each impurity is closely related to factors such as the Fermi energy, chemical potential, and the impurity species itself. To assess the lattice distortion caused by each impurity, a comparison was made between the volume changes before and after doping. Moreover, the solubility of each impurity was determined using the detailed balance theory, thereby enabling a direct measure of the maximum impurity concentration achievable in the material. Based on the CTLs, the impurities in 4H-SiC were classified into four categories: n-type impurities, p-type impurities, amphoteric impurities, and non-electroactive impurities. This comprehensive property database for impurities in 4H-SiC provides valuable insights for tailoring the material properties through controlled doping, thereby ultimately leading to enhanced performance of power electronic devices. [ABSTRACT FROM AUTHOR]

Accurate modeling of intermolecular repulsion is an integral component in force field development. Although repulsion can be explicitly calculated by applying the Pauli exclusion principle, this approach is computationally viable only for systems of limited sizes. Instead, it has previously been shown that repulsion can be reformulated in a "classical" picture: the Pauli exclusion principle prohibits electrons from occupying the same state, leading to a depletion of electronic charge between atoms, giving rise to an enhanced nuclear–nuclear electrostatic repulsion. This classical picture is called the isotropic S2/R approximation, where S is the overlap and R is the interatomic distance. This approximation accurately captures the repulsion of isotropic atoms such as noble gas dimers; however, a key deficiency is that it fails to capture the angular dependence of the repulsion of anisotropic molecules. To include directionality, the wave function must at least be a linear combination of s and p orbitals. We derive a new anisotropic S2/R repulsion model through the inclusion of the anisotropic p orbital term in the total wave function. Because repulsion is pairwise and decays rapidly, it can be truncated at a short range, making it amenable for efficient calculation of energy and forces in complex biomolecular systems. We present a parameterization of the S101 dimer database against the ab initio benchmark symmetry-adapted perturbation theory, which yields an rms error of only 0.9 kcal/mol. The importance of the anisotropic term is demonstrated through angular scans of water–water dimers and dimers involving halobenzene. Simulation of liquid water shows that the model can be computed efficiently for realistic system sizes. [ABSTRACT FROM AUTHOR]

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a "defect genome" that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions. [ABSTRACT FROM AUTHOR]

Polymers, known for their lightweight, high strength, and ease of processing, serve as a key component in engineering materials. Polymers with high thermal conductivity (TC) present enormous potential applications in thermal management for high-performance electronic devices. However, the discovery of thermally conductive polymers is still in a time-consuming and labor-intensive trial-and-error process, which undoubtedly hinders the progress in related applications. Fortunately, machine learning (ML) enables to overcome this obstacle by building precise models to predict the TC of polymers through learning from a large volume of data and it can quickly identify polymers with high TC and provide significant insights to guide further design and innovation. In this mini review, we briefly describe the general process of using ML to predict polymers with high TC and then give guidance for the selection and utilization of three important components: database, descriptor, and algorithm. Furthermore, we summarize the predicted thermally conductive single polymer chains, amorphous polymers, and metal-organic frameworks via ML and identify the key factors that lead to high TC. Finally, we touch on the challenges faced when utilizing ML to predict the TC of polymer and provide a foresight into future research endeavors. [ABSTRACT FROM AUTHOR]

Despite ongoing efforts to improve database accessibility, aggregated database vendors concede that they do not have complete control over document accessibility. Instead, they point to the responsibility of journal publishers to deliver articles in an accessible format. This may increase the likelihood that users with disabilities will encounter articles that are not compatible with a screen reader. To better understand the extent of the problem, a document accessibility audit was conducted of randomly selected articles from EBSCO's Library & Information Source database. Full-text articles from 12 library science journals were evaluated against two measures of screen reader compatibility: HTML format (the optimal format for screen readers) and PDF accessibility conformance. Findings showed inconsistencies in HTML format availability for articles in the selected journals. Additionally, the entire sample of PDF articles failed to meet the minimum standard of PDF Universal Accessibility of containing a tagged structure. However, all PDF articles passed accessibility permissions tests, so could be made accessible retroactively by a third party. [ABSTRACT FROM AUTHOR]

We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load O(N polylog N/p1/ρ*) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ* is the join's fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ* = 2 that requires a load of Ω (N/p1/3) to evaluate. Our lower bound provides solid evidence that the "AGM bound" alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether O(N polylog N/p1/ρ*) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load O(N / p1/ρ*), which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call "canonical edge cover" — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory. [ABSTRACT FROM AUTHOR]

The precise determination of the bond dissociation energy of N2 is crucial for thermochemistry database and theoretical calculations. However, there has been ongoing debate regarding its exact value. In this study, we used the velocity map imaging method combined with an extreme ultraviolet laser to measure the threshold fragment yield (TFY) spectra of N2 in the N(2D) + N(2D) photodissociation channels. By integrating the signals within a small circular area on the fragment velocity map images, we were able to obtain TFY spectra at nine different dissociation thresholds. These spectra are rotational state-resolved for the N2(J″) molecules and spin–orbit state-resolved for the dissociation channels involving N(2D) fragments. By employing the Wigner threshold law to simulate the TFY spectra and conducting statistical analysis on the comprehensive dataset, we determined the N2 bond dissociation energy to be 78 691.09 ± 0.15 cm−1. This work now places N2 among the few diatomic molecules with bond dissociation energies measured at sub-wavenumber precision. [ABSTRACT FROM AUTHOR]

Kohn–Sham density functional theory has been the most popular method in electronic structure calculations. To fulfill the increasing accuracy requirements, new approximate functionals are needed to address key issues in existing approximations. It is well known that nonlocal components are crucial. Current nonlocal functionals mostly require orbital dependence such as in Hartree–Fock exchange and many-body perturbation correlation energy, which, however, leads to higher computational costs. Deviating from this pathway, we describe functional nonlocality in a new approach. By partitioning the total density to atom-centered local densities, a many-body expansion is proposed. This many-body expansion can be truncated at one-body contributions, if a base functional is used and an energy correction is approximated. The contribution from each atom-centered local density is a single finite-range nonlocal functional that is universal for all atoms. We then use machine learning to develop this universal atom-centered functional. Parameters in this functional are determined by fitting to data that are produced by high-level theories. Extensive tests on several different test sets, which include reaction energies, reaction barrier heights, and non-covalent interaction energies, show that the new functional, with only the density as the basic variable, can produce results comparable to the best-performing double-hybrid functionals, (for example, for the thermochemistry test set selected from the GMTKN55 database, BLYP based machine learning functional gives a weighted total mean absolute deviations of 3.33 kcal/mol, while DSD-BLYP-D3(BJ) gives 3.28 kcal/mol) with a lower computational cost. This opens a new pathway to nonlocal functional development and applications. [ABSTRACT FROM AUTHOR]

Purpose: Previous surveys have reported that children with vitamin D deficiency were likely to suffer from early childhood caries (ECC). The aim of this systematic review and meta-analysis was to determine 1. whether the status of vitamin D is intrinsically related to the occurrence of ECC and 2. the optimal level of vitamin D for the prevention of ECC. Materials and Methods: The database of PubMed, Web of Science, Cochrane, Embase and Google scholar were searched for targeted literature. The eligibility criteria were observational studies in which children with ECC were compared to children without ECC in terms of their vitamin D status. Applying the Newcastle-Ottawa tool, study selection, data extraction, and risk of bias assessment were performed by 2 reviewers independently. Meta-analysis was performed using the Cochrane Collaboration’s Review Manager 5.4 software. Results: 501 articles were retrieved from the electronic databases; 11 studies were finally included in systematic review, 10 studies of which were submitted to meta-analysis. The 25(OH)D levels in the ECC group were statistically significantly lower compared with that in the caries-free group (WMD = -13.96, 95% CI: [-19.88,-8.03], p < 0.001), especially in regard to the association between S-ECC and vitamin D (WMD = -18.64, 95% CI: [-20.06,-17.22], p < 0.001). The subgroup analyses in terms of geographical region demonstrated that children with a level of 25(OH)D of 50–75 nmol/l were more likely to have ECC than those with over 75 nmol/l (OR = 1.42, 95% CI: [1.26,1.60], p < 0.001), with data from Asia and Europe combined for analysis Conclusions: The level of vitamin D was lower in children with ECC than in caries-free children, and the correlation between S-ECC and vitamin D was even stronger. The optimal 25(OH)D level for preventing occurrence and development of ECC ≥ 75 nmol/l. Thus, clinicians should view the development of early caries also from a systemic perspective. [ABSTRACT FROM AUTHOR]

This study documented three domains of journal research quality using metrics from six major publishers and two major database services for a large sample of kinesiology-related journals: Rigor/soundness, usage, and prestige. All journal metrics were highly skewed and variable. Median acceptance rates and initial review time were consistent with rigorous peer-review. Four variables were strongly interrelated (0.849 < r < 0.963) and indicate typical usage of 1 to 3 citations for articles in these journals. The 5-year Eigenfactor Score had a relatively weaker association (0.510 < r < 0.758) with the usage metrics, supporting it as an estimate of the prestige afforded these kinesiology journals in Scopus instead of usage. Care must be taken to interpret multiple journal metrics; taking into account the skew, large variability, and confounding factors such as journal size and subject. Differences across quality domains, metrics, and bias from numerous contextual factors refute the insidious misuse of any single journal metric as quality indictor for ranking journals. [ABSTRACT FROM AUTHOR]

One of the well-known limitations of Kohn–Sham density functional theory is the tendency to strongly underestimate bandgaps. Meta-generalized gradient approximations (mGGAs), which include the kinetic energy density in the functional form, have been shown to significantly alleviate this deficiency. In this study, we explore the mechanisms responsible for this improvement from the angle of the underlying local densities. We find that the highest occupied and lowest unoccupied states are distinct in the space of the underlying descriptors. The gap opening is compared to a simple scaling of the local density approximation, and two mechanisms responsible for opening the mGGA gaps are identified. First of all, the relatively large negative derivative of the functional form with respect to reduced kinetic energy tends to elevate the lowest unoccupied state. Second, the curvature of functional, which ensures that it is bounded, tends to lower the highest occupied state. Remarkably, these two mechanisms are found to be transferable over a large and diverse database of compounds. [ABSTRACT FROM AUTHOR]

In a recent paper, Becke et al. [J. Chem. Phys. 158, 151103 (2023)] presented a novel double hybrid density functional, "DH23," whose terms are based on good physics. Its 12 coefficients were trained on the GMTKN55 (general main-group thermochemistry, kinetics, and noncovalent interactions) chemical database of Goerigk et al. [Phys. Chem. Chem. Phys. 19, 32184 (2017)]. The lowest GMTKN55 "WTMAD2" error to date for any hybrid or double hybrid density functional was obtained (1.76 kcal/mol). Here, we make some revisions to DH23 and test its efficacy on reference data beyond GMTKN55, namely, organometallic reaction energies and barrier heights. The results confirm that DH23 is robust outside its training set. In the process, a slightly smaller GMTKN55 WTMAD2 of 1.73 kcal/mol is achieved. [ABSTRACT FROM AUTHOR]

The regularized and restored semi-local meta-generalized gradient approximation (meta-GGA) exchange–correlation functional r2SCAN [Furness et al., J. Phys. Chem. Lett. 11, 8208–8215 (2020)] is used to create adiabatic-connection-derived global double-hybrid functionals employing spin-opposite-scaled MP2. The 0-DH, CIDH, QIDH, and 0–2 type double-hybrid functionals are assessed as a starting point for further modification. Variants with 50% and 69% Hartree–Fock exchange (HFX) are empirically optimized (Pr2SCAN50 and Pr2SCAN69), and the effect of MP2-regularization (κPr2SCAN50) and range-separated HFX (ωPr2SCAN50) is evaluated. All optimized functionals are combined with the state-of-the-art London dispersion corrections D4 and NL. The resulting functionals are assessed comprehensively for their performance on main-group and metal-organic thermochemistry on 90 different benchmark sets containing 25 800 data points. These include the extensive GMTKN55 database, additional sets for main-group chemistry, and multiple sets for transition-metal complexes, including the ROST61, the MOR41, and the MOBH35 sets. As the main target of this study is the development of a broadly applicable, robust functional with low empiricism, special focus is put on variants with moderate amounts of HFX (50%), which are compared to the so far successful PWPB95-D4 (50% HFX, 20% MP2 correlation) functional. The overall best variant, ωPr2SCAN50-D4, performs well on main-group and metal-organic thermochemistry, followed by Pr2SCAN69-D4 that offers a slight edge for metal-organic thermochemistry and by the low HFX global double-hybrid Pr2SCAN50-D4 that performs robustly across all tested sets. All four optimized functionals, Pr2SCAN69-D4, Pr2SCAN50-D4, κPr2SCAN50-D4, and ωPr2SCAN50-D4, outperform the PWPB95-D4 functional. [ABSTRACT FROM AUTHOR]

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Life cycle assessment (LCA) is a tool used to evaluate the environmental impacts and resources used to manufacture a product. The present study proposes an innovative and unprecedented based on LCA of biodiesel production from the methyl transesterification of soybean oil catalyzed by eggshell-derived CaO and using the nonionic surfactant nonylphenol ethoxylate (NP6EO). Biodiesel was produced under mild conditions with a 1:4 methanol-to-oil ratio, 2 wt% CaO, 1 wt% NP6EO, and reaction times of 2 hours with surfactant and 3 hours without, with a yield of 95.18% and 97.75%, respectively. The life cycle impact assessment (LCIA) was performed using the SimaPro software and the Ecoinvent 3.6 database by implementing the CML-IA baseline method. Results indicated that the catalyst preparation process had the lowest environmental impact, while soybean oil used contributed the most across all impact categories. The surfactant-based process was 77% more eco-efficient than the non-surfactant process. Results also showed that surfactant concentration has more influence on impacts than additional electricity consumption due to a longer reaction time. Compared to traditional KOH-catalyzed biodiesel production, the use of CaO from eggshells and NP6EO demonstrated a lower environmental impact, suggesting this method is a promising alternative to conventional processes. [ABSTRACT FROM AUTHOR]

O-GlcNAc is a reversible post-translational modification found on serine and threonine residues of nucleocytoplasmic proteins. Four years ago, we released the O-GlcNAc Database (oglcnac.mcw.edu), a comprehensive catalog of O-GlcNAcylated proteins that has become one of the most cited resources in the field, with hundreds of unique users per month. We are now presenting an updated O-GlcNAc Database, which includes nearly 20,000 O-GlcNAcylated proteins and 48 species, marking substantial growth in data volume and scope. This paper presents the most noteworthy features implemented over the last year, often originating from feedback from the O-GlcNAc community. Among these features, we provide a brief overview of the database content, introduce our new protein viewer mode, and discuss the implementation of subcellular localization information and its applications in the O-GlcNAc score. We also provide an interface to use CytOVS, a tool designed to evaluate and sort O-GlcNAcome datasets derived from MS experiments. In conclusion, this new and improved O-GlcNAc Database represents a significant advancement in providing a comprehensive and expanded resource for researchers in the field of O-GlcNAc biology. [ABSTRACT FROM AUTHOR]

As a post-translational modification, protein glycosylation is critical in health and disease. O-Linked β-N-acetylglucosamine (O-GlcNAc) modification (O-GlcNAcylation), as an intracellular monosaccharide modification on proteins, was discovered 40 years ago. Thanks to technological advances, the physiological and pathological significance of O-GlcNAcylation has been gradually revealed and widely appreciated, especially in recent years. O-GlcNAc informatics has been quickly evolving. Clearly, O-GlcNAc informatics tools have not only facilitated O-GlcNAc functional studies, but also provided us a unique perspective on protein O-GlcNAcylation. In this article, we review O-GlcNAc-focused software tools and servers that have been developed for O-GlcNAc research over the past four decades. Specifically, we will (1) survey bioinformatics tools that have facilitated O-GlcNAc proteomics data analysis, (2) introduce databases/servers for O-GlcNAc proteins/sites that have been experimentally identified by individual research labs, (3) describe software tools that have been developed to predict O-GlcNAc sites, and (4) introduce platforms cataloging proteins that interact with the O-GlcNAc cycling enzymes (i.e., O-GlcNAc transferase and O-GlcNAcase). We hope these resources will provide useful information to both experienced researchers and new incomers to the O-GlcNAc field. We anticipate that this review provides a framework to stimulate the future development of more sophisticated informatic tools for O-GlcNAc research. [ABSTRACT FROM AUTHOR]

Integration of glycan-related databases between different research fields is essential in glycoscience. It requires knowledge across the breadth of science because most glycans exist as glycoconjugates. On the other hand, especially between chemistry and biology, glycan data has not been easy to integrate due to the huge variety of glycan structure representations. We have developed WURCS (Web 3.0 Unique Representation of Carbohydrate Structures) as a notation for representing all glycan structures uniquely for the purpose of integrating data across scientific data resources. While the integration of glycan data in the field of biology has been greatly advanced, in the field of chemistry, progress has been hampered due to the lack of appropriate rules to extract sugars from chemical structures. Thus, we developed a unique algorithm to determine the range of structures allowed to be considered as sugars from the structural formulae of compounds, and we developed software to extract sugars in WURCS format according to this algorithm. In this manuscript, we show that our algorithm can extract sugars from glycoconjugate molecules represented at the molecular level and can distinguish them from other biomolecules, such as amino acids, nucleic acids, and lipids. Available as software, MolWURCS is freely available and downloadable (https://gitlab.com/glycoinfo/molwurcs). [ABSTRACT FROM AUTHOR]

Carbohydrate Structure Database (CSDB) is a curated glycan data collection and a glycoinformatic platform. In this report, its database, analytical, and other components that have appeared for the recent years are reviewed. The major improvements were achieving close-to-full coverage on glycans from microorganisms, launching modules for glycosyltransferases and saccharide conformations, online glycan builder and 3D modeler, NMR simulator, NMR-based structure predictor, and other tools. [ABSTRACT FROM AUTHOR]

Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapid storage and retrieval of data with varying numbers of features and mixed data-types, ensurance of reliable and secure database transactions, extension of stored data row and column-wise and facilitation of data distribution. SQLite and DuckDB are embedded databases that fulfil these requirements. However, they utilize the structured query language (SQL) that hinders their implementation by the uninitiated user, and complicates their use in repetitive tasks due to the necessity of writing SQL queries. This study offers Omilayers, a Python package that encapsulates these two databases and exposes a subset of their functionality that is geared towards frequent and repetitive analytical procedures. Synthetic data were used to demonstrate the use of Omilayers and compare the performance of SQLite and DuckDB. [ABSTRACT FROM AUTHOR]

Stem cell therapies have in many respects revolutionized the way we explore and treat neurological diseases. Characterizing past and ongoing clinical trials using stem cells for neurological diseases will provide important insights for academia, industry and government. Based on the Trialtrove database, we retrospectively analyzed 530 clinical trials initiated by the end of 2023. [ABSTRACT FROM AUTHOR]

Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers. [ABSTRACT FROM AUTHOR]

The main objective of this study is to describe the process of collecting data extracted from Twitter (X) during the Brazilian presidential elections in 2022, encompassing the post-election period and the event of the attack on the buildings of the executive, legislative, and judiciary branches in January 2023. The work of collecting data took one year. Additionally, the study provides an overview of the general characteristics of the dataset created from 282 million tweets, named "The Interfaces Twitter Elections Dataset" (ITED-Br), the third most extensive dataset of tweets with political purposes. The process of collecting and creating the database for this study went through three major stages, subdivided into several processes: (1) A preliminary analysis of the platform and its operation; (2) Contextual analysis, creation of the conceptual model, and definition of Keywords and (3) Implementation of the Data Collection Strategy. Python algorithms were developed to model each primary collection type. The "token farm" algorithm, was employed to iterate over available API keys. While Twitter is generally a "public" access platform and fits into big data standards, extracting valuable information is not trivial due to the volume, speed, and heterogeneity of data. This study concludes that acquiring informational value requires expertise not only in sociopolitical areas but also in computational and informational studies, highlighting the interdisciplinary nature of such research. [ABSTRACT FROM AUTHOR]

This study aims to monitor and identify adverse events (AEs) associated with cetuximab, a drug used to treat various late-stage (metastatic) tumors, to improve patient safety and guide drug use. This study retrospectively analyzed the cases reported in the FDA adverse event reporting system (FAERS) related to the application of cetuximab from 2013 Q1 to 2022 Q4. Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the empirical Bayesian geometric mean (EBGM) algorithms, were employed to quantify the signals of cetuximab-associated AEs. A total of 8364225 reports were contained in the FAERS database, of which 5186 reports of cetuximab were identified as 'primary suspected (PS)' AEs. The application of cetuximab resulted in AEs in 22 system organ classes (SOCs), which preserved 176 significant disproportionality preferred terms (PTs) through the computation of four algorithms. The main SOCs (Skin and subcutaneous tissue disorders, investigations, metabolism and nutrition disorders, and blood and lymphatic system disorders) accounted for 58.63%. Some AEs were not on the drug label: speech disorder, intervertebral discitis, glomerulonephritis rapidly progressive and disseminated intravascular coagulation. This study identified new signals of adverse drug reactions (ADRs) other than those mentioned in the specification associated with cetuximab, providing valuable insights into the relationship between ADRs and cetuximab use. The findings highlight the importance of continuous surveillance to detect and manage AEs effectively, ultimately improving patient safety during treatment with cetuximab. [ABSTRACT FROM AUTHOR]