Salt-Dependent Self-Association of Trinucleotide Repeat RNA Sequences.

Repeat RNA sequences self-associate to form condensates. Simulations of a coarse-grained single-interaction site model for (CAG)n (n = 30 and 31) show that the salt-dependent free energy gap, ΔGS, between the ground (perfect hairpin) and the excited state (slipped hairpin (SH) with one CAG overhang) of the monomer for (n even) is the primary factor that determines the rates and yield of self-assembly. For odd n, the free energy (GS) of the ground state, which is an SH, is used to predict the self-association kinetics. As the monovalent salt concentration, CS, increases, ΔGS and GS increase, which decreases the rates of dimer formation. In contrast, ΔGS for shuffled sequences, with the same length and sequence composition as (CAG)31, is larger, which suppresses their propensities to aggregate. Although demonstrated explicitly for (CAG) polymers, the finding of inverse correlation between the free energy gap and RNA aggregation is general.

Competition between Stacking and Divalent Cation-Mediated Electrostatic Interactions Determines the Conformations of Short DNA Sequences.

Interplay between divalent cations (Mg2+ and Ca2+) and single-stranded DNA (ssDNA) and double-stranded DNA (dsDNA), as well as stacking interactions, is important in nucleosome stability and phase separation in nucleic acids. Quantitative techniques accounting for ion-DNA interactions are needed to obtain insights into these and related problems. Toward this end, we created a sequence-dependent computational TIS-ION model that explicitly accounts for monovalent and divalent ions. Simulations of the rigid 24 base-pair (bp) dsDNA and flexible ssDNA sequences, dT30 and dA30, with varying amounts of the divalent cations show that the calculated excess number of ions around the dsDNA and ssDNA agree quantitatively with ion-counting experiments. Using an ensemble of all-atom structures generated from coarse-grained simulations, we calculated the small-angle X-ray scattering profiles, which are in excellent agreement with experiments. Although ion-counting experiments mask the differences between Mg2+ and Ca2+, we find that Mg2+ binds to the minor grooves and phosphate groups, whereas Ca2+ binds specifically to the minor groove. Both Mg2+ and Ca2+ exhibit a tendency to bind to the minor groove of DNA as opposed to the major groove. The dA30 conformations are dominated by stacking interactions, resulting in structures with considerable helical order. The near cancellation of the favorable stacking and unfavorable electrostatic interactions leads to dT30 populating an ensemble of heterogeneous conformations. The successful applications of the TIS-ION model are poised to confront many problems in DNA biophysics.

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

The first epidemiology of urolithiasis in Northern Vietnam: Urinary stone composition, age, gender, season, and clinical features study.

OBJECTIVES: The aim of this study is to analyze the compositions of urinary stones and investigate their distributions in different ages, genders, seasons, and clinical features of Northern Vietnamese patients. METHODS: A total of 231 patients with urinary stones from Northern Vietnam were collected and analyzed composition from 1/2021-12/2022. For all patients, age, sex, stone location, stone side, urine pH, and hospitalized date (month) were collected. RESULTS: Kidney stones are more frequently found in men than women with the male: female urinary stones ratio in this study being 1.96:1. The highest stone prevalence appeared between 60 and 69 years old. The most common stone composition was calcium oxalate, followed by calcium phosphate, uric acid, struvite, and cysteine. Mix stones of CaOx and CaP were more prevalent than pure stones. Males submitted more CaOx, CaP, and UA stones, whereas females were susceptible to infectious stones. Stones were more frequently found on the left side of the upper urinary tract (51.9%) than on the right side (27.3%) and lower urinary tract (7.8%). Cultural tendency leads to a smaller number of stones during the Lunar new year (February), and Ghost month (August).

Microwave Imaging Based on a Subspace-based Two-step Iterative Shrinkage/Thresholding Method.

This paper presents a subspace-based two-step iterative shrinkage/thresholding method(S-TwIST) based on the Distorted Born iterative method (DBIM) to improve the performance of the original TwIST inverse algorithm. This method retrieves the deterministic part of the induced current from inhomogeneous Green's function operator leading to more accurate total field calculation at each iteration step than that of the original TwIST. Both inverse algorithms have been evaluated with a set of synthetic geometries with fine structures. Compared with TwIST, the results show that S-TwIST has superior accuracy in multiple objects profile (εerr=0.1454%) and 1/16λ resolution at 2GHz. Also, S-TwIST is more robust to initial guess, which means it is less likely to become unstable when the inversion procedure starts without initial guess.

Machine Learning Derived Lifting Technique in People without Low Back Pain.

This paper presents a method for determining the number of lifting techniques used by healthy individuals through the analysis of kinematic data collected from 115 participants utilizing an motion capture system. The technique utilizes a combination of feature extraction and Ward's method to analyse the range of motion in the sagittal plane of the knee, hip, and trunk. The findings identified five unique lifting techniques in people without low back pain. The multivariate analysis of variance statistical analysis reveals a significant difference in the range of motion in the trunk, hip and knee between each cluster for healthy people (F (12, 646) = 125.720, p < 0.0001).Clinical Relevance- This information can assist healthcare professionals in choosing effective treatments and interventions for those with occupational lower back pain by focusing rehabilitation on specific body parts associated with problematic lifting techniques, such as the trunk, hip, or knee, which may lead to improved pain and disability outcomes, exemplifying precision medicine.

Toward Benchmark-Quality Ab Initio Predictions for 3d Transition Metal Electrocatalysts: A Comparison of CCSD(T) and ph-AFQMC.

Generating accurate ab initio ionization energies for transition metal complexes is an important step toward the accurate computational description of their electrocatalytic reactions. Benchmark-quality data is required for testing existing theoretical methods and developing new ones but is complicated to obtain for many transition metal compounds due to the potential presence of both strong dynamical and static electron correlation. In this regime, it is questionable whether the so-called gold standard, coupled cluster with singles, doubles, and perturbative triples (CCSD(T)), provides the desired level of accuracy─roughly 1-3 kcal/mol. In this work, we compiled a test set of 28 3d metal-containing molecules relevant to homogeneous electrocatalysis (termed 3dTMV) and computed their vertical ionization energies (ionization potentials) with CCSD(T) and phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) in the def2-SVP basis set. A substantial effort has been made to converge away the phaseless bias in the ph-AFQMC reference values. We assess a wide variety of multireference diagnostics and find that spin-symmetry breaking of the CCSD wave function and the PBE0 density functional correlate well with our analysis of multiconfigurational wave functions. We propose quantitative criteria based on symmetry breaking to delineate correlation regimes inside of which appropriately performed CCSD(T) can produce mean absolute deviations from the ph-AFQMC reference values of roughly 2 kcal/mol or less and outside of which CCSD(T) is expected to fail. We also present a preliminary assessment of density functional theory (DFT) functionals on the 3dTMV set.

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study.

World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with "neighbors" (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics.

Evaluation and validation of a commercial ELISA versus the in vitro toxin neutralization assay for determination of diphtheria anti-toxin in human serum.

Introduction. Diphtheria is a potentially life-threatening infection and remains endemic in many low- and middle-income countries (LMICs). A reliable, low-cost method for serosurveys in LMICs is warranted to estimate the accurate population immunity to control diphtheria.Hypothesis/Gap Statement. The correlation between the ELISA results against diphtheria toxoid and the gold standard diphtheria toxin neutralization test (TNT) values is poor when ELISA values are <0.1 IU ml-1, which results in inaccurate estimates of susceptibility in populations when ELISA is used for measuring antibody levels.Aim. To explore methods to accurately predict population immunity and TNT-derived anti-toxin titres from ELISA anti-toxoid results.Methodology. A total of 96 paired serum and dried blood spot (DBS) samples collected in Vietnam were used for comparison of TNT and ELISA. The diagnostic accuracy of ELISA measurement with reference to TNT was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and other parameters. Optimal ELISA cut-off values corresponding to TNT cut-off values of 0.01 and 0.1 IU ml-1 were identified by ROC analysis. A method based on the multiple imputation approach was also applied to estimate TNT measurements in a dataset that only included ELISA results. These two approaches were then applied to ELISA results previously generated from 510 subjects in a serosurvey in Vietnam.Results. The ELISA results on DBS samples showed a good diagnostic performance compared to TNT. The cut-off values for ELISA measurement corresponding to the TNT cut-off values of 0.01 IU ml-1 were 0.060 IU ml-1 in serum samples, and 0.044 IU ml-1 in DBS samples. When a cut-off value of 0.06 IU ml-1 was applied to the 510 subject serosurvey data, 54 % of the population were considered susceptible (<0.01 IU ml-1). The multiple imputation-based approach estimated that 35 % of the population were susceptible. These proportions were much larger than the susceptible proportion estimated by the original ELISA measurements.Conclusion. Testing a subset of sera by TNT combined with ROC analysis or a multiple imputation approach helps to adjust ELISA thresholds or values to assess population susceptibility more accurately. DBS is an effective low-cost alternative to serum for future serological studies for diphtheria.

Odd-even disparity in the population of slipped hairpins in RNA repeat sequences with implications for phase separation.

Low-complexity nucleotide repeat sequences, which are implicated in several neurological disorders, undergo liquid-liquid phase separation (LLPS) provided the number of repeat units, n, exceeds a critical value. Here, we establish a link between the folding landscapes of the monomers of trinucleotide repeats and their propensity to self-associate. Simulations using a coarse-grained Self-Organized Polymer (SOP) model for (CAG)n repeats in monovalent salt solutions reproduce experimentally measured melting temperatures, which are available only for small n. By extending the simulations to large n, we show that the free-energy gap, ΔGS, between the ground state (GS) and slipped hairpin (SH) states is a predictor of aggregation propensity. The GS for even n is a perfect hairpin (PH), whereas it is a SH when n is odd. The value of ΔGS (zero for odd n) is larger for even n than for odd n. As a result, the rate of dimer formation is slower in (CAG)30 relative to (CAG)31, thus linking ΔGS to RNA-RNA association. The yield of the dimer decreases dramatically, compared to the wild type, in mutant sequences in which the population of the SH decreases substantially. Association between RNA chains is preceded by a transition to the SH even if the GS is a PH. The finding that the excitation spectrum-which depends on the exact sequence, n, and ionic conditions-is a predictor of self-association should also hold for other RNAs (mRNA for example) that undergo LLPS.

Profiling and Quantification of Anthocyanins in Purple-Pericarp Sweetcorn and Purple-Pericarp Maize.

Purple-pericarp sweetcorn accessions, derived from crossing purple-pericarp maize with white shrunken2 sweetcorn, were assessed for differences in anthocyanin profile at both sweetcorn eating stage and at full kernel maturity. The 'Tim1' sweetcorn line developed a similar total anthocyanin concentration to its 'Costa Rica' parent when assessed at sweetcorn-eating stage. At full maturity it surpassed the purple maize parent, but this was mainly due to the presence of starch diluting the anthocyanin concentration of the latter. The anthocyanin/colour relationship was affected by both total anthocyanin concentration and the ratio of cyanidin- to pelargonidin-based anthocyanins. Malonylation of anthocyanins was also found to vary and did not appear to be linked with either cyanidin:pelargonidin ratio or total anthocyanin concentration. In addition, anthocyanin synthesis was affected by kernel maturity at harvest, with colour development increasing in conjunction with a progression of anthocyanin development across the kernel surface. Pigmentation was present in the aleurone, pericarp and vitreous endosperm of kernels of the purple-pericarp maize parent and purple-pericarp sweetcorn accessions when fully mature, but pigmentation was only apparent in the pericarp at sweetcorn-eating stage. Importantly for consumers, anthocyanin pigmentation covered almost the entire kernel surface at sweetcorn-eating stage.

Breaking the tight genetic linkage between the a1 and sh2 genes led to the development of anthocyanin-rich purple-pericarp super-sweetcorn.

The existence of purple-pericarp super-sweetcorn based on the supersweet mutation, shrunken2 (sh2), has not been previously reported, due to its extremely tight genetic linkage to a non-functional anthocyanin biosynthesis gene, anthocyaninless1 (a1). Generally, pericarp-pigmented starchy purple corn contains significantly higher anthocyanin. The development of purple-pericarp super-sweetcorn is dependent on breaking the a1-sh2 tight genetic linkage, which occurs at a very low frequency of < 1 in 1000 meiotic crossovers. Here, to develop purple-pericarp super-sweetcorn, an initial cross between a male purple-pericarp maize, 'Costa Rica' (A1Sh2.A1Sh2) and a female white shrunken2 super-sweetcorn, 'Tims-white' (a1sh2.a1sh2), was conducted. Subsequent self-pollination based on purple-pericarp-shrunken kernels identified a small frequency (0.08%) of initial heterozygous F3 segregants (A1a1.sh2sh2) producing a fully sh2 cob with a purple-pericarp phenotype, enabled by breaking the close genetic linkage between the a1 and sh2 genes. Resulting rounds of self-pollination generated a F6 homozygous purple-pericarp super-sweetcorn (A1A1.sh2sh2) line, 'Tim1'. Genome sequencing revealed a recombination break between the a1 and yz1 genes of the a1-yz1-x1-sh2 multigenic interval. The novel purple-pericarp super-sweetcorn produced a similar concentration of anthocyanin and sugar as in its purple-pericarp maize and white super-sweetcorn parents, respectively, potentially adding a broader range of health benefits than currently exists with standard yellow/white sweetcorn.

iNSP-GCAAP: Identifying nonclassical secreted proteins using global composition of amino acid properties.

Nonclassical secreted proteins (NSPs) refer to a group of proteins released into the extracellular environment under the facilitation of different biological transporting pathways apart from the Sec/Tat system. As experimental determination of NSPs is often costly and requires skilled handling techniques, computational approaches are necessary. In this study, we introduce iNSP-GCAAP, a computational prediction framework, to identify NSPs. We propose using global composition of a customized set of amino acid properties to encode sequence data and use the random forest (RF) algorithm for classification. We used the training dataset introduced by Zhang et al. (Bioinformatics, 36(3), 704-712, 2020) to develop our model and test it with the independent test set in the same study. The area under the receiver operating characteristic curve on that test set was 0.9256, which outperformed other state-of-the-art methods using the same datasets. Our framework is also deployed as a user-friendly web-based application to support the research community to predict NSPs.

Seroepidemiology and Carriage of Diphtheria in Epidemic-Prone Area and Implications for Vaccination Policy, Vietnam.

In 2019, a community-based, cross-sectional carriage survey and a seroprevalence survey of 1,216 persons 1-55 years of age were conducted in rural Vietnam to investigate the mechanism of diphtheria outbreaks. Seroprevalence was further compared with that of an urban area that had no cases reported for the past decade. Carriage prevalence was 1.4%. The highest prevalence, 4.5%, was observed for children 1-5 years of age. Twenty-seven asymptomatic Coerynebacterium diphtheriae carriers were identified; 9 carriers had tox gene-bearing strains, and 3 had nontoxigenic tox gene-bearing strains. Child malnutrition was associated with low levels of diphtheria toxoid IgG, which might have subsequently increased child carriage prevalence. Different immunity patterns in the 2 populations suggested that the low immunity among children caused by low vaccination coverage increased transmission, resulting in symptomatic infections at school-going age, when vaccine-induced immunity waned most. A school-entry booster dose and improved infant vaccination coverage are recommended to control transmissions.

Leadership in a pandemic: Do more able managers keep firms out of trouble?

The Coronavirus crisis has led to unprecedented economic shocks to the corporate world and challenged how corporate management contributes to business resilience amid the pandemic. Employing a novel measure of managerial ability constructed for a large sample of U.S. publicly listed firms, we document that firms led by higher managerial ability exhibit lower stock return volatility, higher operating performance, and lower levels of default risk amid the pandemic. A difference-in-differences analysis suggests that the impact of managerial ability on firm performance is stronger during the pandemic than during the pre-pandemic period. The effect of managerial competency on corporate resiliency is more pronounced among firms that have high exposure to COVID-19. In addition, firms led by high managerial competency management are associated with higher stock liquidity and are less likely to exhibit employment, healthcare, safety, and consumer protection related violations amid the pandemic.

Risk Factors for Death of Burn Patients With Acute Respiratory Distress Syndrome.

The aim of this study was to investigate factors independently affecting outcomes of post-burn ARDS patients at the time of ARDS onset. A prospective study was conducted on 66 patients with ARDS, treated in the ICU at the Le Huu Trac National Burns Hospital in Hanoi, Viet Nam, from 2014 to 2017. Patients were divided into a survivor and non-survivor group. Demographic criteria, burn severity, inhalation injury, clinical and subclinical features at ARDS onset were compared between the two groups. The results showed that overall mortality of ARDS patients was 62.12%. Logistic regression analysis indicated that at the time of ARDS onset, serum lactate level (OR=6.71), blood platelet count (OR=.99), static lung compliance (OR=.73) and driving pressure (OR=1.69) were independent risk factors for death, while patients' demographics, burn severity and ARDS severity did not significantly affect the mortality rate.

Le but de cette étude est d'évaluer les facteurs indépendants influençant le pronostic d'un SDRA survenant chez un brûlé, présents dès sa survenue. Il s'agit d'une étude prospective conduite sur 66 patients hospitalisés en réanimation de l'hôpital brûlologique national Le Huu Trac de Hanoi entre 2014 et 2017, répartis en groupes survivants et décédés. Nous avons comparé les données démographiques, la gravité de la brûlure, l'existence d'une inhalation de fumée, les données cliniques comme paracliniques au moment du diagnostic de SDRA. La mortalité globale était de 62,12%. En régression logistique, la lactatémie (OR 6,71), le compte de plaquettes (OR 0,99), la compliance pulmonaire statique (OR 0,73) et la pression de travail (OR 1,69) sont des facteurs indépendants de mortalité quand ni les données-patient, ni la sévérité de la brûlure ou celle du SDRA lui-même n'affectent la mortalité.

Correction: Structural, magnetic and hyperthermia properties and their correlation in cobalt-doped magnetite nanoparticles.

[This corrects the article DOI: 10.1039/D1RA07407E.].

Application of Machine Learning in Epileptic Seizure Detection.

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

Cytomegalovirus infection disrupts the influence of short-chain fatty acid producers on Treg/Th17 balance.

BACKGROUND: Both the gut microbiota and chronic viral infections have profound effects on host immunity, but interactions between these influences have been only superficially explored. Cytomegalovirus (CMV), for example, infects approximately 80% of people globally and drives significant changes in immune cells. Similarly, certain gut-resident bacteria affect T-cell development in mice and nonhuman primates. It is unknown if changes imposed by CMV on the intestinal microbiome contribute to immunologic effects of the infection. RESULTS: We show that rhesus cytomegalovirus (RhCMV) infection is associated with specific differences in gut microbiota composition, including decreased abundance of Firmicutes, and that the extent of microbial change was associated with immunologic changes including the proliferation, differentiation, and cytokine production of CD8+ T cells. Furthermore, RhCMV infection disrupted the relationship between short-chain fatty acid producers and Treg/Th17 balance observed in seronegative animals, showing that some immunologic effects of CMV are due to disruption of previously existing host-microbe relationships. CONCLUSIONS: Gut microbes have an important influence on health and disease. Diet is known to shape the microbiota, but the influence of concomitant chronic viral infections is unclear. We found that CMV influences gut microbiota composition to an extent that is correlated with immunologic changes in the host. Additionally, pre-existing correlations between immunophenotypes and gut microbes can be subverted by CMV infection. Immunologic effects of CMV infection on the host may therefore be mediated by two different mechanisms involving gut microbiota. Video Abstract.

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward's method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward's method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.