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.

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.

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.

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.

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.

Resources for Enhancing Alzheimer's Caregiver Health in Vietnam (REACH VN): Exploratory Analyses of Outcomes of a Cluster Randomized Controlled Trial to Test the Feasibility and Preliminary Efficacy of a Family Dementia Caregiver Intervention in Vietnam.

OBJECTIVE: Few support services and caregiving interventions exist to support family caregivers in low- and middle-income countries (LMIC). This paper presents exploratory analyses of outcomes of Resources for Enhancing Alzheimer's Caregiver Health in Vietnam (REACH VN). METHODS: A cluster randomized controlled trial (RCT) was conducted in Soc Son, a semi-rural area in Hanoi. Nine clusters with 60 caregivers were randomized to either an enhanced control group or REACH VN, an in-home, multicomponent, family caregiver support intervention delivered over two to three months. Outcomes were assessed at baseline and three months. RESULTS: Caregivers in the intervention group experienced a significantly greater reduction in frustration levels compared to those in the control group. There were differences in other outcomes (e.g., care recipient problem behaviors and associated caregiver bother) favoring the intervention condition that did not reach statistical significance. CONCLUSION: We found additional evidence that REACH VN is a promising intervention to improve family caregiver outcomes in Vietnam.

Condensates in RNA repeat sequences are heterogeneously organized and exhibit reptation dynamics.

Although it is known that RNA undergoes liquid-liquid phase separation, the interplay between the molecular driving forces and the emergent features of the condensates, such as their morphologies and dynamic properties, is not well understood. We introduce a coarse-grained model to simulate phase separation of trinucleotide repeat RNAs, which are implicated in neurological disorders. After establishing that the simulations reproduce key experimental findings, we show that once recruited inside the liquid droplets, the monomers transition from hairpin-like structures to extended states. Interactions between the monomers in the condensates result in the formation of an intricate and dense intermolecular network, which severely restrains the fluctuations and mobilities of the RNAs inside large droplets. In the largest densely packed high-viscosity droplets, the mobility of RNA chains is best characterized by reptation, reminiscent of the dynamics in polymer melts. Our work provides a microscopic framework for understanding liquid-liquid phase separation in RNA, which is not easily discernible in current experiments.

Resources for Enhancing Alzheimer's Caregiver Health in Vietnam (REACH VN): study protocol for a cluster randomized controlled trial to test the efficacy of a family dementia caregiver intervention in Vietnam.

BACKGROUND: Alzheimer's disease and related dementias (AD/ADRD) are a public health challenge for Vietnam because of its rapidly aging population. However, very few community-based programs exist to support people living with AD/ADRD and their family caregivers. Resources for Enhancing Alzheimer's Caregiver Health in Vietnam (REACH VN) is a culturally adapted family caregiver intervention shown in a pilot study to be feasible and promising in terms of preliminary efficacy. We describe the protocol for a larger cluster randomized controlled trial (RCT) to test the efficacy of REACH VN among family caregivers of people living with dementia in a semi-rural area outside of Hanoi, Vietnam. METHODS: Thirty-two clusters with approximately 350 caregivers will be randomized to either REACH VN intervention or enhanced usual care. REACH VN is a multicomponent intervention delivered in-home or by phone over the course of 2 to 3 months. To be eligible, family caregivers need to be ≥18 years old, be the person who provides the most day-to-day care for people living with dementia, and have a score ≥ 6 on the Zarit Burden Interview-4. The primary outcomes are caregiver burden (Zarit Burden Interview-12) and psychological distress (Patient Health Questionnaire-4). Secondary outcomes include caregiver somatic symptoms (Patient Health Questionnaire-15) and perceived stress (Perceived Stress Scale-10). These outcomes will be assessed at baseline, 3 months, and 6 months. Exploratory analyses to examine potential mediators of primary outcomes are also planned. DISCUSSION: To our knowledge, this is the first large-scale study to test the efficacy of a community-based family dementia caregiver intervention in Vietnam. Results from this study will help inform efforts to widely deliver the REACH VN intervention or similar community-based family dementia caregiver support programs in Vietnam and other low- and middle-income countries (LMIC). TRIAL REGISTRATION: ClinicalTrials.gov NCT04542317 . Registered on 9 September 2020.

Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson's Disease.

Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between "voluntary stopping" and "involuntary stopping" caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal "stop" and "walk" instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping.

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.

A Noncanonical Hippo Pathway Regulates Spindle Disassembly and Cytokinesis During Meiosis in Saccharomyces cerevisiae.

Meiosis in the budding yeast Saccharomyces cerevisiae is used to create haploid yeast spores from a diploid mother cell. During meiosis II, cytokinesis occurs by closure of the prospore membrane, a membrane that initiates at the spindle pole body and grows to surround each of the haploid meiotic products. Timely prospore membrane closure requires SPS1, which encodes an STE20 family GCKIII kinase. To identify genes that may activate SPS1, we utilized a histone phosphorylation defect of sps1 mutants to screen for genes with a similar phenotype and found that cdc15 shared this phenotype. CDC15 encodes a Hippo-like kinase that is part of the mitotic exit network. We find that Sps1 complexes with Cdc15, that Sps1 phosphorylation requires Cdc15, and that CDC15 is also required for timely prospore membrane closure. We also find that SPS1, like CDC15, is required for meiosis II spindle disassembly and sustained anaphase II release of Cdc14 in meiosis. However, the NDR-kinase complex encoded by DBF2/DBF20MOB1 which functions downstream of CDC15 in mitotic cells, does not appear to play a role in spindle disassembly, timely prospore membrane closure, or sustained anaphase II Cdc14 release. Taken together, our results suggest that the mitotic exit network is rewired for exit from meiosis II, such that SPS1 replaces the NDR-kinase complex downstream of CDC15.

Charge Density of Cation Determines Inner versus Outer Shell Coordination to Phosphate in RNA.

Divalent cations are often required to fold RNA, which is a highly charged polyanion. Condensation of ions, such as Mg2+ or Ca2+, in the vicinity of RNA renormalizes the effective charges on the phosphate groups, thus minimizing the intra RNA electrostatic repulsion. The prevailing view is that divalent ions bind diffusively in a nonspecific manner. In sharp contrast, we arrive at the exact opposite conclusion using a theory for the interaction of ions with the phosphate groups using RISM theory in conjunction with simulations based on an accurate three-interaction-site RNA model. The divalent ions bind in a nucleotide-specific manner using either the inner (partially dehydrated) or outer (fully hydrated) shell coordination. The high charge density Mg2+ ion has a preference to bind to the outer shell, whereas the opposite is the case for Ca2+. Surprisingly, we find that bridging interactions, involving ions that are coordinated to two or more phosphate groups, play a crucial role in maintaining the integrity of the folded state. Their importance could become increasingly prominent as the size of the RNA increases. Because the modes of interaction of divalent ions with DNA are likely to be similar, we propose that specific inner and outer shell coordination could play a role in DNA condensation, and perhaps genome organization as well.

Spectroscopic Studies of Mössbauer, Infrared, and Laser-Induced Luminescence for Classifying Rare-Earth Minerals Enriched in Iron-Rich Deposits.

Rare-earth (RE) phosphates often appear as an accessory phase in igneous or metamorphic rocks; however, these rocks are composed of myriad chemical elements and nuclides that interfere with the qualitative or quantitative analyses of the RE phosphates over a range of concentrations in the absence of a pretreatment. In addition, the limit of each analytical methodology constrains the approach as well as the usefulness of the results in geoscience applications. Here, we report the specific mineral characterization of RE-containing ores from Yen Phu mine, Vietnam, using a range of state-of-the-art spectroscopic techniques in conjunction with microscopy: Mössbauer spectroscopy, infrared microspectroscopy, time-resolved laser-induced fluorescence spectroscopy (TRLFS), and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Because the distribution of each element in the deposit differs, such combinatorial works are necessary and could lead to more plausible answers to questions surrounding the point of origin of RE elements. The results of our Mössbauer spectroscopic analysis indicate that the three ores sampled at different locations all contain magnetite-like, hematite-like, and iron(III) salts other than hematite. In addition, we confirmed the presence of phosphate around the grain boundary in the magnetite-like mineral phase by infrared microspectroscopic analysis. The present analytical findings of trace amounts of europium(III) using TRLFS suggest that the europium ions generate identical luminescence spectra despite being embedded in three different matrices of iron minerals. This demonstration highlights the benefits of combinatorial spectroscopic analyses to gain insights into the effects of the environment of REs on their solid-state chemistry and shows the potential utility of TRLFS as a resource mining tool. Further applications of this approach in the analytical screening of rocks and minerals are feasible.

Prevalence of Diabetes and Prediabetes among Children Aged 11-14 Years Old in Vietnam.

AIM: Diabetes in children is becoming more prevalent in some countries. However, in most countries, little is known about the epidemiology of this disease. This study is aimed at estimating the prevalence of type 1 and type 2 diabetes and prediabetes among children in Vietnam and examining factors associated with the conditions. METHODS: A total of 2880 students aged 11-14 years old were recruited for the survey, using a school-based and nationally representative sampling frame. Capillary blood samples of participants were collected to measure fasting glucose level, using glucose meter OneTouch Verio Pro+. Diabetes and impaired fasting plasma glucose were initially diagnosed based on the cut-off points of the American Diabetes Association criteria. Diabetes status and type of diabetes of participants were confirmed at a hospital. Additionally, anthropometric and blood pressure measurements were conducted following a standardized procedure. Multivariate logistic regression was used to examine the association between outcome and independent variables. RESULTS: The overall prevalence of diabetes among the participants was 1.04‰ (three cases), with 2 cases (0.75‰) diagnosed with type 1 diabetes (one known and one newly diagnosed) and 1 case newly diagnosed with type 2 diabetes (0.35‰). The prevalence of impaired fasting glucose was 6.1%. Body mass index, place of residence, and age were found to be significantly associated with the impaired fasting glucose condition in participants. CONCLUSION: The prevalence of type 1 and type 2 diabetes in children in Vietnam is lower than that in some other countries reported recently. However, there is a high prevalence in impaired fasting glucose, requiring attention from policymakers to take action to prevent the occurrence of the epidemic of type 2 diabetes in children in the future.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.