Nonequilibrium Reweighting on the Driven Diffusive Lattice Gas

The nonequilibrium reweighting technique, which was recently developed by the present authors, is used for the study of the nonequilibrium steady states. The renewed formulation of the nonequlibrium reweighting enables us to use the very efficient multi-spin coding. We apply the nonequilibrium reweighting to the driven diffusive lattice gas model. Combining with the dynamical finite-size scaling theory, we estimate the critical temperature Tc and the dynamical exponent z. We also argue that this technique has an interesting feature that enables explicit calculation of derivatives of thermodynamic quantities without resorting to numerical differences.Comment: Accepted for publication in J. Phys. A (Lett.

AMPA Receptor Phosphorylation and Synaptic Colocalization on Motor Neurons Drive Maladaptive Plasticity below Complete Spinal Cord Injury.

Clinical spinal cord injury (SCI) is accompanied by comorbid peripheral injury in 47% of patients. Human and animal modeling data have shown that painful peripheral injuries undermine long-term recovery of locomotion through unknown mechanisms. Peripheral nociceptive stimuli induce maladaptive synaptic plasticity in dorsal horn sensory systems through AMPA receptor (AMPAR) phosphorylation and trafficking to synapses. Here we test whether ventral horn motor neurons in rats demonstrate similar experience-dependent maladaptive plasticity below a complete SCI in vivo. Quantitative biochemistry demonstrated that intermittent nociceptive stimulation (INS) rapidly and selectively increases AMPAR subunit GluA1 serine 831 phosphorylation and localization to synapses in the injured spinal cord, while reducing synaptic GluA2. These changes predict motor dysfunction in the absence of cell death signaling, suggesting an opportunity for therapeutic reversal. Automated confocal time-course analysis of lumbar ventral horn motor neurons confirmed a time-dependent increase in synaptic GluA1 with concurrent decrease in synaptic GluA2. Optical fractionation of neuronal plasma membranes revealed GluA2 removal from extrasynaptic sites on motor neurons early after INS followed by removal from synapses 2 h later. As GluA2-lacking AMPARs are canonical calcium-permeable AMPARs (CP-AMPARs), their stimulus- and time-dependent insertion provides a therapeutic target for limiting calcium-dependent dynamic maladaptive plasticity after SCI. Confirming this, a selective CP-AMPAR antagonist protected against INS-induced maladaptive spinal plasticity, restoring adaptive motor responses on a sensorimotor spinal training task. These findings highlight the critical involvement of AMPARs in experience-dependent spinal cord plasticity after injury and provide a pharmacologically targetable synaptic mechanism by which early postinjury experience shapes motor plasticity

The fission yeast DNA structure checkpoint protein Rad26(ATRIP/LCD1/UVSD )accumulates in the cytoplasm following microtubule destabilization

BACKGROUND: DNA structure checkpoints are conserved eukaryotic signal transduction pathways that help preserve genomic integrity. Upon detecting checkpoint signals such as stalled replication forks or double-stranded DNA breaks, these pathways coordinate appropriate stress responses. Members of the PI-3 kinase related kinase (PIKK) family are essential elements of DNA structure checkpoints. In fission yeast, the Rad3 PIKK and its regulatory subunit Rad26 coordinate the detection of checkpoint signals with pathway outputs. RESULTS: We found that untreated rad26Δ cells were defective for two microtubule-dependent processes: chromosome segregation and morphogenesis. Interestingly, cytoplasmic accumulation of Rad26-GFP occurred following treatment with microtubule destabilizing drugs, but not during treatment with the genotoxic agent Phleomycin. Cytoplasmic accumulation of Rad26-GFP depended on Rad24, a 14-3-3 protein also required for DNA structure checkpoints and morphogenesis. Results of over expression and epistasis experiments confirm that Rad26 and Rad24 define a response to microtubule destabilizing conditions. CONCLUSION: Two DNA structure checkpoint proteins with roles in morphogenesis define a response to microtubule destabilizing conditions

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. METHODS: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. CONCLUSIONS: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA

Association of low arousal threshold obstructive sleep apnea manifestations with body fat and water distribution

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA

Quasiparticle transport in the vortex state of YBa_2Cu_3O_6.9

The effect of vortices on quasiparticle transport in cuprate superconductors was investigated by measuring the low temperature thermal conductivity of YBa_2Cu_3O_6.9 in magnetic fields up to 8 T. The residual linear term (as T \to 0) is found to increase with field, directly reflecting the occupation of extended quasiparticle states. A study for different Zn impurity concentrations reveals a good agreement with recent calculations for a d-wave superconductor, thereby shedding light on the nature of scattering by both impurities and vortices. It also provides a quantitative measure of the gap near the nodes.Comment: 4 pages, 2 included eps figures, significant new analysis wrt other experiments, to appear in Phys Rev Lett 29 March 199

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

OBJECTIVE: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. METHODS: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. RESULTS: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. CONCLUSIONS: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination