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The safety of the geotechnical environment around subways is vitally important for their sustainability. Ground settlement is a very common threat to the safe operation of subways and is related to subway traffic vibrations. Taking Nanjing subway line 10 as an example, field tests, discrete element simulations, and a grey relational analysis are carried out to study the deformation mechanism of the underlying sandy soil induced by vibrations. The results show that the vibration load of Nanjing subway Line 10 is mainly concentrated in the vertical direction. The particles' coordination number below the subway increases under the vibration load, while the coordination number on the side of the tunnel decreases, which may cause a shear dilatancy effect. Among the five microscopic indexes, the local porosity and the coordination number are closely related to the deformation. Although the deformation of the underlying sandy soil is quite small under the subway traffic load, it will accumulate under frequent loading and then lead to urban engineering geological disasters. [ABSTRACT FROM AUTHOR]

Background: Limited evidence has explored the impact of traumatic brain injury (TBI) on posttraumatic epilepsy with control cohort for comparison. In addition, we could not find any review to identify the effect of TBI on the outcomes. Thus, we conducted this study to compare the risk of epilepsy between individuals with TBI and without TBI. Methods: Systematic and comprehensive search was carried out in the following databases and search engines: EMBASE, Cochrane, MEDLINE, ScienceDirect, and Google Scholar from 1954 until January 2022. The Newcastle Ottawa (NO) Scale was utilized to assess the risk of bias. Meta-analysis was carried out using the random-effects model, and pooled odds ratio (OR) along with 95% CI was reported. Results: In total, we included 10 studies satisfying inclusion criteria. Most studies had good to satisfactory quality. The pooled OR was 4.25 (95% CI, 1.77-10.25; I 2 = 100%), indicating that the individuals with TBI had 4.25 times higher risk of having epilepsy than individuals without TBI, and this association was statistically significant (P =.001). Subgroup analysis based on the years of follow-up revealed that the patients within 5 years post-TBI had the highest risk of epilepsy (pooled OR = 7.27; 95% CI, 3.61-14.64). Conclusion: Individuals with TBI had a significantly higher risk of epilepsy than the individuals without TBI, irrespective of the duration of the injury. Hence, long-term follow-up of the individuals with TBI is necessary to prevent any adverse consequences. [ABSTRACT FROM AUTHOR]

The culture of Zen comes into two diverse paths in China and Japan. Compared to China, the Japanese Zen culture has characteristics of natural, plain, sensitive, vanity and silence. It is unrestrained with feeble and desolation. And these characteristics are presented in Japanese domestic ceramic in the unique beauty of its domestic ceramic.

AhFAD2 is a key enzyme catalyzing the conversion of oleic acid into linoleic acid. The high oleic acid characteristic of peanut mainly comes from the homozygous recessive mutation of AhFAD2A and AhFAD2B genes (aabb). However, even in high-oleic-acid varieties with the aabb genotype, the oleic acid content of seeds with different maturity varies significantly. Therefore, in addition to AhFAD2A and AhFAD2B, other FAD2 members or regulators may be involved in this process. Which FAD2 genes are involved in the regulatory processes associated with seed maturity is still unclear. In this study, four stable lines with different genotypes (AABB, aaBB, AAbb, and aabb) were used to analyze the contents of oleic acid and linoleic acid at different stages of seed development in peanut. Three new AhFAD2 genes (AhFAD2–7, AhFAD2–8, and AhFAD2–9) were cloned based on the whole-genome sequencing results of cultivated peanuts. All peanut FAD2 genes showed tissue preference in expression; however, only the expression level of AhFAD2-7 was positively correlated with the linoleic acid concentration in peanut seeds. These findings provide new insights into the regulation of oleic acid accumulation by maturity, and AhFAD2-7 plays an important role in the maturity dependent accumulation of oleic acid and linoleic acid in peanut. [ABSTRACT FROM AUTHOR]

In order to reasonably evaluate the stability of the embankment supported by the rigid pile composite foundation, a nonlinear algorithm calculation and analysis method for the stability of the nanosilica powder soft soil pile foundation is proposed. First, the soft soil foundation reinforced by the composite structure of "piles (prestressed pipe piles, (cement fly-ash gravel, CFG) piles)—pile caps—geotextile pads" is analyzed through the soft soil embankment test section of the Wenfu high-speed railway. Second, the compressive modulus under one-dimensional confining compression is calculated using the layered summation method. Finally, the deep lateral deformation of the pile foundation soil is measured by an inclinometer to evaluate the actual displacement of the pile foundation soil. In the standard method, the empirical coefficient 1.1–1.7 is multiplied on the basis of the calculation result of this method to correct the calculation error. Combined with the test results, it is shown that nanosilica powder can give full play to its excellent characteristics: promoting hydration speed and hydration degree through the reaction of pozzolan refining and consuming Ca(OH)2 crystals produced by cement hydration. Soft soils and modified soft soils: a certain amount of nanosilica powder can significantly improve the strength of soft soil at different ages. [ABSTRACT FROM AUTHOR]

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10−4) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders. [ABSTRACT FROM AUTHOR]

Many monitoring indexes affect the health condition of foundation pits to different extents. How to use a massive on-site monitoring dataset to quantitatively assess the health of foundation pits is a problem that deserves due consideration. This paper proposes a foundation pit health assessment model based on the fuzzy analytical hierarchy process (AHP) method. First, factors affecting the health of foundation pits are classified by the AHP, a hierarchical factor system for foundation pit health assessment is established, and the index scale method is used to assign weights to assessment factors at different hierarchical levels. Combined with the fuzzy mathematical method, the membership functions of four health degree levels (A to D) are constructed, and the determination range of each health degree level is given to realize the quantitative calculation of the health condition from the bottom-level assessment factor to the overall foundation pit. Considering that each assessment factor involves many monitoring points and different monitoring data, a comprehensive assessment operator is also constructed to highlight the most adverse impact. Finally, the proposed model is used to perform a health assessment of an actual foundation pit project, and the variation in the foundation pit health during the entire monitoring period is obtained. The health grade of the foundation pit is determined to be B, which is a basically healthy condition consistent with the on-site inspection results. [ABSTRACT FROM AUTHOR]

Abstract Background Radiation-induced lung injury (RILI) is an important dose-limiting toxicity during thoracic radiotherapy. The purpose of this study is to investigate single photon emission computed tomography (SPECT) perfusion-weighted functional dose-volume histogram (FDVH) for predicting RILI in non-small cell lung cancer (NSCLC) patients treated with definitive chemoradiotherapy. Methods Fifty-seven locally advanced NSCLC patients receiving chemoradiotherapy were enrolled prospectively. Patients had treatment scans and dose calculations to provide a standard dose-volume histogram (DVH). Fusion of SPECT and computed tomography scans provided perfusion-weighted FDVH and associated functional dosimetric parameters (relative volumes of functional lung receiving more than a threshold dose of 5 – 60 Gy at increments of 5 Gy [FV5 – FV60]). The predictive abilities of FDVH and DVH were calculated and compared based on the area under receiver operating characteristic (ROC) curve (AUC). Results The accumulative incidence of ≥ 2 grade RILI was 19.3% with a median follow-up of 12 months. Univariate analysis showed that the functional (FV5 – FV60) and standard (V5 – V40) parameters were associated with RILI (all value of p Conclusions Functional metrics are identified as reliable predictors for RILI, however, this observation still needs to be further verified using a larger sample size.

Abstract Background Functional acupoint specificity is crucial to the clinical efficacy of acupuncture treatment, such as pain relief. Whether acupuncture needling at a peripheral acupoint produces distinct patterns of brain responses remains controversial. Results This fMRI study employed the complex network analysis (CNA) to test the hypothesis that acupuncture stimulation at an acupoint correspondingly induced activity changes in one or more intrinsic or resting-state brain networks. Built upon the sustained effect of acupuncture and its time-varying characteristics, we constructed a dynamic encoding system with the hub anchored at the posterior cingulate cortex and precuneus (PCC/pC). We found that needling at two visual acupoints (GB37 and BL60) and a non-visual acupoint (KI8) induced a spatially converging brain response, which overlapped at the PCC/pC. We also found distinct neural modulations during and after acupoint stimulation. During this period, the PCC/pC interacted with a visual resting-state network in different patterns. Furthermore, there was a delayed functional correspondence between the intrinsic visual network and manipulation over the visual acupoints (i.e., GB37 or BL60), but not the non-visual acupoint (KI8) via the PCC/pC, implicating a specific temporal-spatial encoding/decoding mechanism underlying the post-effect of acupuncture. Conclusions This study provided an integrated view exploring the functional specificity of acupuncture in which both the needling sensation and the following neural cascades may contribute to the overall effect of acupuncture through dynamic reconfiguration of complex neural networks. fMRI, acupoints, posterior cingulate cortex, precuneus, temporal-spatial encoding, resting-state networks

It has been reported that one night of acute sleep deprivation (SD) could induce brain structural changes at the synaptic and neuronal levels in animal studies, and could lead to white matter microstructure and cortical thickness change in human neuroimaging studies. In this study, we focused on changes of brain gray matter density (GMD) after one night of acute SD, which has not been explored previously. Twenty-three normal young participants completed the experiment. Each participant underwent twice T1-weighted structural image scanning with one at 08:00 after normal sleep [resting wakeful (RW)] and the other at 08:00 after 24 h of SD. Using voxel-based morphometry (VBM) analysis by FSL-VBM software, we compared GMD between RW and SD. In addition, the gray matter volume (GMV) and cortical thickness (CT) were also calculated based on volumetric and surface measures with FreeSurfer software. The psychomotor vigilance test (PVT) and the Karolinska Sleepiness Scale (KSS) were performed and evaluated for correlation analysis with GMD, GMV, and CT of the significant regions. Our results showed that the GMD in the right frontal pole (FP), right superior frontal gyrus (SFG), and right middle frontal gyrus significantly increased and GMV and CT in the right temporal pole (TP) significantly decreased after 24 h of acute SD. SD-induced changes in GMD in the right middle frontal gyrus were positively correlated with the changes of KSS scores (Spearman's correlation r = 0.625, p = 0.0014, Bonferroni correction with p < 0.05/25). Taken together, our findings suggested that one night of acute SD could induce substantial brain structure changes and the alterations in GMD in the right middle frontal gyrus (MFG) might be implicated in sleepiness after SD. [ABSTRACT FROM AUTHOR]

Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen's Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification. [ABSTRACT FROM AUTHOR]

Total sleep deprivation (TSD) is common in modern society leading to deterioration of multiple aspects of cognition. Dynamic interaction effect of circadian rhythmicity and homeostatic sleep pressure on sustained attention have been intensively investigated, while how this effect was represented on performance and cerebral responses to working memory, another important element of many neurobehavioral tasks, was not well elucidated. Thirty‐six healthy subjects with intermediate chronotype performed the Sternberg working‐memory task (SWMT) while undergoing functional magnetic resonance imaging every 2 hr from 10:00 p.m. on the first day to 6:00 a.m. on the second day. Using data from three imaging sessions (10:00 p.m., 04:00 a.m., and 06:00 a.m.), we found that the slowest SWMT reaction time and weakest cerebral responses were not at the end of TSD (06:00 a.m.) but during the early morning (04:00 a.m.) hours of the TSD. In addition, during this worst period of TSD, reaction time for the SWMT were found to be negatively correlated with task‐related activation in the angular gyrus and positively correlated with the degree of negative correlation between the control and default networks. Our results revealed a rebound of SWMT reaction time and cerebral responses after the mid‐time point of regular biological sleep night and provided more evidence that different cognitive tasks are differentially affected by sleep loss and circadian rhythmicity. [ABSTRACT FROM AUTHOR]

Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample t -tests with family-wise error correction were used to compare these features that occur more than 500 times during training steps between LPE patients and HCs. Four FCs, (1) between left medial part of orbital frontal cortex (mOFC) and right mOFC, (2) between the left rectus and right postcentral gyrus, (3) between the right insula and left pallidum, and (4) between the right middle part of temporal pole and right inferior part of temporal gyrus showed significant group difference. These results demonstrate that resting-state brain FC might be a discriminating feature to distinguish LPE patients from HCs. These classification features, especially the FC between bilateral mOFC, provide underlying abnormal central functional targets in LPE etiology, which offers a novel alternative target for future intervention in LPE treatment. [ABSTRACT FROM AUTHOR]

Sleep deprivation (SD) impairs the ability of response inhibition. However, few studies have explored the quantitative prediction of performance impairment using Magnetic Resonance Imaging (MRI) data. In this study, structural MRI data were used to predict the change in response inhibition performance (ΔSSRT) measured by a stop-signal task (SST) after 24 h of SD in 52 normal young subjects. For each subject, T1-weighted MRI data were acquired and the gray matter (GM) volumes were calculated using voxel-based morphometry (VBM) analysis. First, the regions in which GM volumes correlated with ΔSSRT were explored. Then, features were extracted from these regions and the prediction process was performed using a linear regression model with four-fold cross-validation. We found that the GM volumes of the left middle frontal gyrus (L_MFG), pars opercularis of right inferior frontal gyrus (R_IFG), pars triangularis of left inferior frontal gyrus, pars opercularis of right rolandic area, left supplementary motor area (L_SMA), left hippocampus, right lingual gyrus, right postcentral gyrus and left middle temporal gyrus (L_MTG) could predict the ΔSSRT with a low mean square error of 0.0039 ± 0.0011 and a high Pearson’s correlation coefficient between the predicted and actual values of 0.948 ± 0.0503. In conclusion, our results demonstrated that a linear combination of structural MRI data could accurately predict the change in response inhibition performance after SD. Further studies with larger sample sizes and more comprehensive sample may be necessary to validate these findings. [ABSTRACT FROM AUTHOR]

Arachis hypogaea L. geocarpy is a unique feature different from other legume plants. Flowering and fertilization occur above ground, while the following processes of pod formation and development proceed in the soil. The zygote divides only few times to develop into pre-embryo and then further embryo developmental process stops when the gynoecium is exposed to light condition or normal day/night period. In this study, eight phytochrome genes were identified in two wild peanuts (four in Arachis duranensis and four in Arachis ipaensis). Using RACE and homologous cloning, the full CDS of AhphyA, AhphyA-like, AhphyB and AhphyE were acquired in cultivated peanut. Protein structure analysis showed that the conservative coding domains of phytochromes from a number of other plant species were found in these proteins. The C-terminal of AhphyA, AhphyA-like and AhphyB could interact with phytochrome-interacting factor 3 in vitro. The expression patterns of these genes in various tissues were analyzed by qRT-PCR, and significant differences were observed. Interestingly, the expression levels of AhphyA-like changed significantly during gynophore growth and early pod development. Furthermore, protein accumulation patterns of AhphyA and AhphyB in gynophore were different during early pod development stages in that AhphyA and AhphyB proteins were not detected in S1 and S2 gynophores, while significant accumulation of AhphyA and AhphyB were detected in S3 gynophore. These results provided evidence that phytochromes mediated light signal transduction may play key roles in peanut geocarpy development. [ABSTRACT FROM AUTHOR]

Primary dysmenorrhea (PDM), characterized by cramping pain in the lower abdomen, is a common gynecological disorder in women of child-bearing age. An increasing number of neuroimaging studies have emphasized that PDM is associated with functional and structural abnormalities in the regions related to the default mode network (DMN). Based on resting-state functional magnetic resonance imaging (fMRI), the aim of this study was to use amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) to investigate changes of the intrinsic brain activity in the DMN in PDM. Pearson correlation analysis was conducted to assess relationships between the neuroimaging findings and clinical symptoms. Forty-eight PDM patients and thirty-eight matched healthy controls participated in this study. Compared to healthy controls, PDM patients had increased ALFF in the precuneus, dorsomedial prefrontal cortex (dmPFC) and anterior cingulate cortex (ACC) and decreased ALFF in the thalamus. PDM patients also had decreased connectivity between the precuneus and left dmPFC and right ACC, while increased connectivity between the precuneus and left thalamus. In addition, the ALFF in the left dmPFC in PDM patients positively correlated with disease duration. Our findings provide further evidence of the DMN-related abnormalities in PDM patients which might contribute to a better understanding of the pathophysiology of this disease. [ABSTRACT FROM AUTHOR]

Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme. [ABSTRACT FROM AUTHOR]