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Background: Sleep disorders are associated with temporomandibular disorders (TMDs). Limited studies have focused on excessive daytime sleepiness (EDS) and its impact on jaw functions in TMD patients. Objective: The aim of the present investigation was to identify the impact of EDS on pain and jaw function in TMD patients. Methods: A total of 338 TMD patients (50 males and 288 females) was included. The Epworth Sleepiness Scale (ESS) was used to classify patients into EDS group (score ≥ 10) and non‐EDS group (score < 10). The Jaw Functional Limitation Scale 8‐item (JFLS‐8) was used to assess the severity of jaw dysfunction. Pain intensity was evaluated using the Visual Analogue Scale (VAS). Anxiety and depression were evaluated using the Generalised Anxiety Disorder 7‐item (GAD‐7) and the Patient Health Questionnaire 9‐item (PHQ‐9). All included patients were diagnosed with pain‐related TMD (PT), intra‐articular TMD (IT) or combined TMD (CT). Results: Compared with non‐EDS patients, EDS patients exhibited more severe jaw dysfunction, greater pain intensity and higher PHQ‐9 scores (p <.05). Multivariate analyses showed that EDS (B = 3.69), female gender (B = 3.69), and elevated GAD‐7 score (B = 0.73) were significantly associated with an increased score on the JFLS‐8 (p <.05). Moreover, bivariate logistic regression analysis indicated a significant relationship between EDS and PT (OR = 2.70, p =.007). Conclusion: The presence of EDS was more closely related to PT, but the causal relationship between them needs to be further confirmed. More concern and intervention to alleviate poor sleep quality might be highlighted during the treatment of TMD, especially PT subtype. [ABSTRACT FROM AUTHOR]

Hierarchical BiVO 4 /Ti 3 C 2 microcapsules report enhanced solar-driven water evaporation and photothermal-catalytic H 2 evolution activity. The flexible aerogel endows efficient evaporation rates (2.01 kg m−2 h−1) under 1kW m−2 solar irradiation, and the complementary heterostructures present photocatalytic H 2 production evolved after 4 h of reaction is 9.39 μmol. [Display omitted] Desalination processes frequently require a lot of energy to generate freshwater and energy, which depletes resources. Their reliance on each other creates tension between these two vital resources. Herein, hierarchical MXene nanosheets and bismuth vanadate (Ti 3 C 2 /BiVO 4)-derived microcapsules were synthesized for a photothermal-induced photoredox reaction for twofold applications, namely, solar-driven water evaporation and hydrogen (H 2) production. For this purpose, flexible aerogels were fabricated by introducing Ti 3 C 2 /BiVO 4 microcapsules in the polymeric network of natural rubber latex (NRL-Ti 3 C 2 /BiVO 4), and a high evaporation rate of 2.01 kg m−2 h−1 was achieved under 1-kW m−2 solar intensity. The excellent performance is attributed to the presence of Ti 3 C 2 /BiVO 4 microcapsules in the polymeric network, which provides balanced hydrophilicity and broadband sun absorption (96 %) and is aimed at plasmonic heating with microscale thermal confinement tailored by heat transfer simulations. Notably, localized plasmonic heating at the catalyst active sites of the Ti 3 C 2 /BiVO 4 heterostructure promotes enhanced photocatalytic H 2 production evolved after 4 h of reaction is 9.39 μmol, which is highly efficient than pure BiVO 4 and Ti 3 C 2. This method turns the issue of water–fuel crisis into a collaborative connection, presenting avenues to collectively address the anticipated demand rather than fostering competition. [ABSTRACT FROM AUTHOR]

For multiple resonance (MR) type thermally activated delayed fluorescence (TADF) emitters, electron cloud distributions of their π‐conjugated planes are crucial for determining their eventual performance. Currently, modulation attempts of MR‐TADF emitters are mainly inside the π‐conjugated planes. Possible out‐of‐plane interactions may also significantly impact the photophysical properties, but the exploration is quite limited. Here, a novel concept of using out‐of‐plane (e.g., π–π and lone pair‐π) interactions to introduce spatial perturbation (SPPT) to improve TADF performance is proposed. Two newly developed MR‐TADF emitters, namely, o‐BNPO and BNPO, which both consist of a popular MR framework, DtBuCzB, and diphenylphosphine oxide (DPPO), are compared in depth. In particular, for o‐BNPO, evident π–π interaction is observed between one side of the DtBuCzB π‐conjugated plane and a phenyl ring from DPPO, and lone pair‐π interaction with the oxygen atom from DPPO is noticed on the other side, resulting in significantly accelerated reverse intersystem crossing and better TADF without sacrificing narrowband emission features. Ultimately, in organic light‐emitting diodes with sensitizer‐free emitting layers, both emitters achieve similar narrowband emissions, while the o‐BNPO‐based device demonstrates a much higher external quantum efficiency of 36% and milder efficiency roll‐off. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients. [ABSTRACT FROM AUTHOR]

The study of the constitutive model under stress loading for rock after undergoing freeze-thaw weathering cycles (FTWCs) treatment has important theoretical significance for the site operation and numerical calculation of rock mass engineering in cold regions. In this work, a series of FTWCs treatment tests of sandstone were carried out, and the evolution characteristics of the T 2 spectrum distribution curve for sandstone before and after FTWCs treatment were analyzed utilizing nuclear magnetic resonance (NMR) technology. The schematic diagram of freeze-thaw damage evolution for sandstone was drawn, and the damage evolution mechanism was analyzed. Evolution laws of the stress-strain curves, peak and residual strength, peak and residual strain, and Young's modulus of sandstone under different FTWCs and confining pressures were analyzed by conventional triaxial compression tests. A piecewise constitutive model under triaxial stress loading for sandstone after undergoing FTWCs treatment was established, and the model considered the significant influence of FTWCs on the nonlinear deformation in the compaction stage and residual strength. By introducing error analysis indexes, the proposed model and published models were compared with the experimental data, it was found that the proposed model's performance is better than the published models, which indicated that the proposed model has good performance and strong universality. [ABSTRACT FROM AUTHOR]

Rapid and effective control of non‐compressible massive hemorrhage poses a great challenge in first‐aid and clinical settings. Herein, a biopolymer‐based powder is developed for the control of non‐compressible hemorrhage. The powder is designed to facilitate rapid hemostasis by its excellent hydrophilicity, great specific surface area, and adaptability to the shape of wound, enabling it to rapidly absorb fluid from the wound. Specifically, the powder can undergo sequential cross‐linking based on "click" chemistry and Schiff base reaction upon contact with the blood, leading to rapid self‐gelling. It also exhibits robust tissue adhesion through covalent/non‐covalent interactions with the tissues (adhesive strength: 89.57 ± 6.62 KPa, which is 3.75 times that of fibrin glue). Collectively, this material leverages the fortes of powder and hydrogel. Experiments with animal models for severe bleeding have shown that it can reduce the blood loss by 48.9%. Studies on the hemostatic mechanism also revealed that, apart from its physical sealing effect, the powder can enhance blood cell adhesion, capture fibrinogen, and synergistically induce the formation of fibrin networks. Taken together, this hemostatic powder has the advantages for convenient preparation, sprayable use, and reliable hemostatic effect, conferring it with a great potential for the control of non‐compressible hemorrhage. [ABSTRACT FROM AUTHOR]

Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel recurrent neural network (RNN) and can overcome some shortcomings of traditional RNNs. Based on NSNP systems, we developed two RC variants for time series classification, RC-SNP and RC-RMS-SNP, which are without and integrated with reservoir model space (RMS), respectively. The two RC variants use NSNP systems as the reservoirs and can be easily implemented in the RC framework. The proposed two RC variants were evaluated on 17 benchmark time series classification datasets and compared with 16 state-of-the-art or baseline classification models. The comparison results demonstrate the effectiveness of the proposed two RC variants for time series classification tasks. [ABSTRACT FROM AUTHOR]

Because of the large-scale variation, counting in scenes of different densities is an extremely difficult task. In this paper, based on the attention mechanism, we propose a new self-weighted multi-scale fusion network structure named SMFNet to solve the problem of multi-scale changes and can significantly improve the effect of crowd counting in monitoring scene. The proposed SMFNet uses VGG as the backbone network to extract multi-scale features, uses a SMFNet as the neck to fuse multiple-scale features, and uses the atrous spatial pyramid pooling (ASPP) network and ordinary convolution as the head to generate both the attention map and the density map. The attention map highlighting crowd regions in the image contributes to a high-quality density map, and the density map records the crowd distribution. The number of crowd in the image can be obtained by summing the pixel values of the density map. We conduct experiments on three crowd counting datasets and one vehicle counting dataset to show that our proposed SMFNet can improve the state-of-the-art counting methods. [ABSTRACT FROM AUTHOR]

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the dynamic analysis of neuroimaging data. We apply VCCP to various simulation datasets and to two fMRI datasets: a reading task and an anxiety inducing experiment. In particular, for the former dataset, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerer's Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the article is available from CRAN. for this article are available online. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN–XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models. [ABSTRACT FROM AUTHOR]

Objective: Autoimmune myocarditis is caused by both innate and adaptive immune responses. Many studies have found that myeloid‐derived suppressor cells (MDSCs) suppress T‐cell responses and reduce immune tolerance, while MDSCs may serve as a key player in inflammatory responses and pathogenesis in variety of autoimmune diseases. However, research into the role of MDSCs in experimental autoimmune myocarditis (EAM) remains lacking. Methods and Results: We discovered that the expansion of MDSCs in EAM was closely related to the severity of myocardial inflammation. At an early stage of EAM, both adoptive transfer (AT) and selective depletion of MDSCs could inhibit the expression of IL‐17 in CD4+ cells and downregulate the Th17/Treg ratio, alleviating excessive inflammation of EAM myocarditis. In another experiment, in addition, MDSCs transferred after selective depletion could increase IL‐17 and Foxp3 expressions in CD4+ cells, as well as the Th17/Treg ratio, contributing to the aggravation of myocardial inflammation. MDSCs promoted the Th17 cell induction under Th17‐polarizing conditions in vitro but suppressed Treg expansion. Conclusion: These findings suggest that MDSCs play a plastic role in sustaining mild inflammation in EAM by shifting Th17/Treg balance. [ABSTRACT FROM AUTHOR]

Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics. [ABSTRACT FROM AUTHOR]

In the hexaploid wheat genome, there are three Gα genes, three Gβ and twelve Gγ genes, but the function of Gβ in wheat has not been explored. In this study, we obtained the overexpression of TaGB1 Arabidopsis plants through inflorescence infection, and the overexpression of wheat lines was obtained by gene bombardment. The results showed that under drought and NaCl treatment, the survival rate of Arabidopsis seedlings' overexpression of TaGB1-B was higher than that of the wild type, while the survival rate of the related mutant agb1-2 was lower than that of the wild type. The survival rate of wheat seedlings with TaGB1-B overexpression was higher than that of the control. In addition, under drought and salt stress, the levels of superoxide dismutase (SOD) and proline (Pro) in the wheat overexpression of TaGB1-B were higher than that of the control, and the concentration of malondialdehyde (MDA) was lower than that of the control. This indicates that TaGB1-B could improve the drought resistance and salt tolerance of Arabidopsis and wheat by scavenging active oxygen. Overall, this work provides a theoretical basis for wheat G-protein β-subunits in a further study, and new genetic resources for the cultivation of drought-tolerant and salt-tolerant wheat varieties. [ABSTRACT FROM AUTHOR]

Water transport is vital for the durability of ultra-high performance concrete (UHPC) in engineering, but its absorption behavior requires further comprehension. This study investigates the impact of silica fume (SF) and metakaolin (MK) on water absorption in UHPC matrix with a high volume of limestone powder (LS) under two curing temperatures, and the variation in water transport with pore size obtained by low field nuclear magnetic resonance (LF-NMR). Relations between cumulative water absorption with other properties were discussed, and the pore size distribution (PSD) measured by Mercury intrusion porosimetry (MIP) was compared with that determined by LF-NMR. Results showed that MK outperformed SF in reducing water absorption in UHPC matrix, containing 30% LS under steam curing due to the synergistic effect between MK and LS. The incorporation of LS greatly affected the water absorption process of UHPC matrix. In samples without LS, capillary and gel pores absorbed water rapidly within the first 6 h and slowly from 6 h to 48 h simultaneously. However, in samples with 30% LS, gel pore water decreased during water absorption process due to the coarsening of gel pores. MK was able to suppress gel pore deterioration caused by the addition of a large amount of LS. Compared with PSD measured by MIP, NMR performed better in detecting micropores (<10 nm). [ABSTRACT FROM AUTHOR]

China emits unproportionately high concentrations of CO2 and, due to rapid population growth and industrialization, suffers from air, water, and soil pollution. However, many of these challenges for sustainable growth are being vigorously addressed, and China aims at a CO2 emission peak by 2030 and carbon neutrality by 2060 ("dual carbon policy"). In addition, nation-wide programs attempt to achieve reforestation and ecological restoration. By 2025, core elements of a "bioeconomy" and a circular economy are expected to be ready. Many of these programs extend into China's international "belt-and-road" initiative (BRI). In this article, we briefly describe the present achievements of China's environmental solutions and the country's visions for a "digital, eco-friendly civilization." Key points: • China's steps towards environmental cleaning, eco-protection, and decarbonization. • Steps towards a future bioeconomy. [ABSTRACT FROM AUTHOR]

Objective. The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone. Method. Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model. Results. The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set. Conclusion. The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone. [ABSTRACT FROM AUTHOR]

Background: Motor imagery training (MIT) has been widely used to improve hemiplegic upper limb function in stroke rehabilitation. The effectiveness of MIT is associated with the functional neuroplasticity of the motor network. Currently, brain activation and connectivity changes related to the motor recovery process after MIT are not well understood. Aim: We aimed to investigate the neural mechanisms of MIT in stroke rehabilitation through a longitudinal intervention study design with task‐based functional magnetic resonance imaging (fMRI) analysis. Methods: We recruited 39 stroke patients with moderate to severe upper limb motor impairment and randomly assigned them to either the MIT or control groups. Patients in the MIT group received 4 weeks of MIT therapy plus conventional rehabilitation, while the control group only received conventional rehabilitation. The assessment of Fugl‐Meyer Upper Limb Scale (FM‐UL) and Barthel Index (BI), and fMRI scanning using a passive hand movement task were conducted on all patients before and after treatment. The changes in brain activation and functional connectivity (FC) were analyzed. Pearson's correlation analysis was conducted to evaluate the association between neural functional changes and motor improvement. Results: The MIT group achieved higher improvements in FM‐UL and BI relative to the control group after the treatment. Passive movement of the affected hand evoked an abnormal bilateral activation pattern in both groups before intervention. A significant Group × Time interaction was found in the contralesional S1 and ipsilesional M1, showing a decrease of activation after intervention specifically in the MIT group, which was negatively correlated with the FM‐UL improvement. FC analysis of the ipsilesional M1 displayed the motor network reorganization within the ipsilesional hemisphere, which correlated with the motor score changes. Conclusions: MIT could help decrease the compensatory activation at both hemispheres and reshape the FC within the ipsilesional hemisphere along with functional recovery in stroke patients. [ABSTRACT FROM AUTHOR]

Background: Degenerative joint disease (DJD) can be associated with disc displacement (DD) in temporomandibular disorder (TMD) patients. However, the relationship between different types of DDs and DJD remains unclear. Objectives: To investigate the odds ratios of different types of sagittal and coronal DDs confirmed by magnetic resonance imaging (MRI) and DJD confirmed by cone‐beam computed tomography (CBCT) in TMD patients. Methods: Radiographic data from 69 males and 232 females were collected for analysis. CBCT was used to diagnose DJD, with criteria including erosion, osteophytes, generalised sclerosis and cysts in the joint. Eight types of DDs were evaluated by sagittal and coronal MRIs: NA, no abnormality; SW, sideways; ADDR, anterior with reduction; ADDR+SW; ADDNR, anterior without reduction; ADDNR + SW; single SW; PDD, posterior; PDD + SW. The odds ratios of DJD in joints with different types of DDs were determined after joint correlation, age and gender adjustment. Results: Compared with NA, the odds ratio of DJD in ADDR was 2.397 (95% CI [confidence interval]: 1.070–5.368), ADDR + SW was 4.808 (95% CI: 1.709–3.528), ADDNR was 29.982 (95% CI: 15.512–57.950) and ADDNR + SW was 25.974 (95% CI: 12.743–52.945). Erosion was significantly increased in ADDR, ADDR + SW, ADDNR and ADDNR + SW; osteophytes were significantly increased in ADDR + SW, ADDNR and ADDNR + SW; and generalised sclerosis and cysts were significantly increased in ADDNR and ADDNR + SW. There were no significant associations between single SW, PDD, PDD + SW and the DJD. Conclusions: ADDR, ADDR+SW, ADDNR and ADDNR+SW were associated with DJD. ADDNR had a significantly higher prevalence of DJD than ADDR. There were no significant relationships between single SW, PDD, PDD + SW and the DJD. [ABSTRACT FROM AUTHOR]

Ultrasound-stimulated piezo-electrocatalysis has been studied for a period; however, the mechanism is still unclear mainly due to the coexistence with other multiple effects like sonocatalysis, which was usually ignored. In this work, with the non-piezoelectric H2Ti3O7 nanowires following the same experimental process in piezo-electrocatalysis, the sonocatalytic reduction performance of CO2 is investigated. By applying vibration under the excitation of ultrasound with various frequencies and powers, it is found that CO is the ultimate product with a selectivity of 100%, and the optimal CO yield of 8.3 μmol g−1 h−1 is achieved with the addition of sacrificial agents. The H2Ti3O7 catalysts are also found to present a good recycling utilization ability. This work indicates that the sonocatalysis effect may exist in the piezo-electrocatalytic process using the ultrasonic excitation, which is suggested to be taken into consideration when exploring the mechanism of piezo-electrocatalysis in the future. [ABSTRACT FROM AUTHOR]

Purpose: To explore the association between the physical health (PH) and physical activity behavior (PAB) of children aged 3 to 6 years, and to provide a basis for the scientific planning of children's physical activity behavior in kindergarten. Methods: A total of 18041 children in China aged 3 to 6 years were selected as the research subject. The PH monitoring indicators were measured according to "The National Physical Fitness Measurement Standards Manual" (Preschool Children Version) (NPFMSM), and PAB was reported by teachers. The chi-square test was used to test for differences in PH and PAB across groups, the percentile method was used to rank PAB, correlation analysis was used to analyze the association between components of PH and PAB, and multiple linear regression was used to determine the independent association between PAB and PH. Results: Participants in this study exhibited poor PH status, and the detection rates of failure were 16.4% for boys and 16.8% for girls, and showed a decreasing trend with increasing age. The grade difference and sex difference in PH components were statistically significant (P<0.01). PAB changes to static activities with increasing age, mainly to static indoor physical activities (SIPAs). The regression effects of the total duration of physical activity (TDPA) and dynamic physical activity (DPA) on PH score and physical fitness (PF) score were significant (P < 0.01) but not statistically significant with static physical activity (SPA) (P>0.05). Dynamic outdoor physical activity (DOPA) is the core factor affecting children's PH, and is significantly positively correlated with the components of PH. Conclusion: PAB in kindergarten can improve children's PH, and reasonable planning of PAB in kindergarten has a targeted effect on PH promotion. [ABSTRACT FROM AUTHOR]

A new method based on the interferometric pseudo-lateral-shearing method is proposed to evaluate the pitch variation of a large-scale planar variable-line-spacing (VLS) grating. In the method, wavefronts of the first-order diffracted beams from a planar VLS grating are measured by a commercial Fizeau form interferometer. By utilizing the differential wavefront of the first-order diffracted beam before and after the small lateral shift of the VLS grating, the pitch variation of the VLS grating can be evaluated. Meanwhile, additional positioning errors of the grating in the lateral shifting process could degrade the measurement accuracy of the pitch variation. To address the issue, the technique referred to as the reference plane technique is also introduced, where the least squares planes in the wavefronts of the first-order diffracted beams are employed to reduce the influences of the additional positioning errors of the VLS grating. The proposed method can also reduce the influence of the out-of-flatness of the reference flat in the Fizeau interferometer by taking the difference between the measured positive and negative diffracted wavefronts; namely, self-calibration can be accomplished. After the theoretical analysis and simulations, experiments are carried out with a large-scale VLS grating to verify the feasibility of the proposed methods. Furthermore, the evaluated VLS parameters are verified by comparing them with the readout signal of an absolute surface encoder employing the evaluated VLS grating as the scale for measurement. [ABSTRACT FROM AUTHOR]

Fish is one of the most important sources of high-quality protein in the human diet. The survival situation (premortem) and freshness (postmortem) of the fish are important indexes in assessing the quality of fish products. A series of traditional analytical techniques have been employed for quality assessments in fish products but suffer from either cost issues or practical limitations. With the capabilities of easy operations and rapid detections, biosensors emerge as strong candidates for the analysis of fish products quality. This review discusses, in a critical way, recent advancements in the field of biosensors for evaluating the quality of fish products. In this review, the designing and fabrication of biosensors are introduced, including the glucose biosensors, biosensors for waterless live fish transportation, electrochemical, and gas biosensors. In addition, we discussed the limitations of the various types of biosensors and critically highlighted their future development trends. As proven by publications in the field, biosensors offer the advantages of simplicity of operation and speed of analysis, which can improve the efficiency of detection of fish product quality. The application of new design ideas and technologies has also led to improved measurement accuracy of biosensors. Nevertheless, including the limited lifespan, damage by environmental factors, the single freshness marker measured and the high cost, among others still limit the large scale application of biosensors in practice. • The design, fabrication and principles of different types of biosensors used to detect the quality of fish products are described. • Biosensors offer the advantages of simplicity of operation and speed of analysis, which can improve the efficiency of detection of fish product quality. • Applying new design ideas and technologies has also led to improved measurement accuracy and portability of biosensors. • Prospects and challenges in biosensors in fish quality testing. [ABSTRACT FROM AUTHOR]

A photo-controlled approach is developed to regulate the interpenetrating polymer network (IPN) topology by varying the connecting structure between the first and second networks. The approach is based on multifunctional inimer (Vinyl-oNB-Br) possessing three moieties, i.e., an acrylate-based double bond for incorporation within a polymer network, a Br group for grafting polymerization to get connect-IPN (c-IPN), and an o-nitrobenzyl spacer for photocleaving to convert the c-IPN to disconnected-IPN (d-IPN) with UV light irradiation. Such design allows for finely controlling the connection degree between two networks. A systematic study on the mechanical property of a series of samples with different connection degrees thus can be conducted. The results reveal that decreasing the connecting degree between two networks of IPN made a negligible contribution to materials' mechanical properties. [ABSTRACT FROM AUTHOR]

Freeze-and-thaw (F&T) weathering cycles induced by day–night and seasonal temperature changes cause a large number of rock mass engineering disasters in cold areas. Investigating the impact of F&T weathering process on the strength and deformation characteristics of frozen–thawed rocks is therefore of critical scientific importance for evaluating the stability and optimizing the design of rock mass engineering in these areas. In this research, the evolution characteristics of F&T damage were analyzed based on T2 spectrum distribution curves of sandstone specimens before and after F&T weathering cycles. The coupling impact of the quantity of F&T weathering cycles and confining pressure on pre-peak and post-peak deformation behaviors of sandstone specimens were analyzed in detail. By introducing the confining pressure increase factor (CPIF), the impact of confining pressure on the triaxial compressive strength (TCS) of sandstone specimens after undergoing different quantities of F&T weathering cycles was further investigated. A novel strength evolution model was proposed that could effectively describe the coupling impact of the quantity of F&T weathering cycles and confining pressure on TCS of rocks after undergoing the F&T weathering process. The proposed strength evolution model was cross-verified with experimental data from the published literature and all correlation coefficients were above 0.95, which proved that the strength evolution model proposed in this paper was reasonable; in addition, this model has strong applicability. [ABSTRACT FROM AUTHOR]

Gut microbiome is related to many major human diseases, and it is of great significance to study the differences in the structure of gut microbiome under different conditions. Due to the phenomenon of zero expansion of the flora data, geometric mean of pairwise ratios (GMPR) was firstly used to normalize the gut microbiome data. This study proposes an improved Spectrum algorithm using the type 2 diabetes mellitus (T2DM) dataset being taken as example. Firstly, a similarity matrix based on feature weighting was used, which can avoid ignoring the weights occupied by the different eigenvalue sizes corresponding to each sample/feature in that sample; secondly, the Laplacian matrix was replaced by the Hessian matrix, which can avoid the sensitivity problem of traditional spectral clustering; the original K-means algorithm was replaced by the ISODATA clustering algorithm, the number of clustering centers K can be effectively adjusted. The experimental results showed that normalized mutual information (NMI) is 0.423, Davies-Boulding index (DBI) is 4.751, the Calinski-Harabasz index (CH) is 25.541, Rand index (RI) is 0.835 and the adjusted Rand index (ARI) is 0.019, which was improved compared with the effect before the improvement, and the algorithm could identify the structural differences in the intestinal flora of different types of patients, unearth the key bacteria of the gut microbiome. [ABSTRACT FROM AUTHOR]

C16H12ClFO5, monclinic, P21/c (no. 14), a = 14.7536(6) Å, b = 7.8281(3) Å, c = 14.0501(7) Å, β = 111.174 (5) ° , V = 1513.13(12) Å3, Z = 4, Rgt(F) = 0.0367, wRref(F2) = 0.0865, T = 296(2) K. [ABSTRACT FROM AUTHOR]

A miniaturized wideband duplex antenna is presented in this paper. The presented duplex antenna is developed based on a dipole antenna. By changing the structure of the dipole antenna, the antenna has dual‐band characteristic. The inductive type feed port was used to improve the impedance matching and isolation. Moreover, to further improve port isolation, all ports of the antenna are connected with miniature filters. The design has been verified by measurements, which show good agreements with simulations. The measured results show that the duplex antenna has good performance in two operating bands of 1.5–2.5 GHz and 5.6–5.95 GHz, the return loss of corresponding ports is greater than 14.5 dB, and the isolation between the two ports is greater than 29.5 dB. [ABSTRACT FROM AUTHOR]

Because optical and synthetic aperture radar (SAR) images are complementary, their registration has received extensive attention in joint applications. However, robust optical and SAR image registration is challenging due to substantial geometric and radiometric differences. To address this problem, we propose a fast and robust registration algorithm for optical and SAR images based on a novel feature type known as the adjacent self-similarity (ASS). The ASS feature of the pixelwise feature representation is defined to quickly and finely capture the structural features of the image. The ASS feature is extracted by using an optimized offset mean filtering method in a neighborhood of the unit pixel radius to accelerate and refine calculations and the local statistics weighted difference operation to suppress coherent speckles. Based on the ASS feature, we extract the minimum self-similarity map (SSM) and the index map, which are robust against radiometric differences and speckles. Then, based on the excellent characteristics of the two maps, we propose a feature detector based on suppressing the local nonmaximum on the minimum SSM and a novel feature descriptor based on calculating the distribution histogram of the index map in a log-polar grid. In addition, we design a rotation invariance enhancement method for the descriptor to improve the rotation invariance robustness of the algorithm. We conduct experiments with both synthetic and real image pairs. The registration results demonstrate that the proposed algorithm has good scale and rotation invariance, as well as good antinoise ability, and that the algorithm performs better than existing state-of-the-art algorithms in terms of registration robustness, accuracy, and efficiency. The registration results on two real optical and SAR image pairs with complex image scenes show the adaptability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Accurately monitoring the State of Health (SOH) of lithium-ion batteries is one of the key technologies critical for the safe and reliable operation of Electric Vehicles (EVs). However, the random charging–discharging behavior of EV users results in differentiated, incomplete, and limited health information in single charging–discharging data, leading to ongoing challenges in SOH estimation. Firstly, an efficient data preprocessing algorithm is designed to automatically handle data slicing, cleaning, alignment, and recombination. Secondly, recognizing the relatively slow change in SOH, multi-neighboring incomplete charging segments are aligned and used as inputs to the SOH estimation model, and then the average capacity of these neighboring charging data is computed as SOH labels. Furthermore, based on the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) structure, an end-to-end SOH estimation model is constructed. This model initially utilizes a multi-channel CNN to fuse health information from multi-neighboring incomplete charging segments and then employs LSTM for SOH prediction. Finally, on a dataset composed of 20 EVs, the proposed SOH estimation method is rigorously validated using K-fold cross-validation. The results demonstrated that the Mean Absolute Error (MAE) is within 2.13%, and the Root Mean Square Error (RMSE) is below 2.74%, highlighting the model's high estimation accuracy. [Display omitted] • The multi-neighboring incomplete charging segments are used as model inputs. • A practical method to extract SOH labels from real EV data. • An end-to-end SOH estimation framework based on CNN-LSTM. • The effects of input dimensions, model structure and charging rates are explored. [ABSTRACT FROM AUTHOR]

This work reports on the synthesis, mechanical, and thermal properties of in situ AlN/Al-12Si composite through laser powder bed fusion (LPBF) by blending Al-12Si powder with 5 vol% nano-sized BN particles. Incorporating nano-BN particles results in (i) formation of thermally stable AlN phase, preventing Si diffusion and breakdown of cellular structure, (ii) improvement of compressive yield strength (CYS), and (iii) reduction in coefficients of thermal expansion (CTE) and thermal conductivity. In addition, compared to Al-12Si alloy, the composite exhibits grain refinement from 38.8 to 1.2 μm in size, and the alteration of columnar grains (Al-12Si) to equiaxed grains (AlN/Al-12Si). At annealing temperatures above 573 K, the CYS of the unadulterated Al-12Si alloy had a ∼ 2.1 times greater reduction (from 285 to 200 MPa) compared to that of the composites (from 301 to 260 MPa). The formation of the AlN phase mitigates the significant reduction in CYS. The CTE of Al-12Si and AlN/Al-12Si are 27.3 × 10−6 K−1 and 24.3 × 10−6 K−1 respectively. There is good agreement between the measured CYS results and the calculated strengthening mechanisms. This work offers both theoretical insights and experimental data to support the use of LPBF AlN/Al-12Si composite in low- and moderate-temperature applications. • Novel in situ AlN/Al-12Si composite is successfully fabricated by laser powder bed fusion. • AlN phase generate from the reaction between BN particle and Al-12Si during laser melting. • AlN/Al-12Si composites have a higher compressive yield strength and lower thermal conductivity. • AlN phase leads to the grain refinement, improving the compressive yield strength. • AlN phase mitigates the reduction in coefficients of thermal expansion at medium temperature. [ABSTRACT FROM AUTHOR]

Effective extraction and representation of action information are critical in action recognition. The majority of existing methods fail to recognize actions accurately because of interference of background changes when the proportion of high-activity action areas is not reinforced and by using RGB flow alone or combined with optical flow. A novel recognition method using action sequences optimization and two-stream fusion network with different modalities is proposed to solve these problems. The method is based on shot segmentation and dynamic weighted sampling, and it reconstructs the video by reinforcing the proportion of high-activity action areas, eliminating redundant intervals, and extracting long-range temporal information. A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field of feature extraction. Compared with existing approaches, the proposed method achieves superior or comparable classification accuracies on benchmark datasets UCF101 and HMDB51. [ABSTRACT FROM AUTHOR]

Background Heterotopic gastric mucosa in the upper esophagus (HGMUE) is reported to be related to gastroesophageal reflux disease (GERD). This study investigated the prevalence of GERD and the use of salivary pepsin to diagnose gastroesophageal reflux, especially proximal reflux, in HGMUE patients. Methods One hundred and fifty-three HGMUE patients and 50 healthy volunteers were studied. All subjects took a reflux symptom index questionnaire (RSI); underwent endoscopy, barium esophagogram, high-resolution manometry (HRM), and 24-hour multichannel intraluminal impedance-pH-metry (MII-pH); and salivary pepsin test. Results Ninety-five (62.1%) HGMUE patients but no control subjects were diagnosed with GERD. The salivary pepsin concentration, RSI score, DeMeester score, acid exposure time (AET), total reflux episodes, proximal acidic reflux episodes, and proximal weakly acidic reflux episodes were significantly higher in the HGMUE group than in the control group (P  < 0.05). The salivary pepsin test showed a sensitivity of 85.9% and specificity of 56.9% for diagnosing GERD using the optimal cut-off value of 75 ng/mL. One hundred and seven (69.9%) and 46 (30.1%) HGMUE patients were categorized as pepsin (+) and pepsin (−), respectively when 75 ng/mL was used as a cut-off value. Male sex, RSI, AET, and proximal acid reflux episodes were positive predictive factors for the occurrence of pepsin (+) in HGMUE patients. Conclusions GERD, especially GERD with proximal acid reflux and related symptoms, was common in HGMUE patients. The salivary pepsin test could be an additional useful test for testing reflux in HGMUE patients, but it will not replace the MII-pH. [ABSTRACT FROM AUTHOR]

This paper presents the implementation and verification of a dual-probe measurement system with a novel method and easily integrated chromatic confocal probes, for synchronous evaluation of not only the inner and outer cylindricity but also the thickness distribution of a ring-type cylinder. A pair of measuring probes mounted on a four-axis scanning stage, which is composed of XYZ -slides and a spindle, is utilized to target the two sides of the ring-type cylinder. In the developed system, to reduce the influence of the positioning errors and tilt misalignments of the confocal probes and the cylinder, alignment methods are developed. The outputs of the measuring probes are employed to accurately evaluate the geometric errors of the ring-type cylinder through performing spiral scanning while separating the Z -slide and spindle motion errors of the scanning stage based on a reversal error separation method. To verify the performance of the proposed system and method, the geometric errors of a thin-walled cylinder which has a nominal outer diameter of 50 mm, an inner diameter of 40 mm, and a length of 100 mm are evaluated in experiments. The experimental results are further compared with those by a coordinate measuring machine (CMM), and the high correspondence of the comparison results well confirm the feasibility of the implemented measurement system. Finally, measurement uncertainty analysis is conducted to demonstrate the reliability of the proposed method for the evaluation of the geometric errors of ring-type cylinders. The measurement accuracy is comparable to that of a CMM. • A dual-probe measurement system was developed for geometrical evaluation of ring-type cylinders. • The inner, outer roundness and thickness distribution of a ring-type cylinder was simultaneously measured. • The measurement uncertainty was analyzed to confirm the reliability of the proposed method. [ABSTRACT FROM AUTHOR]

• A straightforward LC-MS/MS assay was developed to detect 5 EGFR-TKIs and metabolites. • Semi-quantitative analysis of OSI-413 was performed using OSI-420 calibration. • Systematic evaluation were validated to access the method according to the guidelines. • This method was suitable for routine TDM of EGFR-TKIs in clinical laboratory. • Incurred sample reanalysis indicated the satisfying reproducibility of the method. As numerous studies have reported the concentration-exposure relationships of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs), therapeutic drug monitoring is a promising approach in lung cancer treatment, aiming to avoid treatment failure or toxicity. A new method for the simultaneous analysis of five EGFR-TKIs (afatinib, erlotinib, gefitinib, icotinib and osimertinib) and their metabolites in human plasma samples was developed and validated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Afatinib-d 6 , erlotinib-d 6 , OSI-420-d 4, gefitinib-d 6 and osimertinib-C 13 ,d 3 were used as internal standards (ISs). The samples were prepared by liquid-liquid extraction using tert- butyl methyl ether. Chromatographic separation was undertaken on an XBridge C 18 column using a linear gradient elution. LC-MS/MS was conducted in positive ionization mode with multiple reaction monitoring. The proposed method showed satisfactory results in terms of linearity, sensitivity, specificity, precision (intra- and inter-day coefficients of variation ranged from 1.1 to 13.9%), and accuracy (from 93.3 to 111.1%). The IS-normalized matrix factors were below 15%. The sensitivity and linearity were highly appropriate for the expected concentrations according to the analysis of samples from non-small cell lung caner (NSCLC) patients who received EGFR-TKIs. The proposed method showed an acceptable reproducibility, high sensitivity and selectivity, and low matrix effects. This method could be significant for monitoring plasma concentrations of the mentioned EGFR-TKIs in NSCLC patients, aiming to improve the efficacy and safety of targeted therapies. [ABSTRACT FROM AUTHOR]

Vocal fold (VF) scarring is a major etiology of voice disorders and a leading cause for permanent intractable dysphonia. To date, several approaches have been used to address VF injury and scarring, which included scar excision and biomaterial injection and/or implantation. Despite the considerable research progress made in this field, these have not been established as routine clinical procedures due to various restrictions. Tissue engineered scaffolds for delivering growth factors in vivo and implementation of simultaneous delivery of multi-potent cells may achieve better outcome. In this study, we have developed Urine-derived stem cells - Small intestinal submucosa hydrogel composites (USCs@SIS), which are injectable and capable of sustaining the survival and metabolism of the USCs in the scarred VF. The three-dimensional complex showed a robust ability of antiinflammatory polarization, neovascularization, re-epithelialization and anti-fibrosis in a rabbit model for heatinjured VFs. In vitro experiment proved that the hydrogel composites could promote polarization of M2 macrophages, tube formation by human umbilical vein endothelial cells, proliferation and migration by human bronchial epithelial cells, and suppression of differentiation of human fetal lung fibroblasts and human primary fibroblast cells of VF. Application of the USCs@SIS thereby may overcome the substantial limitations of the current strategies for scarless VF regeneration. [ABSTRACT FROM AUTHOR]

• A nonlinear source separation algorithm KICA-R based on reference signals and kernel independent component analysis was proposed to enhance the fault feature information related to the motor, gearbox, and pump in multi-sensor signals. • An adaptive fusion of multi-virtual source features was achieved by introducing a multi-source channel attention mechanism module MS-CAM. • Fault simulation test bench of CRF pump-unit was constructed, and the superiority of the proposed method was verified on imbalanced datasets. The circulating seawater pump-unit (CRF pump-unit) is a crucial equipment that provides cooling water for the secondary circuit of a nuclear power plant (NPP). Its long-term, fault-free operation ensures nuclear power safety and economy. In this study, considering the limited fault-state samples in the historical database and the random fault locations in NPP equipment, a health state identification framework of the CRF pump-unit is proposed, which combines multiple sources of network input and a feature fusion network. The proposed kernelled independent component analysis with reference (KICA-R) is utilized with reference signals to separate virtual sources from multiple raw acceleration signals. It enhances the fault characteristic frequencies related to several subsystems of a complex mechatronic system in the training samples of the network. Based on the newly designed multi-source channel attention module (MS-CAM) that can adaptively allocate fault-sensitive weight coefficients, a health state identification network incorporates the enhanced input features through the virtual sources obtained from KICA-R. By conducting the benchmark analysis on two public datasets and various types of fault simulation tests on the CRF pump-unit simulator, training sets with different levels of data imbalance are constructed, and the optimal network parameters are determined. The comparisons against cICA validate the ability of KICA-R in virtual source separation, and the strong performance of the MS-CAM-based network in identifying different health states of the CRF pump-unit is further demonstrated through detailed comparative analysis. [ABSTRACT FROM AUTHOR]

With the arrival of the first truly global energy crisis, how to precisely forecast the hydroelectric generation becomes a hot spot for allowing governments to obtain more valuable information. A novel forecasting model, Seasonal Fractional Incomplete Gamma Nonlinear Grey Bernoulli Model (SFIGNGBM(1, 1)), is proposed in this paper to precisely forecast the hydroelectric generation in some countries. First, the seasonal raw data are classified into four seasonal groups based on their significant seasonal fluctuations. Second, a novel SFIGNGBM(1, 1) model is established by combining the Bernoulli equation, the fractional-order accumulation operator, and the incomplete gamma function to further optimize partial parameters in the forecasting model and improve the forecasting performance. Third, the Whale Optimized Algorithm (WOA) is employed to optimize the Bernoulli power exponent η , the fractional order parameter r , and the incomplete coefficient h for minimizing the MAPE values and enhancing the fitting precision. Finally, our results present that our proposed model outperforms a set of baseline forecasting models with the smallest three error measure values in all fitting results, and its MAPE values converge before 10 iterations. This indicates that our proposed model has a favorable forecasting performance with fast-convergence for hydroelectric generation in the elected countries. • A novel model is proposed to precisely forecast the hydroelectric generation. • The proposed model uses the seasonal data grouping to enhance the forecasting precision. • The proposed model uses three approaches to further optimize partial parameters. • A meta-heuristic optimization algorithm is used to further enhance the forecasting precision. [ABSTRACT FROM AUTHOR]

NSNP-type neuron is a new type of neuron model inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In order to address the loss problem of edge detail information in edge detection methods based on deep learning, we propose a feature fusion method based on NSNP-type neurons. The architecture of this feature fusion method consists of two modules: feature extraction module and feature fusion module. In particular, the feature fusion module is composed of convolutional blocks constructed by NSNP-type neurons for multi-level feature fusions, and CoT blocks with Transformer style is introduced to extract rich contextual information from low-level features and high-level features. To fuse multi-level features and preserve contextual information, we design a new loss function that not only preserves feature prediction loss and fusion loss, but also considers contour-related and texture-related information. The proposed method is evaluated on BSDS500 and NYUDv2 data sets and compare it with 9 baseline methods and 12 CNN-based methods, and we achieve ODS of 0.808 and OIS of 0.827 on BSDS500. The comparison results demonstrate the advantages of the proposed method for edge detection. The source code is available at https://github.com/xhuph66/FF-CNSNP-master. • Nonlinear mechanism in spiking neurons inspires the NSNP-type neuron model. • NSNP-like neuron model is used to construct a feature fusion module for fusing edge feature maps. • This feature fusion module can effectively fuse the global and local information of the image. • A new loss function is designed to consider information related to contours and textures. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) is a novel class of recurrent neural networks (RNN) models. Nonlinear spiking neural P (NSNP) systems are neural-like computing models with nonlinear spiking mechanisms. By introducing NSNP systems as the reservoir, we propose a new RC model for time series classification task, termed TSC-NSNP model. However, due to the high-dimensional nature of the reservoir state space, the TSC-NSNP model, like existing RC models, will encounter some challenges. To address the challenges. we utilize the reservoir model space representation and dimensionality reduction method to propose two improved models, termed TSC-DR-NSNP model and TSC-RMS-NSNP model. The three RC models can be easily realized and learnt in the RC framework. The proposed three RC models are evaluated on 21 benchmark time series classification data sets, and are compared with 20 classification models. The comparisons demonstrate the effectiveness of the presented three RC models for time series classification tasks. [ABSTRACT FROM AUTHOR]

Global profiling of bile acids (BAs) is imperative for understand their function and disease pathogenesis. But it is still a challenging task, as the collision-induced dissociation (CID) fragment ions of unconjugated BAs showed low ion intensities to insufficient analysis. Herein, we developed a highly sensitive method for pseudotargeted profiling of BAs by chemical derivatization. In the developed method, a labeling reagent, 2-dimethylaminoethylamine (DMED), was adopted to label the carboxyl group of BAs. The results demonstrated that the detection sensitivities of unconjugated BAs were increased by 4–200 folds after DMED-labeling. Moreover, to profile other potential BAs not included in the 91 known targets, diverse survey experiments were performed on Qtrap-MS to search BAs for both precursor and fragment ion species, and retention index (RI) strategy was adopted to facilitate the identification of isomers. Finally, MRM-based LC-MS/MS method was validated for the pseudotargeted profiling of the BAs submetabolome with good linearity (r2 ≥ 0.990 for 89 known BAs) and high sensitivity (0.05–0.5 ng/mL for unconjugated BAs), covering unconjugated, glycine, taurine, sulfuric acid, glucuronic acid, and as well as those doubly-conjugated with above types. With this method, a total of 107 BAs, covering 54 BAs identified by authentic standards and 53 BAs candidates, were successfully determined in human serum of women with intrahepatic cholestasis of pregnancy (ICP). Multivariate analysis revealed deferentially expressed BAs. ICP disease altered the BAs profile with a reduced proportion of unconjugated, sulfate- and doubly-conjugated BAs and an increased proportion of glycine and taurine conjugates. Altered proportion and profile of BAs in ICP groups were gradually recovered during the ursodeoxycholic acid (UDCA) therapy. Overall, the strategy of DMED-labeling technique combined with diverse survey experiments is sufficiently sensitive and robust to comprehensively analysis of metabolic profiling of BAs in human serum. [Display omitted] • A chemical labeling technique was developed for pesudotargeted profiling of BAs. • The detection sensitivities of unconjugated BAs were increased by 4–200 folds. • A MRM-IDA-EPI survey experiment and RI strategy were performed to screen BAs. • 107 potential BAs were screened out from human serum, of which 54 were identified. • Differences occurred for quantitative metabolome of human serum with ICP. [ABSTRACT FROM AUTHOR]

Monolayer t -BN: low diffusion energy barrier, high specific capacity as an anode material for lithium-ion batteries. [Display omitted] Searching for high-performance anode materials with fast charge-discharge rates, high specific capacity, low open circuit voltages, and good reversibility has attracted much attention over the past few decades. Herein, based on first-principles calculations, the performance of t -BN monolayer as an anode material for Li-ion batteries is investigated. The excellent stretchability of t -BN monolayer makes it good reversibility in the case of volume expansion caused by full adsorption. Additionally, t -BN monolayer with Li-adsorbed possesses metallic behavior, indicating excellent electrical conductivity. Furthermore, t -BN monolayer exhibits well-balanced performances with a low diffusion energy barrier (0.35 eV), a high specific capacity (1620 mA h g−1), an appropriate average open-circuit voltage (0.49 V), and small lattice constants change (2.5%). Finally, solvent effects are considered. The adsorption of Li-ions becomes more stable as dielectric constant of the solvent increases. Based on the above excellent properties, we speculate that t -BN monolayer can act as a promising anode material for LIBs. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex. [ABSTRACT FROM AUTHOR]

Summary: Coulomb Counting (CC) method plays an important role in the state of charge (SOC) estimation theory of lithium‐ion batteries, and a lot of improvement and optimization strategies are based on it. With the increasing demand for precise management of lithium‐ion battery systems, the performance of the traditional CC method is no longer suitable for more complex working conditions. First, the battery aging, extreme temperature, and high‐rate discharging were considered as the main influencing factors which limit the SOC estimation accuracy of the CC method, and the performance degradation mechanism of the traditional CC method under the influence of the above factors are experimentally analyzed, especially the change of battery total dischargeable capacity after aging, and the change rules of key parameters in the CC equation are analyzed. Then the initial SOC and the total dischargeable capacity of the CC method are modified and estimated respectively to realize the accurate estimation of SOC under complex working conditions, especially the accurate SOC estimation during the whole life cycle of lithium‐ion batteries. The experimental results show that the improved CC method can effectively deal with complex working conditions, and the comprehensive estimation accuracy of SOC is within 3.6%. [ABSTRACT FROM AUTHOR]

Frequency control can be realized via laminating a novel smart material: light activated shape memory polymer (LASMP) whose Young's modulus varies under light field. In this work, frequency control of plates with LASMP patches is studied. Validation is fulfilled by comparing present results with FEM results for both beams and plates. Furthermore, frequency analysis of plate with LASMP patch pair whose location or size varies is also investigated. Finally, the optimal frequency control scheme of the composite plate is presented and studied in detail. Results show that the optimal scheme has obvious control effect on natural frequency of the plate. [ABSTRACT FROM AUTHOR]

To further understand the relationship between gut microbiota and liver and kidney biochemical indicators in patients with type 2 diabetes with diarrhea. We collected the flora data and liver and kidney index data of 26 patients with type 2 diabetes with diarrhea and 26 patients with type 2 diabetes. One-way variance analysis was used to analyze the differences in liver and kidney indexes between the two groups; linear regression analysis was used to analyze the relationship between liver and kidney indicators and gut microbial alpha diversity; PERMANOVA was used to analyze the difference in structure of gut microbial β diversity; then Spearman rank correlation test was used to analyze the relationship between liver and kidney biochemical indexes and gut microbiota. The results showed that there were significant differences in triglyceride (TG) and blood urea nitrogen (BUN) in liver and kidney indexes between the two groups (P=0.006, P=0.003); the increase of serum bilirubin in patients with type 2 diabetes with diarrhea was related to the decrease of the species diversity of the intestinal flora; Bacteroides, Lacetospirillum, and Prevotella in type 2 diabetes patients with diarrhea decreased, whereas Rumenaceae significantly increased. In addition, Laospirillaceae and Rumenomycetes were positively correlated with serum bilirubin, which was related to the decrease of TG and has nothing to do with the increase of BUN. There is a relationship between liver and kidney indexes of patients with type 2 diabetes with diarrhea and gut microbiota, and these bacteria can provide new targets for the treatment of type 2 diabetes with diarrhea. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex. [ABSTRACT FROM AUTHOR]

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model. [ABSTRACT FROM AUTHOR]

As China assumes a more and more dominant role in global science, this mini-review attempts to provide a bird's eye view on how the bio-digital revolution impacts China's biosciences and bioindustry. Triggered by top-down political programs and the buildup of an impressive infrastructure in science, information technology, and education, China's biomedical and MedTech industries prosper. Plant and animal breeding programs transform agriculture and food supply as much as the Internet of things, and synthetic biology offers new opportunities for the manufacturing of specialty chemicals within the Chinese version of a "bioeconomy." It is already becoming apparent that the new five-year period "145" (2021–2025) will further emphasize emission control, bioenvironmental protection, and more supply of biomass-derived energy. This review identifies key drivers in China's government, industry, and academia behind these developments and details many access points for deeper studies. Key points: Biotechnology in China Biomedical technology New five-year period [ABSTRACT FROM AUTHOR]

Firstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection. [ABSTRACT FROM AUTHOR]