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Abstract Background The triglyceride glucose-body mass index (TyG-BMI) is considered to be a reliable surrogate marker of insulin resistance (IR). However, limited evidence exists regarding its association with the severity of coronary artery disease (CAD), particularly in hypertensive patients with different glucose metabolic states, including those with H-type hypertension. This study aimed to investigate the relationship between TyG-BMI and CAD severity across different glucose metabolism conditions. Methods This retrospective cohort study included 1537 hypertensive patients who underwent coronary angiography. The TyG-BMI was categorized into tertiles and analyzed using logistic regression models and restricted cubic spline (RCS) models to assess its association with multi-vessel CAD. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of TyG-BMI in detecting the severity of CAD in different glucose metabolism states, including normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM). The above method has also been applied to populations of H-type hypertension patients. Results The TyG-BMI was significantly associated with the severity of multi-vessel CAD in hypertensive patients (Odds ratio [OR] 1.043, 95% CI 1.032–1.053). In the diabetic subgroup, after adjusting for risk factors, the risk of multi-vessel CAD in the T3 groups was 3.836-fold (95% CI 1.763–8.347; P = 0.001) higher than in the T1 group, with a non-linear dose–response relationship (P for non-linearity = 0.017). In H-type hypertension patients, the TyG-BMI was also significantly correlated with multi-vessel CAD (OR 5.248, 95% CI 1.821–15.126, P = 0.002) in the DM group. The ROC analysis revealed that TyG-BMI had the highest predictive value for multi-vessel CAD in diabetic patients, with an AUC of 0.720 (95% CI 0.661–0.780, P

Using Arca subcrenata as the research object, this study explored the differences in the utilization of simulated digestion products of Arca subcrenata myofibrillar protein by probiotics, namely Lactiplantibacillus plantarun, Bifidobacterium bifidobacterial, Lacticaseibacillus rhamnosus and Bifidobacterium animalis subsp. Lactis Probio-M8. The study first simulated the in vitro digestion of myofibrillar protein from Arca subcrenata and used it as a nitrogen source to co-culture with probiotics by nitrogen source replacement and nitrogen source addition, respectively. Subsequently, the utilization characteristics by the probiotics on the digestion products of myofibrillar protein from Arca subcrenata were evaluated by monitoring the growth curve, pH and colony number among others. The results showed that, compared to the de Man-Rogosa-Sharpe group, the nitrogen source replacement group could significantly inhibit the proliferation of the four probiotic species (P

Abstract Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios.

Background:: Cerebellar intermittent theta burst stimulation (iTBS) modulates the excitability of the cerebral cortex and may enhance attentional performance. To date, few studies have conducted iTBS on healthy subjects for one week and used electroencephalography (EEG) to investigate the effect of multiple stimulation sessions on resting-state functional brain networks and the daily stimulation effect on attentional performance. Methods:: 16 healthy subjects participated in a one-week experiment, receiving bilateral cerebellar iTBS or sham stimulation and engaging in multi-task attentional training. The primary measures were the one-week attentional performance and pre- and post-experiment resting-state EEG activities. Amplitude Envelope Correlation (AEC) was used to construct the functional connectivity in the eye-open (EO) and eye-closed (EC) phases. Results:: At least three sessions of iTBS were required to enhance multi-task performance significantly, whereas only one or two sessions failed to elicit the improvement. Compared with the control group, iTBS induced significant changes in PSD, AEC functional connectivity, and AEC network properties during the EO phase, while it had little effect during the EC phase. During the EO phase, the network property changes of the iTBS subject were correlated with improved attentional performance. Conclusion:: The multi-task performance requires multiple stimulations to enhance. iTBS affects the resting-state alpha band brain activities during the EO rather than the EC phase. The AEC network properties may serve as a biomarker to assess the attentional potential of healthy subjects.

A 24-year-old male patient complained of mild knee pain after jogging. The subsequent knee MRI demonstrated bilateral lateral thickened tibiofemoral cartilages, evidenced by deformities of the bilateral subchondral bone beneath the lateral femoral condyle cartilage. The corresponding dislocations of almost all the left lateral meniscus and part of the right lateral meniscus to the center of the joint were detected. After excluding diagnoses of congenital ring-shaped meniscus, bucket handle tear of the C-shaped lateral meniscus, and central tear of the discoid meniscus, the displacement of all or part of the lateral meniscus into the intercondylar notch was considered a consequence of congenital thickening of the lateral superior and inferior cartilage. This case may report a new variant of knee joint pathology.

IntroductionMost foreign bodies (FBs) can spontaneously pass through the gastrointestinal tract. Sharp FBs are believed to be able to puncture any part of the gastrointestinal tract, causing perforation and potentially secondary damage to adjacent organs.Case descriptionA 44-year-old man complained of having persistent dull pain in the perianal region. He was diagnosed with a toothpick impacted into the wall of the lower rectum after accepting a digital rectal examination of the lower rectum and a pelvic computed tomography (CT). The surgeon extracted the FB using vascular forceps guided by the operator’s index finger. The patient was discharged after intravenous ceftriaxone was given for 6 days. A follow-up pelvic CT performed 2 weeks after surgery revealed that the perirectal fat and muscles had already normalized.ConclusionA systematic review of relevant literature from the past decade was performed to summarize the imaging features of an orally ingested toothpick perforating the gastrointestinal tract. The location of abdominal pain is an important clue for the diagnosis of toothpick perforation, and a CT examination is recommended as the first option for the detection of an ingested toothpick. Determining the location of the toothpick perforation and assessing the severity of local inflammation are important bases for the selection of treatment.

Aqueous zinc metal batteries (AZMBs) are considered a promising candidate for grid-scale energy storage systems owing to their high capacity, high safety and low cost. However, Zn anodes suffer from notorious dendrite growth and undesirable surface corrosion, severely hindering the commercialization of AZMBs. Herein, a strategy for engineering a dense ZnO coating layer on Zn anodes using the atomic layer deposition (ALD) technique is developed, aiming to improve its long-term cycling stability with fewer Zn dendrites. The surface-modified Zn anode (ZnO@Zn) exhibits an excellent long-cycling life (680 h) and stable coulombic efficiency when being used in a symmetric cell. Moreover, the ZnO@Zn electrode shows a high stability with almost no capacity decay after 1100 cycles at 2C in a full cell using MnO2 as the cathode. The ZnO coating is conducive to reducing corrosion and the generation of by-products, thus increasing the reversibility of Zn2+/Zn stripping/plating. Particularly, density functional theory (DFT) calculation results reveal that the ZnO coating layer could effectively lower the adsorption energy of the Zn(002) plane in ZnO@Zn, inducing the preferential deposition of Zn2+ towards the (002) crystal plane with fewer Zn dendrites. The surface ZnO coating protocol provides a promising approach to achieve a dendrite-free Zn anode for stable AZMBs.

Abstract String instruments, wind instruments and percussion instruments are three traditional categories of musical instruments, among which wind instruments play an important role. Usually, pitches of wind instruments are determined by the vibrating air column, and the musical pitches will be affected by multiple factors of the air flow. In this article, the mechanism of sound production by a pipe is analysed in terms of the coupling of the edge tone and the air column's vibration in the tube. Experiments and computational fluid dynamics numerical calculations are combined to study the influence of the jet velocity on the oscillation frequency of the edge tone and the musical sound produced by the tube, which help to gain deeper insight into the relation between physics and music.

The corrosion of rebars will change the mechanical properties of the reinforced concrete structure, leading to the decline in the structural strength, which seriously threatens the safety and stability of buildings and power equipment foundations. Hence, it is of great significance to detect the internal corrosion of reinforced concrete structures. In this paper, a nondestructive detection method for detecting the corrosion in reinforced concrete structures based on microwave was proposed, and the corrosion state of rebar can be evaluated by the change of microwave information. Compared with other detection methods, microwave nondestructive detection has the advantages of low energy consumption, strong penetration, no contact, and small equipment volume and weight. In order to verify the effectiveness of the method, a microwave nondestructive detection simulation test platform was built and reinforced concrete test models with different corrosion lengths and depths were made. The influences of concrete parameters, the microwave frequency, and the lifting distance of the waveguide were analyzed. The frequency sweeping and moving scanning detection methods of the waveguide were carried out for the test models with different corrosion defects, and the changes of microwave S-parameters under different corrosion defects were obtained. The results show that when the waveguide is placed horizontally by the frequency sweeping detection method, the corrosion length detection effect of rebar is better. When the waveguide is placed vertically by the frequency sweeping detection method, the corrosion depth can be identified and the amplitude of transmission coefficient increases with the increase in the corrosion depth. The corrosion length of rebars can be identified quantitatively by the moving scanning detection method of the waveguide.

Object detection is a focal point in remote sensing applications. Remote sensing images typically contain a large number of small objects and a wide range of orientations across objects. This results in great challenges to small object detection approaches based on remote sensing images. Methods directly employ channel relations with equal weights to construct information features leads to inadequate feature representation in complex image small object detection tasks. Multiscale detection methods improve the speed and accuracy of detection, while small objects themselves contain limited information, and the features are easily lost following down-sampling. During the detection, the feature images are independent across scales, resulting in a discontinuity at the detection scale. In this article, we propose the multiscale context and enhanced channel attention (MSCCA) model. MSCCA employs PeleeNet as the backbone network. In particular, the feature image channel attention is enhanced and the multiscale context information is fused with multiscale detection methods to improve the characterization ability of the convolutional neural network. The proposed MSCCA method is evaluated on two real datasets. Results show that for 512 × 512 input images, MSCCA was able to achieve 80.4% and 94.4% mAP on the DOTA and NWPU VHR-10, respectively. Meanwhile, the model size of MSCCA is 21% smaller than that of its predecessor. MSCCA can be considered as a practical lightweight oriented object detection model in remote sensing images.

Abstract Background Neonatal hyperbilirubinemia (NNH) is a common disease in newborns. This research study aimed to assess the associations between uridine diphospho-glucuronate-glucuronosyltransferase 1A1 (UGT1A1, c.-3279 T > G) polymorphisms and NNH risk. Methods We searched PubMed, the Cochrane Library, and the Embase electronic databases. All published eligible studies before July 1, 2019, were searched for this meta-analysis. Results We identified 7 independent studies including 1560 cases. The data showed that in the general population, compared with the GT + GG vs TT and GG vs TT, c.-3279 T > G (rs4124874) was significantly related to a higher NNH risk (GG vs TT: OR = 1.865, 95% CI: 1.031–3.373, P = 0.039; GT + GG vs TT: OR = 1.331, 95% CI: 1.055–1.679, P = 0.016). Although not statistically significant, the data showed that c.3279 T > G had a tendency to be associated with NNH under the allele model and GG vs GT + TT in the overall population (G vs T: OR = 1.288, 95% CI: 0.982–1.689, P = 0.067; GG vs TT + GT: OR = 1.583, 95% CI: 0.947–2.647, P = 0.080). Conclusion The UGT1A1 gene c.-3279 T > G (rs4124874) polymorphism increased susceptibility to NNH, especially for the comparison of GT + GG vs TT and GG vs TT. In the future, we can use homozygous state of the UGT1A1 gene c.-3279 T > G (rs4124874) polymorphism for the diagnosis and screening of molecular biomarkers in NNH patients.

Recently, the AC/DC hybrid distribution network has become a general trend in energy research. To accommodate the addition of renewable energy, this paper proposes a reliability assessment approach for AC/DC hybrid distribution network considering the new features introduced by the DC distribution technique. First, the probability models of renewable generations and loads are established. Second, the control modes of the voltage source converter (VSC) yielding to different fault scenarios are modeled based on its operating characteristics. Thereafter, an optimal load shedding model that considers the control modes of the VSC is developed, and it can be solved using a second-order cone algorithm. Finally, a reliability assessment method for the AC/DC hybrid distribution network is proposed that is based on the nonsequential Monte Carlo method. Case studies based on a modified AC/DC distribution network are applied to verify the effectiveness of the proposed method. The operational characteristics and reliability performance of the AC/DC distribution network with high integration of renewable energy are demonstrated by comparing it to a corresponding AC distribution network.

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified.

In the face of the radical revolution of energy systems, there is a gradually held consensus regarding the adoption of distributed renewable energy resources, represented by Photovoltaic (PV) and wind generation. Consequently, the distributed Energy Storage Systems (ESSs) have become increasingly important in the distribution networks, as they provide the arbitrage and ancillary services. Determining the optimal installation site and the capacity of the distributed ESSs will defer the network reinforcements, reduce the investment of ESSs, and improve the reliability, flexibility, and efficiency of distribution grids. In order to investigate the optimal ESS configuration and to solve voltage fluctuations brought by the increased penetration of PV, in this study a two-stage heuristic planning strategy has been proposed, which considers both the economic operation and the lifetime of the distributed ESSs, to determine the optimal sitting and sizing of the ESSs, in the distribution grids. The first stage decides the optimal installation site and the economic scheduling of the ESSs, aiming to minimize the fabricating cost of the distributed ESSs and the network losses. Based on the output of the first stage, the second stage planning is further delivered to achieve the optimal ESS capacity, considering the Life-Cycle Cost (LCC) minimization. Finally, the feasibility and effectiveness of the proposed method is verified on a typical distribution case study network.

Abstract The protein corona formed on nanoparticles (NPs) has potential as a valuable diagnostic tool for improving plasma proteome coverage. Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules allows for the detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depletes the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. Employing an optimized data-independent acquisition approach, the inclusion of phosphatidylcholine leads to the detection of 1436 proteins in a single plasma sample. Our molecular dynamics results reveal that phosphatidylcholine interacts with albumin via hydrophobic interactions, H-bonds, and water bridges. The addition of phosphatidylcholine also enables the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate the widespread adoption of this methodology for the identification and clinical translation of biomarkers.