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The netted wireless sensor nodes or coherent accumulation processing in multistatic radar imaging requires high accuracy time synchronization. Although GNSS timing can also be used as a time synchronization method to serve the applications above, its timing accuracy will be limited. In this context, we present the hardware implementation for Two-Way Time-Frequency Real-Time Synchronization (TWTFRTS) with an automatic adaptive jitter elimination algorithm based on Kalman and PID, which is implemented in a real-time, low-cost, portable Xilinx ZYNQ device. A short (2 km) baseline TWTFRTS experiment was done with a pair of devices composed of a master device and a slave device. The result shows a high precision of time synchronization performance with the standard deviation (1 σ) better than 1 ns. &nbsp

Qin Luo,1 Lunchao Chen,2 Dongfang Yu,3 Ke Zhang4 1School of Marxism, Southwest Minzu University, Chengdu, 610041, People’s Republic of China; 2Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University, Chongqing, 400715, People’s Republic of China; 3Finance and Economics, Nanning, 530003, People’s Republic of China; 4Student Affairs Department, China West Normal University, Nanchong, 637002, People’s Republic of ChinaCorrespondence: Qin Luo, School of Marxism, Southwest Minzu University, Chengdu, 610041, People’s Republic of China, Email cxlq519818@163.comPurpose: The link between academic self-efficacy and academic achievement and its potential mediation by learning engagement was investigated in college students in China.Methods: The Chinese versions of the Academic Self-Efficacy Scale, Academic Achievement Scale, and Learning Engagement Scale were administered to 1158 Chinese college students (544 men, 614 women; age [years]: M=19.37, SD=1.16, ranging from 17 to 30 years; college year: 641 freshmen, 302 sophomores, 197 juniors, 18 seniors).Results: The results showed that, among Chinese college students, there were positive correlations between academic self-efficacy and both academic achievement and learning engagement, and between learning engagement and academic achievement. Additionally, a structural equation model demonstrated that learning engagement could mediate the association between academic self-efficacy and achievement.Conclusion: Academic self-efficacy, learning engagement, and academic achievement were found to be significantly and positively associated in Chinese college students, with the association between self-efficacy and achievement observed to be significantly mediated by learning engagement, which mediates the link between self-efficacy and achievement. As the study was cross-sectional, causal inferences were difficult to determine; thus, longitudinal studies should be conducted in the future for further analysis of the causal relationships between these three variables. The present research results reveal the mechanism by which academic self-efficacy of college students impacts their academic achievement, expanding the research perspective of learning engagement, and can help guide the development of interventions to improve college students’ academic achievement.Keywords: academic self-efficacy, academic achievement, learning engagement, Chinese college students, mediating effect

Colletotrin A (1), a new 1,4-benzoxazine derivative, and eight known compounds, including two alkaloids (2-3), one acylamide (4), one benzaldehyde (5), and four sterols (6-9) were obtained from endophytic fungus Colletotrichum gloeosporioides B-142, which was isolated from Dracaena cochinchinensis. Among them, 1,2,3,4-tetrahydroquinoline-4,8-diol (2) was reported for the first time as natural product. The structure of compound 1 was elucidated with help of spectroscopic data including IR, UV, electronic circular dichroism (ECD) calculation, HRESIMS, 1D and 2D NMR (COSY, HSQC and HMBC). The antimicrobial activities were evaluated by disc diffusion method.

It remains unclear what mechanism is driving the evolution of protoplanetary disks. Direct detection of the main candidates, either turbulence driven by magnetorotational instability or magnetohydrodynamical disk winds, has proven difficult, leaving the time evolution of the disk size as one of the most promising observables able to differentiate between these two mechanisms. But to do so successfully, we need to understand what the observed gas disk size actually traces. We studied the relation between $R_{\rm CO,\ 90\%}$, the radius that encloses 90% of the $^{12}$CO flux, and $R_c$, the radius that encodes the physical disk size, in order to provide simple prescriptions for conversions between these two sizes. For an extensive grid of thermochemical models we calculate $R_{\rm CO,\ 90\%}$ from synthetic observations and relate properties measured at this radius, such as the gas column density, to bulk disk properties, such as $R_c$ and the disk mass $M_{\rm disk}$. We found an empirical correlation between the gas column density at $R_{\rm CO,\ 90\%}$ and disk mass: $N_{\rm gas}(R_{\rm CO,\ 90\%}) \approx 3.73\times10^{21}(M_{\rm disk}/\mathrm{M}_{\odot})^{0.34}\ \mathrm{cm}^{-2}$. Using this correlation we derive an analytical prescription of $R_{\rm CO,\ 90\%}$ that only depends on $R_c$ and $M_{\rm disk}$. We derive $R_c$ for disks in Lupus, Upper Sco, Taurus and DSHARP, finding that disks in the older Upper Sco region are significantly smaller ($\langle R_c \rangle$ = 4.8 au) than disks in the younger Lupus and Taurus regions ($\langle R_c \rangle$ = 19.8 and 20.9 au, respectively). This temporal decrease in $R_c$ goes against predictions of both viscous and wind-driven evolution, but could be a sign of significant external photoevaporation having truncated disks in Upper Sco., 23 pages, 12 figures, accepted in ApJ

Antibacterial peptides (AMPs) are a kind of peptide material produced by the organism to resist the invasion of exogenous pathogens. Several antimicrobial peptides in different structures have been reported. To elucidate the relationship between structure and activity. A molecular modelling study on the nonapeptides using three-dimensional quantitative structure–activity relationship (3D-QSAR) was carried out. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were employed for the identification of significant features of antimicrobial peptides. CoMFA and CoMSIA models all gave best parametric results (Rcv2 = 0.563, Rpred2 = 0.654 for CoMFA; Rcv2 = 0.656, Rpred2 = 0.8045 for CoMSIA), which suggested that the derived models are highly robust and predictive. In addition, the derived contour maps would provide structural features to improve binding activity, and indicated that steric, electrostatic, and hydrophobic fields play vital roles in improving the activity. The derived models and information may help to provide an insight into the structural requirements for more potent peptides and facilitate the design and optimisation of novel antimicrobial peptides with promising enhanced activity. Furthermore, some potential candidate antimicrobial peptides have been proposed.

BACKGROUND: To evaluate the diagnostic value of quantitative parameters [T1, T2, and proton density (PD) value] generated from magnetic resonance image compilation (MAGiC) sequence for active sacroiliitis in the patients with axial spondyloarthritis (ax-SpA). METHODS: A total of 90 consecutive ax-SpA patients were recruited and divided into an active group (n=48) and inactive group (n=42) based on the Spondyloarthritis Research Consortium Canada (SPARCC) score in this prospective study. In addition, 47 healthy volunteers were recruited as the control group. All participants underwent magnetic resonance (MR) scanning (including MAGiC sequence and T2 mapping sequence) to obtain the T1 value, T2 value, PD value of MAGiC sequence (MAGiC T1 value, T2 value, PD value), and the T2 value of T2 mapping sequence (T2 map T2 value). Intraclass correlation coefficients (ICC) were calculated to assess the inter‑ and intra‑observer agreement. The correlation between the MAGiC T2 value and the T2 map T2 value was analyzed using Spearman’s Rho. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) analysis were performed for all parameters. RESULTS: For the active group, inactive group, and control group, the MAGiC T1 value, T2 value, PD value, and T2 map T2 value were (1,700.91±725.40, 546.58±59.49, 640.25±95.79 ms), (129.37±23.85, 117.16±20.37, 90.52±12.05 ms), (76.47±15.92, 82.69±9.51, 75.51±9.17 pu), and (96.75±16.06, 87.96±9.27, 82.03±10.17 ms), respectively. The difference of the MAGiC T1 value and the MAGiC T2 value in the three groups was statistically significant (P

BACKGROUND: Brain-derived neurotrophic factor (BDNF) gene rs6265 single-nucleotide polymorphism (SNP) is thought to be involved in neuroplasticity and influence the development of levodopa-induced dyskinesia (LID) in Parkinson’s disease (PD). This study aimed to determine how the BDNF rs6265 SNP regulates cortical thickness and to investigate the association between BDNF and the pathological mechanisms of LID in PD. METHODS: This cross-sectional study recruited 75 patients with PD, including 37 patients with LID and 38 patients without LID, and 33 healthy controls. All the participants underwent T1-weighted magnetic resonance imaging (MRI) scans, clinical evaluations, and BDNF rs6265 genotyping. Two-way factorial analysis of covariance (ANCOVA) was used to explore the primary effects of disease status, rs6265 genotype, and their interactions on cortical thickness. Associations between cortical thickness in the regions of the brain affected by disease status-genotype interactions and clinical symptoms were detected using Spearman’s rank-order correlation. Receiver operating characteristic (ROC) curve analysis was used to test cortical thickness measurements as an indicator of LID. RESULTS: The main effects of disease status were observed in the right pars orbitalis (F=4.229, P=0.017), medial orbitofrontal cortex (F=3.639, P=0.030), and left banks superior temporal sulcus (F=3.172, P=0.046). The left pars orbitalis (F=4.541, P=0.036) and lingual gyrus (F=4.307, P=0.041) were thicker in carriers of the CC genotype than in carriers of the TC/TT genotype. Interaction between disease status and genotype showed that in the LID group, carriers of the CC genotype had a thicker left postcentral gyrus (mean difference =0.103, 95% confidence interval, 0.036 to 0.107, Bonferroni-corrected P

Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers., 10 pages. Accepted as a conference paper at KDD '23

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multi-modal tasks.

Observations of substructure in protoplanetary disks have largely been limited to the brightest and largest disks, excluding the abundant population of compact disks which are likely sites of planet formation. Here, we reanalyze ~0.1'', 1.33 mm ALMA continuum observations of 12 compact protoplanetary disks in the Taurus star-forming region. By fitting visibilities directly, we identify substructures in 6 of the 12 compact disks. We then compare the substructures identified in the full Taurus sample of 24 disks in single star systems and the ALMA DSHARP survey, differentiating between compact (R_eff,90% < 50 au) and extended (R_eff,90% > 50 au) disk sources. We find that substructures are detected at nearly all radii in both small and large disks. Tentatively, we find fewer wide gaps in intermediate-sized disks with R_eff,90% between 30 and 90 au. We perform a series of planet-disk interaction simulations to constrain the sensitivity of our visibility-fitting approach. Under an assumption of planet-disk interaction, we use the gap widths and common disk parameters to calculate potential planet masses within the Taurus sample. We find that the young planet occurrence rate peaks near Neptune masses, similar to the DSHARP sample. For 0.01 $M_J/M_\odot$ $\lesssim$ $M_p/M_*$ $\lesssim$ 0.1 $M_J/M_\odot$, the rate is 17.4$\pm$8.3%; for 0.1 $M_J/M_\odot$ $\lesssim$ $M_p/M_*$ $\lesssim$ 1 $M_J/M_\odot$, it is 27.8$\pm$8.3%. Both of them are consistent with microlensing surveys. For gas giants more massive than 5 $M_J$, the occurrence rate is 4.2$\pm$4.2%, consistent with direct imaging surveys., 30 pages, 15 figures, 6 tables, accepted to be published in The Astrophysical Journal

Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come from stock selectivity ability instead of market timing. However for some reason such as fund capacity limit, fund manager change or market regional switch, it is very hard to find a fund could continuously beat market. Therefore, in order to get excess return from mutual fund industry, we use quantitative way to build the sparse portfolio that take advantage of favorite stocks by mutual fund in China A market. Firstly we do the analysis about favourite stocks by mutual fund and compare the different method to construct our portfolio. Then we build a sparse stock portfolio with constraint on both individual stock and industry exposure using portfolio optimizer to closely track the partial equity funds index 930950.CSI with median 0.985 correlation. This problem is much more difficult than tracking full information index or traditional ETF as higher turnover of mutual fund, just first 10 holding of mutual fund available and fund report updated quarterly with 15 days delay. Finally we build another low risk and balanced sparse portfolio that consistently outperform benchmark 930950.CSI., Portfolio construction, Sparsity, Index tracking, Mutual fund, China A share market

Aiming at high accuracy of dissolved gas content prediction in transformer oil, a novel method based on gated recurrent unit and adaptive graph convolution network (GRU-AGCN) is proposed. For gated recurrent unit (GRU) can selectively choose the feature of time series, it is used to extract time series information of the gas content. Correlation among gases are extracted to improve the accuracy. The original adjacency matrix of the model is constructed according to the grey relational analysis (GRA), and the dynamic relation information between gases is extracted by adaptive graph convolution network (AGCN). The experimental result shows that the GRU-AGCN model can efficiently extract the temporal features and perceive the dynamic relationship of gases. The predictions error of the proposed method is lower than that of RNN, LSTM network and GRU network. The proposed method provides a reliable and accurate result for the prediction of dissolved gas content in transformer oil.

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China's CSI 300 and CSI 500 universe which represent China's large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index., Volatility model, Multi-factors model, Portfolio construction, China A share market

2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1595 and 1403 features for the 3D and 2D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM) based prediction models were developed and optimized for each feature set. 5-fold cross-validation was used to assess the performance of each individual model. The results show that the 2D feature-based model achieved an AUC (area under the ROC curve [receiver operating characteristic]) of 0.84+-0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86+-0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91+-0.01. This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future., For Some modifications and error pruning, we need to withdraw the paper

The key phytohormone auxin is involved in practically every aspect of plant growth and development. Auxin regulates these processes by controlling gene expression through functionally distinct AUXIN RESPONSE FACTORs (ARFs). As a noncanonical ARF, ARF3/ETTIN (ETT) mediates auxin responses to orchestrate multiple developmental processes during the reproductive phase. The arf3 mutation has pleiotropic effects on reproductive development, causing abnormalities in meristem homeostasis, floral determinacy, phyllotaxy, floral organ patterning, gynoecium morphogenesis, ovule development, and self-incompatibility. The importance of ARF3 is also reflected in its precise regulation at the transcriptional, posttranscriptional, translational, and epigenetic levels. Recent studies have shown that ARF3 controls dynamic shoot apical meristem (SAM) maintenance in a non-cell autonomous manner. Here, we summarize the hierarchical regulatory mechanisms by which ARF3 is regulated and the diverse roles of ARF3 regulating developmental processes during the reproductive phase.

BackgroundFear of birth (FOB) is becoming increasingly recognized as a mental health issue that may endanger maternal and infant health and affects women’s subsequent fertility desires. It has also been shown to be related to the choice of delivery mode. Given the differences in healthcare systems and policies between countries, and the gaps in the exploration of women’s experience of fear of birth and its association with the delivery mode in the Chinese cultural context, this study thus attempt to understand Chinese women’s experience with fear of birth and their preferences for delivery mode through a qualitative study.MethodsA descriptive qualitative research was performed among twenty pregnant women from the obstetric outpatient of a tertiary hospital in China. Colaizzi’s method was used to analyze the participants’ data.ResultsWe proposed three themes and nine subthemes on the participants’ experience with fear of birth: (1) an invisible dilemma: trapped in lingering fear (fear of all sides, the untold loneliness, and ambivalence with mixed feelings); (2) an unexpected decision: choose to give birth naturally (initiative selection and passive acceptance); and (3) A strength to confront challenges head-on: move forward with fear (awaken of maternal spirit, hope in bloom, Chinese tolerance culture, and obstetric analgesia).ConclusionFear of birth is a complex emotion, accompanied by feelings of loneliness and ambivalence in addition to fear. We found that women with fear of birth in this study prefer vaginal birth, and it was revealed to be the result of a combined action of intrinsic and extrinsic factors.

Peer reviewed: True, Acknowledgements: We want to thank the Lee Lab, the FinnGen study, the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC), and the Genetic Epidemiology Research on Aging (GERA) for sharing data. XL: the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001). XYW: National Natural Science Foundation of China (81970494) and Key Project of Research and Development Plan of Hunan Province(2019SK2041); SCL: the Swedish Heart Lung Foundation (Hjärt-Lungfonden, 20210351), the Swedish Research Council (Vetenskapsrådet, 2019–00977), and the Swedish Cancer Society (Cancerfonden). Funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication., Funder: Cancerfonden; FundRef: http://dx.doi.org/10.13039/501100002794, BACKGROUND: Whether the positive associations of smoking and alcohol consumption with gastrointestinal diseases are causal is uncertain. We conducted this Mendelian randomization (MR) to comprehensively examine associations of smoking and alcohol consumption with common gastrointestinal diseases. METHODS: Genetic variants associated with smoking initiation and alcohol consumption at the genome-wide significance level were selected as instrumental variables. Genetic associations with 24 gastrointestinal diseases were obtained from the UK Biobank, FinnGen study, and other large consortia. Univariable and multivariable MR analyses were conducted to estimate the overall and independent MR associations after mutual adjustment for genetic liability to smoking and alcohol consumption. RESULTS: Genetic predisposition to smoking initiation was associated with increased risk of 20 of 24 gastrointestinal diseases, including 7 upper gastrointestinal diseases (gastroesophageal reflux, esophageal cancer, gastric ulcer, duodenal ulcer, acute gastritis, chronic gastritis, and gastric cancer), 4 lower gastrointestinal diseases (irritable bowel syndrome, diverticular disease, Crohn's disease, and ulcerative colitis), 8 hepatobiliary and pancreatic diseases (non-alcoholic fatty liver disease, alcoholic liver disease, cirrhosis, liver cancer, cholecystitis, cholelithiasis, and acute and chronic pancreatitis), and acute appendicitis. Fifteen out of 20 associations persisted after adjusting for genetically predicted alcohol consumption. Genetically predicted higher alcohol consumption was associated with increased risk of duodenal ulcer, alcoholic liver disease, cirrhosis, and chronic pancreatitis; however, the association for duodenal ulcer did not remain statistically significant after adjustment for genetic predisposition to smoking initiation. CONCLUSIONS: This study provides MR evidence supporting causal associations of smoking with a broad range of gastrointestinal diseases, whereas alcohol consumption was associated with only a few gastrointestinal diseases. FUNDING: The Natural Science Fund for Distinguished Young Scholars of Zhejiang Province; National Natural Science Foundation of China; Key Project of Research and Development Plan of Hunan Province; the Swedish Heart Lung Foundation; the Swedish Research Council; the Swedish Cancer Society.

Alpine lakes on the Tibetan Plateau are highly sensitive to global change and have been recognized as the sentinel of climate warming. However, anthropogenic impacts in populated area are migrating to these remote areas via transporting particulate nutrients by atmospheric deposition. Whether warming and nutrient deposition would impose additive or synergistic effects on the lake ecosystem remains largely unknown. Here, we present multi-proxy (sediment pigment and geochemistry) records during the past two centuries at the Cuoqia Lake in the southeast Tibetan Plateau. We found that the lake exhibited rapid ecological changes since 1980 AD characterized by an increase in primary productivity due to algal proliferation, with more rapid growth of green algae and diatoms. These findings are in concert with many other lakes (e.g., Moon Lake and Shade Co) in the same area, suggesting a consistent pattern of ecosystem evolution at the region scale. Statistical analyses suggested that nutrient deposition and climate warming were strongly associated with the variations in primary productivity and algae composition, exerting both individual and interactive effects. In addition, scenario analyses with a well-established process-based ecosystem model further revealed that the two factors not only individually, but also synergistically promoted the algal proliferation and community succession. Such synergy is evident in that the effect of lake warming would be more pronounced under higher nutrient deposition scenario, which is potentially due to higher temperature-driven mineralization in warmer conditions, and higher efficiency of nutrient utilization under enhanced light availability attributing to declining ice thickness and duration in cold seasons. Overall, our study proposes the existence and quantifies the synergistic impacts of climate warming and anthropogenic activities in driving the ecological changes in remote alpine lakes on the Tibetan Plateau. The lake ecological consequences driven by individual factor would be worsen by such synergy, so that we cannot predict the lake ecosystem trajectory in the future based on each factor separately, and more efforts than previously expected would be needed for the lake restoration and management.

Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-demanding, especially for medical images. To alleviate this problem, we propose to utilize solely scribble annotations for weakly supervised segmentation. Existing solutions mainly leverage selective losses computed solely on annotated areas and generate pseudo gold standard segmentation by propagating labels to adjacent areas. However, these methods could suffer from the inaccurate and sometimes unrealistic pseudo segmentation due to the insufficient supervision and incomplete shape features. Different from previous efforts, we first investigate the principle of ''good scribble annotations'', which leads to efficient scribble forms via supervision maximization and randomness simulation. Furthermore, we introduce regularization terms to encode the spatial relationship and shape prior, where a new formulation is developed to estimate the mixture ratios of label classes. These ratios are critical in identifying the unlabeled pixels for each class and correcting erroneous predictions, thus the accurate estimation lays the foundation for the incorporation of spatial prior. Finally, we integrate the efficient scribble supervision with the prior into a unified framework, denoted as ZScribbleSeg, and apply the method to multiple scenarios. Leveraging only scribble annotations, ZScribbleSeg set new state-of-the-arts on four segmentation tasks using ACDC, MSCMRseg, MyoPS and PPSS datasets., 31 pages, 10 figures

Reducing carbon emissions is an urgent need in the field of marine power. Gas turbines are of great importance in the marine industry. The use of clean or industrial-associated fuels can increase the fuel adaptability of designed, manufactured, or in-service gas turbines to realize the goal of expanding fuel sources, reducing fuel waste, lowering energy demand, and remitting environmental pressure. By changing from fossil fuel to alternative energy, the change in the physical properties of the combustion products will lead to changes in the working medium of the turbines, which result in a profound effect on the performance. In this study, based on the actual law of working medium property change, the evolution mechanism of turbine characteristics is lucubrated in depth, focusing on the key parameters of the influence of working medium properties on turbine characteristics under alternative fuel conditions, and a correction method is proposed to predict the evolution law of the turbine characteristics as working medium varies.

The Pulmonary Function Test (PFT) is an widely utilized and rigorous classification test for lung function evaluation, serving as a comprehensive tool for lung diagnosis. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder network with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then concatenated to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data collected from 137 subjects were finally used for training. The model is validated by ten-fold and leave-one-out cross-validation first. The accuracy and sensitivity of normal ventilation mode are 0.95 and 1.00, and the f1-score is 0.94. Furthermore, we check the reliability and feasibility of the proposed pipeline by testing it on newly recruited nine subjects. Our results show that the pipeline correctly predicts the ventilation mode of 8 out of 9 subjects. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Additional file 2. SPIRIT 2013 Checklist.

Additional file 1: Table S1. Experimental design and basic sequencing statistics for samples collected from five plant species. Table S3. Experimental design and basic sequencing statistics for samples collected from tomato plants at different growth stages. Table S9. Experimental design and basic traits for samples collected from tomato plants amended with different nitrogen sources. Table S11. Experimental design and basic sequencing statistics from metaproteomic analyses. Figure S1. Order-level community comparison and OTU-based hierarchical clustering analysis of healthy and nematode parasitized samples in five plant species. Figure S2. Unconstrained PCoA (for principal coordinates PCo1 and PCo2) with Bray–Curtis distance showing the root-associated bacteria of healthy and parasitized samples, for each plant species. Figure S3. The composition and relative abundance of major bacterial orders of the tomato root-associated microbiota at different developmental stages. Figure S4. Pairwise analysis for the variation of root-associated microbiota over time at different growth and RKN parasitism stages in tomato. Figure S5. The shared OTUs number and taxonomic composition of the depleted and enriched OUTs in the parasitized root samples (IRH and IRK) comparing with healthy roots (HRC).

Additional file 9: Supplementary Table 2. The diameter of gross specimens collected from CDTX.

Theory of resonator-assisted measurement schemes, detailed fabrication results, and performance comparison of reported waveguide crossings

In this paper, we mainly focus on the rigorous convergence analysis for two fully decoupled, unconditional energy stable methods of the two-phase magnetohydrodynamics (MHD) model, which described in our previous work \cite{2022Highly}. The two methods consist of semi-implicit stabilization method/invariant energy quadratization (IEQ) method \cite{2019EfficientCHEN, Yang2016Linear, Yang2017Efficient, 2019EfficientYANG} for the phase field system, the pressure projection correction method for the saddle point MHD system, the exquisite implicit-explicit treatments for nonlinear coupled terms, which leads to only require solving a sequence of small elliptic equations at each time step. As far as we know, it's the first time to establish the optimal convergence analysis of fully decoupled and unconditional energy stable methods for multi-physics nonlinear two-phase MHD model. In addition, several numerical examples are showed to test the accuracy and stability of the presented methods.

Additional file 3: Figure S1. Protein interaction analysis by STRING database (left) and KEGG pathway clustering analysis (right) in cell lysis group candidate proteins. Figure S2. Protein interaction analysis by STRING database (left) and KEGG pathway clustering analysis (right) in live cell group candidate proteins. Figure S3. Expression and purification of candidate protein. Figure S4. Detailed SPR results of interaction of DA with candidate proteins.

Coronarin E is a main diterpene ever isolated from Hedychium yunnanense. With the aim to enlarge its potential application, four butenolide derivatives (compounds 4a, 4b, 5a and 5b) were obtained from coronarin E via synthetic method, and their antibacterial effects were also evaluated. It is noteworthy that compounds 5a and 5b exhibited stronger antibacterial activities against most of the tested bacterial strains than ampicillin and kanamycin, two first- and second-line antimicrobials in clinical. For example, minimum inhibitory concentration (MIC) of 5a, 5b, ampicillin and kanamycin against Acinetobacter baumanii were 2, 1, 8 and 4 μg/mL, respectively, and MIC of the four compounds mentioned above against Klebsiella pneumonia were 1, 0.5, 16 and 4 μg/mL, respectively. The current studies not only enrich the structural diversity of diterpenes derived from Hedychium genus, but also provide potent candidates for the development of antibacterial medicines.

Additional file 1: Supplementary Figure 1. Gene ontology functional enrichment maps from the CGGA dataset.

Additional file 6. Review history

Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation.

Integrated microwave photonics is an intriguing field that leverages integrated photonic technologies for the generation, transmission, and manipulation of microwave signals in chip-scale optical systems. In particular, ultrafast processing and computation of analog electronic signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters, microwave signal processing, and image recognition. An ideal photonic platform for achieving these integrated MWP processing tasks shall simultaneously offer an efficient, linear and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power, and a low-loss functional photonic network that can be configured for a variety of signal processing tasks, as well as large-scale, low-cost manufacturability to monolithically integrate the two building blocks on the same chip. In this work, we demonstrate such an integrated MWP processing engine based on a thin-film lithium niobate platform capable of performing multi-purpose processing and computation tasks of analog signals up to 92 giga samples per second at CMOS-compatible voltages. We demonstrate high-speed analog computation, i.e., first- and second-order temporal integration and differentiation with computing accuracies up to 98.1 %, and deploy these functions to showcase three proof-of-concept applications, namely, ordinary differential equation solving, ultra-wideband signal generation and high-speed edge detection of images. We further leverage the image edge detector to enable a photonic-assisted image segmentation model that could effectively outline the boundaries of melanoma lesion in medical diagnostic images, achieving orders of magnitude faster processing speed and lower power consumption than conventional electronic processors.

Additional file 1. Trial registration Information.

Supplementary Material 1

Accurately predicting the performance of architecture with small sample training is an important but not easy task. How to analysis and train dataset to overcome overfitting is the core problem we should deal with. Meanwhile if there is the mult-task problem, we should also think about if we can take advantage of their correlation and estimate as fast as we can. In this track, Super Network builds a search space based on ViT-Base. The search space contain depth, num-heads, mpl-ratio and embed-dim. What we done firstly are pre-processing the data based on our understanding of this problem which can reduce complexity of problem and probability of over fitting. Then we tried different kind of models and different way to combine them. Finally we choose stacking ensemble models using GP-NAS with cross validation. Our stacking model ranked 1st in CVPR 2022 Track 2 Challenge., Comment: Ranked 1st in CVPR 2022 Track 2 Challenge, GP-NAS, Stacking Model, Ensemble Model

Supplementary Material 2

Our findings establish a role for gut microbes in the progression of estrogen deficiency-induced lipid metabolism disorders and reveal key target bacteria that may have the potential to regulate acylcarnitine synthesis. These findings suggest a possible route for the use of microbes or acylcarnitine to regulate disorders of lipid metabolism induced by estrogen deficiency.

Additional file 8: Supplementary Table 1. Primers, antibodies, Lentivirus (LV) and adenovirus (ADV).

'Chasing' refers to a tendency to increase betting in an effort to recoup prior losses (i.e., 'loss chasing') or to satisfy an increased gambling desire following wins (i.e., 'win chasing'). Chasing characterizes the transition from recreational to problem gambling, but few studies have examined this behaviour systematically. Using online gambling data, I sought a holistic understanding of chasing by capturing between- and within-session chasing behaviour. Further, I evaluated a behavioural intervention to reduce loss chasing. Chapters 2, 3, and 4 examined between- and within-session chasing using naturalistic data from a gambling website (PlayNow.com in British Columbia, Canada). For between-session chasing, average gamblers returned more slowly after a losing session and returned more quickly after a winning session. Within-session chasing depends on game type, chasing measurement (bet amount vs. quit probability), and outcome timeframe (immediate vs. cumulative outcomes). Across most games, loss chasing depended more on timeframe but not measurement, whereas win chasing depended more on measurement. After losing more on the last bet, gamblers staked larger amounts over a longer session, but when cumulative losses mounted, gamblers staked smaller amounts over a shorter session. After winning more, gamblers bet more over a shorter session. Chapter 4 used enrolment in the Voluntary Self-Exclusion (VSE) program as a proxy for likely gambling problems. While conventional assessments of gambling problems focus on between-session loss chasing, it did not differentiate VSE gamblers from Non-VSE gamblers. Instead, these groups differed in their within-session loss chasing tendencies. Therefore, future research aiming to identify high-risk gambling patterns should also consider within-session behavioural markers. Chapter 5 recruited gamblers from the survey platform, Prolific, and examined the effectiveness of a novel ‘cashing out’ procedure in alleviating loss chasing. In non-problem gamblers, the cashing out manipulation significantly lowered the amount wagered, aligned with the realization effect. There was no interaction between the cashing-out condition and the gambling group, although the cashing-out procedure was not statistically significant in the at-risk or problem gambling groups. These findings provide future directions for identifying gambling problems based on behavioural tracking data, and present proof of concept data for a new digital harm reduction tool.

Additional file 2: Fig. S2. Distributions of aging DMRs in different genomic elements Numbers of aging DMRs found in genomic regions containing gene structures versus intergenic regions (A) or in repeat versus non-repeat sequences (B). Abbreviations: TSS-7k, within 7kB upstream of TSS; GB, gene body; IG, intergenic region; LINE, long interspersed nuclear elements; SINE, short interspersed nuclear elements; SimRep: simple repeats (micro-satellites); Satellite, satellite repeats; Others: other repeat categories as defined in RMSK from the UCSC genome browser ( https://genome.ucsc.edu/cgi-bin/hgTables?db=hg38&hgta_group=rep&hgta_track=rmsk&hgta_table=rmsk&hgta_doSchema=describe+table+schema ); nonRep: non-repeat regions.

Supplemental Material Fabrication of antibacterial polydopamine-carboxymethyl cellulose-Ag nanoparticle hydrogel coating for urinary catheters by Yongwei Cai, Ronghua Gu, Yuhang Dong, Qi Zhao, Ke Zhang, Changyuan Cheng, Hong Yang, Jianxiang Li, and Xinggen Yuan in Journal of Biomaterials Applications

To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this problem, we present CameraPose, a weakly-supervised framework for 3D human pose estimation from a single image, which can not only be applied on 2D-3D pose pairs but also on 2D alone annotations. By adding a camera parameter branch, any in-the-wild 2D annotations can be fed into our pipeline to boost the training diversity and the 3D poses can be implicitly learned by reprojecting back to 2D. Moreover, CameraPose introduces a refinement network module with confidence-guided loss to further improve the quality of noisy 2D keypoints extracted by 2D pose estimators. Experimental results demonstrate that the CameraPose brings in clear improvements on cross-scenario datasets. Notably, it outperforms the baseline method by 3mm on the most challenging dataset 3DPW. In addition, by combining our proposed refinement network module with existing 3D pose estimators, their performance can be improved in cross-scenario evaluation.

Supplemental Material for Short- and Long-term Outcomes of the Minimal Proximal Resection Margin in Total Gastrectomy for Siewert II Adenocarcinoma of the Esophagogastric Junction by Wei Guo, PhD, Jinguo Hao, MD, Xianghuang Mei, PhD, Yangyang Wang, MD, Zhipeng He, MD, Shi Su, MD, Ke Zhang, MD, Xiaoqi Guan, MD, Jingcheng Yang, MD, and Jiake Lv, MD in The American Surgeon.

Previous analyses of mid-infrared water spectra from young protoplanetary disks observed with the Spitzer-IRS found an anti-correlation between water luminosity and the millimeter dust disk radius observed with ALMA. This trend was suggested to be evidence for a fundamental process of inner disk water enrichment, used to explain properties of the Solar System 40 years ago, in which icy pebbles drift inward from the outer disk and sublimate after crossing the snowline. Previous analyses of IRS water spectra, however, were very uncertain due to the low spectral resolution that blended lines together. We present new JWST-MIRI spectra of four disks, two compact and two large with multiple radial gaps, selected to test the scenario that water vapor inside the snowline is regulated by pebble drift. The higher spectral resolving power of MIRI-MRS now yields water spectra that separate individual lines, tracing upper level energies from 900 K to 10,000 K. These spectra clearly reveal excess emission in the low-energy lines in compact disks, compared to the large disks, establishing the presence of a cooler component with $T \approx$ 170-400 K and equivalent emitting radius $R_{\rm{eq}}\approx$ 1-10 au. We interpret the cool water emission as ice sublimation and vapor diffusion near the snowline, suggesting that there is indeed a higher inwards mass flux of icy pebbles in compact disks. Observation of this process opens up multiple exciting prospects to study planet formation chemistry in inner disks with JWST., Comment: Posted as submitted to ApJ Letters; feedback and input from the community is welcome

The cloud is still a popular platform for distributed deep learning (DL) training jobs since resource sharing in the cloud can improve resource utilization and reduce overall costs. However, such sharing also brings multiple challenges for DL training jobs, e.g., high-priority jobs could impact, even interrupt, low-priority jobs. Meanwhile, most existing distributed DL training systems require users to configure the resources (i.e., the number of nodes and resources like CPU and memory allocated to each node) of jobs manually before job submission and can not adjust the job's resources during the runtime. The resource configuration of a job deeply affect this job's performance (e.g., training throughput, resource utilization, and completion rate). However, this usually leads to poor performance of jobs since users fail to provide optimal resource configuration in most cases. \system~is a distributed DL framework can auto-configure a DL job's initial resources and dynamically tune the job's resources to win the better performance. With elastic capability, \system~can effectively adjusts the resources of a job when there are performance issues detected or a job fails because of faults or eviction. Evaluations results show \system~can outperform manual well-tuned resource configurations. Furthermore, in the production Kubernetes cluster of \company, \system~reduces the medium of job completion time by 31\%, and improves the job completion rate by 6\%, CPU utilization by 15\%, and memory utilization by 20\% compared with manual configuration., Comment: 10 pages, and open source system work

Additional file 1: Fig. S1. Analyses of bisulfite-converted genomic DNA (gDNA) libraries (A) Flow chart showing major steps in whole-genome bisulfite sequencing (WGBS). (B) Bioanalyzer analysis of the length and integrity of four examples of bisulfite-converted libraries prepared from 2-m/o (A2R0), 8-m/o (A8R0), and 16-m/o (A16R0) baseline livers and 2-m/o regenerating livers (2d after 70% partial hepatectomy, A2R2). (C) Quality analysis of WGBS data of 12 methylomes of 2, 8, and 16-m/o mouse livers with four biological replicates for each group. (D) Quality analysis of WGBS data of 12 methylomes of 2-m/o regenerating livers at 1d, 2d, and 4d after 70% partial hepatectomy (n=4 for each group).