Causes and treatment strategies of unilateral leaflet escape of bileaflet mechanical prosthetic heart valves after surgery: a case series.

BACKGROUND: During the eleven years from 2010 to 2021, preliminary statistics have shown that Fuwai Hospital completed 23,571 mechanical valve replacements for various types of valves, and 1139 mechanical valve replacements were performed in Guangyuan First People's Hospital. Only two patients developed valve leaflet escape, so valve leaflet escape is a rare postoperative complication. CASE PRESENTATION: In 2010 and 2021, two patients were selected after they had unilateral leaflet escape after having mechanical valve replacements in Fuwai Hospital of Chinese Academy of Medical Sciences and Guangyuan First People's Hospital. Both patients underwent reoperations with the classic operation and the new bileaflet mechanical prosthetic heart valve was sutured. The treatment of detached single lobe and distal vessel was comprehensively determined, and the condition was treated according to the patient's symptoms, CT results, ultrasound results and other test results, as well as whether this detached lobe caused any abnormal hemodynamics of the distal vessel. The patient with mechanical aortic valve escape completed the 10-year follow-up, and patient with mechanical mitral valve escape completed the 3-month follow-up. there was no thrombosis or hematoma at the embolic site; the patient had no lower limb symptoms. CONCLUSIONS: The reason for the leaflet escape may be related to the valve design and the leaflet material. If the detached leaflets are damaged and if the distal blood vessels are affected, simultaneous surgical treatment is required. Those patients whose vessels were not damaged by the valve lobe should be carefully monitored.

Genome-Wide Detection of Quantitative Trait Loci and Prediction of Candidate Genes for Seed Sugar Composition in Early Mature Soybean.

Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean.

Temporal contrast improvement through cascaded second-order nonlinear processes in a thin BBO crystal.

We demonstrate temporal contrast improvement through cascaded second-order nonlinear processes in a 340-µm BBO crystal. The process was initiated by second harmonic (SH) generation, followed by difference frequency generation (DFG) between the SH and the short wavelength part of the fundamental wave (FW). The idler of DFG was selected by a spectral filter, and an output pulse energy of 573 µJ was obtained at 1 kHz with excellent spatial profile and a power fluctuation as low as 0.076% (rms) in 14 hours. The temporal contrast was improved by more than 2 orders of magnitude to approximately 1011, which could be further enhanced with different spectral filters. The excellent stability, energy scalability, and contrast enhancement ability make this simple and robust method very suitable to be integrated into the pulse cleaning system in many different ultra-intense laser facilities.

Effect of 3D-printed hearts used in left ventricular outflow tract obstruction: a multicenter study.

OBJECTIVE: The purpose of this research was to explore the application value of a three-dimensional (3D)-printed heart in surgery for left ventricular outflow tract (LVOT) obstruction. METHODS: From August 2019 to October 2021, 46 patients with LVOT obstruction underwent surgical treatment at our institution. According to the treatment method, 22 and 24 patients were allocated to the experimental and control groups, respectively. In the experimental group, each patient's 3D-printed heart model was used for simulated preoperative surgery, and then the Morrow operation was performed. In the control group, only the Morrow operation was performed, without simulated preoperative surgery using a 3D-printed heart model. The intraoperative and postoperative data of patients in the two groups were recorded, and the clinical data of patients were compared between the two groups. RESULTS: The operation time, cardiopulmonary bypass time, intraoperative blood loss, hospitalization time, LVOT pressure difference (LVP), postoperative interventricular septal thickness (IST), aortic regurgitation (AR), systolic anterior motion (SAM), and postoperative left ventricular flow velocity (LVFV) were significantly lower in the experimental group than in the control group (P < 0.05). The inner diameter of the left ventricular outflow tract (IDLV) was larger in the experimental group than in the control group (P < 0.05). There was no significant difference in the postoperative ejection fraction, atrioventricular block rate or complication rate between the two groups (P > 0.05). CONCLUSION: A 3D-printed heart model for simulated surgery in vitro is conducive to formulating a more reasonable surgical plan and reducing the trauma and duration of surgery, thereby promoting the recovery and maintenance of the heart.

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media.

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery.

Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods.

Biochemical process of ureolysis-based microbial CaCO3 precipitation and its application in self-healing concrete.

Urea hydrolysis has already been considered as the most effective pathway for microbially induced CaCO3 precipitation (MICP). The present work first studied the combination of several key factors including initial pH, temperature, and dosage of urea, which contribute to the biochemical process of MICP. Under an amiable condition of pH and temperature, the dosage of urea has a significant impact on the rate of urea degradation and CaCO3 precipitation. A bacteria-based self-healing system was developed by loading healing agents on ceramsite carriers. The self-healing efficiency was evaluated by visual inspection on crack closure, compressive strength regain, and capillary water absorption. A preferable healing effectiveness was obtained when the bacteria and organic nutrients were co-immobilized in carriers. Image analysis showed that cracks up to 273 µm could be healed with a crack closure ratio of 86% in 28 days. The compressive strength regain increased 24% and the water absorption coefficient decreased 27% compared to the reference. The findings indicated a promising application of ureolysis-based MICP in restoring the mechanical properties and enhancing the durability of concrete.

Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm.

BACKGROUND: Soybean (Glycine max) is a photoperiod-sensitive and self-pollinated species. Days to flowering (DTF) and maturity (DTM), duration of flowering-to-maturity (DFTM) and plant height (PH) are crucial for soybean adaptability and yield. To dissect the genetic architecture of these agronomically important traits, a population consisting of 309 early maturity soybean germplasm accessions was genotyped with the Illumina Infinium SoySNP50K BeadChip and phenotyped in multiple environments. A genome-wide association study (GWAS) was conducted using a mixed linear model that involves both relative kinship and population structure. RESULTS: The linkage disequilibrium (LD) decayed slowly in soybean, and a substantial difference in LD pattern was observed between euchromatic and heterochromatic regions. A total of 27, 6, 18 and 27 loci for DTF, DTM, DFTM and PH were detected via GWAS, respectively. The Dt1 gene was identified in the locus strongly associated with both DTM and PH. Ten candidate genes homologous to Arabidopsis flowering genes were identified near the peak single nucleotide polymorphisms (SNPs) associated with DTF. Four of them encode MADS-domain containing proteins. Additionally, a pectin lyase-like gene was also identified in a major-effect locus for PH where LD decayed rapidly. CONCLUSIONS: This study identified multiple new loci and refined chromosomal regions of known loci associated with DTF, DTM, DFTM and/or PH in soybean. It demonstrates that GWAS is powerful in dissecting complex traits and identifying candidate genes although LD decayed slowly in soybean. The loci and trait-associated SNPs identified in this study can be used for soybean genetic improvement, especially the major-effect loci associated with PH could be used to improve soybean yield potential. The candidate genes may serve as promising targets for studies of molecular mechanisms underlying the related traits in soybean.

Identification and validation of quantitative trait loci for seed yield, oil and protein contents in two recombinant inbred line populations of soybean.

Soybean seeds contain high levels of oil and protein, and are the important sources of vegetable oil and plant protein for human consumption and livestock feed. Increased seed yield, oil and protein contents are the main objectives of soybean breeding. The objectives of this study were to identify and validate quantitative trait loci (QTLs) associated with seed yield, oil and protein contents in two recombinant inbred line populations, and to evaluate the consistency of QTLs across different environments, studies and genetic backgrounds. Both the mapping population (SD02-4-59 × A02-381100) and validation population (SD02-911 × SD00-1501) were phenotyped for the three traits in multiple environments. Genetic analysis indicated that oil and protein contents showed high heritabilities while yield exhibited a lower heritability in both populations. Based on a linkage map constructed previously with the mapping population and using composite interval mapping and/or interval mapping analysis, 12 QTLs for seed yield, 16 QTLs for oil content and 11 QTLs for protein content were consistently detected in multiple environments and/or the average data over all environments. Of the QTLs detected in the mapping population, five QTLs for seed yield, eight QTLs for oil content and five QTLs for protein content were confirmed in the validation population by single marker analysis in at least one environment and the average data and by ANOVA over all environments. Eight of these validated QTLs were newly identified. Compared with the other studies, seven QTLs for seed yield, eight QTLs for oil content and nine QTLs for protein content further verified the previously reported QTLs. These QTLs will be useful for breeding higher yield and better quality cultivars, and help effectively and efficiently improve yield potential and nutritional quality in soybean.

Self-compression of laser pulses induced by asymmetric self-phase modulation aided by backward Raman scattering in periodic density-modulated plasma.

Here a mechanism for self-compression of laser pulses is presented, based on period density-modulated plasma. In this setup, two pump beams intersect at a small angle within the plasma. This interaction is facilitated by the ponderomotive ion mechanism, which causes a modulation in the density of plasma with long wavelengths and low amplitude. This modulation enhances the backward Raman scattering of the probe pulse. The trailing edge of the probe experiences greater energy loss, resulting in a steeper intensity gradient. This, in turn, induces an asymmetric self-phase modulation, which elevates the instantaneous frequency. It is notable that the laser in plasma exhibits opposite group velocity dispersion compared to traditional solid-state media. This unique property allows laser pulses to undergo dispersion compensation while broadening the spectrum, ultimately leading to self-compression. The 2D-PIC simulations demonstrate these phenomena, highlighting how period density-modulated plasma contributes to an asymmetric spectral distribution. The intricate interplay among self-phase modulation, group velocity, and backward Raman scattering results in the self-compressing of the laser pulse. Specifically, the pulses are compressed from their Fourier transform limit duration of 50 fs to a significantly reduced duration of 8 fs at plasma densities below 1/4 critical density, without the transverse self-focusing effects.

Uncertainty Estimation With Neural Processes for Meta-Continual Learning.

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.

[ZHOU Mei-sheng's academic thought and clinical application of "three-phase moxibustion sensation"].

This paper collects professor ZHOU Mei-sheng's academic thought, "three-phases moxibustion sensation" and expounds its clinical value. Proposed by professor ZHOU, in accordance with the occurrence and development characteristics of the moxibustion propagated sensation, three time phases of moxibustion propagated sensation are divided, i.e. directional conduction phase (the first time phase), effect onset phase (the second time phase), and descending suspension and along-meridian re-transmission phase (the third time phase). In terms of the different characteristics among these three time phases, the clinical therapeutic regimens are designed accordingly. It provides a novel approach to the clinical application of moxibustion.

Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging.

Infrared thermal technology plays a vital role in the health condition monitoring of gearbox. In the traditional infrared thermal technology-based methods, Gaussian pyramid is applied as the feature extraction approach, which has disadvantages of noise influence and information missing. Focus on such disadvantages, an improved multi-scale decomposition method combined with convolutional neural network is proposed to extract the fault features of the multi-scale infrared images in this paper. It can enlarge the data length at large scales, and thus reduce the fluctuations of feature values and reserve the fault information. The effectiveness of the proposed method is validated using the experiment infrared data of one industrial gearbox. Results demonstrate that our proposed method has the best performance comparing with five methods.

Agglomerative Neural Networks for Multiview Clustering.

Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K -means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.

Transcriptomic Analysis Reveals the Regulatory Networks and Hub Genes Controlling the Unsaturated Fatty Acid Contents of Developing Seed in Soybean.

Soybean [Glycine max (L.) Merr.] is one of the most important crops, which produces about 25% of the world's edible oil. The nutritional value of soybean oil depends mostly on the relative contents of three unsaturated fatty acids (UFAs), i.e., oleic acid, linoleic acid (LA), and linolenic acid. However, the biosynthetic mechanism of UFAs remains largely unknown, and there are few studies on RNA-seq analysis of developing seeds. To identify the candidate genes and related pathways involved in the regulation of UFA contents during seed development in soybean, two soybean lines with different UFA profiles were selected from 314 cultivars and landraces originated from Southern China, and RNA-seq analysis was performed in soybean seeds at three developmental stages. Using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, a series of genes and pathways related to fatty acid metabolism were identified, and 40 days after flowering (DAF) was found to be the crucial period in the formation of UFA profiles. Further, weighted gene co-expression network analysis identified three modules with six genes whose functions were highly associated with the contents of oleic and LA. The detailed functional investigation of the networks and hub genes could further improve the understanding of the underlying molecular mechanism of UFA contents and might provide some ideas for the improvement in fatty acids profiles in soybean.

Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems.

Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods.

Cardiopulmonary haemodynamics after sieve-shaped atrial septal defect repair: a multicenter study.

OBJECTIVE: The study aimed to collect case data on cardiopulmonary bypass (CPB) sieve-shaped (S-S) and non-sieve-shaped (N-S-S) atrial septal defects (ASDs). METHODS: We analysed and summarized the postoperative blood flow in the cardiopulmonary system. We retrospectively collected 86 patients who underwent CPB S-S and N-S-S ASD repair. The data collected included sex, age, CPB time, ASD area, percentage change in ventricular value (PVV) (preoperative/postoperative), left ventricular wall thickness, ejection fraction (EF) (preoperative/postoperative), fluid inflow value, pulmonary arterial pressure/pulmonary venous pressure, percentage change in total lung resistance (PTLR) (preoperative/postoperative) for statistical analysis and comparison. RESULTS: There were 86 eligible patients in this study, 37 and 49 of whom had S-S and N-S-S ASDs, respectively. The PVV, PTLR, and pulmonary arterial pressure/pulmonary venous pressure (postoperative) were significantly different between the S-S and N-S-S groups. The mean PTLR in the S-S and N-S-S groups was 0.78±0.24 and 0.62±0.28, respectively. The mean PVV in the S-S group was 11.53±7.63, and that in the N-S-S group was 16.47±9.71. Multivariate analysis revealed PVV (OR, 0.143; 95% CI, 0.026-0.790; P=0.026), PTLR (OR, 0.156; 95% CI, 0.049-0.500; P=0.002), and pulmonary arterial pressure/pulmonary venous pressure (postoperative) (OR, 9.014; 95% CI, 2.480-32.755; P=0.001) as significant factors. The rate of pulmonary infection absence postoperatively in the S-S group was 76.52%, and that in the N-S-S group was 42.75%. CONCLUSION: Due to the differences in heart structure between the S-S and N-S-S groups, the haemodynamic index (PVV and PTLR, postoperative pulmonary arterial pressure/pulmonary venous pressure) changes after S-S ASD repair were less than those after N-S-S ASD repair, so the postoperative pulmonary infection rate was higher after N-S-S ASD repair. The pulmonary infection rate was low after S-S ASD repair, and drugs should be reasonably administered to prevent infection.

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus.

Electroencephalogram (EEG)-based neurofeedback has been widely studied for tinnitus therapy in recent years. Most existing research relies on experts' cognitive prediction, and studies based on machine learning and deep learning are either data-hungry or not well generalizable to new subjects. In this paper, we propose a robust, data-efficient model for distinguishing tinnitus from the healthy state based on EEG-based tinnitus neurofeedback. We propose trend descriptor, a feature extractor with lower fineness, to reduce the effect of electrode noises on EEG signals, and a siamese encoder-decoder network boosted in a supervised manner to learn accurate alignment and to acquire high-quality transferable mappings across subjects and EEG signal channels. Our experiments show the proposed method significantly outperforms state-of-the-art algorithms when analyzing subjects' EEG neurofeedback to 90dB and 100dB sound, achieving an accuracy of 91.67%-94.44% in predicting tinnitus and control subjects in a subject-independent setting. Our ablation studies on mixed subjects and parameters show the method's stability in performance.

Nutritional evaluation and transcriptome analyses of short-time germinated seeds in soybean (Glycine max L. Merri.).

Germination is a common practice for nutrition improvement in many crops. In soybean, the nutrient value and genome-wide gene expression pattern of whole seeds germinated for short-time has not been fully investigated. In this study, protein content (PC), water soluble protein content (WSPC), isoflavone compositions were evaluated at 0 and 36 h after germination (HAG), respectively. The results showed that at 36HAG, PC was slightly decreased (P > 0.05) in ZD41, J58 and JHD, WSPC and free isoflavone (aglycones: daidzein, genistein, and glycitein) were significantly increased (P < 0.05), while total isoflavone content was unchanged. Transcriptomic analysis identified 5240, 6840 and 15,766 DEGs in different time point comparisons, respectively. GO and KEGG analysis showed that photosynthesis process was significantly activated from 18HAG, and alternative splicing might play an important role during germination in a complex manner. Response to hydrogen peroxide (H2O2) was found to be down regulated significantly from 18 to 36HAG, suggesting that H2O2 might play an important role in germination. Expression pattern analysis showed the synthesis of storage proteins was slowing down, while the genes coding for protein degradation (peptidase and protease) were up regulated as time went by during germination. For genes involved in isoflavone metabolism pathway, UGT (7-O-glucosyltransferase) coding genes were significantly up regulated (40 up-DEGs vs 27 down-DEGs), while MAT (7-O-glucoside-6''-O-malonyltransferase) coding genes were down regulated, which might explain the increase of aglycones after germination. This study provided a universal transcriptomic atlas for whole soybean seeds germination in terms of nutrition and gene regulation mechanism.