Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network.

Aims: Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results: We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion: Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.

An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.

Evolutionary novelties underlie sound production in baleen whales.

Baleen whales (mysticetes) use vocalizations to mediate their complex social and reproductive behaviours in vast, opaque marine environments1. Adapting to an obligate aquatic lifestyle demanded fundamental physiological changes to efficiently produce sound, including laryngeal specializations2-4. Whereas toothed whales (odontocetes) evolved a nasal vocal organ5, mysticetes have been thought to use the larynx for sound production1,6-8. However, there has been no direct demonstration that the mysticete larynx can phonate, or if it does, how it produces the great diversity of mysticete sounds9. Here we combine experiments on the excised larynx of three mysticete species with detailed anatomy and computational models to show that mysticetes evolved unique laryngeal structures for sound production. These structures allow some of the largest animals that ever lived to efficiently produce frequency-modulated, low-frequency calls. Furthermore, we show that this phonation mechanism is likely to be ancestral to all mysticetes and shares its fundamental physical basis with most terrestrial mammals, including humans10, birds11, and their closest relatives, odontocetes5. However, these laryngeal structures set insurmountable physiological limits to the frequency range and depth of their vocalizations, preventing them from escaping anthropogenic vessel noise12,13 and communicating at great depths14, thereby greatly reducing their active communication range.

[Improved unilateral puncture PVP based on 3D printing technology for the treatment of osteoporotic vertebral compression fracture].

OBJECTIVE: To investigate the clinical effect of unilateral percutaneous vertebroplasty (PVP) combined with 3D printing technology for the treatment of thoracolumbar osteoporotic compression fracture. METHODS: A total of 77 patients with thoracolumbar osteoporotic compression fractures from October 2020 to April 2022 were included in the study, all of which were vertebral body compression fractures caused by trauma. According to different treatment methods, they were divided into experimental group and control group. Thirty-two patients used 3D printing technology to improve unilateral transpedicle puncture vertebroplasty in the experimental group, there were 5 males and 27 females, aged from 63 to 91 years old with an average of (77.59±8.75) years old. Forty-five patients were treated with traditional bilateral pedicle puncture vertebroplasty, including 7 males and 38 females, aged from 60 to 88 years old with an average of(74.89±7.37) years old. Operation time, intraoperative C-arm X-ray times, anesthetic dosage, bone cement injection amount, bone cement diffusion good and good rate, complications, vertebral height, kyphotic angle (Cobb angle), visual analogue scale(VAS), Oswestry disability index (ODI) and other indicators were recorded before and after surgery, and statistically analyzed. RESULTS: All patients were followed up for 6 to 23 months, with preoperative imaging studies, confirmed for thoracolumbar osteoporosis compression fractures, two groups of patients with postoperative complications, no special two groups of patients' age, gender, body mass index (BMI), time were injured, the injured vertebral distribution had no statistical difference(P>0.05), comparable data. Two groups of patients with bone cement injection, bone cement dispersion rate, preoperative and postoperative vertebral body height, protruding after spine angle(Cobb angle), VAS, ODI had no statistical difference(P>0.05). The operative time, intraoperative fluoroscopy times and anesthetic dosage were statistically different between the two groups(P<0.05). Compared with the traditional bilateral puncture group, the modified unilateral puncture group combined with 3D printing technology had shorter operation time, fewer intraoperative fluoroscopy times and less anesthetic dosage. The height of anterior vertebral edge, kyphosis angle (Cobb angle), VAS score and ODI of the affected vertebrae were statistically different between two groups at each time point after surgery(P<0.05). CONCLUSION: In the treatment of thoracolumbar osteoporotic compression fractures, 3D printing technology is used to improve unilateral puncture PVP, which is convenient and simple, less trauma, short operation time, fewer fluoroscopy times, satisfactory distribution of bone cement, vertebral height recovery and kyphotic Angle correction, and good functional improvement.

Enhanced adsorption performance of oxytetracycline in aqueous solutions by Mg-Fe modified suaeda-based magnetic biochar.

Oxytetracycline (OTC) in the waste water can be removed by biochar adsorption. How to separate the biochar adsorbed antibiotics from the wastewater was also a problem. The nontoxic magnetic biochar was prepared from Suaeda biochar (800SBC) by mafic bimetal modification, and used for the removal of OTC. The results of XRD and VSM indicated that the main composition of biochar was ferrite. Then through batch adsorption experiments, the adsorption kinetics, isothermal adsorption, thermodynamics, and coexisting ion and adsorbent regeneration experiments were studied. Through the fitting of the adsorption model, it was found that Mg-Fe@800SBC(1:1) and 800SBC belonged to chemisorption. 800SBC was consistent with the Langmuir model, mainly monolayer adsorption, and Mg-Fe@800SBC(1:1) was consistent with the Freundlich model, mainly multilayer adsorption. The adsorption processes of the two materials were spontaneous, endothermic and entropic decreasing processes. The maximum adsorption capacity of the Mg-Fe@800SBC(1:1) for OTC from the Sips L-F model was 82.83 mg/g. Through various characterizations of magnetic biochar, it was found that the adsorption mechanism of the modified biochar included the hydrogen bonds between the oxygen-containing functional group of biochar and the -NH2 group of OTC, π-π EDA interaction, electrostatic attraction and complexation. Coexistence anions (CO32- and PO43-) have a negative effect on the adsorption process.

The Effects of Negative Pressure Induced by Flow Separation Vortices on Vocal Fold Dynamics during Voice Production.

This study used a two-dimensional flow-structure-interaction computer model to investigate the effects of flow-separation-vortex-induced negative pressure on vocal fold vibration and flow dynamics during vocal fold vibration. The study found that negative pressure induced by flow separation vortices enhances vocal fold vibration by increasing aeroelastic energy transfer during vibration. The result showed that the intraglottal pressure was predominantly negative after flow separation before gradually recovering to zero at the glottis exit. When the negative pressure was removed, the vibration amplitude and flow rate were reduced by up to 20%, and the closing speed, flow skewness quotient, and maximum flow declination rate were reduced by up to 40%. The study provides insights into the complex interactions between flow dynamics, vocal fold vibration, and energy transfer during voice production.

Non-invasive and accurate risk evaluation of cerebrovascular disease using retinal fundus photo based on deep learning.

Background: Cerebrovascular disease (CeVD) is a prominent contributor to global mortality and profound disability. Extensive research has unveiled a connection between CeVD and retinal microvascular abnormalities. Nonetheless, manual analysis of fundus images remains a laborious and time-consuming task. Consequently, our objective is to develop a risk prediction model that utilizes retinal fundus photo to noninvasively and accurately assess cerebrovascular risks. Materials and methods: To leverage retinal fundus photo for CeVD risk evaluation, we proposed a novel model called Efficient Attention which combines the convolutional neural network with attention mechanism. This combination aims to reinforce the salient features present in fundus photos, consequently improving the accuracy and effectiveness of cerebrovascular risk assessment. Result: Our proposed model demonstrates notable advancements compared to the conventional ResNet and Efficient-Net architectures. The accuracy (ACC) of our model is 0.834 ± 0.03, surpassing Efficient-Net by a margin of 3.6%. Additionally, our model exhibits an improved area under the receiver operating characteristic curve (AUC) of 0.904 ± 0.02, surpassing other methods by a margin of 2.2%. Conclusion: This paper provides compelling evidence that Efficient-Attention methods can serve as effective and accurate tool for cerebrovascular risk. The results of the study strongly support the notion that retinal fundus photo holds great potential as a reliable predictor of CeVD, which offers a noninvasive, convenient and low-cost solution for large scale screening of CeVD.

Adsorption properties and mechanism of suaeda biochar and modified materials for tetracycline.

Adsorption was an available way to eliminate Tetracycline (TC) from waste water. Suaeda biochar (800SBC) and iron modified biochar (Fe-800SBC) were prepared using pyrolysis under oxygen-limiting conditions. BET and SEM showed that the surface of Fe-800SBC was rougher, and the specific surface area (SBET) was 7 times that of 800SBC. There existed pore filling, ion exchange, metal ion complexation, hydrogen bonds and cation-π interaction mechanism. Both 800SBC and Fe-800SBC conformed to quasi-second-order kinetics model, belonged to chemisorption. Fe-800SBC conformed to Elovich model too. The adsorption process of 800SBC conformed to Freundlich and Sips L-F models, Fe-800SBC conformed to the Sips L-F and Temkin models, identifying the presence of physical and chemical adsorption during adsorption. Response surface method (RSM) was used to optimize important process parameters. The quadratic model was sufficient to predict TC removal response in the range of studied parameters.

Variation of insulin-related peptides accompanying the differentiation of Aphis gossypii biotypes and their expression profiles.

Insulin signaling plays a critical role in regulating various aspects of insect biology, including development, reproduction, and the formation of wing polyphenism. This leads to differentiation among insect populations at different levels. The insulin family exhibits functional variation, resulting in diverse functional pathways. Aphis gossypii Glover, commonly known as the cotton-melon aphid, is a highly adaptable aphid species that has evolved into multiple biotypes. To understand the genetic structure of the insulin family and its evolutionary diversification and expression patterns in A. gossypii, we conducted studies using genome annotation files and RNA-sequencing data. Consequently, we identified 11 insulin receptor protein (IRP) genes in the genomes of the examined biotypes. Among these, eight AgosIRPs were dispersed across the X chromosome, while two were found in tandem on the A1 chromosome. Notably, AgosIRP2 exhibited alternative splicing, resulting in the formation of two isoforms. The AgosIRP genes displayed a high degree of conservation between Hap1 and Hap3, although some variations were observed between their genomes. For instance, a transposon was present in the coding regions of AgosIRP3 and AgosIRP9 in the Hap3 genome but not in the Hap1 genome. RNA-sequencing data revealed that four AgosIRPs were expressed ubiquitously across different morphs of A. gossypii, while others showed specific expression patterns in adult gynopara and adult males. Furthermore, the expression levels of most AgosIRPs decreased upon treatment with the pesticide acetamiprid. These findings demonstrate the evolutionary diversification of AgosIRPs between the genomes of the two biotypes and provide insights into their expression profiles across different morphs, developmental stages, and biotypes. Overall, this study contributes valuable information for investigating aphid genome evolution and the functions of insulin receptor proteins.

Adsorption of sulfanilamides using biochar derived from Suaeda salsa: adsorption kinetics, isotherm, thermodynamics, and mechanism.

Suaeda biochar (SBC) was prepared by muffle furnace with Suaeda salsa at 600, 700, 800, and 900 ℃. The physical and chemical properties of biochar at different pyrolysis temperatures and the adsorption mechanism of sulfanilamide (SM) were studied by SEM-EDS, BET, FTIR, XRD, and XPS analysis. The adsorption kinetics and adsorption isotherms were fitted. The results showed that the kinetics was in line with the quasi-second-order adsorption model and belonged to chemisorption. The adsorption isotherm conformed to Langmuir adsorption isotherm model and belonged to monolayer adsorption. The adsorption of SM on SBC was spontaneous and exothermic. The adsorption mechanism may be pore filling, hydrogen bonding, and π-π electron donor acceptor (EDA) interaction.

Individualized 3D printed navigation template-assisted atlantoaxial pedicle screws vs. free-hand screws for the treatment of upper cervical fractures.

Objective: This study aims to compare the efficacy and safety of freehand atlantoaxial pedicle screws against custom 3D printed navigation template screws in the treatment of upper cervical fractures. Methods: In our institution from 2010 to 2020, a retrospective cohort analysis of 23 patients with upper cervical fractures was done. These patients were separated into two groups: group A (N = 12), which received customized 3D printed navigation template-assisted screws with virtual reality techniques, and group B (N = 11), which received freehand screws assisted by intraoperative fluoroscopy. Every patient was monitored for more than 1 year. The two groups were contrasted in terms of screw implant accuracy, cervical spine Japanese Orthopaedic Association (JOA) score, American Spinal Injury Association (ASIA) score, visual analogue scale (VAS) score, surgical time, fluoroscopy times, and intraoperative blood loss. Results: A total of 88 atlantoaxial pedicle screws in all, 46 in group A and 42 in group B, were implanted. In group A, the screw insertion accuracy rate was 95.7%, compared to 80.0% in group B (P < 0.05). When compared to group B, group A had shorter surgery times, less blood loss, fewer fluoroscopies, a higher short-term JOA score, and overt pain reduction (P < 0.05). However, there was no discernible difference between the two groups' VAS scores, long-term JOA scores, or ASIA scores (sensory and motor), at the most recent follow-up. Conclusion: Individualized 3D printed guide leads to significant improvement in the screw safety, efficacy, and accuracy, which may be a promising strategy for the treatment of upper cervical fractures.

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.

Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones. Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation. Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007. Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort. METHODS: Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output. RESULTS: We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively. CONCLUSION: Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably. KEY POINTS: • The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.

Deep Adsorption Desulfurization of Fluid Catalytic Cracking Light Gasoline on NiO/ZnO-TiO2 Adsorbents with a High Breakthrough Sulfur Capacity.

Two kinds of NiO/ZnO-TiO2 adsorbents were prepared by equal volume impregnation (NiO/ZnO-TiO2-1) and kneading (NiO/ZnO-TiO2-2) methods. The adsorbents were characterized by X-ray diffraction, mercury intrusion porosimetry, scanning electron microscopy, energy dispersive X-ray spectroscopy, H2 temperature-programmed reduction, and H2 temperature-programmed desorption. It was found that NiO/ZnO-TiO2-2 had a smaller average pore diameter and a larger specific surface area as well as a more uniform distribution of the nickel element. Additionally, more Ni0 active sites together with a stronger interaction between the active component and the support were detected on the surface of NiO/ZnO-TiO2-2, which was beneficial to the inhibition of olefin saturation during desulfurization. The desulfurization performance of the adsorbents was investigated in a fixed bed reactor with fluid catalytic cracking light gasoline as a feed oil. The evaluation results confirmed NiO/ZnO-TiO2-2 with a better desulfurization performance with less olefin saturation. It could reduce the total sulfur content from 300 ppmw to less than 5 ppmw, and the breakthrough time and breakthrough sulfur capacity were 91 h and 6.71% (67.1 mg S/g adsorbent), respectively.

A Deep Learning-Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation.

This paper proposes a deep learning-based generalized empirical flow model (EFM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully connected deep neural network (DNN) is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN-based EFM is coupled with a finite element method (FEM)-based solid dynamics solver for fluid-structure-interaction (FSI) simulation of phonation. The EFM is evaluated by comparing the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.

Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement.

PURPOSE: Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR. PATIENTS AND METHODS: This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell's concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan-Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above. RESULTS: The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79-0.92) vs 0.72 (95% CI: 0.63-0.77) vs 0.70 (95% CI: 0.61-0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan-Meier analysis, BLeNet model had great performance in stratifying high- and low-bleeding risk patients (p < 0.0001). CONCLUSION: Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.

Aerodynamics and motor control of ultrasonic vocalizations for social communication in mice and rats.

BACKGROUND: Rodent ultrasonic vocalizations (USVs) are crucial to their social communication and a widely used translational tool for linking gene mutations to behavior. To maximize the causal interpretation of experimental treatments, we need to understand how neural control affects USV production. However, both the aerodynamics of USV production and its neural control remain poorly understood. RESULTS: Here, we test three intralaryngeal whistle mechanisms-the wall and alar edge impingement, and shallow cavity tone-by combining in vitro larynx physiology and individual-based 3D airway reconstructions with fluid dynamics simulations. Our results show that in the mouse and rat larynx, USVs are produced by a glottal jet impinging on the thyroid inner wall. Furthermore, we implemented an empirically based motor control model that predicts motor gesture trajectories of USV call types. CONCLUSIONS: Our results identify wall impingement as the aerodynamic mechanism of USV production in rats and mice. Furthermore, our empirically based motor control model shows that both neural and anatomical components contribute to USV production, which suggests that changes in strain specific USVs or USV changes in disease models can result from both altered motor programs and laryngeal geometry. Our work provides a quantitative neuromechanical framework to evaluate the contributions of brain and body in shaping USVs and a first step in linking descending motor control to USV production.

Chromosome-level genome assemblies of two cotton-melon aphid Aphis gossypii biotypes unveil mechanisms of host adaption.

The cotton-melon aphid Aphis gossypii is a sap-sucking insect that is considered a serious global pest. The species is distributed over a large geographical range and uses a wide variety of hosts, with some populations being specialized to attack different plant species. Here, we provide de novo chromosome-level genome assemblies of a cotton specialist population (Hap1) and a cucurbit specialist population (Hap3). We achieved this by using a combination of third-generation sequencing platforms, namely Illumina and Hi-C sequencing technologies. We were able to anchor a total of 334.89 Mb (scaffold N50 of 89.13 Mb) and 359.95 Mb (scaffold N50 of 68.88 Mb) to four chromosomes for Hap1 and Hap3, respectively. Moreover, our results showed that the X-chromosome of Hap3 (113.01 Mb) was significantly longer than that of Hap1 (100.26 Mb), with a high level of sequence conservation between the aphid species. We also report variation in the number of protein-coding genes and repeat sequences between Hap1 and Hap3. In particular, olfactory and gustatory receptor genes underwent a high level of gene duplication and expansion events in A. gossypii, including between Hap1 and Hap3. Moreover, we identified two glutathione S-transferase genes which underwent single gene duplications in Hap3, and tandem duplication and inversion events affecting the cytochrome P450 monooxygenase between Hap1 and Hap3, all of which include the CYP3 family. Our results illustrate the variance in the genomic composition of two specialized A. gossypii populations and provide a helpful resource for the study of aphid population evolution, host adaption and insecticide resistance.

Using soil amendments to reduce microcystin-LR bioaccumulation in lettuce.

Contamination of microcystins (MCs) in plant-soil system have become a serious problem worldwide, however, it remains largely unknown how to alleviate the potential risk of consuming MCs-contaminated plants. In the present study, attapulgite, biochar and peat were used as soil amendments to reduce MCs bioaccumulation in lettuce. Lettuce irrigated with 10 µg L-1 microcystin-LR (MC-LR) were growing in two different kinds of soils with or without soil amendments. Results showed that all soil amendments effectively reduced MC-LR bioaccumulation in lettuce roots and leaves. Compared with the control treatment, the MC-LR concentrations in leaves in treatments with attapulgite, biochar and peat decreased by 41.5%, 30.6%, 57.0% in soil A and 38.9%, 43.2%, 54.7% in soil B, respectively. Peat application was most effective in reducing MC-LR bioaccumulation. The decreased soil free MC-LR concentrations were positively correlated with MC-LR concentrations in lettuce, indicating decreased bioavailability of MC-LR by soil amendments. It is noteworthy that soil total MC-LR concentration in peat treatment significantly decreased by 33.3% and 29.4% in soil A and soil B, respectively, compared with the controls. According to the results from high-throughput sequencing, peat amendment increased the α-diversity of soil bacterial community and boosted the abundance of Sphingomonas and Methylobacillus (dozens of MC-degrading bacteria belong to these genera). This was in line with the results of soil total MC-LR concentration. It can be speculated that peat application directly and/or indirectly promoted microbial degradation of MC-LR in soils. This work proposed an effective way to alleviate the potential risks of MCs contamination in plant-soil system.

Computational Modeling of Voice Production Using Excised Canine Larynx.

A combined experimental-numerical work was conducted to comprehensively validate a subject-specific continuum model of voice production in larynx using excised canine laryngeal experiments. The computational model is a coupling of the Navier-Stokes equations for glottal flow dynamics and a finite element model of vocal fold dynamics. The numerical simulations employed a cover-body vocal fold structure with the geometry reconstructed from magnetic resonance imaging scans and the material properties determined through an optimization-based inverse process of experimental indentation measurement. The results showed that the simulations predicted key features of the dynamics observed in the experiments, including the skewing of the glottal flow waveform, mucosal wave propagation, continuous increase of the divergent angle and intraglottal swirl strength during glottal closing, and flow recirculation between glottal jet and vocal fold. The simulations also predicted the increase of the divergent angle, glottal jet speed, and intraglottal flow swirl strength with the subglottal pressure, same as in the experiments. Quantitatively, the simulations over-predicted the frequency and jet speed and under-predicted the flow rate and divergent angle for the larynx under study. The limitations of the model and their implications were discussed.