Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map.

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.

An innovative approach for assessing coronary artery lesions: Fusion of wrist pulse and photoplethysmography using a multi-sensor pulse diagnostic device.

Coronary heart disease (CHD) is a leading cause of mortality globally and poses a significant threat to public health. Coronary angiography (CAG) is a gold standard for the clinical diagnosis of CHD, but its invasiveness restricts its widespread application. In this study, we utilized a pulse diagnostic device equipped with pressure and photoelectric sensors to synchronously and non-invasively capture wrist pressure pulse waves and fingertip photoplethysmography (FPPG) of patients undergoing CAG. The extracted features were utilized in constructing random forest-based models to assessing the severity of coronary artery lesions. Notably, Model 3, incorporating both wrist pulse and FPPG features, surpassed Model 1 (solely utilizing wrist pulse features) and Model 2 (solely utilizing FPPG features). Model3 achieved an Accuracy, Precision, Recall, and F1-score of 78.79%, 78.69%, 78.79%, and 78.70%, respectively. Compared to Model1 and Model2, Model 3 exhibited improvements by 4.55%, 5.25%, 4.55%, and 5.12%, and 6.06%, 6.58%, 6.06%, and 6.54% respectively. This fusion of wrist pulse and FPPG features in Model 3 highlights the advantages of multi-source information fusion for model optimization. Additionally, this research provides invaluable insights into the novel development of diagnostic devices imbued with TCM principles and their potential in managing cardiovascular diseases.

Recruitment of beneficial cucumber rhizosphere microbes mediated by amino acid secretion induced by biocontrol Bacillus subtilis isolate 1JN2.

Introduction: At present, the use of beneficial microorganisms to control cucumber Fusarium wilt is a widely used method, and the rhizosphere microecological reset is one of the mechanisms involved. However, how biocontrol strains reshape cucumber rhizosphere microecology remains to be further studied. Methods: The composition changes of cucumber root exudates induced by biocontrol strain 1JN2, the microbial ecology of cucumber rhizosphere and the colonization ability of biocontrol strain 1JN2 in cucumber rhizosphere were analyzed through UHPLC-MS/MS analysis, Illumina high-throughput sequencing and SEM, respectively. Results: First, cucumber plants treated with biocontrol Bacillus 1JN2 reduced the disease severity of Fusarium wilt by 60%. Significant changes in cucumber root exudates were found after 1JN2 inoculation and the contents of four amino acids including glutamine, tryptophan, glycine and glutamic acid were significantly increased. Second, It was found that the bacterial diversity in the rhizosphere of cucumber was significantly increased in both the strain treatment group and the amino acid mixture treatment group, The number of Bacillus was the largest in all dominant populations, exceeded 20% in all treatment groups. The bacteria of Hydrogenispora and Vicinamibacteria were significantly increased after treatment. Discussion: Overall, the results demonstrated that amino acid substances in cucumber root exudates induced by biocontrol strain 1JN2 can shift the cucumber root microenvironment and prevent the occurrence of Fusarium wilt disease.

Ocu-miR-10a-5p promotes the chondrogenic differentiation of rabbit BMSCs by targeting BTRC-mediated Wnt/ß-catenin signaling pathway.

MicroRNAs (miRNAs) play an important role in articular cartilage damage in osteoarthritis (OA). However, the biological role of miRNAs in the chondrogenic differentiation of bone marrow mesenchymal stem cell (BMSC) remains largely unclear. Rabbit bone marrow mesenchymal stem cells (rBMSCs) were isolated, cultured, and identified. Afterwards, rBMSCs were induced to chondrogenic differentiation, examined by Alcian Blue staining. Differentially expressed miRNAs were identified in rBMSCs between induced and non-induced groups by miRNA sequencing analysis, part of which was validated via PCR assay. Cell viability and apoptosis were assessed by CCK-8 assay and Hoechst staining. Saffron O staining was utilized to assess chondrocyte hyperplasia. The expression of specific chondrogenic markers, including COL2A1, SOX9, Runx2, MMP-13, Aggrecan, and BMP-2, were measured at mRNA and protein levels. The association between beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC) and miR-10a-5p in the miRNA family from rabbit (ocu-miR-10a-5p) was determined by luciferase reporter assay. A total of 76 differentially expressed miRNAs, including 52 downregulated and 24 upregulated miRNAs, were identified in rBMSCs from the induced group. Inhibition of ocu-miR-10a-5p suppressed rBMSC viability and chondrogenic differentiation, as well as downregulated the expression of ß-catenin, SOX9, COL2A1, MMP-13, and Runx2. BTRC was predicted and confirmed as a target of ocu-miR-10a-5p. Overexpression of BTRC rescued the promoting impacts of overexpressed ocu-miR-10a-5p on chondrogenic differentiation of rBMSCs and ß-catenin expression. Taken together, our data suggested that ocu-miR-10a-5p facilitated rabbit BMSC survival and chondrogenic differentiation by activating Wnt/ß-catenin signaling through BTRC.

Mapping the intellectual structure and landscape of nano-drug delivery systems in colorectal cancer.

Background: Colorectal cancer (CRC) is a prevalent malignancy affecting the digestive tract, and its incidence has been steadily rising over the years. Surgery remains the primary treatment modality for advanced colorectal cancer, complemented by chemotherapy. The development of drug resistance to chemotherapy is a significant contributor to treatment failure in colorectal cancer. Nanodrug delivery systems (NDDS) can significantly improve the delivery and efficacy of antitumor drugs in multiple ways. However, there is a lack of visualization of NDDS research structures and research hotspots in the field of colorectal cancer, and the elaboration of potential research areas remains to be discovered. Objective: To comprehensively explore the current research status and development trend of NDDS in CRC research. Methods: Bibliometric analysis of articles and reviews on NDDS for CRC published between 2002 and 2022 using tools including CiteSpace, VOSviewer, R-bibliometrix, and Microsoft Excel was performed. Results: A total of 1866 publications authored by 9,870 individuals affiliated with 6,126 institutions across 293 countries/regions were included in the analysis. These publications appeared in 456 journals. Abnous Khalil has the highest number of publications in this field. The most published journals are the International Journal of Nanomedicine, International Journal of Pharmaceutics, and Biomaterials. Notably, the Journal of Controlled Release has the highest citation count and the third-highest H-index. Thematic analysis identified "inflammatory bowel disease"," "oral drug delivery," and "ulcerative colitis" as areas requiring further development. Keyword analysis revealed that "ulcerative colitis," "exosomes," and "as1411"have emerged as keywords within the last 2 years. These emerging keywords may become the focal points of future research. Conclusion: Our findings reveal the current research landscape and intellectual structure of NDDS in CRC research which helps researchers understand the research trends and hot spots in this field.

Artificial neural networks based multimodal device for autism spectrum disorder.

The neuro developmental condition known as Autism Spectrum Disorder (ASD) affects people on a lifetime basis and exhibits itself in a wide range of ways. In this research work a brand-new semi-supervised training method for the recognition of discrete multi-modal autism spectrum disorder is proposed. At the coarse-grained level, we consider that various methodologies are anticipated to explore equivalent information about child autism. To build DC AlexNet, this combines two small network branches and a large network (trunk network). The network trunk is programmed just to become familiar with the distinguishing characteristics shared by face images at different resolutions. It is built using recently suggested residential components. To project images to a place where their ranges are as little as possible, two branch networks are programmed to learn coupled-mappings (CMs) that are particular to a given resolution. The suggested technique is properly assessed utilizing the databases for the OMEGE and DIAEMO datasets by evaluating it to state-of-the-art techniques in terms of many parameters. Deep Coupled AlexNet is developed to obtain 98.13 % of accuracy, 95.1 % of precision, 94.3 % of recall and 95.4 of F1-score for OMEGE dataset. Moreover, 98.6 % of accuracy, 97.2 % of precision, 98.5 of recall and 97.5 % of F1-score for DIAEMO dataset (Tab. 8, Fig. 10, Ref. 16). Keywords: autism spectrum disorder, artificial neural networks, emotion recognition, interaction design, multimodal factors.

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases.

BACKGROUND: Cardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. OBJECTIVE: Based on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. METHODS: This paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). RESULTS: RF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. CONCLUSIONS: The wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

The microRNA-7322-5p/p38/Hsp19 axis modulates Chilo suppressalis cell-defences against Cry1Ca: an effective target for a stacked transgenic rice approach.

Bacillus thuringiensis (Bt)-secreted crystal (Cry) toxins form oligomeric pores in host cell membranes and are a common element in generating insect-resistant transgenic crops. Although Cry toxin function has been well documented, cellular defences against pore-formation have not been as well developed. Elucidation of the processes underlying this defence, however, could contribute to the development of enhanced Bt crops. Here, we demonstrate that Cry1Ca-mediated downregulation of microRNA-7322-5p (miR-7322-5p), which binds to the 3' untranslated region of p38, negatively regulates the susceptibility of Chilo suppressalis to Cry1Ca. Moreover, Cry1Ca exposure enhanced phosphorylation of Hsp19, and hsp19 downregulation increased susceptibility to Cry1Ca. Further, Hsp19 phosphorylation occurs downstream of p38, and pull-down assays confirmed the interactions between Hsp19 and Cry1Ca, suggesting that activation of Hsp19 by the miR-7322-5p/p38/Hsp19 pathway promotes Cry1Ca sequestration. To assess the efficacy of targeting this pathway in planta, double-stranded RNA (dsRNA) targeting C. suppressalis p38 (dsp38) was introduced into a previously generated cry1Ca-expressing rice line (1CH1-2) to yield a single-copy cry1Ca/dsp38 rice line (p38-rice). Feeding on this rice line triggered a significant reduction in C. suppressalis p38 expression and the line was more resistant to C. suppressalis than 1CH1-2 in both short term (7-day) and continuous feeding bioassays as well as field trials. These findings provide new insights into invertebrate epithelium cellular defences and demonstrate a potential new pyramiding strategy for Bt crops.

The effect of lactose and its isomerization product lactulose on functional and structural properties of glycated casein.

Lactulose is an isomer of lactose, formed under thermal processing of milk. Alkaline conditions favor the isomerization of lactose. As reducing sugar, lactose and lactulose could participate in the Maillard reaction and cause protein glycation in milk products. In this study, the influence of lactose and lactulose on the functional and structural properties of glycated casein was investigated. The results demonstrated that compared with lactose, lactulose led to severer changes in molecular weight, more disordered spatial structure and decrease of tryptophan fluorescence intensity of casein. Besides, the glycation degree and advanced glycation end products (AGEs) results suggested that lactulose exhibited stronger glycation ability than lactose due to the higher proportion of open chain in solution. Furthermore, higher glycation degree induced by lactulose resulted in lower solubility, surface hydrophobicity, digestibility and emulsifying capacity of casein-glycoconjugates compared with lactose. The results of this study are essential for tracking the effects of harmful Maillard reaction products on the quality of milk and dairy products.

The effect of B-type procyanidin on free radical and metal ion induced ß-lactoglobulin glyco-oxidation via mass spectrometry and interaction analysis.

Procyanidin is a group of dietary flavonoids abundant in berry fruits. In this study, the effects and underlying mechanisms of B type procyanidin (PC) on free radical and metal ion (H2O2, AAPH and Fe3+) induced milk protein ß-lactoglobulin (BLG) glyco-oxidation were investigated. The results indicated that PC protected BLG structure changes from cross-link and aggregation induced by free radicals and metal ion. Additionally, it effectively inhibited BLG oxidation by reducing approximately 21%-30% carbonyls and 15%-61% schiff base crosslink formation. Also, PC suppressed BLG glycation by inhibiting 48-70% advanced glycation end-products (AGEs) and reduced the accumulation of intermediate product methylglyoxal (MGO). The corresponding mechanisms were elucidated that PC exhibited great free radical scavenging and metal chelating properties; PC had non-covalent bind with the amino acid residues (preferably lysine and arginine) of BLG and blocked them from glycation; PC interrupted BLG glycation by forming procyanidin-MGO conjugates. Therefore, B type procyanidin was an effective glyco-oxidation inhibitor in milk products.

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.

BACKGROUND: The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality. OBJECTIVE: To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. METHODS: A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared. RESULTS: The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. CONCLUSIONS: The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.

Tongue crack recognition using segmentation based deep learning.

Tongue cracks refer to fissures with different depth and shapes on the tongue's surface, which can characterize the pathological characteristics of spleen and stomach. Tongue cracks are of great significance to the objective study of tongue diagnosis. However, tongue cracks are small and complex, existing methods are difficult to extract them effectively. In order to achieve more accurate extraction and identification of tongue crack, this paper proposes to apply a deep learning network based on image segmentation (Segmentation-Based Deep-Learning, SBDL) to extract and identify tongue crack. In addition, we have studied the quantitative description of tongue crack features. Firstly, the pre-processed tongue crack samples were amplified by using adding salt and pepper noise, changing the contrast and horizontal mirroring; secondly, the annotation tool Crack-Tongue was used to label tongue crack; thirdly, the tongue crack extraction model was trained by using SBDL; fourthly, the cracks on the tongue surface were detected and located by the segmentation network, and then the output and features of the segmentation network were put into the decision network for the classification of crack tongue images; finally, the tongue crack segmentation and identification results were quantitatively evaluated. The experimental results showed that the tongue crack extraction and recognition results based on SBDL were better than Mask Region-based Convolutional Neural Network (Mask R-CNN), DeeplabV3+, U-Net, UNet++ and Semantic Segmentation with Adversarial Learning (SegAN). This method effectively solved the inaccurate tongue crack extraction caused by the tongue crack's color being close to the surrounding tongue coating's color. This method can achieve better tongue crack extraction and recognition results on a small tongue crack data set and provides a new idea for tongue crack recognition, which is of practical value for tongue diagnosis objectification.

Turfgrass-dependent mycotrophic change enhances soil deterioration in dry, cold and high-alkali environments.

AIMS: Soil quality is undergoing severe degradation under anthropogenic effects. Different methods of land management have been implemented for soil reclamation, such as turfing. Although widely accepted to improve soil quality, turfing in specific environments may also culminate in soil deterioration. We aim to know how turfing impacts soils by changing mycobiomes. METHODS AND RESULTS: The soil physicochemical properties and ITS metabarcoding were used to investigate mycobiome diversity and eco-function differences between the eudicot Dianthus plumarius and the monocot Poa pratensis in dry, cold, and high-alkali soil. The effects of plantation and the rhizosphere (e.g. root exudates) were tested. We showed that the change in soil mycobiomes in different planted bulk soils and rhizospheres could mainly be attributed to species turnover, with minor nestedness. Unexpectedly, the soil deteriorates more following turfing. The increasing saprotrophs in planted bulk soil were more marked in the monocot than in the eudicot, even the rhizosphere effect alleviated saprotrophic risks in the rhizosphere. CONCLUSIONS: Turfing deteriorates the health of high-alkali soil by reducing nitrification, and upshift the soil saprotrophs in a dry and cold environment.

Effect of milk fat and its main fatty acids on oxidation and glycation level of milk.

Milk is a highly nutritional food rich in protein and fat that is prone to deterioration by oxidation and glycation reactions at storage and processing. In this study, glycation products and lipid oxidation products contents in skim milk, whole milk, and milk fat simulation groups were determined to evaluate the effect of milk fat components on glycation at 120 °C for 60 min. The increase rate of carbonyl compound, main advanced glycation end products (AGEs) levels, and glycation sites number of α-casein and ß-casein are higher in whole milk than that in skim milk, indicating that milk fat promoted protein glycation significantly. In milk fat simulation groups, oleic acid and linoleic acid (LA) were added to milk fat in skim milk proportionally, promoting the formation of glycation products; however, palmitic acid had no such effect. LA exhibited strong promotion on AGEs formation. Lipid oxidation radicals, protein carbonyl amine condensation, and carbonyl compound formation were critical factors for milk glycation, according to OPLS-DA results. Therefore, radicals of fat oxidation are speculated to trigger the early glycation, and carbonyl compounds of fat oxidation act as important intermediates of glycation, fat type, form, and its degradation rate, thus play essential roles in milk glycation. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-022-05658-z.

Spectroscopic analysis of the effect of glycation on casein structure and aggregation and its dependence on lactose concentration.

In order to study the effect of glycation on the molecular structure of casein (CN), three groups of simulated milk powders with different lactose (Lac) contents, CN:Lac = 1:1.5, 1:2, and 1:2.5 (m:m), were constructed. The glycation level and the structural changes of CN were analyzed by multiple spectra. The results showed that the interaction between Lac and CN molecules was based on the dynamic quenching of collision. A higher quenching constant, KSV, means that the 1:1.5 group showed more distinct glycation levels. In addition, CN glycation reduced the aggregation of casein micelles caused by heating treatment and repaired the structural damage of CN induced during the heating process. There was no positive correlation found between Lac content and protein glycation; thus, decreasing the Lac content of milk powder is not likely to be an effective method to prolong storage stability.

Blocking vicious cycle of cardiac dysfunction by external cardiopulmonary resuscitation is the key to rescue acute aluminium phosphide poisoning.

Aluminium phosphide (ALP) and aluminium zinc phosphide (ZnP), the two main ingredients of fumigation drugs, are commonly used to kill insects or rodents in grain. When exposed to water, highly toxic phosphine gas is released and absorbed through the respiratory or digestive tract. Phosphine gas could non-selectively block cytochrome oxidase, inhibit electron transfer and suppress oxidative phosphorylation, leading to cellular hypoxia and organ dysfunction. The characteristic clinical manifestations are refractory shock and metabolic acidosis with high mortality. However, patients with ALP poisoning have a chance to be cured. Here, we report a case of oral ALP poisoning that was successfully treated by extracorporeal membrane oxygenation (ECMO) combined with continuous renal replacement therapy (CRRT) during frequent ventricular fibrillation and cardiac dysfunction.

Anti-glycation level of pectic oligosaccharide in orange peel and its stability in accelerated storage temperature.

In this study, pectic oligosaccharides of orange peel (OPOs) were isolated and their structure characterized, and then screened according to anti-glycation level compared with aminoguanidine (AG). The results indicated that OPOs mainly included two components, and the main component has more than 50 % inhibition level for the seven glycation products at 60 °C for 40 h. At the accelerated storage temperature, OPOs demonstrated a better anti-glycation level than AG, and this inhibition was concentration-dependent. In addition, the main component in OPOs was separated into 10 fractions by DEAE Sephadex A-25 gel chromatography, the group of 5-7 monosaccharide polymerization showed the best anti-glycation effect, average anti-glycation capability on six products was over 70 % in the 40th hour. The anti-glycation level of this group was closely related to its high content of GalA and molar ratio of GalA: Rha, and positively correlated with time at moderate temperature.

Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network.

Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue body. However, texture discontinuity adversely affects the classification of the tough and tender tongue classification. In order to promote the accuracy and robustness of tongue texture analysis, a novel tongue image texture classification method based on image inpainting and convolutional neural network is proposed. Firstly, Gaussian mixture model is applied to separate the tongue coating and body. In order to exclude the interference of tongue coating on tough and tender tongue classification, a tongue body image inpainting model is built based on generative image inpainting with contextual attention to realize the inpainting of the tongue body image to ensure the continuity of texture and color change of tongue body image. Finally, the classification model of the tough and tender tongue inpainting image based on ResNet101 residual network is used to train and test. The experimental results show that the proposed method achieves better classification results compared with the existing methods of texture classification of tongue image and provides a new idea for tough and tender tongue classification.

Comparative transcriptomics reveal different genetic adaptations of biofilm formation in Bacillus subtilis isolate 1JN2 in response to Cd2+ treatment.

Biofilm plays important roles in the life cycle of Bacillus species, such as promoting host and object surface colonization and resisting heavy metal stress. This study utilized transcriptomics to evaluate the impacts of cadmium on the components, morphology, and function of biofilms of Bacillus subtilis strain 1JN2. Under cadmium ion stress, the morphology of the B. subtilis 1JN2 biofilm was flattened, and its mobility increased. Moreover, differential gene expression analysis showed that the main regulator of biofilm formation, Spo0A, decreased in expression under cadmium ion stress, thereby inhibiting extracellular polysaccharide synthesis through the SinI/SinR two-component regulatory system and the AbrB pathway. Cadmium ion treatment also increased the SigD content significantly, thereby increasing the expression of the flagella encoding and assembly genes in the strain. This promoted poly-γ-glutamic acid production via the DegS/DegU two-component regulatory system and the conversion of biofilm extracellular polysaccharide to poly-γ-glutamic acid. This conferred cadmium stress tolerance in the strain. Additionally, the cadmium ion-mediated changes in the biofilm composition affected the colonization of the strain on the host plant root surface. Cadmium ions also induced surfactin synthesis. These findings illustrate the potential of Bacillus species as biocontrol strains that can mitigate plant pathogenic infections and heavy metal stress. The results also provide a basis for the screening of multifunctional biocontrol strains.

Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short-Term Memory Network.

Purpose: Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method: This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result: The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion: LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.