A multi-source heterogeneous medical data enhancement framework based on lakehouse.

Obtaining high-quality data sets from raw data is a key step before data exploration and analysis. Nowadays, in the medical domain, a large amount of data is in need of quality improvement before being used to analyze the health condition of patients. There have been many researches in data extraction, data cleaning and data imputation, respectively. However, there are seldom frameworks integrating with these three techniques, making the dataset suffer in accuracy, consistency and integrity. In this paper, a multi-source heterogeneous data enhancement framework based on a lakehouse MHDP is proposed, which includes three steps of data extraction, data cleaning and data imputation. In the data extraction step, a data fusion technique is offered to handle multi-modal and multi-source heterogeneous data. In the data cleaning step, we propose HoloCleanX, which provides a convenient interactive procedure. In the data imputation step, multiple imputation (MI) and the SOTA algorithm SAITS, are applied for different situations. We evaluate our framework via three tasks: clustering, classification and strategy prediction. The experimental results prove the effectiveness of our data enhancement framework.

Sensitive fluorescence detection of miRNA-124 in cardiomyocytes under oxidative stress using a nucleic acid probe.

MicroRNAs (miRNAs) are small noncoding RNAs of 18-25 bases. miRNAs are also important new biomarkers that can be used for disease diagnosis in the future. Studies have shown that miR-124 levels are significantly elevated during acute myocardial infarction (AMI) and play a key role in the cardiovascular system. A variety of methods have been established to detect myocardial infarction-related miRNAs. However, most require complex miRNA extraction and isolation, and these methods are virtually undetectable when RNA levels are low in the sample. It may lead to biased results. Thus, it is necessary to develop a technique that can detect miRNA without extracting it, which means that intracellular detection is of great significance. Here, we improved the traditional silicon spheres and obtained a biosensor that could effectively capture and detect specific noncoding nucleic acids through the layer-by-layer assembly method. The sensor is protected by hyaluronic acid so it can successfully escape the lysosome into the cell and achieve detection. With the help of a full-featured microplate reader, we determined that the detection limit of the biosensor could reach 1 fM, meeting the needs of intracellular detection. At the same time, we prepared an oxidative stress cardiomyocyte infarction model and successfully captured the overexpressed miR-124 in the infarcted cells to achieve in situ detection. This study could provide a new potential tool to develop miRNAs for sensitive diagnosis in AMI, and the proposed strategy implies its potential for biomedical research.

Partial Replacement of Fishmeal with Poultry By-Product Meal in Diets for Coho Salmon (Oncorhynchus kisutch) Post-Smolts.

The present study evaluated the effects of partially substituting fish meal (FM) with poultry by-product meal (PBPM) on the growth, muscle composition, and tissue biochemical parameters of coho salmon (Oncorhynchus kisutch) post-smolts. Five isonitrogenous (7.45% nitrogen) and isoenergetic (18.61 MJ/kg gross energy) experimental diets were made by substituting 0%, 10%, 20%, 40%, and 60% FM protein with PBPM protein, which were designated accordingly as PBPM0 (the control), PBPM10, PBPM20, PBPM40, and PBPM60, respectively. Each diet was fed to triplicates of ten post-smolts (initial individual body weight, 180.13 ± 1.32 g) in three floating cages three times daily (6:50, 11:50, and 16:50) to apparent satiation for 84 days. Both specific growth rate (SGR) and protein efficiency ratio did not differ significantly (p > 0.05) among the control, PBPM10, and PBPM20 groups, which were remarkably (p < 0.05) higher than those of the PBPM40 and PBPM60 groups. Feed conversion ratio varied inversely with SGR. The PBPM replacement had no remarkable effects on the morphological indices and proximal muscle components. The control and PBPM10 groups led to significantly higher muscle contents of leucine, lysine, and methionine than groups of higher PBPM inclusion. The groups of PBPM40 and PBPM60 obtained significantly (p < 0.05) higher serum alanine aminotransferase and aspartate aminotransferase activities than the control and low PBPM inclusion groups. The control group had significantly higher albumin and total cholesterol contents than the groups with PBPM inclusion. The control group had significantly higher triglycerides content than the PBPM60 group. The PBPM60 group had significantly lower contents of high-density lipoprotein, low-density lipoprotein, and total protein than the control and PBPM10 groups. The high PBPM replacement level up to 40% and 60% had adverse effects on hepatic malondialdehyde levels. The catalase and superoxide dismutase activities were not affected by low PBPM inclusion, but significantly decreased in high-PBPM-inclusion groups. Based on broken-line regression analysis of SGR and PER, the optimum dietary PBPM replacing level was evaluated to be 16.63-17.50% of FM protein for coho salmon post-smolts.

Detecting Trivariate Associations in High-Dimensional Datasets.

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

Detecting Unbiased Associations in Large Data Sets.

Maximal information coefficient (MIC) explores the associations between pairwise variables in complex relationships. It approaches the correlation by optimized partition on the axis. However, when the relationships meet special noise, MIC may overestimate the correlated value, which leads to the misidentification of the relationship without noiseless. In this article, a novel method of weighted information coefficient mean (WICM) is proposed to detect unbiased associations in large data sets. First, we mathematically analyze the cause of giving an abnormal correlation value to a noisy relationship. Then, the WICM is presented in two core steps. One is to detect the potential overestimation from the relationships with high value, and the other is to rectify the overestimation by calculating information coefficient mean instead of just selecting the maximum element in the characteristic matrix. Finally, experiments in functional relationships and real-world data relationships show that the overestimation can be solved by WICM with both feasibility and effectiveness.

Community Health Nursing Competency and Psychological and Organizational Empowerment of Public Health Nurses: A Cross-Sectional Survey.

AIM: This study explored the effect of public health nurses' current community care nursing competency on the psychological and organizational empowerment of public health services in Taiwan. DESIGN: A cross-sectional nationwide survey design was used. METHODS: A self-developed structured questionnaire was administered to public health nurses. They were recruited using a purposive sampling technique, and they participated in community healthcare workshops. RESULTS: The mean score of Community Care Nursing Competence (CCNC) was 3.92 ± 0.83. The mean score in Community Empowerment (CE) was 3.66 ± 0.90. The study revealed that age and communication competence were crucial factors in public health nurses working in the community. With age and through the accumulation of practical experience, public health nurses' communication competence may also improve, which can further enhance their psychological and organizational empowerment in the nursing workplace.

Global and Local Tensor Factorization for Multi-criteria Recommender System.

In multi-criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user-item rating patterns. However, two-dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion-specific rating data. This study introduces a tensor factorization method to handle three-dimensional user-item-criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi-criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user-item-criterion rating patterns while also exploiting local user-subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real-life data available to the public, demonstrated that the GLTF is superior to well-established baseline methods.

Mechanically and Electronically Robust Transparent Organohydrogel Fibers.

Stretchable conductive fibers are key elements for next-generation flexible electronics. Most existing conductive fibers are electron-based, opaque, relatively rigid, and show a significant increase in resistance during stretching. Accordingly, soft, stretchable, and transparent ion-conductive hydrogel fibers have attracted significant attention. However, hydrogel fibers are difficult to manufacture and easy to dry and freeze, which significantly hinders their wide range of applications. Herein, organohydrogel fibers are designed to address these challenges. First, a newly designed hybrid crosslinking strategy continuously wet-spins hydrogel fibers, which are transformed into organohydrogel fibers by simple solvent replacement. The organohydrogel fibers show excellent antifreezing (< -80 °C) capability, stability (>5 months), transparency, and stretchability. The predominantly covalently crosslinked network ensures the fibers have a high dynamic mechanical stability with negligible hysteresis and creep, from which previous conductive fibers usually suffer. Accordingly, strain sensors made from the organohydrogel fibers accurately capture high-frequency (4 Hz) and high-speed (24 cm s-1 ) motion and exhibit little drift for 1000 stretch-release cycles, and are powerful for detecting rapid cyclic motions such as engine valves and are difficult to reach by previously reported conductive fibers. The organohydrogel fibers also demonstrate potential as wearable anisotropic sensors, data gloves, soft electrodes, and optical fibers.

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most effective method for screening fetal heart malformations. However, standard echocardiograms of the fetal heart, especially four-chamber view images, are difficult to obtain. In addition, the pathophysiological changes in fetal hearts during different pregnancy periods lead to ever-changing two-dimensional fetal heart structures and hemodynamics, and it requires extensive professional knowledge to recognize and judge disease development. Thus, research on the automatic screening for FHD is necessary. In this paper, we proposed a new model named DGACNN that shows the best performance in recognizing FHD, achieving a rate of 85%. The motivation for this network is to deal with the problem that there are insufficient training datasets to train a robust model. There are many unlabeled video slices, but they are tough and time-consuming to annotate. Thus, how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD, in terms of both recognition accuracy and robustness, is very meaningful for FHD screening. The architecture of DGACNN comprises two parts, that is, DANomaly and GACNN (Wgan-GP and CNN). DANomaly, similar to the ALOCC network, but incorporates cycle adversarial learning to train an end-to-end one-class classification (OCC) network that is more robust and has a higher accuracy than ALOCC in screening video slices. For the GACNN architecture, we use FCH (four chamber heart) video slices at around the end-systole, as screened by DANomaly, to train a WGAN-GP for the purpose of obtaining ideal low-level features that can robustly improve the FHD recognition accuracy. A few annotated video slices, as screened by DANomaly, can also be used for data augmentation so as to improve the FHD recognition further. The experiments show that the DGACNN outperforms other state-of-the-art networks by 1%-20% in recognizing FHD. A comparison experiment shows that the proposed network already outperforms the performance of expert cardiologists in recognizing FHD, reaching 84% in a test. Thus, the proposed architecture has high potential for helping cardiologists complete early FHD screenings.

Hough Transform-Based Large Dynamic Reflection Coefficient Micro-Motion Target Detection in SAR.

Special phase modulation of SAR echoes resulted from target rotation or vibration, is a phenomenon called the micro-Doppler (m-D) effect. Such an effect offers favorable information for micro-motion (MM) target detection, thereby improving the performance of the synthetic aperture radar (SAR) system. However, when there are MM targets with large differences in reflection coefficient, the weak reflection components will be difficult to be detected. To find a solution to this problem, we propose a novel algorithm. First, we extract and detect the strongest reflection component. By removing the strongest reflection component from the original azimuth echo one by one, we realize the detection of reflection components sequentially, from the strongest to the weakest. Our algorithm applies to detecting MM targets with different reflection coefficients and has high precision of parameter estimation. The results of simulation and field experiments verify the advantages of the algorithm.

A Highly Efficient Self-Healing Elastomer with Unprecedented Mechanical Properties.

It is highly desirable, although very challenging, to develop self-healable materials exhibiting both high efficiency in self-healing and excellent mechanical properties at ambient conditions. Herein, a novel Cu(II)-dimethylglyoxime-urethane-complex-based polyurethane elastomer (Cu-DOU-CPU) with synergetic triple dynamic bonds is developed. Cu-DOU-CPU demonstrates the highest reported mechanical performance for self-healing elastomers at room temperature, with a tensile strength and toughness up to 14.8 MPa and 87.0 MJ m-3 , respectively. Meanwhile, the Cu-DOU-CPU spontaneously self-heals at room temperature with an instant recovered tensile strength of 1.84 MPa and a continuously increased strength up to 13.8 MPa, surpassing the original strength of all other counterparts. Density functional theory calculations reveal that the coordination of Cu(II) plays a critical role in accelerating the reversible dissociation of dimethylglyoxime-urethane, which is important to the excellent performance of the self-healing elastomer. Application of this technology is demonstrated by a self-healable and stretchable circuit constructed from Cu-DOU-CPU.

Metal-Catalyzed Oxa-[4+2] Cyclizations of Quinone Methides with Alkynyl Benzyl Alcohols.

An oxa-[4+2] cyclization of quinone methides with alkynyl benzyl alcohols has been realized in the presence of a metal catalyst, and the reaction afforded spiroacetal products in overall high yields (up to 99%) and good diastereoselectivities (up to >95:5 dr). By carrying out the reaction under gold catalysis and utilizing alkynyl benzyl alcohols as electron-rich reaction partners, this approach provides a useful strategy for settling the challenges in oxa-[4+2] cyclization of para-quinone methide derivatives. This reaction serves as a good example for metal-catalyzed oxa-[4+2] cyclizations of quinone methides. In addition, it also offers a useful method for the construction of spiroacetal skeletons.

Cooperative Catalysis-Enabled Asymmetric α-Arylation of Aldehydes Using 2-Indolylmethanols as Arylation Reagents.

A catalytic asymmetric α-arylation of aldehydes using 2-indolylmethanols as arylation reagents has been established. This reaction was enabled by a cooperative catalytic system consisting of a gold complex, a Brønsted acid, and a chiral amine, which have a synergistic effect in the reaction process. By using this strategy, a series of α-arylation products of aldehydes were generated in overall acceptable yields and good enantioselectivities (up to 69%, 91:9 er). The control experiments demonstrated that the addition of PPh3AuCl as a gold complex was helpful to improve the yield, and trifluoroacetic acid as a Brønsted acid played a crucial role in the reaction by promoting the generation of carbocation and chiral enamine intermediates, which are two key intermediates of the asymmetric α-arylation reaction. In addition, the enantioselectivity of the reaction was mainly controlled by the chiral amine catalyst via forming a chiral enamine intermediate. This reaction has not only provided a useful protocol for catalytic asymmetric α-arylation of aldehydes but also enriched the research contents of 2-indolylmethanol-involved reactions and asymmetric cooperative catalysis.

[Expressions of CD96 and CD123 and Their Relationship with Prognosis of Patients with Myelodysplastic Syndrome].

OBJECTIVE: To explore the relationship between expression of CD96 and CD123 and prognosis of patients with myelodysplastic syndrome(MDS). METHODS: Eight-nine MDS patients(MDS group) and 20 persons without hematologic disease as controls(Control group) were enrolled. The patients were grouped by the risk. All participants received bone marrow biopsy. Mononuclear cells were extracted, CD34+CD38-CD123+ and CD34+CD38-CD96+ cells were counted by using flow cytometry. Expressions of 2 type cells in control group, MDS group and its subgroups were analyzed. RESULTS: The proportion of CD34+ cells and CD34+CD38- cells in mononuclear cells of patients in MDS group was higher than in control group (P<0.05). The proportions of CD34+CD38-CD123+ cells and CD34+CD38-CD96+ cells in CD34+CD38- cells were significantly higher than that in control group(P<0.05) and the proportion increased with the risk. In the low-and middle-risk group, the rates of complete remission(CR) and partial remission(PR) of patients with CD123- and CD96- were higher than those in patients with CD123+ and CD96+; in the middle-2 and high risk patients, the PR of patients with CD123- was higher than that in patients with CD123+(P<0.05). The CR rate of patients with CD96- was higher than that of patients with CD96+(P<0.05). CONCLUSION: The differentiation of CD34+ cells in bone marrow of MDS patients is abnormal, and the high expression of CD123 and CD96 cells existes. These findings may partially explain the cause of hematopoietic stem cell malignant clone in MDS patients.

Extraction and representation of common feature from uncertain facial expressions with cloud model.

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

Enantioselective Construction of Cyclopenta[b]indole Scaffolds via the Catalytic Asymmetric [3 + 2] Cycloaddition of 2-Indolylmethanols with p-Hydroxystyrenes.

The catalytic asymmetric [3 + 2] cycloaddition of 2-indolylmethanols to p-hydroxystyrenes was established in the presence of a chiral phosphoramide, and this reaction provided chiral cyclopenta[b]indole scaffolds in generally high yields and with good enantioselectivities (up to 98% yield, 99:1 er). The control experiments demonstrated that the dual hydrogen-bonding activation mode of the chiral catalyst toward the two substrates played an important role in the reaction. In addition, the large-scale reaction indicated that this catalytic asymmetric [3 + 2] cycloaddition could be scaled up for the synthesis of chiral cyclopenta[b]indole derivatives.

Radical Deaminative ipso-Cyclization of 4-Methoxyanilines with 1,7-Enynes for Accessing Spirocyclohexadienone-Containing Cyclopenta[c]quinolin-4-ones.

A new C-center radical-triggered bicyclization cascade of N-tethered 1,7-enynes for forming 28 examples of biologically interesting spirocyclohexadienone-containing cyclopenta[c]quinolin-4-ones with two all-carbon quaternary stereocenters has been established under mild conditions. The in situ generated diazonium salts from 4-methoxyanilines and t-BuONO are served as 4-methoxyphenyl precursors without additional oxidant, enabling 6-exo-dig cyclization/5-exo-trig ipso-cyclization to construct three new C-C bonds through metal-free dearomatization. The reaction also features broad substrate scope, annulation efficiency, and high functional group tolerance.

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.

BACKGROUND: Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. METHODS: Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. RESULTS: The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. CONCLUSIONS: Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

Base-Promoted [4 + 1]/[3 + 1 + 1] Bicyclization for Accessing Functionalized Indeno[1,2-c]furans.

A novel three-component bicyclization strategy for the metal-free synthesis of densely functionalized indeno[1,2-c]furans with generally good yields has been established from readily accessible o-phthalaldehydes (OPA), isocyanides, and α-diazoketones. The reaction pathway involves aldol-type addition, 1,2-hydride shift, 5-exo-trig cyclization, and 1,4-addition as well as an oxo-5-exo-dig cyclization sequence, resulting in continuous multiple bond-forming events including C-C and C-O bonds to rapidly build up functional oxo-heterocycles.

Dual rhodium/copper catalysis: synthesis of benzo[b]fluorenes and 2-naphthalenylmethanones via de-diazotized cycloadditions.

A novel synergistic rhodium/copper catalysis has been established, enabling de-diazotized cycloadditions of 1,5-enynes and α-diazocarbonyls toward the selective formation of densely functionalized benzo[b]fluorenes and 2-naphthalenylmethanones. α-Aryldiazoesters result in the formation of triple C-C bonds through bimetallic catalysis whereas a range of functionalized naphthalenes with a 1,5-dicarbonyl unit were accomplished using α-diazocarbonyls without any substituent at the α-position, indicating that substituents at the α-position of the α-diazocarbonyl unit could control the selectivity of the reaction.