High-entropy engineering with regulated defect structure and electron interaction tuning active sites for trifunctional electrocatalysis.

High-entropy alloy nanoparticles (HEANs) possessing regulated defect structure and electron interaction exhibit a guideline for constructing multifunctional catalysts. However, the microstructure-activity relationship between active sites of HEANs for multifunctional electrocatalysts is rarely reported. In this work, HEANs distributed on multi-walled carbon nanotubes (HEAN/CNT) are prepared by Joule heating as an example to explain the mechanism of trifunctional electrocatalysis for oxygen reduction, oxygen evolution, and hydrogen evolution reaction. HEAN/CNT excels with unmatched stability, maintaining a 0.8V voltage window for 220 h in zinc-air batteries. Even after 20 h of water electrolysis, its performance remains undiminished, highlighting exceptional endurance and reliability. Moreover, the intrinsic characteristics of the defect structure and electron interaction for HEAN/CNT are investigated in detail. The electrocatalytic mechanism of trifunctional electrocatalysis of HEAN/CNT under different conditions is identified by in situ monitoring and theoretical calculation. Meanwhile, the electron interaction and adaptive regulation of active sites in the trifunctional electrocatalysis of HEANs were further verified by density functional theory. These findings could provide unique ideas for designing inexpensive multifunctional high-entropy electrocatalysts.

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identification of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the molecular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%-140.0% in three independent, real-world, multi-center cohorts (cohort 1, n = 185; cohort 2, n = 784; and cohort 3, n = 208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n = 471) and 21.4% in cohort 3 (n = 208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool.

The effect of non-insulin-based insulin resistance indices on the prediction of recurrence in patients with atrial fibrillation undergoing radiofrequency catheter ablation.

BACKGROUND: Atrial fibrillation (AF) is acknowledged as a disease continuum. Despite catheter ablation being recommended as a primary therapy for AF, the high recurrence rates have tempered the initial enthusiasm. Insulin resistance (IR) has been established as an independent predictor for the onset of AF. However, the correlation between non-insulin-based IR indices and late AF recurrence in patients undergoing radiofrequency catheter ablation remains unknown. METHODS: A retrospective cohort of 910 AF patients who underwent radiofrequency catheter ablation was included in the analysis. The primary endpoint was late AF recurrence during the follow-up period after a defined blank period. The relationship between non-insulin-based IR indices and the primary endpoint was assessed using multivariate Cox hazards regression models and restricted cubic splines (RCS). Additionally, the net reclassification improvement and integrated discrimination improvement index were calculated to further evaluate the additional predictive value of the four IR indices beyond established risk factors for the primary outcome. RESULTS: During a median follow-up period of 12.00 months, 189 patients (20.77%) experienced late AF recurrence, which was more prevalent among patients with higher levels of IR. The multivariate Cox hazards regression analysis revealed a significant association between these IR indices and late AF recurrence. Among the four indices, METS-IR provided the most significant incremental effect on the basic model for predicting late AF recurrence. Multivariable-adjusted RCS curves illustrated a nonlinear correlation between METS-IR and late AF recurrence. In subgroup analysis, METS-IR exhibited a significant correlation with late AF recurrence in patients with diabetes mellitus (HR: 1.697, 95% CI 1.397 - 2.063, P < 0.001). CONCLUSION: All the four non-insulin-based IR indices were significantly associated with late AF recurrence in patients undergoing radiofrequency catheter ablation. Addressing IR could potentially serve as a viable strategy for reducing the late AF recurrence rate.

Effect of lactation on the distribution of mineral elements in goat milk.

The distribution of mineral elements in milk is crucial for their absorption and utilization, however, there has been limited attention given to the status of mineral elements in goat milk. In this study, goat milk was collected at 4 lactation periods (1-3, 90, 150, 240 d) and separated into 4 fractions (fat, casein, whey, and aqueous phase). The concentrations of Mg, Ca, Na, K, Zn, Fe, Cu, Mn, Co, Ni, Mo, and Cr in 4 fractions were analyzed using an inductively coupled plasma emission spectrometer. Our findings reveal that Ca, Zn, Fe, Cu, Mn, and Cr exhibit the highest levels in casein, while Mo demonstrates the highest content in whey. Additionally, Mg, Na, K, and Ni display the highest concentrations in the aqueous phase. Specifically, the contents of Ca, Cu and Fe in casein decrease from 1-3 to 150 d of lactation but increase from 150 to 240 d of lactation. Furthermore, the content of Mg in the aqueous phase decreases from 1-3 to 90 d of lactation but increases from 90 to 240 d of lactation. The content of Na and K in the aqueous phase decreases from 1-3 to 150 d of lactation. Notably, the content of Mo in whey increases from 1-3 to 150 d of lactation and decreases from 150 to 240 d. Our research contributes to the advancement of understanding the bioavailability of mineral elements in goat milk.

Effect of processing methods on the distribution of mineral elements in goat milk fractions.

Milk and dairy products are excellent sources of mineral elements, including Ca, P, Mg, Na, K, and Zn. The purpose of this study was to determine the effect of nonthermal (homogenization) and thermal (heat treatment) treatments on the distribution of mineral elements in 4 milk fractions: fat, casein, whey protein, and aqueous phase. The study results revealed that the distribution of mineral elements (such as Mg and Fe) in fat fractions is extremely low, whereas significant mineral elements such as Ca, Zn, Fe, and Cu are mostly dispersed in casein fractions. For nontreated goat milk, Mo is the only element identified in the whey protein fraction, whereas K and Na are mostly found in the aqueous phase. Mineral element concentrations in fat (K, Zn, and so on) and casein fractions (Fe, Mo, and so on) increased dramatically after homogenization. Homogenization greatly decreased the concentration of mineral elements in the whey protein fraction (Ca, Na, and so on) and aqueous phase (Fe, Cu, and so on). After heat treatment, the element content in the fat fraction and casein fraction increased greatly when compared with raw milk, such as Cu and Mg in the fat fraction, Na and Cu in the whey protein fraction, the concentration of components such as Mg and Na in casein fraction increased considerably. In contrast, after homogenization, Zn in the aqueous phase decreased substantially, whereas Fe increased significantly. Therefore, both homogenization and heat treatment have an effect on the mineral element distribution in goat milk fractions.

Comparative analysis of changes in whey proteins of goat milk throughout the lactation cycle using quantitative proteomics.

The composition and content of goat milk proteins are affected by many factors and have been extensively studied. However, variation in whey protein composition in goat milk throughout the lactation cycle has not been clarified. In the current study, 15 dairy goats were selected, and milk samples were collected at 1, 3, 30, 90, 150, and 240 d after delivery. Whey proteins were separated and digested and then identified using data-independent acquisition (DIA) and data-dependent acquisition proteomics approaches. Protein profiles identified using DIA were consistent with those of the data-dependent acquisition proteomics approach according to clustering and principal component analyses. Significant differences in the abundance of 238 proteins around the lactation cycle were identified using the DIA approach. Developmental changes of the whey proteome corresponding to lactation stage were revealed: plasminogen, α-2-macroglobulin, and fibronectin levels decreased from d 1 to 240, whereas polymeric immunoglobulin receptor, nucleobindin 2, fatty acid-binding protein 3, and lactoperoxidase increased from d 1 to 240. Protein-protein interaction analysis showed that fibronectin with a higher degree of connectivity is a central node. The findings are of great significance to better understanding the potential role of specific proteins and the mechanism of protein biosynthesis or intercellular transport in the mammary glands related to the physiological changes of dairy goats.

Developmental changes in proteins of casein micelles in goat milk using data-independent acquisition-based proteomics methods during the lactation cycle.

Casein micelles (CM) play an important role in milk secretion, stability, and processing. The composition and content of milk proteins are affected by physiological factors, which have been widely investigated. However, the variation in CM proteins in goat milk throughout the lactation cycle has yet to be fully clarified. In the current study, milk samples were collected at d 1, 3, 30, 90, 150, and 240 of lactation from 15 dairy goats. The size of CM was determined using laser light scattering, and CM proteins were separated, digested, and identified using data-independent acquisition (DIA) and data-dependent acquisition (DDA)-based proteomics approaches. According to clustering and principal component analysis, protein profiles identified using DIA were similar to those identified using the DDA approach. Significant differences in the abundance of 115 proteins during the lactation cycle were identified using the DIA approach. Developmental changes in the CM proteome corresponding to lactation stages were revealed: levels of lecithin cholesterol acyltransferase, folate receptor α, and prominin 2 increased from 1 to 240 d, whereas levels of growth/differentiation factor 8, peptidoglycan-recognition protein, and 45 kDa calcium-binding protein decreased in the same period. In addition, lipoprotein lipase, glycoprotein IIIb, and α-lactalbumin levels increased from 1 to 90 d and then decreased to 240 d, which is consistent with the change in CM size. Protein-protein interaction analysis showed that fibronectin, albumin, and apolipoprotein E interacted more with other proteins at the central node. These findings indicate that changes in the CM proteome during lactation could be related to requirements of newborn development, as well as mammary gland development, and may thus contribute to elucidating the physical and chemical properties of CM.

Research Progress and Future Trends of Low Temperature Plasma Application in Food Industry: A Review.

Food nutrition, function, sensory quality and safety became major concerns to the food industry. As a novel technology application in food industry, low temperature plasma was commonly used in the sterilization of heat sensitive materials and is now widely used. This review provides a detailed study of the latest advancements and applications of plasma technology in the food industry, especially the sterilization field; influencing factors and the latest research progress in recent years are outlined and upgraded. It explores the parameters that influence its efficiency and effectiveness in the sterilization process. Further research trends include optimizing plasma parameters for different food types, investigating the effects on nutritional quality and sensory attributes, understanding microbial inactivation mechanisms, and developing efficient and scalable plasma-based sterilization systems. Additionally, there is growing interest in assessing the overall quality and safety of processed foods and evaluating the environmental sustainability of plasma technology. The present paper highlights recent developments and provides new perspectives for the application of low temperature plasma in various areas, especially sterilization field of the food industry. Low temperature plasma holds great promise for the food industry's sterilization needs. Further research and technological advancements are required to fully harness its potential and ensure safe implementation across various food sectors.

Sparse phase retrieval using a physics-informed neural network for Fourier ptychographic microscopy.

In this paper, we report a sparse phase retrieval framework for Fourier ptychographic microscopy using the recently proposed principle of physics-informed neural networks. The phase retrieval problem is cast as training bidirectional mappings from the measured image space with random noise and the object space to be reconstructed, in which the image formation physics and convolutional neural network are integrated. Meanwhile, we slightly modify the mean absolute error loss function considering the signal characteristics. Two datasets are used to validate this framework. The results indicate that the proposed framework is able to reconstruct sparsely sampled data using a small aperture overlapping rate without additional data driving whereas conventional methods cannot.

Diagnostic yield and clinical impact of exome sequencing in early-onset scoliosis (EOS).

BACKGROUND: Early-onset scoliosis (EOS), defined by an onset age of scoliosis less than 10 years, conveys significant health risk to affected children. Identification of the molecular aetiology underlying patients with EOS could provide valuable information for both clinical management and prenatal screening. METHODS: In this study, we consecutively recruited a cohort of 447 Chinese patients with operative EOS. We performed exome sequencing (ES) screening on these individuals and their available family members (totaling 670 subjects). Another cohort of 13 patients with idiopathic early-onset scoliosis (IEOS) from the USA who underwent ES was also recruited. RESULTS: After ES data processing and variant interpretation, we detected molecular diagnostic variants in 92 out of 447 (20.6%) Chinese patients with EOS, including 8 patients with molecular confirmation of their clinical diagnosis and 84 patients with molecular diagnoses of previously unrecognised diseases underlying scoliosis. One out of 13 patients with IEOS from the US cohort was molecularly diagnosed. The age at presentation, the number of organ systems involved and the Cobb angle were the three top features predictive of a molecular diagnosis. CONCLUSION: ES enabled the molecular diagnosis/classification of patients with EOS. Specific clinical features/feature pairs are able to indicate the likelihood of gaining a molecular diagnosis through ES.

Changes in Whey Proteome between Mediterranean and Murrah Buffalo Colostrum and Mature Milk Reflect Their Pharmaceutical and Medicinal Value.

Milk represents an integrated meal for newborns; its whey protein is rich in many health beneficial components and proteins. The current study aimed to investigate the differences between colostrum and mature milk from Mediterranean and Murrah buffaloes using labeled proteomics and bioinformatics tools. In the current work, LC-MS/MS analysis led to identification of 780 proteins from which 638 were shared among three independent TMT experiments. The significantly changed proteins between the studied types were analyzed using gene ontology enrichment and KEGG pathways, and their interactions were generated using STRING database. Results indicated that immunological, muscular development and function, blood coagulation, heme related, neuronal, translation, metabolic process, and binding proteins were the main terms. Overall, colostrum showed higher levels of immunoglobulins, myosins, actin, neurofascin, syntaxins, thyroglobulins, and RNA-binding proteins, reflecting its importance in the development and activity of immunological, muscular, cardiac, neuronal, and thyroid systems, while lactoferrin and ferritin were increased in mature milk, highlighting its role in iron storage and hemoglobin formation.

A preliminary study on two-dimensional grid-free compressive beamforming for arbitrary planar array geometries.

The two-dimensional (2D) grid-free compressive beamforming based on atomic norm minimization is a promising solution for estimating the source direction-of-arrival and quantifying the source strength. However, the existing method is limited to measuring with rectangular grid arrays. To overcome this limitation, a 2D grid-free compressive beamforming strategy for arbitrary planar array geometries is proposed, where the microphone pressure is transformed into a representation of 2D Fourier series expansion. The term truncation method of Fourier series polynomials is established. The effectiveness and feasibility of the proposed method in the acoustic source identification are preliminarily verified by an experimental case, and topics worthy of further study are discussed based on the numerical simulations.

Comparative proteomic characterization of bovine milk containing ß-casein variants A1A1 and A2A2, and their heterozygote A1A2.

BACKGROUND: Genetic variants of ß-casein are cosnidered to affect the components of milk. However, limited data are available on the bovine protein components correlated with ß-casein variants at the proteome level. In the present study, cows producing milk containing ß-casein variants (A1A1 and A2A2) and their heterozygote (A1A2) were identified using a high-resolution melting method, and milk samples were collected and tested. Comparative analyses of casein micelles, whey and milk fat globule membrane fractions in each milk variant were performed using a label-free proteomics approach. RESULTS: The results obtained showed that ceruloplasmin and cathelicidin-2 were the most abundant proteins in milk containing variant A1A1; lactoferrin and CD5 molecule-like were the most abundant proteins in milk containing variant A2A2; and selenoprotein P and osteopontin were the most abundant proteins in milk containing heterozygote A1A2. Differences in protein components in milk containing the different ß-casein variants were visualized using hierarchical clustering, and profiles were separated using principal components analysis. The differentially expressed proteins in milk containing A1A1, A2A2 or A1A2 were predominantly involved in response to stress and defense response according to their Gene Ontology annotations. CONCLUSION: Our findings provide new insights into differentially expressed milk proteins corresponding to the presence of different ß-casein variants. This knowledge will help determine their potential biological functions in dairy products and the effects on human health. © 2020 Society of Chemical Industry.

Two-dimensional Newtonized orthogonal matching pursuit compressive beamforming.

Conventional compressive beamforming assumes that the acoustic sources fall on the discretized grid points. The performance degrades when the acoustic source lies off the discretized grid point, that is, when the basis mismatch occurs. This paper proposes a two-dimensional Newtonized orthogonal matching pursuit compressive beamforming, including single and multiple snapshot versions, which constructs the maximum likelihood estimation model, taking the position and strength of sources on a two-dimensional continuous plane as parameters. This method first captures the grid point near the source based on the discrete grid. Then it optimizes the coordinate estimation within the local continuous plane by a combination of the two-dimensional Newton optimization and a feedback mechanism to converge to the actual source position. It allows acoustic source identification in the near field utilizing arbitrary geometry planar array and works without the prior knowledge of signal-to-noise ratio and/or regularization parameters. Simulations and experiments show that the proposed method can overcome the basis mismatch issue and provide high spatial resolution, obtaining an accurate estimation for the position and strength of the acoustic source. Moreover, the multiple snapshot version outperforms the single snapshot version, especially under low signal-to-noise ratio. The larger the number of snapshots, the better the performance.

Analyses of histological and transcriptome differences in the skin of short-hair and long-hair rabbits.

BACKGROUND: Hair fibre length is an important economic trait of rabbits in fur production. However, molecular mechanisms regulating rabbit hair growth have remained elusive. RESULTS: Here we aimed to characterise the skin traits and gene expression profiles of short-hair and long-hair rabbits by histological and transcriptome analyses. Haematoxylin-eosin staining was performed to observe the histological structure of the skin of short-hair and long-hair rabbits. Compared to that in short-hair rabbits, a significantly longer anagen phase was observed in long-hair rabbits. In addition, by RNA sequencing, we identified 951 genes that were expressed at significantly different levels in the skin of short-hair and long-hair rabbits. Nine significantly differentially expressed genes were validated by quantitative real-time polymerase chain reaction. A gene ontology analysis revealed that epidermis development, hair follicle development, and lipid metabolic process were significantly enriched. Further, we identified potential functional genes regulating follicle development, lipid metabolic, and apoptosis as well as important pathways including extracellular matrix-receptor interaction and basal cell carcinoma pathway. CONCLUSIONS: The present study provides transcriptome evidence for the differences in hair growth between short-hair and long-hair rabbits and reveals that lipid metabolism and apoptosis might constitute major factors contributing to hair length.

Minimum Connected Dominating Set Algorithms for Ad Hoc Sensor Networks.

To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which expands the selection of the dominating point from two-hop neighbor to three-hop neighbor. The coverage efficiency has been improved under the condition of complete coverage. For the second stage, this paper respectively proposes an improved Kruskal-Steiner tree construction algorithm (IK-ST) and a maximum leaf nodes Steiner tree construction algorithm (ML-ST), both of which can make the result closer to the optimal solution. Finally, the simulation results show that the algorithm proposed in this paper is a great improvement over the previous algorithm in optimizing the scale of the connected dominating set (CDS).

Evaluation of six sample preparation procedures for qualitative and quantitative proteomics analysis of milk fat globule membrane.

Proteomic analysis of membrane proteins is challenged by the proteins solubility and detergent incompatibility with MS analysis. No single perfect protocol can be used to comprehensively characterize the proteome of membrane fraction. Here, we used cow milk fat globule membrane (MFGM) proteome analysis to assess six sample preparation procedures including one in-gel and five in-solution digestion approaches prior to LC-MS/MS analysis. The largest number of MFGM proteins were identified by suspension trapping (S-Trap) and filter-aided sample preparation (FASP) methods, followed by acetone precipitation without clean-up of tryptic peptides method. Protein identifications with highest average coverage was achieved by Chloroform/MeOH, in-gel and S-Trap methods. Most distinct proteins were identified by FASP method, followed by S-Trap. Analyses by Venn diagram, principal-component analysis, hierarchical clustering and the abundance ranking of quantitative proteins highlight differences in the MFGM fraction by the all sample preparation procedures. These results reveal the biased proteins/peptides loss occurred in each protocol. In this study, we found several novel proteins that were not observed previously by in-depth proteomics characterization of MFGM fraction in milk. Thus, a combination of multiple procedures with orthologous properties of sample preparation was demonstrated to improve the protein sequence coverage and expression level accuracy of membrane samples.

Sensitivity Analysis of Geometrical Parameters on the Aerodynamic Performance of Closed-Box Girder Bridges.

In this study, the influence of two critical geometrical parameters (i.e., angles of wind fairing, α; and lower inclined web, β) in the aerodynamic performance of closed-box girder bridges was systematically investigated through conducting a theoretical analysis and wind tunnel testing using laser displacement sensors. The results show that, for a particular inclined web angle β, a closed-box girder with a sharper wind fairing angle of α = 50° has better flutter and vortex-induced vibration (VIV) performance than that with α = 60°, while an inclined web angle of β = 14° produces the best VIV performance. In addition, the results from particle image velocimetry (PIV) tests indicate that a wind fairing angle of α = 50° produces a better flutter performance by inducing a single vortex structure and a balanced distribution of the strength of vorticity in both upper and lower parts of the wake region. Furthermore, two-dimensional three-degrees-of-freedom (2D-3DOF) analysis results demonstrate that the absolute values of Part A (with a reference of flutter derivative A2*) and Part D (with a reference of A1*H3*) generally decrease with the increase of β, while the change of the participation level of heaving degrees of freedom (DOF) in torsion-dominated coupled flutter initially increases, reaches its peak, and then decreases with the increase of β.

Validation of iScore and PLAN Score for Death in Thrombectomy in Acute Stroke Due to Anterior Circulation Large Artery Occlusion.

BACKGROUND: To validate iScore and PLAN score in acute anterior circulation large vessel occlusion stroke patients undergoing thrombectomy. METHODS: iScore and PLAN score were calculated for consecutive acute ischemic stroke undergoing thrombectomy were included and death at 1 month and death at 3 months were recorded. The area under the receiver operating characteristic curve was used to assess the discrimination ability of the scales for death. RESULTS: Two hundred and twenty-nine patients were included, 25.3% (58 of 229) of patient died at 1 month after thrombectomy and 25.8% (59 of 229) of them died at 3 months after thrombectomy. The receiver operator curve analysis found that iScore (area under the curve [AUC] = .76, 95% confidence interval [CI] .69-.83) was numerically better than PLAN score (AUC = .73, 95% CI .66-.81) for predicting death at day 90. The cut-off for iScore is 193, with sensitivity 64%, specificity 79%, positive predictive value 75% and negative predictive value 69%. CONCLUSIONS: The iScore scale is a valid predictive tool for death in anterior circulation large vessel occlusions undergoing thrombectomy.

Detection of plant protein adulterated in fluid milk using two-dimensional gel electrophoresis combined with mass spectrometry.

The illegal or unlabelled addition of plant protein in milk can cause serious anaphylaxis. For sustainable food security, it is therefore important to develop a methodology to detect non-milk protein in milk products. This research aims to differentiate milk adulterated with plant protein using two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry. According to the protein spots highlighted on the gel of adulterated milk, ß-conglycinin and glycinin were detected in milk adulterated with soy protein, while legumin, vicilin, and convicilin indicated the addition of pea protein, and ß-amylase and serpin marked wheat protein. These results suggest that a 2-DE-based protein profile is a useful method to identify milk adulterated with soy and pea protein, with a detection limit of 4% plant protein in the total protein.