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Metal nanoparticles play an outstanding role in the field of wound healing due to their excellent properties, and the significance of iron, one of the most widely used metals globally, cannot be overlooked. The purpose of this review is to determine the importance of iron nanoparticles in wound-healing dressings. Prolonged, poorly healing wounds may induce infections; wound infections are a major cause of chronic wound formation. The primary components of iron nanoparticles are iron oxide nanoparticles, which promote wound healing by being antibacterial, releasing metal ions, and overcoming bacterial resistance. The diameter of iron oxide nanoparticles typically ranges between 1 and 100 nm. Magnetic nanoparticles with a diameter of less than 30 nm are superparamagnetic and are referred to as superparamagnetic iron oxide nanoparticles. This subset of iron oxide nanoparticles can use an external magnetic field for novel functions such as magnetization and functionalization. Iron nanoparticles can serve clinical purposes not only to enhance wound healing through the aforementioned means but also to ameliorate anemia and glucose irregularities, capitalizing on iron's properties. Iron nanoparticles positively impact the healing process of chronic wounds, potentially extending beyond wound management. [ABSTRACT FROM AUTHOR]

The tremendous personal and economic burden worldwide caused by low back pain (LBP) has been surging in recent years. While intervertebral disc degeneration (IVDD) is the leading cause of LBP and vast efforts have been made to develop effective therapies, this problem is far from being resolved, as most treatments, such as painkillers and surgeries, mainly focus on relieving the symptoms rather than reversing the cause of IVDD. However, as stem/progenitor cells possess the potential to regenerate IVD, a deeper understanding of the early development and role of these cells could help to improve the effectiveness of stem/progenitor cell therapy in treating LBP. Single-cell RNA sequencing results provide fresh insights into the heterogeneity and development patterns of IVD progenitors; additionally, we compare mesenchymal stromal cells and IVD progenitors to provide a clearer view of the optimal cell source proposed for IVD regeneration. [ABSTRACT FROM AUTHOR]

In this work, the mechanical behavior and energy absorption characteristics of flexible fabric under hypervelocity impact (HVI) were investigated. Basalt fabric, ultra-high molecular weight polyethylene (UHMWPE) fabric, and aluminum alloy (Al) plate were chosen to be the sample materials for their excellent mechanical properties and applicative prospect in spacecraft shielding. HVI experiments had been conducted with the help of a two-stage light-gas gun facility, wherein Al projectile with 3.97 mm diameter was launched at velocities in the range 4.1~4.3 km/s. Impact conditions and areal density were kept constant for all targets. The microstructural damage morphology of fiber post-impact was characterized using a scanning electron microscope (SEM). Analysis results show that a brittle fracture occurred for Basalt fiber during HVI. On the contrary, the ductile fractures with large-scale plastic deformation and apparent thermal softening/melting of the material had happened on the UHMWPE fiber when subjected to a projectile impact. According to the HVI shielding performance and microstructural damage analysis results, it can be inferred that ductile fractures and thermal softening/melting of the material were the prevailing energy absorption behaviors of UHMWPE fabric, which leads to absorbing more impact energy than Basalt fabric and eventually, contributes the superior shielding performance. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis. [ABSTRACT FROM AUTHOR]

In this article, the anti-poison performance of novel Al-6Y-2P master alloy for impurity Ca in hypereutectic Al-Si alloys was investigated in detail. According to the microstructural analysis, it can be found that the primary Si and eutectic Si particles could be relatively modified and refined. In order to investigate the influence mechanism of Ca on the limited refinement performance of Al-6Y-2P master alloy, types of Al-xSi-2Ca-3Y-1P (x = 0, 6, 12, 18 and 30) alloys were prepared. It is observed that Ca takes the form of more stable Ca3P2 compounds by reacting with YP and the surface of Ca3P2 particles are unsmooth, and even some have wrinkles in Al Al-2Ca-3Y-1P alloy. With the increase of Si content in Al-xSi-2Ca-3Y-1P (x = 6, 12, 18 and 30) systems, the multi-encapsulation structures, i.e., the phosphide (AlP and YP), hexagonal Al2Si2Ca, the Al3Si2Y2 or primary Si from inside to outside in order were examined.The excapsulation of YP and AlP caused by Al2Si2Ca might be the reason for the limited refinement effect of Al-6Y-2P master alloy for hypereutectic Al-18Si alloys. [ABSTRACT FROM AUTHOR]

Simple Summary: The history of oyster culture consists of a succession of developmental phases using different species, followed by collapses associated with infectious diseases. The Pacific oyster (Crassostrea gigas), native to the northwest Pacific Ocean, was characterized by wide adaptability, and translocated to support major aquaculture industries in many countries around the world. A deadly herpesvirus, Ostreid herpesvirus-1 (OsHV-1), has hit the superior oyster since 1991. OsHV-1 infection, combined with subsequent bacteraemia by opportunistic bacteria, has brought heavy losses to the main production regions of the Pacific oyster. Because of the considerable economic importance of Pacific oysters, there is a wealth of information available on diseases that affect them around the world. Little comprehensive information is available about the epidemiology status of major disease-causing agents in China. On the other hand, the scale and yields of Pacific oyster increased rapidly in recent years, as a result of the introduction and popularization of triploid oysters. In the present study, a comprehensive survey of potential pathogens associated with mortality events of Pacific oysters was carried out. Our results highlight the potential risks of OsHV-1, Vibrio natriegens, and Vibrio alginolyticus to the aquaculture industry of the Pacific oyster in China. The Pacific oyster (Crassostrea gigas) aquaculture industry increased rapidly in China with the introduction and promotion of triploid oysters in recent years. Mass mortalities affecting different life stages of Pacific oysters emerged periodically in several important production areas of Northern China. During 2020 and 2021, we conducted a passive two-year investigation of infectious pathogens linked to mass mortality. Ostreid herpesvirus-1 (OsHV-1) was detected to be associated with mass mortalities of hatchery larvae, but not juveniles and adults in the open sea. Protozoan parasites, such as Marteilia spp., Perkinsus spp. and Bonamia spp. were not detected. Bacterial isolation and identification revealed that Vibrio natriegens and Vibrio alginolyticus were the most frequently (9 out of 13) identified two dominant bacteria associated with mass mortalities. Pseudoalteromonas spp. was identified as the dominant bacteria in three mortality events that occurred during the cold season. Further bacteriological analysis was conducted on two representative isolates of V. natriegens and V. alginolyticus, designated as CgA1-1 and CgA1-2. Multisequence analysis (MLSA) showed that CgA1-1 and CgA1-2 were closely related to each other and nested within the Harveyi clade. Bacteriological investigation revealed faster growth, and more remarkable haemolytic activity and siderophore production capacity at 25 °C than at 15 °C for both CgA1-1 and CgA1-2. The accumulative mortalities of experimental immersion infections were also higher at 25 °C (90% and 63.33%) than at 15 °C (43.33% and 33.33%) using both CgA1-1 and CgA1-2, respectively. Similar clinical and pathological features were identified in samples collected during both naturally and experimentally occurring mortalities, such as thin visceral mass, discolouration, and connective tissue and digestive tube lesions. The results presented here highlight the potential risk of OsHV-1 to hatchery production of larvae, and the pathogenic role of V. natriegens and V. alginolyticus during mass mortalities of all life stages of Pacific oysters in Northern China. [ABSTRACT FROM AUTHOR]

Background: Hydrogen peroxide (H2O2)-induced oxidative stress has been demonstrated to induce afterdepolarizations and triggered activities in isolated myocytes, but the underlying mechanisms remain not fully understood. We aimed to explore whether protein kinase C (PKC) activation plays an important role in oxidative stress-induced afterdepolarizations. Methods: Action potentials and ion currents of isolated rabbit cardiomyocytes were recorded using the patch clamp technique. H2O2 (1 mM) was perfused to induce oxidative stress and the specific classical PKC inhibitor, Gö 6983 (1 μM), was applied to test the involvement of PKC. Results: H2O2 perfusion prolonged the action potential duration and induced afterdepolarizations. Pretreatment with Gö 6983 prevented the emergence of H2O2-induced afterdepolarizations. Additional application of Gö 6983 with H2O2 effectively suppressed H2O2-induced afterdepolarizations. H2O2 increased the late sodium current (INa,L) (n = 7, p < 0.01) and the L-type calcium current (ICa,L) (n = 5, p < 0.01), which were significantly reversed by Gö 6983 (p < 0.01). H2O2 also increased the transient outward potassium current (Ito) (n = 6, p < 0.05). However, Gö 6983 showed little effect on H2O2-induced enhancement of Ito. Conclusions: H2O2 induced afterdepolarizations via the activation of PKC and the enhancement of ICa,L and INa,L. These results provide evidence of a link between oxidative stress, PKC activation and afterdepolarizations. [ABSTRACT FROM AUTHOR]

The strain rate effect of reinforcing steel bars is generally indispensable for modeling the dynamic responses of reinforced concrete structures in blast and impact events. A systematic experimental investigation was conducted on the strain rate behavior of reinforcing steel bar grades HPB235, HRB335, HRB400, and HRB500 which are widely used in the field of civil engineering in China. The dynamic testing was performed using a servo-hydraulic Instron VHS160/100-20 in a strain rate range from 2 to 75 s-1. Stress-strain curves at preset strain rates were obtained. The test data were then used to derive the parameters in a model based on the dynamic increase factors (DIFs) of strengths and the Johnson-Cook constitutive model. Results indicated that a significant strain rate effect was observed for the four rebar grades. The dynamic yield strengths increased from 13% to 41% and their ultimate strengths improved from 9% to 19% in the strain rate range during testing. The strain rate behavior of the four rebar grades could be appropriately predicted using the parameters in the model based on the DIFs of strengths and the Johnson-Cook model. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is a high incidence metabolic disease. Glimepiride, metformin, and their combination are the most commonly used therapeutics for T2DM in the clinic, but little is known about the metabolic responses of these therapies. In this study, ultrahigh-pressure liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC/ESI-QTOF-MS)-based metabolomics was applied to detect changes in the urinary metabolomic profile of Zucker diabetic fatty (ZDF) rats in response to these treatments. Additionally, standard biochemical parameters (e.g., fasting plasma glucose, glycosylated hemoglobin, oral glucose tolerance, urinary glucose, triglyceride, total cholesterol, and insulin) and liver histopathology were monitored and observed. Six metabolites, including 3-galactosyl lactose, citric acid, sphingosine, phytosphingosine, ribothymidine, and succinoadenosine, were found significantly reverted to the normal level after these therapies. The present study is the first to present citric acid and sphinganine as the potential markers of T2DM, which could be used as indicators to observe the anti-diabetic effects of glimepiride, metformin, and their combination treatments. [ABSTRACT FROM AUTHOR]

Intelligent composite structures with self-aware functions are preferable for future aircrafts. The real-time location of damaged areas of composites is a key step. In this study, deep transfer learning was used to achieve the real-time location of damaged areas. The sensor network obtained acoustic emission signals from different damaged areas of the aluminum alloy plate. The acoustic emission time-domain signal is transformed into the input image by continuous wavelet transform. The convolutional neural network-based model automatically localized the damaged area by extracting features from the input image. A small amount of composite acoustic emission data was used to fine-tune some network parameters of the basic model through transfer learning. This enabled the model to classify the damaged area of composites. The accuracy of the transfer learning model trained with 900 samples is 96.38%, which is comparable to the accuracy of the model trained directly with 1800 samples; the training time of the former is only 17.68% of that of the latter. The proposed method can be easily adapted to new composite structures using transfer learning and a small dataset, providing a new idea for structural health monitoring. [ABSTRACT FROM AUTHOR]

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions. [ABSTRACT FROM AUTHOR]

Background/Objectives: There have been widespread reports of persistent symptoms in both children and adults after SARS-CoV-2 infection, giving rise to debates on whether it should be regarded as a separate clinical entity from other postviral syndromes. This study aimed to characterize the clinical presentation of post-acute symptoms and conditions in the Korean pediatric and adult populations. Methods: A retrospective analysis was performed using a national, population-based database, which was encoded using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We compared individuals diagnosed with SARS-CoV-2 to those diagnosed with influenza, focusing on the risk of developing prespecified symptoms and conditions commonly associated with the post-acute sequelae of COVID-19. Results: Propensity score matching yielded 1,656 adult and 343 pediatric SARS-CoV-2 and influenza pairs. Ninety days after diagnosis, no symptoms were found to have elevated risk in either adults or children when compared with influenza controls. Conversely, at 1 day after diagnosis, adults with SARS-CoV-2 exhibited a significantly higher risk of developing abnormal liver function tests, cardiorespiratory symptoms, constipation, cough, thrombophlebitis/thromboembolism, and pneumonia. In contrast, children diagnosed with SARS-CoV-2 did not show an increased risk for any symptoms during either acute or post-acute phases. Conclusions: In the acute phase after infection, SARS-CoV-2 is associated with an elevated risk of certain symptoms in adults. The risk of developing post-acute COVID-19 sequelae is not significantly different from that of having postviral symptoms in children in both the acute and post-acute phases, and in adults in the post-acute phase. These observations warrant further validation through studies, including the severity of initial illness, vaccination status, and variant types. [ABSTRACT FROM AUTHOR]

To test the hypothesis that a platelet-rich plasma (PRP) plus bioactive glass (BG) mixture could shorten the tendon-bone healing process in rotator cuff tendon repair, thirty mature male New Zealand white rabbits were randomly divided into three groups, Control, PRP, and PRP + BG. All groups underwent a surgical procedure to establish a rotator cuff tendon healing model. Mechanical examinations and histological assays were taken to verify the adhesion of the tendon-bone. Real-time PCR was adopted to analyze Bone Morphogenetic Protein-2 (BMP-2). The maximum load-to-failure value in mechanical examinations was significantly higher in the PRP + BG group than that in the control group after six weeks (Control 38.73 ± 8.58, PRP 54.49 ± 8.72, PRP + BG 79.15 ± 7.62, p < 0.001), but it was not significantly different at 12 weeks (PRP 74.27 ± 7.74, PRP + BG 82.57 ± 6.63, p = 0.145). In histological assays, H&E (hematoxylin-eosin) staining showed that the interface between the tendon-bone integration was much sturdier in the PRP + BG group compared to the other two groups at each time point, and more ordered arranged tendon fibers can be seen at 12 weeks. At six weeks, the mRNA expression levels of BMP-2 in the PRP + BG group were higher than those in the other groups (PRP + BG 0.65 ± 0.11, PRP 2.284 ± 0.07, Control 0.12 ± 0.05, p < 0.05). However, there was no significant difference in the mRNA expression levels of BMP-2 among the three groups at 12 weeks (p = 0.922, 0.067, 0.056). BMP-2 levels in PRP and PRP+BG groups were significantly lower at 12 weeks compared to six weeks (p = 0.006, <0.001).We found that the PRP + BG mixture could enhance tendon-bone healing in rotator cuff tendon repair. [ABSTRACT FROM AUTHOR]

At present, due to the growing attention focused on the issue of tendon-bone healing, we carried out an animal study of the use of genetic intervention combined with cell transplantation for the promotion of this process. Here, the efficacy of bone marrow stromal cells infected with bone morphogenetic protein-2 (BMP-2) on tendon-bone healing was determined. A eukaryotic expression vector containing the BMP-2 gene was constructed and bone marrow-derived mesenchymal stem cells (bMSCs) were infected with a lentivirus. Next, we examined the viability of the infected cells and the mRNA and protein levels of BMP-2-infected bMSCs. Gastrocnemius tendons, gastrocnemius tendons wrapped by bMSCs infected with the control virus (bMSCs+Lv-Control), and gastrocnemius tendons wrapped by bMSCs infected with the recombinant BMP-2 virus (bMSCs+Lv-BMP-2) were used to reconstruct the anterior cruciate ligament (ACL) in New Zealand white rabbits. Specimens from each group were harvested four and eight weeks postoperatively and evaluated using biomechanical and histological methods. The bMSCs were infected with the lentivirus at an efficiency close to 100%. The BMP-2 mRNA and protein levels in bMSCs were significantly increased after lentiviral infection. The bMSCs and BMP-2-infected bMSCs on the gastrocnemius tendon improved the biomechanical properties of the graft in the bone tunnel; specifically, bMSCs infected with BMP-2 had a positive effect on tendon-bone healing. In the four-week and eight-week groups, bMSCs+Lv-BMP-2 group exhibited significantly higher maximum loads of 29.3 ± 7.4 N and 45.5 ± 11.9 N, respectively, compared with the control group (19.9 ± 6.4 N and 21.9 ± 4.9 N) (P = 0.041 and P = 0.001, respectively). In the eight-week groups, the stiffness of the bMSCs+Lv-BMP-2 group (32.5 ± 7.3) was significantly higher than that of the bMSCs+Lv-Control group (22.8 ± 7.4) or control groups (12.4 ± 6.0) (p = 0.036 and 0.001, respectively). Based on the histological findings, there was an increased amount of perpendicular collagen fibers formed between the tendon and bone in the bMSCs+Lv-Control and bMSCs+Lv-BMP-2 group, compared with the gastrocnemius tendons. The proliferation of cartilage-like cells and the formation of fibrocartilage-like tissue were highest within the bone tunnels in the bMSCs+Lv-BMP-2 group. These results suggest that this lentivirus can be used to efficiently infect bMSCs with BMP-2. Furthermore, tendons wrapped by bMSCs+Lv-BMP-2 improved tendon-bone healing. [ABSTRACT FROM AUTHOR]

In this study, the interfacial reactions and mechanical properties of solder joints after multiple reflows were observed to evaluate the applicability of the developed materials for high-temperature soldering for automotive electronic components. The microstructural changes and mechanical properties of Sn-Cu solders regarding Al(Si) addition and the number of reflows were investigated to determine their reliability under high heat and strong vibrations. Using differential scanning calorimetry, the melting points were measured to be approximately 227, 230, and 231 °C for the SC07 solder, SC-0.01Al(Si), and SC-0.03Al(Si), respectively. The cross-sectional analysis results showed that the total intermetallic compounds (IMCs) of the SC-0.03Al(Si) solder grew the least after the as-reflow, as well as after 10 reflows. Electron probe microanalysis and transmission electron microscopy revealed that the Al-Cu and Cu-Al-Sn IMCs were present inside the solders, and their amounts increased with increasing Al(Si) content. In addition, the Cu6Sn5 IMCs inside the solder became more finely distributed with increasing Al(Si) content. The Sn-0.5Cu-0.03Al(Si) solder exhibited the highest shear strength at the beginning and after 10 reflows, and ductile fracturing was observed in all three solders. This study will facilitate the future application of lead-free solders, such as an Sn-Cu-Al(Si) solder, in automotive electrical components. [ABSTRACT FROM AUTHOR]

The effects of Ag nanoparticle (Ag NP) addition on interfacial reaction and mechanical properties of Sn–58Bi solder joints using ultra-fast laser soldering were investigated. Laser-assisted low-temperature bonding was used to solder Sn–58Bi based pastes, with different Ag NP contents, onto organic surface preservative-finished Cu pads of printed circuit boards. The solder joints after laser bonding were examined to determine the effects of Ag NPs on interfacial reactions and intermetallic compounds (IMCs) and high-temperature storage tests performed to investigate its effects on the long-term reliabilities of solder joints. Their mechanical properties were also assessed using shear tests. Although the bonding time of the laser process was shorter than that of a conventional reflow process, Cu–Sn IMCs, such as Cu6Sn5 and Cu3Sn, were well formed at the interface of the solder joint. The addition of Ag NPs also improved the mechanical properties of the solder joints by reducing brittle fracture and suppressing IMC growth. However, excessive addition of Ag NPs degraded the mechanical properties due to coarsened Ag3Sn IMCs. Thus, this research predicts that the laser bonding process can be applied to low-temperature bonding to reduce thermal damage and improve the mechanical properties of Sn–58Bi solders, whose microstructure and related mechanical properties can be improved by adding optimal amounts of Ag NPs. [ABSTRACT FROM AUTHOR]

In this paper, an easy-to-implement domain-type meshless method—the generalized finite difference method (GFDM)—is applied to simulate the bending behavior of functionally graded (FG) plates. Based on the first-order shear deformation theory (FSDT) and Hamilton's principle, the governing equations and constrained boundary conditions of functionally graded plates are derived. Based on the multivariate Taylor series and the weighted moving least-squares technique, the partial derivative of the underdetermined displacement at a certain node can be represented by a linear combination of the displacements at its adjacent nodes in the GFDM implementation. A certain node of the local support domain is formed according to the rule of "the shortest distance". The proposed GFDM provides the sparse resultant matrix, which overcomes the highly ill-conditioned resultant matrix issue encountered in most of the meshless collocation methods. In addition, the studies show that irregular distribution of structural nodes has hardly any impact on the numerical performance of the generalized finite difference method for FG plate bending behavior. The method is a truly meshless approach. The numerical accuracy and efficiency of the GFDM are firstly verified through some benchmark examples, with different shapes and constrained boundary conditions. Then, the effects of material parameters and thickness on FG plate bending behavior are numerically investigated. [ABSTRACT FROM AUTHOR]

The goal of this study is to develop a disturbance observer-based prescribed performance fault-tolerant controller (PPFTC) for a multi-area interconnected power system (MIPS) with a hybrid energy storage system (HESS). The mathematical model of the MIPS with HESS is introduced first. Then the load disturbance estimation is obtained using a disturbance observer (DO) approach. By introducing two additional functions, the tracking error is bounded to achieve the desired response. The PPFTC is further developed based on the DO ensuring that the area control error (ACE) of the MIPS asymptotically converges to zero. Finally, the effectiveness of the given PPFTC and DO are inspected through the use of the Lyapunov theory and simulated results. [ABSTRACT FROM AUTHOR]

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance. [ABSTRACT FROM AUTHOR]

An observer-based sliding mode fault-tolerant controller is developed in this paper, which is applied to an interconnected power system with a hybrid energy storage system (HESS). The model of the interconnected power system with HESS is introduced first. An observer is then proposed to estimate the unknown but bounded load disturbances and the actuator fault. The sliding mode fault-tolerant controller is further designed based on the observer ensuring that the area control error of the interconnected power system asymptotically converges to zero. The stability and the convergence of the whole system are proven based on the Lyapunov stability theory. Finally, the effectiveness of the proposed fault-control scheme is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

This paper presents a novel outer rotor permanent-magnet vernier machine (PMVM) for in-wheel direct-drive application. The overhang structures of the rotor and flux modulation pole (FMP) are introduced. The soft magnetic composite (SMC) was adopted in the FMP overhang to allow more axial flux. The 3-D finite element analysis (FEA) was carried out to prove that the proposed machine can effectively utilize the end winding space to enhance the air-gap flux density. Hence the PMVM can offer 27.3% and 14.5% higher torque density than the conventional machine with no overhang structure and the machine with only rotor overhang structure, respectively. Nevertheless, the efficiency of the proposed machine is slightly lower than the conventional ones due to the extra losses from the overhang structures. [ABSTRACT FROM AUTHOR]

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying "quantitatively tissue-specific expressed genes" capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function. [ABSTRACT FROM AUTHOR]

Simple Summary: It is known that COVID-19 causes dynamic changes in the immune system. At different stages of the course of COVID-19, the immune cells may exhibit different patterns, which have not been fully uncovered. In this study, a machine-learning-based method was designed to deeply analyze the scRNA-seq data of three types of immune cells from patients with COVID-19, including B cells, T cells, and myeloid cells. Four levels of COVID-19 severity/outcome were involved for each cell type. As a result, several essential genes were obtained, and some of them could be confirmed to be related to SARS-CoV-2 infection. As COVID-19 develops, dynamic changes occur in the patient's immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system. [ABSTRACT FROM AUTHOR]

Vaccines trigger an immunological response that includes B and T cells, with B cells producing antibodies. SARS-CoV-2 immunity weakens over time after vaccination. Discovering key changes in antigen-reactive antibodies over time after vaccination could help improve vaccine efficiency. In this study, we collected data on blood antibody levels in a cohort of healthcare workers vaccinated for COVID-19 and obtained 73 antigens in samples from four groups according to the duration after vaccination, including 104 unvaccinated healthcare workers, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination. Our work was a reanalysis of the data originally collected at Irvine University. This data was obtained in Orange County, California, USA, with the collection process commencing in December 2020. British variant (B.1.1.7), South African variant (B.1.351), and Brazilian/Japanese variant (P.1) were the most prevalent strains during the sampling period. An efficient machine learning based framework containing four feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and maximum relevance minimum redundancy) and four classification algorithms (decision tree, k-nearest neighbor, random forest, and support vector machine) was designed to select essential antibodies against specific antigens. Several efficient classifiers with a weighted F1 value around 0.75 were constructed. The antigen microarray used for identifying antibody levels in the coronavirus features ten distinct SARS-CoV-2 antigens, comprising various segments of both nucleocapsid protein (NP) and spike protein (S). This study revealed that S1 + S2, S1.mFcTag, S1.HisTag, S1, S2, Spike.RBD.His.Bac, Spike.RBD.rFc, and S1.RBD.mFc were most highly ranked among all features, where S1 and S2 are the subunits of Spike, and the suffixes represent the tagging information of different recombinant proteins. Meanwhile, the classification rules were obtained from the optimal decision tree to explain quantitatively the roles of antigens in the classification. This study identified antibodies associated with decreased clinical immunity based on populations with different time spans after vaccination. These antibodies have important implications for maintaining long-term immunity to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Climate change has caused high salinity in many fields, particularly in the mud flats in coastal regions. The resulting salinity has become one of the most significant abiotic stresses affecting the world's rice crop productivity. Developing elite cultivars with novel salinity-tolerance traits is regarded as the most cost-effective and environmentally friendly approach for utilizing saline-alkali land. To develop a highly efficient green strategy and create novel rice germplasms for salt-tolerant rice breeding, this study aimed to improve rice salinity tolerance by combining targeted CRISPR/Cas9-mediated editing of the OsRR22 gene with heterosis utilization. The novel alleles of the genic male-sterility (GMS) and elite restorer line (733Srr22-T1447-1 and HZrr22-T1349-3) produced 110 and 1 bp deletions at the third exon of OsRR22 and conferred a high level of salinity tolerance. Homozygous transgene-free progeny were identified via segregation in the T2 generation, with osrr22 showing similar agronomic performance to wild-type (733S and HZ). Furthermore, these two osrr22 lines were used to develop a new promising third-generation hybrid rice line with novel salinity tolerance. Overall, the results demonstrate that combining CRISPR/Cas9 targeted gene editing with the "third-generation hybrid rice system" approach allows for the efficient development of novel hybrid rice varieties that exhibit a high level of salinity tolerance, thereby ensuring improved cultivar stability and enhanced rice productivity. [ABSTRACT FROM AUTHOR]

Neurofilament light chain (NF-L) plays critical roles in synapses that are relevant to neuropsychiatric diseases. Despite postmortem evidence that NF-L is decreased in opiate abusers, its role and underlying mechanisms remain largely unknown. We found that the microinjection of the histone deacetylase (HDAC) inhibitor Trichostatin A (TSA) into the ventrolateral orbital cortex (VLO) attenuated chronic morphine-induced behavioral sensitization. The microinjection of TSA blocked the chronic morphine-induced decrease of NF-L. However, our chromatin immunoprecipitation (ChIP)-qPCR results indicated that this effect was not due to the acetylation of histone H3-Lysine 9 and 14 binding to the NF-L promotor. In line with the behavioral phenotype, the microinjection of TSA also blocked the chronic morphine-induced increase of p-ERK/p-CREB/p-NF-L. Finally, we compared chronic and acute morphine-induced behavioral sensitization. We found that although both chronic and acute morphine-induced behavioral sensitization were accompanied by an increase of p-CREB/p-NF-L, TSA exhibited opposing effects on behavioral phenotype and molecular changes at different addiction contexts. Thus, our findings revealed a novel role of NF-L in morphine-induced behavioral sensitization, and therefore provided some correlational evidence of the involvement of NF-L in opiate addiction. [ABSTRACT FROM AUTHOR]

The early detection of breast cancer using mammogram images is critical for lowering women's mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained. [ABSTRACT FROM AUTHOR]

Globally, the incidence of kidney stones (urolithiasis) has increased over time. Without better treatment, stones in the kidneys could result in blockage of the ureters, repetitive infections in the urinary tract, painful urination, and permanent deterioration of the kidneys. Hence, detecting kidney stones is crucial to improving an individual's life. Concurrently, ML (Machine Learning) has gained extensive attention in this area due to its innate benefits in continuous enhancement, its ability to deal with multi-dimensional data, and its automated learning. Researchers have employed various ML-based approaches to better predict kidney stones. However, there is a scope for further enhancement regarding accuracy. Moreover, studies seem to be lacking in this area. This study proposes a smart toilet model in an IoT-fog (Internet of Things-fog) environment with suitable ML-based algorithms for kidney stone detection from real-time urinary data to rectify this issue. Significant features are selected using the proposed Improved MBPSO (Improved Modified Binary Particle Swarm Optimization) to attain better classification. In this case, sigmoid functions are used for better prediction with binary values. Finally, classification is performed using the proposed Improved Modified XGBoost (Modified eXtreme Gradient Boosting) to prognosticate kidney stones. In this case, the loss functions are updated to make the model learn effectively and classify accordingly. The overall proposed system is assessed by internal comparison with DT (Decision Tree) and NB (Naïve Bayes), which reveals the efficient performance of the proposed system in kidney stone prognostication. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20–85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients' quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications. [ABSTRACT FROM AUTHOR]

Anemoside B4 (B4) is a saponin that is extracted from Pulsatilla chinensis (Bge.), and Regel exhibited anti-inflammatory, antioxidant, antiviral, and immunomodulatory activities. However, its hypoglycemic activity in diabetes mellitus has not been evaluated. Here, we explored the effect of B4 on hyperglycemia and studied its underlying mechanism of lowering blood glucose based on hyperglycemic rats in vivo and L6 skeletal muscle cells (L6) in vitro. The rats were fed a high-fat diet (HFD) for one month, combined with an intraperitoneal injection of 60 mg/kg streptozotocin (STZ) to construct the animal model, and the drug was administrated for two weeks. Blood glucose was detected and the proteins and mRNA were expressed. Our study showed that B4 significantly diminished fasting blood glucose (FBG) and improved glucose metabolism. In addition, B4 facilitated glucose utilization in L6 cells. B4 could enhance the expression of glucose transporter 4 (GLUT4) in rat skeletal muscle and L6 cells. Mechanistically, B4 elevated the inhibition of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways. Furthermore, we confirmed the effect of B4 on glucose uptake involved in the enhancement of GLUT4 expression in part due to PI3K/AKT signaling by using a small molecule inhibitor assay and constructing a GLUT4 promoter plasmid. Taken together, our study found that B4 ameliorates hyperglycemia through the PI3K/AKT pathway and promotes GLUT4 initiation, showing a new perspective of B4 as a potential agent against diabetes. [ABSTRACT FROM AUTHOR]

Psoriasis is a chronic inflammatory skin disease associated with various factors. Recently, alterations in the gut and skin microbiomes have been shown to interact with host immunity, affect skin barrier function, as well as development and progression of psoriasis. We aimed to analyze the microbiota of the scalp of patients with psoriasis and determine the characteristics of the microbiome according to disease severity. We investigated the scalp microbiome of 39 patients with psoriasis scalp lesions and a total of 47 samples were analyzed. The patients were divided into mild, moderate, and severe groups according to the European recommendations for scalp psoriasis. For bacterial identification, we utilized the SILVA database targeting the V3 region of the 16 S rRNA gene. The mean Shannon index escalated along with disease severity, and the diversity of the scalp microbiome tended to increase with disease severity (R = 0.37, p < 0.01). The relative abundance of Pseudomonas was increased in severe scalp psoriasis (0.49 ± 0.22) compared to the mild group (0.07 ± 0.03, p = 0.029), and Diaphorobacter was enriched in the mild group (0.76 ± 0.16%) compared to the severe group (0.44 ± 0.22, p < 0.001). We identified that increased diversity of the scalp microbiome and the relative abundance of Pseudomonas are associated with the severity of scalp psoriasis. [ABSTRACT FROM AUTHOR]

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy. [ABSTRACT FROM AUTHOR]

However the following combination of characters places the fossil in a separate entity within Cosmoscartini, as this tribe is currently understood: angulate border angulate border of frontoclypeus in dorsal view, anterior margin of pronotum protruded, posterior margin with distinct incision, long mesonotum (distinctly longer than half of the length of the pronotum in midline), more basal forking of ScP + R, long clavus, short tornus, and subtriangular shape of subgenital plates place the fossil separately within Cosmoscartini as this tribe is currently understood. MAP: Figure 7 Localities of fossil taxa ascribed to Cercopidae and the modern relatives (note that the taxonomic placement of the fossil record presented here is in need of revision). The family Cercopidae includes 178 genera and 1561 species [[7]], most closely related to the paraphyletic Aphrophoridae [[6]], which bear transverse compound eyes instead of the globose compound eyes of Cercopidae [[8]]. The oldest confirmed fossil known for the Cercopidae ( I Allocercopis punctatis i Lin, 1997) dates back to the Cretaceous-Paleocene [[11]] and two dubious cercopid fossils were recorded from the Cretaceous [[12]]. [Extracted from the article]

Vitelline membrane proteins (VMPs) are the main proteins that form the inner shell (vitelline membrane layer) of insect eggs and are an integral part of egg formation and embryo development. Here, we characterized the molecular structure and expression patterns of the VMP26 gene and analyzed its reproductive functions in diamondback moth, Plutella xylostella (L.), a worldwide migratory pest of cruciferous plants. The PxVMP26 gene was shown to be a single exon gene that contained an open reading frame of 852 base pairs (bp) encoding 283 amino acids. Both qPCR and western blot analyses showed that PxVMP26 was specifically expressed in female adults and was significantly highly expressed in the ovary. Further anatomical analysis indicated that the expression level of PxVMP26 in the ovarian tube with an incomplete yolk was significantly higher than that in the ovarian tube with a complete yolk. CRISPR/Cas9-induced PxVMP26 knockout successfully created two homozygous strains with 8- and 46-bp frameshift mutations. The expression deficiency of the PxVMP26 protein was detected in the mutant strains using immunofluorescence and western blot. No significant difference was found in the number of eggs laid within three days between wild and mutant individuals, but there was a lower egg hatchability. The loss of the PxVMP26 gene changed the mean egg size, damaged the structure of the vitelline membrane, and increased the proportion of abnormal eggs due to water loss, resulting in egg collapse. This first analysis of the roles of the VMP gene in the oocyte formation and embryonic development of P. xylostella, using CRISPR/Cas9 technology, provides a basis for screening new genetic control targets of P. xylostella. [ABSTRACT FROM AUTHOR]

Large magnetic field-induced strains can be achieved in modulated martensite for Ni-Mn-In alloys; however, the metastability of the modulated martensite imposes serious constraints on the ability of these alloys to serve as promising sensor and actuator materials. The phase stability, magnetic properties, and electronic structure of the modulated martensite in the Ni2Mn1.5In0.5 alloy are systematically investigated. Results show that the 6M and 5M martensites are metastable and will eventually transform to the NM martensite with the lowest total energy in the Ni2Mn1.5In0.5 alloy. The physical properties of the incommensurate 7M modulated martensite (7M–IC) and nanotwinned 7M martensite ( 7 M − (5 2 ¯) 2 ) are also calculated. The austenite (A) and 7 M − (5 2 ¯) 2 phases are ferromagnetic (FM), whereas the 5M, 6M, and NM martensites are ferrimagnetic (FIM), and the FM coexists with the FIM state in the 7M–IC martensite. The calculated electronic structure demonstrates that the splitting of Jahn–Teller effect and the strong Ni–Mn bonding interaction lead to the enhancement of structural stability. [ABSTRACT FROM AUTHOR]

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects' flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes a new COVID-19 detection system called CGENet, based on computer vision and chest computed tomography images. First, an optimal backbone selection algorithm was proposed to determine the best backbone network for the CGENet adaptively. Then, we introduced a novel graph embedding mechanism to fuse the spatial relationship into the feature vectors. Finally, we chose the extreme learning machine as the classifier of the proposed CGENet to boost the classification performance. The proposed CGENet was evaluated on a public dataset using 5-fold cross-validation and compared with other algorithms. The results revealed that the proposed model achieved state-of-the-art classification performance. In all, the CGENet can be an effective and efficient tool that can assist COVID-19 diagnosis. Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Due to its portability, convenience, and low cost, incompletely closed near-infrared (ICNIR) imaging equipment (mixed light reflection imaging) is used for ultra thin sensor modules and have good application prospects. However, equipment with incompletely closed structure also brings some problems. Some finger vein images are not clear and there are sparse or even missing veins, which results in poor recognition performance. For these poor quality ICNIR images, however, there is additional fingerprint information in the image. The analysis of ICNIR images reveals that the fingerprint and finger vein in a single ICNIR image can be enhanced and separated. We propose a feature-level fusion recognition algorithm using a single ICNIR finger image. Firstly, we propose contrast limited adaptive histogram equalization (CLAHE) and grayscale normalization to enhance fingerprint and finger vein texture, respectively. Then we propose an adaptive radius local binary pattern (ADLBP) feature combined with uniform pattern to extract the features of fingerprint and finger vein. It solves the problem that traditional local binary pattern (LBP) is unable to describe the texture features of different sizes in ICNIR images. Finally, we fuse the feature vectors of ADLBP block histogram for a fingerprint and finger vein, and realize feature-layer fusion recognition by a threshold decision support vector machine (T-SVM). The experimentation results showed that the performance of the proposed algorithm was noticeably better than that of the single model recognition algorithm. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer. [ABSTRACT FROM AUTHOR]

The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein-protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity-disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity-disease co-occurrence and provide useful insights for disease prevention and intervention. [ABSTRACT FROM AUTHOR]

Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein--protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method. [ABSTRACT FROM AUTHOR]

Background: We assumed that the pharmacokinetics of lovastatin could be changed by the induction effect of berberine. Methods: An UPLC-MS/MS method was developed and validated for the pharmacokinetics tudy of lovastatin to investigate the in vivo drug-drug interactions between lovastatin and berberine. SD male rats were random divided into lovastatin group and berberine induced prior to lovastatin group for the in vivo pharmacokinetic studies. Meanwhile HepG2 cells were induced by berberine for three days to study the metabolism of lovastatin. Results: The AUC (p < 0.01) and Cmax (p < 0.01) could be significantly decreased in the berberine-induced group in vivo, and the metabolic activity of HepG2 cell ccould be increased by berberine induction in vitro. The metabolism parameters of lovastatin such as CL, Vmax and Km were increased after the induction of berberine. From the pharmacokinetic study of lovastatin induced with berberine, we obtained pharmacokinetic parameters which are compliance with the metabolic parameters of lovastatin in HepG2 cells with berberine induction in vitro. Conclusions: From the in vivo pharmacokinetics study and the HepG2 cell metabolism study in vitro, berberine could be an inducer for the metabolism of lovastatin according to our previous research on berberine induction effects on HepG2 cells, which may be relevant to the fact that berberine possesses induction effects through the CYP 450 3A4 enzyme. [ABSTRACT FROM AUTHOR]

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them. [ABSTRACT FROM AUTHOR]

In the present study, post inflammation irritable bowel syndrome (PI-IBS) rats were firstly established by intracolonic instillation of acetic acid with restraint stress. Then the pharmacokinetics of berberine in the rat plasma were compared after oral administration of berberine hydrochloride (25 mg/kg) to normal rats and PI-IBS rats. Quantification of berberine in the rat plasma was achieved by using a sensitive and rapid UPLC-MS/MS method. Plasma samples were collected at 15 different points in time and the pharmacokinetic parameters were analyzed by WinNonlin software. Compared with the normal group, area under the plasma concentration vs. time curve from zero to last sampling time (AUC0-t) and total body clearance (CL/F) in the model group significantly increased or decreased, (2039.49 ± 492.24 vs. 2763.43 ± 203.14; 4999.34 ± 1198.79 vs. 3270.57 ± 58.32) respectively. The results indicated that the pharmacokinetic process of berberine could be altered in PI-IBS pathological conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Deep learning has become a popular technique in modern computer-aided (CAD) systems. In breast cancer CAD systems, breast pectoral segmentation is an important procedure to remove unwanted pectoral muscle in the images. In recent decades, there are numerous studies aiming at developing efficient and accurate methods for pectoral muscle segmentation. However, some methods heavily rely on manually crafted features that can easily lead to segmentation failure. Moreover, deep learning-based methods are still suffering from poor performance at high computational costs. Therefore, we propose a novel deep learning segmentation framework to provide fast and accurate pectoral muscle segmentation result. In the proposed framework, the novel network architecture enables more useful information to be used and therefore improve the segmentation results. The experimental results using two public datasets validated the effectiveness of the proposed network. As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46 % , global pixel accuracy of 99.48 % , Dice similarity coefficient of 96.30 % , and Jaccard of 93.33 % , respectively. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia is a significant pollutant throughout the year, necessitating standardized measurement and identification of emission factors. We performed a quantized evaluation of ammonia concentrations at various locations in and around Seoul, South Korea. The established testing methods of the Radiello Passive Sampler were used for ammonia sampling, and the method was validated using annular denuder sampling. Urban and suburban areas were studied to gain a deeper understanding of the factors responsible for ammonia pollution. This study aimed to establish the fluctuations in concentration over one year, by analyzing the seasonal and regional variation in ammonia concentration. Livestock and agricultural areas recorded the highest concentration of ammonia among all sites, with the highest concentration recorded in autumn. However, at most of the other studied sites, the highest and lowest ammonia concentrations were recorded during summer and winter, respectively. This study attempted to establish a correlation between ammonia concentration and temperature, as well as ammonia concentration and altitude. [ABSTRACT FROM AUTHOR]

Simple Summary: One of the hottest areas in deep learning is computerized tumor diagnosis and treatment. The identification of tumor markers, the outline of tumor growth activity, and the staging of various tumor kinds are frequently included. There are several deep learning models based on convolutional neural networks that have high performance and accurate identification, with the potential to improve medical tasks. Breakthroughs and updates in computer algorithms and hardware devices, and intelligent algorithms applied in medical images have a diagnostic accuracy that doctors cannot match in some diseases. This paper reviews the progress of tumor detection from traditional computer-aided methods to convolutional neural networks and demonstrates the potential of the practical application of convolutional neural networks from practical cases to transform the detection model from experiment to clinical application. Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient's secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future. [ABSTRACT FROM AUTHOR]

Given a protein-forming system, i.e., a system consisting of certain number of different proteins, can it form a biologically meaningful pathway? This is a fundamental problem in systems biology and proteomics. During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different (biological and graphic) features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the "Minimum Redundancy Maximum Relevance" and the "Incremental Feature Selection" techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic. [ABSTRACT FROM AUTHOR]

The COVID-19 virus has swept the world and brought great impact to various fields, gaining wide attention from all walks of life since the end of 2019. At present, although the global epidemic situation is leveling off and vaccine doses have been administered in a large amount, confirmed cases are still emerging around the world. To make up for the missed diagnosis caused by the uncertainty of nucleic acid polymerase chain reaction (PCR) test, utilizing lung CT examination as a combined detection method to improve the diagnostic rate becomes a necessity. Our research considered the time-consuming and labor-intensive characteristics of the traditional CT analyzing process, and developed an efficient deep learning framework named CSGBBNet to solve the binary classification task of COVID-19 images based on a COVID-Seg model for image preprocessing and a GBBNet for classification. The five runs with random seed on the test set showed our novel framework can rapidly analyze CT scan images and give out effective results for assisting COVID-19 detection, with the mean accuracy of 98.49 ± 1.23%, the sensitivity of 99.00 ± 2.00%, the specificity of 97.95 ± 2.51%, the precision of 98.10 ± 2.61%, and the F1 score of 98.51 ± 1.22%. Moreover, our model CSGBBNet performs better when compared with seven previous state-of-the-art methods. In this research, the aim is to link together biomedical research and artificial intelligence and provide some insights into the field of COVID-19 detection. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew's correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study. [ABSTRACT FROM AUTHOR]