A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix.

Calculation of the time-varying (TV) matrix generalized inverse has grown into an essential tool in many fields, such as computer science, physics, engineering, and mathematics, in order to tackle TV challenges. This work investigates the challenge of finding a TV extension of a subclass of inner inverses on real matrices, known as generalized-outer (G-outer) inverses. More precisely, our goal is to construct TV G-outer inverses (TV-GOIs) by utilizing the zeroing neural network (ZNN) process, which is presently thought to be a state-of-the-art solution to tackling TV matrix challenges. Using known advantages of ZNN dynamic systems, a novel ZNN model, called ZNNGOI, is presented in the literature for the first time in order to compute TV-GOIs. The ZNNGOI performs excellently in performed numerical simulations and an application on addressing localization problems. In terms of solving linear TV matrix equations, its performance is comparable to that of the standard ZNN model for computing the pseudoinverse.

Various solitons induced by relative phase in the nonlinear Schrödinger Maxwell-Bloch system.

We study the effect of relative phase on the characteristics of rogue waves and solitons described by rational solutions in the nonlinear Schrödinger Maxwell-Bloch system. We derived the rational rogue wave and soliton solutions with adjustable relative phase and present the parameter range of different types of rogue waves and solitons. Our findings show that the relative phase can alter the distribution of rational solitons and even change the type of rational solitons, leading to a rich array of rational soliton types by adjusting the relative phase. However, the relative phase does not affect the structure of the rogue wave, because the relative phase of the rogue wave changes during evolution. In particular, we confirm that the rational solitons with varying relative phases and the rogue waves at corresponding different evolution positions share the same distribution mode. This relationship holds true for rogue waves or breathers and their stable counterparts solitons or periodic waves in different nonlinear systems. The implications of our study are significant for exploring fundamental excitation elements in nonlinear systems.

The Protective Effect of Chronic Intermittent Hypobaric Hypoxia on Preventing the Destruction of CD34+ Haematopoietic Stem Cells in Aplastic Anaemia by Modulating the Th1/Th2 Balance.

Aplastic anaemia (AA) is a haematopoietic disorder caused by immune-mediated attack on haematopoietic stem cells (HSCs). Stem cell transplantation and immunosuppressive therapy remain the major treatment choice for AA patients but have limited benefits and undesired side effects. The aim of our study was to clarify the protective role of immunity of chronic intermittent hypobaric hypoxia (CIHH) and the underlying mechanism in AA. Our integrative analysis demonstrated that CIHH pre-treatment significantly improved haematopoiesis and survival in an AA rat model. We further confirmed that CIHH pre-treatment was closely associated with the Th1/Th2 balance and a large number of negative regulatory haematopoietic factors, such as TNF-α and IFN-γ, produced by hyperactive Th1 lymphocytes released in AA rats, which induced the death program in a large number of CD34+ HSCs by activating the Fas/FasL apoptosis pathway, while CIHH pre-treatment effectively downregulated the expression of TNF-α and IFN-γ, resulting in a reduction in Fas antigen expression in CD34+ HSCs. In summary, this study provides evidence that CIHH has good protective effect against AA by modulating immune balance in Th1/Th2 cells and may provide a new therapeutic strategy.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Neural Networks for Portfolio Analysis in High-Frequency Trading.

High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve 5.50 % and 5.47 % less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.

Advances on intelligent algorithms for scientific computing: an overview.

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

Phosphodiesterase type 10A inhibitor attenuates lung fibrosis by targeting myofibroblast activation.

Pulmonary fibrosis (PF) is a fatal and irreversible respiratory disease accompanied by excessive fibroblast activation. Previous studies have suggested that cAMP signaling pathway and cGMP-PKG signaling pathway are continuously down-regulated in lung fibrosis, whereas PDE10A has a specifically expression in fibroblasts/myofibroblasts in lung fibrosis. In this study, we demonstrated that overexpression of PDE10A induces myofibroblast differentiation, and papaverine, as a PDE10A inhibitor used for vasodilation, inhibits myofibroblast differentiation in human fibroblasts, Meanwhile, papaverine alleviated bleomycin-induced pulmonary fibrosis and amiodarone-induced oxidative stress, papaverine downregulated VASP/ß-catenin pathway to reduce the myofibroblast differentiation. Our results first demonstrated that papaverine inhibits TGFß1-induced myofibroblast differentiation and lung fibrosis by VASP/ß-catenin pathway.

Time­dependent changes in NLRP3 and Nrf2 levels in lipopolysaccharide­induced acute lung injury.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe clinical conditions with a high mortality rate. Nucleotide­binding oligomerization domain (NOD)­like receptor containing pyrin domain 3 (NLRP3) and nuclear factor E2­related factor 2 (Nrf2) have been reported to be associated with ALI. However, the dynamic changes in the levels of these factors in lipopolysaccharide (LPS)­induced lung injury remain unclear. Thus, the present study aimed to determine the LPS­induced activation of immunological cascades, as well as the NLRP3/Nrf2 signaling pathway at different stages of lung injury. For this purpose, mice were divided into six groups as follows: The control, LPS­4 h, LPS­24 h, LPS­48 h, LPS­96 h and LPS­144 h groups. LPS (4 mg/kg) was administered intratracheally to induce lung injury. Flow cytometry was used to determine the changes in macrophages, neutrophils and T­cell subsets in lung tissue, hematoxylin and eosin staining were used to measure the histopathological changes in lung tissues, ELISA was performed to evaluate the levels of cytokines, western blot analysis was used to measure the levels of inflammatory proteins, and reverse transcription­quantitative PCR used to determine the mRNA level of a target gene. Following LPS administration, evident histopathological damage with neutrophil infiltration was observed which peaked at 48 h. The levels of interleukin­1ß, keratinocyte­derived chemokine, macrophage inflammatory protein 2 and tumor necrosis factor a were markedly increased in bronchoalveolar lavage fluid and serum from the mice, and these levels peaked at 4 h. Moreover, LPS promoted Toll like receptor­4 expression and reactive oxygen species production, thus activating NLRP3/Nrf2 signaling and pyroptosis. Collectively, the present study demonstrates that LPS triggers multiple inflammatory molecules and immune cells during ALI, which may be closely involved in the irregular redox status, NLRP3/Nrf2 pathway and pyroptosis.

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization.

A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem.

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.

The fate of antibiotic resistance genes and their association with bacterial and archaeal communities during advanced treatment of pig farm wastewater.

Advanced wastewater treatment plants are widely used in most large-scale pig farms in southern China. However, the fate of antibiotic resistance genes (ARGs) and their association with bacterial and archaeal communities during advanced wastewater treatment remain unclear. In this study, the profiles of ARGs in typical advanced wastewater treatment plants were surveyed using metagenomic analysis. The results showed that 279- 326 different subtypes of ARGs were detected in raw wastewater, with a total abundance of 5.98 ± 0.48 copies per bacterial cell. During the advanced wastewater treatment, the abundance and number of ARGs were significantly reduced. Microbial communities (bacteria and archaea) contributed the most to the variation in ARG abundance and composition (PCA axis_1), accounting for 10.8 % and 15.7 %, respectively, followed by mobile genetic elements (MGEs) and physicochemical factors. Special attention should be given to potential pathogenic bacteria such as Escherichia, Streptococcus, Enterococcus and Staphylococcus and archaea such as Methanocorpusculum, Candidatus Methanoplasma and Candidatus Methanomethylophilus, which were important potential ARG hosts. Bacterial communities may indirectly affect ARG variation by affecting archaeal communities. These findings indicated that ARG levels in pig farm wastewater can be effectively reduced during advanced treatment and highlighted the important role played by archaea, which should not be ignored.

Beetle Antennae Search: Using Biomimetic Foraging Behaviour of Beetles to Fool a Well-Trained Neuro-Intelligent System.

Deep Convolutional Neural Networks (CNNs) represent the state-of-the-art artificially intelligent computing models for image classification. The advanced cognition and pattern recognition abilities possessed by humans are ascribed to the intricate and complex neurological connection in human brains. CNNs are inspired by the neurological structure of the human brain and show performance at par with humans in image recognition and classification tasks. On the lower extreme of the neurological complexity spectrum lie small organisms such as insects and worms, with simple brain structures and limited cognition abilities, pattern recognition, and intelligent decision-making abilities. However, billions of years of evolution guided by natural selection have imparted basic survival instincts, which appear as an "intelligent behavior". In this paper, we put forward the evidence that a simple algorithm inspired by the behavior of a beetle (an insect) can fool CNNs in image classification tasks by just perturbing a single pixel. The proposed algorithm accomplishes this in a computationally efficient manner as compared to the other adversarial attacking algorithms proposed in the literature. The novel feature of the proposed algorithm as compared to other metaheuristics approaches for fooling a neural network, is that it mimics the behavior of a single beetle and requires fewer search particles. On the contrary, other metaheuristic algorithms rely on the social or swarming behavior of the organisms, requiring a large population of search particles. We evaluated the performance of the proposed algorithm on LeNet-5 and ResNet architecture using the CIFAR-10 dataset. The results show a high success rate for the proposed algorithms. The proposed strategy raises a concern about the robustness and security aspects of artificially intelligent learning systems.

Deletion of Nf2 in neural crest-derived tongue mesenchyme alters tongue shape and size, Hippo signalling and cell proliferation in a region- and stage-specific manner.

OBJECTIVES: The mammalian tongue develops from the branchial arches (1-4) and comprises highly organized tissues compartmentalized by mesenchyme/connective tissue that is largely derived from neural crest (NC). This study aimed to understand the roles of tumour suppressor Neurofibromin 2 (Nf2) in NC-derived tongue mesenchyme in regulating Hippo signalling and cell proliferation for the proper development of tongue shape and size. MATERIALS AND METHODS: Conditional knockout (cKO) of Nf2 in NC cell lineage was generated using Wnt1-Cre (Wnt1-Cre/Nf2cKO ). Nf2 expression, Hippo signalling activities, cell proliferation and tongue shape and size were thoroughly analysed in different tongue regions and tissue types of Wnt1-Cre/Nf2cKO and Cre- /Nf2fx/fx littermates at various stages (E10.5-E18.5). RESULTS: In contrast to many other organs in which the Nf2/Hippo pathway activity restrains growth and cell proliferation and as a result, loss of Nf2 decreases Hippo pathway activity and promotes an enlarged organ development, here we report our observations of distinct, tongue region- and stage-specific alterations of Hippo signalling activity and cell proliferation in Nf2cKO in NC-derived tongue mesenchyme. Compared to Cre- /Nf2fx / fx littermates, Wnt1-Cre/Nf2cKO depicted a non-proportionally enlarged tongue (macroglossia) at E12.5-E13.5 and microglossia at later stages (E15.5-E18.5). Specifically, at E12.5 Nf2cKO mutants had a decreased level of Hippo signalling transcription factor Yes-associated protein (Yap), Yap target genes and cell proliferation anteriorly, while having an increased Yap, Yap target genes and cell proliferation posteriorly, which lead to a tip-pointed and posteriorly widened tongue. At E15.5, loss of Nf2 in the NC lineage resulted in distinct changes in cell proliferation in different regions, that is, high in epithelium and mesenchyme subjacent to the epithelium, and lower in deeper layers of the mesenchyme. At E18.5, cell proliferation was reduced throughout the Nf2cKO tongue.

A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor.

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of NF1 and cooperating tumor suppressors, including CDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of LATS1/2 of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a GEM-MPNST model that recapitulated the human disease genetically, histologically, and molecularly. METHODS: We combined 2 genetically modified alleles, an Nf1;Trp53 cis-conditional allele and an inducible Plp-CreER allele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditional Lats1;Lats2 knockout mice. We performed histopathologic analyses of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors. RESULTS: Postnatal Nf1;Trp53 cis-deletion resulted in GEM-MPNST that were histologically more similar to human MPNST than the widely used germline Nf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level, Nf1;p53-driven GEM-MPNST were distinct from Lats-driven GEM-MPNST and resembled human MPNST more closely than do Lats-driven tumors. CONCLUSIONS: The NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly.

YAP/TAZ maintain the proliferative capacity and structural organization of radial glial cells during brain development.

The Hippo pathway regulates the development and homeostasis of many tissues and in many species. It controls the activity of two paralogous transcriptional coactivators, YAP and TAZ (YAP/TAZ). Although previous studies have established that aberrant YAP/TAZ activation is detrimental to mammalian brain development, whether and how endogenous levels of YAP/TAZ activity regulate brain development remain unclear. Here, we show that during mammalian cortical development, YAP/TAZ are specifically expressed in apical neural progenitor cells known as radial glial cells (RGCs). The subcellular localization of YAP/TAZ undergoes dynamic changes as corticogenesis proceeds. YAP/TAZ are required for maintaining the proliferative potential and structural organization of RGCs, and their ablation during cortical development reduces the numbers of cortical projection neurons and causes the loss of ependymal cells, resulting in hydrocephaly. Transcriptomic analysis using sorted RGCs reveals gene expression changes in YAP/TAZ-depleted cells that correlate with mutant phenotypes. Thus, our study has uncovered essential functions of YAP/TAZ during mammalian brain development and revealed the transcriptional mechanism of their action.

The Potentially Therapeutic Role of EPAC in Curbing the Process of Idiopathic Pulmonary Fibrosis via Differential Cellular Pathways.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrosis disease caused by genetic susceptibility (causative) and other indirect risk factors such as smoking, micro-aspiration and air pollution. Repeated damage of lung epithelial cells can cause fibroblast activation and excessive collagen will lead the scar formation and severe fibrosis. It has been decades since drugs for the treatment of IPF were developed, but clinical choices were limited. Exchange Protein directly Activated by cAMP (EPAC), as a newly emerging cAMP (adenosine 3',5'-cyclic monophosphate) downstream molecule, plays a vital role in the cellular pathways of IPF such as inhibiting fibroblast proliferation, stress fiber formation and epithelium cell adhesion, so it may be a novel target for drug development and treatment for curbing IPF. Here, we hypothesize that EPAC may participate in the signaling pathways related to IPF in different cell types (fibroblasts; airway smooth muscle cells; vascular endothelial cells; lung epithelial cells; macrophages; mesenchymal stem cells; T cells), thereby playing a potentially therapeutic role in resisting the process of fibrosis. We summarize the current correlation between EPAC and IPF in these different cell types, and further insights into EPAC will help to optimize the pharmacological treatment for IPF.

YAP and TAZ maintain PROX1 expression in the developing lymphatic and lymphovenous valves in response to VEGF-C signaling.

Lymphatic vasculature is an integral part of digestive, immune and circulatory systems. The homeobox transcription factor PROX1 is necessary for the development of lymphatic vessels, lymphatic valves (LVs) and lymphovenous valves (LVVs). We and others previously reported a feedback loop between PROX1 and vascular endothelial growth factor-C (VEGF-C) signaling. PROX1 promotes the expression of the VEGF-C receptor VEGFR3 in lymphatic endothelial cells (LECs). In turn, VEGF-C signaling maintains PROX1 expression in LECs. However, the mechanisms of PROX1/VEGF-C feedback loop remain poorly understood. Whether VEGF-C signaling is necessary for LV and LVV development is also unknown. Here, we report for the first time that VEGF-C signaling is necessary for valve morphogenesis. We have also discovered that the transcriptional co-activators YAP and TAZ are required to maintain PROX1 expression in LVs and LVVs in response to VEGF-C signaling. Deletion of Yap and Taz in the lymphatic vasculature of mouse embryos did not affect the formation of LVs or LVVs, but resulted in the degeneration of these structures. Our results have identified VEGF-C, YAP and TAZ as a crucial molecular pathway in valve development.

Theoretical Investigation of 6-Mercaptopurine Isomers' Adsorption on the Au(001) Surface: Revealing the Fate of Different Isomers.

Density functional theory (DFT) calculations were performed for the adsorption of different isomers of 6-mercaptopurine on the Au(001) surface. All of the configurations of four thione and two thiol isomers were considered. The results show that the thione isomers adsorbed more strongly on the Au(001) surface compared with the thiol ones. In all of the configurations, the calculated binding energy of ma-8 is the largest, in which the S atom of 6-mercaptopurine binds strongly with one Au atom on the monodentate sites and 6-mercaptopurine retains a flat geometry, predominantly with an approximately 30° orientation between the C-S bond and the Au-Au bond of the catalyst. Additionally, the 6-mercaptopurines in ma-2, mb-5, and mc-3 also bind more strongly onto the surface, which show relatively higher stability on the surfaces, indicating a high preference for adsorption. Charge density differences and TDOS analyses for the four configurations also show that the electronic charges are accumulated between Au and S atoms in the Au-S bonds, indicating occurrence of adsorption and chemical-bond formation.

The Hippo Pathway Prevents YAP/TAZ-Driven Hypertranscription and Controls Neural Progenitor Number.

The Hippo pathway controls the activity of YAP/TAZ transcriptional coactivators through a kinase cascade. Despite the critical role of this pathway in tissue growth and tumorigenesis, it remains unclear how YAP/TAZ-mediated transcription drives proliferation. By analyzing the effects of inactivating LATS1/2 kinases, the direct upstream inhibitors of YAP/TAZ, on mouse brain development and applying cell-number-normalized transcriptome analyses, we discovered that YAP/TAZ activation causes a global increase in transcription activity, known as hypertranscription, and upregulates many genes associated with cell growth and proliferation. In contrast, conventional read-depth-normalized RNA-sequencing analysis failed to detect the scope of the transcriptome shift and missed most relevant gene ontologies. Following a transient increase in proliferation, however, hypertranscription in neural progenitors triggers replication stress, DNA damage, and p53 activation, resulting in massive apoptosis. Our findings reveal a significant impact of YAP/TAZ activation on global transcription activity and have important implications for understanding YAP/TAZ function.

Process optimization of ultrasound-assisted treatment for soya bean protein isolate/polyacrylamide composite film.

In this paper, composite films composed of soya bean protein isolate (SPI) and polyacrylamide (PAM) were prepared under variations of ultrasonic power, treatment time and heating temperature. The effects of the major processing parameters related to ultrasonic-assisted treatment were evaluated and optimized through the single-factor analysis and Box-Behnken design (BBD), respectively, when the tensile strength of composite films was considered as the response value. The single-factor analysis was carried out to study the effects of ultrasonic power, treatment time and heating temperature on the viscosity and cohesion of the slurry and the tensile strength of SPI/PAM composite films, which also provided a reasonable data range of each factor for further optimization. Experiment results indicated that these three factors play a significant role in the tensile strength of films. Then BBD was applied to optimize the treatment conditions of these three factors, using the tensile strength of films as the response value. According to the interactive second-order polynomial model of three factors and the three-dimensional response surface, the maximum tensile strength of films was obtained under the optimal condition. To verify the reliability of the model, the experiment with the optimal condition was conducted, and results demonstrated that the observed tensile strength was in agreement with the predicated one. Also, the morphology and water solubility of the films showed that the film can be coated on the yarns evenly and removed clearly.