Target Function Research Articles

Randomized semi-quantum matrix processing

We present a hybrid quantum-classical framework for simulating generic matrix functions more amenable to early fault-tolerant quantum hardware than standard quantum singular-value transformations. The method is based on randomization over the Chebyshev approximation of the target function while keeping the matrix oracle quantum, and is assisted by a variant of the Hadamard test that removes the need for post-selection. The resulting statistical overhead is similar to the fully quantum case and does not incur any circuit depth degradation. On the contrary, the average circuit depth is shown to get smaller, yielding equivalent reductions in noise sensitivity, as explicitly shown for depolarizing noise and coherent errors. We apply our technique to partition-function estimation, linear system solvers, and ground-state energy estimation. For these cases, we prove advantages on average depths, including quadratic speed-ups on costly parameters and even the removal of the approximation-error dependence.

Mechanics of Materials: Multiscale Design of Advanced Materials and Structures

Materials can now be designed and architectured like structural components for targeted mechanical and physical properties. Structures and microstructures should not be studied independently and their design will benefit from a multiscale approach combining nonlinear continuum mechanics approaches and physical descriptions of elasticity, viscoplasticity, phase transformations and damage of microstructures, at various scales. The aim of the workshop was to gather outstanding junior and senior researchers in the various branches of mathematics, physics and engineering sciences suited to address the question of design of materials and structures by means of multiscale discrete and continuum approaches to their constitutive behavior. Examples include atomic or macroscopic lattices, random or periodic cellular materials, smart materials like shape memory alloys, 3D woven composites, acoustic and electromagnetic metamaterials, etc. Modern continuum mechanics relies on sophisticated constitutive laws for anisotropic materials exhibiting elastoviscoplastic behavior, still a field of intense research with new mathematical concepts. In particular size-dependent properties are addressed by resorting to generalized continua such as gradient or micromorphic and phase field models. The latter are attractive for the simulation of microstructure evolution coupled with mechanics, due to thermodynamic and metallurgical processes and damage. Scale transition and homogenization methods for continuous and discrete systems are required for the determination of effective material and structural behavior. Metamaterials are architectured materials specifically designed to achieve certain propagation and dispersion properties of elastic and plastic waves. Optimization strategies for the design of optimal architectures are involved in the design process. Target functions for optimization are now based on multicriteria (stiffness, strength, thermal expansion, transport properties, anisotropy etc.).

Exploring the therapeutic potential of Xiangsha Liujunzi Wan in Crohn's disease: from network pharmacology approach to experimental validation

Ethnopharmacological relevanceXiangsha Liujunzi Wan (LJZW) is a traditional Chinese medicine (TCM) formula containing a variety of traditional Chinese herb components. Its principal components are often used in the treatment of gastrointestinal diseases and contribute to the treatment of Crohn's disease (CD). Aim of the studyTo explore the therapeutic potential of LJZW in CD through network pharmacology, bioinformatics, molecular docking, and experimental verification. MethodsThe principal bioactive components and corresponding targets of LJZW were ascertained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential targets for CD were identified in GeneCards, OMIM, DrugBank, DisGeNET, CTD, and Gene Expression Omnibus (GEO) databases. Intersection targets of LJZW and CD were identified using a Venn diagram and visualized using Cytoscape 3.8.0 to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to assess the function of intersection targets. AutoDockTools and PyMOL were used for molecular docking to recognize the association between the core ingredients of LJZW and the core targets of CD. Subsequently, a series of experiments were conducted for validation. ResultsThe network pharmacology results indicated that there were 156 bioactive components and 268 corresponding targets for LJZW, 3023 primary relevant targets for CD, and 169 intersection targets for LJZW and CD. The PPI network was employed to identify five hub genes and six clusters. The GO functional analysis indicated that intersection targets are primarily correlated with oxidative stress and inflammatory responses. KEGG pathway analysis revealed that these targets were primarily associated with the phosphotylinosital 3 kinase (PI3K)-protein kinase B (AKT) and mitogen-activated protein kinase (MAPK) signaling pathways. The molecular docking results showed that the core ingredients of LJZW had good binding ability with the core targets of CD. A series of experiments demonstrated that LJZW could effectively attenuate TNBS-induced colitis symptoms, inhibit the inflammatory response, and protect intestinal barrier function by inhibiting the PI3K-AKT and MAPK signaling pathways, thus preventing and treating CD. ConclusionLJZW has the characteristics of multi-component, multi-target, and multi-pathway treatment, which helps to improve the treatment of CD, protect the intestinal barrier, and exert the effect of anti-inflammatory therapy by inhibiting PI3K-AKT and MAPK signaling pathways.

OPTIMIZATION OF LOADING AND UNLOADING WORKS AND PLACEMENT OF TARED AND PIECE CARGOES IN VEHICLES AND WAREHOUSES USING THE METHODS OF ERGONOMICS AND THEORY OF SYSTEMS

Purpose. The tasks of optimizing the placement of tared and piece cargoes (or the task of loading) in a limited volume (capacity) are typical for cases of loading with tared and piece cargoes (mainly large-sized) of transport containers, car bodies, ship holds in the merchant fleet, transport aircraft, railway wagons, areas of the warehouse complex. At the same time, the safe and efficient performance of loading and unloading operations with tared and piece cargoes, including large-sized ones, in a limited space is directly related to taking into account the requirements and using ergonomic methods. The purpose of this paper is to develop a mathematical model for the optimization of the placement of tared and piece cargoes (or about loading) in a limited volume (capacity), which is used to select a subset of cargo units with the maximum relative utility, while observing the limit on the total volume (or mass) of cargo. Methods. The analysis of literary sources made it possible to establish that for modeling and solving the problem of optimizing the placement of cargo (or loading) in a limited volume (capacity), it is rational to use one of the exact solution methods (dynamic programming method), the number of stages of which is the number of types of cargo. The work uses the method of dynamic programming, methods of theory of systems, and linear optimization. Results. The article presents a mathematical model of the problem of optimal placement of tared and piece cargoes (or about loading) in a limited volume, and for the case of placing weapons, ammunition, weapons, it is proposed to maximize not the function of the total cost (value) of the cargo, but the function that would take into account the relative usefulness of each cargo. The relative usefulness of each cargo is proposed to be determined by expert methods of systems theory, for example, the Delphi method, which is well formalized and suitable for computer processing of results. Scientific novelty. The scientific novelty consists in the development of a mathematical model that allows solving the problem of optimal placement of container loads (or loading) in a limited volume, and the target function of the optimization model is proposed to be the total relative usefulness of the cargo. Practical significance. Defined ergonomic requirements for cargo operations with tared and piece cargoes in a limited space (vehicles, warehouses). A method of numerical calculation based on the model is proposed (the case of loading a car body with tared and piece cargoes). In further research, the obtained results will be used for computer simulation according to the developed model using the Solver tool of Microsoft Excel.

Secular and modulator-specific drifts in the predictive performance of a rapid lung function decline algorithm: a cystic fibrosis patient registry study

PurposePredicting the onset of pulmonary exacerbation for people with cystic fibrosis (CF) is critical to identify those at high risk in advance and allows timely clinical intervention. The FEV1-indicated exacerbation signal (FIES) has been proposed by the CF Learning Network to identify pulmonary exacerbation events, but temporal predictive performance of this marker is unknown.MethodsTo assess the epidemiologic impact of secular trends in CF care and treatments on algorithms aimed at prediction of pulmonary exacerbation, we evaluated prediction model “drift” using a longitudinal retrospective cohort of 29,476 individuals from the U.S. CF Foundation Patient Registry (2011–2018). The FIES marker was defined for each clinical encounter using relative drops in lung function, measured as forced expiratory volume in 1 s of % predicted (FEV1pp). We formed predictive probabilities for each FIES event via a target function for a predictive algorithm of FEV1pp. Models were trained using 2 year data windows with prediction performance tested on subsequent years.ResultsYear-over-year FIES event discrimination remained consistent (area under the receiver-operator characteristic curve, AUC > 80%). Annual drift was < 1% with actual drop in AUC ~ 3% in 6 years. The model performed best in individuals with moderate-to-low baseline FEV1pp (AUC range: 84–85%). For relatively higher FEV1pp levels, discrimination was slightly lower with higher AUC variability.ConclusionWhile model fit improved by accounting for modulator therapy, prediction performance was similar. Most impactful model drifts occurred after 2012 coinciding with increased ivacaftor modulator use. Additional evaluation will be necessary given recent, broad uptake of highly effective modulators.

Improvement of simultaneous prediction using principal components approach

Prediction has a fundamental role both in linear regression analysis and in many applied sciences. The predictions of actual values or mean values of the response variable were obtained separately, earlier than defined the target function. It has been attracting a lot of interest because obtaining simultaneous predictions both of the mean values and actual values is more reasonable in many applied studies since the target function was reported. This paper purposes to improve the simultaneous prediction with the target function when the problem of multicollinearity exists in the linear regression model by defining new predictors based on the principal components estimator, which is more resistant to multicollinearity than the ordinary least squares estimator. The comparisons of proposed predictors are done theoretically by using the mean squared error of predictions. Then theoretical results are supported by an exhaustive simulation study. Also, performance comparisons of the newly suggested predictors are made for two real-life data sets, real estate valuation and cruise ship info. The findings show that the use of the proposed predictors will make a crucial contribution to applied studies in obtaining better predictions.

Systems pharmacology-based drug discovery from Amaryllidaceae alkaloids and investigation of mechanisms of action in treatment of Alzheimer’s disease

Abstract Objectives Given the success of galanthamine in treating Alzheimer’s disease, this study aims to establish an effective method to find drugs from Amaryllidaceae alkaloids and to clarify its mechanism in treating Alzheimer’s disease. Methods The pharmacodynamic basis and mechanism of action between Amaryllidaceae alkaloids and Alzheimer’s disease were explored by constructing a compound-target-disease network, targets protein-protein interaction, gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and molecular docking verification. Key findings In total, a chemical library of 357 potential alkaloids was constructed. A total of 100 active alkaloid components were identified. Thirty-nine associated targets were yielded based on network construction, and the key targets were defined as HSP90AA1, ESR1, NOS3, PTGS2, and PPARG using protein–protein interaction network. Gene ontology items (490) and 68 Kyoto Encyclopedia of Genes and Genomes pathways were selected through the enrichment of target functions, including neuroactive ligand–receptor interaction, calcium signaling pathway, cAMP signaling pathway, Alzheimer disease, and serotonergic synapse that were related to Alzheimer’s disease. Lastly, molecular docking demonstrated good stability in combining selected alkaloids with targets. Conclusions This study explained the mechanisms of Amaryllidaceae alkaloids in preventing and treating Alzheimer’s disease and established a novel strategy to discover new drugs from biological chemical sources.

Huang-Jin-Shuang-Shen Decoction promotes CD8+ T-cell-mediated anti-tumor immunity by regulating chemokine CXCL10 in gastric cancer

BackgroundResearch on immunotherapy for gastric cancer is currently receiving significant attention, with particular emphasis on the role of CD8+ T cells in anti-tumor immune responses. In recent years, the importance of the chemokine CXCL10 in promoting anti-tumor immunity has been increasingly recognized because it plays a crucial role in recruiting CD8+ T cells to the tumor microenvironment. The Huang-Jin-Shuang-Shen (HJSS) Decoction, a Chinese medicine, has been used as an adjuvant drug for gastric cancer chemotherapy. Its mechanism of action may be related to the activation of anti-tumor immunity. PurposeTo assess the role of the HJSS Decoction in regulating the immune microenvironment of gastric cancer and elucidate its mechanism. Study design/methodsUltra-high performance liquid chromatography Q Exactive-mass spectrometry was used to analyze the main components of the HJSS Decoction and evaluate the therapeutic effect of the HJSS Decoction synergized with 5-fluorouracil (5-FU) on gastric cancer. The proportions of CD8+ T cells and killing markers were determined using flow cytometry. Mechanisms of action and targets were screened using network pharmacology. The level of CXCL10 was detected using enzyme-linked immunosorbent assay and western blot, and nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) related signaling pathway was detected in vitro. The target function was validated by siRNA transfection. ResultsThe combination of HJSS Decoction and 5-FU demonstrated a synergistic effect in impeding the progression of subcutaneous gastric cancer. This was achieved through the facilitation of apoptosis and suppression of proliferation. Furthermore, HJSS Decoction exhibited the ability to enhance the population of CD8+ T cells and augment their cytotoxic capabilities, both in laboratory settings and in living organisms. Notably, HJSS Decoction upregulated the expression of CXCL10, and mechanistically, it activated the NFκB-related signaling pathway to initiate subsequent transcription of chemokines. ConclusionThe present study aimed to investigate the pharmacological mechanism of the HJSS Decoction and its potential clinical application in inhibiting gastric cancer in mice. HJSS Decoction can cooperate with 5-FU to inhibit gastric cancer, and the optimal dose is medium. HJSS Decoction exerts anti-tumor immunity by activating the NFκB-related signaling pathway and promoting the expression of CXCL10, which in turn recruits CD8+ T cells into the tumor immune microenvironment. Overall, these findings provide valuable evidence for the potential clinical application of HJSS Decoction.

Apigenin inhibits lipid metabolism of hepatocellular carcinoma cells by targeting the histone demethylase KDM1A

BackgroundThe development of cancer is accompanied by metabolic reprogramming, and the liver serves as a central hub for lipid transportation. Apigenin, a plant-derived flavonoid, demonstrates potent anticancer properties across various cancer types and exhibits promising potential as a therapeutic agent for cancer treatment. However, there are limited studies focusing on the downstream targets of apigenin. Moreover, there are few reports on the impact of apigenin in lipid metabolism within liver cancer cells. PurposeThe objective is to elucidate the metabolic mechanism underlying the inhibitory effect of apigenin on liver cancer progression, search for downstream targets and provide reliable data support for the clinical trials of apigenin. MethodsAnticancer effects of apigenin were detected at cellular and molecular levels in vitro, and downstream targets of apigenin, especially metabolic pathway genes, were analyzed by transcriptome. Next, the downstream target of apigenin was verified and the biological function of the downstream target was examined. Finally, the downstream target of apigenin was further verified by restoring target gene expression. ResultsCellular molecular experiments showed that Apigenin inhibited the proliferation, migration, invasion and lipid metabolism of hepatocellular carcinoma (HCC) cells. Transcriptome analysis showed apigenin widely regulates histone demethylase, particularly histone H3K4 lysine demethylase 1A (KDM1A). Apigenin treatment inhibited the expression of KDM1A protein and mRNA levels in liver cancer cells, molecular docking predicted the interaction between apigenin and KDM1A. Furthermore, downregulation KDM1A inhibited the proliferation and lipid metabolism of HCC cells, in the same way, overexpressing KDM1A promoted proliferation of HCC cells. Finally, restoring KDM1A expression partially attenuated the effects of apigenin on lipid metabolism in HCC cells. ConclusionIn conclusion, our study provides compelling evidence that apigenin inhibits liver cancer progression and elucidates its mechanism of action in regulating lipid metabolism. Specifically, we find that apigenin suppresses the progression of HCC cells by downregulating genes involved in lipid metabolism. Additionally, our results indicate that KDM1A acts as a downstream target of apigenin in the inhibition of lipid metabolism in HCC. These findings offer experimental support for the potential use of apigenin as a therapeutic agent for liver cancer, highlighting its relevance in future clinical applications.

Abstract PR-19: Cell polarity proteins as novel regulators of macropinocytosis

Abstract Macropinocytosis has emerged as a nutrient-scavenging pathway that cancer cells exploit to survive the nutrient-deprived conditions of the tumor microenvironment. Cancer cells are especially reliant on glutamine for their survival, and in pancreatic ductal adenocarcinoma (PDAC) cells, glutamine deficiency can enhance the stimulation of macropinocytosis, allowing the cells to escape metabolic stress through the production of extracellular-protein-derived amino acids. Here, we identify the atypical protein kinase C (aPKC) enzymes, PKCz and PKCi, as novel regulators of macropinocytosis. In normal epithelial cells, aPKCs are known to regulate cell polarity in association with the scaffold proteins Par3 and Par6, controlling the function of several targets, including the microtubule-associated Par1 kinases. In PDAC cells, we identify that each of these cell polarity proteins are required for glutamine stress-induced macropinocytosis. Mechanistically, we find that the aPKCs are regulated by EGFR signaling or by the transcription factor CREM to promote the relocation of Par3 to microtubules, facilitating macropinocytosis in a dynein-dependent manner. Importantly, we determine that cell fitness impairment caused by aPKC depletion in glutamine stress is rescued by the restoration of macropinocytosis and that aPKCs support PDAC growth in vivo. These results identify a previously unappreciated role for the cell polarity protein network in the regulation of macropinocytosis and provide a better understanding of the mechanistic underpinnings that control macropinocytic uptake in the context of metabolic stress. Citation Format: Guillem Lambies Barjau, Szu-Wei Lee, Karen Duong-Polk Duong-Polk, David Dawson, Cheska Marie Galapate, Swetha Maganti, Cosimo Commisso. Cell polarity proteins as novel regulators of macropinocytosis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-19.

Rational frequency range and criteria for diagnosing endogenous fire zones in coal massifs by using the georadiolocation method

Relevance. The necessity to improve the accuracy of analysis and prediction of potential fire hazard of rock-coal massifs by using electromagnetic methods of endogenous fire detection. Taking into consideration that the increase in coal temperature changes a number of its parameters, such as dielectric permittivity and electrical resistivity, it is reasonable to use the method of electromagnetic reconnaissance in locating the focus of fire. Aim. To analyze the theoretical and practical knowledge about the anomalies formed in the area of spontaneous combustion and to evaluate the effectiveness of electromagnetic methods for locating the spontaneous combustion zones of a coal massif. Objects. Physical parameters of the ignition zone of the coal massif, such as dielectric permittivity and electrical resistivity, as well as the range of the GPR central frequency, allowing clearly defining the boundaries of the ignited zone. Method. Reviewing the proposed methods of determining the parameters serving for correct location of the fire zone. For GPR it is necessary to determine the center frequency of the standard antenna unit and resolution, then to estimate the rational frequency range of GPR. Results. Allow us to draw conclusions about the methods used to determine the rational range of GPR center frequency – it can be calculated by solving the system of equations of the signal attenuation function and energy potential of GPR, by the experimental value of the target function, including the depth and detail of sounding as a function of frequency, or on the basis of a complex parameter of GPR, including the radiated power of the antenna, the number of accumulations and the reflection coefficient from the boundaries. Taking into account such physical features of the fire zone as drying the rock-angle massif and the shape of the anomalous zone, the rational range of the center frequency for the GPR "OKO-2" was determined.

Gradient Descent Provably Escapes Saddle Points in the Training of Shallow ReLU Networks

AbstractDynamical systems theory has recently been applied in optimization to prove that gradient descent algorithms bypass so-called strict saddle points of the loss function. However, in many modern machine learning applications, the required regularity conditions are not satisfied. In this paper, we prove a variant of the relevant dynamical systems result, a center-stable manifold theorem, in which we relax some of the regularity requirements. We explore its relevance for various machine learning tasks, with a particular focus on shallow rectified linear unit (ReLU) and leaky ReLU networks with scalar input. Building on a detailed examination of critical points of the square integral loss function for shallow ReLU and leaky ReLU networks relative to an affine target function, we show that gradient descent circumvents most saddle points. Furthermore, we prove convergence to global minima under favourable initialization conditions, quantified by an explicit threshold on the limiting loss.

Multi-Omics Exploration of the Mechanism of Curcumol to Reduce Invasion and Metastasis of Nasopharyngeal Carcinoma by Inhibiting NCL/EBNA1-Mediated UBE2C Upregulation.

Nasopharyngeal carcinoma (NPC) is closely linked to Epstein-Barr virus (EBV) infection. Curcumae Rhizoma, a traditional Chinese herb, has shown antitumor effects, primarily through its component curcumol (Cur), which has been shown to reduce NPC cell invasion and migration by targeting nucleolin (NCL) and Epstein-Barr Virus Nuclear Antigen 1 (EBNA1). We constructed an EBV-positive NPC cell model using C666-1 cells and performed transcriptomics studies after treatment with curcumol, which revealed a significant enrichment of ubiquitin-mediated proteolysis, the PI3K-AKT and mTOR signaling pathways, cell cycle and apoptosis involved in tumor invasion and migration. To investigate the importance of NCL and EBNA1 in curcumol-resistant EBV-positive NPC, we performed a multi-omics study using short hairpin NCL (shNCL) and shEBNA1 EBV-positive NPC cells, and the proteomics results showed enrichment in complement and coagulation cascades and ubiquitin-mediated proteolysis signaling pathways. Here, we focused on ubiquitin-conjugating enzyme E2C (UBE2C), which plays an important role in the ubiquitin-mediated proteolysis signaling pathway. In addition, metabolomics revealed that UBE2C is highly associated with 4-Aminobutanoic acid (GABA). In vitro studies further validated the function of the key targets, suggesting that UBE2C plays an important role in NCL and EBNA1-mediated curcumol resistance to nasopharyngeal carcinoma invasion and metastasis.

Выбор элемента(ов) прототипа модели медицинской организации в целях оптимизации процессов регионального проекта с использованием методов динамического программирования.

Changes in federal legislation regulating the provision of patients suffering from cardiovascular diseases initiate a revision of the regional legislation, and inevitably raises questions of practical adaptation and optimization of the processes of ongoing projects, taking into account resources in the established management model in medical organizations. Finding elements of a prototype model of a medical organization implementing a regional project as objects of influence for theoretical proof of hypotheses using the dynamic programming method (callback function, recursion) is the task of optimizing the project processes considered in this article. The purposeof the research question was to optimize the processes of a regional project using the dynamic programming method, which is focused on finding the objective function F(i) – the number of ways to return the function. The subgoal was to justify the use of dynamic programming methods. Materials and methods. The information base for the study was federal and regional regulatory legal acts published in the legislative reference and legal systems of the Russian Federation, and electronic resources. The study was carried out using methods of dynamic programming, optimization, recursion, and modeling. Results. We did not find similar studies in the available literature. This study was carried out using the dynamic programming method, which is designed to solve the problem of finding the target function(s) F(i) of regional projects. The authors attempted to find a prototype of an operating procedure at the level of project executors, which is indivisible and elementary for subsequent optimization of the variable (non-constant) elements of the prototype of a medical organization model from the found objective function. Finding F(i) by categories of patients (object of influence) registered at the dispensary, obtained using the callback function F(0), F(1), F(2), F(3), …F(k); F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion) consists of decomposing the problem into smaller subproblems, finding the optimal solution to the subproblems recursively and using the resulting solution subtasks for constructing a solution to the original problem – optimizing an element of a prototype model of a medical organization, a regional project as a whole. Conclusion. The main tasks associated with project optimization are the tasks of minimizing the cost of the project for a given deadline for its implementation and minimizing (maximizing) the project completion period for a given marginal cost of its implementation. In search of a solution to optimization issues, the authors proposed finding F(i) by categories of patients (object of influence) registered as “D”, obtained using the callback function F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion).

Sequential Targeted Enzyme-Instructed Self-Assembly Supramolecular Nanofibers to Attenuate Intervertebral Disc Degeneration.

As an age-related disease, intervertebral disc degeneration is closely related to inflammation and aging. Inflammatory cytokines and cellular senescence collectively contribute to the degradation of intervertebral disc. Blocking this synergy reduces disc extracellular matrix damage caused by inflammation and aging. In this study, drug-loaded nanofibers with sequential targeting functions are constructed through intelligent response, hydrophilicity, and in situ self-assembly empowerment of flurbiprofen. The peptide precursor responds to the cleavage of overexpressed MMP-2 in the degenerative intervertebral disc microenvironment (intracellular and extracellular), resulting in the formation of self-assembled nanofibers that enable the on-demand release of flurbiprofen and COX-2 response. In vitro, Comp. 1 (Flurbiprofen-GFFYPLGLAGEEEERGD) reduces the expression of inflammation-related genes and proteins and the polarization of M1 macrophages by competitively inhibiting COX-2 and increases the expression of extracellular matrix proteins COL-2 and aggrecan. Additionally, it can reduce the expression of Senescence-Associated Secretory Phenotype and DNA damage in aged nucleus pulposus cells and promote the recovery of proliferation and cell cycle. In vivo, drug-loaded nanofibers delay intervertebral disc degeneration by inhibiting inflammation and preventing the accumulation of senescent cells. Therefore, the sequentially targeted self-assembled drug-loaded nanofibers can delay intervertebral disc degeneration by blocking the synergistic effect of inflammatory cytokines and cellular senescence.

Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks

The past decade has seen increasing interest in applying Deep Learning (DL) to Computational Science and Engineering (CSE). Driven by impressive results in applications such as computer vision, Uncertainty Quantification (UQ), genetics, simulations and image processing, DL is increasingly supplanting classical algorithms, and seems poised to revolutionize scientific computing. However, DL is not yet well-understood from the standpoint of numerical analysis. Little is known about the efficiency and reliability of DL from the perspectives of stability, robustness, accuracy, and, crucially, sample complexity. For example, approximating solutions to parametric PDEs is a key task in UQ for CSE. Yet, training data for such problems is often scarce and corrupted by errors. Moreover, the target function, while often smooth, is a potentially infinite-dimensional function taking values in the PDE solution space, which is generally an infinite-dimensional Banach space. This paper provides arguments for Deep Neural Network (DNN) approximation of such functions, with both known and unknown parametric dependence, that overcome the curse of dimensionality. We establish practical existence theorems that describe classes of DNNs with dimension-independent architecture widths and depths, and training procedures based on minimizing a (regularized) ℓ2-loss which achieve near-optimal algebraic rates of convergence in terms of the amount of training data m. These results involve key extensions of compressed sensing for recovering Banach-valued vectors and polynomial emulation with DNNs. When approximating solutions of parametric PDEs, our results account for all sources of error, i.e., sampling, optimization, approximation and physical discretization, and allow for training high-fidelity DNN approximations from coarse-grained sample data. Our theoretical results fall into the category of non-intrusive methods, providing a theoretical alternative to classical methods for high-dimensional approximation.

Multi-layer topology optimization of dual-fluid convective heat transfer in printed circuit heat exchangers

Optimizing channel configurations in printed circuit heat exchangers is essential for enhancing heat transfer and minimizing pumping power. The present study aims to optimize channel configurations by developing a multi-layer topology optimization (TO) model that simultaneously optimizes the fin structures in both the cold and hot fluid layers. The TO model directly integrates the heat transfer rate as the target function while using the pressure drop from an airfoil structure as the constraint. Counterintuitive channel configurations are generated by the TO model under various Reynolds numbers (Re). The evaluation under both laminar and turbulent conditions demonstrates that the TO-designed channel configuration outperforms conventional channel configurations including empty, airfoil, heatric, louver, modified louver, sine curve, and S-shaped designs. Compared with the airfoil structure, at Re = 300, the optimized structure can reduce pumping power by 17.0 % while increasing heat transfer by 10.8 %, and at Re = 20000 it reduces pumping power by 52.0 % and enhances heat transfer by 1.2 %. The present study introduces a promising method for designing novel printed circuit heat exchangers.

Kinesin Family Member C1: Function in liver hepatocellular carcinoma and potential target for chemotherapeutic

MiR-105 exerts inhibitory effects on the development and progression of various cancers, including breast cancer, lung cancer, and gastric cancer. Through GEO data analysis, we observed decreased expression of miR-105 in liver cancer tissues compared to adjacent tissues. Furthermore, miR-105 downregulates KIFC1 expression levels by targeting its 3' UTR. KIFC1 (Kinesin Family Member C1), a Protein Coding gene, may play a role in mitotic metaphase plate polymerization and mitotic spindle assembly. However, our findings suggest that this gene could serve as a potential chemotherapeutic target for Liver hepatocellular carcinoma (LIHC). We obtained the LIHC dataset from the TCGA database and genotype Tissue Expression Project (GTEx) normal tissue data for differential analysis. Additionally, we utilized the cBioPortal database, tumor immune single-cell center (TISCH) database, gene set enrichment analysis (GSEA), and R software to investigate the possible functions and mechanisms of KIFC1. These findings were further validated through experiments such as immunohistochemistry and wound healing assays. Our results indicate that KIFC1 might be involved in DNA repair and cell cycle regulation in LIHC cells which subsequently impacts tumor cell proliferation; moreover, miR-105 influences hepatoma cell line proliferation via its interaction with KIFC1. Collectively, these results highlight the potential therapeutic significance of targeting KIFC1 for chemotherapy treatment in LIHC patients.

Enhanced Anti‐Cancer Efficacy of Hesperidin through Smart Polymeric Nanoparticles Targeting Prostate Cancer

AbstractThe rapid progress of various nanotechnology tools is currently being utilized in the management of several fatal illnesses, including cancer. Nanopolymers that encapsulate anti‐cancer medication present a highly encouraging substitute to traditional therapies, primarily because of their specific targeting and accurate functionality, making them suitable for a variety of uses. Poly(2‐hydroxyethyl methacrylate) (pHEMA) is a non‐toxic polymer derived from the monomer HEMA, which is known for its toxicity. It can be combined with various other polymers and is associated with minimal immune response. The objective of the current investigation is to produce smart polymeric nanoparticles that are responsive to changes in pH and temperature, and capable of encapsulating hesperidin. These hesperidin‐loaded poly(2‐Hydroxyethyl methacrylate‐N‐isopropylacrylamide‐Vinyl imidazole) nanoparticles, referred to as HesSNPs, aim to enhance the bioavailability of hesperidin and augment its therapeutic effectiveness in combating prostate cancer. N‐isopropylacrylamide and Vinyl imidazole were employed as monomers sensitive to temperature and pH, respectively. Nanopolymers that were synthesized underwent characterization through dynamic light scattering (DLS) analyses, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) to analyze particle size and charge, surface morphology, and functional group determinations, respectively. HesSNPs decreased cell viability of DU‐145 cells. Flow cytometry analysis revealed that apoptosis is the main mechanism underlying cell death after HesSNPs treatment.

Reducing cold start delay in serverless computing using lightweight virtual machines

In recent years, serverless computing has gained considerable attention in academic, professional, and business circles. Unique features such as code development flexibility and the cost-efficient pay-as-you-go pricing model have led to predictions of widespread adoption of serverless services. Major players in the cloud computing sector, including industry giants like Amazon, Google, and Microsoft, have made significant advancements in the field of serverless services. However, cloud computing faces complex challenges, with two prominent ones being the latency caused by cold start instances and security vulnerabilities associated with container escapes. These challenges undermine the smooth execution of isolated functions, a concern amplified by technologies like Google gVisor and Kata Containers. While the integration of tools like lightweight virtual machines has alleviated concerns about container escape vulnerabilities, the primary issue remains the increased delay experienced during cold start instances in the execution of serverless functions. The purpose of this research is to propose an architecture that reduces cold start delay overhead by utilizing lightweight virtual machines within a commercial architecture, thereby achieving a setup that closely resembles real-world scenarios. This research employs supervised learning methodologies to predict function invocations by leveraging the execution patterns of other program functions. The goal is to proactively mitigate cold start scenarios by invoking the target function before actual user initiation, effectively transitioning from cold starts to warm starts. In this study, we compared our approach with two fixed and variable window strategies. Commercial platforms like Knative, OpenFaaS, and OpenWhisk typically employ a fixed 15-minute window during cold starts. In contrast to these platforms, our approach demonstrated a significant reduction in cold start incidents. Specifically, when calling a function 200 times with 5, 10, and 20 invocations within one hour, our approach achieved reductions in cold starts by 83.33%, 92.13%, and 90.90%, respectively. Compared to the variable window approach, which adjusts the window based on cold start values, our proposed approach was able to prevent 82.92%, 91.66%, and 90.56% of cold starts for the same scenario. These results highlight the effectiveness of our approach in significantly reducing cold starts, thereby enhancing the performance and responsiveness of serverless functions. Our method outperformed both fixed and variable window strategies, making it a valuable contribution to the field of serverless computing. Additionally, the implementation of pre-invocation strategies to convert cold starts into warm starts results in a substantial reduction in the execution time of functions within lightweight virtual machines.