Value Of Function Research Articles

Potential regulatory mechanism and clinical significance of synaptotagmin binding cytoplasmic RNA interacting protein in colorectal cancer

BACKGROUND Colorectal cancer (CRC) causes many deaths worldwide. Synaptotagmin binding cytoplasmic RNA interacting protein (SYNCRIP) is an RNA-binding protein that plays an important role in multiple cancers by epigenetically targeting some genes. Our study will examine the expression, potential effect, biological function and clinical value of SYNCRIP in CRC. AIM To examine the expression, potential effect, biological function and clinical value of SYNCRIP in CRC. METHODS The expression of SYNCRIP was examined by immunohistochemistry arrays and high-throughput data. The effect of SYNCRIP gene in CRC cell growth was evaluated by CRISPR-Cas9 technology. The target genes of SYNCRIP were calculated using various algorithms, and the molecular mechanism of SYNCRIP in CRC was explored by mutation analysis and pathway analysis. The clinical value of SYNCRIP in prognosis and radiotherapy was revealed via evidence-based medicine methods. RESULTS The protein and mRNA levels of SYNCRIP were both highly expressed in CRC samples compared to nontumorous tissue based on 330 immunohistochemistry arrays and 3640 CRC samples. Cells grew more slowly in eleven CRC cell lines after knocking out the SYNCRIP gene. SYNCRIP could epigenetically target genes to promote the occurrence and development of CRC by boosting the cell cycle and affecting the tumor microenvironment. In addition, CRC patients with high SYNCRIP expression are more sensitive to radiotherapy. CONCLUSION SYNCRIP is upregulated in CRC, and highly expressed SYNCRIP can accelerate CRC cell division by exerting its epigenetic regulatory effects. In addition, SYNCRIP is expected to become a potential biomarker to predict the effect of radiotherapy.

Antagonism of the ATP-gated P2X7 receptor inhibits the proliferation of hepatocellular carcinoma cells.

The P2X7 receptor, an ATP-gated ion channel which belongs to the P2X receptor family, plays critical roles in recognizing extracellular adenosine 5'-triphosphate (ATP) and is widely expressed in most tumor cells as well as inflammatory cells. Previously, the P2X7 receptor has been demonstrated to modulate the progression of various malignancies, including glioblastoma, pancreatic cancer, lung cancer, leukemia, and lymphoma. However, the biological function and prognostic values of P2X7 receptor in hepatocellular carcinoma remain to be determined. Here, we investigated the expression level of P2X7 receptor in patients with hepatocellular carcinoma. Then MTS and EdU assays were carried out to study the role of P2X7 receptor blockade in the proliferation of hepatocellular carcinoma cells. In addition, the underlying mechanism was further elucidated by bulk RNAseq. Compared to the control group, the P2X7 receptor was significantly up-regulated in the hepatocellular carcinoma group. Interestingly, A740003 and A438079, two selective antagonists at P2X7 receptor, significantly blocked Ca2+ influx and decreased the proliferative rate of hepatocellular carcinoma cells. Furthermore, the expression level of chondroitin sulfate synthase 1 (CHSY1), an enzyme that mediates the polymerization step of chondroitin sulfate, was reduced by both A740003 and A438079. In conclusion, inhibition of the P2X7 receptor attenuated the proliferation of hepatocellular carcinoma cells, and this process was largely modulated by CHSY1. Thus, our findings reveal a previously unknown role for P2X7 receptor in the proliferation of hepatocellular carcinoma cells and imply that the P2X7 receptor may represent a new target for the treatment of hepatocellular carcinoma.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

On Some Works of Boris Teodorovich Polyak on the Convergence of Gradient Methods and Their Development

The paper presents a review of the current state of subgradient and accelerated convex optimization methods, including the cases with the presence of noise and access to various information about the objective function (function value, gradient, stochastic gradient, higher derivatives). For nonconvex problems, the Polyak–Lojasiewicz condition is considered and a review of the main results is given. The behavior of numerical methods in the presence of a sharp minimum is considered. The aim of this review is to show the influence of the works of B.T. Polyak (1935–2023) on gradient optimization methods and their surroundings on the modern development of numerical optimization methods.

Photon blockade in a double-transmon system with ultrastrong coupling

The circuit quantum electrodynamics (QED) system has brought us into an ultrastrong and deep coupling regime in the light–matter interaction community, in which the quantum effect has attracted significant interest. In this study, we theoretically investigated the photon blockade phenomenon in a double-transmon system operating in an ultrastrong coupling regime. We considered the effect of the counter-rotating wave terms in the interaction Hamiltonian and derived the master equation in the eigenpresentation. We found that photon blockade occurred in only one of the eigenmodes, and the counter-rotating wave terms enhanced the blockade by reducing the minimum value of the second-order correlation function. This study will be beneficial for the design of single-photon devices in circuit QED systems, especially in the ultrastrong coupling regime.

Characterization of the temporal profile of the antinociceptive effects of an intravenous bolus of ketamine using the analgesia nociception index in no-anesthetized adult patients.

An effect-site target-controlled infusion (TCI) would allow a more precise titration of intravenous analgesics effect. The analgesia nociception index (ANI) continuously monitors the analgesia/nociception balance during general anesthesia. This study aims to derive a PKPD model of ketamine antinociceptive effect using the Domino PK parameter set and the ANI response data in awake patients without other drugs affecting the ANI response. Twenty awake adult patients were prospectively studied before general anesthesia. Patients received a single intravenous bolus of ketamine 0.1mg·kg- 1, and the subsequent ANI values were recorded. An effect compartment model incorporating the Domino PK parameter set was used to characterize the time lag between ketamine plasma concentrations and the ANI response. The model was parameterized with a single parameter Ke0. An Emax pharmacodynamic model was used to fit the ANI response data. Model parameters were estimated with NONMEM® 7.5. The minimum objective function value guided the model construction. After the ketamine administration, basal ANI values increased from 38.5 ± 4.95 to a maximum of 53.5 ± 4.95 with an observed time-to-peak effect of 1.83 ± 0.74min. Modeling analysis revealed hysteresis between predicted plasma concentrations from the Domino model and observed ANI data. Hysteresis was characterized, incorporating an estimated Keo of 0.238 (CI95% 0.20-0.28) min-1 to the described PK parameters set. The developed PKPD model, using Domino's PK parameters and the ANI response data, adequately characterized the temporal profile of ketamine's antinociceptive effect. The current estimated model parameters can be used to perform an effect-site TCI of ketamine for analgesic purposes.

Analysis of impact of limb segment length variations during reinforcement learning in four-legged robot.

Crawling robots are becoming increasingly prevalent in both industrial and private applications. Despite their many advantages over other robot types, they have complex movement mechanics. Artificial intelligence can simplify this by reinforcement learning. This process requires configuring the training environment and defining input parameters, including a robot model for movement training. To translate the virtual results into real-world scenarios, a 3D model with appropriate mechanical parameters must be developed.These parameters can vary significantly between multiple mechanical configurations, which will further impact the reinforcement learning process of such a robot. For this reason, it was decided to test which limb configurations would work best in this process. Initially, various kinematic types of walking robots were analysed, drawing on the anatomy of mammals, reptiles, and insects for the biological model. The reptilian model was chosen for its balance of stability, dynamics, and energy efficiency. The article reviews the preparation of robot models and the configuration of the Unity3D development environment using the ML-Agents toolkit. The experiment examined how different limb lengths affect training, resulting in movement algorithms for various quadruped robot configurations using artificial neural networks. Based on the numerical results, the best configuration was the default, with the same length of the tibia as the thigh, achieving a reward function value of 883.9 and an episode length of 245.5. Taking into account the same criteria, the least efficient configuration was definitely the one characterised by the shortest thigh and the longest tibia among those considered. In its case, the reward function reached a value of only 526.2 with an episode lasting 999.0, which means that it never achieved the intended goal.

Assessment of fuzzy logic to enhance species distribution modelling of two cryptic wood boring beetle species in Australia.

Fuzzy logic presents a promising approach for Species Distribution Modelling by generating a value that can be used for comparative purposes termed 'environmental favourability'. In contrast to 'presence probability', 'environmental favourability' remains robust regardless of species prevalence. This characteristic facilitates effective comparisons across species with varying levels of prevalence. In this study, presence probability was predicted using three commonly used Species Distribution Models: Generalised Linear Model, Generalised Additive Modelling, and Boosted Regression Trees for two beetle species, Euwallacea fornicatus and Euwallacea perbrevis in Australia. Fuzzy logic was then employed to derive environmental favourability values based on these models. Additionally, Maxent modelling was included to compare prediction outputs and facilitate a comprehensive analysis. Model performance was evaluated using standard metrics (Area under the receiver operating characteristic curve, True statistical skill, Correct classification rate), as well as Hosmer-Lemeshow test. The research explored fuzzy similarity, fuzzy intersection and potential biotic interaction of these closely related borers, and revealed a favourable distribution pattern for Euwallacea fornicatus across Australia. This study supports the efficacy of fuzzy logic in Species Distribution Modelling and highlights the value of environmental favourability function in enhancing the comparative analysis of the geographical relationship across species. This approach offers a more nuanced perspective on Species Distribution Modelling.

Robust Adaptive Extremum Seeking Control Without Persistence of Excitation: Theory to Experiment

ABSTRACTIn this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios.

Conflicting Consumer Beliefs Influencing Eco‐Innovation Adoption: Motives and Barriers for Accepting the Laser Marking of Organic Products

ABSTRACTIn response to regulatory requirements and consumer demand for sustainable products, producers of organic products are beginning to use laser marking to reduce packaging and, thereby, packaging waste. However, the consumer responses to this “high‐tech” eco‐innovation remain unexplored. Using a mixed‐method approach, we collected qualitative and quantitative data on responses to the laser marking of organic products from 328 French participants. Guided by the theory of consumption values and innovation resistance theory, we conducted a thematic analysis of answers to an open‐ended question which probed consumers' motives for and barriers to adopting laser marking. The most frequently stated motive was ecological benefits, and the most reported barriers were risks and tradition. Structural equation modeling revealed that attitudes toward laser‐marked organic products are positively impacted by social, emotional, and functional values and are negatively impacted by barriers related to images and emotions. Consumers' attitudes toward laser‐marked organic products strongly affect their willingness to buy such products. To increase the acceptance of laser marking, managers and policymakers should mitigate false‐negative consumer perceptions, including doubts about its eco‐friendliness and safety, thereby facilitating greater acceptance of this eco‐innovation.

Orbit-finite linear programming

An infinite set is orbit-finite if, up to permutations of atoms, it has only finitely many elements. We study a generalisation of linear programming where constraints are expressed by an orbit-finite system of linear inequalities. As our principal contribution we provide a decision procedure for checking if such a system has a real solution, and for computing the minimal/maximal value of a linear objective function over the solution set. We also show undecidability of these problems in case when only integer solutions are considered. Therefore orbit-finite linear programming is decidable, while orbit-finite integer linear programming is not.

Changing Nature of Consumer Values Toward Negative Word of Mouth: Voice and Risk Avoidance as Moderators

ABSTRACTGiven the increasing role of word of mouth (WOM), and primarily the impact of negative WOM on the decision‐making of today's retail consumers, it is essential to understand the changing nature of consumer values (monetary, functional, emotional and social), emotion and attitude toward negative WOM. The current study's data were collected using two online surveys in 2015 and 2019, with 290 participants in Study 1 and 201 in Study 2. The intention behind using two different time points over a 4‐year span was to capture the differences in consumers' views over the two periods. The study's findings suggested that social and emotional values remained unchanged; however, monetary and functional values changed over time. Interestingly, attitudes altered toward complaining, although the relationships between taking external action and negative WOM were unchanged in both studies. Consumers raising their voice and consumers' risk‐taking attributes were found to be significant moderators in Study 1 but not in Study 2. The findings provide useful insights into the negative WOM literature and have implications for marketing practitioners, as highlighted in the paper's conclusion.

Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

Antioxidants, ACE I Inhibitory Peptides, and Physicochemical Composition, with a Special Focus on Trace Elements and Pollutants, of SPRING Spawning Atlantic Herring (Clupea harengus) Milt and Hydrolysates for Functional Food Applications

Norwegian spring spawning (NVG) herring milt is a raw material with high nutritional and functional values. However, its incorporation into food presents physicochemical and sensory challenges. Its high DNA content, the presence of TMA/TMAO and possibly heavy metal and/or environmental pollutants, and its bitter taste due to amino acids or peptides requires a careful approach to food development. Hydrolysis with food-grade enzymes enable an improvement in both the functional and sensory properties of the substrate and the increased stability of the raw materials and end products. HLPC, GC-MS, and in vitro protocols were used for the characterisation of manually extracted material (sample code: HMC) and milt from a fish-filleting line from early spring/late autumn catches. Three different food-grade protein hydrolysates were prepared from these raw materials (sample codes: H1, H2, and H3) as a means to estimate their functional food development potential. Combinations of three commercial enzymatic preparations were applied, targeting specific sensory properties. Parameters related to consumer safety (e.g., the presence of heavy metals and TMA/TMAO); beneficial health effects, such as antioxidant or antihypertensive bioactivities (measured using in vitro TAC, ORAC, DPPH, and ACE I inhibitory activity assays); the presence of beneficial fatty acids and micronutrients; and the protein quality were studied. On the basis of their total amino acid compositions, freeze-dried herring milt and hydrolysates could provide high-quality protein with most of the essential amino acids and taurine. Powdered milt has a particularly high fatty acid profile of bioavailable omega-3 fatty acids (2024.06 mg/100 g docosahexaenoic acid (DHA; 22:6n-3) and 884 mg/100 g eicosapentaenoic acid (EPA; 20:5n-3)). The experimentally measured levels of arsenic (3.9 ± 1.2 mg/kg) and cadmium (0.15 ± 0.05 mg/kg) were higher than the levels of the other two heavy metals (mercury and lead). The bioactivity is concentration-dependent. Overall, this work presents complementary information for the future utilisation of C. harengus powdered milt (possibly obtained directly from a fish-filleting line) and some of its protein hydrolysates as food ingredients.

Advances in the Management of Lung Cancer Brain Metastases

Lung cancer, both non-small cell and small cell, harbors a high propensity for spreading to the central nervous system. Radiation therapy remains the backbone of the management of brain metastases. Recent advances in stereotactic radiosurgery have expanded its indications and ongoing studies seek to elucidate optimal fractionation and coordination with systemic therapies, especially targeted inhibitors with intracranial efficacy. Efforts in whole-brain radiotherapy aim to preserve neurocognition and to investigate the need for prophylactic cranial irradiation. As novel combinatorial strategies are tested and prognostic/predictive biomarkers are identified and tested, the management of brain metastases in lung cancer will become increasingly personalized to optimally balance intracranial efficacy with preserving neurocognitive function and patient values.

Further results on outer independent triple Roman domination

An outer-independent triple Roman dominating function (OI[3]RDF) on a graph G = ( V , E ) is function f : V → { 0 , 1 , 2 , 3 , 4 } having the property that (i) if f ( v ) = 0 , then v must have either a neighbor assigned 4 or two neighbors one of which is assigned 3 and the other at least 2 or v has three neighbors all assigned 2; (ii) no two vertices assigned 0 are adjacent; (iii) if f ( v ) = 1 , then v must have either a neighbor assigned at least 3 or two neighbors assigned 2; (iv) if f ( v ) = 2 , then v must have one neighbor assigned at least 2. The weight of an OI[3]RDF is the sum of its function value over the whole set of vertices, and the outer-independent triple Roman domination number of G is the minimum weight of an OI[3]RDF on G. In this paper, we continue the study of outer-independent triple Roman domination number in graphs. First, we characterize all graphs with small or large outer-independent triple Roman domination number, and then we establish relationships with some other related parameters. Finally we present a sharp upper bound for the outer-independent triple Roman domination number of trees.

Characterization of the function and clinical value of ERCC family genes in lung adenocarcinoma

IntroductionERCC genes, responsible for encoding enzymes involved in base excision repair, have been implicated in various cancers, contributing to chemoresistance. However, a comprehensive analysis of the prognostic and therapeutic significance of this gene family in lung adenocarcinoma (LUAD) is lacking.MethodsThis study conducted a multidimensional assessment of ERCC family genes in LUAD using bioinformatic approaches, including mRNA expression level, gene methylation, and copy number variation (CNV), as well as their correlations with clinical outcome, gene set variations, and tumor-infiltrating lymphocytes (TILs). In addition, We evaluated the anti-tumor effects of ERCC8 in cell lines, demonstrating its clinical potential on an experimental level.ResultsOverall, the expression of ERCC genes exhibited a negative correlation with good prognosis, with ERCC6L and ERCC8 demonstrating the most reliable predictive performance. Gene methylation level and CNV increases of ERCC genes generally displayed negative and positive associations with their expression levels, respectively. Additionally, GSVA analysis suggested that ERCC expression was positively correlated with cell cycle and apoptosis pathways but negatively correlated to the TSC/mTOR pathway. Furthermore, the expression of ERCC genes exhibited a complex relationship with TILs and the response to anti-tumor drugs. The results of in vitro cellular experiments show that inhibiting ERCC8 can alleviate the malignant phenotype of LUAD cells.DiscussionOur study revealed the multifaceted biological and clinical significance of ERCC family members in LUAD. These findings provide new insights into the function of ERCC family genes in LUAD and their potential clinical applications.

Predicting guest satisfaction and willingness to pay premium prices for green hotels

Green transformation in the hotel industry represents an undeniable trend yet poses financial challenges, particularly in emerging economies. Expanding on the service quality-satisfaction-behavioral intention paradigm, this study investigates the influences of green emotional attachment, attitude towards green hotels, green image, green practice performance, word-of-mouth, guest satisfaction, functional value, and green experiential loyalty on green hotel premium payment. We collected 573 data points from China using purposive sampling. Partial least squares structural equation modeling was used to analyze the data. The findings revealed the positive effect of attitudes towards green hotels and green practices on guest satisfaction. The findings confirmed a positive correlation between guest satisfaction and willingness to pay a premium price for green hotels. This study also confirms the moderating effect of green experiential loyalty. Moreover, it recognizes gender, age, and income as partial moderating factors. These findings offer valuable insights into green hotel consumption research and assist practitioners in enhancing guest willingness to pay premiums and fostering sustainable industry development. The active implementation of strategies to boost guest satisfaction and willingness to pay premiums can further environmental conservation and support zero-carbon goals in the hospitality sector.

Into the Revolution of NanoFusion: Merging High Performance and Aesthetics by Nanomaterials in Textile Finishes

AbstractThe field of technical textiles has grown significantly during the last two decades, with a focus on functionality rather than aesthetics. However, the advancement of NanoFusion technology provides a novel potential to combine better functionality and aesthetic value in textile finishes. NanoFusion incorporates nanoparticles into textile treatments to improve waterproofing, stain resistance, durability, and breathability. This is performed without affecting the textile's visual appeal or aesthetics and may even improve them. This textile finishing revolution is expected to impact industries such as athletics, outdoor clothing, car upholstery, and luxury fashion. It offers cutting‐edge functionality while maintaining style and design integrity. Furthermore, the use of nanoparticle textile coatings opens up new opportunities for personalization and modification. Manufacturers and designers can now experiment with different color combinations, patterns, and textured finishes while maintaining performance characteristics. NanoFusion technology has the potential to transform the textile industry by providing hitherto unattainable levels of performance and aesthetics. This study reviews the current state of the art in nanofinishes for garment textiles, focusing on their many varieties, techniques, mechanisms, and applications. In addition, it addresses significant concerns such as sustainability and the environmental footprint, paving the way for a new era in textile manufacturing.

A pan-cancer study of ADAM9’s immunological function and prognostic value particularly in liver cancer

A pan-cancer analysis summarizing the overall changes in mRNA and protein stability of ADM9, as well as its oncogenic function on immune cell line modulation and checkpoints within the tumor microenvironment (TME), is lacking, despite the fact that ADM9 up-regulation is correlated with the progression of many cancers. Therefore, in this study, we comprehensively analyzed the role of ADAM9 expression and its prognostic value in different cancers to fill this gap. Multiple bioinformatics databases such as Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to evaluate the ADAM9 genetic alternation, phosphorylation, and methylation, and indicated highly positive correlated genes that might play a critical interaction with ADAM9 and their molecular function with GO analysis. We also evaluate the effect of higher ADAM9 with prominent immune modulatory genes and immune infiltration especially in liver cancer pathogenesis stimulates lower NK cell effector functions based on its role in MICA shedding and increasing the Tregs infiltration. Immunohistochemistry (IHC) staining from 90 pathologically verified samples proved the positive correlation between ADAM9 and tumor stages and proved the higher expression of ADAM9 correlated genes (SNX9, APP, TNF, CDH1, ITGAV, MAD2L2) in HCC pathogenesis. In conclusion, this pan-cancer study provides a comprehensive understanding of the prognostic value of ADAM9 in various tumors emphasizing its importance to be considered as an innovative treatment approach, especially in tumor immunity shortly.