Target Platform Research Articles

Targeting online sales through last‐mile delivery platform integration

AbstractWe analyze channel integration between a last‐mile delivery platform and a general merchandise retailer in two distinct stages: (1) platform delivery access (PDA), where the retailer continues to offer standard delivery through its own website but directs customers to the platform's website for new same‐day delivery; and (2) integrated delivery access (IDA), where customers can continue to use same‐day delivery service at the delivery platform website but can purchase products in a single order with both same‐day and standard delivery options at the retailer's website. We perform a quasi‐experiment using consumer spending data from retailer, target, and delivery platform, Shipt. We find that PDA provides positive impacts to the delivery platform through increased sales. IDA, on the other hand, increases the retailer's online channel sales but does not impact the delivery platform's sales. Moreover, we find that the positive effects of PDA on the delivery platform's sales are stronger in markets where online grocery penetration is lower, indicating that the effects were likely driven by increased purchases for groceries. Finally, the positive effect of IDA on the retailer's online channel sales is stronger in markets where the retailer has a greater loyal customer base and online grocery penetration is lower.

Technical infrastructure as a hidden terrain of disinformation

ABSTRACT While social media disinformation has received significant academic and policy attention, more consequential forms of intentional manipulation target the underlying digital infrastructures upon which society depends. Infrastructure-based deception, less visible than deception targeting content and platforms, has consequences for internet security, stability and trust. This article examines terrains of disinformation in digital infrastructure, including in the Domain Name System, access and interconnection, public key infrastructures, cyber-physical systems and emerging technologies. Infrastructure disinformation is largely a cybersecurity problem. By bringing technical infrastructure into the epistemic realm of disinformation, this paper shifts policy conversations around content moderation to encompass stronger cybersecurity architectures.

Mendelian randomization analysis of causal and druggable circulating inflammatory proteins in schizophrenia.

Schizophrenia (SZ) is a severe mental disorder with complex origins. Observational studies suggested that inflammatory factors may play a role in the pathophysiology of SZ and we aim to investigate the potential genetic connection between them by examining the causal impact of circulating inflammatory proteins on SZ. We utilized Mendelian randomization (MR) analysis to assess the causal relationship between circulating inflammatory proteins and SZ and the GWAS summary datasets were sourced from public databases. The SZ dataset comprised 74,776 cases and 101,023 controls, while the summary results for 91 plasma proteins in 14,824 participants were obtained through the Olink Target platform. Moreover, to identify and evaluate potential drug targets, we searched the Drug-Gene Interaction Database (DGIdb). The results of the MR study confirmed that nine inflammatory proteins had a causal effect on SZ. Among these proteins, IL1A (OR: 0.93), TNFB (OR: 0.94), TNFSF14 (OR: 0.96), and CD40 (OR: 0.95) exhibited protective effects against SZ. Conversely, CCL23 (OR: 1.04), CCL19 (OR: 1.04), 4EBP1 (OR: 1.06), TWEAK (OR: 1.08), and DNER (OR: 1.10) were associated with an increased risk of SZ. The MR-Egger and weighted median methods also supported the direction of these effects. According to the Gene-Drug analysis, LTA, IL1A, CD40, and 4EBP1 can serve as drug targets. Our study established causal relationships between circulating inflammatory proteins and SZ. It may be beneficial to personalize the treatment of SZ by incorporating inflammation management into the treatment regimen.

Genome-wide association study of CMR image-based aortic stiffness in 45,789 individuals identifies loci linked with cardiovascular diseases

Abstract Background The use of artificial intelligence on large-scale cardiac magnetic resonance (CMR) imaging data has enabled detailed characterisation of cardiac shape and function for use in genome-wide association studies (GWAS), leading to improved understanding of genetic aetiology of cardiovascular diseases (CVDs). Aortic stiffness is associated with increased risk of cardiovascular events in observational studies. Here, we present a novel CMR-based pressure-independent measure of aortic stiffness, identify genetic associations and explore links with CVDs. Purpose Identify genetic contributions to pressure-independent aortic stiffness and links to CVDs using large-scale GWAS analysis on the UK Biobank cohort. Methods A pre-trained neural network was used to segment CMR images of participants in the UK Biobank to quantify ascending aorta diameter, which together with contemporaneous systolic and diastolic blood pressure measurements (SBP, DBP, respectively) were used to calculate a pressure-independent measure of aortic stiffness (β0). The measurement assumes an exponential relationship between pressure and aortic diameter. We then performed a GWAS on β0 in 45,789 participants of European ancestry without CVDs. A multi-trait-based conditional & joint analysis (mtCOJO) was performed to estimate genetic effects independent of SBP, DBP and aortic area. We mapped the independent lead variants associated with aortic stiffness at P < 5x10-8 to nearest gene, and characterised shared genetic association across CVDs using Open Targets Platform database. Results The GWAS identified 17 independent lead variants, including 6 that remained significant in the conditional analysis. Four of these lead variants, not previously reported in GWAS of other aortic traits, were located near CTD-2203A3.1, MCF2L, CFDP1 and CDH13 genes (red annotations in the Figure). MCF2L, CFDP1 and CDH13 have been associated with coronary artery disease, and CFDP1 with myocardial infarction and coronary atherosclerosis in previous studies. Conclusion A GWAS of CMR-derived aortic stiffness identified 4 loci distinct from other aortic traits that also associate with other CVDs, suggesting genetic links between aortic stiffness and CVDs. Further analyses are required to explore biological mechanisms underlying these genetic associations.Aortic Stiffness Manhattan Plot

Virtual screening and network pharmacology-based synergistic coagulation mechanism identification of multiple components contained in compound Kushen Injection against hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a primary liver malignancy commonly encountered in the setting of chronic liver disease and cirrhosis. Compound Kushen Injection (CKI) has been widely used in HCC, however, the underlying mechanisms are scarce. ObjectiveTo explore the molecular mechanisms of CKI for HCC.To explore the molecular mechanisms of CKI for HCC. Materials and MethodsThe chemical ingredients of CKI were reviewed from published articles and the potential targets were got from Herbal Ingredients’ Targets Platform. Coagulation-related targets were from Kyoto Encyclopedia of Genes and Genomes and HCC-related targets were from Therapeutic Target Database, Gene Expression Omnibus, and The Cancer Genome Atlas. Then the CKI-Herb-Target and CKI-Herb-Target-HCC networks were built. The shared targets between CKI and HCC were used for functional enrichment through Metascape and the shared coagulation-related target was used for molecular docking and survival analysis. ResultsA total of 23 chemical ingredients and 41 potential targets shared between CKI and HCC were obtained. The results of functional enrichment indicated that several canonical pathways of CKI mostly participated in the treatment of HCC. Furthermore, a chemical ingredient of CKI formed a stable hydrogen bond link with the ASN-189 on PLG, with a best binding energy of −4.7 kcal/mol. Finally, PLG was confirmed as the shared coagulation-related target and interrelated with the prognosis of HCC. ConclusionCKI probably improves HCC prognosis through PLG. Our research undoubtedly deepened the understanding of the molecular mechanism of CKI anti-HCC.

Realistic Realizability: Specifying ABIs You Can Count On

The Application Binary Interface (ABI) for a language defines the interoperability rules for its target platforms, including data layout and calling conventions, such that compliance with the rules ensures “safe” execution and perhaps certain resource usage guarantees. These rules are relied upon by compilers, libraries, and foreign-function interfaces. Unfortunately, ABIs are typically specified in prose, and while type systems for source languages have evolved, ABIs have comparatively stalled, lacking advancements in expressivity and safety. We propose a vision for richer, semantic ABIs to improve interoperability and library integration, supported by a methodology for formally specifying ABIs using realizability models. These semantic ABIs connect abstract, high-level types to unwieldy, but well-behaved, low-level code. We illustrate our approach with a case study formalizing the ABI of a functional source language in terms of a reference-counting implementation in a C-like target language. A key contribution supporting this case study is a graph-based model of separation logic that captures the ownership and accessibility of reference-counted resources using modalities inspired by hybrid logic. To highlight the flexibility of our methodology, we show how various design decisions can be interpreted into the semantic ABI. Finally, we provide the first formalization of library evolution, a distinguishing feature of Swift’s ABI.

Aluminum phthalocyanine tetrasulfonate conjugated to surface-modified Iron oxide nanoparticles as a magnetic targeting platform for photodynamic therapy of Ehrlich tumor-bearing mice

BackgroundPhotodynamic therapy (PDT) is a targeted treatment option for cancers that are non-responding to ordinary anticancer therapies. It involves activating a photosensitizer with a light source of a specific wavelength to destroy targeted cells and their surrounding vasculature. Aluminum phthalocyanine tetra sulfonate (AlPcS4) has gained attention as a second-generation photosensitizer for its strong absorption in the red-light region. AlPcS4 can be conjugated to magnetic iron oxide nanoparticles (IONs) to provide targeted drug delivery to the tumor cells while reducing its undesired effect on healthy tissues in other body parts. MethodsMagnetic glutamine functionalized iron oxide nanocomposites loaded with AlPcS4 (IONs-NH2-AlPcS4) were synthesized via the co-precipitation method. The conjugate (IONs-NH2-AlPcS4) was characterized by TEM, Zeta potential, DLS, FTIR, and UV–VIS absorption spectroscopy. Furthermore, its photodynamic activity was investigated using albino mice with induced Ehrlich solid tumors. ResultsAlPcS4 was successfully conjugated to IONs-NH2 with a high loading efficiency of 54±2%. The synthesized conjugate exhibited a spherical shape, with 7 ± 2 nm particle size. The In vivo experiment revealed that the albino mice with induced Ehrlich solid tumor that were treated by combined PDT and magnetic targeting conjugate exhibited significant tumor regression and notably higher levels of necrotic tissue compared to the animals in other groups. ConclusionPDT mediated by magnetic targeting significantly inhibited tumor growth with minimal adverse effects, indicating its great potential as a promising strategy for solid cancer treatment.

Next-generation cell-penetrating antibodies for tumor targeting and RAD51 inhibition.

Monoclonal antibody therapies for cancer have demonstrated extraordinary clinical success in recent years. However, these strategies are thus far mostly limited to specific cell surface antigens, even though many disease targets are found intracellularly. Here we report studies on the humanization of a full-length, nucleic acid binding, monoclonal lupus-derived autoantibody, 3E10, which exhibits a novel mechanism of cell penetration and tumor specific targeting. Comparing humanized variants of 3E10, we demonstrate that cell uptake depends on the nucleoside transporter ENT2, and that faster cell uptake and superior in vivo tumor targeting are associated with higher affinity nucleic acid binding. We show that one human variant retains the ability of the parental 3E10 to bind RAD51, serving as a synthetically lethal inhibitor of homology-directed repair in vitro. These results provide the basis for the rational design of a novel antibody platform for therapeutic tumor targeting with high specificity following systemic administration.

Adaptive multi-epitope targeting and avidity-enhanced nanobody platform for ultrapotent, durable antiviral therapy.

Pathogens constantly evolve and can develop mutations that evade host immunity and treatment. Addressing these escape mechanisms requires targeting evolutionarily conserved vulnerabilities, as mutations in these regions often impose fitness costs. We introduce adaptive multi-epitope targeting with enhanced avidity (AMETA), a modular and multivalent nanobody platform that conjugates potent bispecific nanobodies to a human immunoglobulin M (IgM) scaffold. AMETA can display 20+ nanobodies, enabling superior avidity binding to multiple conserved and neutralizing epitopes. By leveraging multi-epitope SARS-CoV-2 nanobodies and structure-guided design, AMETA constructs exponentially enhance antiviral potency, surpassing monomeric nanobodies by over a million-fold. These constructs demonstrate ultrapotent, broad, and durable efficacy against pathogenic sarbecoviruses, including Omicron sublineages, with robust preclinical results. Structural analysis through cryoelectron microscopy and modeling has uncovered multiple antiviral mechanisms within a single construct. At picomolar to nanomolar concentrations, AMETA efficiently induces inter-spike and inter-virus cross-linking, promoting spike post-fusion and striking viral disarmament. AMETA's modularity enables rapid, cost-effective production and adaptation to evolving pathogens.

Clustering and Cooperative Guidance of Multiple Decoys for Defending a Naval Platform against Salvo Threats

The threat to naval platforms from missile systems is increasing due to recent advancements in radar seeker technology, which have significantly enhanced the accuracy and effectiveness of missile targeting. In scenarios where a naval platform with limited maneuverability faces salvo attacks, the importance of an effective defense strategy becomes crucial to ensuring the protection of the platform. In this study, we present a multi-agent reinforcement learning-based decoy deployment approach that employs six decoys to increase the survival likelihood of a naval platform against salvo missile strikes. Our approach entails separating the decoys into two clusters, each consisting of three decoys. Subsequently, every cluster is allocated to a related missile threat. This is accomplished by training the decoys with the multi-agent deep reinforcement learning algorithm. To compare the proposed approach across different algorithms, we use two distinct algorithms to train the decoys; multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed deep deterministic policy gradient (MATD3). Following training, the decoys learn to form groups and establish effective formation configurations within each group to ensure optimal coordination. We assess the proposed decoy deployment strategy using parameters including decoy deployment angle and maximum decoy speed. Our findings indicate that decoys positioned on the same side outperform those positioned on different sides relative to the target platform. In general, MATD3 performs slightly better than MADDPG. Decoys trained with MATD3 succeed in more successful formation configurations than those trained with the MADDPG method, which accounts for this enhancement.

Multi-Stream Scheduling of Inference Pipelines on Edge Devices - a DRL Approach

Low-power edge devices equipped with Graphics Processing Units (GPUs) are a popular target platform for real-time scheduling of inference pipelines. Such application-architecture combinations are popular in Advanced Driver-assistance Systems for aiding in the real-time decision-making of automotive controllers. However, the real-time throughput sustainable by such inference pipelines is limited by resource constraints of the target edge devices. Modern GPUs, both in edge devices and workstation variants, support the facility of concurrent execution of computation kernels and data transfers using the primitive of streams , also allowing for the assignment of priority to these streams. This opens up the possibility of executing computation layers of inference pipelines within a multi-priority, multi-stream environment on the GPU. However, manually co-scheduling such applications while satisfying their throughput requirement and platform memory budget may require an unmanageable number of profiling runs. In this work, we propose a Deep Reinforcement Learning (DRL)-based method for deciding the start time of various operations in each pipeline layer while optimizing the latency of execution of inference pipelines as well as memory consumption. Experimental results demonstrate the promising efficacy of the proposed DRL approach in comparison with the baseline methods, particularly in terms of real-time performance enhancements, schedulability ratio, and memory savings. We have additionally assessed the effectiveness of the proposed DRL approach using a real-time traffic simulation tool IPG CarMaker.

Cognitive performance of grey mouse lemurs (Microcebus murinus) during a discrimination learning task: Effect of the emotional valence of stimuli.

Emotions are omnipresent in many animals' lives. It is a complex concept that encompasses physiological, subjective, behavioural and cognitive aspects. While the complex relationship between emotion and cognition has been well studied in humans and in some nonhuman primates, it remains rather unexplored for other nonhuman primate species, such as lemurs. In our study, we evaluated the performance of N = 48 grey mouse lemurs (Microcebus murinus) in a discrimination learning task using visual emotional stimuli. We tested whether the type of visual stimulus (positive, negative or neutral) influenced the cognitive performance of mouse lemurs. Individuals had to learn to discriminate between two platforms according to the associated visual stimuli and to jump to the target platform (leading to a reward). Our main finding was that emotional stimuli, whether positive or negative in valence, impaired cognitive performance when used as a target. Specifically, the lowest success rate occurred when the target was associated with the emotional stimuli, and the highest success rate occurred when it was associated with neutral stimuli. Our results show a similar pattern to that found in other primate species and support the adaptive role of emotion. Our results also support that individual differences could be a factor impacting the relation between emotion and cognition. This study is the first to explore how emotions interfere with the cognitive abilities of a lemur species and highlights the importance of acknowledging emotion in mouse lemurs as well as studying the emotion-cognition interaction in a wider range of primate species.

Drug Repurposing Approach to Identify Candidate Drug Molecules for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, with a high mortality rate due to the limited therapeutic options. Systemic drug treatments improve the patient's life expectancy by only a few months. Furthermore, the development of novel small molecule chemotherapeutics is time-consuming and costly. Drug repurposing has been a successful strategy for identifying and utilizing new therapeutic options for diseases with limited treatment options. This study aims to identify candidate drug molecules for HCC treatment through repurposing existing compounds, leveraging the machine learning tool MDeePred. The Open Targets Platform, UniProt, ChEMBL, and Expasy databases were used to create a dataset for drug target interaction (DTI) predictions by MDeePred. Enrichment analyses of DTIs were conducted, leading to the selection of 6 out of 380 DTIs identified by MDeePred for further analyses. The physicochemical properties, lipophilicity, water solubility, drug-likeness, and medicinal chemistry properties of the candidate compounds and approved drugs for advanced stage HCC (lenvatinib, regorafenib, and sorafenib) were analyzed in detail. Drug candidates exhibited drug-like properties and demonstrated significant target docking properties. Our findings indicated the binding efficacy of the selected drug compounds to their designated targets associated with HCC. In conclusion, we identified small molecules that can be further exploited experimentally in HCC therapeutics. Our study also demonstrated the use of the MDeePred deep learning tool in in silico drug repurposing efforts for cancer therapeutics.

A systematic exploration of unexploited genes for oxidative stress in Parkinson’s disease

Human disease-associated gene data are accessible through databases, including the Open Targets Platform, DisGeNET, miRTex, RNADisease, and PubChem. However, missing data entries in such databases are anticipated because of curational errors, biases, and text-mining failures. Additionally, the extensive research on human diseases has led to challenges in registering comprehensive data. The lack of essential data in databases hinders knowledge sharing and should be addressed. Therefore, we propose an analysis pipeline to explore missing entries of unexploited genes in the human disease-associated gene databases. Using this pipeline for genes in Parkinson’s disease with oxidative stress revealed two unexploited genes: nuclear protein 1 (NUPR1) and ubiquitin-like with PHD and ring finger domains 2 (UHRF2). This methodology enhances the identification of underrepresented disease-associated genes, facilitating easier access to potential human disease-related functional genes. This study aims to identify unexploited genes for further research and does not include independent experimental validation.

Integrated network pharmacology and molecular docking to investigate the potential mechanism of Tufuling on Alzheimer's disease

ObjectiveThis study aimed to investigate the mechanism of Tu Fu Ling in treating Alzheimer's disease (AD) using network pharmacology and molecular docking. MethodsThe TCMSP and Swiss target prediction databases were utilized to confirm the active components of Tu Fu Ling and their corresponding targets, with target gene names converted using the UniProt database. Genes related to AD were collected from DisGeNET, GeneCards, and the Open Target Platform databases. Common target genes between the disease and the drug were obtained using Venny 2.1 tools and visualized using Cytoscape software. Protein-protein interaction (PPI) data were further analyzed to determine correlations between common target genes, and GO and KEGG pathway enrichment analyses were performed for intersecting genes. Finally, PYmol, AutoDock Tool, Discovery Studio 2020, and PyRx software were used for preliminary computer virtual verification and visualization of active drug ingredients and target proteins. ResultsNine active ingredients meeting the screening criteria yielded a total of 168 genes after removing duplicates. A total of 3833 target genes were collected, with 129 overlapping target genes identified. GO enrichment analysis identified 643 biological processes, 82 cellular components, and 147 molecular functions. KEGG pathway enrichment analysis also revealed a pathway closely related to AD (hsa05010: Alzheimer's disease). In molecular docking analysis, the binding affinity between the 9 active ingredients and 10 core targets ranged from −3.5 to −12.3 kcal/mol, indicating strong binding. ConclusionThis study preliminarily verified the combination of Tu Fu Ling's screened active ingredient and the calculated core target, suggesting a potential mechanism of action to improve the symptoms of AD patients through multi-target and multi-pathway approaches. This provides a valuable reference for further exploration of the pharmacological mechanism of AD and the formulation of drug therapy.

A Thorough Examination of Peltophorum pterocarpum Phytochemicals in Network Pharmacology-Based Management of Acne Vulgaris.

Introduction Acne vulgaris is a common skin problem caused by inflammation of the sebaceous glands and hair follicles as a result of hormonal fluctuations, bacteria, and overproduction of oil. The plant Peltophorum pterocarpum (P. pterocarpum) has been investigated for possible medical uses. Its anti-inflammatory, antibacterial, and antioxidant qualities are well recognised, and they may be applied to treat several diseases. This study investigates the plant's phytochemicals for their effectiveness in treating acne. Methods The Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database was utilised to extract the phytochemicals from P. pterocarpum. The absorption, distribution, metabolism, and excretion (ADME) analysis was conducted using the online tool SwissADME. The SwissTargetPrediction tool determines the potential targets of these phytochemicals. Targets for acne have been identified using the Open Targets Platform database. The common targets of P. pterocarpum and acne were identified using the Venn diagram drawing tool Venny 2.1.0, and a protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Following that, hub genes were identified by Cytoscape 3.10.2. The web tool ShinyGO 0.80 has enabled simpler evaluation of enrichment analysis for these hub genes. Results Five genes were shown to be key targets because they are directly engaged in the relaxin signalling pathway by pathway analysis: epidermal growth factor receptor (EGFR) and various matrix metalloproteinases (MMPs) (MMP1, MMP2, MMP9, and MMP13). The phytochemicals found in P. pterocarpum, including quercetin, rhamnetin, hirsutidin, and (+)-leucocyanidin, target these key genes. Conclusion This study highlights the potential of P. pterocarpum as a multi-target therapeutic agent for acne vulgaris. By targeting key genes in the relaxin signalling pathway, the phytochemicals from P. pterocarpum present a promising approach for acne management.

The Open Pediatric Cancer Project.

In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

Development of a high performance MagLIF target platform using high aspect ratio coated liners and low-mix laser preheat

Development of a high performance MagLIF target platform using high aspect ratio coated liners and low-mix laser preheat

The effect of miR‑155‑5p on long‑term memory of sleep‑deprived mice through the BDNF/NF‑κB pathway.

Sleep deprivation (SD) is a prevalent sleep issue in modern society that significantly impairs neurological function and quality of life in affected individuals. This study seeks to investigate the involvement of the miR‑155‑5p/BDNF axis in SD mice, aiming to establish a theoretical foundation for potential treatment strategies. Male C57BL/6 mice were utilized in the construction of a SD model using the flower pot technique. HT22 cells were selected for cellular experiments. The Morris water maze was employed to assess the learning and memory capabilities of the mice. HE staining was utilized to observe pathological changes in hippocampal tissue. Levels of IL‑1β, IL‑6, and TNF‑α were analyzed using ELISA. The expression level of miR‑155‑5p was quantified via RT‑qPCR. The binding between miR‑155‑5p and brain‑derived neurotrophic factor (BDNF) was confirmed through a dual‑luciferase reporter assay. Apoptosis of hippocampal neurons was assessed using TUNEL. Western blot analysis was conducted to evaluate the expression levels of BDNF, p65, and p‑p65. The Morris water maze test revealed that the mice exhibited prolonged escape latency, decreased swimming velocity, and reduced time spent in the target platform quadrant, which are indicative of a successful construction of the SD model. The observed cognitive deficits in the mice were associated with SD‑induced damage to the hippocampal tissue, leading to increased levels of miR‑155‑5p and decreased levels of BDNF. miR‑155‑5p was found to directly bind to BDNF, thereby suppressing its mRNA and protein expression. The upregulation of BDNF effectively mitigated hippocampal damage by attenuating cell apoptosis and reducing inflammation levels in SD mice. Additionally, the BDNF/NF‑κB pathway was found to be suppressed in SD mice through the downregulation of miR‑155‑5p. Therefore, the silencing of miR‑155‑5p inhibited the activation of the NF‑κB pathway by upregulating BDNF, which improved long‑term memory and reduced neuronal damage in SD mice.

Verification of Control System Runtime Using an Executable Semantic Model

The paper outlines a methodology for validating the accuracy of a control system’s runtime implementation. The runtime takes the form of a virtual machine executing portable code compliant with IEC 61131-3 standards. A formal model, comprising denotational semantics equations, has been devised to specify machine instruction decoding and operations, including arithmetic functions across various data types, arrays, and subprogram calls. The model also encompasses exception-handling mechanisms for runtime errors, such as division by zero and invalid array index access. This denotational model is translated into executable form using the functional F ♯ language. Verification involves comparing the actual implementation of the virtual machine against this executable model. Any disparities between the model and implementation indicate deviations from the specification. Implemented within the CPDev engineering environment, this approach ensures consistent and predictable control program execution across different target platforms.