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Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage.

Primary sensory neurons are generally considered the only source of dorsal horn calcitonin gene-related peptide (CGRP), a neuropeptide critical to the transmission of pain messages. Using a tamoxifen-inducible Calca CreER transgenic mouse, here we identified a distinct population of CGRP-expressing excitatory interneurons in lamina III of the spinal cord dorsal horn and trigeminal nucleus caudalis. These interneurons have spine-laden, dorsally directed, dendrites, and ventrally directed axons. As under resting conditions, CGRP interneurons are under tonic inhibitory control, neither innocuous nor noxious stimulation provoked significant Fos expression in these neurons. However, synchronous, electrical non-nociceptive Aβ primary afferent stimulation of dorsal roots depolarized the CGRP interneurons, consistent with their receipt of a VGLUT1 innervation. On the other hand, chemogenetic activation of the neurons produced a mechanical hypersensitivity in response to von Frey stimulation, whereas their caspase-mediated ablation led to mechanical hyposensitivity. Finally, after partial peripheral nerve injury, innocuous stimulation (brush) induced significant Fos expression in the CGRP interneurons. These findings suggest that CGRP interneurons become hyperexcitable and contribute either to ascending circuits originating in deep dorsal horn or to the reflex circuits in baseline conditions, but not in the setting of nerve injury. The ability to sense pain is critical to our survival. Normally, pain is provoked by intense heat or cold temperatures, strong force or a chemical stimulus, for example, capsaicin, the pain-provoking substance in chili peppers. However, if nerve fibers in the arms or legs are damaged, pain can occur in response to touch or pressure stimuli that are normally painless. This hypersensitivity is called mechanical allodynia. A protein called calcitonin gene-related peptide, or CGRP, has been implicated in mechanical allodynia and other chronic pain conditions, such as migraine. CGRP is found in, and released from, the neurons that receive and transmit pain messages from tissues, such as skin and muscles, to the spinal cord. However, only a few distinct groups of CGRP-expressing neurons have been identified and it is unclear if these nerve cells also contribute to mechanical allodynia. To investigate this, Löken et al. genetically engineered mice so that all nerve cells containing CGRP produced red fluorescent light when illuminated with a laser. This included a previously unexplored group of CGRP-expressing neurons found in a part of the spinal cord that is known to receive information about non-painful stimuli. Using neuroanatomical methods, Löken et al. monitored the activity of these neurons in response to various stimuli, before and after a partial nerve injury. This partial injury was induced via a surgery that cut off a few, but not all, branches of a key leg nerve. The experiments showed that in their normal state, the CGRP-expressing neurons hardly responded to mechanical stimulation. In fact, it was difficult to establish what they normally respond to. However, after a nerve injury, brushing the mice’s skin evoked significant activity in these cells. Moreover, when these CGRP cells were artificially stimulated, the stimulation induced hypersensitivity to mechanical stimuli, even when the mice had no nerve damage. These results suggest that this group of neurons, which are normally suppressed, can become hyperexcitable and contribute to the development of mechanical allodynia. In summary, Löken et al. have identified a group of nerve cells in the spinal cord that process mechanical information and contribute to touch-evoked pain. Future studies will identify the nerve circuits that are targeted by CGRP released from these nerve cells. These circuits represent a new therapeutic target for managing chronic pain conditions related to nerve damage, specifically mechanical allodynia, which is the most common complaint of patients with chronic pain.

Health in early life is crucial for health later in life. Exposure to air pollution during embryonic and early-life development can result in placental epigenetic modification and foetus reprogramming, which can influence disease susceptibility in later life. Objectives: The aim of this paper was to investigate the placental adaptation in the level of global DNA methylation and differential gene expression in the methylation cycle in new-borns exposed to high fine particulate matter in the foetal stage. This is a nested case-control study. We enrolled pregnant healthy women attending prenatal care clinics in Tehran, Iran, who were residents of selected polluted and unpolluted regions, before the 14th week of pregnancy. We calculated the regional background levels of particle mass- particles with aerodynamics diameter smaller than 2.5 μm (PM2.5) and 10 μm (PM10)-of two regions of interest. At the time of delivery, placental tissue was taken for gene expression and DNA methylation analyses. We also recorded birth outcomes (the new-born's sex, birth date, birth weight and length, head and chest circumference, gestational age, Apgar score, and level of neonatal care required). As regards PM2.5 and PM10 concentrations in different time windows of pregnancy, there were significantly independent positive correlations between PM10 and PM2.5 in the first trimester of all subjects and placental global DNA methylation levels (p-value = 0.01, p-value = 0.03, respectively). The gene expression analysis showed there was significant correlation between S-adenosylmethionine expression and PM2.5 (p = 0.003) and PM10 levels in the first trimester (p = 0.03). Our data showed prenatal exposures to air pollutants in the first trimester could influence placental adaptation by DNA methylation.

A novel nanocomposite-modified electrode based on reduced graphene oxide (rGO) decorated with 18-crown-6 (Cr.6) and gold nanoparticles (GNPs) on the surface of a glassy carbon electrode (GCE) was successfully fabricated to investigate the electrochemical sensing of the biomarker L-tryptophan (L-Trp) in the presence of dopamine (DA), ascorbic acid (AA), urea, and glucose. The rGO-GNPs-Cr.6/GCE displayed high electrochemical catalytic activity for L-Trp determination using square-wave voltammetry (SWV). The electrochemical behavior of L-Trp at the rGO-GNPs-Cr.6/GCE displayed higher oxidation current and potential (oxidation peak current of 40 μA at 0.85 V) than rGO-GNPs/GCE, Cr.6/GCE, GNPs/GCE, rGO/GCE, and bare GCE. The SWV demonstrated a linear range of L-Trp concentration from 0.1 to 2.5 μM. A low limit of detection (LOD) was found for L-Trp, with LOD of about 0.48 μM and 0.61 μM in diabetic and normal serum, respectively. The fabricated sensor demonstrated high selectivity and sensitivity, and good stability and reproducibility for L-Trp sensing. Finally, the nanocomposite (rGO-GNPs-Cr.6)-modified GCE was applied for the determination of L-Trp in normal and diabetic human serum samples, and displayed excellent LOD and recoveries higher than 91.8%.

Underwater sensor networks (UWSNs) have recently attracted much attention due to their ability to discover and monitor the aquatic environment. However, acoustic communication has posed some significant challenges, such as high propagation delay, low available bandwidth, and high bit error rate. Therefore, proposing a cross-layer protocol is of high importance to the field to integrate different communication functionalities (i.e, an interaction between data link layer and network layer) to interact in a more reliable and flexible manner to overcome the consequences of applying acoustic signals. In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance by providing the interaction between the MAC and routing layers. In CLMD, an Autonomous Underwater Vehicle (AUV) is used to periodically visit a group of clusters which are responsible for data collection from members. The communications are managed by using a distributed cross-layer solution to enhance network performance in terms of packet delivery and energy saving. The cluster heads are replaced with other candidate members at the end of each operational phase to prolong the network lifetime. The effectiveness of CLMD is verified through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio with varying number of nodes. The effects of MAC protocols are also studied by studying the network performance under various MAC protocols in terms of packet delivery ratio, goodput, and energy consumption with varying density of nodes.

Background Opioid dependence, particularly morphine, has been linked to gut microbiota dysbiosis and systemic inflammation, yet the interplay between gut microbial alterations and hepatic inflammatory responses remains poorly understood. Methods Fifty male Wistar rats were separated into two groups, one received escalating morphine doses (5 to 30 mg/kg over 10 days), while the other acted as a saline control. Fecal samples were collected at baseline, on days 5 and 10 of treatment, and after a 10-day withdrawal. DNA was extracted for qPCR analysis of Lactobacillus , Bifidobacterium , Clostridium , Bacteroides , and Faecalibacterium . Liver tissues were examined for inflammatory markers ( TNF-α , IFN-γ , IL-6 , NF-κB ) using RT-qPCR after treatment and withdrawal. Results A significant decline in Lactobacillus ( P  = 0.011) and Bifidobacterium ( P  = 0.003) following morphine treatment, with partial recovery observed after withdrawal ( P  = 0.014; P  = 0.0009), yet levels remained below baseline. Conversely, Clostridium levels increased significantly during treatment ( P  = 0.0001), persisting at elevated levels post-withdrawal ( P  = 0.0001). Bacteroides and Faecalibacterium also exhibited decreased abundances during morphine treatment ( P  > 0.05; P  = 0.00009), with limited recovery thereafter ( P  > 0.05; P  = 0.00008). Hepatic analysis revealed elevated levels of TNF-α ( P  < 0.0001), IL-6 ( P =  0.005), and NF-κB ( P =  0.41), alongside a significant reduction in IFN-γ ( P  < 0.001) expression in the morphine group compared to controls. After withdrawal, TNF-α ( P  < 0.01) and IFN-γ ( P  = 0.004) levels decreased, while NF-κB ( P  = 0.03) and IL-6 ( P =  0.4(remained elevated, indicating persistent inflammatory responses. Conclusion Morphine causes lasting gut dysbiosis and liver inflammation, indicating disruption of the gut-liver axis in opioid dependence. These results emphasize morphine’s impact on gut microbiota and liver health, suggesting significant long-term effects of opioid use. Targeting microbiota modulation and anti-inflammatory approaches may offer therapeutic options for opioid-related conditions.

Effective corporate social responsibility (CSR) implementation is essential for construction enterprises to achieve sustainable development. However, existing reviews on CSR indicators and performance measures predominantly employ a single review method or focus on non-construction sectors, with limited exploration of their interrelationships. To address this gap, this state-of-the-art review synthesizes findings from 77 relevant papers published over the past two decades in Scopus, adopting a combined methodological approach that integrates science mapping and systematic review techniques. The scientometric analysis, conducted using VOSviewer, examines annual publication trends, key journals, prominent keywords, contributing countries, and influential documents. A subsequent systematic discussion utilizing content analysis identifies seven critical CSR indicators (e.g., environmental sustainability, corporate practices, and employee well-being) and eight performance dimensions (e.g., customer satisfaction and corporate reputation). A conceptual linkage framework is developed to elucidate the relationships between these indicators and performance dimensions, highlighting the most influential CSR factors. To enhance the robustness of the findings, a post-survey interview method is employed to validate and compare the systematic discussion results, revealing several cognitive gaps between academic perspectives and industry practices. Finally, future research directions and study limitations are discussed. By integrating the mixed-review results with voices of the construction industry, this review provides an objective and holistic reference for CSR scholars in the construction sector and offers managerial and policy insights for industry stakeholders and policymakers.

Construction 4.0 has become a buzzword since the penetration of building information modeling (BIM), cyber-physical systems, and digital and computing technologies into the construction industry. Among emerging technologies, distributed ledger technology (DLT), or blockchain, is a powerful business enhancer whose potential can disrupt projects, AEC (architecture, engineering, and construction) firms, and construction supply chain, and in a broader sense, the whole construction industry. This technology has not reached the plateau of productivity due to several barriers and challenges. Previous studies have started to investigate the barriers to implementing DLT in various sectors and segmentations. However, we still need further surveys in the construction industry. This study evaluates the applicability of identified challenges and barriers based on a sustainability perspective. Precisely, we will answer which challenges need to be addressed for the sustainability of the construction industry. To meet the research objective, the ordinal priority approach (OPA) in multiple attributes decision-making (MADM) was utilized. This novel method determines the weight of sustainability attributes and barriers simultaneously. The results show that DLT implementation needs (i) infrastructure for data management, (ii) advanced applications and archetypes, and (iii) customers’ demand, interest, and tendency, and (iv) taxation and reporting. Solving high-ranked challenges is the key to social sustainability from the aspects of “supply chain management and procurement”; “transparency, anti-corruption, and anti-counterfeiting”; and “fair operation and honest competition.”

In graph theory, the Randic index ( R ) is a topological graph invariant widely used as a physicochemical descriptor in the mathematical modeling of molecular structures. However, traditional molecular graphs fail to capture the heterogeneity of chemical bonds, since they treat all edges as uniform, ignoring variations in bond lengths and strengths. To overcome this limitation, we adopt a fuzzy graph structure, which provides a more realistic framework for representing molecular interactions. This dual perspective developing rigorous theoretical bounds for the Randic index on fuzzy graph classes and simultaneously applying them to real pharmaceutical compounds ensure both mathematical novelty and practical relevance. We derive analytical bounds for the Randic index over fuzzy versions of complete graphs, paths, stars, and cycles, as well as for graph operations such as Cartesian product, union, join, and composition, confirming the sharpness of the results in each case. To demonstrate the applicability, we compute the Randic index for fuzzy graph representations of Parkinson’s disease drugs (Levodopa, Procyclidine, Trihexyphenidyl, and Apomorphine). The findings indicate that the Randic index, within the fuzzy graph framework, reliably estimates key physicochemical properties such as polarizability, molar refractivity, surface tension, and molar volume, highlighting the strength of combining theoretical results with drug modeling applications.

Background and Aims The ongoing COVID‐19 pandemic necessitates a deeper understanding of risk factors associated with severe outcomes. Chronic Periodontitis, a persistent inflammatory condition affecting the gums, may be linked to increased COVID‐19 severity. This study aimed to determine the relationship between periodontitis and the severity of lung infection caused by COVID‐19. Methods This observational study was conducted at Valiasr Hospital, Zanjan, Iran, between 2019 and 2020. Participants included individuals with COVID‐19‐related pneumonia (cases) and a control group without COVID‐19. Pneumonia severity was assessed using the Pneumonia Severity Index, while periodontal status was evaluated through clinical parameters such as the Plaque Index, Gingival Index, and probing depth (PD). Statistical analyses included Chi‐square, Fisher's exact, Mann‐Whitney U tests, and multivariate models to examine associations and control for potential confounders, including age, gender, education, and place of residence. Results The study included 160 participants, with 86 classified as COVID‐19 cases and 74 as controls. Analysis revealed no significant disparities in demographic variables between the two groups. Additionally, no notable differences were observed in the distribution of periodontal conditions. However, a significant correlation emerged between periodontal indices and COVID‐19 severity (p < 0.05). Further analysis showed a significant relationship between periodontal conditions and the severity of lung involvement in COVID‐19. Logistic regression analysis identified PD as the only significant predictor of COVID‐19 severity, with an odds ratio of 1.083 (95% CI: 1.002–1.171, p = 0.04), indicating an 8.3% increase in the odds of severe COVID‐19 per unit increase in PD. Additionally, multinomial logistic regression highlighted associations between PD, extent of involvement, and disease type with the severity of COVID‐19 pulmonary involvement, reinforcing their potential as predictive factors. Conclusion Further research is warranted to validate these observations, elucidate the underlying mechanisms, and explore potential interventions targeting periodontal health as a strategy for COVID‐19 risk reduction.