Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

Background: Inflammatory demyelination and impaired recovery processes result in permanent neurodegeneration and neurological disability in patients with multiple sclerosis (MS). In terms of smoldering MS, chronic neuroinflammation develops in the early period of the disease and leads to confirmed disability accumulation. There is a great need to identify biomarkers of neuro-degeneration and disease progression. Methods: We evaluated the concentrations of the param-eters of brain injury (NF-H, GPAF, S100B, UCHL1) in the cerebrospinal fluid (CSF) and the se-lected interleukins (ILs) in serum of 123 relapsing-remitting MS (RRMS) and 88 progressive MS (PMS) patients. Results: The levels of GFAP, S100B and UCHL were higher in the PMS group compared to RRMS as opposed to the levels of NF-H. We observed a positive correlation be-tween the selected pro-inflammatory cytokines and the parameters of brain injury. The Ex-panded Disability Status Scale score (EDSS) increased with GFA and NF-H levels and correlated with the selected ILs. The concentrations of S100B, UCHL1 and NF-H reflected the duration of MS symptoms. Conclusions: The levels of brain injury parameters in the CSF and the selected serum ILs in MS patients seem to be promising biomarkers to determine neurodegeneration and neuroinflammation in smoldering MS. Further studies are warranted in this respect.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. Spiking Neural Networks (SNNs) can utilize biological temporal coding features, such as noise-induced stochastic resonance and dynamical synapses to increase a model’s performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified Convolutional Neural Network analog, this paper demonstrates two key results: (1) SNNs solve the problem linearly inseparable in ANN with over 10% improvement in the optimal setting, and (2) in SNNs, the synaptic noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results.

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes to a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure under the above described constraints.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

The autonomic regulation of hepatic metabolism offers a novel target for the treatment of non-alcoholic fatty liver disease (NAFLD). However, the molecular characteristics of neurons that regulate the brain-liver axis remain unclear. Since mice lacking neuronal lipoprotein lipase (LPL) develop perturbations in neuronal lipid-sensing and systemic energy balance, we reasoned that LPL might be a component of pre-autonomic neurons involved in the regulation of hepatic metabolism. Here we show that despite obesity, mice with reduced neuronal LPL (NEXCreLPLflox [LPL KD]) show improved glucose tolerance and reduced hepatic lipid accumulation with aging compared to WT controls (LPLflox). To determine the effect of LPL deficiency on neuronal physiology, liver-related neurons were identified in the paraventricular nucleus (PVN) of the hypothalamus using the transsynaptic retrograde tracer PRV-152. Patch-clamp studies revealed reduced inhibitory post-synaptic currents in liver-related neurons of LPL KD mice. Fluorescence Lifetime Imaging Microscopy (FLIM) was used to visualize metabolic changes in LPL-depleted neurons. Quantification of the free vs. bound Nicotinamide Adenine Dinucleotide (NADH) and Flavin Adenine Dinucleotide (FAD) revealed increased glucose utilization and TCA cycle flux in LPL-depleted neurons compared to controls. Global metabolomics from hypothalamic cell lines either deficient in, or over-expressing, LPL recapitulated these findings. Our data suggest that LPL is a novel feature of liver–related preautonomic neurons in the PVN. Moreover, LPL loss is sufficient to cause changes in neuronal substrate utilization and function, which may precede changes in hepatic metabolism.

The study presents a mathematical modeling for supporting the premise that our body is composed of blockchain systems. A cell population is considered a space with peer-to-peer communication networks for applying blockchain protocol. Transaction is defined as gene expression that constantly occurs for protein synthesis in each cell. The transaction process proceeds according to the blockchain protocol with sharing and recording the data on the blockchain ledger. The premise comes from the clues of the previous research such that the cell population is a complex structured network system having cell-to-cell connections. Although individual cells exhibit stochastic nonlinear dynamic behavior, cell population shows consensus behavior that reaches to ensemble through interaction among cells. It is inferred that gene expression is not regulated by the corresponding cell only nor determined by external intelligence such as the brain. This is achieved through a consensus through stochastic interactions between cells over the whole cell population. These findings imply that mathematical blockchain modeling is a suitable for gene expression process. The original contribution of the study is a methodology for applying mathematically the blockchain protocol to the real biological gene expression process. In other words, the DNA sequence is converted into a numeral bit sequence that makes it possible to implement the blockchain protocol. A new DNA sequence scheme is proposed with adding methylated cytosine and adenine as the 5th and 6th bases for including epigenetic information which has profound effect on gene expression and regulation. The methodology was applied to the real biological synthesis process of protein samples. The protein is composed of amino acids that are encoded by triplet codons of 216 kinds with 6 base RNA sequence. The gene expression information is traced backward from a synthesized protein sample, amino acids of codons, RNA transcript up to DNA sequence. One of the results is a numeric value in the form of a bit sequence with which mathematical blockchain modeling is applicable. The cryptographic authentication and the consensus process are mathematically proven to work properly by the blockchain protocol. It implies that the same protein is synthesized, but with different epigenetic data, then protein's latent material properties will certainly be different. Although the result has not been justified by the biologic experiment yet, it is sure that the biological hidden algorithm inside DNA sequence will be revealed by the binary bit-logic with physical on/off states which is mathematically proven. The research will greatly contribute to disease treatment and medicine development as well as epigenetics in the future.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Autophagy is a cellular homeostatic process by which cells degrade and recycle their malfunctioned contents, and impairment in this process leads to Parkinson's disease (PD) pathogenesis. Dioscin, a steroidal saponin, has induced autophagy in several cell lines and animal models. The role of dioscin-mediated autophagy in PD remains to be investigated. Therefore, this study aims to investigate the hypothesis that dioscin-regulated autophagy and autophagy-related genes (ATG) could protect neuronal cells in PD via reducing apoptosis and enhancing neurogenesis. In this study, the 1-methyl-4-phenylpyridinium ion (MPP+) were used to induce neurotoxicity and impair autophagic flux in a human neuroblastoma cell line (SH-SY5Y). The result showed that dioscin pre-treatment counters MPP+-mediated autophagic flux impairment and alleviates MPP+-induced apoptosis by downregulating activated caspase-3 and Bax expression while increasing Bcl-2 expression. In addition, dioscin pre-treatment was found to increase neurotrophic factors and tyrosine hydroxylase expression, suggesting that dioscin could ameliorate MPP+-induced degeneration in dopaminergic neurons and benefit the PD model. Interestingly, the neuroprotective activities of dioscin were suppressed when co-treated with chloroquine (CQ), an autophagosome-lysosome inhibitor. In summary, we showed dioscin’s neuroprotective activity in neuronal SH-SY5Y cells might be partly related to its autophagy induction and suppression of the mitochondrial apoptosis pathway.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

Although many details remain still elusive, it became increasingly evident in recent years that mechanosensing of microenvironmental biophysical cues and subsequent mechanotransduction are strongly involved in the regulation of neuronal cell development and functioning. This review gives an overview about the current understanding of brain and neuronal cell mechanobiology and how it impacts on neurogenesis, neuronal migration, differentiation, and maturation. Therein; we are focussing particularly on the events in the cell/microenvironment interface and the decisive extracellular matrix (ECM) parameters (i.e. rigidity and nanometric spatial organisation of adhesion sites) that modulate integrin adhesion complex-based mechanosensing and mechanotransductive signalling. It will also be outlined how biomaterial approaches mimicking essential ECM features help to understand these processes and how they can be used to control and guide neuronal cell behaviour by providing appropriate biophysical cues. In addition, principal biophysical methods will be highlighted that have been crucial for the study of neuronal mechanobiology.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

: This Systematic Review focused on the use of Bifidobacterium infantis and Bifidobacterium breve in neurodegenerative diseases. The study of metabolomics has demonstrated the relationship between dysbiosis and neuronal death. The modulation of the gut microbiota in the intervention of these pathologies has become a promising area of study, showing beneficial results such as the improvement of synaptic function, the reduction of neuroinflammation, as well as the improvement of associated neuropsychiatric symptoms. Several studies have addressed the administration of Bifidobacterium infantis and Bifidobacterium breve, alone, in conjunction or in combination with other strains, in the intervention of neurodegenerative diseases. This Systematic Review includes the results of 17 research articles (n=5 in humans and n=12 in animal models) and the main result was that the use of these Bifidobacterium strains, alone, in conjunction or in combination with other strains, produced improvements in neurodegenerative disease-related symptomatology and degenerative cellular processes. However, some studies shown neutral results in certain variables. These data suggest that the use of these probiotics has a neuroprotective effect that may delay the progression of the disease. This research provides relevant and updated information on the use of these Bifidobacterium strains in neurodegenerative pathologies such as Alzheimer's disease and Parkinson's disease.

This study measures the noise levels in a baseball stadium and analyzes baseball fans’ attitude of effect of recreational noise exposure on their hearing. In the baseball stadium, noise levels were measured in four seating sections using a sound level meter during the games. The LAeq average of the 16 measures produced 91.7 dBA, showing a significantly high noise level in the red and navy sections. As a function of frequency by LZeq analysis, the noise levels were significantly higher in low frequencies than other frequencies. For the survey sample, 688 randomly selected participants completed a 16-question survey on their noise exposure during the game and on the potential risk of hearing loss. Despite the very high noise levels, 70% of the respondents preferred sitting in either the red or the navy section to be closer to the cheerleaders and to obtain a good view. Most respondents reported that they did not consider wearing earplugs, and one-third experienced hearing muffled speech after the game. We conclude that the noise levels in baseball stadiums are high enough to cause hearing damage and/or tinnitus later, but expect these results to improve public education regarding safe noise exposure during popular sports activities.

Two new dimacrolide sesquiterpene pyridine alkaloids (DMSPAs), dimacroregelines A (1) and B (2), were isolated from the stems of Tripterygium regelii. The structures of both compounds were characterized by extensive 1D and 2D NMR spectroscopic analyses, as well as HRESIMS data. Compounds 1 and 2 are two rare DMSPAs possessing unique 2-(3′-carboxybutyl)-3-furanoic acid units forming the second macrocyclic ring, representing the first example of DMSPAs bearing an extra furan ring in their second macrocyclic ring system. Compound 2 showed inhibitory effects on the proliferation of human rheumatoid arthritis synovial fibroblast cell (MH7A) at a concentration of 20 μM.

A model of a single-server queuing-inventory system (QIS) with catastrophes and a finite waiting room for consumer customers (c-customers) has been developed. When a catastrophe occurs, all inventory in the system's warehouse is destroyed, but c-customers in the system are still waiting for restocking. In addition to c-customers, negative customers (n-customers) are also taken into account, which displaces one c-customers (if any). The policy (?, ?) is used to replenish stocks. If upon arrival of the c-customer the inventory level is zero, then according to Bernoulli’s scheme this c-customer is either lost or joining the queue. The mathematical model of the studied QIS is constructed in the form of a two-dimensional continuous-time Markov chain (2D CTMC). Exact and approximate methods for calculating the steady-state probabilities of constructed 2D CTMCs are developed and closed-form expressions are derived for calculating the performance measures. The results of numerical experiments are presented, demonstrating the high accuracy of the developed approximate formulas, as well as the behavior of performance indicators depending on the reorder point. In addition, an optimization problem is solved to obtain the optimal reorder point to minimize the expected total cost.

High level of noise is a disturbance to the human environment. Noise in industries is also an occupational hazard because of its attendant effects on workers’ health. Noise presents health and social problems in industrial operations, and the source is related to the machineries used in the industries. One of the unique features of the noise associated with wood machinery is the level of exposure and duration. Equipment used in a factory can be extremely loud. They can produce noise at decibels high enough to cause environmental health and safety concerns. The mechanically driven transport and handling equipment, cutting, milling, shaping and dust extractor installations in the wood industry generate noise. The sources of noise pollution have increased due to non-compliance with basic safety practices. The increased use of locally fabricated machine in the industry has increased the level of noise and vibration. The effects of industrial noise pollution as discussed include: increase in blood pressure; increased stress; fatigue; vertigo; headaches; sleep disturbance; annoyance; speech problems; dysgraphia, which means reading/learning impairment; aggression; anxiety and withdrawal. As presented in this paper, noise control techniques include; sound insulation, sound absorption, vibration damping and Vibration isolation.

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and diseases. Several methods have emerged to pinpoint these influential entities in networks that are represented as graphs. In these graphs, nodes corre-spond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We categorize structural central-ity into two: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both mi-cro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson's correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the SIR model, portraying disease spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a nota-ble correlative efficacy across various real-world networks relative to other centrality metrics.

The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of scienThe Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.ce, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

Reviewed are recent efforts leading to chemo-enzymatic syntheses of strongly fluorescent ribosides of nucleobase analogs, potentially applicable in analytical biochemistry and cell biology. Described are syntheses and properties of fluorescent purine, 8-azapurine and etheno-purine ribosides, obtained using various types of the purine nucleoside phosphorylase (PNP) as catalysts of ribosylation, and -ribose-1-phosphate (r1P) as a second substrate. In several instances, the ribosylation sites were different than the canonical purine N9. Some of the obtained ribosides show fluorescence yields close to 100%. Their main applications are assays of PNP, nucleoside hydrolases, and other enzyme activities both in vitro and, possibly, within the living cells, using fluorescence microscopy.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x104 – 2x107 CFU per ml and has a sensitivity of S = 1.655x105 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10-9 grams (g) which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

In this paper, the mechanism of combined process of ion beam sputtering (IBS) and HF acid etching on the chemical structure defects of fused silica and its laser damage resistance perfor-mance were investigated. During the removal process of surface material, the sputtering effect causes lattice atoms to detach from their original positions, forming a large number of unsaturated structures on silica surface, which improves the chemical activity of Si and O atoms, accelerates the chemical reaction process between surface atoms and water molecules, increases the content of hydroxyl (OH-) groups in the shallow layer, and enhances the photothermal weak absorption intensity. However, the presence of OH- groups reduces the bonding strength of Si-O, destroys the spatial network structure of silica bulk, and reduces the mechanical strength of silica surface, resulting in a decrease in its laser damage resistance performance. The paper reveals for the first time the mechanism of IBS to change the structure characteristics of silica material, accelerate the surface hydroxylation process, and thereby reduces the laser damage resistance performance. This work provides technical guidance for effectively suppressing chemical structure defects on silica surface and improving laser damage resistance performance of optical components under high-flux laser irradiation.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

Neurologic disorders such as traumatic brain injury, multiple sclerosis, Alzheimer’s disease, and drug-resistant epilepsy have a high socioeconomic impact around the world. Current therapies for these disorders are often not effective. This creates a demand for the development of new therapeutic approaches to treat these disorders. Recent data suggest that autoreactive naturally occurring immunoglobulins produced by subsets of B cells called B1 B cells combined with complement are actively involved in the processes of restoration of neuronal functions during pathological conditions and remyelination. The focus of this review is to discuss the possibility of creating specific therapeutic antibodies that can activate and fix complement to enhance neuronal survival and promote central nervous system repair after injuries associated with many types of neurodegenerative diseases.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a to a mix between noise-induced auditory lesions and energetic masking of speech signal. For years, the monitoring of conventional audiometric thresholds has been the usual way to check and preserve the auditory function. But the highlighting of supra-threshold deficits, notably speech in noise understanding difficulties, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise under-standing in order to suggest a new way of regular monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modula-tion detection thresholds, conventional and extended high frequency audiometric thresholds) variables were collected in a population of individuals presenting a relative homogeneous occu-pational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict scores of three different speech in noise tests and a self-report of speech in noise ability. The extended high frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise exposed professionals.

Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, the analysis covers the theoretical and practical aspects of sound attenuation in a turboengine testing stand. This paper presents a preliminary analysis and also an evaluation of a procedure for projecting a noise attenuator for industrial application and especially for turboengine test stands. While primarily focusing on the static acoustic behavior of the attenuator, considerations were also made regarding flow dynamics, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective in selecting the optimal design configuration. The study investigates different calculation methods for assessment of the noise reduction for linear and staggered baffles, applied on a scaled reduced model of attenuator. Thus, the critical parameters and projecting requirements necessary for effective noise reduction in high-performance turboengine testing environments will be evaluated in a down-scaled model. Key factors examined include the selection of design parameters and configurations from various options. Advanced computational methods, like analytic and finite element analysis (FEM) are utilized to predict the acoustic performance and identify potential design optimizations. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The findings indicate that a well-designed sound attenuator module can significantly reduce noise levels without compromising the operational performance of the turboengine inside a test cell.

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

Aural rehabilitation with hearing aids can decrease the attentional requirements of cognitive resources by amplifying the deteriorated-frequency sound in hearing loss patients and improve the auditory discrimination ability like speech-in-noise perception. As aural rehabilitation with an intelligible-hearing sound also can be hopeful, the aim of this study was to evaluate the effectiveness of the aural rehabilitation with intelligible-hearing sound for hearing loss patients. Adult native Japanese speakers (17 males and 23 females, 68.43±9.23 years) showed over 30 dB at 125, 250, 500, 1000, 2000, 3000, 4000, 8000, 10000, and 12000Hz on any side, were recruited and underwent Mini-Mental State Examination Japanese. we conducted self-evaluation questionnaire for hearing problems of voice, gap detection test, fast speech test, and speech-in-noise test, pure tone audiogram, speech perception test using Japanese 67-S, cortical auditory evoked fields, and magnetic mismatch negativity before and after the non-intelligible-hearing (N=20) and intelligible-hearing (N=20) sound therapy with listening to music for one hour a day for 35 days. Better hearing ear was defined using a four-frequency pure-tone average at the thresholds of 500, 1000, 2000 and 4000 Hz. After the sound therapy, speech-in-noise test with signal to noise ratio +10 in better hearing ear showed significant improvement (p&lt;0.05), and N1m-P2m amplitudes showed the significant increase in Lt superior temporal gyrus at the stimulus from better hearing ear (p&lt;0.05). The significant enhancement of the magnetic mismatch negativity amplitude at Lt superior temporal gyrus was exhibited after the sound therapy (p&lt;0.01). Intelligible-hearing sound therapy can improve the ability of speech-in-noise perception in better hearing ear and the enhancement of central cortex response, which reflect the ability of working memory, was proved by cortical auditory evoked fields and magnetic mismatch negativity. Intelligible-hearing sound therapy can be a valuable aural rehabilitation method for sensory neural hearing loss as same as hearing aids.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables and cable sag determination. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of OPL cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly and, respectively, the calculation by the alternative method, which uses another group of sensors, to generate both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application. The main modules of the application software are: the software module for reading quantities from the sensor network through a ModBUS Input/Output (I/O) server, the software module for real-time calculation of the OPL cable sag, the software module for read/write operations based on MySQL type data, the software module for transferring data to the user's intranet through a web server, and the software module for viewing data by authorised users based on user and password through an IoT server. The development software environments used are Matlab and Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Research into the causes of neurotoxicity in Alzheimer’s Dementia (AD) has focused on neurofibrillary tangles and beta amyloid (Aβ) plaques. This paper proposes the heterodox theory that these hallmarks of AD are the visible effects, not direct causes of neuronal necrosis. Rather AD results from a combination of age-induced, disproportional decline in physiological support for aerobic metabolism, and dysregulation of the sleep cycle processes. The hypothesis is that the decimation of neurons in AD results from a combination of neurotoxicity and increased apoptosis caused by: 1. direct damage from toxic waste products of anaerobic glycolysis due to a progressive decline in the capacity of neurons to perform oxidative phosphorylation (OXPHOS) and an increased reliance on anaerobic glycolysis to meet metabolic needs; 2. impaired cellular repair and effluent release due to dysregulation of non-rapid eye movement (NREM) sleep allowing damage to cell membranes and synaptic junctions to accumulate inducing a chronic inflammatory response; 3. indirect damage from products produced by inflammatory reaction to toxic metabolites; 4. neuronal apoptosis from the AβPP-mediated pathway due to the age-induced decline of growth hormone (GH), GH-releasing hormone (GHRH) and insulin-like growth factor (IGF).

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

Multidrug resistant organisms (MDROs) outbreaks have been steadilyincreasing in Intensive care units (ICUs). Still, healthcare institutions and workers (HCW) reached no unanimity on how and when implementing infection prevention and control (IPC) strategies. We aimed to provide a pragmatic, physician practice-oriented, resume of strategies towards different MRDOs outbreaks in ICU. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios, education, isolation, decolonization, screening and hygiene practices, outbreak reporting, cost-effectiveness, reproduction-number(R0), and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician losing the big picture. IPC strategies proved their cost-effectiveness regardless typology, country, or pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, a rapid outbreak recognition tool elaboration (i.e., an easy-to-use mathematical model) would improve early diagnosis and spreading prevention. Further studies are needed to express in favor or against MDROs decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategies application has made, and still makes ICUs a major MDROs reservoir into the community. In a not-too-distant future genetic engineering and phage therapies could represent a plot-twist in MDROs IPC strategies.

Microsporidia are intracellular parasites infecting a wide range of vertebrates and invertebrates. In bees, they cause a diarrheal disease called nosematosis, which represents a growing problem contributing to the decline of bee colonies. This parasitic disease infects the gastrointestinal tract of bees, disrupts their immune system, weakens them, and shortens their lifespan. There are two known species of this microsporidial parasite with a global distribution: Vairimorpha apis and Vairimorpha ceranae, with current research indicating that the species V. ceranae is gradually replacing V. apis. In this study, we focused on the detection and occurrence of Vairimorpha spp. in bee colonies kept in different environments, as well as on comparing the impact of hive location on its occurrence. The methods used included sampling from winter bee carcasses, microscopic examination of bee gut contents, and subsequent molecular duplex PCR analysis. Altogether, we examined 82 hives located at 4 different sites and focused on the impact of transportation as an abiotic factor disturbing bees leading to the development of Vairimorpha spp. disease. The results indicate an increased occurrence of this parasite in hives located in urban sites and hives situated in intravillage locations, which may be attributed to transportation and associated vibrations as an abiotic stress factor. Detection revealed that all samples were positive for Vairimorpha ceranae.

Despite the recognized influence of body mass on combat techniques, the relationship between body segment mass (BSM) and combat moves remains unexplored. This study aimed to examine the relationship between striking arm mass (SAM), kicking leg mass (KLM), and body mass (BM) with the dynamic forces of the direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and countermovement jumps (CMJ) performance. Sixteen male military cadets (22.3 ± 1.8 years, 181.4 ± 7.0 cm, 82.1 ± 8.5 kg) performed combat techniques, with performance measured by using a force plate and body segment mass assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis, the Wilcoxon test, and Cohen's d were applied. Results indicated the relationship between KLM or BM and FK impulse (r = 0.64, p = 0.01; r = 0.52, p = 0.04, respectively) and CMJ impact force (r = 0.80, p ≤ 0.01; r = 0.70, p ≤ 0.01, respectively). Moreover, FK peak and impact forces were moderately correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01). The highest relationship was found between KLM and FK impulse; however, the difference in variability explained by KLM versus body mass is only 12%. This suggests that knowledge of BSM did not provide a significantly better estimate of the dynamic forces of the punches and FK than BM.

In recent years, the prevalence of tobacco and electronic cigarette (e-cigarette) use among adolescents has raised significant public health concerns worldwide. This study aims to investigate respiratory symptoms among Polish adolescents. We conducted an online survey among Polish school students from all provinces, collecting data over two months in Spring 2021. Students voluntarily complete the anonymous survey, answering questions about respiratory symptoms, smoking habits (both traditional and electronic cigarettes), and demographic information. Analysis focused on four subgroups: non-tobacco users, traditional cigarette smokers, e-cigarette users, and dual users, totaling 10,388 pupils aged 12-18 years, predominantly attending secondary technical and comprehensive schools. A total of 10388 pupils participated in the study, 55.6 % (5778) of whom were girls and 44.4 % (4610) boys. Adolescents, who admitted using both e-cigarettes and traditional cigarettes experienced more frequent episodes of cough during the day (39.70%) and at night (18.40%) compared to their peers in other groups. Chest discomfort, including pain and pressure, was also reported more often by adolescents, who used e-cigarettes and traditional cigarettes concurrently (27.60%) compared to their peers in other groups. Chest pressure was experienced less commonly by non-smoking adolescents (14.40%) than by smokers (18.90%). Higher severity of cough during the day and at night was observed in the group of adolescents using traditional cigarettes and e-cigarettes concurrently compared to the other groups. The adolescents in the dual user group experienced more severe dyspnoea and wheezing compared to the other groups included in the comparison. The results of this study confirm the correlation between the occurrence of respiratory symptoms in adolescents who smoke cigarettes, use e-cigarettes and in dual users. The respiratory symptoms occur most frequently and are the most severe in the group of adolescents who use e-cigarettes and traditional cigarettes

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

Steel surface protection with hard coatings is essential in the metalworking industry, yet developing high-performance hard coatings still remains a challenge. Quaternary nitride coatings, particularly TiAlSiN, have been extensively studied, but commercial TiAlSiN coatings vary widely, and there is a lack of consistent data. This study presents findings on the development and evaluation of TiAlSiN and TiAlSiN/TiN thin films, with an 800 nm thick TiAlSiN top coating and a 100 nm thick TiN mid-coating. The films were deposited on C120 tool steel discs via reactive DC magnetron sputtering. Ti and TiAlSi 75–20–5 (at.%) disc-shaped targets (50.8 mm diameter × 6.3 mm thickness), fabricated by our team through spark plasma sintering, were used for the sputtering process. Coatings were grown on smooth steel substrates heated to 300 °C, resulting in a uniform micro-structure with nanoparticles of globular (TiN) or pyramidal shape (TiAlSiN), as evidenced by SEM analysis. Nanoindentation tests showed superior mechanical properties for all coatings compared to the steel substrate. Micro-scratch tests indicated better scratch resistance for TiAlSiN/TiN bilayer (BL) coatings than for TiAlSiN single-layer (SL) coatings. Electrochemical impedance spectroscopy tests in a 3.5 wt.% NaCl solution revealed that the BL coatings demonstrated superior corrosion resistance compared to the SL coatings, even after thermal aging at 800 °C for 1 hour. The superior properties of the synthesized TiAlSiN/TiN films recommend them as protective coatings for tool steel surfaces.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

Capsinoids (CAP) are potential antioxidant agents capable of reducing oxidative damage and the resulting complications triggered by obesity. Thus, the aim of this study was to investigate the effects of CAP on adiposity and biomarkers of cardiac oxidative stress in rats with obesity induced by a high-fat diet. Male Wistar rats were exposed to a high-fat diet for 27 consecutive weeks. After characterization of obesity (week 19), some of the obese (Ob) animals began to receive capsinoids (10 mg/kg/day) by orogastric gavage. Adiposity and comorbidities were assessed. In the heart, remodeling, injury and biomarkers of oxidative stress were determinated. The treatment did not reduce the adiposity induced d by obesity, but they were efficient in reducing cholesterol levels. CAP treatment did not cause a difference in heart and LV mass, despite having reduced troponin I concentrations. Furthermore, capisinoids did not reduce the increase in advanced oxidation of proteins products (AOPP) and carbonylated proteins (CBO) caused by obesity in cardiac tissue. Besides that, it was observed that ObCap rats presented high levels of malondialdehyde and greater activity of antioxidant enzymes compared to Ob. In conclusion, treatment with capsinoids did not lead to significant positive effects in reducing obesity-induced oxidative stress.

Traumatic spinal cord injury (SCI) elicits an acute inflammatory response which comprises numerous cell populations. It is driven by the immediate response of macro-phages and reactive M1 microglia, which triggers activation of genes responsible for the dysregulated microenvironment within the lesion site and in the spinal cord parenchyma immediately adjacent to the lesion. Recently published data indicate that microglia induces astrocyte activation and determines the fate of astrocytes. Conversely, astrocytes have the potency to trigger microglial activation and control their cellular functions. Here we review current information about the release of diverse signaling molecules (pro-inflammatory vs anti-inflammatory) in individual cell phenotypes (microglia, astrocytes, blood inflammatory cells) in acute and subacute SCI stages, and how they contribute to delayed neuronal death in a the surrounding spinal cord tissue which is spared and functional but reactive. In addition, temporal correlation in progressive degeneration of neurons and astrocytes and their functional interactions after SCI are discussed. Finally, the review highlight the time-dependent transformation of reactive mi-croglia (M1) and astrocytes (A1) into their neuroprotective phenotypes (M2a, M2c and A2) which are crucial for spontaneous post-SCI locomotor recovery. We also provide sug-gestions on how to increase functional outcome after SCI and discuss key therapeutic approaches.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

The medicinal herb Centaurium erythraea is well-known for its natural ability to treat digestive issues like dyspepsia and liver insufficiency by promoting the secretions of the liver and esophagus. This study aimed to analyze the effectiveness of Centaurium erythraea extracts in treating diabetes. Decoction and Soxhlet procedures were used to examine the extracts, with water serving as the solvent. The extracts were tested for antioxidant capacity, antimicrobial effects, and inhibitory effects on alpha-amylase activity. In comparison to the aqueous extract, the decocted extract had higher levels of polyphenols, total tannins, and anthocyanins, as well as more effective antioxidant capacities. Due to its reduction of alpha-amylase activity and enteric absorption of glucose, Centaurium erythraea has also shown considerable anti-hyperglycemic action.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

Consumption of ultra-processed foods is associated with abdominal obesity, but this association has not yet been investigated in individuals on hemodialysis. This cross-sectional study, developed with 1014 individuals, aimed to analyze the prevalence of abdominal obesity and its association with the consumption of ultra-processed foods in individuals on hemodialysis. Consumption of ultra-processed foods was measured using a questionnaire, whose list of foods was based on the Surveillance System of Risk and Protective Factors for Chronic Non-Communicable Diseases through Telephone Enquiry. Waist circumference was used to determine the presence of abdominal obesity. The difference in proportions between the independent variables and the outcome was verified using Pearson's chi-square test. Hierarchical logistic regression was used to determine the association between the outcome and the independent variables. The prevalence of abdominal obesity was 77.22%. In the final model, higher consumption of ultra-processed foods, be male and an adult, have between 8 and 11 years of schooling and practice physical activity below the recommended level increased the chances of having abdominal obesity. The research provides a basis for the creation and improvement of public policies that promote improvements in these health determinants.

The Active Distribution Networks (ADNs) are Multi-Input Multi-Output (MIMO) systems with coupled dynamics which cause interactions among the control loops of the Distributed Energy Resources (DERs). This undesired effect lead to performance degradation in the voltage control. To mitigate the effects of these unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In details, each DER is equipped with a double loop PI to control the active and reactive powers output by the Voltage Source Converter (VSC) which connects the DER to the network's node. Furthermore, to guarantee Ancillary Services (ASs), the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a two sequential steps procedure based on the (Internal Model Control) IMC technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in presence of model parameter uncertainty in the MIMO model.

The microtubule-associated protein TAU is sorted into the axon in healthy brain neurons. Somatodendritic missorting of TAU is a pathological hallmark of many neurodegenerative diseases called tauopathies, including Alzheimer’s Disease (AD). Cause, consequence, and (patho)physiological mechanisms of TAU sorting and missorting are understudied, in part also due to the lack of readily available human neuronal model systems. The human neuroblastoma cell line SH-SY5Y is widely used for studying TAU physiology and TAU-related pathology in AD and related tauopathies. SH-SY5Y cells can be differentiated into neuron-like cells (SH-SY5Y-derived neurons) using various substances. This review evaluates whether SH-SY5Y-derived neurons are a suitable model for i) investigating intracellular TAU sorting in general, and ii) with respect to neuron subtype-specific TAU vulnerability. I) SH-SY5Y-derived neurons show pronounced axodendritic polarity, high levels of axonally localized TAU protein, expression of all six major human brain isoforms, and TAU phosphorylation similar to the human brain. As proliferative cells, SH-SY5Y cells are readily accessible for genetic engineering, stable transgene integration and leading-edge genome editing are valuable and promising tools for TAU-related studies. II) Depending on the used differentiation procedure, SH-SY5Y-derived neurons resemble cells of distinct subcortical nuclei, i.e. the Locus coeruleus (LC), Nucleus basalis (NB) and Substantia nigra (SN), all of which early affected in many tauopathies. This allows to analyse neuron-specific TAU isoform expression and intracellular localization, also in the context of vulnerability to TAU pathology. Limitations are e.g. the lack of mimicking age-related tauopathy risk factors and the difficulty to define the exact neuronal subtype of SH-SY5Y-derived neurons. In brief, this review discusses the suitability of SH-SY5Y-derived neurons for investigating TAU (mis)sorting mechanisms and neuron-specific TAU vulnerability in disease paradigms.

Highly-doped graphene samples show the conductance reduced and the shot-noise power enhanced compared to standard ballistic systems in two-dimensional electron gas. These features can be understood within a model assuming incoherent scattering of Dirac electrons between two interfaces separating the sample and the leads. Here we find, by adopting the above-mentioned model for the edge-free (Corbino) geometry and by means of the computer simulation of quantum transport, that another graphene-specific feature should be observable when the current flow through a doped disk is blocked by high magnetic field. In case the conductance drops to zero, the Fano factor approaches the value of $F\approx{}0.56$, with a very weak dependence on the disk radii ratio. The role of finite source-drain voltages and the system behavior upon tuning the electrostatic potential barrier from a rectangular to parabolic shape are also discussed.

This study explores the implementation of the ANSI/373: Sustainable Production of Natural Dimension Stone standard within the North American dimension stone industry to identify driving forces behind the initiative and consider potential outcomes as it advances. The method involves a thorough review of ANSI/373, comparing it with voluntary sustainability initiatives (VSIs) in the forest industry to assess its effectiveness and challenges, and semi-structured interviews with contacts in the dimension stone field. Key drivers such as corporate social responsibility and market pressures influence the adoption of sustainability standards. The results highlight the embedded environmental considerations within the emerging dimension stone standards and their implications for sustainable practices in the industry. While the ANSI/373 standard promotes sustainable awareness and practices, it faces challenges in certification credibility and industry acceptance, particularly due to inconsistency among firms in their response, rigorous certification demands, and skepticism about certification credibility. The research advocates for enhanced collaborative frameworks to increase the standard’s adoption and impact, suggesting that industry stakeholders prioritize showcasing dimension stone as a practical, environmentally considerate material.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

Polymer inclusion membranes (PIMs), which contained cellulose tri-acetate used as a polymer base, a plasticizer in the form of o-NPOE, and meso-tetra methyl tetrakis-[methyl-2-(4-acetlphenoxy)] calix[4]pyrrole (KP) used as a carrier, were prepared and tested for the effective purification of aqueous solutions of methylene blue dye. The scanning electron microscope (SEM), thermogravimetric analysis (TGA), and attenuated total reflection Fourier transform infrared (ATR-FTIR) were used to define the microstructure and surface of PIMs. Experimental results showed that with the increased concentration of methylene blue in an aqueous solution, the removal percentage also increased. Further observation also showed that the flux increased with the rise of the source phase pH values from 3 to 10. The carrier and plasticizer content in the membrane significantly influenced the membrane’s transport properties. The optimal composition of the membrane in per cent by weight was for KP: 74% plasticizer; 18% support; 8% carrier. It was shown that the membrane with optimal composition showed good reusability and enabled the easy and spontaneous separation of methylene blue from aqueous solutions.

Diarrhea is a common occurrence following vascular organ transplantation, including kidney transplants. It can be induced by non-infectious factors, adverse effects of immunosuppressive drugs, or infectious agents such as viruses, bacteria, fungi, or parasites. Intestinal protozoan parasites, include Cryptosporidium spp., are causes of infectious diarrhea particularly in immunocompromised patients. This review aims to summarize reports on epidemiology, pathogenesis, clinical presentation, diagnosis, and treatment of Cryptosporidium spp. infection in adult patients, particularly following kidney transplantation.

Diabetes is a major health concern that the next-generation of on-demand insulin releasing implants may overcome for personalized therapy. Therein, 3D-printed phenylboronic acid-containing subcutaneous implants with on-demand glucose-triggered drug release abilities are produced using high resolution stereolithography technology. To that end, the methacrylation of phenylboronic acid is targeted following a two-step reaction. The resulting photocurable phenylboronic acid derivative is accordingly incorporated within bioinert polyhydroxyethyl methacrylate-based hydrogels at varying loadings. The end result are sub-centimeter scaled 3D-printed bionert implants that can be remotely activated with 1,2-diols and 1,3-diols such as glucose for on-demand drug administration such as insulin. As a proof of concept, varying glucose concentration from hypoglycemia to hyperglycemia levels readily allow the release of pinacol, i.e. a model molecule with similar release properties than insulin, at respectively low and high rates. In addition, the results demonstrated that adjusting the geometry and size of the 3D-printed part is a simple and suitable method for tailoring the release behavior and dosage.

The present paper is aimed at presenting an updated review of prediction methods for the aerodynamic noise of ducted rotor-stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and increasingly used, for both aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced, from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered as the best suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons.

Cybercrime Threat Intelligence (CTI) allows us to change our actions from reactive to proactive in the fight against threat actors, and to make more informed, data-driven security decisions. Thus this study proposes the practical implementation of CTI in the Arab world. This study focuses on strengthening CTI by integrating Indicators of Compromise (IoCs) and collecting realistic security alerts from honeypot systems and open source intelligence. The collected data is stored in the Malware Information Sharing Platform (MISP), an open source platform that allows users to create and share IoCs with other organizations while staying informed about new threats. It features an intuitive interface for data analysis and threat identification, facilitating sharing, storage, and correlation of IoCs. Therefore, we leveraged MISP to generate IoCs based on the collected data and analyzed the results to identify potential cyber threats. The implemented platform aims to provide organizations with actionable information to prevent, detect and respond to cybercrime. This study presents a practical approach to strengthening CTI in the Arab world and provides an opportunity to strengthen the region’s security posture.

IgE-mediated wheat allergy is a gluten-related disorder. Wheat is one of the five most common food allergens in children. However, the natural history of IgE-mediated wheat allergy has seldom been described in the research literature. This study presents the current state of knowledge about the IgE-mediated wheat allergy in children.

The insulin-regulated aminopeptidase (IRAP; oxytocinase) is part of the M1 aminopeptidase family and is highly expressed in many tissues, including the neocortex and hippocampus of the brain. IRAP is involved in various physiological functions and has been identified as a receptor for the endogenous hexapeptide Angiotensin IV (Ang IV). Binding of Ang IV inhibits the enzymatic activity of IRAP and has proven to enhance learning and memory in animal models. The macrocyclic compound 9 (C9) is a potent synthetic IRAP inhibitor developed from the previously reported inhibitor HA08. In this study, we have examined compound C9 and its effects on cognitive markers drebrin, microtubule-associated protein 2 (MAP2), and glial fibrillary acidic protein (GFAP) in primary hippocampal and cortical cultures. Cells from Sprague Dawley rats were cultured for 14 days before treatment with C9 for 4 consecutive days. The cells were analysed for protein expression of drebrin, MAP2, GFAP, glucose transporter type 4 (GLUT4), vesicular glutamate transporter 1 (vGluT1), and synapsin I using immunocytochemistry. The gene expression of related proteins was determined using qPCR, and viability assays were performed to evaluate toxicity. The results showed that protein expression of drebrin and MAP2 was increased, and the corresponding mRNA levels were decreased, after treatment with C9 in the hippocampal cultures. The ratio of MAP2 positive neurons and GFAP positive astrocytes was altered in both hippocampal and cortical cultures, and there were no toxic effects observed. In conclusion, IRAP inhibitor compound C9 enhance the expression of pro-cognitive markers drebrin and MAP2 which further confirms IRAP as a relevant pharmaceutical target and C9 as a promising candidate for further investigation.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Named Entity Recognition (NER) serves as a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries.To address these challenges, a corpus, labeled datasets and a lexicon specific to pig diseases for Chinese named entity recognition were constructed. Subsequently, a Pig Disease Chinese Named Entity Recognition (PDCNER) model was proposed. The model integrates external lexicon knowledge of pig disease by employing Lexicon-enhanced BERT and enhance feature representation by incorporating contrastive learning. Experimental results show that the model achieved the best recognition results, with a precision of 86.92%, a recall of 85.08%, and an F1-score of 85.99% respectively. Furthermore, the model exhibits robustness and generalizability across few-shot and publicly available datasets. Experimental results illustrate the proposed model could effectively identify Chinese named entities of pig diseases, outperforming several existing baseline methods. Moreover, the proposed model can be extended to other animal disease domains, such as chicken and cattle, thereby facilitating seamless adaptation for named entity identification across diverse contexts.

Zinc is a highly abundant cation in the brain, where it is essential for cellular function, including transcription, enzymatic activity, and cell signaling. However, zinc can also trigger injurious cascades in neurons, contributing to the pathology of neurodegenerative diseases. Mitochondria, critical for meeting the high energy demands of the central nervous system (CNS), are a principal target of the deleterious actions of zinc. An increasing body of work suggests that intracellular zinc, can, under certain circumstances, contribute to neuronal damage by inhibiting mitochondrial energy processes, including dissipation of the mitochondrial membrane potential, leading to ATP depletion. Additional consequences of zinc-mediated mitochondrial damage include reactive oxygen species (ROS) generation, mitochondrial permeability transition, and calcium deregulation. Zinc can also induce mitochondrial fission, resulting in mitochondrial fragmentation, as well as inhibition of mitochondrial motility. Here, we review the known mechanisms responsible for the deleterious actions of zinc on the organelle, within the context of neuronal injury associated with neurodegenerative processes. Elucidating the critical contributions of zinc-induced mitochondrial defects to neurotoxicity and neurodegeneration may provide insight into novel therapeutic targets in the clinical setting.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Background/Objectives: It is known that balance disorders involve occupational hazards. However, the inverse relationship (between certain occupations and an increased incidence of vertigo or dizziness) has been scarcely studied. The objective of this work was to analyze the occupation of a group of patients with vertigo, compared to economically active general population, and to evaluate the prevalence of occupational noise and/or vibration-exposure in both groups. Methods: A cross-sectional, observational, case-control study was carried out including 461 patients (220: Meniere’s disease; 73: vestibular migraine; 28: vestibular neuritis; 140: BPPV) (310 women and 151 men). These patients were compared to a control group from the general population, obtained from 6th EWCS-Spain (2015). Possible differences regarding sex, age, occupation, exposure to noise, and exposure to mechanical vibrations were analyzed. Results: Differences in distribution of occupations between patients with vertigo and general population were observed (Chi-square, p=6.142 e-30). Patients with vertigo were significantly more exposed to noise (p=8.19 e-12; OR=2,127, CI95% (1.720;2.630)) and vibrations (p=1.11 e-12; OR=2.288, CI95% (1.835;2.853)) than the control group. These differences were observed both between men and women. Conclusions: A relationship between occupational exposure to noise and/or vibrations and the presence of vertigo was observed. Protective and preventive measures could help prevent the occurrence of some diseases involving vertigo.

Walls-like structured monoclinic WO3 thin films were obtained by spray deposition method for further integration in gas sensors. For sensing applications, it is very important to achieve a high surface-to-volume ratio surface together with a proper thin films structure. The present communication reports on the effect of the sprayed solution volume variation (as a thickness variation element) on the detailed Raman spectroscopy for WO3 thin films with different thicknesses grown from precursor solutions with two different concentrations. Detailed analysis of the two series of samples shows that the growing thickness strongly affects the films morphology while their crystalline structure is only slightly affected. The Raman analysis contribute to refining the structural features clarifications. It was observed that, for both concentrations, the increase in thickness seems to slightly affect the stoichiometry of material and, in association with surface morphology variations, provide new possibilities for fine tuning of films properties towards enhanced as sensing applications. Further studies are ongoing.

Obesity has increasingly become a worldwide epidemic, as demonstrated by epidemiological and clinical studies. Obesity may lead to development of a broad spectrum of cardiovascular diseases (CVDs), such as coronary heart disease (CHD), hypertension, heart failure (HF), cerebrovascular disease, atrial fibrillation (AF), ventricular arrhythmias, and sudden cardiac death (SCD). In addition to hypertension, there are other cardiometabolic risk factors (CRFs) such as visceral adiposity, dyslipidemia, insulin resistance, diabetes, elevated levels of fibrinogen, and C-reactive protein and others, all of which increase the risk of CVD events. Mechanisms involved between obesity and CVD mainly include insulin resistance, oxidative stress, inflammation, and adipokines dysregulation, which cause maladaptive structural and functional alterations of the heart, particularly left ventricular (LV) remodeling and diastolic dysfunction. Natural products of plants, provide a diversity of nutrients and different bioactive compounds, including phenolics, flavonoids, terpenoids, carotenoids, anthocyanins, vitamins, minerals, fibers, and others, which possess a wide range of biological activities including antihypertensive, antilipidemic, antidiabetic, and other activities, thus conferring cardiometabolic benefits. In this review, we discussed the main therapeutic interventions using extracts from herbs and plants in preclinical and clinical trials with protective properties targeting CRFs. Molecular mechanisms and therapeutic targets of herbs and plants extracts for the prevention and treatment of CRFs are also reviewed.

The demand for improved service in mobile networks is always increasing. That means signal transmission with higher capacity is required. To achieve that goal a combined optical and wireless link using double polarization multiplexing has been developed. A high quality signal transmission is achieved by applying high polarization extinction ratio. The new concept is validated by experiments varying the bit rate and fiber length. A signal with 12 Gbit/s bit rate was transmitted over a 25 km long combined link with about 1.10-8 bit error rate. That result is much better than the already published data which were measured on links transmitting information with 2.5 Gbit/s bit rate. The polarization multiplexing technique is very practical for capacity enhancement in a specific section of an existing optical fiber by inserting a new link into it without deploying a new fiber.

During embryonic development stem cells undergo the differentiation process so that they can specialise for different functions within the organism. Complex programs of gene transcription are crucial for this process to happen. Epigenetic modifications and the architecture of chromatin in the nucleus, by the formation of specific regions of active as well as inactive chromatin, allow the coordinated regulation of the genes for each cell fate. In this mini review, we discuss the current knowledge regarding the regulation of three-dimensional chromatin structure during neuronal differentiation. We also focus on the role played in neurogenesis by the nuclear lamina that ensures the tethering of the chromatin to the nuclear envelope.

Introduction: This review explores the literature and presents institutional cases of concurrent robotic multisite surgery—in particular, robot-assisted radical prostatectomy (RARP) and robot-assisted partial nephrectomy (RAPN) for synchronous prostate and kidney cancers. We highlight dual-console synchronous robotic procedures, emphasizing the unique docking tech-nique developed. Aim: The aims of the study were to evaluate the feasibility, safety, and clinical outcomes of concurrent RARP and RAPN for synchronous prostate cancer and small renal tumors, as well as other concurrent robotic multisite surgeries, and to compare institutional findings with the existing literature. Materials and Methods: We describe the unique docking technique used in our procedures, which facilitates atraumatic transitions between surgeries using the same port sites. Additionally, eligible studies on concurrent robotic multisite surgery that were published in English were retrieved from the MEDLINE, Embase, and Cochrane Library databases. Data on operative time, blood loss, complications, and postoperative outcomes were extracted. Results: Eight cases from our institution demonstrated the feasibility and safety of the proposed approach, which is consistent with previous reports. The unique docking technique and dual-console setup enhance surgical precision, offering significant benefits in patient outcomes. As part of the litera-ture review, nine retrospective studies involving 40 cases of concurrent RARP and RAPN were reviewed. Additionally, eleven retrospective studies involving 392 cases of concomitant RARP and robotic transabdominal preperitoneal inguinal hernia repair (RTAPPIHR) were analyzed. Conclusions: Concurrent RARP and RAPN for synchronous prostate and kidney tumors is a viable and effective approach. Our early experience suggests that this and other combined procedures can optimize patient care and reduce costs. Prospective trials are needed to validate these findings and further refine the techniques.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Global warming and the associated increasing frequency of heatwaves are on the rise leading to numerous health complications. Diabetic patients are more vulnerable to heat exposure than healthy individuals, owing to their impaired sweating ability, however, little is known about how heat exposure may impact the function of skin cells, commonly keratinocytes. Our recent report provided the first proof-of-concept revealing that the metabolic composition of sweat EVs is associated with metabolic changes in healthy individuals and type 2 diabetes patients. Given this and the shared cellular proprieties of eccrine sweat glands with keratinocytes, we questioned whether the metabolic changes in sweat EVs may mirror the responses of keratinocytes upon heat exposure. To this, we exposed HaCat, which were maintained in low (mimicking healthy condition) and high glucose (mimicking diabetes condition) to acute (90 min), chronic (48 h), and repetitive heat exposure at 40 ⁰C. Through conducting a set of experiments including a targeted metabolomics approach, Greiss assay, western blotting analysis, and electron microscopy studies. The data demonstrate that heat exposure results in substantial changes of metabolite levels belonging to amino acids, pyrimidine metabolism, and glycolysis pathways in high glucose maintained HaCat cells when compared to those noted in low glucose condition. Exploring the molecular mechanisms through which heat exposure may impact the metabolic composition in keratinocytes, our discoveries highlight nitric oxide (NO) as a novel biomarker that the level is correlated with heat duration and frequency. Furthermore, data reveal that the HSP-70 protein levels and mitochondrial morphology are consequently altered. This study provides evidence of shared metabolic alterations in keratinocytes and sweat EVs collected from clinical study and proposes, for the first time, NO as a novel biomarker for monitoring changes in health and diabetes upon heat exposure.

The brain, rather than being homogeneous, displays an almost infinite topological genus, because is punctured with a very high number of topological vortexes, i.e., .e., nesting, non-concentric brain signal cycles resulting from inhibitory neurons devoid of excitatory oscillations. Starting from this observation, we show that the occurrence of topological vortexes is constrained by random walks taking place during self-organized brain activity. We introduce a visual model, based on the Pascal’s triangle and linear and nonlinear arithmetic octahedrons, that describes three-dimensional random walks of excitatory spike activity propagating throughout the brain tissue. In case of nonlinear 3D paths, the trajectories in brains crossed by spiking oscillations can be depicted as the operation of filling the numbers of the octahedrons in the form of “islands of numbers”: this leads to excitatory neuronal assemblies, spaced out by empty area of inhibitory neuronal assemblies. These procedures allow us to describe the topology of a brain of infinite genus, to assess inhibitory neurons in terms of Betti numbers, and to highlight how non-linear random walks cause spike diffusion in neural tissues when tiny groups of excitatory neurons start to fire.

The mutation in NF2 is the most common alteration associated with meningioma oncogenesis, and it is related to the loss of a suppressing protein called merlin. At the same time, alterations in energy production are visible in cancer cells where increased demand for energy is observed. Fatty acid oxidation could be one of the ways cancer cells obtain energy. This metabolic pathway uses the acylcarnitine shuttle system, which is responsible for the acylation of fatty acids and their transport through mitochondria. Therefore, this study aimed to profile acylcarnitines with short-, medium- and long-acyl chain length in meningiomas to assess their changes in tumors with different NF2 mutation statuses. For the analysis, solid-phase microextraction (SPME) coupled with liquid chromatography high resolution mass spectrometry (LC-HRMS) was used. The presented sampling method enables low invasive and easy collection of the analytes from the studied lesions, which can be crucial for future analysis of potential biomarkers in the surgery room. It was observed that higher levels of these analytes characterized meningiomas with NF2 mutation. Moreover, increased energy consumption and elevated levels of acylcarnitines show that these analytes can be considered as a marker of increased fatty acid oxidation in NF2 mutated cells.

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to constrain the depositional ages of lake sediments and examine their physical, chemical, and biological characteristics, aiming to construct historical vegetation, environment and climate patterns in Chinese lake watersheds. In this study, we obtained relevant literature on lake sediment records by searching the key word 'age-depth' from the databases – Web of Science and China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in the watershed to lake ecology, and provides a solid foundation for better understanding regional climate change and the preservation of lake environments and ecosystems. In the future, resulting data from environmental reconstruction with lake sediments will be needed to integrate with emerging information processing technologies (e.g., artificial intelligence and meta-analysis), to disentangle the complex interplay between Earth surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

This work presents a novel compact CMOS potentiostat designed circuit for electrochemical cell. The proposed topology functions as a circuit interface, controlling the polarization of voltage signals at the sensor electrodes and facilitating current measurement during the oxidation-reduction process of a analyzed solution. The potentiostat, designed for CMOS technology, comprises a two-stage amplifier, two current mirror blocks coupled to this amplifier, and a CMOS push-pull output stage. It employs the electrochemical method of cyclic voltammetry, operating within voltage range of ±0.8 V and scan rates of 10mV/s, 25mV/s, 100mV/s and 250 mV/s. The circuit is capable of reading currents ranging from 10 µA to 500 µA. Experimental results were obtained using a potassium ferrocyanide (K3(FeCN)6) redox solution with concentrations of 10, 15 and 20 mmol/L, and their corresponding voltammograms were evaluated. The experimental results from a discrete circuit demonstrate that the proposed potentiostat topology produces outcomes consistent with those of classical topologies presented in the literature and industrial equipment.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.

Background - Compared to the da Vinci platform, there is limited experience with the Hugo RAS platform for colorectal surgery in Europe. This difference is especially notable when considering a single complex procedure, such as total colectomy. Aim - To demonstrate the feasibility and safety of using the Hugo RAS for assisted total colectomy. Clinical case- Female patient: eighteen-year-old with Familial Adenomatous Polyposis (FAP). Her IBM was 19. She underwent a total colectomy with ileorectal anastomosis using the Hugo RAS platform. The procedure took 230 minutes and had no complications. The postoperative period was uneventful. She was discharged from the hospital on the third postoperative day. Conclusion - The Hugo RAS platform is one of the emergent minimally invasive robotic platforms that can be used even in total colectomy if the proper patient selection is made. The placement choice of arms and trocars was crucial to obtain a similar operative time of the single laparoscopic approach. The certification of Hugo’s new instruments, such as energy devices and staplers, will make this platform even more competitive.

Despite the recognized influence of maximum or acceleration strength on combat techniques and countermovement jump (CMJ) performance, the relationship between body segment weights and combat techniques remains unexplored. This study aimed to examine the impact of body segment weights, including striking arm weight (SAW), kicking leg weight (KLW), and trunk weight (TW), on the dynamics of direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and CMJ performance. Sixteen male military cadets (22.3±1.8 years, 181.4±7.0 cm, 83.0±8.1 kg) performed combat techniques, with performance measured by using a force plate and body segment weights assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis was applied. Results indicated a strong correlation between KLW and FK impulse (r = 0.64, p ≤ 0.01) and CMJ impact force (r = 0.80, p ≤ 0.01). SAW showed moderate correlations with DP impulse (r = 0.53, p ≤ 0.05) and ES impulse (r = 0.54, p ≤ 0.05). Additionally, FK peak and impact forces were strongly correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01), and FK peak force moderately correlated with CMJ peak velocity (r = 0.63, p ≤ 0.05). These findings suggest targeted arm strength training could enhance striking abilities, while improvements in leg acceleration strength may directly boost kicking performance and vertical jump capabilities, which are crucial in combat scenarios.

Although a growing number of evidence emphasizes superior of VATS over conventional thoracotomy, still is little known about early postoperative diaphragm muscle function after lobectomy via these two approahces. To fill the gap in existing literature, we conducted comparative study between VATS and conventional thoracotomy in terms of postoperative diaphragm muscle function, assessing its contractility, strength, magnitude of effort and potential risk of dysfunction such as: atrophy and paralysis. A total of 59 patients (30 after VATS), who underwent anatomical pulmonary resection at our institution, were enrolled into this study. The control group was consisted of 28 health subjects without medical condition could contribute to diaphragm dysfunction. Diaphragm muscle was assessed before and after surgery using ultrasonography. We found that both surgical approaches were associated with postoperative impairment of diaphragm muscle function - compared to baseline data. Postoperative reduction of diaphragm contraction was demonstrated in all of 59 patients. In case of control group, the differences between measurements were no observed. We noted that lobectomy via thoracotomy was linked with greater percentage of patients with diaphragm paralysis or/and atrophy that VATS. Similar findings were observed in refer to diaphragm magnitude effort, as well as, diaphragm contraction strength, where minimal invasive surgery was associated with better diaphragm function parameters - in comparison to thoracotomy. Disturbance of diaphragm work was reported both at operated and non-operated side. Upper right and left lobectomy was connected with greater diaphragm function impairment than other segments. In conclusion, VATS technique seems to be less invasive than conventional thoracotomy providing a better postoperative function of a main respiratory muscle.

The nematode Caenorhabditis elegans, with its highly sensitive olfactory system, has emerged as a promising tool for testing chemotaxis. In the field of cancer diagnostics, there is a growing interest in the development of non-invasive screening methods for the detection of volatile organic compounds in a patient's urine. The objective of this study was to contribute to the existing body of knowledge by evaluating the ability of a Caenorhabditis elegans-based chemotaxis assay to discriminate between urine samples from healthy individuals and patients diagnosed with breast or colon cancer. Following synchronization of the developmental stages of C. elegans, nematodes were exposed to the urine of cancer patients and healthy individuals. Subsequently, chemotactic indices were calculated for each urine sample. Our results demonstrated a statistically significant difference in the chemotactic response of C. elegans to urine samples from cancer patients compared to healthy volunteers (p < 0.001). Moreover, the test exhibited promising diagnostic utility, with a sensitivity of 98%, a specificity of 62%, and a detection rate of 78%. Our findings expand on previous observations, confirming the remarkable sensitivity of C. elegans hermaphrodites to discriminating cancer-related volatile organic compounds in urine samples.

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASS). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML) based algorithms and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCC) by using real-time data to optimise their operations, enhance safety and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity aspects must be considered and properly addressed to prevent such complex systems from being tampered with. In this paper, we propose an approach to secure communication channels between the SCCs and the vessels using SECOM standard and the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly detection. The proposed approach is validated against a real implementation of the SCC deployed at Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Incident rates of neurodegenerative diseases have steadily increased globally, but there is no therapeutic access available. We newly prescribed medicinal herbal remedy including five different herbal plants called, Chen-Ma-Dan-Sam-Ga-Mi-Bang (CMST), purposed to prove for pharmacological properties and corresponded actions on hippocampus neuronal cell injury by hypoxia-induced mice model. Mice were adapted to normoxia or hypoxia with or without CMST for 5 days. We gathered pharmacological effects of CMST on cell injury by enhancement of dihydroethidium and 4-hydroxynonenal signals which were correlated with abnormal redox status in the protein or gene expression levels (abnormal elevations of nitric oxide, reactive oxygen species, lipid peroxidation and deteriorations of total glutathione, total antioxidant capacity, and activities of superoxide dismutase and catalase) due to hypoxia. CMST also notably exerted to attenuates molecules for neuronal cell injury markers such as p-tau, cleaved caspase-3 due to DNA oxidations (53bp1and phosphor-histone H2AX), inflammatory cytokines, and hemeoxigenase-1. We further figured out the underlying actions of CMST by in vitro experiment through inactivation of microglial cell which can mediate neuronal cell injury. Collectively, CMST prevented from hippocampal neuronal cells via inactivation of microglial cell with normalization of redox status on hypoxia-induced hippocampus neuronal cell injury.

With the ongoing amendment of the EU legislation on the treatment of urban wastewater, stricter requirements for the removal of pollutants are expected, which calls for the need for innovative wastewater treatment technologies. Biological systems are still the first choice. The survey of typical bioreactors applied in wastewater treatment is presented. Wastewater treatment objective, bio-chemical environment and microbial growth are selected as a main criterion for classification of these bioreactors. Hydraulic and kinetic aspects are considered, advantages and drawbacks of these bioreactors regarding the selection of appropriate type of the reactor as well as regarding operation of reactors are mentioned. The aim of this paper is to provide operators and designers with a brief overview of selected traditional and advanced processes, reactors, and technologies for nutrient removal from municipal wastewater. Another goal is to discuss the possibilities and limitations to comply with more strict effluent standards. From the evaluation of the published papers, it follows that the currently applied traditional methods of nutrients removal have the potential to convey also with the expected stricter limits.

We derive the thermal noise spectrum of the Fourier transform of the electric field operator of a given wave vector starting from the quantum-statistical definitions and relate it to the complex frequency and wave vector dependent complex conductivity in a homogeneous, isotropic system of electromagnetic interacting electrons. We analyze separately the longitudinal and transverse case with their peculiarities. The Nyquist formula for vanishing frequency and wave vector, as well as its modification for non-vanishing frequencies and wave vectors follow immediately. Furthermore we discuss also the noise of the photon occupation numbers. It is important to stress that no additional assumptions at all were used in this straightforward proof.

Latin America and the Caribbean offer the most favorable natural and social conditions for the emergence of cattle ranching that regenerate and maintains the ecosystem services on which life on Earth depends, that is energy efficient and that contributes to meeting fundamental human food needs. Unlike other major global producers, cattle farming in Latin America and the Caribbean is fundamentally based on pasture. Therein lies both its strength (due to the regenerative potential of well-managed pastures) and its vulnerability (due to the pervasive link between deforestation and pasture formation, especially in the Amazon, where 40% of the Brazilian herd is concentrated). The diversification of pastures (today marked by impressive monotony, with brachiaria accounting for 85% of the species in Brazil), the introduction of legumes and tree plantations within them, and the proper management of herds, open the way to the possibility of drastically reducing the area currently occupied by the activity (especially in the Amazon) while maintaining the supply of its products. Agricultural research has disseminated forms of moderate intensification of cattle ranching, which represent a low opportunity cost of animal feed and, at the same time, regeneration of ecosystem services that have so far been destroyed by the prevailing methods of farming.

Understanding HCPs COVID-19 vaccine concerns can provide critical insights to optimize success of the vaccine rollout program as they are a trusted source of health information to patients and the general population. We sought to understand reasons for health care providers’ COVID-19 vaccine hesitancy in Kenyatta National teaching and referral Hospital. From April to July 2021, we interviewed 60 HCPs purposively sampled from the Infectious Disease Unit (IDU) (n=20) and other departments (n=40). We analyzed data using inductive and deductive approaches to understand reasons for COVID-19 vaccine hesitancy. The HCPs had a median age of 37 years, (Interquartile range: [IQR 20.0-58.0]), 57% were female, 30% nurses among other cadres. Most HCPs cited low confidence in the vaccine such as mistrust in healthcare system and government, vaccine safety and efficacy concerns. Constraints such as uncertainty about the second vaccine dose availability, and misinformation caused hesitancy. Further, few were reluctant receiving the vaccine because of perceived herd immunity from a recent COVID-19 infection, and the need for more information before deciding. The study highlights the need to address any misinformation about vaccines safety and efficacy, and ensure accurate information is easily accessible to HCPs which can translate to higher vaccine uptake.

Pulmonary hypertension (PH) is a progressive and potentially fatal complication of sickle cell disease (SCD) affecting 6-10% of adult SCD patients. Various mechanisms and theories have been evaluated to explain the pathophysiology of the disease. However, questions remain, particularly regarding the clinical heterogeneity of the disease in terms of symptoms, complications, and survival. Beyond the classical mechanisms that have been thoroughly investigated and include hemolysis, nitric oxide availability, endothelial disorders, thrombosis, and left heart failure, attention is focused nowadays on the potential role of the genes involved in such processes. Potential candidate genes are investigated through next-generation sequencing, with the TGF-β pathway being the initial target. This field of research may also provide novel targets for pharmacologic agents in the future as is already the case with idiopathic PH. The collection and processing of data and samples from multiple centers can yield safe results that will allow a better understanding of SCD-related PH as a part of the disease’s clinical spectrum. The following review attempts to capture the most recent findings of studies on gene polymorphisms that have been associated with PH in SCD patients.