Search Results (44,699)

Background/Objectives: Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). Methods: In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with ${\mathrm{I}\mathrm{C}}_{50}$ values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. Results: A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. Conclusions: This study provides an effective method for screening AISMs, potentially impacting drug discovery and design. Full article

In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment. Full article

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models. Full article

CNNs have become one of the most commonly used computational tools in the past two decades. One of the primary downsides of CNNs is that they work as a “black box”, where the user cannot necessarily know how the image data are analyzed, and therefore needs to rely on empirical evaluation to test the efficacy of a trained CNN. This can lead to hidden biases that affect the performance evaluation of neural networks, but are difficult to identify. Here we discuss examples of such hidden biases in common and widely used benchmark datasets, and propose techniques for identifying dataset biases that can affect the standard performance evaluation metrics. One effective approach to identify dataset bias is to perform image classification by using merely blank background parts of the original images. However, in some situations, a blank background in the images is not available, making it more difficult to separate foreground or contextual information from the bias. To overcome this, we propose a method to identify dataset bias without the need to crop background information from the images. The method is based on applying several image transforms to the original images, including Fourier transform, wavelet transforms, median filter, and their combinations. These transforms are applied to recover background bias information that CNNs use to classify images. These transformations affect the contextual visual information in a different manner than it affects the systemic background bias. Therefore, the method can distinguish between contextual information and the bias, and can reveal the presence of background bias even without the need to separate sub-image parts from the blank background of the original images. The code used in the experiments is publicly available. Full article

Mucopolysaccharidosis (MPS) comprises a group of inherited metabolic diseases. Each MPS type is caused by a deficiency in the activity of one kind of enzymes involved in glycosaminoglycan (GAG) degradation, resulting from the presence of pathogenic variant(s) of the corresponding gene. All types/subtypes of MPS, which are classified on the basis of all kinds of defective enzymes and accumulated GAG(s), are severe diseases. However, neuronopathy only occurs in some MPS types/subtypes (specifically severe forms of MPS I and MPS II, all subtypes of MPS III, and MPS VII), while in others, the symptoms related to central nervous system dysfunctions are either mild or absent. The early diagnosis of neuronopathy is important for the proper treatment and/or management of the disease; however, there are no specific markers that could be easily used for this in a clinical practice. Therefore, in this work, a comparative analysis of shared and specific gene expression alterations in neuronopathic and non-neuronopathic MPS types was performed using cultures of cells derived from patients. Using transcriptomic analyses (based on the RNA-seq method, confirmed by measuring the levels of a selected gene product), we identified genes (including PFN1, ADAMTSL1, and ABHD5) with dysregulated expression that are common for all, or almost all, types of MPS, suggesting their roles in MPS pathogenesis. Moreover, a distinct set of genes (including ARL6IP6 and PDIA3) exhibited expression changes only in neuronopathic MPS types/subtypes, but not in non-neuronopathic ones, suggesting their possible applications as biomarkers for neurodegeneration in MPS. These findings provide new insights into both the molecular mechanisms of MPS pathogenesis and the development of differentiation method(s) between neuronopathic and non-neuronopathic courses of the disease. Full article

Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading. CER-MADDPG emphasizes collaboration between UAVs and uses historical UAV experiences to classify and obtain optimal strategies. It enables collaboration among edge devices through the design of the ’critic’ network. Additionally, by defining good and bad experiences for UAVs, experiences are classified into two separate buffers, allowing UAVs to learn from them, seek benefits, avoid harm, and reduce system overhead. The performance of CER-MADDPG was verified through simulations in two aspects. First, the influence of key hyperparameters on performance was examined, and the optimal values were determined. Second, CER-MADDPG was compared with other baseline algorithms. The results show that compared with MADDPG and stochastic game-based resource allocation with prioritized experience replay, CER-MADDPG achieves the lowest system overhead and superior stability and scalability. Full article

Background: Lymphedema represents a frequent cause of disability for patients undergoing oncological treatments and, being a chronic, non-reversible pathology, requires targeted and continuous rehabilitation treatments. To date, the studies available on the use of ultrasound in patients with lymphedema mainly report descriptive data; therefore, with this study, we wanted to describe in a more objective way the typical ultrasound alterations found in these patients, measuring the thickness of the different superficial structures, and defining subcutis echogenicity. Methods: 14 patients affected by secondary lymphedema of the upper limbs were enrolled in this cross-sectional observational study (12 had breast cancer and 2 with melanoma as their primary diagnosis). All patients were classified as stage II according to the ISL classification. Patients were examined between March and July 2023 with a clinical and an ultrasound evaluation. Ultrasound evaluation was performed following a protocol and took into consideration thickness of the cutis, subcutis, superficial and deep fascia, and subcutis echogenicity. Results: The cutis of the affected limbs was thicker in the distal anterior region of the arm and throughout the anterior region of the forearm. The subcutaneous tissue was thicker in the posterior region of the distal arm and throughout the forearm, including the dorsum of the hand and excluding only the proximal posterior region of the forearm. Fascial structures did not demonstrate statistically significant differences in thickness between pathological and healthy limbs, despite undergoing significant changes from a qualitative point of view (loss of the trilaminar skin appearance and the development of anechoic areas due to fluid accumulation around the hyperechoic adipose lobule). A statistically significant difference in the echogenicity of subcutaneous tissue was found at the distal anterior region of the arm and at the entire anterior forearm. Conclusions: High-resolution ultrasound has been confirmed to be a tool capable of supporting the diagnosis of lymphedema and identifying the most compromised regions of the limb. A tailored rehabilitation plan can be developed based on the non-uniform alterations in subcutaneous tissue, where some areas are affected earlier than others. This compartmentalization should be considered in lymphedema staging and management. Ultrasound may provide early detection of these changes, guiding a more precise therapeutic approach. Full article

Abstract: Background: Re-occlusion of initially recanalized arteries after thrombectomy is a significant concern that may lead to poor outcomes. This study aimed to identify the risk factors and evaluate the prognosis of arterial re-occlusion following successful thrombectomy in patients diagnosed with emergent large-vessel occlusion (ELVO). Methods: We retrospectively analyzed data from 155 consecutive patients with ELVO who underwent mechanical thrombectomy (MT). Patients were classified into two groups according to whether the initial recanalized artery was re-occluded within 7 days after successful thrombectomy: re-occlusion and non-occlusion groups. Multivariate analysis was performed for potentially associated variables with p < 0.20 in the univariate analysis to identify the independent risk factors of re-occlusion. Differences in clinical outcomes were also assessed in these two groups. Results: Re-occlusion occurred in 10.3% of patients (16/155). Multivariate analysis demonstrated that large artery atherosclerosis (odds ratio [OR]: 3.942, 95% confidence interval [CI]: 1.247–12.464; p = 0.020), the number of device passes (OR: 2.509, 95% CI: 1.352–4.654; p = 0.004), and residual thrombus/stenosis (OR: 4.123, 95% CI: 1.267–13.415; p = 0.019) were independently associated with re-occlusion. Patients with re-occlusion had significantly worse NIHSS scores at discharge and lower opportunities for achieving functional independence at 3 months after MT than patients without re-occlusion. Conclusion: Large artery atherosclerosis, a high number of thrombectomy device passes, and residual thrombus/stenosis seemed to promote re-occlusion after successful recanalization. Timely identification and proper treatment strategies to prevent re-occlusion are warranted to improve clinical outcomes, especially among high-risk patients. Full article

The Xyloglucan endotransglucosylase/hydrolase (XTH) family, a group of cell wall-modifying enzymes, plays crucial roles in plant growth, development, and stress adaptation. The quality and yield of Chinese jujube (Ziziphus jujuba) fruit are significantly impacted by environmental stresses, including excessive salinity, drought, freezing, and disease. However, there has been no report of the XTH encoding genes present in the Chinese jujube genome and their response transcription level under various stresses. This study provides an in-depth analysis of ZjXTH genes in the genome of Chinese jujube and elucidates their structural motifs, regulatory networks, and expression patterns under various stresses. A total of 29 ZjXTH genes were identified from the Ziziphus jujuba genome. Phylogenetic analysis classifies ZjXTH genes into four distinct groups, while conserved motifs and domain analyses reveal coordinated xyloglucan modifications, highlighting key shared motifs and domains. Interaction network predictions suggest that ZjXTHs may interact with proteins such as Expansin-B1 (EXPB1) and Pectin Methylesterase 22 (PME22). Additionally, cis-regulatory element analysis enhances our understanding of Chinese jujube plant’s defensive systems, where TCA- and TGACG-motifs process environmental cues and orchestrate stress responses. Expression profiling revealed that ZjXTH1 and ZjXTH5 were significantly upregulated under salt, drought, freezing, and phytoplasma infection, indicating their involvement in biotic and abiotic stress responses. Collectively, these findings deepen our understanding of the functional roles of Chinese jujube XTHs, emphasizing their regulatory function in adaptive responses in Chinese jujube plants. Full article

This review examines the integration of advanced ultrasonic techniques and artificial intelligence (AI) for monitoring and analyzing concrete structures, focusing on detecting and classifying internal defects. Concrete structures are subject to damage over time due to environmental factors and dynamic loads, compromising their integrity. Non-destructive techniques, such as ultrasonics, allow for identifying discontinuities and microcracks without altering structural functionality. This review addresses key scientific challenges, such as the complexity of managing the large volumes of data generated by high-resolution inspections and the importance of non-linear models, such as the Hammerstein model, for interpreting ultrasonic signals. Integrating AI with advanced analytical models enhances early defect diagnosis and enables the creation of detailed maps of internal discontinuities. Results reported in the literature show significant improvements in diagnostic sensitivity (up to 30% compared to traditional linear techniques), accuracy in defect localization (improvements of 25%), and reductions in predictive maintenance costs by 20–40%, thanks to advanced systems based on convolutional neural networks and fuzzy logic. These innovative approaches contribute to the sustainability and safety of infrastructure, with significant implications for monitoring and maintaining the built environment. The scientific significance of this review lies in offering a systematic overview of emerging technologies and their application to concrete structures, providing tools to address challenges related to infrastructure degradation and contributing to advancements in composite sciences. Full article

This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries. Full article

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This study systematically reviews the global SSB patent landscape using a cross-sectional bibliometric and thematic analysis to identify innovations addressing key technical challenges. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including year, geographic distribution, inventor engagement, award latency, and technological focus. The analysis reveals significant advancements in electrolyte materials, electrode designs, and manufacturability. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions. Full article

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (p < 0.05; false discovery rate > 0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies. Full article

Objectives: Dementia is becoming a major health problem in the world, and chronic brain ischemia is an established important risk factor in predisposing this disease. Astrocytes, as one major part of the blood–brain barrier (BBB), are activated during chronic cerebral blood flow hypoperfusion. Reactive astrocytes have been classified into phenotype pro-inflammatory type A1 or neuroprotective type A2. However, the specific subtype change of astrocyte and the mechanisms of chronic brain ischemia are still unknown. Methods: In order to depict the phenotype changes and their possible roles during this process, a rat bilateral common carotid artery occlusion model (BCAO) was employed in the present study. Meanwhile, the signaling pathways that possibly regulate these changes were investigated as well. Results: After four-week occlusion, astrocytes in the cortex of BCAO rats were shown to be the A2 phenotype, identified by the significant up-regulation of S100a10 accompanied by the down-regulation of Connexin 43 (CX43) protein. Next, we established in vitro hypoxia models, which were set up by stimulating primary astrocyte cultures from rat cortex with cobalt chloride, low glucose, or/and fibrinogen. Consistent with in vivo data, the cultured astrocytes also transformed into the A2 phenotype with the up-regulation of S100a10 and the down-regulation of CX43. In order to explore the mechanism of CX43 protein changes, C6 astrocyte cells were handled in both hypoxia and low-glucose stimulus, in which decreased pERK and pJNK expression were found. Conclusions: In conclusion, our data suggest that in chronic cerebral ischemia conditions, the gradual ischemic insults could promote the transformation of astrocytes into A2 type instead of A1 type, and the phosphorylation of CX43 was negatively regulated by the phosphorylation of ERK and JNK. Also, our data could provide some new evidence of how to leverage the endogenous astrocytes phenotype changes during CNS injury by promoting them to be “protector” and not “culprit”. Full article

This study investigates the relationship between economic growth and energy consumption—both renewable and non-renewable—in European Union countries during the green transition. Using a panel dataset of 28 EU countries from 1995 to 2021, we employ econometric techniques—including the Westerlund cointegration test and a fixed-effect panel threshold model—to assess long-term equilibrium relationships. The results indicate that while both renewable and non-renewable energy consumption are associated with economic growth, their roles differ. Renewable energy consumption shows a positive but less robust relationship with economic growth. In contrast, non-renewable energy consumption demonstrates a more robust bidirectional causality with economic growth, indicating a more intertwined relationship with economic growth during the study period. Interestingly, in countries with high levels of non-renewable energy consumption—classified as regime 2 in the panel threshold model—increased non-renewable energy consumption is associated with a decrease in economic activity. Our results have significant policy recommendations, indicating that promoting renewable energy sources does not hinder economic growth. Moreover, such promotion has the potential to contribute substantially to economic growth in the future. Therefore, in addition to other crucial benefits, such as increased energy security, the development of renewable energy sources does not threaten the economy. This is particularly relevant as many EU countries, including Poland, Romania, Hungary, Bulgaria, Slovakia, and Lithuania, still have underdeveloped renewable energy sectors. Full article