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The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models. Even though forecast stations are expanding across the United States, the coverage is usually limited to major rivers and urban areas. Most rural and sub-urban areas, including recreational areas such as the Window Cliffs State Natural Area, do not have such forecast stations and as such, are prone to the dire effects of unforeseen flooding. In this study, four machine learning model architectures were developed based on the long short-term memory, random forest, and support vector regression techniques to forecast water depths at the Window Cliffs State Natural Area, located within the Cane Creek watershed in Putnam County, Tennessee. Historic upstream and downstream water levels and absolute pressure were used to forecast the future water levels downstream of the Cane Creek watershed. The models were tested with lead times of 3, 4, 5, and 6 h, revealing that the model performances reduced with an increase in lead time. Even though the models yielded low errors of 0.063–0.368 ft MAE, there was an apparent delay in predicting the peak water depths. However, including rainfall data in the forecast showed a promising improvement in the models’ performance. Tests conducted on the Cumberland River in Tennessee showed a promising improvement in model performance when trained with larger data.

The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric’s low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties.

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Saffron is a spice derived from the flower of Crocus sativus used as a flavoring and coloring agent in the food industry which also possesses medicinal properties. In the current study, the optimum Solid Phase Extraction (SPE) conditions for separating picrocrocin and crocins from aqueous saffron extracts were investigated, using Rotatable-Central Composite Design (RCCD) in combination with Response Surface Methodology (RSM). The optimized factors were volume of saffron extract (3 mL), elution solvent (15% v/v ACN/water for picrocrocin; and 50% v/v ACN/water for crocins), and volume of elution solvent (ACN/water) (10 mL). The response factor measured was the UV-Vis absorbance. The presence of picrocrocin and crocins in the solutions obtained from SPE was confirmed using Ultra-Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (UPLC-QToF-MS). The above optimized SPE procedure provides economy in reagents and consumables.

This study aimed to examine whether we could use the discrepancy consistency method on CAS-2: Brief data collected in Cyprus. A total of 438 Grade 6 children (201 boys, 237 girls, Mage = 135.75 months, SD = 4.05 months) from Cyprus were assessed on the Cognitive Assessment System-2: Brief that is used to operationalize four neurocognitive processes, namely Planning, Attention, Simultaneous, and Successive (PASS) processing. They were also assessed on two measures of reading (Wordchains and CBM-Maze) and mathematics (Mathematics Achievement Test and Mathematics Reasoning Test). The results showed that 31.5% of our sample had a PASS disorder, and 8% to 10% of our sample had both a PASS disorder and an academic disorder. These numbers are similar to those reported in previous studies that used DCM in North America and suggest that the method can be used to inform instruction, particularly in places where no screening for learning disabilities is available.

Background/Aim: Activin A is involved in the pathogenesis of human liver diseases, but its therapeutic targeting is not fully explored. Here, we tested the effect of novel, highly specific small-molecule-based activin A antagonists (NUCC-474/555) in improving liver regeneration following partial hepatectomy and halting fibrosis progression in models of chronic liver diseases (CLDs). Methods: Cell toxicity of antagonists was determined in rat hepatocytes and Huh-7 cells using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay. Hepatocytes and hepatic stellate cells (HSCs) were treated with activin A and NUCC-555 and analyzed by reverse transcription–polymerase chain reaction and immunohistochemistry. Partial hepatectomized Fisher (F)344 rats were treated with NUCC-555, and bromodeoxyuridine (BrdU) incorporation was determined at 18/24/36/120/240 h. NUCC-555 was administered into thioacetamide- or carbon tetrachloride-treated F344 rats or C57BL/6 mice, and the fibrosis progression was studied. Results: NUCC-474 showed higher cytotoxicity in cultured hepatic cells; therefore, NUCC-555 was used in subsequent studies. Activin A-stimulated overexpression of cell cycle-/senescence-related genes (e.g., p15INK4b, DEC1, Glb1) was near-completely reversed by NUCC-555 in hepatocytes. Activin A-mediated HSC activation was blocked by NUCC-555. In partial hepatectomized rats, antagonizing activin A signaling resulted in a 1.9-fold and 2.3-fold increase in BrdU+ cells at 18 and 24 h, respectively. Administration of NUCC-555 in rats and mice with progressing fibrosis significantly reduced collagen accumulation (7.9-fold), HSC activation indicated by reduced alpha smooth muscle actin+ and vimentin+ cells, and serum aminotransferase activity. Conclusions: Our studies demonstrate that activin A antagonist NUCC-555 promotes liver regeneration and halts fibrosis progression in CLD models, suggesting that blocking activin A signaling may represent a new approach to treating people with CLD.

Small ruminants, such as sheep (Ovisaries) and goats (Capra hircus), contribute to approximately 475 million metric tons of carbon dioxide equivalent (MtCO2e) greenhouse gas (GHG) emissions, accounting for approximately 6.5% of the global emissions in the agriculture sector. Crop residues, silage, grasses, hay, browse, plant leaves, shrubs, agro-industrial by-products, poultry litter, and other alternative feed sources are frequently utilized for small ruminant production. The use of these valuable alternative feeds can significantly improve animal productivity and reduce carbon footprints and GHG fluxes, making it both environmentally friendly and cost-effective. Additionally, these alternative feeds possess antioxidant, antimicrobial, and antiseptic properties that can enhance the quality of the meat and milk produced. By impacting the bacteria involved in ruminal biohydrogenation, alternative feeds can reduce methane emissions and contribute to a decrease in the carbon footprint. Overall, the use of alternative feed sources for small ruminants generally improves their apparent nutrient digestibility and productivity, and has an impact on the production of greenhouse gases, especially methane. Finally, this review recommends evaluating the economic analysis of reducing methane emissions in small ruminants by utilizing different feed sources and feeding techniques.

The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support.

Seasonality in sheep reproduction and related limitations make milk production challenging throughout the year. In the present study, we investigated the response to estrus induction treatments in three indigenous breeds, Florina, Chios, and Karagouniko, as well as the melatonin receptor 1A gene variants in relation to this response. The three distinct synchronization methods were A: intravaginal sponges, B: GNRH use, and C: male effect. In group A, fertility was 85%, and Florina ewes expressed estrus at 90% in July. Ewes from Karagouniko and Chios had fecundity rates of 95% and 99%, respectively, and 100% estrus expression. The Florina ewes in group B expressed estrus at a percentage of 60%, with a fecundity rate of 57%, the Karagouniko ewes at a percentage of 65%, with a fecundity rate of 54%, and the Chios breed animals at a percentage of 87%, with a fecundity rate of 85%. Twenty to twenty-five days after ram induction, 68% of the Florina breed in group C showed signs of estrus, compared to 84% and 94% of Karagouniko and Chios breeds, respectively. In both Florina and Karagouniko breeds, all treatments showed a substantial difference in the frequency of the four identified SNPs in the MTNR1A gene between ewes who expressed estrus and ewes who did not. The genetic improvement based on the alleles analyzed in the current study is expected to decrease seasonality rates in indigenous sheep breeds.

A combined computational and experimental study of 3D-printed scaffolds made from hybrid nanocomposite materials for potential applications in bone tissue engineering is presented. Polycaprolactone (PCL) and polylactic acid (PLA), enhanced with chitosan (CS) and multiwalled carbon nanotubes (MWCNTs), were investigated in respect of their mechanical characteristics and responses in fluidic environments. A novel scaffold geometry was designed, considering the requirements of cellular proliferation and mechanical properties. Specimens with the same dimensions and porosity of 45% were studied to fully describe and understand the yielding behavior. Mechanical testing indicated higher apparent moduli in the PLA-based scaffolds, while compressive strength decreased with CS/MWCNTs reinforcement due to nanoscale challenges in 3D printing. Mechanical modeling revealed lower stresses in the PLA scaffolds, attributed to the molecular mass of the filler. Despite modeling challenges, adjustments improved simulation accuracy, aligning well with experimental values. Material and reinforcement choices significantly influenced responses to mechanical loads, emphasizing optimal structural robustness. Computational fluid dynamics emphasized the significance of scaffold permeability and wall shear stress in influencing bone tissue growth. For an inlet velocity of 0.1 mm/s, the permeability value was estimated at 4.41 × 10−9 m2, which is in the acceptable range close to human natural bone permeability. The average wall shear stress (WSS) value that indicates the mechanical stimuli produced by cells was calculated to be 2.48 mPa, which is within the range of the reported literature values for promoting a higher proliferation rate and improving osteogenic differentiation. Overall, a holistic approach was utilized to achieve a delicate balance between structural robustness and optimal fluidic conditions, in order to enhance the overall performance of scaffolds in tissue engineering applications.

The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states (real or imaginary) in a private BCI dataset, including EEG recordings of 32 participants. As such, the recorded brain activation data were employed to illustrate the differences between visual- and auditory-event-related responses during task performance. Our methodology involved a two-pronged approach. Firstly, EEG data were collected, capturing both the visual- and auditory-event-related signals that corresponded to each of the four movement classes. Secondly, we performed a comparative analysis of the collected dataset using various correlation algorithms, such as Pearson, Spearman, and Kendall, among others, to evaluate their effectiveness in differentiating between movements and states. The results demonstrated distinctive correlation patterns, as the selected indices effectively distinguished between real and imaginary movements, as well as between different lower limp movements in most cases. Moreover, the correlation schemas of certain individuals presented greater sensitivity in discerning nuances within the dataset. In this regard, it can be inferred that the chosen correlation indices can provide valuable insights into the aforementioned differentiation in EEG data. The results open up potential paths for improving BCI interfaces and contributing to more accurate prediction models.

The objective of the present study was to evaluate the effects of an olive leaf extract obtained with an up-to-date laboratory method, when supplemented at different levels in laying hens’ diets, on egg quality, egg yolk antioxidant parameters, fatty acid content, and liver pathology characteristics. Thus, 96 laying hens of the ISA-Brown breed were allocated to 48 experimental cages with two hens in each cage, resulting in 12 replicates per treatment. Treatments were: T1 (Control: basal diet); T2 (1% olive leaf extract); T3 (2.5% olive leaf extract); T4 (Positive control: 0.1% encapsulated oregano oil). Eggshell weight and thickness were improved in all treatments compared to the control, with T2 being significantly higher till the end of the experiment (p < 0.001). Egg yolk MDA content was lower for the T2 and T4 groups, while total phenol content and Haugh units were greater in the T2. The most improved fatty acid profile was the one of T3 yolks. The α-tocopherol yolk content was higher in all groups compared to T1. No effect was observed on cholesterol content at any treatment. Based on the findings, it can be inferred that the inclusion of olive leaf extract at a concentration of 1% in the diet leads to enhancements in specific egg quality attributes, accompanied by an augmentation of the antioxidant capacity.

Cervical spine pathologies often stem from deformations of the intervertebral discs and spinal canal. This work introduces a computational method for boundary extraction of these structures. The proposed method employs an active shape model (ASM) and is bimodal, in the sense that computed tomography (CT) images are used for ASM training and magnetic resonance (MR) images are used for ASM testing. The proposed method is less dependent on large amounts of training samples than deep learning methods, whereas it involves limited user intervention. Still, it is comparable to state-of-the-art methods in terms of segmentation quality, as demonstrated in our experimental comparisons.

Spasticity following a stroke often leads to severe motor impairments, necessitating comprehensive and personalized rehabilitation protocols. This paper presents Rehabotics, an innovative rehabilitation platform incorporating a multi-component design for the rehabilitation of patients with post-stroke spasticity in the upper limbs. This system incorporates a sensor-equipped soft glove, a robotic exoskeleton hand, and an augmented reality (AR) platform with serious games of varying difficulties for adaptive therapy personalization. The soft glove collects data regarding hand movements and force exertion levels when the patient touches an object. In conjunction with a web camera, this enables real-time physical therapy using AR serious games, thus targeting specific motor skills. The exoskeleton hand, facilitated by servomotors, assists patients in hand movements, specifically aiding in overcoming the challenge of hand opening. The proposed system utilizes the data collected and (in combination with the clinical measurements) provides personalized and refined rehabilitation plans and targeted therapy to the affected hand. A pilot study of Rehabotics was conducted with a sample of 14 stroke patients. This novel system promises to enhance patient engagement and outcomes in post-stroke spasticity rehabilitation by providing a personalized, adaptive, and engaging therapy experience.

Driving with fatigue is a major contributor to traffic accidents and is closely linked to central nervous system functions. To investigate the evolution of brain dynamics during simulated driving under different EEG rhythms, we conducted an experiment in which participants performed a 1 h driving task while their EEG signals were recorded. We used the complex network theory to analyze data derived from the driving stimulation and found that as fatigue deepened, small-world metrics, namely the path lengths, clustering coefficients, and measures of efficiency (global, local, nodal), showed alterations against the driving time. Additionally, a major correlation (corr = 0.98) was observed between the cluster coefficient with local efficiency in all frequency bands (theta, alpha, beta). Our findings suggest that driving fatigue can cause significant trends in brain network characteristics, such as path length (m = −103 to −93), (m = 98) for specific rhythms (beta, alpha, theta band, respectively) and their related brain functions, which could serve as objective indicators when evaluating the fatigue level and in the future, preventing driving fatigue and its consequences.

We examined what executive functioning (EF) components predict reading and mathematics within the same study and whether the effects of behavioral ratings of EF overlap or complement those of performance-based measures. One hundred and nine Grade 2 Mandarin-speaking Chinese students from Chengdu, China (55 girls, 54 boys, Mage = 8.15 years), were assessed on measures of EF (planning, inhibition, shifting, and working memory), speed of processing, reading and mathematics. Parents also rated their children’s EF skills using the Childhood Executive Functioning Inventory. Results of hierarchical regression analyses showed that only working memory among the performance-based EF measures predicted reading and mathematics. In addition, none of the behavioral ratings of EF made a significant contribution to reading and mathematics after controlling for mother’s education and speed of processing. Taken together, these findings suggest that working memory is a domain general predictor of academic achievement, but only when measured with cognitive tasks.

The purpose of this study was to examine the role of intelligence—operationalized in terms of Planning, Attention, Simultaneous, and Successive (PASS) processing skills—in reading and mathematics. Two hundred and forty-two Grade 6 Greek-speaking students (114 boys and 128 girls, Mage = 135.65 months, SD = 4.12 months) were assessed on PASS processes, speed of processing (Visual Matching), reading (Wordchains and CBM-Maze), and mathematics (Mathematics Achievement Test and Mathematics Reasoning Test). The results of the hierarchical regression analyses showed that, after controlling for family’s socioeconomic status and speed of processing, Attention and Successive processing predicted reading and Planning and Simultaneous processing predicted mathematics. Taken together, these findings suggest that different PASS processes may account for individual differences in reading and mathematics.

Both reading difficulties (RD) and mathematics difficulties (MD) are common neurodevelopmental disorders. The co-occurrence of RD and MD, known as comorbid RDMD, is estimated to range between 21% and 45% of children with learning disabilities. Deficits in working memory have been reported in both RD and MD groups, as well as among comorbid RDMD. However, previous comorbidity studies have only examined the role of some components of working memory, and they do not strictly match their groups on relevant reading and mathematics tasks. Thus, the purpose of this study is to examine the nature of working memory deficits in comorbid RDMD after matching groups based on reading and mathematics tasks. We assessed four groups of children (RD [n = 21, Mage = 10.96 years], MD [n = 24, Mage = 11.04 years], comorbid RDMD [n = 26, Mage = 10.90 years], and chronological-age controls [n = 27, Mage = 10.96 years]) on measures of the phonological loop (word span and digit span forward tasks), central executive (complex word and digit span), and updating tasks (word and digit 2-back). The results of ANCOVA (covarying for gender and non-verbal IQ) showed first that the RD and RDMD groups performed significantly worse than the MD and control groups in both measures of the phonological loop. For the central executive and updating tasks, we found an effect based on stimulus type. For word-related tasks, the RD and comorbid RDMD groups performed worse than the MD and control groups, and for number-related tasks, the MD and comorbid RDMD groups performed worse than the RD and control groups. Taken together, our findings provide support for the correlated liability model of comorbidity, which indicates that working memory deficits experienced by the RDMD group are an additive combination of deficits observed in the RD and MD groups, suggesting that working memory tasks used to examine underlying deficits in reading and/or mathematics difficulties may dictate whether or not significant group differences are found.

Antibodies to glutamic acid decarboxylase (GAD) have been predominantly associated with stiff-person syndrome (SPS), which is often accompanied by organ-specific autoimmune diseases, such as late-onset type 1 diabetes. Autoimmune retinal pathology in SPS has recently been suggested to coexist in patients suffering from this disease; however, evidence reporting potential treatment options for the neurological and visual symptoms these patients experience remains scarce. We provide a review of the relevant literature, presenting a rare case of a middle-aged woman with autoimmune retinopathy (AIR) followed by stiff-leg syndrome who responded to intravenous immune globulin treatment (IVIg). Our report adds to previously reported data supporting the efficacy of IVIg in SPS spectrum disorders while also proposing the potential effect of IVIg in treating SPS spectrum patients with coexisting AIR.

In recent years, the tremendous increase in data traffic carried by wireless communication networks has generated the urgent need for establishing more energy-efficient wireless communication systems. Recent advances in semiconductor and light devices have triggered remarkable research interest to the development of these optical wireless communication (OWC) links. Among them, free-space optical (FSO) links and, more recently, ultraviolet links which operate within the (UV-C) spectral band, have been considered as prime candidates to create both high speed and power effective line-of-sight (LOS) and non-light-of-sight (NLOS) free-air communication links, respectively. Moreover, transdermal optical wireless (TOW) links for telemetry with medical implants minimize the expense of power for the implant. In the current review, a background on the energy efficiency challenges in wireless communication is presented. Each of these OWC technologies is mainly discussed in terms of key energy consumption requirements and major limiting factors that affect their power performance. Energy-efficient modulation formats as well as other powerful techniques for performance enhancement such as diversity and relaying are assessed. The survey is concluded with a discussion regarding their future energy consumption requirements and trends.

Next generation in-to-out-of body biomedical applications have adopted optical wireless communications (OWCs). However, by delving into the published literature, a gap is recognized in modeling the in-to-out-of channel, since most published contributions neglect the particularities of different types of tissues. In this paper, we present a novel pathloss and scattering models for in-to-out-of OWC links. Specifically, we derive extract analytical expressions that accurately describe the absorption of the five main tissues’ constituents, namely fat, water, melanin, and oxygenated and de-oxygenated blood. Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue. Next, by incorporating the impact of scattering in the aforementioned model, we formulate the complete pathloss model. The developed model is verified by means of comparisons between the estimated pathloss and experimental measurements from independent research works. Finally, we illustrate the accuracy of the proposed model in estimating the optical properties of any generic tissue based on its constitution. The extracted channel model is expected to enable link budget analysis, performance analysis, and theoretical framework development, which will boost the design of optimized communication protocols for a plethora of biomedical applications.

The data on tumor molecular profiling of European patients with prostate cancer is limited. Our aim was to evaluate the prevalence and prognostic and predictive values of gene alterations in unselected patients with prostate cancer. The presence of gene alterations was assessed in patients with histologically confirmed prostate cancer using the ForeSENTIA® Prostate panel (Medicover Genetics), targeting 36 clinically relevant genes and microsatellite instability testing. The primary endpoint was the prevalence of gene alterations in homologous recombination repair (HRR) genes. Overall, 196 patients with prostate cancer were evaluated (median age 72.2 years, metastatic disease in 141 (71.9%) patients). Gene alterations were identified in 120 (61%) patients, while alteration in HRR genes were identified in 34 (17.3%) patients. The most commonly mutated HRR genes were ATM (17, 8.7%), BRCA2 (9, 4.6%) and BRCA1 (4, 2%). The presence of HRR gene alterations was not associated with advanced stage (p = 0.21), age at diagnosis (p = 0.28), Gleason score (p = 0.17) or overall survival (HR 0.72; 95% CI: 0.41–1.26; p = 0.251). We identified clinically relevant somatic gene alterations in European patients with prostate cancer. These molecular alterations have prognostic significance and therapeutic implications and/or may trigger genetic testing in selected patients. In the era of precision medicine, prospective research on the predictive role of these alterations for innovative treatments or their combinations is warranted.

Visible light positioning systems (VLP) have attracted significant commercial and research interest because of the many advantages they possess over other applications such as radio frequency (RF) positioning systems. In this work, an experimental configuration of an indoor VLP system based on the well-known Lambertian light emission, is investigated. The corresponding results are also presented, and show that the system retains high enough accuracy to be operational, even in cases of low transmitted power and high background noise.

Currently, single-board computers (SBCs) are sufficiently powerful to run real-time operating systems (RTOSs) and applications. The purpose of this research was to investigate the timing performance of an NXP TWR-K70F120M device with μClinux OS on concurrently running tasks with real-time features and constraints, and provide new and distinct technical data not yet available in the literature. Towards this goal, a custom-built multithreaded application with specific compute-intensive sorting and matrix operations was developed and applied to obtain measurements in specific timing metrics, including task execution time, thread waiting time, and response time. In this way, this research extends the literature by documenting performance results on specific timing metrics. The performance of this device was additionally benchmarked and validated against commonly used platforms, a Raspberry Pi4 and BeagleBone AI SBCs. The experimental results showed that this device stands well both in terms of timing and efficiency metrics. Execution times were lower than with the other platforms, by approximately 56% in the case of two threads, and by 29% in the case of 32-thread configurations. The outcomes could be of practical value to companies which intend to use such low-cost embedded devices in the development of reliable real-time industrial applications.

Electropolymerized thionine was used as a redox-active polymer to create a two-mediated microbial biosensor for determining biochemical oxygen demand (BOD). The electrochemical characteristics of the conducting system were studied by cyclic voltammetry and electrochemical impedance spectroscopy. It has been shown that the most promising in terms of the rate of interaction with the yeast B. adeninivorans is the system based on poly(thionine), single-walled carbon nanotubes (SWCNT), and neutral red (kint = 0.071 dm3/(g·s)). The biosensor based on this system is characterized by high sensitivity (the lower limit of determined BOD concentrations is 0.4 mgO2/dm3). Sample analysis by means of the developed analytical system showed that the results of the standard dilution method and those using the biosensor differed insignificantly. Thus, for the first time, the fundamental possibility of effectively using nanocomposite materials based on SWCNT and the redox-active polymer poly(thionine) as one of the components of two-mediator systems for electron transfer from yeast microorganisms to the electrode has been shown. It opens up prospects for creating stable and highly sensitive electrochemical systems based on eukaryotes.

Epicutaneous patch testing is a well-established diagnostic method for identifying substances that may cause Allergic Contact Dermatitis (ACD), a common skin condition caused by exposure to environmental allergens. While the patch test remains the gold standard for identifying allergens, it is prone to observer bias and consumes valuable human resources. Deep learning models can be employed to address this challenge. In this study, we collected a dataset of 1579 multi-modal skin images from 200 patients using the Antera 3D® camera. We then investigated the feasibility of using a deep learning classifier for automating the identification of the allergens causing ACD. We propose a deep learning approach that utilizes a context-retaining pre-processing technique to improve the accuracy of the classifier. In addition, we find promise in the combination of the color image and false-color map of hemoglobin concentration to improve diagnostic accuracy. Our results showed that this approach can potentially achieve more than 86% recall and 94% specificity in identifying skin reactions, and contribute to faster and more accurate diagnosis while reducing clinician workload.

In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient deployment of artificial intelligence models and techniques, the view is rapidly changing, with a shift towards machine and deep learning approaches becoming apparent, even without a complete evaluation of the superiority of the new approach over the classic statistical algorithms. Our work constitutes an extensive review of the published scientific literature regarding the comparison of ARIMA and machine learning algorithms applied to time series forecasting problems, as well as the combination of these two approaches in hybrid statistical-AI models in a wide variety of data applications (finance, health, weather, utilities, and network traffic prediction). Our review has shown that the AI algorithms display better prediction performance in most applications, with a few notable exceptions analyzed in our Discussion and Conclusions sections, while the hybrid statistical-AI models steadily outperform their individual parts, utilizing the best algorithmic features of both worlds.

The Byzantine religious tradition includes Greek Orthodox Church hymns, which significantly differ from other cultures’ religious music. Since the deep learning revolution, audio and music signal processing are often approached as computer vision problems. This work trains from scratch three different novel convolutional neural networks on a hymns dataset to perform hymns classification for mobile applications. The audio data are first transformed into Mel-spectrograms and then fed as input to the model. To study in more detail our models’ performance, two state-of-the-art (SOTA) deep learning models were trained on the same dataset. Our approach outperforms the SOTA models both in terms of accuracy and their characteristics. Additional statistical analysis was conducted to validate the results obtained.

Desmin is a class III intermediate filament protein highly expressed in cardiac, smooth and striated muscle. Autosomal dominant or recessive mutations in the desmin gene (DES) result in a variety of diseases, including cardiomyopathies and myofibrillar myopathy, collectively called desminopathies. Here we describe the clinical, histological and radiological features of a Greek patient with a myofibrillar myopathy and cardiomyopathy linked to the c.734A>G,p.(Glu245Gly) heterozygous variant in the DES gene. Moreover, through ribonucleic acid sequencing analysis in skeletal muscle we show that this variant provokes a defect in exon 3 splicing and thus should be considered clearly pathogenic.

Coaches need reliable methods of quantifying rugby union scrum force performance in order to make data-driven decisions. The purpose of this study is to present the reliability of a replicable instrumented scrum machine. We performed 3 phases of deadweight calibration on 8 S-type load cells; during deadweight calibration, each load cell was loaded with ~20–200 kg. Phase 1 compared power sources (wall outlet vs. portable power station). Phase 2 tested the inter-session reliability of the load cells after 15, 30, 45, 60, and 420 min of run time. Phase 3 tested between-session reliability, comparing days 0, 1, 7, and 180. We also performed a phase of inter-trial reliability when humans pushed on the fully instrumented scrum machine. Fourteen collegiate rugby players performed four warm-up trials and then five 100%-effort trials; peak and average voltage during the push were compared between the 100%-effort trials. For all phases, statistical analyses show near-perfect reliability. Therefore, we conclude that our novel instrumented scrum machine is ready for in vivo data collection; other coaches or researchers could duplicate our methods to create their own reliable instrumented scrum machine.

Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.

Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.

This study is aimed at the design, calibration, and development of a near-infrared (NIR) liquid crystal multifunctional automated optical polarimeter, which is aimed at the study and characterization of the polarimetric properties of polymer optical nanofilms. The characterization of these novel nanophotonic structures has been achieved, in terms of Mueller matrix and Stokes parameter analyses. The nanophotonic structures of this study consisted of (a) a matrix consisting of two different polymer domains, namely polybutadiene (PB) and polystyrene (PS), functionalized with gold nanoparticles; (b) cast and annealed Poly (styrene-b-methyl methacrylate) (PS-PMMA) diblock copolymers; (c) a matrix of a block copolymer (BCP) domain, PS-b-PMMA or Poly (styrene-block-methy methacrylate), functionalized with gold nanoparticles; and (d) different thicknesses of PS-b-P2VP diblock copolymer functionalized with gold nanoparticles. In all cases, backscattered infrared light was studied and related to the polarization figures-of-merit (FOM). The outcome of this study indicates that functionalized polymer nanomaterials, depending upon their structure and composition, exhibit promising optical characteristics, modulating and manipulating the polarimetric properties of light. The fabrication of technologically useful, tunable, conjugated polymer blends with an optimized refractive index, shape, size, spatial orientation, and arrangement would lead to the development of new nanoantennas and metasurfaces.

This article focuses on the recent advances in the field of reinforcement learning (RL) as well as the present state–of–the–art applications in games. First, we give a general panorama of RL while at the same time we underline the way that it has progressed to the current degree of application. Moreover, we conduct a keyword analysis of the literature on deep learning (DL) and reinforcement learning in order to analyze to what extent the scientific study is based on games such as ATARI, Chess, and Go. Finally, we explored a range of public data to create a unified framework and trends for the present and future of this sector (RL in games). Our work led us to conclude that deep RL accounted for roughly 25.1% of the DL literature, and a sizable amount of this literature focuses on RL applications in the game domain, indicating the road for newer and more sophisticated algorithms capable of outperforming human performance.

Mental workload has a major effect on the individual’s performance in most real-world tasks, which can lead to significant errors in critical operations. On this premise, the analysis and assessment of mental workload attain high research interest in both the fields of Neuroergonomics and Neuroscience. In this work, we implemented an EEG experimental design consisting of two distinct mental tasks (mental arithmetic task, n-back task), each with two conditions of complexity (low and high) to investigate the task-related and task-unrelated workload effects. Since mental workload is an intricate phenomenon involving multiple brain areas, we performed a graph theoretical analysis estimating the Phase Locking Index (PLI) in four frequency bands (delta, theta, alpha, beta). The brainwave-dependent network results show statistically significant reductions in clustering coefficient, characteristic path length, and small-worldness metrics with higher workload in both tasks across several bands. Moreover, functional connectivity analysis indicates a task-independent fashion of the brain topological re-organization with increasing mental load. These results revealed how the brain network is re-organized with increasing mental workload in a task-independent way. Finally, the network metrics were used as classification features, leading to high performance in workload level discrimination.

The filamentation of focused 300 fs laser pulses with variable polarization azimuth in bulk of synthetic HPHT diamond demonstrates the possibility of polarization-dependent bandgap control for crystal dielectric photoexcitation. This directly affects the value of the filamentation threshold power, which exhibits the distinct dependence on the polarization azimuth angle. The nonlinear photoluminescence yield, when focusing ultrashort laser pulses with variable polarization in bulk of the synthetic diamond, indicates different polarization-dependent regimes in the dynamics of electron-hole plasma formation, arising due to different processes of photoexcitation and recombination of free carriers during the filamentation process. Thus, at the onset of the filamentation process, at relatively low intensities, the photoluminescence yield rate depends on polarization azimuth controlling bandgap, while at high intensities the resulting dense absorbing plasma exhibits isotropy with respect to laser radiation polarization, and photoluminescence yield weakly depends on polarization azimuth.

The detection of mental fatigue is an important issue in the nascent field of neuroergonomics. Although machine learning approaches and especially deep learning designs have constantly demonstrated their efficiency to automatically detect critical features from raw data, the computational resources for training and predictions are usually very demanding. In this work, we propose a shallow convolutional neural network, with three convolutional layers, for fatigue detection using electroencephalogram (EEG) data that can alleviate the computational burden and provide fast mental fatigue detection. As such, a deep learning model was created utilizing time-frequency domain features, extracted with Morlet wavelet analysis. These features, combined with the higher-level characteristics learnt by the model, resulted in a resilient solution, able to attain very high prediction accuracy (97%), while reducing training time and computing costs. Moreover, by incorporating a subsequent SHAP values analysis on the characteristics that contributed in the model creation, indications of low frequency (theta and alpha band) brain wave characteristics were indicated as prominent mental fatigue detectors.

Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools’ detection and annotation results, regarding the waves of interest, were then compared to the clinicians’ manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals.

The Chinese pharmaceutical industry and traditional Chinese medicine (TCM) are both vital components of Chinese culture. Some traditional methods used to prepare TCMs have lost their conformity, and as a result, are producing lower-quality medicines. In this regard, the TCM sector has been looking for new ways to boost productivity and product quality. Membrane technology is environmentally-friendly, energy-saving technology, and more efficient than traditional technologies. Membrane separation is the most effective method for separating and cleaning the ingredients of the non-pharmaceutical common compounds from traditional Chinese medicine (TCM). Membrane technology is currently being employed for the concentration, purification, and separation of TCMs. This review paper discusses how membranes are fabricated and their role in non-pharmaceutical common compound separation and TCM purification. Accordingly, the membrane applicability and the technological advantage were also analyzed in non-pharmaceutical common compound separation. Researchers pay attention to the choice of membrane pore size when selecting membranes but often ignore the influence of membrane materials and membrane structure on separation, resulting in certain blindness in the membrane selection process.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants’ reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.

In recent years, the THz frequency band (0.3 THz–10 THz) has attracted an increasing research interest for the realization of emerging high-speed wireless communication links. Nevertheless, the propagation of THz signals through the atmospheric channel is primarily subjected to signal attenuation due to free space path loss (FSPL), water vapor, adverse weather conditions along with atmospheric turbulence-induced and misalignment-induced scintillations. Therefore, in this work, a multi-hop line-of-sight THz system that utilizes serially connected decode-and-forward relays is proposed to extend the total THz coverage distance under the presence of fog, rain or clear weather conditions, as well as water vapor, atmospheric turbulence, non-zero boresight pointing errors and FSPL. Under these circumstances, an average bit error rate (ABER) analysis is performed. In this context, novel closed-form ABER expressions are derived. Their analytical results demonstrate the influence of each of the above limiting factors as well as their joint impact on the ABER performance. Finally, the feasibility of extending the total THz link distance through multi-hop relaying configurations is also evaluated.

An important factor in the successful marketing of natural ornamental rocks is providing sets of tiles with matching textures. The market price of the tiles is based on the aesthetics of the different quality classes and can change according to the varying needs of the market. The classification of the marble tiles is mainly performed manually by experienced workers. This can lead to misclassifications due to the subjectiveness of such a procedure, causing subsequent problems with the marketing of the product. In this paper, 24 hand-crafted texture descriptors and 20 Convolution Neural Networks were evaluated towards creating aggregated descriptors resulting from the combination of one hand-crafted and one Convolutional Neural Network at a time. A marble tile dataset designed for this study was used for the evaluation process, which was also released publicly to further enable the research for similar studies (both on texture and dolomitic ornamental marble tile analysis). This was done to automate the classification of the marble tiles. The best performing feature descriptors were aggregated together in order to achieve an objective classification. The resulting model was embodied into an automatic screening machine designed and constructed as a part of this study. The experiments showed that the aggregation of the VGG16 and SILTP provided the best results, with an AUC score of 0.9944.

This paper presents a novel Image-Based Visual Servoing (IBVS) control approach for Floating Base Mobile Manipulator Systems (FBMMSs) that imposes prescribed transient and steady-state response on the image feature coordinate errors while satisfying the visibility constraints that arise owing to the camera’s limited field of view. The proposed control strategy does not incorporate any knowledge on either the FBMMS dynamic model, the exogenous disturbances, or the inevitable camera calibration and depth measurement errors. More specifically, it guarantees: (i) predefined behavior in terms of overshoot, convergence rate, and maximum steady-state error value of the image features and system velocities tracking errors; (ii) satisfaction of camera field of view constraints; (iii) bounded closed-loop control signals, and (iv) reduced design and implementation complexity. Additionally, the performance of the developed scheme is solely determined by certain designer-specified performance functions/parameters, and it is fully decoupled by the control gains selection. The efficiency of the proposed scheme is demonstrated via a realistic simulation study, using an eye-in-hand Underwater Vehicle Manipulator System (UVMS) as a test-bed FBMMS platform.

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.

The object of this paper was the application of machine learning to a clinical dataset that was anonymized using the Mondrian algorithm. (1) Background: The preservation of patient privacy is a necessity rising from the increasing digitization of health data; however, the effect of data anonymization on the performance of machine learning models remains to be explored. (2) Methods: The original EHR derived dataset was subjected to anonymization by applying the Mondrian algorithm for various k values and quasi identifier (QI) set attributes. The logistic regression, decision trees, k-nearest neighbors, Gaussian naive Bayes and support vector machine models were applied to the different dataset versions. (3) Results: The classifiers demonstrated different degrees of resilience to the anonymization, with the decision tree and the KNN models showing remarkably stable performance, as opposed to the Gaussian naïve Bayes model. The choice of the QI set attributes and the generalized information loss value played a more important role than the size of the QI set or the k value. (4) Conclusions: Data anonymization can reduce the performance of certain machine learning models, although the appropriate selection of classifier and parameter values can mitigate this effect.

A very important task of Natural Language Processing is text categorization (or text classification), which aims to automatically classify a document into categories. This kind of task includes numerous applications, such as sentiment analysis, language or intent detection, heavily used by social-/brand-monitoring tools, customer service, and the voice of customer, among others. Since the introduction of Fuzzy Set theory, its application has been tested in many fields, from bioinformatics to industrial and commercial use, as well as in cases with vague, incomplete, or imprecise data, highlighting its importance and usefulness in the fields. The most important aspect of the application of Fuzzy Set theory is the measures employed to calculate how similar or dissimilar two samples in a dataset are. In this study, we evaluate the performance of 43 similarity and 19 distance measures in the task of text document classification, using the widely used BBC News and BBC Sports benchmark datasets. Their performance is optimized through hyperparameter optimization techniques and evaluated via a leave-one-out cross-validation technique, presenting their performance using the accuracy, precision, recall, and F1-score metrics.

Multicore and multithreaded architectures increase the performance of computing systems. The increase in cores and threads, however, raises further issues in the efficiency achieved in terms of speedup and parallelization, particularly for the real-time requirements of Internet of things (IoT)-embedded applications. This research investigates the efficiency of a 32-core field-programmable gate array (FPGA) architecture, with memory management unit (MMU) and real-time operating system (OS) support, to exploit the thread level parallelism (TLP) of tasks running in parallel as threads on multiple cores. The research outcomes confirm the feasibility of the proposed approach in the efficient execution of recursive sorting algorithms, as well as their evaluation in terms of speedup and parallelization. The results reveal that parallel implementation of the prevalent merge sort and quicksort algorithms on this platform is more efficient. The increase in the speedup is proportional to the core scaling, reaching a maximum of 53% for the configuration with the highest number of cores and threads. However, the maximum magnitude of the parallelization (66%) was found to be bounded to a low number of two cores and four threads. A further increase in the number of cores and threads did not add to the improvement of the parallelism.

Climate change is considered a major factor affecting honeybees’ behavior and productivity with major consequences in both honey and agricultural production. Many research studies have expressed serious concerns about the mass losses of bee colonies and the role of bees as pollinators, while others have underlined important issues for the impact of the increase in temperature on honeybee abundance and honey yields. In the present work, we draw our attention to Marchalina hellenica, which is the most important honeydew-producing insect in Greece. A statistically significant forecasting model for the effect of cold periods in February on the life cycle of the insect is constructed, with the aid of the Cumulative Logit Model and the theory of runs. The forecasting model may help beekeepers plan the timely exploitation of honeydew secretions of pine trees, which will be beneficial for beekeepers, the rural economy, and forest protection. The new suggested model also indicates that, in view of the climate change scenarios seen in the literature, the life cycle of M. hellenica is expected to be drastically shorter.