Surface water plays a crucial role in climate change, human production, and life, making accurate monitoring and observation of surface water particularly important. However, due to the significant diversity and complexity of water distribution in surface space, accurate mapping of surface water faces considerable challenges. When extracting water bodies from medium-resolution satellite remote sensing images, CNN methods may suffer from limitations in receptive fields and insufficient context modeling capabilities, leading to the loss of water body boundary details and poor fusion of multiscale features. Currently, there is relatively little research on this issue; therefore, it is necessary to explore new combinations of deep learning networks to address these challenges. The purpose of this study is to address the above issues. We propose a new combination of deep learning networks that fully utilize multiscale information to enhance water features. Specifically, we first combine deformable convolutions with the Swin Transformer to increase effective receptive fields while better integrating global semantic information. This combination can capture features of water bodies at different scales, improve the accuracy and integrity of water extraction, and provide reliable technical support for detailed water body extraction. We tested the newly constructed model using Sentinel-2 satellite images. Our model achieved results of over 90%, with an average accuracy of 97.89%, average precision of 94.98%, average recall of 90.05%, and an average F1 score of 92.33%. In addition, our model achieved an accuracy of 98.03% in mountainous areas. Our experiments and results validate the potential of combining the Swin Transformer and deformable convolutions in detailed water body extraction.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Background: A new access technique was developed to reduce postoperative adverse events after the extraction of impacted maxillary wisdom teeth. Hence, this study aimed to assess the occurrence of adverse events after extraction of maxillary impacted wisdom teeth using traditional or modified access. Methods: Two different surgical incision designs were used for bilateral wisdom tooth extractions in 30 patients. The traditional incision was performed distal to the second molar in the center of the tuberosity, followed by a buccal releasing incision. After tooth extraction the wound was secured by sutures. The new technique consists of an oblique incision from the distal palatal aspect of the tuberosity towards the buccal aspect of the second molar. After tooth extraction, cyanoacrylate glue was used on the wound. Results: Lower pain was reported by patients at the site treated with the new technique (p<0.01). Edema, postsurgical bleeding, and hematoma were similar in both groups. The surgical time was shorter for the new technique (p<0.01). Conclusions: The new technique applied for the extraction of impacted maxillary wisdom teeth reduced postsurgical pain and the duration of surgery.

Pyrethroid pesticides (PYRs) as the third most widely used pesticides, following organophosphorus and carbamate pesticides, which are of significant importance in the analysis and detection of vegetables. However, the current pretreatment technology for PYRs confronts challenges of difficult separation and low enrichment efficiency, resulting in a cumbersome and time-consuming pretreatment process with poor selectivity. Here, a simple and efficient magnetic solid-phase extraction (MSPE) strategy was developed to simultaneously purify and enrich five PYRs in vegetables, with the magnetic covalent organic framework nanomaterial COF-SiO2@Fe3O4 as an adsorbent. The COF-SiO2@Fe3O4 was prepared by a simple solvothermal conditions method, using Fe3O4 as magnetic core, benzidine and 3,3,5,5-tetraaldehyde biphenyl as two building units. COF-SiO2@Fe3O4 could effectively capture the targeted PYRs by virtue of its abundant π-electron system and hydroxyl groups. The impact of various experimental parameters on extraction efficiency was investigated to optimize the MSPE conditions, including adsorbent amount, extraction time, elution solvent type and elution time. Subsequently, method validation was conducted under the optimal conditions in conjunction with gas chromatography-mass spectrometry (GC-MS). Within the range of 5.00–100 μg·kg-1 (1.00–100 μg·kg-1 for bifenthrin and 2.5–100 μg·kg-1 for fenpropathrin), the five PYRs exhibited a strong linear relationship, with determination coefficients ranging from 0.9990 to 0.9997. The limits of detection (LODs) were 0.3–1.5 μg·kg-1, and the limits of quantification (LOQs) were 0.9–4.5 μg·kg-1. The recoveries were 80.2–116.7% with relative standard deviations (RSDs) below 7.0%. Finally, COF-SiO2@Fe3O4, NH2-SiO2@Fe3O4 and Fe3O4 were compared as MSPE adsorbents for PYRs. The results indicated that COF-SiO2@Fe3O4 was an efficient and rapid selective adsorbent for PYRs. This method holds promising prospects for the determination of PYRs in real samples.

The perovskite solar cells (PSCs) have been significantly improved by utilizing inorganic hole transporting layer (HTL), such as nickel oxide. Despite the promising properties, there are still limitations due to defects. Recently, research on self-assembled monolayers (SAM) is being actively conducted, which show promise in reducing defects and enhancing device performance. In this study, we successfully engineered a p-i-n perovskite solar cell structure utilizing HC-A1 and HC-A4 molecules. These SAM molecules were found to enhance the grain morphology and uniformity of the perovskite film, which are critical factors in determining optical properties and device performance. Notably, HC-A4 demonstrated superior performance due to its distinct hydrophilic properties with a contact angle of 50.3°, attributable to its unique functional groups. Overall, the HC-A4 applied film exhibited efficient carrier extraction properties, attaining a carrier lifetime of 117.33 ns. Furthermore, HC-A4 contributed the superior device performance with its favorable band alignment, achieving the highest device efficiency of 20%.

In this study, the extraction of rare earth elements (REEs) from chloride solutions containing mixtures of these metals with solutions of the mixtures of P507 and Cyanex 272 (1:1) of various concentrations was experimentally studied. It was shown that the distribution coefficients of all REEs decrease with increasing concentration of these metals in the initial solution, which is associated with the loading of the organic phase. The most significant improvement in the extraction is observed for heavy group of rare earth elements. The extractability of REEs enhances with increasing atomic number of the element, as is typical for the extraction of these metals with acidic organophosphorus extractants. The data obtained show that separation factors of adjacent rare earth elements mainly decrease with increasing concentration of metals in the initial aqueous solution. Increasing the concentration of the extractant mixture does not have a significant effect on the values of the adjacent REE separation factors. Analysis of the experimental results shows that two commercial products can be obtained from a concentrate of the medium-heavy group. In the first cascade, yttrium can be separated, in the second, a concentrate of dysprosium and terbium can be obtained.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

The present study evaluated the antioxidant, antidiabetic properties, and biocompatibility of Medicago sativa and Solidago virgaurea extracts enriched in flavonoid and isoflavonoid compounds. The extracts were obtained by accelerated solvent extraction and laser irradiation. Then, nanofiltration was used for the concentration of flavonoid and isoflavonoid compounds from extracts. The extracts were analyzed for antioxidant capacity using DPPH radical scavenging and reducing power methods, while the antidiabetic property was tested by α-amylase and α-glucosidase inhibition and in vitro on a murine insulinoma cell line (β-TC-6). M. sativa obtained by laser irradiation and concentrated by nanofiltration had the highest DPPH• scavenging (IC50 = 105.2±1.1 and reducing power activities (IC50 = 40.98±0.2 µg/mL). M. sativa extracts had higher inhibition on α-amylase (IC50 = 23.9±1.2, respectively 26.8±1.1), while S. virgaurea had the highest α-glucosidase inhibition (9.3±0.9 ?g/mL respectively 8.6±0.7 ?g/mL). The results obtained after evaluating the antidiabetic in vitro activity showed that the treatment with M. sativa and S. virgaurea flavonoid- and isoflavonoid-rich extracts stimulated the insulin secretion of β-TC-6 cells, both under normal conditions as well as in hyperglycemic conditions. This paper argued that M. sativa and S. virgaurea flavonoid-rich and isoflavonoid-rich extracts could be an excellent natural source with promising antidiabetic potential.

Supercritical fluid extraction (SFE) is an innovative green technology for the extraction of phytochemicals from plants. Therefore, this study aimed to evaluate the application of SFE and to optimize the extraction condition of the Thai herbal formula, Kleeb Bua Daeng (KBD). A Box-Behnken design (BBD) with response surface methodology was used to determine the effect of extraction time, temperature, and pressure on response variables including extraction yield, total phenolic content (TPC), total flavonoid content (TFC), total carotenoid content (TCC) and total anthocyanin content (TAC) of KBD formula. The highest percentage extraction yield, TPC, TFC, and TCC of the extracts were 3.81%, 464.56 mg gallic acid equivalents/g extract, 217.19 mg quercetin equivalents/g extract, and 22.26 mg β-carotene equivalents/g extract, respectively. The results indicated that SFE is a suitable method of extraction for green recovery of some phytochemicals from the KBD formula.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE-value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling to reduce the data acquisition period significantly. The extracted feature set is then inputted into fundamental classification algorithms, with the Support-Vector-Machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step of a large project aiming to identify specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Coal permeability data are critical in the prevention and control of coal and gas outbursts in mines and are an important reservoir parameter for the development of coalbed methane. The mechanism by which permeability is affected by gas pressure is complex. We used a self-developed true triaxial seepage experimental device that collects lignite and anthracite coal samples, sets fixed axial pressure and confining pressure, and changes gas pressure by changing the orientation of the coal seam to study the influence of the gas pressure on the permeability of the coal seam under the conditions of different coal types and different bedding orientations. Coal permeability decreased rapidly and then decreased slowly and tended to be stable with the increase in gas pressure. This conforms to the power exponential fitting relationship, and the fitting degree reaches more than 99%. The permeabilities of lignite and anthracite were basically the same under various pressure conditions, indicating that the influence of coal type on coal permeability was not significant. The comparison of the two anthracite coal samples showed that the sample's permeability with a bedding plane vertical to the seepage direction was significantly lower than that of the bedding plane parallel to the seepage direction, indicating that gas seeped more easily along the bedding. The sensitivity coefficient of permeability with the change in gas pressure was calculated. The analysis showed that coal permeability was sensitive to changes in gas pressure during the low-pressure stage. When the gas pressure was greater than 0.8 MPa, the sensitivity coefficient was significantly reduced, which may be related to the slow increase in the amount of gas absorbed by the coal seam in the high-pressure stage. A theoretical calculation model of coal seam permeability considering adsorption/desorption and seepage effects was proposed and then verified with experimental results showing that the theoretical model better reflected the permeability characteristics of coal and predicted its permeability. Using the finite element simulation software COMSOL, the extraction efficiency of the coal seam gas under different gas pressure conditions was simulated. The results showed that with an increase in gas pressure, coal permeability and extraction efficiency decreased. In the low-pressure stage, the reduction in the extraction efficiency was more obvious than that in the high-pressure stage.

In pursuit of carbon neutrality, the demand for metals that are necessary for the development of clean energy technologies is rapidly increasing. Metallurgical waste, such as slag, presents a promising secondary source of these key metals. This research aims to develop an eco-friendly hydrometallurgical process to recover Cu, Ni, and Co from discarded copper/nickel slag. The high-pressure acid leaching (HPAL) was used to selectively leach Ni, Cu, and Co from the fayalite slag, yielding high leaching efficiencies of 99.9%, 89.4%, and 99.9%, respectively, with low Fe and Si tenors to the pregnant leach solution (PLS). The solvent extraction (SX) technique utilizing LIX 984N was used to selectively extract and enrich copper from the dilute PLS to about 23 g/L Cu with a very low Fe concentration of 0.05 g/L. Potassium amyl xanthate (PAX) solution was used to form Ni and Co xanthate complexes from the raffinate solution. Nickel was selectively recovered using ammonia solution, while cobalt xanthate complex was thermally decomposed and recovered as cobalt oxide solids of about 25 wt.% Co. A comprehensive process flowsheet is presented, furthermore, to realize the real application of the developed slag cleaning process, a preliminary economic evaluation was performed.

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since the calm weather is mostly noticed at the local site, making the dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward-back propagation neural network with 12-month measurements at Fujairah site (UAE). Both daily and monthly wind speed data sets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Due to the random nature of wind speed at the local site, a two-layer with 4 neurons per layer neuro network is used to locate the most variable and intense speed during 8-hours in the day. Forecasted wind speed is determined with errors arising from different sources such as the utilization of only 3rd-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.

Abstract: Pomegranate peels are important source of polyphenols with remarkable interest in food, pharmaceutical and cosmetic industry. An improved extraction of total polyphenolic com-pounds (TPC) from pomegranate peels by Solid-State Fermentation (SSF) was achieved. A Box Hunter & Hunter (BHH) followed by Central Composite Design (CCD) were performed to assess the effect of the process variables on TPC release. The statistic designs indicate that the best TPC extraction (234.85 mg GAE/gdm) by means of SSF occurs, at 42°C, 50 % moisture, pH 5.0, mineral solution (g/L): NaNO3 (3.83), KH2PO4 (1.52), MgSO4 (4.66) and KCl (1.52) at 36 h. At the best fer-mentation conditions, TPC (248.78±1.24 mgGAE/gdm) increased 5.96-fold than values previously reported and antioxidant activity [AA] increased 5.81-fold than the value obtained before the SSF optimization. High-valued citric acid, α and β punicalin, α and β punicalagin, punigluconin, gal-loyl-HHDP hexoside and ellagic acid molecules were identified. The increased extraction of TPC by SSF provides a suitable alternative for pomegranate peels valorization through the re-covery of molecules with high added value with potential use in food, pharmacy and cosmetic industries; a diversification in the use of food agroindustry by-products is obtained as approach to the circular economy model through biotechnological processes.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

Sesquiterpene lactones (STLs) are a large group of terpenoids most commonly found in plants of the Asteraceae family, e. g. in chicory plants, displaying a wide range of interesting biological activities. However, further studies on the biological potential of chicory derived STLs and analogues are challenging as only four of these molecules are commercially available (as analytical standards), and to date there are no published or patented simple extraction-purification processes capable of large scale STLs isolation. In this work we describe a novel three-step large scale extraction and purification method for the simultaneous purification of 11,13-dihydrolactucin (DHLc) and lactucin (Lc) starting from a chicory genotype rich in these STLs and in the corresponding glycosyl and oxalyl conjugated forms. After a small-scale screening on 100 mg of freeze-dried chicory root powder, the best results were achieved with a 17 h water maceration at 30°C. With these conditions we managed to increase the content of DHLc and Lc, at the same time favouring the hydrolysis of their conjugated forms. On a larger scale, the extraction of 750 g of freeze-dried chicory root powder, followed by a liquid-liquid extraction step and a reversed-phase chromatography, allowed the recovery of 642.3 ± 76.3 mg of DHLc and 175.3 ± 32.9 mg of Lc. The two pure STLs were subsequently used in the context of semi-synthesis to generate analogues for biological evaluation as antibacterial agents. In addition, other described chicory STLs that are not commercially available were also synthesized or extracted to serve as analytical standards for the study. In particular, lactucin-oxalate and 11,13-dihydrolactucin-oxalate were synthesized in two steps starting from Lc and DHLc, respectively. On the other hand, 11β,13-dihydrolactucin-glycoside was obtained after a MeOH/H₂O (70/30) extraction, followed by a liquid-liquid extraction step and a reversed-phase chromatography. Together, this work will help facilitate the evaluation of the biological potential of chicory derived STLs and their semisynthetic analogues.

A unique two-column ²³⁰U/²²⁶Th generator has been developed. The focus was hold on obtaining ²²⁶Th of high purity in a solution amenable to further labeling. The first column of the proposed generator filled with TEVA Resin held ²³⁰U, from which ²²⁶Th was eluted with 7 M HCl solution. UTEVA Resin pretreated with nitric acid solution was used as a sorbent for the second column for thorium retention. ²²⁶Th was extracted with 0.01-0.05 M citric buffer solution. One cycle of generator milking took 5-7 minutes and produced > 90% of ²²⁶Th in 1.5 ml of eluate, pH 4.5-5.0. The proposed two-column ²³⁰U/²²⁶Th generator was tested over two months including a second loading of ²³⁰U additionally accumulated from ²³⁰Pa. The ²³⁰U impurity in ²²⁶Th eluate was less than 0.01% allowing to use it directly in synthesis of radiopharmaceutical compounds.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

The aquaculture industry has become a sustainable provider of food to humans. Remaining challenges include disease issues, as well as ethical concerns for discomfort and stress among farmed fish. There is a need for reliable biomarkers to monitor welfare in fish, and the stress hormone cortisol has been suggested as a good candidate. This study presents a novel method for measurement of cortisol in fish feces based on enzymatic hydrolysis, liquid-liquid extraction, derivatization, and finally instrumental analysis by liquid chromatography coupled with tandem mass spectrometry. Hydrolysis and extraction conditions were optimized. Cortisol appeared to be mostly conjugated to sulfate and less conjugated to glucuronic acid in the studied samples of feces from farmed Atlantic salmon. The method was suitable for quantification of cortisol after enzymatic deconjugation by either combined glucuronidase and sulfatase activity, or by glucuronidase activity alone. The limit of detection was 0.15 ng/g and the limit of quantification was 0.34 ng/g, and the method was linear (R2>0.997) up to 380 ng/g, for measurement of cortisol in wet feces. Method repeatability and intermediate precision were acceptable, both with a CV of 11%. Stress level was high in fish released into seawater, and significantly reduced after eight days.

Electrical Resistance Tomography (ERT) has been used in the literature to monitor the gas-liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the rele-vant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10x less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000x faster than solving the inverse problem non-iteratively via the Gauss-Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the aver-age estimation error below 6 mm in the worst case scenario.

For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

Inclusions in steels and alloys are known as factors that could lower deformation, mechanical, corrosion and other properties. Study of inclusions in nickel based alloys is important since these materials could suffer from corrosion degradation in harsh operational conditions, where inclusions could lead to a pitting initiation. For estimation of a harmful effect of different inclusions on corrosion resistance of Ni-based alloys, inclusion characteristics (such as composition, morphology, size, number and location) on film filter and dissolution of metal matrix around different inclusions on surfaces of metal samples after electrolytic extraction (EE) were investigated in two samples of industrial Ni-based alloys (alloy 718 and EP718). It was found that both Ni-based alloys have various inclusions: carbides (large size NbTi-C and small multicomponent carbides), nitrides TiNb-N and sulfides (TiNb-S in EP718 alloy). The higher harmful effect on corrosion resistance of metal was detected around sulfides and small carbides containing Mo, W, Cr. Dissolution effect was also observed around large carbides and nitrides, especially around inclusions having size more than 10 µm. Moreover, the dissolution of metal matrix around inclusions and clusters located on the grain boundaries is 2.1-2.7 times larger compared to that for those inclusions inside of grains of the given alloy samples.

Cold brew coffee has emerged as a new trend during the last decade. However, “cold brew” is an extraction style of ground roasted coffee with water at less than body temperature (typically 8°C or room temperature) rather than a beverage per se. Cold brew extraction poses several challenges including the need of specific optimization depending on the multivariate influences of coffee variety and processing, roast degree, grinding, dosage, water composition, turbulence, brew system (drip, immersion etc.), time and temperature. While cold brew is typically characterized by a floral sweetness, over-extraction may lead to abundant acidity and bitterness. To avoid this, an extraction degree of 70% was suggested using rather shorter times (i.e. 2 h at 15°C with 80 g/L coffee with optimized medium roast profiles). Due to the lack of sterilizing temperatures during preparation, cold brew is special in the coffee sector because hygiene and food safety aspects pose specific challenges for food industry. To avoid microbiological contamination and spoilage, cold brew should be as freshly prepared as possible and shelf-life should be minimized.

The presented research faces the problem of early detection of sepsis for patients in the Intensive Care Unit. The PhysioNet/Computing in Cardiology Challenge 2019 facilitated the development of automated, open-source algorithms for the early detection of sepsis from clinical data. A labeled clinical records dataset for training and verification of the algorithms was provided by the challenge organizers. However, a relatively small number of records with sepsis, supported by Sepsis-3 clinical criteria, led to highly unbalanced dataset (only 2% records with sepsis label). A high number of unbalanced data records is a great challenge for machine learning model training and is not suitable for training classical classifiers. To address these issues, a number of various models were investigated. A solution including feature selection and data balancing techniques was proposed in this paper. In addition, several performance metrics were investigated. Results show, that for successful prediction, a particular model having few or more predictors based on the length of stay in the Intensive Care Unit should be applied.

Silybum marianum (L.) Gaertn. (aka milk thistle) constitute the almost exclusive source of silymarin, a mixture of different flavonolignans, and is thus considered as a unique model for their extraction. The present research deals with ultrasound-assisted extraction (UAE) of S. marianum flavonolignans and their quantification using LC system. The optimal conditions for UAE were: aqueous EtOH 54.5% (v/v) as solvent, applying an ultrasound frequency of 36.6 kHz during an extraction time of 60 min at 45°C with a liquid to solid ratio of 25:1 ml/g DW. Following optimization, the extraction method was validated according to international standards of the association of analytical communities (AOAC) in order to ensure its precision and accuracy for the quantitation of the individual silymarin components. The efficiency of UAE was compared with maceration protocol of the same duration. The optimized and validated conditions allowed highest extraction yields of flavonolignans in comparison to maceration. The antioxidant capacity of the extracts was confirmed by the CUPRAC assays and inhibition of advanced glycation end products. The skin anti-aging action was also confirmed toward the strong in vitro inhibition capacity of the obtained extract against collagenase and elastase enzymes. The procedure presented here allows a green efficient extraction and quantification of the main flavonolignans from the fruits of S. marianum with attractive antioxidant and anti-aging activities for future cosmetic applications.

Singing melody extraction is the task that identifies the melody pitch contour of singing voice from polyphonic music. Most of the traditional melody extraction algorithms are based on calculating salient pitch candidates or separating the melody source from the mixture. Recently, classification-based approach based on deep learning has drawn much attentions. In this paper, we present a classification-based singing melody extraction model using deep convolutional neural networks. The proposed model consists of a singing pitch extractor (SPE) and a singing voice activity detector (SVAD). The SPE is trained to predict a high-resolution pitch label of singing voice from a short segment of spectrogram. This allows the model to predict highly continuous curves. The melody contour is smoothed further by post-processing the output of the melody extractor. The SVAD is trained to determine if a long segment of mel-spectrogram contains a singing voice. This often produces voice false alarm errors around the boundary of singing segments. We reduced them by exploiting the output of the SPE. Finally, we evaluate the proposed melody extraction model on several public datasets. The results show that the proposed model is comparable to state-of-the-art algorithms.

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was chosen at last based on the comparison of the binding energy of the complexes between the template (OC) and functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method and NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts with 2 g of MIPs with the best binding properties through gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time.

The theory and working principle of fabric phase sorptive extraction (FPSE) is presented that eloquently explains the mystery behind this new and powerful sample preparation technique. FPSE innovatively integrates the benefits of sol-gel coating technology and the rich surface chemistry of cellulose/polyester/fiberglass fabric, resulting in a microextraction device with very high sorbent loading in the form of an ultra-thin coating. This porous sorbent coating and the permeable substrate synergistically facilitate very fast extraction equilibrium. The flexibility of the FPSE device allows for direct insertion into original, unmodified samples of different origin. Strong chemical bonding between the sol-gel sorbent and the fabric substrate permits the exposure of FPSE devices to any organic solvent for analyte back-extraction/elution and to highly acidic or basic environments (pH 1-12) if required. A sol-gel derived sorbent, highly polar sol-gel poly(ethylene glycol) coating, was generated on cellulose substrates. Five cm2 segments of these coated fabrics were used as the FPSE devices for sample preparation using direct immersion. An important class of environmental pollutants, substituted phenols, was used as model compounds to evaluate the extraction performance of FPSE. The high primary contact surface area (PCSA) of the FPSE device and porous structure of the sol-gel coatings resulted in very high sample capacities and incredible extraction sensitivities for both the compound classes in a relatively short period of time. Different extraction parameters were evaluated and optimized. The new extraction devices demonstrated part per trillion level detection limits for substituted phenols, a wide range of detection linearity, and good performance reproducibility.

Individuals have different rumination patterns after experiencing traumatic events in different cultural backgrounds and situations. This study was aimed to explore the experience of Chinese young and middle-aged patients with acute coronary syndrome (ACS) to understand the content and nature of their rumination. Fourteen participants were selected using the purposive sampling method in XXX hospital from May 2023 to December 2023. Data were collected using semi-structured interviews and analyzed using Colaizzi’ s 7-step phenomenological method. The content of rumination in young and middle-aged patients with ACS can be summarized into 2 categories: non-adaptive rumination and adaptive rumination. Non-adaptive rumination included 3 themes: worry or anxiety, scare, and depression. Adaptive rumination included 4 themes: tracing of disease processes, enhancement of disease cognition, improvement of health awareness, and adjustment of lifestyle cognition. In conclusion, although Chinese young and middle-aged patients with ACS experience negative emotions after a traumatic cardiac event, they gradually make positive changes, and optimism and information support play important roles in this transition, which provide a fundamental understanding of rumination experiences in Chinese young and middle-aged patients with ACS and provide new data for healthcare providers when designing intervention programs to enhance post-traumatic growth in these patients.

In this study, the team employed full-scale crash tests to investigate the impact of post-densification technology on the safety performance of separated central median W-beam barriers under low compaction conditions. By precisely simulating the design and construction environment of the central median, the study comprehensively evaluated the barrier's containment, redirection, buffering, and anti-intrusion performances, as well as the deformation of both the barrier and vehicles under various collision scenarios. The results indicate that under low compaction soil conditions, the A-level separated central median barriers reinforced with post-densification technology effectively meet the specified protection requirements. By comparing the influence of soil strength variations on barrier performance under simulated rainfall conditions, it was confirmed that enhancing soil compaction significantly improves soil stability, effectively increases the overall strength and stability of the barriers, reduces deformation, and significantly enhances safety performance. This study not only validates the effectiveness of the reinforcement scheme but also provides new perspectives for the scientific research of safety barriers and offers crucial technical support for the optimal design of road safety protection facilities.

The COVID-19 pandemic period, from 2020 - 2022 had a significant impact on maternal infant health with mothers impacted more than their infants. We questioned whether there have been any lingering effects from the pandemic. We examined intermediate and long-term pandemic effects of maternal and neonatal outcomes before, during and after the COVID-19 pandemic period. We reviewed mother-infant pairs from three epochs: 1) the pre-COVID-19 period, 2) the COVID-19 pandemic period, and 3) the post-pandemic period. The Case Mix Index (CMI) for the neonates from all 3 epochs were detailed. Post-pandemic we noted a rising trend of LGA infants (10%), and an increase in SGA infants (13%). For women in 2023 we noted an increase in hypertension, preeclampsia, diabetes, and a higher BMI than the pre- pandemic period. There have also been more congenital anomalies (9%), and neonatal CMI increased in the post-pandemic period. Well after the pandemic period, maternal-infant health continues to be affected. For women, increase in hypertension and diabetes during pregnancy is concerning. For the infants, being LGA or SGA may have long-term consequences. The post-pandemic increase in infants with congenital anomalies compared to the pre-pandemic era is an area that needs ongoing review.

N-methyl-D-aspartate receptors (NMDARs) are the main class of ionotropic receptor for the ex-citatory neurotransmitter glutamate. It plays a crucial role in the permeability of Ca2+ ions and excitatory neurotransmission in the brain. Being heteromeric receptors, they are composed of sev-eral subunits, including two obligatory GluN1 subunits (eight splice variants) and regulatory GluN2 (GluN2A~D) and/or GluN3 (GluN3A~B) subunits. Widely distributed in the brain, they regulate other neurotransmission systems and are therefore involved in essential functions such as synaptic transmission, learning and memory, plasticity, excitotoxicity. The present review will discuss the physiopathological impacts of NMDAR and particularly GluN2A and GluN2B subu-nits in cognitive processes and neurodegenerative diseases (Alzheimer's disease, Huntington's disease, Parkinson's disease). The pharmacology of different NMDAR antagonists and their po-tentialities will be presented. In particular, a focus will be given on fluoroethylnormemantine (FENM), an investigational drug with very promising developments as a tomography radiotracer for NMDARs, an anxiolytic in post-traumatic stress disorder and a neuroprotective agent in Alzheimer's disease.

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted the carbon balance of Earth and forest ecosystems, mainly by altering soil respiration (SR) in addition to decreased gross primary productivity. SR is considered the second highest flux on Earth, emitting 78-98 Gt C yr-1 to the atmosphere, thus contributing to global warming. Post-disturbance development is marked by changes in the size of SR. To investigate the impact of bark beetle (Ips typographus) infestations in mountain spruce forests on SR, we measured SR at infested sites by bark beetles and adjacent undisturbed stands in the High Tatra Mountains (Slovakia). The measurements were conducted during the vegetation period (May-September) in 2016 and 2017, five to six years after the initial attack by bark beetles, at an altitudinal gradient (1100-1400 m a.s.l.). SR varied throughout different months and altitudes, and we observed that SR at infested sites reached higher values (in most cases) than at uninfested stands. Average monthly and altitude SR rates during both vegetation periods showed higher values at infested sites, but not all of them were statistically significant. We observed an insignificant change in SR rate between 2016 and 2017 at both infested and uninfested sites. The highest rates of SR were observed in 2016 from July to August at both infested and undisturbed sites. On the other hand, in 2017 the highest SR rates were observed in June and July at undisturbed sites and from June to August at infested sites. However, yearly SR from May-September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites. However, this pattern was not observed in 2017. Based on our observation and results we postulate that SR rates at infested sites are boosted by needle fall and debris from dead and dying trees, which increases heterotrophic SR and compensates the decrease of autotrophic SR from tree roots. This study provides important insights into the impact of bark beetle infestations on SR and highlights the need for further research on the long-term effects of forest disturbances on carbon cycling.

The success of Toxoplasma gondii (intracellular parasite) host cell invasion relies on the apical complex, a specialized microtubule cytoskeleton structure associated with secretory organelles. The genome encodes three isoforms of both α- and β-tubulin which are altered by specific post-translational modifications (PTMs), changing the biochemical/biophysical proprieties of microtubules, and modulating their interaction with associated proteins. Tubulin PTMs are a powerful and evolutionarily conserved mechanism to generate tubulin diversity, forming a biochemical ‘tubulin code’ that can be ‘read’ by microtubule-interacting factors. The T. gondii tubulin PTMs are: α-tubulin acetylation, α-tubulin detyrosination, Δ5α-tubulin, Δ2α-tubulin, α- and β-tubulin polyglutamylation, and α- and α-tubulin methylation. Tubulin glutamylation is a key candidate to assist microtubule remodeling in Toxoplasma, being involved in the regulation of microtubule stability, dynamics, interaction with motor proteins, and severing enzymes. The correct balance of tubulin glutamylation is achieved by the coordinated action of polyglutamylases and deglutamylating enzymes. In this work we will review and discuss the current knowledge on T. gondii tubulin glutamylation. By in silico identification of mammalian protein orthologs we explored and updated the identification of putative proteins related to glutamylation, contributing to a better understanding of the role of tubulin glutamylation in T. gondii.

In this inclusive review article, the focus is on navigating the dynamic landscape of literature education, particularly the profound pedagogical shifts observed in the aftermath of the pandemic. The article synthesizes global insights, delving into how educators have adapted to hybrid models and embraced digital tools, fundamentally transforming the reading, analysis, and engagement with literary works. Exploring the integration of digital tools in literature education, the review uncovers the transformative impact of technology on traditional pedagogies. It analyzes the challenges and opportunities inherent in adapting literature education to the digital era, emphasizing the need for inclusive practices and adaptability. The article concludes by suggesting future research directions, encompassing AI integration, inclusive practices, cultural variations, long-term effects of hybrid models, and effective pedagogical strategies for technology integration. With a comprehensive approach, this review not only summarizes the current state of literature education but also discusses its implications, offering a holistic understanding of the contemporary educational landscape and setting the stage for future research endeavors.

The coronavirus disease 2019 (COVID-19), instigated by the zoonotic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), rapidly transformed from an outbreak in Wuhan, China, into a widespread global pandemic. A significant post-infection condition, known as ‘long- COVID’, emerges in a substantial subset of patients, manifesting with a constellation of over 200 reported symptoms that span multiple organ systems. This condition, also known as 'post-acute sequelae of SARS-CoV-2 infection' (PASC), presents a perplexing clinical picture with far-reaching implications, often persisting long after the acute phase. While initial research focused on the immediate pulmonary impact of the virus, the recognition of COVID-19 as a multiorgan disruptor has unveiled a gamut of protracted and severe health issues. This review provides a summary of the primary effects of long COVID on the cardiovascular, nervous, and respiratory systems. It also delves into the mechanisms underlying these impacts and underscores the critical need for a comprehensive understanding of long COVID's pathogenesis.

Post-operative pain is the unpleasant sensory and emotional experience after surgery, its origin being both the inflammatory reaction induced by the surgical trauma on the abdominal wall, and the splanchnic pain, induced by the activation of nociceptors of the viscera, which are highly sensi-tive to distension, ischemia and inflammation. Nowadays, it is well recognized that there is a close relationship between the gut microbiome and pain perception, and that microbiome is highly af-fected by both anaesthesia and surgical manipulation. Thus, efforts to restore the disturbed micro-biome by supplementation with beneficial bacteria, namely probiotics, seems to be effective. In this article the knowledge gained mainly from experimental research on this topic is analyzed, the con-cluding message being that each probiotic strain works in its own way towards pain relief.

Post-harvest handling and ripening techniques have an impact on peach quality and shelf life, which has a big impact on consumer satisfaction and market competitiveness. This review paper examines recent advancements in ripening techniques and post-harvest technologies with the goal of improving peach fruit quality and sustainability. The factors impacting fruit quality after harvest and the physiological changes that occur throughout peach ripening are fully explained. For maintaining peach freshness and reducing losses, novel handling methods like modified atmosphere packaging (MAP) and controlled atmosphere storage (CAS) have been investigated. The study explores the possibilities of nanotechnology applications and low-temperature storage for prolonging shelf life while maintaining texture, flavor, and aroma. Examining the effectiveness and waste reduction potential of automation and mechanization in post-harvest activities. The paper also discusses ethylene-based and non-ethylene-based ripening agents, as well as innovative techniques including gene editing and RNAi technology for controlled and delayed ripening. Analyses are done on how these technologies affect the sensory qualities and nutrient profiles of peaches. The study emphasizes the significance of sustainable practices in the peach industry by focusing on waste reduction, resource efficiency, and circular economy integration. Post-harvest technologies' potential environmental consequences have been taken into consideration and the paper encourages more study and cooperation to increase sustainability.

Background: As the world tends to recover from the crisis created by the COVID-19 pandemic, vaccines have served as one of the most valuable tools of combat and large-scale immunization has helped to tide over the catastrophe. However, several reports of post -vaccination neurological complications around the globe have surfaced up in recent literature. This study was conducted with an aim to describe the spectrum of neurological ailments requiring hospitalization following COVID-19 vaccination.Methods: An ambispective observational study was carried out from March 2021 to February 2022 at a tertiary care institute with 25 patients who satisfied the inclusion criteria of a new onset neurological illness requiring hospitalization within 4 days to 42 days of vaccination. The patients were duly followed upto months after discharge.Results: The median age of the sample was 36 years and the median latency of onset of neurological events was 14 days. Guillain-Barré syndrome was documented in 11 patients; majority were acute motor axonal neuropathy (45.45%) followed by acute inflammatory demyelinating polyradiculoneuropathy (36.36%) and acute motor sensory axonal neuropathy (18.18%). Central nervous system demyelination was reported in 9 patients; two patients had acute disseminated encephalomyelitis, 2 patients were diagnosed with myelin oligodendrocyte glycoprotein antibody-associated disease, one patient with aquaporin-4 positive neuromyelitis optica spectrum disorder , and the rest were labelled as seronegative central nervous system demyelination. Vascular events were noted in 5 patients comprising of 2 cases each of cerebral venous sinus thrombosis and intracerebral hemorrhage and a case of Moyamoya angiopathy. All patients were conservatively managed and had a complete to partial recovery suggestive of a favorable outcome after 3 months. Conclusion: The pathomechanisms of post -vaccination neurological complications is yet to be elucidatedand any causal association attributable to vaccination warrants further research. Compared to the enormity of the vaccinated population, the adverse effects are miniscule to hinder and discourage vaccination.

The menopausal transition often accompanies distressing manifestations, including vasomotor symptoms, sleep disruptions, and depressive syndrome. Omega-3 fatty acids have emerged as a potential intervention to alleviate these symptoms. This review aimed to comprehensively assess the impact of omega-3 supplementation on vasomotor symptoms, sleep quality, and depression among postmenopausal women. We conducted a systematic search focusing on randomized control trials across the Cochrane Library, Web of Science, PubMed, CINAHL, EMBASE, and SCOPUS databases from inception to August 2023. Among the initial pool of 163 studies, 9 studies met the inclusion criteria and were incorporated into this review. Four studies suggested potential benefits of omega-3 intake for improving hot flashes and night sweats. Sleep quality outcomes displayed heterogeneity across the studies. Incorporating diverse scales such as the Hamilton Depression Rating Scale-21, the Patient Health Questionnaire depression scale, and Generalized Anxiety Disorder-7 for depression outcomes, the review found inconclusive evidence on omega-3's impact on depression. Overall, the combined analysis of these studies did not provide substantial evidence to support the efficacy of omega-3 fatty acids in improving vasomotor symptoms, sleep quality, and depression. Further well-designed RCTs with larger participant groups are crucial to validate and generalize these results.

Coronary artery disease (CAD) is a prevalent cardiovascular condition characterized by the accumulation of plaque within coronary arteries. This plaque accumulation obstructs blood flow to the heart, resulting in a heart attack. While distinct features of CAD have been identified, its causes remain largely unclear, with the exception of environmental and nutritional factors. This study aimed to investigate the connection between genetic factors and CAD, focusing on the thymidylate synthase (TS) gene, a gene involved in one-carbon metabolism. Therefore, our research targeted single nucleotide polymorphisms that could potentially impact TS gene expression and lead to dysfunction. Our findings strongly associate the TS 1100T>C and 1170A>G genotypes with CAD susceptibility. We observed that TS 1100T>C polymorphisms increased disease susceptibility in several groups, while the TS 1170A>G polymorphism displayed a decreasing trend for disease risk when interacting with clinical factors. Furthermore, our results demonstrate the potential contribution of the TS 1100/1170 haplotypes to disease susceptibility, indicating a synergistic interaction with clinical factors in disease occurrence. Based on these findings, we propose that polymorphisms in the 3'-UTR miRNA binding site of the TS gene could serve as clinically useful biomarkers for the prevention, prognosis, and management of CAD.

The blackening of cut carrots decreases their shelf life and causes severe economic losses but the molecular and metabolic mechanisms that underpin this phenomenon remain poorly characterized. Studies were therefore undertaken to determine the molecular and metabolic causes of the blackening. The susceptibility of blackening was dependent on the period of time that the crop was stored underground prior to harvest. The structure of the cell walls in the black regions was substantially changed compared to the orange regions. The black regions of carrot batons had decreased immunodetection of xyloglucan, HG-pectin, RG-I pectin, galactan and arabinan but had higher levels of lignin and phenolic compounds compared to the orange regions. Transcript profiling analysis revealed that phytohormone signalling processes were activated in the black regions. Transcripts associated with auxin signalling and ethylene-responsive transcription factors were increased in the black regions. In contrast, the levels of transcripts encoding proteins associated with secondary metabolism were decreased in the black regions. These findings implicate ethylene and auxin-related processes in the control of the primary to secondary metabolism shift that results in lignification and cell wall disruption that underpin the blackening process.

A developing energy-saving approach of cold sintering in a pure aqueous medium was applied to the preparation of α-Al2O3 ceramics and performed on spark plasma sintering equipment. The initial γ-Al(OH)3 and γ-AlOOH powders and the cold-sintered ceramics were studied by X-ray diffraction analysis, infrared spectroscopy, thermal analysis, and scanning electron microscopy to reveal the chemical and structural transformations they experienced during the cold sintering. At 450°C and 70 MPa, initial γ-AlOOH transformed into a fragile α-Al2O3 material. Porous α-Al2O3 ceramics with about 60% porosity was obtained after cold sintering of γ-Al(OH)3 in the same conditions combined with subsequent annealing at 1250°C for 3 h. The role of water molecules in the studied processes was considered as the enhancement of structural mobility in the cold-sintered material due to its reversible hydroxylation similar to earlier investigated supercritical water action on the precursors during α-Al2O3 formation. Further improvement of the cold sintering setup and regimens would open prospects in α-Al2O3 ceramics manufacturing by an ecologically benign route.

Macrophage-derived chemokine belongs to the CC subfamily. It is produced by dendritic cells (DCs) and macrophages with or without external stimulation. We have previously shown a statistically significant depletion of MDC/CCL22 concentrations in a number of studies concerning COVID-19. These shifts in concentrations demonstrated stability unrelated to the SARS-CoV-2 genetic variant and remained noticeable even in convalescent patients. In this work, we analyze MDC/CCL22 dynamics in various diseases, including those that manifest with inflammation in lung tissue. In addition, we provide our hypothesis on such a decrease in MDC/CCL22 concentrations in COVID-19. If its secretion by producer cells is unperturbed, then it is possible for viral products to bind to this chemokine and to block its functional activity. There is, however, another possible explanation directly linked to depletion in DC subpopulations and the inhibition of their function. We also discuss MDC/CCL22's role in the immunology of novel coronavirus infection, based on both our own data and other studies.

Over the last few decades, the approach to geological and geomorphological surveys has changed remarkably. The advent of digital tools has allowed significant advances in the acquisition and management of survey data. In this paper, we demonstrate the development and testing of a new and effective digital survey method that allows for the fast acquisition and collaborative storage and management of data and information. This method was tested in collaboration with five universities for the mapping and classification of landslides in 249 survey areas in Central Italy and, more precisely, in the municipalities affected by the 2016 Central Italy Earthquake. Geological and geomorphological surveys were carried out in the field with tablet PCs, GPS, and cameras. The survey project for collecting field data was based on the structure of the Italian Landslide Inventory (IFFI) and the Territorial Resilience Central Apennines Earthquake Reconstruction (ReSTART) projects. The structure of the database and input forms were implemented for these aims. Moreover, the data and information were retrieved and organised in detailed records useful to the administrative entities.

Acute SARS-CoV-2 infections in children and adolescents are usually mild. However, they can suffer from ongoing symptoms generally referred as long COVID. Sleep disorders are one of the most frequent complaints in long COVID although precise data are missing. We assessed the sleep behavior of children and adolescents who presented at our outpatient clinic between January 2021 and May 2022 with the Children's Sleep Habits Questionnaire (CSHQ-DE). We compared sleep behavior at three different time points: pre-COVID-19, post-COVID-19 at initial presentation and post-COVID-19 at re-presentation. Data from 45 patients were analyzed. Of those, 64% were female and the median age was 10 years (range 0-18 years). Asymptomatic or mild COVID-19 disease was experienced in 89% of patients, whilst 11% experienced moderate disease. Initial presentation occurred at a median of 20.4 weeks (6 weeks - 14 months) after infection. The CSHQ-DE score increased significantly from pre-COVID-19 (45.82+8.7 points) to post-COVID-19 (49.40+8.3 points; p=<0.01). The score then normalized at re-presentation (46.98+7.8; p=0.1). The greatest changes were seen in the CSHQ-DE subscale score "daytime sleepiness". Our data show that children and adolescents with long COVID often suffer from sleep disturbance. For most children and adolescents these sleep disorders decreased over time without further medical intervention, aside from a basic sleep consultation.

Viruses feature an evolutionary shaped minimal genome that is obligately dependent on the cellular transcription and translation machinery for propagation. To suppress host cell immune responses and ensure efficient replication, viruses employ numerous tactics to favor viral gene expression and protein synthesis. This necessitates a carefully balanced network of virus- and host-encoded components, of which the RNA-based regulatory mechanisms have emerged as particularly interesting albeit insufficiently studied, especially in unicellular organisms. Here, recent advances that further our understanding of RNA-based translation regulation, mainly through post-transcriptional chemical modification of ribonucleosides, codon usage, and (virus-encoded) transfer RNAs, will be discussed in the context of viral infection.

Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. The objective of our study was to understand the genesis of the LD hypothesis in care pathways. In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were: suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. A majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. The diagnosis of Lyme disease was primarily triggered by identification with clinical stories circulating in the media. Most of participants had conducted the diagnostic investigation themselves. Diagnostic wandering in the diagnostic process suggests a failure of modern medicine to propose solutions for medically unexplained symptoms. Clinicians should systematically explore patients’ etiologic representations in a patient-centred care approach.

At present, there are no hardware or biochemical systems allow to assess the severity of post-COVID syndrome in vivo. The hardware of the proposed biotechnical system is based on routine transthoracic electrical impedance rheography, which makes it possible to register the frequency characteristics of the patient's bioimpedance response to controlled stress stimulation, thereby simultaneously fixing the characteristics of his productive heart, the state of the hemomicrocirculatory bed, the efficiency of the gas transport function of his blood, and also reliably assess personal reactivity and adaptive potential. Subsequent mathematical approximation of the obtained biometric data by an original neural network makes it possible to rank the results obtained and automatically generate a program of medical rehabilitation for a particular patient, depending on the severity of his post-COVID syndrome. The study results proved two reliable physiological signs confirming the presence of latent post-COVID complications: a decrease in the base impedance value for light exercise and an increase in the length of the systolic arc of the rheocardiogram.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Manual visual inspections typically conducted after an earthquake are high-risk, subjective, and time-consuming. Delays from inspections often exacerbate the social and economic impact of the disaster on affected communities. Rapid and autonomous inspection using images acquired from unmanned aerial vehicles offer the potential to reduce such delays. Indeed, a vast amount of re-search has been conducted toward developing automated vision-based methods to assess the health of infrastructure at the component and structure level. Most proposed methods typically rely on images of the damaged structure, but seldom consider how the images were acquired. To achieve autonomous inspections, methods must be evaluated in a comprehensive end-to-end manner, incorporating both data acquisition and data processing. In this paper, we leverage recent advances in computer generated imagery (CGI) to construct a 3D synthetic environment for simulation of post-earthquake inspections that allows for comprehensive evaluation and valida-tion of autonomous inspection strategies. A critical issue is how to simulate and subsequently render the damage in the structure after an earthquake. To this end, a high-fidelity nonlinear finite element model is incorporated in the synthetic environment to provide a representation of earthquake-induced damage; this finite element model, combined with photo-realistic rendering of the damage, is termed herein a physics-based graphics models (PBGM). The 3D synthetic en-vironment with PBGMs provide a comprehensive end-to-end approach for development and validation of autonomous post-earthquake strategies using UAVs, including: (i) simulation of path planning of virtual UAVs and image capture under different environmental conditions; (ii) au-tomatic labeling of captured images, potentially providing an infinite amount of data for training deep neural networks; (iii) availability of the ground truth damage state from the results of the finite-element simulation; and (iv) direct comparison of different approaches to autonomous as-sessments. Moreover, the synthetic data generated has the potential to be used to augment field datasets. To demonstrate the efficacy of PBGMs, models of reinforced concrete moment-frame buildings with masonry infill walls are examined. The 3D synthetic environment employing PBGMs is shown to provide an effective testbed for development and validation of autonomous vision-based post-earthquake inspections that can serve as an important building block for ad-vancing autonomous data to decision frameworks.

The large production of non-degradable petrol-based plastics has become a major global issue due to its environmental pollution. Biopolymers produced by microorganisms such as polyhydroxyalkanoates (PHAs) are gaining potential as a sustainable alternative, but the high cost associated to their industrial production has been a limiting factor. Post-transcriptional regulation is a key step to control gene expression in changing environments and has been reported to play a major role in numerous cellular processes. However, limited reports are available concerning the regulation of PHA accumulation in bacteria, and many essential regulatory factors still need to be identified. Here, we review studies where the synthesis of PHA has been reported to be regulated at the post-transcriptional level, and we analyze the RNA-mediated networks involved. Finally, we discuss the forthcoming research on riboregulation, synthetic and metabolic engineering which could lead to improved strategies for PHAs synthesis in industrial production, thereby reducing the costs currently associated with this procedure.

The rapid spread of COVID-19 worldwide was accompanied by intense fears, confusion, worries, anger and stress threatening people’s mental health. Unprecedented measures to slow down and prevent the transmission of COVID-19 have had various impacts on the population’s health behaviour and mental health. The main purpose of the present study is to investigate the lockdown’s effects on university students’ mental health in Romania. Based on a cross-sectional design, the survey data were collected from a sample of 722 participants (247 males; M = 21.1 years; SD ± 1.73). A path analysis was performed to verify the hypothesised direct and indirect effects included in the multiple mediation model. The findings showed a positive association between stress and boredom proneness, missing daily social interactions, spending more time on phone conversations and the increasing interest in following news about the pandemic. The path analysis revealed an excellent fit between the proposed multiple mediation model and the sample data. Boredom proneness and missing daily social interactions both affected stress, directly and indirectly, through more time spent on phone conversations. In addition, it was found that the increased interest in following news about the pan-demic mediated the relationship between boredom proneness and perceived stress. In terms of gender differences, our findings revealed that female students experienced significantly higher stress levels than male students, perceived to a greater extent the lack of daily social interactions and spent more time on phone conversations. Overall, the findings further extend the empirical evidence on university students’ mental health in the context of the COVID-19 pandemic. Therefore, universities need to organise support programmes focused on developing university students’ coping strategies to maintain their mental health even in adverse contexts.

On March 2020 strict measures took place in Finland to limit the COVID -19 pandemic. A majority of the Finnish COVID -19 –patients have been located in the southern Finland and consequently cared for in the HUS Helsinki University Hospital. During the ongoing pandemic, HUS personnel’s psychological symptoms are followed via an electronic survey, which also delivers information on psychosocial support services. The baseline survey in June 2020 was sent to 25494 HUS employees out of whom 4804 (19%) answered; altogether 62.4% of the respondents were nursing staff and 8.9% medical doctors. While the follow-up continues for a year and a half, this report shares the sociodemographic characteristics of the respondents and the first results of psychological symptoms from the baseline survey. Out of those who were directly involved in pandemic patients` care, 43.4% reported potentially traumatic COVID-19 pandemic-related experiences vs. 21.8% among the other (p < 0.001). While over a half of the personnel was symptomless, a group of respondents reported pandemic work –related traumatic events and concurrent depressive, insomnia and anxiety symptoms. This highlights the need to ensure appropriate psychosocial support services to all traumatized personnel and PTEs were present especially among nursing staff.

Climacteric fruits are characterized by a dramatic increase in autocatalytic ethylene production, which is accompanied by a spike in respiration, at the onset of ripening. The change in the mode of ethylene production from autoinhibitory to auto-stimulatory is known as the system 1 (S1) to system 2 (S2) transition. Existing physiological models explain the basic and overarching genetic, hormonal, and transcriptional regulatory mechanisms governing the S1 to S2 transition of climacteric fruit. However, the links between ethylene and respiration, the two main factors that characterize the respiratory climacteric, have been largely understudied at the molecular level. Results of recent studies indicate that the AOX respiratory pathway may play an important role in mediating cross talk between ethylene response, carbon metabolism, ATP production, and ROS signaling during climacteric ripening. New genomic, metabolic, and epigenetic information sheds light on the interconnectedness of ripening-associated metabolic pathways, necessitating expanding the current, ethylene-centric physiological models. Understanding points at which ripening responses can be manipulated may reveal key, speciesand cultivar-specific targets for regulation of ripening enabling superior strategies for reducing postharvest wastage.

Background: This study investigated the prevalence of worsening problems using Post Stroke Checklist (PSC) at 3, 6, and 12 months post-stroke and their associations with health-related quality of life. Methods: In stroke patients admitted between June 2014 and December 2015, PSC and EuroQol-5Dthree level (EQ-5D-3L) were assessed at post-stroke 3 (n=181), 6 (n=175), and 12months (n=89). The prevalence of worsening problems and its association withEQ-5D-3L at post-stroke 3 and 6months were analyzed. Results: An average of 0.59 (range 0–12), 1.47 (range 0–12), and 1.00 (range 0–10) worsening problems per patient was identified at 3, 6, and 12months after stroke, respectively. The most frequently and continuously identified worsening problems were mood disturbances (reported by 8.8%, 16.0% and13.5% of patients at 3, 6, and 12 months post-stroke, respectively). Worsening mobility was significantly associated with worse EQ-5D index at post-stroke 3 months (β,-0.583; 95% CI, -1.045 to -0.120). The worsening of mobility and communication was significantly associated with worse EQ-5D index at post-stroke 6 months (mobility: β,-0.170; 95% CI, -0.305 to -0.034, communication: β,-0.164; 95% CI, -0.309 to -0.020). Conclusions: PSC may be useful for the detection of various subjective worsening problems during serial clinical follow-up after stroke. Appropriate rehabilitation and management strategy to solve the identified problems could improve the quality of life in stroke survivors.

The prerogative to narrate the experience of marginal identities, particularly ethno-religious ones, appeared only in the post-occupation era in Iraqi fiction. Traditionally, secular Iraqi discourse struggled to openly address “sectarianism” due to the prevalent notion that sectarian identities are mutually exclusive and oppositional to national identity. It is distinctly in post-2003 Iraq—more precisely, since the sectarian violence of 2006–2007 began to cut across class, civil society, and urban identities—that works which consciously refuse to depict normative Iraqi identities with their mainstream formulations become noticeable. We witness this development first in the Western diaspora, where Iraqi novels exhibit a fascination with the ethno-religious culture of the Iraqi margins or subalterns and impart a message of pluralistic secularism. This paper investigates the origins of the taboo that proscribed articulations of ethno-religious subjectivities in 20^th-century Iraqi fiction, and then culls examples of recent diasporic Iraqi novels in which these subjectivities are encoded and amplified in distinct ways. In the diasporic novel, I argue, modern Iraqi intellectuals attain the conceptual and political distance necessary for contending retrospectively with their formative socialization experiences in Iraq. Through a new medium of marginalization—the diasporic experience of the authors themselves—they are equipped with a newfound desire to unmask subcultures in Iraq and to write more effectively about marginal aspects of Iraqi identity inside and outside the country. These new diasporic writings showcase processes of ethnic and religious socialization in the Iraqi public sphere. The result is deconstruction of mainstream Iraqi identity narratives and instrumentalization of marginal identities in a nonviolent struggle against sectarian violence.

This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets, with a specific application focus on wood surface defect detection. The proposed framework integrates sparse modeling techniques, particularly Lasso and proximal gradient methods, into a comprehensive pipeline for efficient and interpretable feature selection. Leveraging pre-trained models such as VGG19 and incorporating anomaly detection methods like Isolation Forest and Local Outlier Factor, our methodology addresses the challenge of extracting meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score, alongside visualizations, are employed to assess the performance of the sparse modeling techniques. Through this work, we aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

This research introduces a hybrid feature extraction approach that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) methods to address the challenges of reducing feature vector dimensionality and accurately classifying smartphone-based human activities. Moreover, to refine activity classification accuracy, Support Vector Machine (SVM) optimization with Stochastic Gradient Descent (SGD) is employed. LDA, a statistical tool, is leveraged to derive a new feature space for data projection, enhancing class separation and test feature label prediction. The proposed approach, named LMSS, was evaluated using the UCI-HAR dataset and compared with state-of-the-art models. The results demonstrate that the proposed approach outperformed the best-performing method over this dataset. It achieved an accuracy rate of 99.52%, precision of 99.55%, recall of 99.53%, and an F1-score of 99.54%, highlighting the effectiveness of the proposed method in accurately classifying the data.

Optical coherence tomography (OCT) can help in the diagnosis and treatment of infectious keratitis, but it has not been studied in cases occurring after corneal refractive surgery procedures such as femtosecond laser in situ keratomileusis (FS-LASIK) and keratorefractive lenticule extraction (KLEx). In these procedures a surgical interface is created, where infections usually start, thus determining a different OCT pattern compared to non-surgical infections, which instead begin in the corneal surface. A total of 4 cases of post-refractive surgery infectious keratitis are reported (2 after FS-LASIK and 2 after KLEx), in which the OCT identified an initial infiltrate in the interface, followed by interface inflammation. In 1 case after FS-LASIK, interface fluid accumulation occurred. In 1 case after KLEx, diffuse interface inflammation led to stromal reabsorption, later compensated by stromal reformation and epithelial hyperplasia, well documented by OCT.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

Industrial hemp (Cannabis sativa L.) is an attractive candidate for sustainable pest management due to its abundance of bioactive compounds with potential pesticidal properties. Solvent choice has a significant impact on the extraction efficiency of bioactive compounds. Deep Eutectic Solvents (DESs) are gaining popularity in extraction because they are safe and environmentally friendly, making them viable alternatives to organic solvents (OSs). This research first compared the extraction efficiency of OSs on the extraction of phytochemicals from the infloresences of two hemp varieties, Citrus and Cherry Dwarf. Inflorescences were extracted using three OSs, ethanol, ethyl acetate, and hexane. The highest level of cannabidiol (CBD; 0.69%) was extracted from Cherry Dwarf using ethanol while the level of delta-9 tetrahydrocannabinol THC (0.19%) was essentially the same in both. Therefore, Cherry Dwarf was selected to compare the extraction efficiency of DESs to the OSs. The DESs were choline chloride/ethylene glycol, citric acid/ethylene glycol, menthol/lauric acid, choline chloride/urea and choline chloride/glycerol. In the targeted analysis, choline chloride/ethylene glycol extracted the highest amount of CBD (0.87%) followed by choline chloride/urea (0.78%). As some DESs outperformed ethanol, the popular solvent for extracting cannabinoids, DESs are viable candidates to replace organic solvents.

Using straightforward and cost-effective methods, persimmon leaves were converted into a high-quality powder. This powder was applied as an adsorbent for the removal of Cu(II) and Cd(II) from aqueous solutions. Scanning electron microscopy (SEM) indicated the presence of particles with non-homogeneous sizes and rough texture. Elemental analysis and the EDX technique confirmed the presence of elements such as sulfur, phosphorus, nitrogen, and oxygen. The results of ¹³C nuclear magnetic resonance (¹³C-NMR), obtained through the cross-polarization technique, suggest the presence of groups containing sulfur and oxygen. Infrared spectroscopy (FTIR) indicated the existence of amine and hydroxyl groups. The material was used in solid-phase extraction of Cu(II) and Cd(II) in batch experiments, and its adsorption capacity was evaluated as a function of time, pH, and analyte concentration. Kinetic equilibrium was reached within 5 minutes, and the experimental data were fitted to the pseudo-second-order kinetic model. The optimum pH for adsorption of both metal species was observed to be around 5.0. The adsorption isotherms were adjusted using the modified Langmuir equation, and the maximum amount of metal species extracted from the solution was determined to be 0.213 mmol g^-1 for Cu(II) and 0.215 mmol g^-1 for Cd(II), with high linear correlation coefficients for both metals. The persimmon leaves are typically abundant during the growing season, and since they are seasonal, the Diospyros kaki L.f. tree undergoes the natural process of leaf abscission, ensuring the availability of leaves for application.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

A simple and rapid method for the extraction of D-series resolvins (RvD1, RvD2, RvD3, RvD4, RvD5) released into Leibovitz's L-15 complete medium by head kidney cells from Atlantic salmon and further liquid chromatography triple quadrupole mass spectrometry determination is proposed. A three-level factorial design was proposed to select the optimal concentrations of internal standards that were used in the evaluation of the performance parameters such as linear range (0.1-50 ng/mL), limits of detection and quantification (0.05 and 0.1 ng/mL, respectively) and recovery values (98.99±0.00). The optimized method was used to determine the stimulated production of resolvins by head kidney cells exposed to docosahexaenoic acid, and the results indicated that it is possible that the production was controlled by circadian responses.

Accurate evaluation of health status of lithium-ion batteries must be deemed as of great significance, insofar as utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as fast or non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap, by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum, to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked to establish a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

Symbolic pole/zero analysis is an important step when designing an analog operational amplifier. Generally, a simplified symbolic analysis of analog circuits suffers from NP-hardness, i.e., an exponential growth of the number of symbolic terms of the transfer function with the circuit size. In this study, we present a mathematical model combined with a heuristic-metaheuristic solution method for the symbolic pole/zero simplification in operational transconductance amplifiers (OTA). At first, the circuit is symbolically solved and an improved root splitting method is applied to extract symbolic poles/zeroes from the exact expanded transfer function. Then, a hybrid algorithm based on heuristic information and a metaheuristic technique using simulated annealing is performed for the simplification of the derived symbolic pole/zero expressions. The developed method has been tested on three analog OTAs. The obtained results show the effectiveness of the proposed method to achieve accurate simplified symbolic pole/zero expressions with the least complexity.

Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is ​​calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.

A polymer inclusion membrane (PIM) composed of 50 wt% poly(vinylidenefluoride-co-hexafluoropropylene) (PVDF-HFP) as its base polymer, 40 wt% Aliquat® 336 as its extractant and 10 wt% dibutyl phthalate (DBP) as plasticizer provided efficient extraction of vanadium(V) from its sulfate solutions adjusted to pH 2.5. It was suggested that V(V) was extracted as VO2SO4− via an anion exchange mechanism. Quantitative back-extraction was achieved in a sulfuric acid solution (6 mol L-1) containing 1 v/v% of hydrogen peroxide. It was assumed that the back-extraction process involved the oxidation of VO2+ to VO(O2)+ by hydrogen peroxide. The newly developed PIM with the optimized composition mentioned above exhibited excellent selectivity for V(V) in the presence of metallic species present in digests of spent alumina hydrodesulfurization catalysts (i.e., Al(III), Co(II), Cu(II), Fe(III), Mn(II), and Ni(II)). The co-extraction of Mo(VI) with V(V) was eliminated by its selective extraction at pH 1.1. The optimized PIM was characterized by contact angle measurements, atomic-force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA)/derivatives thermogravimetric analysis (DTGA), and the stress-strain measurements.

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixe assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

Cold brew coffee is a new trend in coffee industry. This paper presents pilot studies into several aspect of this beverage. Using an online survey, the current practices of cold brew coffee preparation were investigated identifying a rather large variability with a preference for extraction of medium roasted Arabica coffee using 50-100 g/l at 8°C for about 1 day. Sensory testing using ranking and triangle tests showed that cold brew may be preferred over iced coffee (cooled down hot extracted coffee). Extraction experiments at different conditions combined with nuclear magnetic resonance (NMR) analysis showed that the usual extraction time may be longer than necessary as most compounds are extracted within only a few hours, while increasing turbulence (e.g. using ultrasonication) and temperature may additional increase the speed of extraction. NMR analysis also revealed a possible chemical differentiation between cold brew and hot brew using multivariate data analysis. Decreased extraction time and reduced storage times could be beneficial for cold brew product quality as microbiological analysis of commercial samples detected samples with spoilage organisms and contamination with Bacillus cereus.

This paper presents a concise exploration of several analytical methods for extracting the parameters of the single-diode model (SDM) of a photovoltaic (PV) module under standard test conditions (STC). The paper investigates six methods and presents the detailed mathematical analysis leading to the development of each method. To evaluate the performance of these methods, MATLAB-based software has been developed and deployed to synthesize the results of each method when used to extract the SDM parameters of various PV test modules of different PV technologies. Similar software has also been developed to extract the same parameters using well-established numerical and iterative techniques. A comparison is subsequently made between the synthesized results and those obtained using numerical and iterative methods. The comparison indicates that in spite of the fact that analytical methods may involve fair amount of approximations, their accuracy can be comparable to that of their numerical and iterative counterparts, with the added advantage of significant reduction in computational complexity, and without the initialization and convergence difficulties, which are normally associated with numerical methods.

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

Waste orange peel represents a heavy burden for the orange juice industry, estimated in several million tons per year worldwide; nevertheless, this by-product is endowed with valuable bioactive compounds, such as pectin, polyphenols and terpenes. The potential value of the waste orange peel has stimulated the search for extraction processes, alternative or complementary to landfilling or to the integral energy conversion. This study introduces controlled hydrodynamic cavitation processes, as a new route to the integral valorization of this by-product, based on simple equipment, speed, effectiveness and efficiency, scalability, and compliance with green extraction principles. Waste orange peel, in batches of several kg, was processed in more than 100 L of water, absent any other raw materials, in a device comprising a Venturi-shaped cavitation reactor. The extractions of pectin, endowed with a very low degree of esterification, polyphenols (flavanones and hydroxycinnamic acid derivatives), and terpenes (mainly d-limonene) were effective and fast (high yield, few min of process time), as well as the biomethane generation potential of the process residues was effectively exploited. The achieved results proved the viability of the proposed route to the integral valorization of waste orange peel, though wide margins exist for further improvements.

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

A nonrenewable resource extraction game model is analyzed in a differential game theory framework with random duration. If the c.d.f. of the final time is discontinuous, the related subgames are differentiated based on the position of the initial instant with respect to the jump. We investigate properties of optimal trajectories and of imputation distribution procedures if the game is played cooperatively.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

Lyme disease is a highly prevalent tick-borne infection caused by Borrelia burgdorferi spirochetes in North America and Eurasia. Diagnosis of the early disease remains difficult with the currently approved serological tests in the absence of noticeable tick bite or erythema migrans due to presentation of non-specific symptoms and the absence of antibodies early in infection. Furthermore, it is difficult to distinguish persistence of disease and reinfection in the endemic regions. If not treated early, infection spreads throughout the body and affects most organs with disease manifestations including arthritis, musculoskeletal pain, neuroborreliosis and acrodermatitis chronicum atrophicans. Although most patients recover after antibiotic treatment, ~10-12% of show recurrence of symptoms known as post-treatment Lyme disease syndrome (PTLDS). Various causes of PTLDS are not well-defined; however, the spirochete persisters, antigenic debris, dysregulation of immunological response in individuals or a combination of these factors are proposed to result in continuation of Lyme disease manifestations. Based on these considerations, this review highlights currently employed treatment approaches with antimicrobials and vaccines being tried for prevention of B. burgdorferi infection. We also summarize combinatorial approaches that could be employed to alleviate symptoms to improve the quality of life of the individuals suffering from long Lyme disease.

Volunteering has many social, emotional and health benefits and could be an option for people with younger/early onset dementia to engage in stage/age-appropriate, personally meaningful activity within their local community. Audio data of people with young onset dementia discussing their volunteering experiences were taken from the Dementia Diaries and co-analysed. This study explored how people with dementia volunteer in the UK, the benefits and barriers, and how we can improve access to volunteering opportunities. The findings suggest that stigma is the overarching concept (or net) which influences people’s experiences of volunteering with dementia. Within the stigma net, three themes were developed that employed a lifespan approach to challenging said stigma: 1) Valuing the past, 2) Valuing the present and 3) Valuing the future, each with additional subthemes. This research contributes to the limited pool of literature that highlights the clear benefits of volunteering for people with young onset dementia, while also considering how dementia stigma can create practical and institutional barriers to accessing volunteering. Guidance is provided for how to deliver meaningful and accessible volunteering opportunities.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

Sustainable manufacturing practices are becoming increasingly necessary due to the growing concerns regarding climate change and resource scarcity. Consequently, material recycling technologies have gradually become preferred over conventional processes. This study aimed to recycle waste polylactic acid (PLA) from household-disposed cups and lids to create 3D-printed parts and promote sustainable manufacturing practices. To achieve this, the current study utilised virgin and post-consumer PLA (sourced from household waste) blends. The PC-PLA wastes were shredded and sorted by size with the aid of a washing step, resulting in a filament with a 1.70 ± 0.5 mm diameter without fragmentation or dissolution. A 50:50 wt.% blend of virgin PLA (vPLA) and PC-PLA was selected as the standard recycling percentage based on previous research and resource conservation goals. The study investigated the impact of three 3D printing parameters (layer height (LH), infill density (I), and nozzle temperature (NT))on the quality of 3D-printed parts using a three-level L9 Taguchi orthogonal array. The findings revealed that blending PC-PLA with vPLA led to significant improvements in the tensile, flexural, and impact strengths by 18.40%, 8%, and 9.15%, respectively, compared to those of recycled PLA. This conclusion was supported by the investigation of the fracture surface area, which revealed fractographic features associated with printing parameters, such as plastic deformation and interfilament debonding. An ANOVA analysis revealed a positive influence of larger layer height and high nozzle temperature on the mechanical properties. Subsequently, the optimal printing parameters (LH: 0.3 mm, I: 100%, and NT: 215 °C) were determined using the S/N ratio, and a confirmation test using the optimum printing parameters exhibited a strong correlation with statistically predicted outcomes. Finally, the study used optimum printing parameters to fabricate 100% post-consumer recycled PLA 3D printed parts, demonstrating their potential for low-strength applications. Based on these findings, it can be concluded that virgin and post-consumer recycled PLA blended filament for fabricating 3D printed components is an effective way to support plastic recycling in the context of a circular economy.

C. auris is a newly emerging yeast, raising public health concerns due to its outbreak potential, lack of protocols for decontamination and isolation of patients or contacts, increased resistance to common antifungals and associated high mortality. This research aimed to describe the challenges related to identifying the outbreak, limiting further contamination, and treating affected individuals. We retrospectively analysed all cases of C. auris detected between October 2022 and August 2023, but our investigation focused on a three-month-long outbreak in the department of cardio-vascular surgery and the related intensive care unit. Along with isolated cases in different wards, we identified 13 patients who became infected or colonized in the same area and time, even though the epidemiological link could be traced only in 10 patients, according to the epidemiological investigation. In conclusion, our study emphasizes the substantial challenge encountered in clinical practice when attempting to diagnose and limit the spread of an outbreak. Therefore, it is crucial to promptly apply contact precaution measures and appropriate environmental cleaning, from the first case detected positive.

Whether and how P2X7 receptor knockout (KO) modulates central post-stroke pain (CPSP) induced by lesions of the ventrobasal complex (VBC) of the thalamus regarding behavioral, molecular, and electrical recording parameters remains unclear. Following the experimental protocols of a rat hemorrhage CPSP model, the VBC of the thalamus of wild-type and P2X7 receptor KO mice was injected with 10 mU/0.2 μl type IV collagenase to produce an animal model of stroke-like thalamic hemorrhage. Behavioral data showed that the CPSP group induced thermal and mechanical pain, while the P2X7 receptor KO group showed reduced thermal and mechanical pain behaviors. Molecular assessments revealed that the CPSP group had lower expression of NeuN and KCC2 and higher expression of GFAP, IBA1, and BDNF. The P2X7 KO group showed lower expression of GFAP, IBA1, and BDNF and higher expression of KCC2 than the CPSP group. The expression of NKCC1, GABAa receptor, and TrkB did not differ significantly between the control, CPSP, and P2X7 receptor KO groups. Muscimol application increased multiunit numbers and [Cl-] influx in the membrane in the CPSP group, while P2X7 receptor KO reduced multiunit activity and [Cl-] outflux. P2X4 receptor expression was significantly decreased in the 100 kb but not the 50 kb site in the P2X7 receptor KO group. Altogether, the P2X7 hypothesis of CPSP was proposed, wherein P2X7 receptor KO changed the CPSP pain behavior, numbers of astrocytes and microglia, CSD amplitude of the anterior cingulate cortex and the medial dorsal thalamus, BDNF and KCC2 expression, [Cl-], and P2X4 activation in 100 kb with P2X7 receptors. The present findings have implications for the clinical treatment of CPSP symptoms.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

The purpose of the present study was to investigate the effect of post-activation performance enhancement (PAPE) induced by eccentric half-squat exercise on vertical jump performance in male jumpers. The jumping height, peak power, and work were measured and evaluated in twenty male jumpers. Participants performed five eccentric half-squats at 85% of their one-repetition maximum (1 RM), with a knee angle below 90°, followed by immediate and 2min delayed jump assessments using Squat Jump (SJ) and Countermovement Jump (CMJ) tests. Results showed that this specific PAPE protocol did not significantly improve jump performance for both SJ and CMJ tests. Factors such as stimulus specificity, rest intervals, muscle length, and the balance between potentiation and fatigue may explain these results. The study highlights the complexity of PAPE responses and suggests that a single set of eccentric squats with a short rest may not improve jump performance in male jumpers. Further research is required to optimize the interplay between conditioning stimulus and rest periods to maximize PAPE effects in athletic performance enhancement.

Hypertension is one of the most common and significant cardiovascular risk factor. With the emergence of COVID-19, concerns have arisen about its potential to exacerbate cardiovascular morbidity and mortality. Several theories have emerged linking hypertension with COVID-19. However, the relationship between SARS-CoV-2 infection and long-term risk of hypertension remains insufficiently explored. Therefore, our study aimed to investigate the impact of COVID-19 infection on blood pressure and long-term risk of hypertension developing. A comprehensive search was conducted in the PubMed and SCOPUS databases up to 10th August 2023 for relevant papers. The analysis included 35 eligible articles. The majority of the latest studies pointed to increase BP after COVID-19 infection. However, several observations contradicted the influence of SARS-CoV-2 on the hypertension development or suggesting a transient effect. The possible role of the loss of angiotensin-converting enzyme 2, cytokine storm, increased sympathetic activity, and endothelial dysfunction were considered. On the other hand, BP changes following COVID-19 could be a consequence of lifestyle changes, which often accompanied COVID-19 infection. Our findings highlight the urgent need for comprehensive research on the COVID-19 and hypertension link. These insights are crucial for implementing effective prevention and management strategies for individuals with the history of COVID-19 infection.

This study aimed to explore the experiences of teachers in the recovery of educational skills. Specifically, ten teachers of Andap National High School are selected for the in-depth interview. This study employed a qualitative-phenomenological research design of which in-depth interview (IDI) was used in gathering the data. The study utilized phenomenological approach to study a particular phenomenon concerning the post-pandemic experiences of teachers in the implementation of the face-to-face classes. After gathering sufficient amount of data through the conduct of in-depth interviews, thematic analysis was used to analyse further the results and to check the disparities and similarities of all responses gathered. This was the time wherein responses were categorized and organized into themes. The findings of the study revealed that the challenges encountered of educational skills recovery were sources of learning materials, learning gaps, learners characteristics, difficulty with comprehension and lack of interest. It was also revealed that the most challenging teaching experiences in the recovery of educational skills were catching attention, learning gaps, communicating to students, need for remedial instruction, retroactive interference and difficulty in understanding English Language. They frequently have to quickly convert lesson material they had intended to teach in a classroom setting to any learning modality implemented. Further, adjust and learn new trend, provide opportunities to develop new skills, helps to manage stress, improved educational outcomes, become well-rounded teacher, networking and learning and continue doing responsibilities helped in these insights had in confronting challenges brought by the pandemic.

The Coronavirus Disease 2019 (COVID-19) pandemic has had a major impact on the mental and physical health of hospitalized patients. In our study we focused on the onset of symptoms correlated with Post-traumatic stress disorder (PTSD), depression and physical disabilities in patients admitted to the Intensive Care Unit (ICU) because of a severe respiratory distress related to COVID-19 (COVID Group) compared with patients admitted to the same ICU for trauma and other medical conditions than COVID-19 (No-COVID Group). The physical symptoms and the level of disability were evaluated with the Glasgow Outcome Scale-Extended (GOS-E), the Quality of Life after Brain Injury (QOLIBRI) and the 3 levels version of EQ-5D (EQ-5D-3L) questionnaire; psychiatric symptoms were investigated using the Impact of Event Scale-Revised 22-item (IES-R), the Patient Health Questionnaire, 9-Item Version (PHQ-9) and the Generalized Anxiety Disorder Assessment, 7-items version (GAD-7). These questionnaires were administered 6 months after discharge. Patients in the No-COVID Group showed statistically significant more severe scores in all the physical assessments while similar relevant PTSD and depressive symptoms were reported in both groups. The results of the present study underline the psychopathological impact of being hospitalized in ICU because of COVID-19 even after 6 months from discharge ,suggesting the importance of assessing the psychiatric effects of COVID-19 in the long term in order to create supportive measures.

Mild TBI (mTBI) accounts for most TBI cases, the leading cause of morbidity and mortality worldwide. Despite their high incidence, mTBI pathophysiology remains largely unknown. Recent studies have shown that the inflammatory response is activated early after mTBI and can persist for several weeks or months. However, limited evidence on the utility of inflammatory biomarkers as predictors of clinical outcomes in mTBI was previously provided. Thus, this study aims to provide an overview of the current knowledge on the role of inflammation in the pathogenesis of mTBI and the potential of some inflammatory biomolecules as biomarkers of mTBI. Thus, it was shown that the increase of IL-6, TNF-α, and IL-1β plasma levels could be implicated in the development of the early post-concussion symptoms. On the other hand, the persistence of the increased plasmatic concentrations of IL-10 and IL-8 for as long as six months following the brain injury event could suggest chronic inflammation leading to neuroinflammation and late or persistent symptoms. In this context, our findings showed that the inflammatory biomarkers could be relevant in diagnosing or predicting recovery or long-term outcomes of mTBI.

The COVID-19 pandemic has struck educational experience systems around the globe. This paper investigates and evaluates the student participants’ perceptions who joined the international exchange seminar on global citizenship and peace held at a University in Hiroshima, Japan. Approximately seventy students and faculty members from nine to ten different universities from around the globe joined this summer program in August 2021 (online) and 2022 (face-to-face). This study is a mixed-method study. The first part consists of a quantitative analysis of BEVI data obtained from the students in the seminar before COVID-19 and after. The research concludes that there are no changes in the way students learn. The second part consists of qualitative data. This data shows the perceptions of students of online teaching versus hybrid teaching. It compares the differences in participants’ perceptions reported in students’ feedback on the programs during and post-COVID-19. Our results confirm prominent differences exist in the students’ perceptions of their learning experience during the pre-pandemic and post-pandemic periods. The findings of this study suggest that universities need to strive and define a meaning and purpose of international seminars, which enables students to experience a high level of intercultural social interaction face-to-face.

In celIs, microtubule location, length, and dynamics are regulated by a host of microtubule-associated proteins and enzymes that read where to bind and act based on the microtubule “tubulin code,” which is predominantly encoded in the tubulin carboxy-terminal tail (CTT). Katanin is a highly conserved AAA ATPase enzyme that binds to the tubulin CTTs to remove dimers and sever microtubules. We have previously demonstrated that short CTT peptides are able to inhibit katanin severing. Here, we examine effects of CTT sequences on this inhibition activity. Specifically, we examine CTT sequences found in nature, alpha1A (TUBA1A), detyrosinated alpha1A, Δ2 alpha1A, beta5 (TUBB/TUBB5), beta2a (TUBB2A), beta3 (TUBB3), and beta4b (TUBB4b). We find that these natural CTTs have distinct abilities to inhibit, most noticeably beta3 cannot inhibit katanin. Two non-native CTT tail constructs are also unable to inhibit – despite having 94% sequence identity with alpha1 or beta5 sequences. Surprisingly, we demonstrate that poly-E and poly-D peptides are capable of inhibiting katanin significantly. An analysis of the hydrophobicity of the CTT constructs indicates that more hydrophobic polypeptides are less inhibitory than more polar polypeptides. These experiments not only demonstrate inhibition, but also likely interaction and targeting of katanin to these various CTTs when they are part of a polymerized microtubule filament.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.