Macrobrachium rosenbergii is a valuable freshwater prawn in Asian aquaculture. In recent years, a new symptom that was generally called as ‘white head’ caused high mortality in M. rosenbergii farms in China. Samples of M. rosenbergii, M. nipponense, Procambarus clarkii, M. superbum, Penaeus vannamei, and Cladocera from a farm suffering from ‘white head’ in Jiangsu Province were collected and analyzed in this study. Pathogen detection showed that all samples were positive for Shrimp hemocyte iridescent virus (SHIV). Histopathological examination revealed dark eosinophilic inclusions and pyknosis in hematopoietic tissue, hepatopancreas and gills of M. rosenbergii and M. nipponense. Blue signals of in situ DIG-labeled LAMP (ISDL) appeared in hematopoietic tissue, hemocytes, hepatopancreatic sinus, and antennal gland. TEM of ultrathin sections showed a large number of SHIV particles with a mean diameter about 157.9 nm. The virogenic stromata and budding virions were observed in hematopoietic cells. Quantitative detection by TaqMan probe based real-time PCR of different tissues in natural infected M. rosenbergii showed that hematopoietic tissue contained the highest SHIV load with a relative abundance of (25.4±16.9)%. Hepatopancreas and muscle contained the lowest SHIV load with a relative abundance at (2.44±1.24)% and (2.44±2.16)%, respectively. Above results verified that SHIV is the pathogen causing ‘white head’ in M. rosenbergii, and M. nipponense and Pr. clarkii are also the susceptible species of SHIV.

Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot and cold stored parboiled rice to widely available medium grain white rice. Method: Twenty-eight healthy volunteers participated in a three treatment experiment where postprandial blood glucose was measured over 120 minutes after consumption of 140g of rice. Three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 ºC. All rice was served warm at 65 ºC. Chewing time was recorded. Results: The 24-hours cold-stored and reheated parboiled rice resulted in a 42% and 12% lower blood glucose concentration trajectory than freshly cooked medium-grain white rice and freshly cooked parboiled rice. Chewing time for 10g cold stored parboiled rice was 6 seconds (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium grain (all P<0.05). Conclusions:. For regular consumers of rice reheating cooked rice after cold storage would lower the dietary glycaemic load and long term may reduce risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

In this study, rheological properties of the bread wheat flour dough from 6 wheat genotypes were determined. For the pereparation of flour, 3 red bread (Pandas, Sagitorya, Pehlivan) and 3 white bread (Kaşifbey, Göktan, Ceyhan-99) were selected as wheat genotype. To determine the rheological properties of the wheat flour dough, farinograph, extensograph, mixolab and glutograph devices were used. According to the results of the Farinograph analysis, the average development time of wheat White and red genotypes were 1.95 minutes and 8.96 minutes, respectively. According to the extensograph results of the flour samples, the most extended stability value was determined with 7.47 min in Ceyhan-99 cultivar. As a result of the research, it was determined that flour yields of red bread varieties were higher other genotypes, gas retention capacities of white bread flours were showed high result in extensograph application and resistance of dough to elongation was higher. In the Mixolab analysis, it determined that white bread wheat varieties have higher values in terms of kneading properties and gluten properties, and red bread wheat varieties have higher values in values of viscosity, amylase value and starch retrogradation.

Among non-conventional yeasts of industrial interest, the dimorphic oleaginous yeast Yarrowia lipolytica appears as one of the most attractive for a large range of white biotechnology applications, from heterologous proteins secretion to cell factories process development. The past, present and potential applications of wild type, traditionally improved or genetically modified Yarrowia lipolytica strains will be resumed, together with the wide array of molecular tools now available to genetically engineer and metabolically remodel this yeast. The present review will also provide a detailed description of Yarrowia lipolytica strains and highlight the natural biodiversity of this yeast, a subject little touched upon in most previous reviews. This work intends to fill this gap by retracing the genealogy of the main Yarrowia lipolytica strains of industrial interest, by illustrating the search for new genetic backgrounds and by providing data about the main publicly available strains in yeast collections worldwide. At last, it will focus on exemplifying how advances in engineering tools can leverage a better biotechnological exploitation of the natural biodiversity of Yarrowia lipolytica and of other yeasts from the Yarrowia clade.

Liquorpheresis (CSF filtration) comprises a therapeutical approach that has been proposed to treat several neurological conditions where antibodies, inflammatory mediators or abnormal peptides are the cause or play an important role in the pathogenesis of the disease. CSF replacement may be an alternative approach not explored so far. Here, we review previous experiences in the use of liquorpheresis in autoimmune and degenerative neurological diseases. Then we describe previous developments and provide some new technical innovations in order to design bidirectional CSF shunting systems. These systems can be complemented either with a deposit of artificial CSF or with a CSF filter, allowing CSF replacement or liquorpheresis respectively. Both options would lead to mechanical dilution of the patient’s CSF.

Today, Colebrook’s equation is mostly accepted as an informal standard for modeling of turbulent flow in hydraulically smooth and rough pipes including transient zone in between. The empirical Colebrook’s equation relates the unknown flow friction factor (λ) with the known Reynolds number (R) and the known relative roughness of inner pipe surface (ε/D). It is implicit in unknown friction factor (λ). Implicit Colebrook’s equation cannot be rearranged to derive friction factor (λ) directly and therefore it can be solved only iteratively [λ=f(λ, R, ε/D)] or using its explicit approximations [λ≈f(R, ε/D)]. Of course, approximations carry in certain error compared with the iterative solution where the highest level of accuracy can be reached after enough number of iterations. The explicit approximations give a relatively good prediction of the friction factor (λ) and can reproduce accurately Colebrook’s equation and its Moody’s plot. Usually, more complex models of approximations are more accurate and vice versa. In this paper, numerical values of parameters in various existing approximations are changed (optimized) using genetic algorithms to reduce maximal relative error. After this improvement computational burden stays unchanged while accuracy of approximations increases in some of the cases very significantly.

Αbstract: To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980–1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

The first part of the paper was aimed at analyzing the given nonlinear problem by different methods of computation of the Lyapunov exponents (Wolf method [1], Rosenstein method [2], Kantz method [3], method based on the modification of a neural network [4, 5], and the synchronization method [6, 7]) for the classical problems governed by difference and differential equations (Hénon map [8], hyper-chaotic Hénon map [9], logistic map [10], Rössler attractor [11], Lorenz attractor [12]) and with the use of both Fourier spectra and Gauss wavelets [13]. It was shown that a modification of the neural network method [4, 5] makes it possible to compute a spectrum of Lyapunov exponents, and then to detect a transition of the system regular dynamics into chaos, hyper-chaos, hyper hyper-chaos and deep chaos [14-16]. Different algorithms for computation of Lyapunov exponents were validated by comparison with the known dynamical systems spectra of the Lyapunov exponents. The carried out analysis helps comparatively estimate the employed methods in order to choose the most suitable/optimal one to study different kinds of dynamical systems and different classes of problems in both this and the next paper parts.

Background: Previous in vitro and in vivo studies have demonstrated that storage of cooked rice at 4 °C for 24 h and reheating to 65 °C significantly reduced starch digestibility and postprandial glycaemic responses. Moreover, the effect was greater for parboiled rice compared to other rice varieties commonly consumed in New Zealand. This study aimed to evaluate consumer preferences of related sensory attributes and consumer acceptability of several rice varieties freshly cooked or reheated. Method: Sixty-four consumers volunteered and recorded on Visual Analoge Scales their preference and acceptability of freshly prepared or cold-stored and reheated medium grain white, medium grain brown and parboiled rice. Results: All six rice samples were accepted by participants (average 54%). Reheated parboiled rice and reheated medium grain brown rice were both accepted by participants as a preferred staple meal compared to other rice samples. Among all rice samples, the sweetness and the flavour of freshly cooked warm medium-grain white rice were less preferred (scored 42.1% and 45.0% respectively) compared with other samples (P = 0.05). Participants who prepared and consumed brown rice at home regularly (more than 10 times per month), preferred the reheated brown rice (73.8% (67.4, 80.2)) and reheated parboiled rice (74.3% (67.9, 80.7)) (P < 0.001). Conclusions: It is suggested that reheated parboiled rice, with the lowest starch digestibility and glycaemic impact (both in vitro glucose release and in vivo glucose response) could be accepted as a healthier alternative for the daily staple meal.

The dye effluent is usually difficult to be degraded by conventional wastewater treatment in leather industry. In order to develop efficient and cost-effective treatment methods, we evaluate the effect of white-rot fungus immobilization for dye decolorisation in this paper. The Phanerochaete chrysosporium BKM-F-1767 was used for immobilization. This research found that the white-rot fungus immobilization had an obviously decolorisation effect in dye wastewater treatment, and plant carriers such as sorghum stalk and corn cob were helpful to the growth of Phanerochaete chrysosporium in white-rot fungus immobilization. Due to the stability and recyclability, the white-rot fungus co-immobilization was considered as the most suitable treatment for decolorisation of dye effluent which enjoyed the advantages of both adsorption immobilization and entrapment immobilization. Furthermore, the dye decolorisation evaluation was carried out to find the most suitable carrier for co-immobilization, and it found that sorghum stalk - calcium-alginate gel spherical particle (SS-CGPB) has better decolorisation effect than corn cobs - calcium-alginate gel spherical particle (CC-CGPB), and the dye decolorisation rate was 86.77%. After 5 cycles, the dye decolorisation rate was 85.87% which indicated the SS-CGPB preserved functional integrity successfully. By further analyzing the biodegradation process with white-rot fungus immobilization, the intermediate products were observed and the degradation pathway of acid golden yellow dye molecular was proposed. The results showed that the C-N single bonds attached to the central benzene in the dye molecule were attacked and destroyed in white-rot fungus co-immobilization treatment, thus the structure of dye molecule could be successfully degraded into small molecules which would be more easily treated by conventional treatment methods. Therefore, the white rot fungus co-immobilization might be appropriate for pre-treatment as an important biotechnology for the advanced treatment of dye effluent.

The central promise of personalized medicine is individualized treatments that target molecular mechanisms underlying the physiological changes and symptoms arising from disease. We demonstrate a bioinformatics analysis pipeline as a proof-of-principle to test the feasibility and practicality of comparative transcriptomics to classify two of the most popular in vivo diet-induced models of coronary atherosclerosis, apolipoprotein E null mice and New Zealand White rabbits. Transcriptomics analyses indicate the two models extensively share dysregulated genes albeit with some unique pathways. For instance, while both models have alterations in the mitochondrion, the biochemical pathway analysis revealed, Complex IV in the electron transfer chain is higher in mice, whereas the rest of the electron transfer chain components are higher in the rabbits. Several fatty acids anabolic pathways are expressed higher in mice, whereas fatty acids and lipids degradation pathways are higher in rabbits. This reflects the differences between two translational models of atherosclerosis. This study validates transcriptome analysis as a potential method to precisely identify altered cellular and molecular pathways in atherosclerotic disease, which can be used to individualize treatment even in the absence of genetic data.

The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

“White matter disease” identifies series of different conditions and pathological mechanisms: autoimmune, infectious, toxic-metabolic and vascular. Each of these leads to a global impairment of the neural myelination process through the secondary destruction of previously myelinated structures. To date, the imaging spectrum represents an irreplaceable tool to detect these lesions, describe their distribution patterns and stage them over time. This study aims presents a pictorial review of white matter disease, from pathology to imaging spectrum, reporting current main staging systems with greater emphasis to relationship between cerebrovascular disease and white matter hyper-intensity appearance, and the newest advances in this field.

This work communicates a review on Balmer series hydrogen beta line measurements and applications for analysis of white dwarf stars. Laser-induced plasma investigations explore electron density and temperature ranges comparable to white dwarf star signatures such as Sirius B, the companion to the brightest star observable from the earth. Spectral line shape characteristics of the hydrogen beta line include width, peak separation, and central dip-shift, thereby providing three indicators for electron density measurements. The hydrogen alpha line shows two primary line-profile parameters for electron density determination, namely, width and shift. Both Boltzmann plot and line-to-continuum ratios yield temperature. The line-shifts recorded with temporally- and spatially- resolved optical emission spectroscopy of hydrogen plasma in laboratory settings can be larger than gravitational redshifts that occur in absorption spectra from radiating white dwarfs. Published astrophysical spectra display significantly diminished Stark or pressure broadening contributions to red-shifted atomic lines. Gravitational redshifts allow one to assess the ratio of mass and radius of these stars, and subsequently, the mass from cooling models.

This article explores the cross-cultural significance of three iconic fairy tales: Cinderella, Snow White, and Sleeping Beauty. Using a comparative analysis approach, the study investigates recurring themes, motifs, and symbolism found in these tales across different cultures and time periods. The research draws on primary and secondary sources such as literary works and scholarly articles to uncover the underlying messages that resonate with audiences worldwide. The historical and cultural contexts of the tales are also examined to provide a deeper understanding of their significance in shaping cultural identity and values. The findings of the research demonstrate the enduring legacy of these tales and their continued relevance in contemporary society. The study highlights how the tales have evolved and adapted over time to reflect changing cultural values and beliefs. The interpretation of the findings offers insights into the role of storytelling in shaping cultural identity and values, and the ways in which these stories continue to inspire and captivate audiences worldwide. Overall, this research provides a comprehensive and nuanced understanding of the significance of these three fairy tales across cultures and time. By illuminating the similarities and differences in these tales, the study offers a fresh perspective on the power of storytelling and its ability to transcend cultural boundaries. The research contributes to the fields of folklore, cultural studies, and cross-cultural communication, inspiring further exploration into the captivating world of fairy tales.

Physics behind collimated highly directional nature of lasers, and factors that keep the seven coloured waves that form white light together during their journey from Sun to Earth, in the face of the natural disruptive forces, is not fully understood. Energy levels were measured, in terms of alterations in induced current and voltage, in beams from a red laser, white LED light and the Sunlight before and during their disruption by diffusers (frosted glass for the lasers) and diffractors (diffraction grating for the white light) using a photovoltaic solar cell panel attached to a digital multimeter. Results show that disruption of the beams results in release of extra energy named as ‘Latent Light Binding’ Energy’. It is hypothesized that the ‘binding’ energy keeps laser waves firmly bound together both end-on and side-on enabling laser beams to travel long distances in collimated manner. Likewise, the 7 coloured waves that constitute white light are kept together, probably side-on, in their journey from the Sun to the Earth. The observation that diffraction of sunbeam is associated with increased power generation provides a new lead to improve harnessing of solar energy, where, currently, the focus is mainly on improving efficiency of photovoltaic cell.

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

With proper processing and utilization, biofortified cassava may contribute to the nutritional status of the consumers, thus, the need for this study. High-quality cassava flour from white- (TME 419) and biofortified (TMS 01/1368) cassava varieties were produced at a commercial processing factory, after which the flour is composite with wheat flour to produce bread. The nutritional composition, physical properties and sensory quality of the composite bread were analyzed using standard methods. Results showed that composite bread from 20% biofortified cassava flour (20-YCF) had a higher value of total β-carotene (0.74 μg/g), moisture (37.83%) and ash (2.29%) contents. The fat (3.72%) and protein (12.83%) contents were higher in 20% white cassava flour (20-WCF) composite bread. The 20-YCF composite bread had the highest loaf volume (3286.2 cm3), elasticity (6.32), chewiness (40.51 N) and gumminess (6.41), 20-WCF composite bread had higher specific volume (3.59 cm3/g) and hardness (176.50 N). The 100% wheat bread had higher cohesiveness (0.10) and loaf weight (932.35 g). A significant negative correlation (r = - 0.98, p≤0.05) exist between bread hardness and protein content. The composite bread compared favourably with the 100% wheat bread in terms of weight and aroma, but, the 100% wheat bread was more acceptable.

Background: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin. Method: We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2. Results: The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their method in public repositories. Conclusions: We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction is evaluated. According to the results, ANNGWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and latest heuristic approaches reported in the literature.

The aim of this article is to make a multi-criteria analysis of various exhibition spaces of an originally non-exhibition character and to determine how these spaces affect the selection of works and the exhibition concept. The analysis is based on the exhibitions of art objects at collective exhibitions in unconventional architectural spaces: commercial, i.e. the modern office building of PBG Gallery Skalar Office Centre in Poznań, post-industrial i.e. in the former Zakłady Przemysłu Ziemniaczanego Lubanta S.A. and in the historic interior of the "U Jezuitów" Gallery of the Cultural Integration Centre in Poznań. The multi-criteria comparative analysis shows a variety of features of the studied spaces as well as the relationship between architecture and art and their mutual interaction. The participatory role of the non-exhibition space in the process of creating an exhibition and selecting works has been proven. It has also been confirmed that the presentation of works of art in originally non-exhibition spaces creates a new quality of the artwork. Unconventional architectural space, when used for the exhibition of works of art, expands and strengthens the area of ​​their influence through the interaction between the work and the architectural space. The specificity of the space adapted for exhibition needs, the presence and type of architectural details in the interior, the quantity and quality of light and its distribution in space, the volume and colour of the interior determine the exhibition space and influence the shape of the exhibitions organised and the reception of the artworks. The only condition for the change of the original function of an architectural space into that of an exhibition space is a coherent artistic vision of the creator. This should take into account the appropriate selection of the exhibited objects, where the process of searching for the relationship between architecture and art determines the features of the architectural space as integral components influencing the realisation of the exhibition .

Although the most common forms of brain injury in preterm infants have been associated with adverse neurodevelopmental outcomes, existing MRI scoring systems lack specificity, do not incorporate clinical factors, and are technically challenging to perform. The objective of this study was to develop a web-based, clinically-focused prediction system which differentiates severe from normal-moderate neurodevelopmental outcomes at two years. Infants were retrospectively identified as those who were born ≤30 weeks gestation, had MR imaging at term-equivalent age, and neurodevelopmental testing at 18-24 months. Each MRI was scored on injury in three domains (intraventricular hemorrhage, white matter injury, and cerebellar hemorrhage) and clinical factors strongly predictive of outcome were investigated. A binary logistic regression model was then generated from the composite of clinical and imaging components. A total of 154 infants were included (mean GA = 26.1±1.8 weeks, BW = 889.1±226.2 grams). The final model (imaging score + ventilator days + delivery mode + antenatal steroids + ROP requiring surgery) had strong discriminatory power for severe disability (AUC=0.850), with a PPV of 76% and NPV of 90%. Available as a web-based tool, it can be useful for prognostication and targeting early intervention services to infants who may benefit most from such services.

We present a robust, fresh-frozen approach to immunohistochemistry (IHC), without committing the tissue to IHC via fixation and cryopreservation while maintaining long-term storage, using LiCor-based infrared (IR) quantification for sensitive assessment of TH in immunoreacted mid-brain sections for quantitative comparison across studies. In fresh-frozen tissue stored up to 1 year prior to IHC reaction, we found our method to be highly sensitive to rotenone treatment in 3-month-old Sprague-Dawley rats, and correlated with a significant decline in rotarod laten-cy-to-fall measurement by approximately 2.5 fold. The measured midbrain region revealed a 31% lower TH signal when compared to control (p<0.01 by t test, n=5). Bivariate analysis of in-tegrated TH counts versus rotarod latency-to-fall indicates a positive slope and modest but sig-nificant correlation of R2=0.68 (p<0.05, n=10). These results indicate this rapid, instrument-based quantification method by IR detection successfully quantifies TH levels in rat brain tissue, while taking only 5 days from euthanasia to data output. This approach also allows for the identifica-tion of multiple targets by IHC with the simultaneous performance of downstream molecular analysis within the same animal tissue, allowing for the use of fewer animals per study.

White spot lesions (WSLs), a side effect of orthodontic treatment, can result in reversible and unaesthetic results. Graphene oxide (GO) with a bioactive glass (BAG) mixture(BAG@GO) was added to Low Viscosity Transbond XT(LV) in a ratio of 1, 3, 5%. The composite’s characterization and its physical and biological properties were verified with scanning electron microscopy(SEM) and X-ray diffraction(XRD); its microhardness, shear bond stress (SBS), cell viability, and adhesive remnant index (ARI) were also assessed. Efficiency in reducing WSL was evaluated using antibacterial activity of S. mutans. Anti-demineralization was analyzed using a cycle of the acid-base solution. Adhesives with 3 or 5 wt.% of BAG@GO showed significant increase in microhardness compared with LV. The sample and LV groups showed no significant differences in SBS or ARI. The cell viability test confirmed that none of the sample groups showed higher toxicity compared to the LV group. Antibacterial activity was higher in the 48-hour group than in the 24-hour group; the 48-hour test showed that BAG@GO had a high antibacterial effect, which was more pronounced in 5 wt.% of BAG@GO. Anti-demineralization effect was higher in the BAG@GO-group than in the LV-group; the higher the BAG@GO concentration, the higher the anti-demineralization effect.

Flexibility is perceived as a primary reason for working as a gig worker. However, there are not enough studies that investigate gig workers' motivations. Hence, this study aims to examine the motivation of white-collar gig workers using a sample size of 327 gig workers in Mongolia, a developing country’s labor market. The purpose of this study is to investigate intrinsic and extrinsic motivation factors for white-collar gig workers and their impact on behavioral intentions such as those of remote workers, freelancers, and any representatives of digital gig workers, not location-based gig workers. The primary data used in this study were collected through questionnaires from an online platform that attracts white-collar gig workers. The characteristics of this group are analyzed using Smart PLS4.0 software through the PLS-SEM technique to test the hypothesized relationships. Overall, the study contributes to an extension of current literature by understanding the white-collar digital gig workforce’s motivations and reasons behind it.

Some wind farms have implemented automated camera\textendash based monitoring systems e.g. IdentiFlight to mitigate the impact of wind turbines on protected raptors. These systems have effectuated the collection of large amounts of data that can be used to describe flight behavior in a novel way. This data uniquely provides both flight trajectories and images of individual birds throughout their flight trajectories. The aim of this study was to evaluate how this unique data could be used to create a robust quantitative behavioral analysis, that could be used to identify risk prone flight behavior and avoidance behavior thereby in the future assess collision risk. This was attained through a case study at a wind farm on the Swedish island Gotland, where golden eagles (Aquila chrysaetos), white-tailed eagles (Haliaeetus albicilla), and red kites (Milvus milvus), were chosen as the selected bird species. The results demonstrate that flight trajectories and bird images can be used to identify high risk flight behavior and thereby also used to evaluate collision risk and avoidance behavior. This study presents a promising framework for future research, demonstrating how data from camera\textendash based monitoring systems can be utilized to quantitatively describe risk prone behavior and thereby assess collision risk and avoidance behavior.

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

The abnormal assembly of α-synuclein (α-Syn) is an initial step in the formation of Lewy bodies in the brain, which finally causes the neuronal death, being considered as a pathological hallmark in Parkinson’s disease (PD). Certain food bioactives or their metabolites at very low concentrations can trespass the blood brain barrier (BBB) that might, thereafter, act simultaneously. The aim of this work was to evaluate the inhibitory and destabilising capacities on α-Syn kinetics and the neuroprotective effects of three well-known bioactive compounds able to cross the BBB and present in foods; melatonin (MEL), protocatechuic acid (PCA) and hydroxytyrosol (HT), and their combinations. For this purpose, different in vitro techniques (Thioflavin T (ThT), Transmission Electronic Microscopy (TEM), electrophoresis and MTT assay) were used. All tested compounds and their combinations were able to abolish the toxicity induced by α-Syn. In addition, the combination of PCA (100 µM) +HT (100 µM) showed the highest inhibitory effect against α-Syn fibril formation and destabilises α-Syn fibrils (88 and 62%, respectively). This is the first time that MEL, PCA and HT prove a joint effect against α-Syn aggregation and toxicity when they are tested together.

Chronic gastro-abdominal pain with altered bowel habits are associated with pathologies including gastroenteritis, autoimmune and inflammatory bowel disease, and irritable bowel syndrome (IBS). In IBS, diagnostic evidence of infection or inflammation are absent, yet symptoms nonetheless include chronic abdominal pain and alterations of stool frequency and consistency, with most common subtypes including diarrhea-predominant (IBS-D) or constipation predominant (IBS-C). [1, 2] IBS is a common clinical complaint in westernized nations, with females more frequently diagnosed than males [3]. Obesity is also associated with increased likelihood of chronic pain [4], and is associated with intestinal dysbiosis, and systemic inflammatory signatures [5]. Improving personalized medicine therefore requires patient stratification based on a combination of biological factors contributing to the individual’s symptom and biomarker spectrum. The “buffy coat” method isolates leukocytes and PBMCs from whole blood, comprises the immune cell population in peripheral blood. These cells are relatively inactive but are primed to respond to generalized and localized immune activation signals; they are under active investigation as diagnostic biomarkers [6]. Collection of buffy coat is a standard method in clinical laboratories; detection of immune activation via RNA expression biomarkers will be informative of differential biological response, and be tractable to collect in clinical settings. Results of differential expression, pathway analysis, and cell-type abundance analysis from buffy coat RNA, is presented here using variables for BMI, Gender, and IBS-subtype from publicly available Nanostring RNA expression and phenotypic data from the NCBI GEO database.

Brown adipose tissue (BAT) function may depend on its anatomical location and developmental origin. Interscapular BAT (iBAT) regulates acute macronutrient metabolism, whilst perivascular BAT (PVAT) regulates vascular function. Although phenotypically similar, whether these depots respond differently to acute nutrient excess is unclear. Given their distinct anatomical locations and developmental origins and we hypothesised that iBAT and PVAT would respond differently to brief period of nutrient excess. Sprague-Dawley rats aged 12 weeks (n = 12) were fed either a standard (10% fat, n = 6) or high fat diet (HFD: 45% fat, n = 6) for 72 h and housed at thermoneutrality. Following an assessment of whole body physiology, fat was collected from both depots for analysis of gene expression and the proteome. HFD consumption for 72 h induced rapid weight gain (c. 2.6%) and reduced serum NEFA with no change in either total adipose or depot mass. In iBAT, an upregulation of genes involved in insulin signalling and lipid metabolism was accompanied by enrichment of lipid-related processes and functions, plus glucagon and PPAR signalling pathways. In PVAT, HFD induced a pronounced down-regulation of multiple metabolic pathways which was accompanied with increased abundance of proteins involved in apoptosis (e.g. Hdgf and Ywaq) and toll-like receptor signalling (Ube2n). There was also an enrichment of DNA-related processes and functions (e.g., nucleosome assembly and histone exchange) and RNA degradation and cell adhesion pathways. In conclusion, we show that iBAT and PVAT elicit divergent responses to short-term nutrient excess highlighting early adaptations in these depots before changes in fat mass.

Exopolysaccharides or extracellular polysaccharides (EPS, sPS) represent valuable metabolite compound synthesized from red microalgae. It is a non toxic natural agent and can be applied as immunostimulant. Toxicity test of exopolysaccharides from Porphyridium has been done in-vivo using zebrafish (Danio rerio) embryonic model, or the ZET (Zebrafish Embryotoxicity Test). The administration of extracellular polysaccharide or exopolysaccharides (EPS) from microalgae Porphyridium cruentum (synonym: P. purpureum) on shrimps Litopenaeus vannamei was investigated to determine the effect of this immunostimulant on their non specific immune response and to test if this compound can be used as a protective agent for shrimp related to Vibrio infection. For immune response, exopolysaccharides was given to shrimps by immersion method on day 1 and booster on day 8. Shrimp hemocytes were taken on day 1 (EPS administration), day 7 (no treatment), day 8 (EPS booster) and day 9 (Vibrio infection) and tested for their immune response on each treatment. Result shows that the EPS is not toxic as represented by the normal embryonic development and the mortality data. In the Pacific whiteshrimps, it show an increase in values ​​of all immune parameters in line with the increasing EPS concentration, except the Differential Haemocyte Count (DHC). In detail, an increase was noted in total hemocytes (THC) value, Phagocytotic Activity (PA), Respiratory Burst (RB) in line with the EPS concentration increase. These results and other previous studies indicate that EPS from Porphyridium is safe and it enhances immune parameters in shrimp rapidly and has the ability as an immunostimulant or an immunomodulator. It is a good modulator for the non-specific immune cells of Pacific white shrimps, and it can be used as a preventive agent against vibriosis.

Hypoxia is one of the most common pathological conditions which results from ischemic injury, trauma, inflammatory conditions, tumors, The adaptation of the body to hypoxia is a phenomenon that is of great importance both in normal conditions and in Most of the cellular response’ reactions to hypoxia is associated with a family of transcription factors called hypoxia-inducible factors (HIF). They induce the expression of a wide range of genes that help cells adapt to a hypoxic HIF functions are currently being extensively studied. In 2019, William G. Kaelin and Gregg Semenza from the USA and Sir Peter J. Ratcliffe from the UK received the Nobel Prize in Physiology or Medicine for the discovery of the basic mechanisms of adaptation to hypoxia and investigation of the role of HIF factor in the regulation of the hormone erythropoietin Based on its pivotal physiological importance, the HIF factor attracts more and more attention as a new potential target for treating a large number of diseases associated with Most of the experimental work dealing with the HIF factor is focused on its role in liver and However, increasing amount of experimental results clearly demonstrates that the HIF factor-based response represents an universal adaptation mechanism for all kinds of tissues, including the nervous system where HIF is critical for regulating neurogenesis, nerve cell differentiation, and neuronal This review provides actual overview about the complex role of HIF-1 in the adaptation of nerve cells to hypoxia with the focus on its potential role by various neuronal

The eighty years old empirical Colebrook function widely used as an informal standard for hydraulic resistance relates implicitly the unknown flow friction factor , with the known Reynolds number and the known relative roughness of a pipe inner surface ; . It is based on logarithmic law in the form that captures the unknown flow friction factor in a way from which it cannot be extracted analytically. As an alternative to the explicit approximations or to the iterative procedures that require at least a few evaluations of computationally expensive logarithmic function or non-integer powers, this paper offers an accurate and computationally cheap iterative algorithm based on Padé polynomials with only one -call in total for the whole procedure (expensive -calls are substituted with Padé polynomials in each iteration with the exception of the first). The proposed modification is computationally less demanding compared with the standard approaches of engineering practice, but does not influence the accuracy or the number of iterations required to reach the final balanced solution.

White matter hyperintensity (WMH) has been considered the primary biomarker from small-vessel cerebrovascular disease to Alzheimer’s disease (AD) and has been reported for its correlation of brain structural changes. To perform WMH related analysis with brain structure, both T1-weighted (T1w) and (Fluid Attenuated Inversion Recovery(FLAIR) are required. However, in a clinical situation, it is limited to obtain 3D T1w and FLAIR images simultaneously. Also, the most of brain segmentation technique supports 3D T1w only. Therefore, we introduced the semi-supervised learning method that can perform brain segmentation using FLAIR image only. Our method achieved a dice overlap score of 0.86 for brain tissue segmentation on FLAIR, with the relative volume difference between T1w and FLAIR segmentation under 4.8%, which is just as reliable as the segmentation done by its paired T1w image. We believe our semi-supervised learning method has a great potential to be used to other MRI sequences and provide encouragement to people who seek brain tissue segmentation from a non-T1w image.

This study documents the conditions associated to white spruce and balsam fir regeneration after partial cutting. Measurements were collected 9 to 30 years after partial cutting in 12 natural fir stands and 5 white spruce plantations. We estimated seed input, measured light reaching the undergrowth, recorded seedlings (<150 cm) and their age on 6 different seedling establishment substrates: mineral soil, moss, rotten wood, litterfall, herbaceous and dead wood. Partial cutting generally favours the establishment and growth of seedlings. The number of fir and spruce seedlings is always greater in natural stands than in plantations, a trend likely associated with the reduced abundance of preferential establishment substrate in the latter. White spruce significantly prefers rotten wood while fir settles on all types of substrates that cover at least 10% of the forest floor. There is a strong relationship between light intensity and the median height of spruce seedlings, but this relationship is non-significant for fir. Seedlings of both species can survive at incident light intensities as low as 3%, but an intensity of 15% or more seems to offer the best growth conditions. The conditions for successful forest regeneration proposed in this study should be applied when the goal is to establish a new stand prior to clear cutting or to convert stand structure.

Ageing wine is a commonly practice used in winemaking since the quality and sensory profile increase due to the extractable compounds coming from wood by means of barrels or chips. The quantitative and qualitative compounds from wood depend on the species, its origins and the treatments applied in cooperages. Traditionally, oak wood species are most often used in cooperage, specifically Quercus alba, Known as American oak and Q. robur and Q. petraea both known as French oak. However, although this stage is very common for red wines, its use is still restricted in the case of white wines. However, this topic is particularly interesting, since due to the sensorial benefits of wood contact, the option for ageing white wines in barrels or chips is increasingly and widely chosen by winemakers. This review compiles the novel strategies applied to white wines by means of wood contact in the last years with the aim to increase wine quality and sensorial features.

Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and brain volumes and white matter integrity (FA and MD) with magnetic resonance imaging in the BrainSCALE cohort, consisting of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years of the twins. Genes influenced externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to –0.20); with sizeable genetic correlations (FA ra up to +0.42; MD ra up to –0.33). Cortical gray matter (CGM; rph up to –0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genotypically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence from the same genetic factor.

Empirical Colebrook equation from 1939 is still accepted as an informal standard to calculate friction factor during the turbulent flow (4000 < Re < 10⁸) through pipes from smooth with almost negligible relative roughness (ε/D→0) to the very rough (up to ε/D = 0.05) inner surface. The Colebrook equation contains flow friction factor λ in implicit logarithmic form where it is, aside of itself; λ, a function of the Reynolds number Re and the relative roughness of inner pipe surface ε/D; λ = f (λ, Re, ε/D). To evaluate the error introduced by many available explicit approximations to the Colebrook equation, λ ≈ f(Re, ε/D), it is necessary to determinate value of the friction factor λ from the Colebrook equation as accurate as possible. The most accurate way to achieve that is using some kind of iterative methods. Usually classical approach also known as simple fixed point method requires up to 8 iterations to achieve the high level of accuracy, but does not require derivatives of the Colebrook function as here presented accelerated Householder’s approach (3^rd order, 2^nd order: Halley’s and Schröder’s method and 1^st order: Newton-Raphson) which needs only 3 to 7 iteration and three-point iterative methods which needs only 1 to 4 iteration to achieve the same high level of accuracy. Strategies how to find derivatives of the Colebrook function in symbolic form, how to avoid use of the derivatives (Secant method) and how to choose optimal starting point for the iterative procedure are shown. Householder’s approach to the Colebrook’s equations expressed through the Lambert W-function is also analyzed. One approximation to the Colebrook equation based on the analysis from the paper with the error of no more than 0.0617% is shown.

An accurate outbreak prediction of COVID-19 can successfully help to get insight into the spread and consequences of infectious diseases. Recently, machine learning (ML) based prediction models have been successfully employed for the prediction of the disease outbreak. The present study aimed to engage an artificial neural network-integrated by grey wolf optimizer for COVID-19 outbreak predictions by employing the Global dataset. Training and testing processes have been performed by time-series data related to January 22 to September 15, 2020 and validation has been performed by time-series data related to September 16 to October 15, 2020. Results have been evaluated by employing mean absolute percentage error (MAPE) and correlation coefficient (r) values. ANN-GWO provided a MAPE of 6.23, 13.15 and 11.4% for training, testing and validating phases, respectively. According to the results, the developed model could successfully cope with the prediction task.

The Colebrook equation ζ is implicitly given in respect to the unknown flow friction factor λ ; λ=ζ(Re,ε*,λ) which cannot be expressed explicitly in exact way without simplifications and use of approximate calculus. Common approach to solve it is through the Newton-Raphson iterative procedure or through the fixed-point iterative procedure. Both requires in some case even eight iterations. On the other hand numerous more powerful iterative methods such as three-or two-point methods, etc. are available. The purpose is to choose optimal iterative method in order to solve the implicit Colebrook equation for flow friction accurately using the least possible number of iterations. The methods are thoroughly tested and those which require the least possible number of iterations to reach the accurate solution are identified. The most powerful three-point methods require in worst case only two iterations to reach final solution. The recommended representatives are Sharma-Guha-Gupta, Sharma-Sharma, Sharma-Arora, Džunić-Petković-Petković; Bi-Ren-Wu, Chun-Neta based on Kung-Traub, Neta, and Jain method based on Steffensen scheme. The recommended iterative methods can reach the final accurate solution with the least possible number of iterations. The approach is hybrid between iterative procedure and one-step explicit approximations and can be used in engineering design for initial rough, but also for final fine calculations.

This work discusses laboratory measurements of atomic and diatomic molecular species in laser-plasma generated in gases. Noticeable self-absorption of the Balmer series hydrogen alpha line occurs for electron densities of the order of one tenth of standard ambient temperature and pressure density. Emission spectra of selected diatomic molecules in air or specific gaseous mixtures at or near atmospheric pressure reveal minimal plasma re-absorption. Abel inversion of the plasma in selected gases and gas mixtures confirm expansion dynamics that unravel regions of atomic and molecular species of different electron temperature and density. Time resolved spectroscopy diagnoses self-absorption of hydrogen alpha and hydrogen beta lines in ultra-high pure hydrogen gas. Radiation from a Nd:YAG laser device induces micro-plasma for pulse widths in the range of 6 ns to 14 ns, energies in the range of 100 mJ to 800 mJ, and for peak irradiance of the order 1 to 10 TW/cm². Atomic line profiles yield electron density and temperature from fitting of line profiles to wavelength and sensitivity corrected spectral radiance data. Analysis of measured diatomic emission data yields excitation temperature of primarily molecular recombination spectra. Applications of the laboratory experiments extend to investigations of stellar astrophysics white dwarf spectra.

I wish to suggest a hypothesis regarding the physiological function of alpha-synuclein, a protein implicated in Parkinson disease, whose cell biology is still debated. The essence is the concept of Carnot cycle of refrigeration in which membrane fluidity depends on the heat produced by mitochondria. The hypothesis has the potential to explain the diverse phenotypes attributed to alpha-synuclein under both physiological and pathological conditions.

Lipid metabolism is clearly associated to Parkinson´s disease (PD). Although lipid homeostasis has been widely studied in multiple animal and cellular models as well as in blood derived from PD individuals, the cerebrospinal fluid (CSF) lipidomic profile in PD remains largely unexplored. In this study, we have characterized the CSF lipidomic imbalance between neurologically intact controls (n=10) and PD subjects (n=20). The combination of dual extraction with ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC-ESI-qToF-MS/MS) allowed to monitor 257 lipid species across all samples. Complementary multivariate and univariate data analysis pointed out that glycerolipids (mono-, di-, and triacylglycerides), saturated and mono/polyunsaturated fatty acids, primary fatty amides, glycerophospholipids (phosphatidylcholines, phosphatidylethanolamines), sphingolipids (ceramides, sphingomyelins), N-acylethanolamines and sterol lipids (cholesteryl esters, steroids) were significantly increased in the CSF of PD compared to control group. These results, despite the limitation of being obtained in a small population, demonstrate and extensive CSF lipid remodelling in PD, shedding new light on the deployment of CSF lipidomics as a promising tool to identify potential lipid markers as well as discriminatory lipid species between PD and other atypical parkinsonisms.

The objective of this study was to evaluate the efficacy of fungal treatment (Pleurotus ostreatus) of urea-treated rice stubble on growth performance in slow-growing goats. Eighteen crossbred Thai native x Anglo-Nubian male goats (average body weight: 20.4 ± 2.0 kg) were randomly assigned to three experimental total mixed rations containing 35% rice stubble (RS) that was either untreated (URS), urea treated (UTRS) or treated with urea and fungi (UFTRS). URS and UTRS were cultivated and harvested from aseptically fungal spawn, incubated at 25-30 °C for 25 days. Indicators of growth performance were monitored and faeces were collected quantitatively to assess nutrient digestibility, during a 12-week feeding trial. All goats remained healthy throughout experiment. The goats fed UFTRS had a lower feed conversion ratio (kg feed/kg growth) compared to goats fed URS or UTRS. Compared to URS, dietary UFTRS increased nutrient digestibility of slow-growing goats, such as OM (+8.5%), CP (+5.5%), NDF (+39.2%), and ADF (+27.4%). Likewise, dietary UFTRS tended to increase rumen ammonia concentrations but rumen pH and volatile fatty acids were not affected by UFTRS. In conclusion, the present study indicates that the fungal treatment of RS is an effective tool to improve the growth performance of slow-growing goats.

Although working memory (WM) is crucial for intellectual abilities, not much is known about its brain underpinnings, especially the structural connectivity. We used diffusion tensor imaging (DTI) to look across the whole brain for the white matter integrity correlates of the individual differences in the reading span (verbal WM capacity during reading) in healthy adults. Right-handed healthy native Russian speakers (N = 67) underwent DTI on a 3T Philips Ingenia scanner. Verbal WM was assessed with the Daneman-Carpenter reading span test (Russian version). Fractional anisotropy maps from each participant were entered into the group tract-based spatial statistics analysis with the reading span as a covariate; the results were TFCE-corrected. After taking into account effects of age, sex, education and handedness, reading span positively correlated with the white matter integrity in multiple sites: the body, the genu and the splenium of corpus callosum; bilateral corona radiata (anterior, posterior, and superior); bilateral superior longitudinal fasciculus; several tracts in the right hemisphere only, including the internal and external capsule; bilateral superior parietal and frontal white matter. Although the left hemisphere is central for verbal processing, we revealed the important role of the right hemisphere white matter for the verbal WM capacity. Our finding indicates that larger verbal working memory span may originate from additional processing resources of the right hemisphere.

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10^-5.

Cyrene was evaluated for the first time as a potential sustainable mobile phase solvent in re-versed-phase chromatography. As a benign biodegradable solvent, Cyrene is an attractive re-placement to classical non-green organic chromatographic solvents such as acetonitrile and a modifier, co-eluent to known green solvents such as ethanol. Compared to ethanol, Cyrene is less toxic, non-flammable, bio-based, biodegradable and cheaper solvent. A fire safety spider Chart was generated to compare the properties of Cyrene to ethanol and show its superiority as a greener solvent. Cyrene's behaviour, advantages, and drawbacks in reversed-phase chromatography, including cut-off value, elution power, selectivity, and effect on the column, were investigated using a model drug mixture of moxifloxacin and metronidazole. A monolithic C18 (10 x 4.6 mm) column was used as a stationary phase. Different ratios of Cyrene: ethanol with an aqueous portion of sodium acetate buffer mobile phases were tested. A mobile phase consisting of Cyrene: ethanol: 0.1 M sodium acetate buffer pH 4.25 (8:13:79, v/v) was selected as most suitable mobile phase system for separating and simultaneously determining metronidazole and moxifloxacin. The greenness and whiteness of the method were evaluated using the qualitative green assessment tool AGREE and the white analytical chemistry assessment tool RGB12. Further potentials of Cyrene as a solvent or modifier in normal phase chromatography, liquid chromatography-mass spectrometry, and supercritical fluid chromatography are discussed.

It is not established whether SARS-CoV-2 (COVID-19) patients with movement disorders, are at greater risk for more serious outcomes than the larger COVID-19 population beyond the susceptibility associated with greater age. We reviewed electronic health records and conducted telephone interviews to collect the demographics and clinical outcomes of patients seen at our Movement Disorders Center who tested positive for COVID-19 from 8 March 2020 through 6 June 2020. Thirty-six patients were identified, 23 men and 13 women, median age of 74.5 years. They primarily carried diagnoses of idiopathic Parkinson disease (n=22; 61%) and atypical parkinsonism (n=7; 19%) with the balance having other diagnoses. Twenty-seven patients (75%) exhibited alteration in mental status and fifteen (42%) had abnormalities of movement as common manifestations of COVID-19; in 61% and 31%, these were the presenting symptoms of the disease. 67% of patients in our cohort required hospitalization, and the mortality rate was 39%.. These data demonstrate that in patients with movement disorders, the likelihood of hospitalization and death after contracting COVID-19 was substantially greater than in the general population. Patients with movement disorders frequently presented with altered mental status, generalized weakness, or worsening mobility but not anosmia.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

This work communicates laser-plasma experiments in a gaseous mixture of hydrogen and nitrogen. Time-resolved spectroscopy measures the first four Balmer series hydrogen lines together with selected neutral and ionized nitrogen lines. Optical breakdown plasma is generated in a 1:1 hydrogen:nitrogen mixture at ambient temperature and 0.27-atm pressure. Time-resolved spectroscopy records emitted radiation with spatial resolution along the slit height for the H_α, H_β, H_γ, and H_δ lines. For 13 selected time delays from 0.25 µs to 3.25 µs and 0.025 µ gate-widths, micro-plasma diagnostics is evaluated. Of interest are the peak-separation and width of H_δ and width of H_γ for electron densities in the range of 0.1 to 1.0×10¹⁷ cm^-3, and comparisons with H_β and H_α diagnostics. Integral inversions interrogate spatial distributions of the plasma expansion. Applications include laboratory and stellar astrophysics plasma diagnosis.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

This work communicates the connection of measured shadowgraphs from optically induced air breakdown with emission spectroscopy in selected gas mixtures. Laser-induced optical breakdown is generated using 850 mJ and 170 mJ, 6-ns pulses at a wavelength of 1064 nm, the shadowgraphs are recorded using time-delayed 5-ns pulses at a wavelength of 532 nm and a digital camera, and emission spectra are recorded for typically a dozen of discrete time-delays from optical breakdown by employing an intensified charge-coupled device. The symmetry of the breakdown event can be viewed as close-to spherical symmetry for time-delays of several 100 ns. Spectroscopic analysis explores well-above hypersonic expansion dynamics using primarily the diatomic molecule cyanide and atomic hydrogen emission spectroscopy. Analysis of the air breakdown and selected gas breakdown events permits the use of Abel inversion for inference of the expanding species distribution. Typically, species are prevalent at higher density near the hypersonically expanding shockwave, measured by tracing cyanide and a specific carbon atomic line. Overall, recorded air breakdown shadowgraphs are indicative of laser-plasma expansion in selected gas mixtures, and optical spectroscopy delivers analytical insight into plasma expansion phenomena.

This research aimed to establish the relationship between carbon-nitrogen nutritional factors and copper sulfate on laccase activity (LA) by Pleurotus ostreatus. Culture media composition was tested to choose the nitrogen source. Yeast extract (YE) was selected as a better nitrogen source than ammonium sulfate. Then, the effect of glucose and YE concentrations on biomass production and LA as response variables was evaluated using central composite experimental designs with and without copper. The results showed that the best culture medium composition was glucose 45 gL-1 and YE 15 gL-1, simultaneously optimizing these two response variables. The fungal transcriptome was obtained in this medium with or without copper, and the differentially expressed genes were found. Main up-regulated transcripts included three laccase genes (lacc2, lacc6, and lacc10) regulated by copper, whereas the principal down-regulated transcripts included a copper transporter (ctr1) and a regulator of nitrogen metabolism (nmr1). These results suggest that Ctr1, which facilitates the entry of copper in the cell, is regulated by nutrient-sufficiency conditions. Once inside, copper induces transcription of laccase genes. This finding could explain why a 10 to 20-fold increase in LA occurs with copper compared to cultures without copper when using the optimal concentration of YE as nitrogen sources.

Abstract: Systemic inflammatory response syndrome (SIRS) is a clinical diagnosis based on abnormalities in patients' vital signs and white blood cell count. Neutrophil-lymphocyte ratio (NLR) is used as an easily accessible parameter to assess the patient's inflammatory status and stress. There are some studies showing that NLR affected by the inflammatory response is a criterion to support the diagnosis of SIRS. In this study, it was aimed to compare the interrelation between SIRS (+) and SIRS (-) groups in patients with high positive feline coronavirus (FCoV) in the light of NLR. Based on the anamneses, physical examination findings, laboratory findings, enzyme immunoassay antibody test kit titer were included in the sick groups. These patients were further categorized based on the presence of SIRS symptoms, vital signs and laboratory findings. The NLR as diagnostic marker demonstrated to differ between high antibody titer FCoV positive and healthy cats. However the diagnostic significance of NLR remains questionable between the high antibody titer FCoV positive cats with SIRS (+) and SIRS (-). As a result of our study NLR can be used as a blood parameter like albumin/globulin ratio and elevated total bilirubin in cats with high FCoV antibody titer and high probability of developing FIP.

The anarchic hand syndrome refers to an inability to control the movements of one’s own hand which acts as if it had a will of its own. The symptoms may differ depending on whether the brain lesion is anterior, posterior, callosal or subcortical, but the relative classifications are not conclusive. This study investigates the role of white matter disconnections in a patient whose symptoms are inconsistent with the mapping of the lesion site. A repeated neuropsychological investigation was associated with a review of the literature on the topic to identify the frequency of various different symptoms relating to this syndrome. Furthermore, an analysis of the neuroimaging regarding structural connectivity allowed us to investigate the grey matter lesions and white matter disconnections. The results indicated that some of the patient’s symptoms were associated with structures that, although not directly damaged, were dysfunctional due to a disconnection in their networks. This suggests that the anarchic hand may be considered as a disconnection syndrome involving the integration of multiple antero-posterior, insular and interhemispheric networks. In order to comprehend this rare syndrome better, the clinical and neuroimaging data need to be integrated with the clinical reports available in the literature on the topic.

The thermodynamics of black holes is discussed for the case, when the Newton constant G is not a constant, but is the thermodynamic variable. This gives for the first law of the Schwarzschild black hole thermodynamics: d S BH = − A d K + d M T BH , where the gravitational coupling K = 1 / 4 G , M is the black hole mass, A is the area of horizon, and T BH is Hawking temperature. From this first law it follows that the dimensionless quantity M 2 / K is the adiabatic invariant, which in principle can be quantized if to follow the Bekenstein conjecture. From the Euclidean action for the black hole it follows that K and A serve as dynamically conjugate variables. This allows us to calculate the quantum tunneling from the black hole to the white hole, and determine the temperature and entropy of the white hole.

The sources of dark matter and its spatial distribution is not in the typical standard model. The study thought to induct the physical causalities with empirical evidence on the theory of black hole and white hole juxtapose. The research applied a mixed method of data analysis and observational cosmology. With the cosmological observations on the traces of white holes, the research finds that the accretion phenomenon of black holes is largely contributed by its thermonuclear binding with the white hole. The antimatter saturated plasma of cold fusion and hot fission are key to the phenomenon of black hole and white hole juxtapose. The force between the black hole and the white hole is stronger than the strong force, and sheds new directions on cosmological and gravitation studies. The research concludes that black holes are white hole entropy. The higher order of the universe in matter forms can exist beyond the observed force. The thermal arrow of time is critical to future general relativity dependent instrumentation discoveries.

Neurodegenerative diseases (NDs) affect millions of people worldwide, and to date Alzheimer's and Parkinson's disease are the most common NDs. The risk of developing NDs increases dra-matically with age, and genetic and environmental factors can be crucial in their progression. The main feature of NDs is the abnormal presence of specific proteins that can lead to neuronal death, proteotoxic stress, and alteration of catabolic systems, which are major players in the processes of neurodegeneration and neuroinflammation. Despite research efforts to date, the therapies cur-rently in use for the treatment of NDs provide marginal therapeutic benefit, so the focus is now being direct toward the search of natural products that could be a valuable aid in combating these diseases, including the natural compound Andrographis paniculata (Ap) and especially its main constituent Andrographolide (Andro). Preclinical studies have shown that the aqueous extract of Ap is capable to modulate neuroinflammatory and neurodegenerative responses, determining a reduction in inflammatory markers and oxidative stress in various NDs. Therefore, in this review we will focus on the molecular mechanisms through which Ap and Andro are able to modulate the processes of neurodegeneration and neuroinflammation that are major causes of neuronal death and cognitive decline.

White-rot fungi (WRF) have specific enzymes to degrade lignocellulosic and phenolic compounds. Therefore, their direct application could be an alternative to biodegrade complex lignocellulosic biomass such as olive mill solid waste (OMSW). The aim of this study was to evaluate the capacity of A. discolor and S. hirsutum to grow in OMSW as the sole substrate under static conditions and evaluate the phenolic removal compounds and lignin degradation. The lignolytic enzyme activity was determined, as was the phenolic compound removal. At the same time, lignin degradation and structural changes were evaluated by confocal laser scanning microscopy (CLSM) and scanning electron microscope (SEM), respectively. Both strains were able to grow using OMSW as the sole substrate without adding other nutrients, oxygen and/or agitation. The higher ligninolytic enzyme activity was found at day 8, and the highest phenolic removal (more than 80% with both strains) was reached after 24 days of incubation. The CLSM analysis confirmed lignin degradation through the drop in lignin fluorescence from 3967 for untreated OMSW to 235 and 221 RFU after 24 days of treatment by A. discolor and S. hirsutum respectively. The results indicate that both WRF could be suitable candidates to design an in-situ pretreatment step of OMSW, as long as in future research the WRFs have the same performance in non-sterile conditions.

Post-traumatic stress disorder (PTSD) is a chronic psychiatric disorder that occurs following exposure to traumatic events. The prevalence of PTSD is estimated to be 5-10% worldwide, and it is associated with significant distress and disability. Recent evidence suggests that PTSD may be a risk factor for the development of subsequent neurodegenerative disorders, including Alzheimer’s dementia and Parkinson’s disease. Identification of biomarkers known to be associated with neurodegeneration in patients with PTSD would shed light on the pathophysiological mechanisms linking these disorders, and would also help in the development of preventive approaches aimed at reducing the risk of neurodegenerative disorders in PTSD. In the current review, the PubMed and Scopus databases were searched for studies aimed at identifying genetic, biochemical, neuroimaging or behavioral markers associated with neurodegeneration in patients with PTSD. Out of a total of 342 citations retrieved, 29 relevant studies were identified for inclusion in the review. The results of these studies suggest that several potentially relevant biological markers, including cerebral cortical thinning, disrupted white matter integrity, specific genetic polymorphisms, immune-inflammatory alterations, vitamin D deficiency, metabolic syndrome and parasomnias, may be associated with an increased risk of neurodegeneration in patients with PTSD. Though many of these results need replication, they highlight a number of biological pathways that plausibly link PTSD with neurodegenerative disorders, and suggest potentially valuable avenues for prevention and early intervention in this patient population.

In Latvia, livestock depredation by wolves has increased during the last two decades. Most attacks had occurred in summer and autumn within wolf hunting season. Cumulative numbers of wolf attacks and number of affected sheep per year at regional forest management units were analyzed in relation to estimated wolf density, extent of culling and proportion of juveniles, as well as sheep density and estimated number of wild prey. The response variables (cumulative number of attacks and cumulative number of affected sheep) were modelled by a negative binomial regression, testing effects of every covariate separately and building models from the significant covariates. Depredation level was related to sheep density and estimated wolf population size. No reducing effect was found for culling, and even greater depredation rate was expected at higher proportions of culled wolves. Estimated number of wild prey or proportion of juvenile wolves had an insignificant effect. However, greater numbers of affected sheep were expected at higher red deer density, suggesting increased opportunistic livestock depredation when the red deer may locally outcompete the preferred wolf prey – roe deer.

In this study, divalent Yb2+-doped silica fiber was fabricated using rod-in-tube technology. The fiber core of Yb2+-doped silica glass was prepared by high-temperature melting technology under vacuum conditions. The spectroscopic properties of the Yb2+-doped glass and fiber were studied. The experiments indicate that divalent Yb2+-doped glass has a high quantum efficiency and superbroadband fluorescence in the visible region with an excitation wavelength of 405 nm. In additionally, the results suggest that Yb2+-doped fiber has a potential for application in visible fiber laser and fiber amplification.

Mucolares are an ancient group of fungi encompassing the causal agents for the lethal infection mucormycosis. The high lethality rates, the emerging character of this disease, and the broad antifungal resistance of its causal agents are mucormycosis features alarming clinicians and researchers. Thus, the research field around mucormycosis is currently focused on finding specific weaknesses and targets in Mucorales for developing new treatments against mucormycosis. In this work, we tested the role of the white-collar genes family in the virulence potential of Mucor lusitanicus. In this regard, the study of the role in virulence of the three genes of this family, mcwc-1a, mcwc-1b, and mcwc-1c, resulted in a marked functional specialization, as only mcwc-1a was essential to maintain the virulence potential of M. lusitanicus. The traditional role of wc-1 genes regulating light-dependent responses is a thoroughly studied field, whereas their role in virulence remains uncharacterized. In this work, we investigated the mechanism involving mcwc-1a in virulence from an integrated transcriptomic and functional approach during the host-pathogen interaction. Our results revealed mcwc-1a as a master regulator controlling an extensive gene-network. Further dissection of this gene-network clustering its components by type of regulation and functional criteria disclosed a multifunctional mechanism depending on diverse pathways. In the absence of phagocytic cells, mcwc-1a controlled pathways related to cell motility and cytoskeleton that could be associated with the essential tropism during tissue invasion. After phagocytosis, several oxidative responding pathways dependent on mcwc-1a were activated during the germination of M. lusitanicus spore inside of phagocytic cells, which is the first stage of the infection. The third relevant group of genes involved in virulence and regulated by mcwc-1a belonged to the “unknown function,” indicating that new and hidden pathways are involved in virulence. The unknown function category is especially pertinent in the study of mucormycosis, as it is highly enriched in specific fungal genes that represent the most promising targets for developing new antifungal compounds. These results essentially unveil a complex multifunctional mechanism used by wc-1 genes to regulate the pathogenic potential in Mucorales that could also apply to other fungal pathogens.

Iron overnutrition has been implicated with higher risk of developing metabolic and cardiovascular diseases, including metabolic syndrome (MetS), while iron deficiency anemia exacerbates many underlying chronic conditions. Hemoglobin (Hb) concentration in the blood, which reflects a major functional iron (i.e., heme iron) in the body, may serve as a surrogate of iron nutrition status. We conducted sex-specific observational association studies where we carefully titrated the association between Hb deciles and MetS and its components among the Taiwanese Han Chinese (HC) from the Taiwan Biobank and Europeans of White ancestry from the UK Biobank, representing two large ethnicities. Our data show that at higher-than-normal levels of Hb, increasing deciles of Hb concentration were significantly associated with MetS across all sex subgroups in both ethnicities, with the highest deciles resulting in up to three times greater risks than the reference group [Taiwanese HC: OR=3.17 (95% CI, 2.75-3.67) for Hb >16.5 g/dL in men, OR=3.11 (2.78-3.47) for Hb >14.5 g/dL in women; European Whites: OR=1.89 (1.80-1.98) for Hb >16.24 g/dL in men, OR=2.35 (2.24-2.47) for Hb >14.68 g/dL in women]. The association between stronger risks and increasing Hb deciles was similarly observed with all metabolic components except diabetes. Here we found that both the highest Hb decile groups and contrarily the lowest ones, with respect to the reference, were associated with higher odds of diabetes in both ethnic groups [e.g., Taiwanese HC men: OR=1.64 (1.33-2.02) for Hb >16.5 g/dL, OR=1.71 (1.39-2.10) for Hb <13.5 g/dL; European Whites women: OR=1.39 (1.26-1.45) for Hb >14.68 g/dL, OR=1.81 (1.63-2.01) for Hb <12.39 g/dL]. These findings confirm that elevated Hb concentrations, a potential indicator of iron overnutrition, may play a role in the pathophysiology of MetS and metabolic components.

Background: Parkinson's disease (PD) is a global disease. However, there is a lack of research specifically focused on the AfrAbia population. Climate change, driven mainly by global warming, is linked to detrimental health impacts, including an increased frequency and intensity of heat waves. Neurological diseases, including neurodegenerative syndromes like Alzheimer's disease, Parkinson's disease, and motor neuron illnesses, have been increasingly seen and recorded in scientific literature. Although there is much literature demonstrating the growth in neurodegenerative disorders, the relationship between environmental variables such as global warming, air pollution, heavy metals, and pesticides and the higher occurrence of these illnesses is still unknown. Objective: The current article primarily aims to examine the relationship between climatic changes, air pollution, pesticides, heavy metals, and the occurrence of PD. Initially, we evaluate the influence of exposure to these variables on the cause or origin of Parkinson's disease. In addition, we will investigate how these variables disrupt processes such as excitotoxicity, oxidative stress, and neuroinflammation, all of which are strongly linked to Parkinson's disease (PD). Conclusions: This research presents a plausible rationale for the adverse effects of environmental influences on the brain, which are pivotal in the progression or onset of Parkinson's disease from an AfrAbian standpoint.

Due to production space and operating environment requirements, mine production equipment often breaks down, which seriously affects the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the number of the equipment, while and the ore quality was stable. Moreover, the improved optimization algorithm converged fast and was characterized by high robustness.

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps,obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Recent data highlight similarities between neurodegenerative diseases including PD and type 2 diabetes mellitus (T2DM) suggesting a crucial interplay between the gut–brain axis. GLP-1R agonists, known for their use in T2DM treatment, are currently extensively studied as novel PD modifying agents. For this narrative review article, we searched PubMed and Scopus databases for peer-reviewed research, review articles and clinical trials regarding GLP-1R agonists and PD, published in the English language with no time restrictions. We also screened the references of the selected articles for possible additional articles in order to include most of the key recent evidence. Our research was conducted between February 2023 and February 2024. We used the terms “GLP-1”, “GLP-1 receptor”, “GLP-1R agonists”, “PD”, “T2DM”, “insulin”, “neurodegenerative disease”, “glucose”, “animal models”, “clinical trials” in different combinations. Many data on animal models and preclinical studies show that GLP1-R agonists can restore dopamine levels, inhibit dopaminergic loss, attenuate neuronal degeneration and alleviate motor and non-motor features of PD. Evidence from clinical studies are also very promising enhancing the possibility of adding GLP1-R agonists in the current armamentarium of drugs available for PD treatment.

In the present study, we studied the effect of apolipoprotein A-1 (APOA1) on the spatial and molecular characteristics of bone marrow adipocytes, using well-characterized ApoA1 knockout mice. APOA1 is a central regulator of high-density lipoprotein cholesterol (HDL-C) metabolism, and thus HDL; our recent work showed that deficiency of APOA1 increases bone marrow adiposity in mice. We found that ApoA1 deficient mice have greatly elevated adipocytes within their bone marrow compared to wild type counterparts. Morphologically, the increased adipocytes were similar to white adipocytes, and displayed proximal tibial-end localization. Marrow adipocytes from wild type mice were significantly fewer and did not display bone-end distribution pattern. The mRNA levels of the brown/beige adipocyte-specific markers Ucp1, Dio2, Pat2, Pgc1a, and the expression of leptin were greatly reduced in the ApoA1 knock-out in comparison to the wild-type mice. In the knock-out mice adiponectin was remarkably elevated. In keeping with the close ties of hematopoietic stem cells and marrow adipocytes, we found that the elevated adiposity in the ApoA1 knock out mice is associated with a significant reduction of the hematopoietic stem cells and common myeloid, but not common lymphoid, progenitors. Moreover, the “beiging”-related marker osteopontin and the angiogenic factor VEGF were also reduced in the ApoA1 knock-out mice, further supporting the notion that APOA1, and most probably HDL-C, regulate bone marrow microenvironment, favouring beige/brown adipocyte characteristics.

The relationship between Parkinson's disease (PD), the second most common neurodegenerative disease after Alzheimer's disease, and palmitoylation, a post-translational lipid modification, is not well understood. In this study, we have analyzed the differential palmitome present in the cerebral cortex of PD patients compared to controls (n=4 per group) to better understand the role of protein palmitoylation in PD and the pathways altered in this disease. Data-mining of the cortical palmitome from PD patients and controls has allowed to: i) detect a set of 150 proteins with altered palmitoylation in PD subjects in comparison with controls, ii) describe the biological pathways and targets predicted to be altered by these palmitoylation changes, and iii) depict the overlap between the differential palmitome identified in our study with protein interactomes of the PD-linked proteins α-synuclein, LRRK2, DJ-1, PINK1, GBA and UCHL1. In summary, we have partially characterized the altered palmitome in the cortex of PD patients which is predicted to impact cytoskeleton, mitochondrial and fibrinogen functions, as well as cell survival. Our study points out that protein palmitoylation could have a role in the pathophysiology of PD, and that comprehensive palmitoyl-proteomics offers a powerful approach for elucidating novel cellular pathways modulated in this neurodegenerative disease.

Neurological disorders are the leading cause of cognitive and physical disability worldwide, affecting 15% of the global population. Due to the demographics of aging, the prevalence of neurological disorders, including neurodegenerative diseases, will double over the next two decades. Unfortunately, while available therapies provide symptomatic relief for cognitive and motor impairment, there is an urgent unmet need to develop disease-modifying therapies that slow the rate of pathological progression. In that context, biomarkers could identify at-risk and prodromal patients, monitor disease progression, track response to therapy, and parse causality of molecular events to identify novel targets for further clinical investigation. Thus, identifying biomarkers that discriminate between diseases and reflect specific stages of pathology would catalyze the discovery and development of therapeutic targets. This review will describe the prevalence, known mechanisms, ongoing or recently concluded therapeutic clinical trials, and biomarkers of three of the most prevalent neurodegenerative diseases, including Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD).

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, Optimal Reactive Power Dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. On the other hand, installing Distributed Generation Unit (DGU) into a power system can have an important effect on power quality and power loss minimization. In this paper, Grey Wolf Optimization (GWO method is applied to ORPD problem for the first time, to find optimal placement of newly installed DGUs at the power system. Active power loss and voltage minimization have been minimized by optimizing the control variables while keeping them within their upper and lower bounds, and by finding the optimal placement of a newly installed DGU in power system. On the basis of IEEE 30 bus and IEEE 118 bus systems, performance of the suggested approach is investigated. A comparison is also performed among the proposed method and some other methods. Test results demonstrate that the resulting configuration contributes to growing the rate of renewable integration to a power system. The results also indicate that the suggested optimization approach applied to ORPD problem has improved power system performance in terms of both on power loss and voltage deviation. The promising results highlight the potential of the GWO algorithm to facilitate the integration of renewable power sources, and its role in promoting sustainable energy solutions.

Phospholipid metabolism, including phosphatidylcholine (PC) biosynthesis, is crucial for various biological functions and is associated to longevity. Phosphatidylethanolamine N-methyltransferase (PEMT) is a protein that catalyzes the biosynthesis of PC, the levels of which change in various organs such as brain and kidney during aging. However, the role of PEMT for systemic PC supply is not fully understood. To address how PEMT affects aging-associated energy metabolism in tissues responsible for nutrient absorption, lipid storage and energy consumption, we employed NMR-based metabolomics to study liver, plasma, intestine (duodenum, jejunum, ileum), brown/white adipose tissues (BAT, WAT), and skeletal muscle of young (9–10 weeks) and old (96–104 weeks) wild-type (WT) and PEMT knockout (KO) mice. We found that the effect of PEMT-knockout was tissue-specific and age-dependent. Deficiency of PEMT affected the metabolome of all tissues examined, among which the metabolome of BAT from both young and aged KO mice was dramatically changed in comparison to WT mice, whereas the metabolome of jejunum was only slightly affected. As for aging, the absence of PEMT increased the divergence of metabolome during aging of liver, WAT, duodenum and ileum and decreased the impact on skeletal muscle. Overall, our results suggest that PEMT plays a previously unexplored critical role in both aging and energy metabolism.

The Iberian wolf (Canis lupus signatus) is recolonizing historical distribution areas after decades of absence. As in other human-dominated landscapes, finding a balance to protect this species by favoring recolonization and mitigating human-wildlife conflicts is a challenge. Since wolves are often generalist opportunistic predators, we studied their diet composition in the Central Spain to evaluate the consumption of domestic ungulates and provide reliable data that help local authorities to deal with the current wolf-cattle ranchers conflict and coexistence. Diet composition (% prey occurrence, % prey ingested biomass) was analyzed through the identification of prey hairs present in 671 scats collected between 2017 and 2021. Wolves fed more on wild ungulates (82% occurrence) than domestic ones (18%). Wild boar (Sus scrofa, 44% occurrence) and roe deer (Capreolus capreolus, 35%) were the most consumed prey. Wolves positively selected these two species. The wolf's diet varied between seasons, years, and forest regions, but a diet based on wild ungulates predominated over domestic ones. Food niche breadth and showed variations depending on seasons and years. Preserving the availability and diversity of wild ungulates may favor reducing livestock attacks and would be an achievable goal that help to conserve this species and reduce conservation conflicts.

Background:Caregiving for individuals with neurological disease involves providing physical, emotional, and social support, which can be a challenging and stressful experience. This study aims to compare the caregivers' burden, including the impact on mental and physical health, social support, and quality of life, in different neurodegenerative disorders. Methods:Fifty-five primary caregivers (F=41;mean age=53.07±12.29) of patients with Parkinson's Disease (PD=26), Multiple System Atrophy (MSA=16), and Amyotrophic Lateral Sclerosis (ALS=13), took part in the study. Standardized screening scales for the quality of life (EQ-5D/EQ-VAS and PQoL), levels of the caregiving burden (CBI and FSQ), and the assessment of the anxiety-depressive axis (HADS) were administered. Results: The mean CBI score was higher for caregivers of MSA patients (28.81±21.90) and ALS (25.15±16.23) than for caregivers of PD patients (16.08±10.34; p=0.036). Critical burden (cut-off>36) was present in the caregivers of MSA (31.3%), ALS (23.1%), and PD (19.2%) patients. Caregivers of patients with ALS had a worse perception of quality of life (EQ-5D total score) than caregivers of patients with MSA and PD (p

Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.

White noise is fundamentally linked to many processes, it has a flat power spectral density and a delta-correlated autocorrelation. Operators acting on white noise can result in coloured noise, whether they operate in the time domain, like fractional calculus, or in the frequency domain, like spectral processing. We investigate whether any of the white noise properties remain in the coloured noises produced by the action of an operator. For a coloured noise, which drives a physical system, we provide evidence to pinpoint the mother process from which it came. We demonstrate the existence of two indices, that is, kurtosis and codifference, whose values can categorise coloured noises according to their mother process. Four different mother processes are used in this study: Gaussian, Laplace, Cauchy, and Uniform white noise distributions. The mother process determines the kurtosis value of the coloured noises that are produced. It maintains its value for Gaussian, never converges for Cauchy, and takes values for Laplace and Uniform that are within a range of its white noise value. In addition, the codifference function maintains its value for zero lag-time essentially constant around the value of the corresponding white noise.

The brain being arguably the most complex organ in the human body, its detailed functioning is yet to be fully deciphered, let alone accurately modeled. Nonetheless, the medical community has gathered quite a remarkable amount of studies about the consequences of illnesses and injuries on its development and functioning. This bibliographic review aims to cover mostly the findings related to the changes in the physical distribution of neurons and their connections - the connectome -, both structural and functional, as well as their modelling. It does not intend to offer an extensive medical description of all injuries and diseases affecting the brain, rather presenting the most common ones succinctly so the need for accurate brain modelling can be fully understood and pondered, offering propositions to this aim.

Imaginary dimensions in physics require an imaginary set of base Planck units and some negative parameter $c_n$ corresponding to the speed of light in vacuum $c$. The second, negative fine-structure constant $\alpha_2^{-1} \approx -140.178$ present in Fresnel coefficients for the normal incidence of electromagnetic radiation on monolayer graphene leads to the imaginary Planck units. Furthermore, it sets $c_n \approx -3.06 \times 10^8~\text{[m/s]}$. It follows that electric charges are the same in real and imaginary dimensions. Neutron stars and white dwarfs, objects emitting perfect black-body radiation, need energy exceeding their mass-energy equivalence ratios. Complex energies are defined in terms of real and imaginary Planck units. Their imaginary parts, inaccessible for direct observation, store the excess of these energies. It follows that black holes are fundamentally uncharged, charged micro neutron stars and white dwarfs with masses lower than $5.7275 \times 10^{-10}~[\text{kg}]$ are unphysical, and the radii of white dwarfs' cores are limited to $R_{\text{WD}} < 6.7933~G M_{\text{WD}}/c^2$. It is conjectured that the maximum atomic number $Z=238$. A black-body object is in the equilibrium of complex energies of masses, charges, and wavelengths if its radius $R_\text{eq} \approx 2.7665~G M_{\text{BBO}}/c^2$, which corrects the value of the photon sphere radius $R_{\text{ps}}=3G M/c^2$, taking into account the value(s) of the fine-structure constant(s), which is otherwise neglected in general relativity.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based system. In this paper, we present a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consume substantial computational resources and increase the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we propose a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduce a lightweight superpixel-based Graph Neural Network (GNN) and break new ground by introducing superpixel metric learning to segment nucles and cytoplasm. Remarkably, our proposed segmentation model (SMGNN) achieves state-of-the-art segmentation performance while utilizing at most 10000$\times$ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes show satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories.

Studies of dietary preferences of migratory species are of great importance as these species connect food webs of habitats across the migration route and thus represent trophic relationships between the spatially disjointed communities. Here we describe dietary preferences of threespine stickleback G. aculeatus in the White Sea during the spawning season using stable isotope and stomach content analyses. Both analyses indicated that during the spawning season, when sticklebacks spend most of the time in the inshore, their diet significantly consist of benthic species in contrast to the start of the spawning season when fishes migrating from the offshore are feeding on zooplankton. Also, we show that stickleback eggs contribute greatly to the diet of both male and female fishes. Using Bayesian mixing modelling we show that dietary preferences in females were broader than in males, and more variable during the spawning season. Males fed on eggs almost while guarding their nests. Both stomach contents and isotope signatures demonstrate that by the end of the spawning season sticklebacks again increase consumption of plankton, and isotope analysis proved to be more reliable tool to trace this change than stomach content analysis. Our results show that stable isotope and stomach content analyses well supplement each other in understanding of seasonal changes in dietary composition of stickleback.

The ability of 2D hybrid structures formed by boron, nitrogen and carbon atoms (h-BNCs) as potential substrates for surface enhanced Raman spectroscopy (SERS) detection of dioxin-like pollutants is theoretically analyzed. The strong confinement and high tunability of the electromagnetic response of the carbon nanostructures embedded within the h-BNC sheets point out that these hybrid structures could be promising for applications in optical spectroscopies, such as SERS. In this work, two model dioxin-like pollutants, TCDD and TCDF, and a model h-BNC surface composed by a carbon nanodisk of ninety-six atoms surrounded by a string of borazine rings, BNC96, are employed for the simulation of the adsorption complexes and the static and pre-resonance Raman spectra of the adsorbed molecules. A large affinity of BNC96 by these pollutants is reflected on the large interaction energies obtained for the most stable stacking complexes, with dispersion being the most important contribution to their stability. The large vibrational coupling of some active modes of TCDF and, specially, of TCDD makes the Raman static spectra display a ‘pure’ chemical enhancement of one order of magnitude. On the other hand, due to the strong electromagnetic response of BNC96, confined within the carbon nanodisk, pre-resonance Raman spectra obtained for TCDD and TCDF display large enhancement factors of 108 and 107, respectively. Promisingly, laser excitation wavelengths frequently used in SERS experiments also provoke significant Raman enhancements, around 104, for the TCDD and TCDF signals. Both the strong confinement of the electromagnetic response within the carbon domains and the high modulation of the resonance wavelengths within the visible and/or UV ranges in h-BNCs should lead to a higher sensitivity than graphene and white graphene parent structures, solving one of the main disadvantages of using 2D substrates for SERS applications.

Depression is one of the most severe sequelae of COVID-19, with major depressive disorder often characterized by disruption in white matter (WM) connectivity , stemming from changes in brain myelination. This study aimed to quantitatively assess brain myelination in clinically diagnosed post-COVID depression using recently proposed MRI method, macromolecular proton fraction (MPF) mapping. The study involved 63 recovered COVID-19 patients (52 mild, 11 moderate, 2 severe) at 13.5±10.0 months post-recovery, with matched controls without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, while a comparison group (noPCD, n=38) included participants with neurological COVID-19 complications, excluding clinical depression. Fast MPF mapping revealed extensive demyelination in PCD patients, particularly in juxtacortical WM (predominantly occipital lobe and medial surface), WM tracts (inferior fronto-occipital fasciculus (IFOF), posterior thalamic radiation, external capsule, sagittal stratum, tapetum), and grey matter (GM) structures (hippocampus, putamen, globus pallidus, and amygdala). The noPCD group also displayed notable demyelination but with less magnitude and propagation. Multiple regression analysis highlighted IFOF demyelination as the primary predictor of PCD presence and severity according to Hamilton scores. The number of post-COVID symptoms was a significant predictor of PCD presence while the number acute of symptoms was a significant predictor of PCD severity. This study, for the first time, reveals extensive demyelination in numerous WM and GM structures in PCD, outlining IFOF demyelination as a key biomarker.

Human endogenous retroviruses (HERV) are remnants of ancient retroviral infections that, over millions of years, became integrated into the human genome. While normally inactive, environmental stimuli such as infections have contributed to the transcriptional reactivation of HERV promoting pathological conditions including the development of autoimmunity, neurodegenerative disease and cancer. What infections then trigger HERV activation? Mycobacterium avium subspecies paratuberculosis (MAP) is a pluripotent driver of human disease. Aside from granulomatous diseases Crohn’s, sarcoidosis and Blau syndrome, MAP is associated with autoimmune disease: type one diabetes (T1D), multiple sclerosis (MS), rheumatoid arthritis and autoimmune thyroiditis. MAP is also associated with Parkinson’s disease. Autoimmune diabetes, multiple sclerosis and rheumatoid arthritis are the diseases with the strongest MAP/HERV association. There are several other diseases associated with HERV activation including diseases whose epidemiology and/or pathology would prompt speculation for a causal role of MAP. These include non-solar uveal melanoma, colon cancer, glioblastoma and amyotrophic lateral sclerosis (ALS). This article further points to MAP infection as a contributor to autoimmunity, neurodegenerative disease and cancer via unsilencing of HERV. We examine the link between the ever-increasing number of MAP- associated diseases, the MAP/HERV intersection with these diverse medical conditions and propose treatment opportunities based upon this association.

Egg white lysozyme was modified by chemical methods using organic acids. Caffeic acid and p-coumaric acid in organic acids were used as modifiers, and 1-Ethyl-3- (3-dimethylaminopropyl) carbodiimide hydrochloride and N-hydroxy succinimide were used as dehydration condensation agents during modification. A certain degree of modified lysozyme was obtained through appropriate modification conditions. The antibacterial properties and structure of the obtained two organic acid modified lysozymes were compared with natural enzymes. The results showed that compared with the natural enzyme, the activity of modified lysozyme decreased, but the inhibitory effect on Gram-negative bacteria was enhanced. The minimum inhibitory concentrations of caffeic acid modified enzyme and p-coumaric acid modified enzyme on Escherichia coli and Pseudomonas aeruginosa were 0.5 mg/mL and 0.75 mg/mL, respectively. However, the antibacterial ability of modified lysozyme to Gram-positive bacteria was lower than that of natural enzyme. The minimum inhibitory concentration of caffeic acid modified enzyme and p-coumaric acid modified enzyme to Staphylococcus aureus and Bacillus subtilis was 1.25 mg/mL. The peak fitting results of the amide-I band (1600 cm-1-1700 cm-1) absorption peak in the infrared spectroscopy showed that the content of the secondary structure of the two modified enzymes obtained after modification was different from that of natural enzymes.

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality, particularly the soil properties, is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of white oak mortality and examine the influence of soil properties on mortality rates. Multicycle Forest Inventory and Analysis data were compiled to capture all white oaks across the eastern US. White oak tree mortality data were collected across plot systems that utilized Diameter at Breast Height between two periods. Soil variables were analyzed to assess soil properties. Spatial analysis techniques, including geostatistics and regression modeling, were used to analyze the relationship between white oak mortality and soil characteristics. Results found clustered spatial patterns of white oak mortality across a broad scale depicting the significant effects of coarser soil textures (p = 0.00), nutrient-deficient sites, and extreme soil moisture levels (p <0.1). Our findings demonstrated the importance of soil properties in shaping the spatial pattern of white oak mortality rate. This idea can inform forest management practices for the conservation of white oak populations. Future research is needed for comprehensive soil assessment including biotic and abiotic factors for forest management strategies at a broader scale aimed at mitigating white oak mortality.

Disease detection and prediction have been one of the most frequent uses of machine learning models in the last decade, and Parkinson's disease is no exception. It is common for people in their old age to have one form of disease or another depending on the circumstances in which they find themselves. This study analyzed and evaluated various supervised machine learning models to detect the presence of Parkinson's disease in elderly individuals. An extreme gradient boosting model, along with other models, such as naïve Bayes, random forest, K-nearest neighbor, and support vector machine (SVM), was implemented to determine the best outcome. The extreme gradient boosting (XGBoost) model is a powerful boosting machine learning technique, particularly in terms of speed and accuracy. The results showed that the XGBoost model, which is an optimized version of the other boosting models, is capable of projecting the presence of Parkinson's disease with very high accuracy compared to other algorithms. The implemented classification algorithm extreme gradient boosting method achieved approximately 98 % accuracy, 97.5 % precision, 100.0 % recall, and 98.7% f1-score which are high compared with the results found in the existing literature.

Abstract Background: Parkinson's disease (PD) is a neurodegenerative disorder featured with motor and non-motor deficits. Using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced dopamine neuron degeneration has been widely used to produce reliable animal models of the PD. However, most of previous preclinical studies focused on the motor dysfunctions, few non-motor symptoms were evaluated. So far there is a lack of comprehensive investigation of the non-motor symptoms in animal models. Objectives: In this study, we aim to use a battery of behavioral methods to evaluate the non-motor symptoms in MPTP-induced non-human primate PD models. Methods: Cognitive functions, sleep and psychiatric behaviors were evaluated in MPTP-treated cynomolgus monkeys. The tests consisted of delayed matching-to-sample (DMTS), physical activity monitor (PAM), apathy feeding task (AFT), human intruder test (HIT), novel fruit test (NFT) and predator confrontation test (PCT). In addition, we tested whether the dopamine receptor agonist pramipexole (PPX) will improve these non-motor symptoms. Results: The present results show that the MPTP-treated monkeys exhibited cognitive deficits, abnormal sleep, and anxiety-like behaviors as compared to the control monkeys. These symptoms were relieved partially by PPX. Conclusions: These results suggest that MPTP-induced PD monkeys displayed non-motor symptoms that were similar to those found in PD patients. PPX treatment showed moderate therapeutic effects on these non-motor symptoms. This battery of behavioral tests may provide a valuable model for future preclinical research.

Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in pre-venting adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an Ensemble Heuristic-Metaheuristic Feature Fusion Learning (EHMFFL) algorithm for heart disease diagnosis. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic-metaheuristic approach that integrates the heuristic knowledge of Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning, aimed at constructing a robust prediction model. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art machine learning, ensemble learning, and feature selection techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson's Disease (PD) or Essential Tremors (ET). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3~Hz and 6~Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to as ET, vary between 4~Hz and 12~Hz. We simulated and used these signals as reference signals in adaptive algorithms, Filtered-x Least Mean Square (Fx-LMS), Filtered-x Normalized Least Mean Square (Fx-NLMS), and a hybrid Fx-LMS\&NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS\&LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we have used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

Cardiovascular diseases are a broadly understood concept focusing on vascular and heart dys-function. Lack of physical exercise, type 2 diabetes, obesity, hypertension, dyslipidemia, throm-boembolism, kidney, and lung diseases all contribute to the development of heart and blood vessel dysfunction. Although effective and important, traditional treatment with diuretics, statins, beta–blockers, calcium inhibitors, ACE inhibitors, and anti–platelet drugs remain a second–line treatment after dietary intervention and lifestyle changes. Scientists worldwide are still looking for an herbal product that would be quite effective and free from side effects, either taken together or before the standard pharmacological intervention. Such herbal–originated medication therapy may include Morus alba L. (white mulberry), Elaeagnus rhamnoides (L.) A. Nelson (sea–buckthorn), Allium sativum L. (garlic), Convallaria majalis L. (lily of the valley), Leonurus cardiaca L. (mother-wort), and Crataegus spp. (hawthorn). Valuable herbal raw materials include leaves, fruits, seeds, and even thorns. This short review focuses on six herbs that can constitute an interesting and po-tential therapeutic option in the management of cardiovascular disorders.

Age-related myelination decrease is considered one of the likely mechanisms of cognitive decline. The present study is based on the longitudinal assessment of global and regional myelination of the normal adult human brain using the fast macromolecular fraction (MPF) mapping. Additional markers were age-related changes in white matter (WM) hyperintensities on FLAIR-MRI and the levels of anti-myelin autoantibodies in serum. Eleven healthy subjects (33–60 years in the first study) were scanned twice, 7 years apart. An age-related decrease in MPF was found in global WM, GM, and mixed WM-GM, as well as in 48 out of 82 examined WM and GM regions. The greatest decrease in MPF was observed for the frontal WM (2-5%), genu of the corpus callosum (CC) (4.0%) and caudate nucleus (5.9%). The age-related decrease in MPF significantly correlated with an increase in the level of antibodies against myelin basic protein (MBP) in serum (r = 0.69 and r = 0.63 for global WM and mixed WM-GM, correspondingly). The volume of FLAIR hyperintensities increased with age but did not correlate with MPF changes and the levels of anti-myelin antibodies. MPF mapping showed high sensitivity to age-related changes in brain myelination, providing the feasibility of this method in clinics.

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders globally, and leads to an excessive loss of dopaminergic neurons in the substantia nigra of the brain. Circulating cell-free DNA (ccf-DNA) are double-stranded DNA fragments of different sizes and origins that are released into the serum and cerebrospinal fluid (CSF) due to cell death (i.e., necrosis and apoptosis) or are actively released by viable cells via exocytosis and NETosis. Methods: Using droplet digital polymerase chain reaction (ddPCR) we comprehensively analyzed and distinguished ccfDNA and ccf mtDNA in the serum and CSF of PD and control patients. Results: The quantitative analysis of serum ccf-DNA in PD patients demonstrated significant increase of ccf mtDNA and ccfDNA compared to healthy control patients and significantly higher copy of ccf mtDNA when compared to ccfDNA. Next, the serum ccf mtDNA levels significantly increased in male PD patients compared to healthy male controls. Furthermore, CSF ccf mtDNA in PD patients increased significantly compared to ccfDNA, and ccf mtDNA decreased in PD patients compared to healthy controls. These decreases were not statistically significant, but were in agreement with previous data. In-terestingly, ccf mtDNA increased in healthy control patients in both serum and CSF as compared to ccfDNA. Limitations: the small sample size of serum and CSF were the main limitations of this study. Conclusion: To the best of our knowledge, this is the first comprehensive study on serum and CSF of PD patients using ddPCR and indicating the distribution of the copy number of ccf mtDNA, as well as ccfDNA. If validated, we suggest that ccf mtDNA has greater than ccfDNA potential to lead the development of novel treatments for PD patients.

In China, the demand for rabbit meat is also increasing, and the production for rabbit meat is important. However, the underlying mechanisms of regulate skeletal muscle growth and development in rabbits remain unclear. The purpose of this study was to identify candidate genes related to skeletal muscle growth in rabbits and explore their potential regulatory mechanisms. RNA-seq technique was used to compare the differences of skeletal muscle transcriptome in Fujian white rabbits at different developmental stages (day 20 and 26 of embryos and birth 1, 30 and 60-day-olds) with a total of 25 individual selected. A total of 9737 DEGs that were acquired from the five groups were annotated into three ontologies of the GO database: biological process (BP), cell component (CC), and molecular function (MF). For KEGG analysis, there are 8249 genes were enriched in 1148 pathways, of which 67 pathways were significantly enriched, mainly in Hypertrophic cardiomyopathy (HCM), the PPAR signaling pathway, MAPK signaling pathway. Among all comparison groups, SE was the most abundant AS event. Real-time PCR verified that the expression patterns of differential genes were consistent with the transcriptome sequencing results. These results will provide a molecular regulation mechanism of muscle growth and development in Fujian white rabbits, and should serve an important theoretical basis for improving meat performance and growth rate of Chinese local meat rabbit breeds.

Apex predators were extirpated from the UK hundreds of years ago. Practitioners have recently advocated for reintroductions of predators in the UK given their role as keystone species. For a reintroduction to take place, a survey of public desirability needs to be conducted. We aimed to understand the perspectives of people across the United Kingdom on the reintroduction of Eurasian lynxes, grey wolves, and brown bears. We collected data via questionnaires from 78 key stakeholders, such as farmers, general public, nature conservationists, and those in environmental sectors, with these areas being seen as the most affected by these reintroductions. It was organizations which front these stakeholder groups which mainly distributed the survey, as well as social media groups. We ran a path analysis, which highlighted that there was a negative correlation between respondents viewing the species as a danger towards livestock and humans, and the respondent seeing it being good to have that species in their region. Overall, participants who stated that rewilding their region was a priority also stated that it would be good to have Eurasian lynx in their region. That said, 70% of farmers strongly disagreed with the reintroduction of Eurasian lynx. These findings showed that much of the United Kingdom would welcome the reintroduction of Eurasian lynx, but both farmers and the public would fear the loss of livestock that may come along with these species. Support can be gained through education and knowledge, a necessary step needed especially targeting farmers before any reintroduction plans can be made.

The influence of pH, temperature, biopolymer ratio, total concentration, and ionic concentration on the interaction between egg white protein (EWP) and chitosan (CS) was investigated through turbidity, zeta potential, and state diagram in our research. In addition, phase behavior was observed under various conditions. The turbidity of EWP remained low (turbidity&lt;0.03) and basically unchanged at a wide range of pH (4.0-8.0), while the turbidity of CS was slightly higher (turbidity&lt;0.2) after pH 7.0 than before. Moreover, under the same conditions, a sharply rising peak pattern was observed for the complex between EWP and CS. The maximum turbidity value was observed at 55°C, and the temperature had a mild effect on turbidity. The optimum EWP to CS ratio was found to be 12:1 based on the experiment of the turbidity curves and state diagrams influenced by different biopolymer mixing ratios. With the enhanced concentrations of total biopolymer, the maximum turbidity rose insignificantly above 0.1%.

The use of social media is increasing worldwide, and thus, some new problems that negatively affect life are coming to the fore. The “White Bear Thought Suppression” problem, which was put forward in previous years, has re-emerged in the digital environment as psychological disorders accompanying excessive social media use. The reason for this study was that people constantly think about what is happening on social media, cannot distinguish between digital and reality, and cannot manage their mental process. The aim of this study is to examine the relationship between social media addiction and White Bear Thought Suppression and the factors affecting it from the perspective of White Bear Suppression on social media. The sample of the research consists of 356 volunteer participants in Turkey between the ages of 18-71. The research was conducted by analyzing the data collected with the Social Media Addiction Scale (SMAS) and White Bear Suppression Inventory (WBSI) scales in accordance with the quantitative method. Some of the results obtained in the research are as follows: (a) Women’s social media addiction and White Bear Suppression were found to be high, (b) Generation Z is the generation in which social media addiction and White Bear Suppression are seen at the highest level, (c) As daily social media use increases, the level of social media addiction and White Bear Suppression increases; (d) The frequency of opening live chats and having more than one profile increases social media addiction and White Bear Suppression. (e) The frequency of sleep disorders and perceived loneliness affect both phenomena. At the end of the research, it was emphasized that social media impacted White Bear Suppression and that new clinical studies were needed, especially with young people.

Background: Cord blood represents a link between intrauterine and early extrauterine development. Cord blood cells map an important time frame in human immune imprinting processes. It is unknown whether sex of the newborn affects the lymphocyte subpopulations in cord blood. Methods: 9 B and 21 T cell subpopulations were characterized by flow cytometry in human cord blood from 16 male and 21 female newborns, respectively. Results: Except for marginal zone B cells and transitional B cells, B cell count in all subsets was higher in cord blood of male newborns than in female newborns. Frequency of naive thymus negative Th cells was significantly higher in male cord blood whereas the remaining T cell subpopulations showed a higher count in cord blood of female newborns. Conclusion: Our study is the first revealing sex differences in B and T cell subpopulations of human cord blood. These results indicate that sex might have a higher impact for the developing immune system urging the need to expand research in this area.