Fragile X syndrome (FXS) is a neurodevelopmental disorder associated with a wide range of cognitive, behavioral and medical problems. It arises from the silencing of the fragile X mental retardation 1 (FMR1) gene, and consequently, in the absence of its encoded protein, FMRP (Fragile X Mental Retardation Protein). FMRP is a ubiquitously expressed and multifunctional RNA-binding protein, primarily considered as a translational regulator. Pre-clinical studies of the past two decades have therefore focus on this function to relate FMRP’s absence to the molecular mechanisms underlying FXS physiopathology. Based on these data, successful pharmacological strategies were developed to rescue fragile X phenotype in animal models. Unfortunately, these results did not translate into human, as clinical trials using same therapeutic approaches did not reach the expected outcomes. These failures highlight the need to put into perspectives the different functions of FMRP in order to get a more comprehensive understanding of FXS pathophysiology. In this review, FMRP’s involvement on noteworthy molecular mechanisms are pointed out; ultimately contributing to various biochemicals alterations composing the fragile X phenotype.

Abstract: The Willows, belonging to the genus Salix, encompass a vast array of species and hybrids, totaling between 330 to 500 species along with approximately 200 hybrids. These plants are found across continents such as Africa, North America, Europe, and Asia, exhibiting diverse forms ranging from trees to shrubs and prostrate plants. Traditionally, Willows have been utilized in folk medicine due to their rich reservoir of biologically active compounds, with salicin being a notable example, serving as a prodrug for salicylic acid.The chemical composition of Willows is highly diverse, with 322 secondary metabolites identified thus far. These include various classes such as flavonoids (94 types encompassing flavonols, flavones, flavanones, isoflavones, flavan-3-ols like catechins and procyanidins, chalcones, dihydrochalcone, anthocyanins, and dihydroflavonols), phenolic glycosides (76), organic acids (28), non-phenolic glycosides (17), sterols, and terpenes (17), simple phenolics (13), lignans (7), as well as volatiles and fatty acids (69). This rich chemical di-versity underpins the various pharmacological activities exhibited by Willows. The pharmacological properties attributed to Willows are extensive and include analgesic, an-ti-inflammatory, antioxidant, anticancer, cytotoxic, antidiabetic, antimicrobial, antiobesity, neuroprotective, and hepatoprotective effects. These properties make Willows a subject of con-siderable interest in the fields of medicine and pharmacology. In summary, Willows represent a valuable natural resource with a diverse chemical composition and a wide range of pharmacological activities. This study aimed to enhance our comprehension of the medicinal characteristics of willow bark extract, focusing on its antimicrobial properties. This investigation holds significance in validating the traditional usage of willow bark and investi-gating its potential utility in contemporary medicine.

The treatment of acute myeloid leukemia (AML) with adverse genetics remains unsatisfactory, with very low response rates to standard chemotherapy and shorter durations of remission commonly observed in these patients. The complex biology of AML with adverse genetics is continuously evolving. Herein, we discuss recent advances which have investigated the contribution of cell intrinsic mechanisms as well as of the immune system to myeloid leukemogenesis in this specific subset of AML. We focus on the biological rationales for combining targeted therapy and immunotherapy, which are currently being investigated in ongoing trials, and could hopefully ameliorate the poor outcomes for these patients

(1) Background: The prediction of cervical lesions evolution is a challenge for clinicians. The prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors for the prediction of progression, regression or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions that underwent follow-up. Descriptive statistics and sensitivity analysis for individual or combined index tests were used for data analysis; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model that comprised a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least 3 clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesions regression or persistence was modest; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction.

Dysferlinopathies comprise a spectrum of muscular dystrophies characterized by progressive muscle weakness and degeneration due to mutations in the DYSF gene, which encodes the dysferlin protein critical for muscle membrane repair. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We explore the phenotypic heterogeneity of dysferlinopathies, highlight the incomplete understanding of genotype-phenotype correlations, and discuss the implications of various DYSF mutations. Furthermore, we examine the utility of animal models in elucidating disease mechanisms and the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease's progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. By culminating the complexities inherent in dysferlinopathies, this article emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modelling of gene expression networks that involve large amounts of data. The integration of data from DNA, RNA, and protein activities has resemblance to pharmacogenomics, which is of utmost importance in the development of cancer drugs. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Expanding the study and application of functional genomics has great promise to advance the process of discovering and repurposing drugs, especially in the field of oncology.

The impacts of inflation on international finance are generally reflected in the decline of buying power, risks of investment and deposit loss from currency devaluation. For the purpose of optimized allocation and management of asset values, i.e., annual income or research grants, a framework of a three-level model for static financial management is presented to maximize banking interests, bonuses, and the remaining balance after spending necessary expenditures. Meanwhile, in retrospect to higher-level financial inflation, strategies of dynamic investment, i.e., posing regular tests of flipping coins, exploiting variations of the currency exchange rate, as well as applying the theory of broad money, are presented to increase the appreciation rate of net assets in and post the era of COVID-19. Quantitative evaluations in case studies verify the feasibility of improving the financial situation, indicating broader applications for budgeting and investing when US inflation rate is below 3.0%. Besides, combining approaches of static and dynamic strategies, the expected annual growth rate of individual zero-risk asset investment is predicted as within the range of 8.30~9.62% according to budgeted appreciation on net assets in 12 months, which is capable of overcoming negative impacts from higher level annual inflation, i.e., those as highest as 8.01% in the Year 2022.

High blood cholesterol levels are one of the main risks for atherosclerotic disease. A purified aqueous extract of Fucus vesiculosus, characterized as rich in phlorotannins and peptides, was described as having the capacity to inhibit cholesterol biosynthesis and intestinal absorption. In this work, the effect of this extract on intestinal cells metabolites and proteins was analysed, aiming to expand the knowledge about its mode of action targeting cholesterol metabolism, in particular exogenous cholesterol absorption and transport. Caco-2 cells differentiated into enterocytes were exposed to the purified aqueous extract of F. vesiculosus and analysed by undirected metabolomics and proteomics. The results of the metabolomic analysis showed only statistically significant differences in glutathione content of the cells exposed to the extract relatively to the control cells, with a decreased glutathione expression in exposed cells. The proteomic analysis showed an increased expression for cells exposed to the extract of NPC1, an important protein known to be involved in cholesterol transport. To extent of our knowledge this is the first study using untargeted metabolomics and proteomic analysis to study the effect of F. vesiculosus on differentiated Caco-2 cells, which aims to provide some insight about the molecular mechanism of extract compounds on intestinal cells.

Inflammatory bowel diseases, familial adenomatous polyposis (FAP) and colorectal cancer (CRC) are associated with alterations of the intestinal microbiota. However, few data are available on the perpetuation of FAP and ulcerative colitis (UC) in relation to microbial dysbiosis. This study evaluated the UC and genetically confirmed FAP patients’ gut microbial balance in concordance to clinical outcome. Fecal materials (average mass of 0.54 g) were collected from three FAP and five UC patients to compare with healthy individuals as control group. Genomic materials of micro-biota were isolated for next generation sequencing of 16S rRNA that was performed by using QIAseq 16S/ITS panel in Illumina Miseq Platform. Data processing and bioinformatics analysis were performed via CLC Genomic Workbench bioinformatics tool. The comparison between FAP, UC and control group revealed an alteration in the intestinal microbial composition. More in details, relative abundance of class levels showed statistical significance differences among FAP, UC and control groups. Our preliminary data focused on the explanation of how dysbiosis can lead to inflammation and drive processes together with host genetic profile that leads to colorectal carcinogenesis.

Lumpy skin disease (LSD), caused by a virus within the Poxviridae family and Capripoxvirus genus, induces nodular skin lesions in cattle, spreading through direct contact and insect vectors, significantly affecting global cattle farming. Despite available vaccines, their effectiveness is limited by poor bioavailability and adverse effects. Our study aimed to identify potential inhibitors targeting LSD-associated DNA polymerase protein, selecting LSDV039 for further investigation through comprehensive analysis and computational methods. Virtual screening revealed rhein and taxifolin as potent binders among 380 phytocompounds, with respective affinities of -8.965 and -7.195 kcal/mol. Canagliflozin and tepotinib exhibited strong affinities (-9.858 and -8.856 kcal/mol) among 718 FDA-approved antiviral drugs. Molecular dynamics simulations of canagliflozin, tepotinib, rhein, and taxifolin highlighted taxifolin's superior stability and binding energy. Rhein displayed compactness in RMSD and RMSF but fluctuations in Rg and SASA, while canagliflozin demonstrated stability compared to tepotinib. The study highlights the promising potential of repurposed drugs and phytocompounds as LSD therapeutics. However, extensive validation through in-vitro, in-vivo, and clinical trials is crucial for their practical application.

The emergence of drug-resistant parasitic nematodes of both humans and livestock calls for development of alternative and cost-effective control strategies. For the economically important ruminant strongylid Haemonchus contortus, Barbervax® remains the only registered vaccine available. Here we compared the microbiome, genome-wide diversity and transcriptome of H. contortus adult male populations that survived vaccination with an experimental vaccine after inoculation in sheep. Our genome-wide SNP analysis revealed 16 putative candidate vaccine evasion genes. However, we did not identify any evidence for changes in microbial community profiling based of 16S rRNA gene sequencing results of vaccine surviving parasite populations. A total of 58 genes were identified as significantly differentially expressed with six being long non-coding (lnc) RNAs and no putative candidate SNP associated genes. The genes highly upregulated in surviving parasites from vaccinated animals were associated with GO terms belonging to predominantly molecular function and a few biological processes that may have facilitated evasion or potentially lessened the effect of the vaccine. These included five targets: astacin (ASTL), carbonate dehydratase (CA2), phospholipase A2 (PLA2), glutamine synthetase (GLUL) and fatty acid-binding protein (FABP3). We searched all five DEG targets against the proteomes of selected Nematoda (Clades III, V, IV, C, I) and Platyhelminthes (Clades Monogenea, Trematoda, Cestoda, Rhabditophora) to determine homologs within the H. contortus NZ_HCO_NP v1.0 genome and identified single-copy orthologous groups (OGs) in selected proteomes. All but one (FABP3) demonstrated high levels of duplication and wide-spread occurrence in closely related Caenorhabditis elegans and Pristionchus pacificus, with complete absence of all five gene targets among other Clade III (Toxocara canis) and V (Ascaris suum, Ascaris lumbricoides and Parascaris univalens) nematodes, further supporting their vital biological functions in nematodes. Phylogenetic analyses inferred the presence of only ASTL and CA2 in almost all Nematoda, platyhelminthes and metazoans examined, with loss of GLULs observed among all outgroup vertebrate species and the presence of FABP3 in only three other species (Schmidtea mediterranea, Fasciola gigantica and F. hepatica). Our tertiary structure predictions and modelling analyses were used to perform in silico searches of all published and commercially available inhibitor molecules or substrate analogues with potential broad-spectrum efficacy against nematodes of human and veterinary importance.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

Conventional monitoring relies on "in vivo" (non-destructive) measurements of aboveground organ traits (such as stems, branches, and leaves) to analyze plantation growth trends and assess the effectiveness of management practices like watering and fertilization. This study reveals the necessity of “ex vivo” (destructive) approaches, post-excavation, to ensure fully reliable monitoring of plantation performance, including belowground organ growth and development trends. We showcase the effectiveness of dendrometric analysis, which measures the annual ring width in the secondary xylem (wood) of the taproot and stem. Monitoring above- and belowground organ growth trends allow for the exploration of similarities and differences in how different tree organs respond to internal and external factors. Such monitoring is especially crucial for assessing plantation performance in challenging climates, such as those found in arid and semi-arid lands.

Probiotics are microorganisms infused in products for health benefits including acceleration of nutrient digestion, however, it is also important to ensure the safety prior to incorporation. Here, we present evidence of the ability of two probiotic isolates,Lacticaseibacillus paracasei BCRC-16100 and Lacticaseibacillus paracasei ZFM54, in the (1) enhancement of carbohydrate digestion, (2) tolerance to stress, and (3) antibiotic resistance. Approaches include whole genome sequence (WGS) analysis and bioactivity assays. WGS revealed genes suggesting the ability of the two isolates to promote carbohydrate digestion, tolerance to stress and antibiotic resistance. Carbohydrate digestive ability was confirmed through a biochemical assay where the two isolates cause glucose release from starch. The two isolates also showed versatility in a range of temperature and alcohol concentration. For antibiotic resistance particularly on vancomycin, there are three mechanisms namely transporter control, transcriptional regulation, and efflux pump. Furthermore, promoter and transposable element analysis showed that some of the active antibiotic resistant (AbR) genes can be laterally transferred. Altogether, we show the potential of two probiotic isolates to be incorporated in products for improved carbohydrate digestion and the need to address the removal of active and mobile antibiotic resistance genes that may compromise safety.

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disor-ders, even nearly a century after its discovery, due to the elusive nature of its causes. The develop-ment of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, open-ing new possibilities for treatment. Despite these advancements, conventional treatments for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, under-scoring the need for innovative solutions. This necessity has led to the incorporating of advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Understanding mechanisms of ageing remains a complex challenge for biogerontologists, but recent adaptations of evolutionary ageing theories offer a compelling lens in which to view both age-related molecular and physiological deterioration. Ageing is commonly associated with a progressive loss of biochemical processes and degeneration of molecular material, however, the mechanisms of diminishing function are not simply a result of aberrant expression of these pathways. Natural selection pressures are at their highest in youthful periods to select genes advantageous to maximising reproductive capacity. After sexual maturation, selective pressure diminishes, subjecting individuals to maladaptive pleiotropic gene function once beneficial for developmental growth, but pathogenic later in life. Due to this selective ‘shadowing’ in ageing, mechanisms to counter hyperfunctional genes are unlikely to evolve within a given species. These genetic ‘run-on’ programmes are not dysfunctional per se and, in fact, remain highly robust. Thus, interventions capable of optimising gene function during the ageing process represent as attractive therapeutic modalities. Caenorhabditis elegans offers a unique model to explore high-throughput screening of interventions to combat hyperfunctional pathways involved in ageing. However, the majority of research to date employ whole-life vs late-life pathway manipulation of developmental/growth-related systems to claim ageing attenuation, despite significant impairments in early-life fitness and discordance with healthspan. Here, we discuss data supporting hyperfunctional changes at a global molecular and functional level in C. elegans, and how classical lifespan-extension mutants alter these dynamics. We later discuss the relevance of such mutant models for exploring mechanisms of ageing, highlighting that post-reproductive gene manipulation represents a far more translatable approach for C. elegans research not routed within pleiotropic constraints. More so, lifespan has become a routine readout for quantifying ‘ageing’, however, we make the argument that a compression of gerospan vs lifespan extension is a more meaningful goal for anti-ageing regimes with current knowledge. Similarly, mechanisms governing lifespan are distinct from those regulating health, and slowing senescent pathology accumulation appears more applicable for the abstraction to human ageing.

Multivariate statistical approaches (MSA), such as principal components analysis and multidimensional scaling, seek to uncover meaningful patterns within datasets by considering multiple response variables in a concerted fashion. Although these techniques are readily used by ecologists to visualize and explain differences between study sites, they could theoretically be employed to differentiate organisms within an experimental framework while simultaneously identifying response variables that drive documented experimental differences. Therefore, MSA were used herein to attempt to understand the response of the common, Indo-Pacific reef coral Seriatopora hystrix to temperature changes using data from laboratory-based temperature challenge studies performed in Southern Taiwan. Gene expression and physiological data partitioned experimental specimens by time of sampling, treatment temperature, and site of origin upon employing MSA, signifying that S. hystrix and its dinoflagellate endosymbionts display physiological and molecular signatures that are characteristic of sampling time, site of colony origin, and/or temperature regime. These findings promote the utility of MSA for documenting biologically meaningful shifts in the physiological and/or sub-cellular response of marine invertebrates exposed to environmental change.

In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties, including anti-inflammatory, antiviral and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures, but also new synthetic routes developed during the last years as well as promising therapeutic applications.

Engineering Education needs to be approached in different, innovative ways to meet the needs of society, learners, and education providers. This ranges from new educational programs and innovative pedagogies in individual classrooms, to online and immersive learning, to micro-credentials and modularity along with how to enable rapid adaptation for reskilling and upskilling, to entirely new pathways of restructuring education. Gamification is the application of game elements and digital game design techniques to non-game problems, such as learning challenges. It is about using game-based mechanics, aesthetics, and game thinking to enhance users’ engagement, motivate action, and support learning. At first view, the concept of gamification does not contain game design elements, but with the addition of additional elements such as the video game component that is applied in different contexts, the users are encouraged to participate in learning activities and complete learning challenges successfully. The first step in the process of designing a gamification activity is to think like a game designer. According to Cook (2013), any process can be gamified when the following requirements are covered: (i) the gamified activity is easy to learn, (ii) user progress can be measured, (iii) continuous feedback is provided to users.

Legionella pneumophila can cause a large panel of symptoms beside the classic pneumonia presentation. We present here a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of legionnaires’ disease in the same ward. While the first one was easily assumed as nosocomial based on the date of symptoms onset, the other case needed to rely on clear typing results to be either assigned as nosocomial and related to the same environmental source as the first one, or community acquired. To untangle this specific question, we applied Core-genome MultiLocus Typing (MLST), whole-genome Single Nucleotide Polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large scale epidemiological investigations, NGS typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in environment.

No doubt pollution is a global problem which must be holistically tackled. In doing this, adequate knowledge of the sources of pollution is important, therefore the aim of this paper is to review source apportionment with reference to top-down and bottom-up methods. In this paper, dispersion modeling, emissions inventory, and sampling methods were discussed. Also, analytical methods involved in top-down source apportionment were mentioned. The two techniques are needed to evaluate pollutants and their sources. Based on these two approaches, pollution control strategy would be developed and decisions can be made in deciding the right approach to solve or reduce the pollution problems.

Wheat, a component of the staple diet globally, is a common food allergen in children. The symptoms of wheat allergy (WA) range from skin rash to shortness of breath, significantly impairing quality of life. Following initial clinical suspicion, individuals may undergo wheat allergen-specific skin prick test (SPT), blood test for serum immunoglobulin E (sIgE) levels or oral food challenge. Conventional management of WA lies in wheat avoidance, yet accidental consumption may be inevitable owing to the ubiquity of wheat in various food products. This article aims to provide an overview of the immunologic pathway of WA, followed by its novel diagnostic methods, namely alcohol-soluble SPT extracts, component-resolved diagnosis and basophil activation test (BAT). The mechanisms underlying wheat allergen-specific oral immunotherapy (OIT) as well as a summary of the efficacy, tolerability and safety of related clinical trials will then be discussed.

This work aims to generate some types of M_(j )–neighborhoods. Relationships among these M_(j )-neighborhoods and some other types of neighborhoods are discussed. Additionally, M_(j )-lower, M_(j )-upper approximations and M_(j )-accuracy in terms of M_(j )-neighborhoods are presented. Their properties, with some examples, are investigated. Finally, We addressed the comparisons among M_(j )approach and other approaches.

Scrub typhus (ST) is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the ‘tsutsugamushi triangle’, diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters to human body through the bite of trombiculid mite (Chigger) of the genus Leptotrombidium deliense.The diagnosis of the disease becomes challenging as its early symptoms mimic other febrile illnesses like dengue, influenza, and corona viruses. Lack of rapid, reliable, and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent population, habitat characteristics, and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns, and behavioral interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

For a significant period, conventional breeding and genetic modification were the key techniques that were effective in managing biotic and abiotic stresses in crops and adding desirable traits. However, the recent appearance of novel diseases and unexpected climatic changes that have substantial implications for agriculture worldwide have urged scientists to look for alternative methods to quickly manage seasonal crises. The primary emphasis of this chapter is on the obstacles and diverse biotechnological methods employed to enhance crop resilience against a range of biotic and abiotic stressors in plants. Furthermore, we consider gene transformation, omics techniques, genome editing, and other sophisticated biotechnological tools that utilize transcriptomic, proteomic, metabolomic, phenomics, RNA interference, and epigenome modifications for enhancing plant resilience. Lastly, we examine the potential of merging these eco-friendly and innovative methods with conventional breeding to promote modern agriculture and aid in enhancing tolerance to different biotic, abiotic stresses and growth promotion.

Early disease detection in livestock allows for target treatment decreasing antibiotics use and allow advancements in precision veterinary medicine. MicroRNA (miRNA) -driven signaling cascades play a crucial role in the context of farm animal disease diagnostics and prediction, and their proper understanding remains a challenge. In livestock farm animals, only a small number of miRNAs have been fully validated with respect to disease conditions and physiological or behavioral traits. Low abundance of miRNAs in blood and bodily fluids, along with a small number of nucleotides, makes detection and discrimination tedious and challenging task in. miRNAs usually are homologous, owing to which detection specificity becomes next to impossible when screening for multiple miRNAs in the same analyte sample. Hence, a concurrent, multiplexing, approach becomes crucial for the development of on-farm point-of-care based detection systems. Comprehensive screening methods demand broad dynamic range and enhanced specificity. For on-farm handheld platform development, the ability to screen for multiple varieties of miRNA is essential. In this review paper, I provide an overview of the recent developments of miRNA sensing and the current bottlenecks in the realization of the sensors for detecting miRNAS as target analyte for various livestock disease detection applications. Due to the nascent stages of this research, the possibilities of exploiting miRNAs as a biomarker opens up ways to move from reactive to predictive possibilities in diseases detection in the modern digital livestock farming.

There has been a surge in the interest for (semi)transparent photovoltaics (sTPVs) in recent years, since the more traditional, opaque, devices are not ideally suited for a variety of innovative appli-cations spanning from smart and self-powered windows for buildings to those for vehicle inte-gration. Additional requirements for these photovoltaic applications are a high conversion effi-ciency (despite the necessary compromise to achieve a degree of transparency) and an aesthetical-ly pleasing design. One potential realm to explore in the attempt to meet such challenges is the bio-logical world, where evolution has led to highly efficient and fascinating light-management structures. In this mini-review, we explore some of the biomimetic approaches that can be used to improve both transparent and semitransparent photovoltaic cells, such as moth-eye inspired structures for improved performance and stability or tuneable, coloured, and semi-transparent devices inspired by beetles’ cuticles. Lastly, we will briefly discuss possible future developments for bio-inspired and potentially bio-compatible sTPVs.

Despite several decades’ effort to detect and identify phytoplasmas (Tenericutes: Mollicutes) using PCR and Sanger sequencing focusing on diseased plants, knowledge of phytoplasma biodiversity and vector associations remains highly incomplete. To improve protocols for documenting phyto-plasma diversity and ecology, we used DNA extracted from phloem-feeding insects and com-pared traditional Sanger sequencing with a next-generation sequencing method, Anchored Hy-brid Enrichment (AHE) for detecting and characterizing phytoplasmas. Among 22 of 180 leaf-hopper samples that initially tested positive for phytoplasmas using qPCR, AHE yielded phyto-plasma 16Sr sequences for 20 (19 complete and 1 partial sequence) while Sanger sequencing yield-ed sequences for 16 (11 complete and 5 partial). AHE yielded phytoplasma sequences for an addi-tional 7 samples (3 complete and 4 partial) that did not meet the qPCR threshold for phytoplasma positivity or yielded non-phytoplasma sequences using Sanger sequencing. This suggests that AHE is more efficient for obtaining phytoplasma sequences. Twenty-three samples with sufficient data were classified into eight 16Sr subgroups (16SrI-B, I-F, I-AO, III-U, V-C, IX-J, XI-C, XXXVII-A), three new subgroups (designated as 16SrVI-L, XV-D, XI-G) and three possible new groups. Our results suggest that screening phloem-feeding insects using qPCR and AHE sequencing may be the most efficient method for discovering new phytoplasmas.

Metabolite transport across cellular membranes is a key feature of living organisms. Specialized proteins or protein complexes mediate such transport processes and are accessible to genetic engineering approaches. This special issue will reflect on the different transport between cellular compartments. It will provide an overview about different transporter classes and metabolic engineering approaches in different organisms ranging from Bacteria to Eukarya. The transport mechanisms energetics of these compounds were thought little of, and most consideration was given to the designing of metabolic pathways. Gram-negative microbes discharge a wide scope of proteins whose capacities incorporate biogenesis of organelles, for example, pilli and flagella, supplement securing, destructiveness, and efflux of medications and different poisons. Six particular emission frameworks have been appeared to intervene protein send out through the inward and external layers of Gram-negative microbes. These pathways are exceptionally monitored all through the Gram-negative bacterial species. In Gram-positive microscopic organisms, discharged proteins are usually moved across the single layer by the Sec pathway or the two-arginine (Tat) pathway. Recently efforts focused on transporter and metabolite engineering, visualize the improvement and enhancement of microbial cell to pass such metabolites these metabolite carrier proteins are inserted into the inner membrane of mitochondria across the inner membrane, specialized carrier proteins are necessary that mediate the transport into and out of the mitochondrial matrix. Two main strategies for metabolic engineering in plants: the introduction of genes encoding new enzymes and the use of TFs controlling specialized metabolic pathways

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

Stroke represents one of the neurological diseases most responsible for death and permanent disability in the world. Different factors, such as thrombus, emboli and atherosclerosis, take part in the intricate pathophysiology of stroke. Comprehending the molecular processes involved in this mechanism is crucial to developing new, specific and efficient treatments. Some common mechanisms are excitotoxicity and calcium overload, oxidative stress, and neuroinflammation. Furthermore, non-coding RNAs (ncRNAs) are critical in pathophysiology and recovery after cerebral ischaemia. ncRNAs, particularly microRNAs, and the long non-coding RNAs (lncRNAs) are essential for angiogenesis and neuroprotection, and they have been suggested to be therapeutic, diagnostic and prognostic tools in cerebrovascular diseases, including stroke. This review summarizes the intricate molecular mechanisms underlying ischemic and hemorrhagic stroke and delves into the function of miRNAs in the development of brain damage. Furthermore, we will analyze new perspectives on treatment based on molecular mechanisms in addition to traditional stroke therapies.

Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are secreted from the gut upon nutrient stimulation and regulate postprandial metabolism. These hormones are known as classical incretin hormones and are responsible for a major part of postprandial insulin release. The incretin effect is severely reduced in patients with type 2 diabetes, but it was discovered that administration of GLP-1 agonists was capable of normalizing glucose control in these patients. Over the last decades, much research has been focused on the development of incretin-based therapies for type 2 diabetes. These therapies include incretin receptor agonists and inhibitors of the incretin-degrading enzyme dipeptidyl peptidase-4. Especially the development of diverse GLP-1 receptor agonists has shown immense success, whereas studies of GIP monotherapy in patients with type 2 diabetes have consistently been disappointing. Interestingly, both GIP-GLP-1 co-agonists and GIP receptor antagonists administered in combination with GLP-1R agonists appear to be efficient with respect to both weight loss and control of diabetes, although the molecular mechanisms behind these effects remain unknown. This review describes our current knowledge of the two incretin hormones and the development of incretin-based therapies for treatment of type 2 diabetes.

This study addresses the central question that how can a precise measurement of the gluon Parton Distribution Function (PDF) be attained, thereby enhancing our understanding of the intricate quark-gluon composition within nucleons? Through a dual approach involving Quantum Chromodynamics (QCD) and high-energy particle collisions, the research combines computational tools and experimental techniques, focusing on protons and neutrons. Scattering experiments and advanced detectors are utilized to probe nucleon structure. Theoretical frameworks like QCD equations and the Momentum Sum Rule investigate Parton Distribution Functions at specific momentum fractions and energy scales. The primary goal is to achieve an accurate measurement of the gluon PDF, crucial for unraveling strong force complexities and internal nucleon dynamics. The study's theoretical plots and equations illustrate hypothetical behaviors of gluon distribution, quark distribution, and splitting functions, contributing to a deeper understanding of fundamental particles.

Bacteriotherapy is emerging as a strategic and effective approach to treat infections by providing putatively harmless bacteria (i.e., probiotics) as antagonists to pathogens. A proper delivery of probiotics or their metabolites (i.e., post-biotics) can avail itself of biomaterial encapsulation by innovative manufacturing technologies. This review paper aims at providing the most recent biomaterial-assisted strategies proposed to treat infections or disbiosis using bacteriotherapy. We revised the encapsulation processes across multiscale biomaterial approaches, which could be ideal to target different tissues and suit diverse therapeutic opportunities. Hydrogels, and in particular polysaccharides, are the focus of this review, as they have been reported to better sustain vitality of the live cells incorporated. Specifically, the approaches for fabricating hydrogel-based devices with increasing dimensionality (D), namely, 0D (i.e., particles), 1D (i.e., fibers), 2D (i.e., fiber meshes), and 3D (i.e., scaffolds) endowed with probiotics, were detailed by describing their advantages and challenges, along with a future overlook in the field. Electrospinning, electrospray and 3D bioprinting were investigated as new biofabrication methods for probiotic encapsulation within multidimensional matrices. Finally, examples of biomaterial-based systems for cell and post-biotic release were reported.

The study of the development of the vertebrate retina can be addressed from several perspectives: from purely qualitative to a more quantitative approach that takes into account its spatiotemporal features, its three dimensional structure and also the regulation and properties at the systems level. Here we review the ongoing transition towards a full four-dimensional characterization of the developing vertebrate retina, focusing on the challenges at the experimental, image acquisition, image processing and quantification. Using the developing zebrafish retina, we illustrate how quantitative data extracted from these type of highly dense three-dimensional tissues depends strongly on the image quality, image processing and algorithms used to segment and quantify. Therefore, we propose that the scientific community that focuses on developmental systems could strongly benefit from a more detailed disclosure of the tools and pipelines used to process and analyze images from biological samples.

In this paper, we propose 2 novel methods for brain tumor detection in MRI images. In the first proposed approach, we build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. In the second approach, we expand upon prior studies on convolutional neural networks: a convolutional neural network involving a specific module of layers is used for classification. The first approach achieved accuracy scores of 0.98 and the second approach achieved a score of 0.863, outperforming a benchmark VGG-16 model. Considerations to granular computing and circuit complexity theory are given in the paper as well.

Background: Differentiating essential tremor (ET) and functional tremor (FT) remains challenging due to their overlapping clinical presentations. Objective: In this study, we aimed to elucidate the demographic and psychometric differences between the aforementioned tremor types to enhance the diagnostic accuracy and therapeutic strategies. Methods: In this prospective study, we included 96 patients diagnosed with ET or FT and analysed their related demographic data, clinical symptoms, and psychometric evaluation scores. Our statistical analysis involved Pearson’s chi-square tests, Fisher’s exact tests, and logistic regression to assess how the different variables impact tremor diagnosis. Results: Our study demonstrated a higher ET prevalence in males (p = 0.015). Furthermore, we demonstrated that patients with ET displayed a significantly lower body mass index and a lower age of onset compared to those with FT (p = 0.050 and p = 0.023, respectively). Our psychometric assessments revealed higher cognitive and body image scores in patients with ET, whereas those with FT scored higher on depression and anxiety scales. The misdiagnosis rate was 14.5%, emphasising the need for improved diagnostic criteria. Conclusions: We established specific demographic and psychometric distinctions between ET and FT, which could potentially benefit clinicians in making accurate diagnoses and tailoring treatment approaches. These findings support the inclusion of comprehensive psychometric evaluations into standard diagnostic procedures to better differentiate tremor types.

Spinal cord tumors, though rare, present formidable challenges in clinical management due to their intricate nature. Traditional treatment modalities like surgery, radiation therapy, and chemotherapy have been the mainstay for managing these tumors. However, despite significant advancements, challenges persist, including the limitations of surgical resection and the potential side effects associated with radiation therapy. In response to these limitations, a wave of innovative approaches is reshaping the treatment landscape for spinal cord tumors. Advancements in gene therapy, immunotherapy, and targeted therapy are offering groundbreaking possibilities. Gene therapy holds the potential to modify the genes responsible for tumor growth, while immunotherapy harnesses the body's own immune system to fight cancer cells. Targeted therapy aims to strike a specific vulnerability within the tumor cells, offering a more precise and potentially less toxic approach. Additionally, novel surgical adjuncts are being explored to improve visualization and minimize damage to surrounding healthy tissue during tumor removal. These developments pave the way for a future of personalized medicine for spinal cord tumors. By delving deeper into the molecular makeup of individual tumors, doctors can tailor treatment strategies to target specific mutations and vulnerabilities. This personalized approach offers the potential for more effective interventions with fewer side effects, ultimately leading to improved patient outcomes and a better quality of life. This evolving landscape of spinal cord tumor management signifies the crucial integration of established and innovative strategies to create a brighter future for patients battling this complex condition.

Musculoskeletal injuries such as equine osteoarthritis, osteoarticular defects, tendonitis/desmitis, and muscular disorders are prevalent among sport horses, with a fair prognosis for returning to exercise or previous performance levels. The field of equine medicine has witnessed rapid and fruitful development, resulting in a diverse range of therapeutic options for musculoskeletal problems. Staying abreast of these advancements can be challenging, prompting the need for a comprehensive review of commonly used and recent treatments. The aim is to compile current therapeutic options for managing these injuries, spanning from simple to complex physiotherapy techniques, conservative treatments including steroidal and non-steroidal anti-inflammatory drugs, hyaluronic acid, and polyacrylamides, to the promising regenerative therapies such as hemoderivatives and stem cell-based therapies. Each therapeutic modality is scrutinized for its benefits, limitations, and potential synergistic actions to facilitate their most effective application for intended healing/regeneration of the injured tissue/organ and subsequent patient recovery. While stem cell-based therapies emerge as particularly promising for equine musculoskeletal injuries, a multidisciplinary approach is underscored throughout the discussion, emphasizing the importance of considering various therapeutic modalities in tandem.

Rhabdomyosarcoma is a rare cancer arising in skeletal muscle that typically impacts children and young adults. It is a worldwide challenge in child health as treatment outcomes for metastatic and recurrent disease still pose a major concern for both basic and clinical scientists. The treatment strategies for rhabdomyosarcoma include multi-agent chemotherapies after surgical resection with or without ionization radiotherapy. In this comprehensive review, we first provide a detailed clinical understanding of rhabdomyosarcoma including its classification and subtypes, diagnosis, and treatment strategies. Later we focus on chemotherapy strategies for this childhood sarcoma and discuss the impact of three mechanisms that are involved in chemotherapy response including apoptosis, macro-autophagy, and the unfolded protein response. Finally, we discuss in vivo mouse and zebrafish models, and in vitro 3-dimensional bioengineering models of Rhabdomyosarcoma to screen future therapeutic approaches and promote muscle regeneration.

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Polydimethylsiloxane (PDMS) is one of the most popular elastomers and has been used in different fields, especially in biomechanics research. Among the many interesting features of this material, its hyperelastic behaviour stands out, which allows the use of PDMS in various applications, like the ones that mimic soft tissues. However, the hyperelastic behaviour is not linear and needs detailed analysis, especially the characterization of shear strain. In this work, two approaches, numerical and experimental, were proposed to characterize the effect of shear strain on PDMS. The experimental method was implemented as a simple shear testing associated with 3D digital image correlation and was made using two specimens with two thicknesses of PDMS (2 and 4 mm). A finite element software was used to implement the numerical simulations, in which four different simulations using the Mooney-Rivlin, Yeoh, Gent, and Polynomial hyperelastic constitutive models were performed. These approaches showed that the maximum value of shear strain occurred in the central region of the PDMS, and higher values emerged for the 2 mm PDMS thickness. Qualitatively, in the central area of the specimen, the numerical and experimental results have similar behaviours and the values of shear strain are close. For higher values of displacement and thicknesses, the numerical simulation results move further away from experimental values.

Bacterial biofilms can cause widespread infection. In addition to causing urinary tract infections and pulmonary infections in patients with cystic fibrosis, biofilms can help microorganisms adhere to the surfaces of various medical devices, causing biofilm-associated infections on the surfaces of biomaterials such as venous ducts, joint prostheses, mechanical heart valves, and catheters. Biofilms provide a protective barrier for bacteria and provide resistance to antimicrobial agents, which increases the morbidity and mortality of patients. This review introduces the formation process and drug resistance mechanism of biofilms in detail, and further summarizes the main characteristics of clinical persistent infection caused by biofilms and the many methods of treating biomaterial-related biofilm. This provides ideas and directions for the development of new biofilm infection strategies related to therapeutic materials.

In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

A healthy cornea is a nonvascularized, transparent tissue. It is nourished by diffusion through the tear film, aqueous fluid and neurotrophins supplied by the corneal innervation. The cornea is devoid of both blood and lymphatic vessels, which makes it an immunologi-cally privileged tissue. Corneal neovascularization develops due to an imbalance between angiogenic and anti-angiogenic factors and can lead to a significant decrease in visual acui-ty. It may cause scarring, lipid keratopathy, and corneal edema. The resulting pathological vascularization of the cornea reduces its immune privilege and significantly worsens the prognosis after subsequent penetrating keratoplasty, increasing the risk of rejection of the transplanted flap. Neovascularization is favored by many disease processes, including auto-immune diseases, viral inflammations, mainly herpes and varicella-zoster, and blepharitis [1]. There are several options to treat corneal neovascularization, both pharmacological and surgical. Angiostatic treatment involves inhibiting the growth of new vessels by suppressing inflammatory responses. VEGF inhibitors, steroids, cyclosporine A, and amniotic membrane grafts secured to the cornea work this way. VEGF inhibitors are the agents with the direct and most potent angiostatic effect. Angioregressive treatment, on the other hand, results in the regression of already existing pathological vessels. This therapy is also based on VEGF inhibitors, which, however, have the most potent effect on newly formed and small vessels but are less effective in reducing large-caliber and mature vessels. A more effective method for such vessels is ablation, which involves surgical closure. The most commonly used an-gio-occlusive method is needle diathermy and argon or yellow laser therapy. In advanced corneal neovascularization, corneal transplant surgery may be necessary. It is best performed after pretreatment with anti-VEGF preparations, often continued after corneal transplanta-tion to reduce the risk of transplant rejection [2]. This paper discusses the pathogenesis of corneal neovascularization and the current and future trends in its treatment.

Building electric energy is characterized by a significant increase of its uses (e.g. vehicle charging), a rapidly declining cost of all related data collection and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting related issues. First, on the forecasting explainability, i.e. the ability to understand and explain to the user what shapes the forecast. To this extent we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that a fast training would allow for faster deployment of forecasting in real life solutions. And third, we explore the concept of counterfactual analysis on actionable features, i.e. features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. As for GP models, we have found comparable and in some cases superior accuracy when compared to its neural-network and time-series counterparts but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found, and report here on an important potential especially for practical, decision support, solutions of counterfactuals built on actionable features and short training timeframes.

Greenery-covered towers are a new generation of tall buildings characterized by substantial integration of vegetation. They aim to improve the quality of urban life by reducing air pollution, enhancing biodiversity, creating microclimates, and providing aesthetic and psychological benefits. This paper examines the innovative design approaches of greenery-covered towers, drawing on examples from different countries and contexts. It analyzes the technical, environmental, social, and economic aspects of these projects, as well as the challenges and opportunities they pose for the future of sustainable architecture. The paper concludes that greenery-covered towers should be further developed and refined to become a potential solution for addressing dense cities’ environmental and health issues.

Depth maps produced by LiDAR based approaches are sparse. Even high-end LiDAR sensors produce highly sparse depth maps, which are also noisy around the object boundaries. Depth completion is the task of generating a dense depth map from a sparse depth map. While the traditional approaches focus on directly completing this sparsity from the sparse depth maps, modern techniques use RGB images as a guidance tool to resolve this problem. Whilst many others rely on affinity matrices for depth completion. Based on these approaches, we have sub-divided the literature into two major categories; traditional approaches and backbone-based approaches. The latter is further sub-divided into two-branch, and spatial propagation approaches. The two-branch approaches still have a sub-category named guided-kernel approaches. In this paper, for the first time ever we present a comprehensive survey of depth completion methods. We present a novel taxonomy of depth completion approaches, review and detail different state-of-the art techniques within each category for depth completion of LiDAR data, and provide quantitative results for the approaches on KITTI and NYUv2 depth completion benchmark datasets.

Convolutional Neural Networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the Discrete Wavelet Transform and the other on the Constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across three benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art performance across all three data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification (code available at https://github.com/LorisNanni).

Although the need to assess hydration is well recognized, laboratory tests and clinical impressions are impractical and lack sensitivity to be clinically meaningful. Different approaches use bioelectrical impedance measurements to overcome some of these limitations and aid in classification of hydration status. One indirect approach utilizes single or multiple frequency bioimpedance in regression equations and theoretical models, respectively, with anthropometric measurements to predict fluid volumes (bioelectrical impedance spectroscopy; BIS) and estimate fluid overload based on deviation of calculated to reference extracellular fluid volume. Alternatively, bioimpedance vector analysis (BIVA) uses direct phase-sensitive measurements of resistance and reactance, measured at 50 kHz, normalized for height, then plotted on a bivariate graph, resulting in a vector with length related to fluid content normalized by standing height, and direction with phase angle that indexes hydration status. Comparison with healthy population norms enables BIVA to classify (normal, under- and over-) and rank (change relative to pre-treatment) hydration independent of body weight. Each approach has wide-ranging uses in evaluation and management of clinical groups with overhydration with an evolving emphasis on prognosis. This review discusses the advantages and limitations of BIS and BIVA for hydration assessment with comments on future applications.

Colpodella species are free-living opportunistic pathogens that cause human and animal infections and use ticks and flies as vectors. Trophozoite and cyst stages of Colpodella species can be distinguished from stages of the prey Parabodo caudatus using Sam-Yellowe’s trichrome staining. Colpodella species obtain nutrients by attaching to their prey, aspirating the prey’s cytoplasmic contents into a posterior food vacuole and encysting. It is unclear whether both trophozoite and cyst stages are present during infection. Molecular techniques identified Colpodella species in all reported infections. However, no morphological information was reported to aid life cycle stage identification in hosts. This review discusses the increased incidence of Colpodella infection in animals and in vectors and the need to identify stages used for transmission and pathogenesis. The potential for zoonotic infections through tick and fly bites is a public health concern. We previously used Sam-Yellowe’s trichrome staining to identify life cycle stages of Colpodella sp. In order to gain a better understanding of transmission and pathogenesis, identifying Colpodella life cycle stages in infected tissue and in vectors will provide important insights regarding nutrient uptake in hosts by determining whether attachment of trophozoites to host cells occurs, and identifying trophozoites and cysts in infected hosts.

The spread of high-performance personal computers, frequently equipped with powerful Graphic Processing Units (GPU), raised the interest on a set of techniques able to extract models of electromagnetic phenomena (and devices) directly from available examples of the desired behavior. Such approaches are collectively referred to as Machine Learning (ML). A typical representative ML approach is the so called “Neural Network” (NN). Using such data-driven models allows evaluating the output in a much shorter time when a theoretical model is available, or allows predicting the behavior of the systems and devices when no theoretical model is available. With reference to a simple yet representative benchmark electromagnetic problem, some of the possibilities and the pitfalls of the use of NN for the interpretation of measurements (inverse problem) or to obtain the required measurements (optimal design problem) are discussed. The investigated aspects include the choice of the NN model; the generation of the dataset(s); the selection of hyperparameters (hidden layers, training paradigm). Finally, the capabilities in the handling of ill-posed problems are critically revised.

The field of water management is continually changing. Water has been subject to external shocks in the form of climate change and globalization. Analysis of water management is subject to disciplinary developments and inter-disciplinary interactions. Are these developments well documented in the literature? Initial observations on interdisciplinary literature suggest that results are fragmented, implying that a state-of-the-art review is needed. The objective of this paper is to close this gap by reviewing recent developments in water economics that address the increasing perceptions of water scarcity by looking first at changes in supply and quality of water, and then at impacts of climate change on water extremes. Among responses to such challenges, the paper identifies changes to water use patterns by including and co-managing water from different sources—surface and groundwater, wastewater, and desalinated water. Technological advancements also are among the resources that address water challenges. Water challenges reflect also on management of internationally shared water. A recent surge in scientific work identified international treaties as playing a significant role in water management. The paper reviews recently employed economic tools, such as experimental economics, game theory, institutional economics, and valuation methods. And finally, it explores modeling approaches, including hydro-economic and computable general equilibrium models that are being used to deal with water challenges.

Every land plant exists in close relationship with microbial communities of several niches: rhizosphere, endosphere, phyllosphere, etc. The growth and yield of potato – a critical food crop worldwide – highly depend on the diversity and structure of bacterial and fungal communities with which the potato plant coexists. The potato plant has a specific part – tubers, and the soil near the tubers as sub-compartment is usually called “geocaulosphere”, which is associated with storage process and tare soil microbiome. Specific microbes can help the plant to adapt to particular environment conditions and resist to pathogens. There is a number of approaches to modulate the microbiome that provides organisms with desired traits during inoculation. The mechanisms of plant–bacterial communication remain understudied, and for the further engineering of microbiomes with particular features the knowledge on potato microbiome should be summarized. The most recent approaches to microbiome engineering include construction of a synthetic microbial community or management of plant microbiome using genome engineering. In this review, the various factors that form the microbiome of potato and the role of these factors in overcoming the negative impact of drought and pathogens are surveyed.

The participation of the public in the decision, use and maintenance of the heritage structures in the cities brings positive results to that city. Istanbul Kadikoy-Hasanpasa Gasworks was closed in 1993 and lost its function, and in 2021, it was reopened for use as a culture and art center. In the transformation decisions of the gasworks, the Gasworks Environmental Volunteers, consisting of the people of the environment, have played an active role for 27 years and continue to play a role after the transformation. This study examines the impact and efforts of Gasworks Environmental Volunteers before and after the transformation and describes a collective struggle in the participatory management of heritage. As a result of face-to-face interviews, observations, and literature research conducted with a qualitative research method, the effects of non-governmental organizations on heritage sites were examined. The data obtained will increase the awareness of the society on the heritage areas and contribute to the fact that the society will be a factor in the transformation decisions by protecting the heritage.

Neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease, are caused by a combination of multiple events that damage neuronal function. A well-characterized biomarker of neurodegeneration is proteinaceous aggregates accumulation in the brain. However, the gradually worsening symptoms of neurodegenerative diseases are unlikely to be solely due to the result of a mutation in a single gene, but rather a multi-step process involving epigenetic changes. Multiple epigenetic studies have revealed that modification of histones accompanied by both the local and global remodeling of the chromatin structure and alternations in transcriptional patterns are closely associated with the pathogenesis of neurodegenerative diseases. Unlike DNA mutations, epigenetic alterations are reversible, and therefore raise the possibilities for therapeutic intervention including dietary modifications. Additionally, reactive oxygen species may contribute to the pathogenesis of Alzheimer’s disease and Parkinson’s disease. Given that the antioxidant properties of plant-derived phytochemicals are likely to exhibit pleiotropic effects against ROS-mediated epigenetic alternation, dietary intervention may be promising for the management of neurodegeneration in these diseases. In this review, the state-of-the-art applications using single-cell multi-modal omics approaches including genetics, and epigenetics, and dietary approaches for the identification of novel biomarkers and therapeutic approaches for the treatment of neurodegenerative diseases are discussed.

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

Crop genetic diversity is most for the long-term sustainable production system. The breeding and production strategies of developing and growing uniform and homogenous varieties have created many different problems. Such populations are static and very sensitive to unpredictable stresses. In Nepal, more than 80% of seed system is informal s which has contributed greatly to creating and maintaining genetic diversity within the field particularly contributing to landrace diversity. This paper aims to assess and present the approaches and advantages of increased crop genetic diversity in the fields. The paper is developed based on experiences of implementing on-farm conservation activities carried out in Nepal since last two decades. Some of the evidences have been derived from on-going evolutionary plant breeding project being implemented in Nepal. The information is supplemented with field assessment, focus group discussion, and literature review. The major approaches to increase crop genetic diversity are evolutionary plant breeding, cultivar mixture, landrace enhancement, informal seed system, bulk method, diversifying the sources, participatory plant breeding, open pollination, etc. EPB and cultivar mixture are very simple and effective approaches to increase crop genetic diversity at field level. The involvement of farmers in these approaches helps to accelerate the population improvement particularly for landraces. The major advantages of increased crop genetic diversity are: seed maintenance by farmers themselves, minimal risk of crop failure, resilience to unpredictable stresses, increased amount of diversified nutrition, production increment each year, ease to produce organically, etc. However, there are some issues and problems associated with mixtures and diverse varieties, for example, not being able to harvest by machine, mature at a different date, difficulty in maintaining seeds and registration, etc. Crop genetic diversity should be considered for a climate-resilient and self-dependent production system. The higher the genetic diversity in farming land, the more chance of getting the multiple benefits in the agriculture system (Vernooy 2022).

The is a sequential article to an initial review suggesting that Microbiome First medical approaches to human health and wellness could both aid the fight against noncommunicable diseases and conditions (NCDs) and help to usher in sustainable healthcare. This current review article specifically focuses on public health programs and initiatives and what has been termed by medical journals as a catastrophic record of recent failures. Included in the review is a discussion of the four priority behavioral modifications (food choices, cessation of two drugs of abuse, and exercise) advocated by the World Health Organization as the way to stop the ongoing NCD epidemic. The lack of public health focus on the majority of cells and genes in the human superorganism, the microbiome, is highlighted as is the “regulatory gap” failure to protect humans, particularly the young, from a series of mass population toxic exposures (e.g., asbestos, trichloroethylene, dioxin, polychlorinated biphenyls, triclosan, bisphenol A and other plasticizers, polyfluorinated compounds, herbicides, food emulsifiers, high fructose corn syrup, certain nanoparticles, endocrine disruptors, obesogens). The combination of early life toxicity for the microbiome and connected human physiological systems (e.g., immune, neurological), plus a lack of attention to the importance of microbial rebiosis has facilitated rather than suppressed, the NCD epidemic. This review article concludes with a call to place the microbiome first and foremost in public health initiatives as a way to both rescue public health effectiveness and reduce the human suffering connected to co-morbid NCDs.

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on inflammation, nutrition, and homeostasis. Although driven by a complex combination of intrinsic and extrinsic factors, many chronic diseases such as diabetes, cancer, Inflammatory Bowel Disease among others are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites etc have recently been elucidated, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to pragmatic limitations with respect to parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a systems and community level. In the past decade, various computational biology and machine learning methodologies and approaches have been developed with an aim to fill these existing gaps. Due to the agnostic nature of the tools, they have been applied in various disease contexts to analyze and infer the interactions between the microbiome and host molecular components, and in the case of a few selected tools, on downstream host processes. Generally, most of the tools are enabled by frameworks to statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic microbiome-host interactions and their potential benefit for basic and clinical research. These could include but are not limited to the development of activity and mechanism based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Angiogenesis is a term that describes the formation of new blood and lymphatic vessels from a pre-existing vasculature. This allows tumour cells to acquire sustenance in the form of nutrients and oxygen and the ability to evacuate metabolic waste. As one of the hallmarks of cancer, angiogenesis has been studied extensively in animal and human models to enable better understanding of cancer biology and the development of new anti-cancer treatments. Angiogenesis plays a crucial role in the process of tumour genesis, because solid tumour need a blood supply if they are to grow beyond a few millimeters in size. On the other hand, there is growing evidence that some solid tumour exploit existing normal blood supply and do not require a new vessel formation to grow and to undergo metastasis. This review of the literature will present the current understanding of this intricate process and the latest advances in the use of angiogenesis-targeting therapies in the fight against cancer.

Maximum hardness and hardened depth are the responses of interest in relation to the laser hardening process. These values define heat treatment quality and have a direct impact on mechanical performance. This paper aims to develop models capable of predicting the shape of the hardness profile depending on laser process parameters for controlling laser hardening quality (LHQ), or rather the response values. An experimental study was conducted to highlight hardened profile sensitivity to process input parameters such as laser power (P_L), beam scanning speed (V_S) and initial hardness in the core (H_C). LHQ modeling was conducted by modeling attributes extracted from the hardness profile curve using two effective techniques based on the punctual and geometrical approaches. The process parameters with the most influence on the responses were laser power, beam velocity and initial hardness in the core. The obtained results demonstrate that the geometrical approach is more accurate and credible than the punctual approach according to performance assessment criteria.

Pulsed-field gradient (PFG) diffusion experiments can be used to measure anomalous diffusion in many polymer or biological systems. However, it is still complicated to analyze PFG anomalous diffusion, particularly the finite gradient pulse width (FGPW) effect. In practical applications, the FGPW effect may not be neglected such as in clinical diffusion magnetic resonance imaging (MRI). Here, two significantly different methods are proposed to analyze PFG anomalous diffusion: the effective phase shift diffusion equation (EPSDE) method and an observing the signal intensity at the origin method. The EPSDE method describes the phase evolution in virtual phase space, while the method to observe the signal intensity at the origin describes the magnetization evolution in real space. However, these two approaches give the same general PFG signal attenuation including FGPW effect, which can be numerically evaluated by a direct integration method. The direct integration method is fast and without overflow. It is a convenient numerical evaluation method for Mittag-Leffler function type PFG signal attenuation. The methods here provide a clear view of spin evolution under field gradient, and their results will help the analysis of PFG anomalous diffusion.

Pipelines are widely used for transportation of hydrocarbon fluids over millions of miles over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distributions deport. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses on pipelines leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Particulate matter (PM) contains heavy metals that affect various cellular functions and gene expression associated with an array of acute and chronic diseases, in humans. However, their specific effects on the stem cells remain unclear. Here, we report the effects of PM collected from Jeddah city on bone marrow mesenchymal stem cells (BM-MSCs) on proliferation, cell death, related gene expression and systems biological analysis aiming to understand the underlying mechanisms. Two different sizes (PM_2.5-₁₀) were tested in vitro at various concentrations (15 to 300 µg/ml) and durations (24 to 72 h). PMs induced cellular stress including membrane damage, shrinkage and death. Lower concentrations of PM_2.5 increased BM-MSCs proliferation, while higher concentrations decreased it. PM₁₀ decreased BM-MSCs proliferation in a concentration-dependant manner. The X-Ray Fluorescence spectrometric analysis showed that PM contains high levels of heavy metals. Ingenuity Pathway Analysis (IPA) and hierarchical clustering analyses showed that heavy metals were associated with signalling pathways involving cell stress/death, cancer and chronic diseases. qRT-PCR results showed differential regulation of the apoptosis genes (BCL2, BAX); upregulation of inflammation associated genes (TNF-a and IL-6) and downregulation of cell cycle regulation gene (P53). We conclude that PM could affect different cellular functions and predispose to debilitating diseases.

With a view to understand and resolve the complexity of the food matrix, ohmic technologies alone or in combination were extensively employed. In this sense, the newest developments and advances of proteomics, metabolomics, lipidomics whith their unique benefits could simplify and understand the link between physiological and pathological activities in biology, physiology, pathology, and food science and processing. This review aims to briefly introduce the basis of proteomics, metabolomics, lipidomics, then expansively review their impact on the assessment of meat quality and safety. Here, also, we discuss the application of proteomics, metabolomics, lipidomics for the authentication and adulteration of meat and meat derivatives from different sources, and provides some perspectives regarding the use of emerging techniques such as rapid mass spectrometry (MS) and non-invasive measurements for the analysis of meat quality and safety. This paper summarizes all significant investigation in these matters, and underlines the advances in analytical chemistry technologies and meat science areas. By emphasizing the requirement for additional examinations, this paper attempts a comprehensive knowledge of “foodomics” and their potential to improve their employment in meat science.

Chlorella vulgaris has considerable promise as a raw material for biofuel production since it has a high productivity per unit area, has a minimum influence on the environment, and does not significantly affect food security. Nevertheless, the low fatty content of their biomass poses a considerable economic obstacle for commercialization. Increasing the amount of biomass and lipids produced by these algae is essential for improving the economic feasibility of using them as biofuel sources. This review highlights the significance of identifying appropriate algal strains, namely those found in local environments, and utilising mutagenesis and genetic engineering techniques to create 'platform strains'. Prior endeavours have primarily concentrated on altering environmental and dietary parameters to enhance the production of biomass and lipids. Here, we review a scientific literature that examines biotechnological approaches and develops methodologies designed to increase lipid production, emphasising the importance of aligning engineering efforts with breakthroughs in DNA manipulation tools. In addition, the review research evaluates the present economic and commercial situation of algal biorefineries, including any related disadvantages. In summary, this thorough research highlights the importance of using creative methods to tackle the intricacies of lipogenesis and enhance the economic viability of producing biofuels from algae.

Proteolysis Targeting Chimeras (PROTACs) describe compounds that bind to and induce degradation of a target by simultaneously binding to a ubiquitin ligase. More generally referred to as bifunctional degraders, PROTACs have led the way in the field of targeted protein degradation (TPD) with several compounds currently undergoing clinical testing. Alongside bifunctional degraders, single-moiety compounds, or molecular glue degraders (MGDs), are increasingly being considered as a viable approach for development of therapeutics, driven by advances in rational discovery approaches. This review focusses on drug discovery with respect to bifunctional and molecular glue degraders within the ubiquitin proteasome system, including analysis of mechanistic concepts and discovery approaches, with an overview of current clinical and pre-clinical degrader status in oncology, neurodegenerative and inflammatory disease.

The uterine leiomyoma is the most common benign tumor in women of childbearing age. It may lead to problem of conception or complications during gestational period. The methods of treatment can be surgical (myomectomy and hysterectomy, embolization of arteries) and therapeutic treatment (Ulipristal acetate, Leuprolide acetate, Cetrorelix, Goserelin, Mifeprestone). Both approaches are efficient, but are incompatible with pregnancy planning. Therefore, there is a call for medical practice in developing therapeutical means of preventing leiomyoma onset in patients planning on pregnant. Based on the analysis of GWAS data on the search for mononucleotide polymorphisms associated with the risk of leiomyoma, meta-transcriptomic and meta-methylomic studies, target proteins have been proposed. Prospective therapeuticals of leiomyoma may be based on chemical compounds, humanized recombinant antibodies, vaccines based on markers of the uterine leiomyoma cells that are absent in the adult organism, DNA and RNA preparations. Three different nosological forms of the disease associated with driver mutations in the MED12, HMGA2 and FH genes should be considered when developing or prescribing drugs. E.g. synthetic inhibitors and vaccines based on matrix metalloproteinases MMP11 and MMP16 are expected to be effective only for the prevention of the occurrence of MED12-dependent nodules.

This essay review explores the concept of climate-smart forestry as an approach to mitigating and adapting to climate change. It highlights the need for a holistic approach to forestry that considers regional differences and synergies between ecosystem services. The essay discusses various management strategies at different scales, including species selection, mixed species cultivation, and the promotion of the bioeconomy. It also examines the trade-offs and synergies between different forest uses and the effects of forest management on ecosystem services. Furthermore, the review addresses major abiotic and biotic damage risks to forests, such as wildfires, windstorms, and bark beetle attacks, and proposes risk management strategies. Lastly, the review delves into the management of peatland forests, discussing the role of drainage, the impact of ditch network maintenance, and the challenges of peat ash fertilization. Overall, the review provides valuable insights into climate-smart forestry and its potential to contribute to climate change mitigation and adaptation.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Aphasia is one of the most socially disabling post-stroke deficits. Although traditional therapies have been shown to induce adequate clinical improvement, aphasic symptoms often persist. Therefore, new rehabilitation techniques which act as a substitute or as an adjunct to traditional approaches are urgently needed. The present review provides an overview of the efficacy and safety of the most innovative approaches which have been proposed over the last twenty years. First, we examined the effectiveness of the pharmacological approach, principally used as an adjunct to language therapy, reporting the mechanism of action of each single drug for the recovery of aphasia. Results are conflicting but promising. Secondly, we discussed the application of Virtual Reality (VR) which has been proved to be useful since it potentiates the ecological validity of the language therapy by using virtual contexts which simulate real-life everyday contexts. Finally, we focused on the use of Transcranial Direct Current Stimulation (tDCS), both discussing its applications at the cortical level and highlighting a new perspective, which considers the possibility to extend the use of tDCS over the motor regions. Although the review revels an extraordinary variability among the different studies, substantial agreement has been reached on some general principles, such as the necessity to consider tDCS only as an adjunct to traditional language therapy.

Anthelmintic drugs are the major line of defense against parasitic nematode infections, but the arsenal is limited and resistance threatens sustained efficacy of the available drugs. Discoveries of the modes of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss how our understanding of anthelmintic resistance and modes of action came from the interplay of results from each of these species. We argue that this “cycle of discovery”, where results from one species inform the design of experiments in the other, can use the complementary strengths of both to understand anthelmintic modes of action and mechanisms of resistance.

The recent outbreak of Coronavirus Disease 2019 (COVID-19) is a major threat to human health and the global economy. In addition to the development of vaccines there is an urgent need for preventive and therapeutic strategies towards severe COVID-19. Key to this would be a better understanding of the molecular mechanismsoiuuuu affected by SARS-COV-2. To address this, we performed a systems biology approach by integrating available RNA-seq datasets from post-mortem lung tissue of COVID-19 patients and cell culture models infected with SARS-COV-2, Respiratory Syncytial virus or influenza virus. We identified two gene-expression modules that are commonly regulated by the three viral diseases and one that is specific to COVID-19. All 3 gene-expression modules represent key inflammatory processes. We identified several proteins within these networks that can be targeted by FDA approved drugs. Key examples are TNF, NFkB, INTERLEUKIN-1 and ALOX5 signaling pathways. Our data also suggest that Vitamin D supplementation and a ketogenic diet should be further analyzed as preventive strategies. In conclusion, our data highlights the potential of transcriptomics to unravel the pathological processes related to COVID-19 and guide the initiation of clinical trials.

Male obesity secondary hypogonadism (MOSH) impairs fertility, sexual function, bone mineralization, fat metabolism, cognitive function, deteriorates muscle mass and alters body composition. The aim of this pilot study was to evaluate the effect of dietary intervention and physical activity on the MOSH patient’s hormonal profile after a 10% weight loss compared to baseline. Fourteen male patients were enrolled. Hormonal, lipid, glycemic profiles and body composition were determined at baseline and after a 10% weight loss. Aging Male Symptoms Scale (AMS) and Yale Food Addiction Scale (YFAS) were administered to patients in order to investigate hypogonadal symptoms and food addiction. Compared to baseline, a significant increase of Total Testosterone (TT) (300.2 ± 79.5 ng/dl vs 408.3 ± 125.9, p = 0.002, 95% CI 26.8; 167.7) and a reduction of 17-Beta Estradiol level (48.3 ± 14.9 pg/mL vs 39.2 ± 15.2, p = 0.049, 95% CI 3.1; 0.0) were observed. Total Fat Mass (FM) percentage, android and gynoid fat mass percentage (39.2 ± 6.4% vs 36.2 ± 5.8%, p = 0.0001, 95% CI 22.5; 62.3; 51.5 ± 6.8% vs 47.6 ± 6.8%, p = 0.001, 95% CI 0.6; 1.8, vs 39.2 ± 6.2% vs 36.5 ± 6.3% p= 0.0001, 95% CI 0.9; 2.0 respectively) were significantly decreased after nutritional intervention. In addition, total Fat Free Mass (FFM) in kg was significantly reduced after 10% weight loss (62.3± 2.8 kg vs 60.3± 7.7 kg, p = 0.002, 95% CI 45.0; 93.0). Lifestyle changes, specifically dietotherapy and physical activity, induce positive effects on hypogonadism due to obesity.

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in the animal welfare research, the animal affective computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in nascent stage. By capitalizing on the immense potential of biometric sensors, the artificial intelligence enabled big data methods substantially offers advancement of animal welfare standards and meet the urgent need of caretakers to respond effectively to maintain the wellbeing of their animals. Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are no scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time consuming, interrupt farming processes and involve subjective judgments. Biometric sensors data enabled by Artificial Intelligence are an emerging smart solution to unobtrusively monitoring livestock, but their potential for quantifying affective states and groundbreaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and be-havior in real time are a near-term possibility.

Genomic selection (GS) is a predictive approach that was build up to increase the rate of genetic gain per unit of time in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effect. GS enables the prediction of breeding value of candidate genotypes for selection. In this work we address important issues related to GS and its implementation in tomato breeding context. Genomic constrains and critical parameters affecting the accuracy of prediction in such crop such as phenotyping, genotyping training population composition and size and statistical method should be carefully evaluated. Comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding program are also discussed. GS applied to tomato breeding has already shown to be feasible. We illustrated how GS can improve the rate of gain in elite lines selection, descendent and in backcross schemes. The GS schemes begin to be delineated and computer science can provide support for future selection strategies. A new breeding framework is beginning to emerge for optimizing tomato improvement procedures.

This paper introduces an ecological approach to quantifying diversity in musical compositions. The approach considers notations with distinct pitches and duration as equivalents of species in ecosystems, measures within a composition as equivalents of ecosystems, and the sum of measures (i.e., the entire composition) as a landscape in which ecosystems are embedded. Structural diversity can be calculated at the level of measures (“alpha diversity”) and the entire composition (“gamma diversity”). An additional metric can be derived that quantifies the structural differentiation between measures in a composition (“beta diversity”). We demonstrate the suitability of the approach in music using specifically composed examples and real songs that vary in complexity. We discuss the potential of the approach with selected examples from a potentially ample spectrum of applications within musicology research. The method seems particularly suitability for hypothesis testing to objectively identify many of the intricate phenomena in music. Because the approach extracts information present in the compositions – it lets the songs tell their structure – it can complement more complex modeling approaches used by music scholars. Combined such approaches provide opportunities for interdisciplinary research. They can help to fill knowledge gaps, stimulate further research and increase our understanding of music.

Background: Spinal schwannomas and meningiomas are the most common spinal cord benign tumors in adults and total excision is the goal of the treatment. The traditional surgical approach is performed by total laminectomy, but recently unilateral hemilaminectomy has been recognized as a valuable surgical alternative. We report the clinical results of patients affected by spinal schwannoma and meningioma in whom total laminectomy or unilateral hemilaminectomy were performed. Material and Methods: A retrospective study of patients who underwent resection of spinal meningioma or schwannoma by laminectomy or unilateral hemilaminectomy approaches in our Institute from January 2013 to January 2023 was made. The baseline data of these patients, operative approach and postoperative outcome were compared. Results: The study was performed on 41 patients with mean age of 62.5 ±14.6 years. Our series included 24 (59%) cases of schwannomas and 17 (41%) cases of meningiomas, with a prevalent location in the thoracic spine. The average schwannomas volume was 3,6 ±2,7 cm3, whereas the average meningiomas volume was 1.1 ±0.5 cm3. Total excision was achieved in all cases. Operative time of schwannomas was longer (p=0.001) respect to meningiomas with higher intra-operative blood loss (p=0.000). Total laminectomy approach was performed in 24 out of 41 patients (59%) and unilateral hemilaminectomy approach in 17 out of 41 (41%) patients. Unilateral hemilaminectomy was associated with shorter postoperative bed rest (p=0.003), shorter hospitalization (p=0.028) and lower postoperative pain (p=0.02) respect to total laminectomy. Conclusions: Both spinal schwannomas and spinal meningiomas in various size can be safely and effectively treated with unilateral hemilaminectomy approach. This approach grants more advantages compared with standard total laminectomy, including shorter postoperative bed rest, shorter hospitalization and reduced postoperative pain.

Smart home and building systems are popular solution that support maintaining comfort, safety and improving energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things, are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that mix technologies of field-level and ICT networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, along with a systematic review of the literature, case-studies and an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a SWOT analysis of its usability. Future works are proposed as well.

In genetic conflicts, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables a selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

Mycobacterium avium ssp paratuberculosis (MAP) is the causative agent of Johne’s disease (JD) in animals and a suspected cause of Crohn’s disease (CD) in humans. Crohn’s Disease is a progressive, detrimental granulomatous enteritis of which common therapies are targeted to suppressing certain components of the immune system. The efficacy of these treatments can be variable and temporary with no clear process of selection for the individual patient. However, diagnosis of MAP has been elusive in patients with Crohn’s Disease. In animals it can be detected by several different techniques, including bacterial cell culture, polymerase chain reaction (PCR), and immunological targets. To move to an understanding of the possible pathogenicity of MAP and the sup-posed cause of CD in humans, it is necessary to demonstrate the presence of cell wall deficient mycobacterial (CWDM) forms of MAP in blood and tissue cultures from patients with CD. From this, more tailored and accurate therapies can be implemented and be developed to eradicate this infectious organism from humans. Current anti-mycobacterial antibiotics have limited efficacy as MAP is subtly different from tuberculosis. Newer methods, such as vaccination and gut microbiome remediation will be essential as part of combination therapies.

This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are taken using two approaches: i) an optimization and ii) a rule-based approach, followed by a comparative study. A MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions, using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the other hand, the complexity of the problem and the experience of production engineers and plant operators can significantly impact the quality of the obtained solutions for the rule-based approach.

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capabilities in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibility and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering was minimally discussed in literature neither. This paper first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model into generating new game levels within two well-known game settings; as well as validating the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments were also utilized for evaluating the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and spatial results, several potential drawbacks and future enhancement of the established model were outlined, with the aim of maximizing the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

One of the most frequent brain diseases, Alzheimer's is defined by poor cognitive function brought on by the build-up of Beta Amyloid plaques and the gradual death of neurons. Glucose metabolism and the development of amyloid plaques are being studied together. Under physiologically normal circumstances, glucose is the primary substrate for the adult human brain. The prodromal phases of AD are significantly influenced by glucose hypometabolism. Hypometabolism of glucose in the brain is a clear sign of mitochondrial dysfunction and bioenergetic system impairment. By regulating energy synthesis and cell death, mitochondria play a crucial role in the functioning of cells. Increased formation of reactive oxygen species (ROS) and oxidative stress are a result of mitochondrial dysfunction, which also accelerates the development of Alzheimer's disease. For the maintenance of balance, autophagy is crucial because it selectively destroys damaged mitochondria. AD affects this route for mitochondrial breakdown. Targeting specific mitochondrial ligands by interventions along this pathway might be a useful therapeutic approach. Due to a number of biological obstacles, this method has significant limitations. As a result, many nanocarriers have been created to improve drug delivery effectiveness. All potential nanotechnology-based treatments for AD have been examined in this study, with a particular emphasis on medication delivery to the mitochondria

Integrated landscape approaches (ILA) aim to reconcile multiple, often competing, interests across agriculture, nature conservation, and other land uses. Recognized ILA design principles provide guidance for their implementation, yet application remains challenging, and a strong performance evidence-base is yet to be formed. A comprehensive literature review and focus group discussions with practitioners identified considerable diversity of ILA in actors, temporal, and spatial scales, inter alia. This diversity hampers learning from and steering these integrated planning approaches because of its intractable nature. Therefore, we developed a tool—an ‘ILA mixing board’—to structure the complexity of ILA into selectable and scalable attributes in a replicable way to allow planning, diagnostics, and comparative assessment of ILA. The ILA mixing board tool presents seven qualifiers, each representing a key attribute of ILA design and performance such as project flexibility, inclusiveness of the dialogue, and the centrality of the power distribution. Each qualifier has five (non-normative) outcome indicators that can be registered as present or absent. This process in turn guides planners, evaluators and other participating stakeholders involved in landscape management to diagnose the ILA type, and or its performance. We apply the ILA mixing board as a diagnostic tool to three ILA cases in Nicaragua, Madagascar, and the Congo Basin to show some of the many possible configurations of qualifiers on the mixing board. Overall, the tool allows comparative analyses of the complexity of ILA in a structured and manageable way.

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

Tryptophan metabolism, via the kynurenine (Kyn) pathway, and microbial transformation of tryptophan to indolic compounds, are fundamental for host health; both of which are altered in colon carcinogenesis. Alterations in tryptophan metabolism begin early in colon carcinogenesis as an adaptive mechanism for the tumor to escape immune surveillance and metastasize. The microbial community is a key part of the tumor microenvironment and influences cancer initiation, promotion and treatment response. A growing awareness of the impact of the microbiome on tryptophan (Trp) metabolism in the context of carcinogenesis has prompted this review. We first compare the different metabolic pathways of Trp under normal cellular physiology to colon carcinogenesis, in both the host cells and the microbiome. Second, we review how the microbiome, specifically indoles, influence host tryptophan pathways under normal and oncogenic metabolism. We conclude by proposing several dietary, microbial and drug therapeutic modalities that can be utilized in combination to abrogate tumorigenesis.

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC remains its fatality rate about half of the infected populations globally. The major screening biomarkers and therapeutic target identification have now become a global concern. The present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from the transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4,643 differentially expressed genes. The up-regulatory genes are mainly concentrating on immune-inflammatory response and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment and they were mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC. Furthermore, the survival of the hub genes was also assessed, and among them, finally, CXCR4 has identified as one of the most potential differentially expressed gene that might play a vital role to the survival of CC patients. Thus CXCR4 could be used as a prognostic biomarker and development of a drug target for CC.

Coral reefs are declining worldwide due to global changes in the marine environment. The increasing frequency of massive bleaching events in the tropics is highlighting the need to better understand the stages of coral physiological responses to extreme conditions. Moreover, like many other coastal regions, coral reef ecosystems are facing additional localized anthropogenic issues such as nutrient loading, increased turbidity, and coastal development. Different strategies have been developed to measure the health status of a damaged reef, ranging from the resolution of individual polyps to an entire coral community, but techniques for measuring coral physiology in situ are not yet widely implemented. For instance, while there are many studies of the coral holobiont response in single or limited-number multiple stressor experiments, they provide only partial insights to metabolic performance under more complex temporally and spatially variable natural conditions. Here, we discuss the current status of coral reefs and their global and local stressors in the context of experimental techniques that measure core processes in coral metabolism (respiration, photosynthesis, and biocalcification) in situ, and their role in indicating the health status of colonies and communities. We highlight the need to improve the capability of in situ studies in order to better understand the resilience and stress response of corals under multiple global and local scale stressors.

Classic Hodgkin lymphoma (cHL) is characterized by a few tumor cells surrounded by a protective, immunosuppressive tumor microenvironment composed of normal cells that are an active part of the disease. Hodgkin and Reed-Sternberg (HRS) cells evade the immune system through a variety of different mechanisms. They are invisible to antitumor effector T cells and natural killer cells and promote T cell exhaustion. Using cytokines and extracellular vesicles, they recruit normal cells, induce their proliferation, and “educate” (i.e., reprogram) them to become immunosuppressive and protumorigenic. Therefore, alternative treatment strategies, targeting not only tumor cells but also the tumor microenvironment, are being developed. Here we summarize current knowledge on the ability of HRS cells to build their microenvironment and to educate normal cells to become protective or immunosuppressive. We also describe therapeutic strategies to counteract formation of the tumor microenvironment and related processes leading to T cell exhaustion and repolarization of immunosuppressive tumor-associated macrophages.

Nucleosomes are non-uniformly distributed across eukarytic genomes, with stretches of ‘open’ chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and damaged genomic material. Here we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.

Effective management of water distribution systems requires an integrated approach, but none of the available frameworks for one are in broad use in the water supply industry. Frameworks developed include a management standard of the American Water Works Association and Distribution System Optimization, a methodology for physical, hydraulic, and water quality performance assessment. The intelligent systems framework also offers a pathway to integration, but it lacks a definite structure. The voluntary aspect of adopting innovations within the fragmented and unregulated nature of the water utility industry poses a barrier to adoption of such innovations. Another barrier is the uncoordinated arrangements of water research stakeholders with different incentive structures. Intelligent water systems offer a way to incentive utilities to encourage implementation. They can provide a bottom-up approach where utilities see advantages, as opposed to a top-down approach where they are expected to adopt a method without seeing clear benefits. Research to develop new and improved tools is needed, but the research roadmap should prioritize implementation.

There have been significant collaborative efforts over the past three years to develop therapies against COVID-19. During this journey, there has also been a lot of focus on understanding at-risk groups of patients who either have pre-existing conditions or have developed concomitant health conditions due to the impact of COVID-19 on immune system. There was a high incidence of COVID-19 induced pulmonary fibrosis (PF) observed in patients. PF can cause significant morbidity, long-term disability, and lead to death in the long run. Additionally, being a progressive disease, PF can also impact the patient for a long time after COVID infection and affect the overall quality of life. Although current therapies are being used as the mainstay for treating PF, there is no therapy specifically for COVID-induced PF. As observed in the treatment of other diseases, nanomedicine can show a significant promise in overcoming the limitations of current anti-PF therapies. In this review, we summarize the efforts reported by various groups to develop nanomedicine therapeutics to treat COVID-induced PF.

Conventional diagnostic techniques are based on utilization of analyte sampling, sensing and signalling on separate platforms for detection purposes, that must be evaded and integrated to a single step procedure in point of care (POC) testing devices. Therefore, the trend has been shifted towards utilization of microfluidic platforms for detection of analytes in biochemical, clinical and food technology due to its being expeditious. Microfluidic systems moulded with polymer substances or glass offer specific and sensitive detection of infectious and non-infectious diseases by providing innumerable benefits including less cost, good biological affinity, strong capillary action and simple process of fabrication. In the case of nucleic acid-based nanosensors, there are some challenges that need to be addressed such as cellular lysis, isolation and amplification of nucleic acid required to be accomplished prior to its detection. To avoid utilization of laborious steps for executing these steps, advances have been deployed in this perspective for on-chip sample preparation, amplification and detection which not only improves sensitivity and selectivity but also saves time and resources. This review emphasizes the significance of microfluidic technology for nucleic acid detection of infectious and non-infectious diseases. The implementation of isothermal amplification in concomitance with lateral flow assay greatly increases the binding efficiency of nanoparticles and biomolecules, improves limit of detection and sensitivity. Most importantly deployment of paper-based material made of cellulose reduces the overall cost. Microfluidic technology in nucleic acid testing has been discussed by explicating its applications in different fields. This review concludes with the prospects and proposes future directions in microfluidic based methods in disease diagnosis.

Chronic venous disease (CVD) occurs in a substantial proportion of the world's population. If the onset of CVD looks like a cosmetic defect, then over time, it can develop into serious problems that require surgical intervention. The aim of the work is to use deep learning (DL) methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs. The required for DL algorithms images of legs with CVD were obtained by using Internet Data Mining. For images preprocessing, the binary classification problem “legs - no legs” was solved based on Resnet50 with accuracy 0.998. The application of this filter made it possible to collect a data set of 11,118 good quality leg images with various stages of CVD. For classification of various stages of CVD according to CEAP classification, the multi classification problem was set and resolved by using two neural networks with completely different architecture - Resnet50 and DeiT. The model based on DeiT without any tuning shows better results than the model based on Resnet50 (precision = 0.770 (DeiT) and 0.615 (Resnet50)). To demonstrate the results of the work, a telegram bot was developed, in which fully functioning DL algorithms are implemented. This bot allows evaluating the condition of the patient's legs with a fairly good accuracy for the CVD classification.