Search

Background: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery. Methods: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD. Results: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD. Conclusions: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis. [ABSTRACT FROM AUTHOR]

Motivation De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. Results In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. Availability and implementation DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream. [ABSTRACT FROM AUTHOR]

Background: In the last few years, several studies confirmed the effectiveness of music therapy (MT) for the rehabilitative management of patients with neurological disorders.Aim: Here we discuss the feasibility and disadvantages of tele-neurological MT (tele-NMT) compared to the traditional MT programmes.Methods: We selected all the articles registered in the Web of Knowledge, PubMed, Google Scholar and ScienceDirect from March 2020 to November 2021 concerning tele-NMT during the COVID-19 outbreak, collecting same examples and experiences.Results: With the advent of the COVID-19, several music-based interventions (MBIs) have been adapted from "in person" to a "remote and virtual" mode (through the telemedicine).Discussion: Tele-NMT could represent a promising option to provide constant care and support to people with neurological diseases during the pandemic. [ABSTRACT FROM AUTHOR]

Simple Summary: Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer. [ABSTRACT FROM AUTHOR]

The spatial variability of the aquifers' hydraulic properties can be satisfactorily described by means of scaling laws. The latter enable one to relate the small (typically laboratory) scale to the larger (typically formation/regional) ones, therefore leading de facto to an upscaling procedure. In the present study, we are concerned with the spatial variability of the hydraulic conductivity K into a strongly heterogeneous porous formation. A strategy, allowing one to identify correctly the single/multiple scaling of K, is applied for the first time to a large caisson, where the medium was packed. In particular, we show how to identify the various scaling ranges with special emphasis on the determination of the related cut-off limits. Finally, we illustrate how the heterogeneity enhances with the increasing scale of observation, by identifying the proper law accounting for the transition from the laboratory to the field scale. Results of the present study are of paramount utility for the proper design of pumping tests in formations where the degree of spatial variability of the hydraulic conductivity does not allow regarding them as "weakly heterogeneous", as well as for the study of dispersion mechanisms. [ABSTRACT FROM AUTHOR]

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers. [ABSTRACT FROM AUTHOR]

When the modified cells are injected into the patient as a one-time infusion, the cells are expected to produce the ARSA enzyme that breaks down the build-up of sulfatides in the nerve cells and other cells of the patient's body. This holds great promise for the patients and their carers since both diseases are associated with a shortened life expectancy and a heavy disease burden. Treatment with Libmeldy should only be carried out in qualified specialised treatment centres, and patients and their carers should receive extra educational material to warn them of the symptoms of leukaemia and lymphoma. [Extracted from the article]

Background: The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. Results: Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. Conclusions: CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs. [ABSTRACT FROM AUTHOR]

Water infiltration through coal stocks exposed to weather elements represents a key issue for many old mining sites and coal-fired power plants from the environmental point of view, considering the negative impact on human health of the deriving groundwater, soil and air pollution. Within this context, the paper investigates the hydraulic behaviour of a self-weight compacted unsaturated coal mass and its impact on the numerical prediction of infiltration induced by rainfall events. In particular, the work focuses on the experimental investigation carried out at different representative scales, from the grain scale to physical modelling. The material, when starting from uncompacted conditions, seems to be characterized by metastable structure, which tends to collapse under imbibition. In addition, direct numerical predictions of the seepage regime through a partially saturated coal mass have been performed. As the compaction of the coal stock induced by dozers has not been taken into account, the numerical simulations represent a conservative approach for the assessment of chemical pollution hazard associated to water infiltration into a real stockpile under operational conditions. [ABSTRACT FROM AUTHOR]

DNA methylation alterations are related to multiple molecular mechanisms. The DNA context of CpG sites plays a crucial role in the maintenance and stability of methylation patterns. The quantitative relationship between DNA composition and DNA methylation has been studied in normal as well as pathological conditions, showing that DNA methylation status is highly dependent on the local sequence context. In this work, we describe this relationship by analyzing the DNA sequence context associated to methylation profiles in both physiological and pathological conditions. In particular, we used DNA motifs to describe methylation stability patterns in normal tissues and aberrant methylation events in cancer lesions. In this manuscript, we show how different groups of DNA sequences can be related to specific epigenetic events, across normal and cancer tissues, and provide a thorough structural and functional characterization of these sequences. [ABSTRACT FROM AUTHOR]

The specific gravity of soil solids, G s , is a significant material property in geomechanics. It is determined in the laboratory by water, gas or kerosene pycnometer, following a rather complex procedure. This Note suggests an original, simple and quick – albeit not empirical –method, which only requires knowledge of the soil unit weight γ sat and the water content w in fully saturated conditions and allows one to obtain the soil solids specific gravity G s as a derived quantity. The method can be also used for unsaturated soils, provided that they (or a paste made of the soil itself) are previously fully saturated. G s values obtained applying this method to a variety of soils ranging from pure clays to fine-medium sands are in very close agreement with those determined using the pycnometer standard method. As a by-product, the method is a applicable also for assessing the in-situ unit weight of a saturated soil sample γ sat , even of cohesionless fine-medium sandy soil, however disturbed it may be. The proposed method is not intended as a substitute for the standard pycnometer method, but as a simple and useful addition to the tool-bag for the characterization of ordinary soils. [ABSTRACT FROM AUTHOR]

In the last decades, transcriptome profiling has been one of the most utilized approaches to investigate human diseases at the molecular level. Through expression studies, many molecular biomarkers and therapeutic targets have been found for several human pathologies. This number is continuously increasing thanks to total RNA sequencing. Indeed, this new technology has completely revolutionized transcriptome analysis allowing the quantification of gene expression levels and allele-specific expression in a single experiment, as well as to identify novel genes, splice isoforms, fusion transcripts, and to investigate the world of non-coding RNA at an unprecedented level. RNA sequencing has also been employed in important projects, like ENCODE (Encyclopedia of the regulatory elements) and TCGA (The Cancer Genome Atlas), to provide a snapshot of the transcriptome of dozens of cell lines and thousands of primary tumor specimens. Moreover, these studies have also paved the way to the development of data integration approaches in order to facilitate management and analysis of data and to identify novel disease markers and molecular targets to use in the clinics. In this scenario, several ongoing clinical trials utilize transcriptome profiling through RNA sequencing strategies as an important instrument in the diagnosis of numerous human pathologies. [ABSTRACT FROM AUTHOR]

The integrator complex has been recently identified as a key regulator of RNA Polymerase II-mediated transcription, with many functions including the processing of small nuclear RNAs, the pause-release and elongation of polymerase during the transcription of protein coding genes, and the biogenesis of enhancer derived transcripts. Moreover, some of its components also play a role in genome maintenance. Thus, it is reasonable to hypothesize that their functional impairment or altered expression can contribute to malignancies. Indeed, several studies have described the mutations or transcriptional alteration of some Integrator genes in different cancers. Here, to draw a comprehensive pan-cancer picture of the genomic and transcriptomic alterations for the members of the complex, we reanalyzed public data from The Cancer Genome Atlas. Somatic mutations affecting Integrator subunit genes and their transcriptional profiles have been investigated in about 11,000 patients and 31 tumor types. A general heterogeneity in the mutation frequencies was observed, mostly depending on tumor type. Despite the fact that we could not establish them as cancer drivers, INTS7 and INTS8 genes were highly mutated in specific cancers. A transcriptome analysis of paired (normal and tumor) samples revealed that the transcription of INTS7, INTS8, and INTS13 is significantly altered in several cancers. Experimental validation performed on primary tumors confirmed these findings. [ABSTRACT FROM AUTHOR]

In vermicomposting, the main product is the worm casts, but a leachate is generated that contains large amounts of plant nutrients. This leachate is normally diluted to avoid plant damage. We investigated how dilution of vermicompost leachate combined with different concentrations of nitrogen (N) - phosphorus (P) - potassium (K) triple 17 fertilizer, and polyoxyethylene tridecyl alcohol as dispersant and polyethylene nonylphenol as adherent to increase efficiency of fertilizer uptake, affected sugarcane plant development. The vermicomposting leachate with pH 7.8 and electrolytic conductivity 2.6 dS m−1, contained 834 mg potassium (K) l−1, 247 mg nitrate (NO3−) l−1 and 168 mg phosphate (PO43−) l−1, was free of pathogens and resulted in a 65% germination index. Vermicompost leachate did not inhibit sugarcane growth and mixed with 170 g l−1 NPK triple 17 fertilizer resulted in the best plant development. No dispersant or adherent was required to improve plant height and stem development. [ABSTRACT FROM AUTHOR]

The article discusses the importance of studying rare neurological diseases (RNDs) and the advancements in genetic research that have allowed for a better understanding of these conditions. It highlights the role of Italian neurology centers in the study of RNDs and their participation in international rare disease organizations. The article emphasizes the need for early and accurate diagnosis, collaboration among researchers, patient-centered approaches, and raising awareness about RNDs. The Italian Society of Neurology is committed to addressing these challenges and driving positive transformation in healthcare and research. [Extracted from the article]

Type 2 diabetes (T2D) is one of the most frequent mortality causes in western countries, with rapidly increasing prevalence. Anti-diabetic drugs are the first therapeutic approach, although many patients develop drug resistance. Most drug responsiveness variability can be explained by genetic causes. Inter-individual variability is principally due to single nucleotide polymorphisms, and differential drug responsiveness has been correlated to alteration in genes involved in drug metabolism (CYP2C9) or insulin signaling (IRS1, ABCC8, KCNJ11 and PPARG). However, most genome-wide association studies did not provide clues about the contribution of DNA variations to impaired drug responsiveness. Thus, characterizing T2D drug responsiveness variants is needed to guide clinicians toward tailored therapeutic approaches. Here, we extensively investigated polymorphisms associated with altered drug response in T2D, predicting their effects in silico. Combining different computational approaches, we focused on the expression pattern of genes correlated to drug resistance and inferred evolutionary conservation of polymorphic residues, computationally predicting the biochemical properties of polymorphic proteins. Using RNA-Sequencing followed by targeted validation, we identified and experimentally confirmed that two nucleotide variations in the CAPN10 gene-currently annotated as intronic-fall within two new transcripts in this locus. Additionally, we found that a Single Nucleotide Polymorphism (SNP), currently reported as intergenic, maps to the intron of a new transcript, harboring CAPN10 and GPR35 genes, which undergoes non-sense mediated decay. Finally, we analyzed variants that fall into non-coding regulatory regions of yet underestimated functional significance, predicting that some of them can potentially affect gene expression and/or post-transcriptional regulation of mRNAs affecting the splicing. [ABSTRACT FROM AUTHOR]

Motivation Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a 'plug and play' system is provided. Availability and implementation The package and used data are available at GitHub: https://github.com/fhaive/VOLTA and 10.5281/zenodo.5171719. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Alternative splicing is a pervasive mechanism of RNA maturation in higher eukaryotes, which increases proteomic diversity and biological complexity. It has a key regulatory role in several physiological and pathological states. The diffusion of Next Generation Sequencing, particularly of RNA-Sequencing, has exponentially empowered the identification of novel transcripts revealing that more than 95% of human genes undergo alternative splicing. The highest rate of alternative splicing occurs in transcription factors encoding genes, mostly in Krüppel-associated box domains of zinc finger proteins. Since these molecules are responsible for gene expression, alternative splicing is a crucial mechanism to "regulate the regulators". Indeed, different transcription factors isoforms may have different or even opposite functions. In this work, through a targeted re-analysis of our previously published RNA-Sequencing datasets, we identified nine novel transcripts in seven transcription factors genes. In silico analysis, combined with RT-PCR, cloning and Sanger sequencing, allowed us to experimentally validate these new variants. Through computational approaches we also predicted their novel structural and functional properties. Our findings indicate that alternative splicing is a major determinant of transcription factor diversity, confirming that accurate analysis of RNA-Sequencing data can reliably lead to the identification of novel transcripts, with potentially new functions. [ABSTRACT FROM AUTHOR]

Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the "optimal motor system" and the "optimal stimulus encoders." [ABSTRACT FROM AUTHOR]

Abstract: Mitochondria are involved in ATP supply to cells through oxidative phosphorylation (OXPHOS), synthesis of key molecules and response to oxidative stress, as well as in apoptosis. They contain many redox enzymes and naturally occurring inefficiencies of oxidative phosphorylation generate reactive oxygen species (ROS). CNS functions depend heavily on efficient mitochondrial function, since brain tissue has a high energy demand. Mutations in mitochondrial DNA (mtDNA), generation and presence of ROS and environmental factors may contribute to energy failure and lead to neurodegenerative diseases. Many rare metabolic disorders have been associated with mitochondrial dysfunction. More than 300 pathogenic mtDNA mutations involve proteins that regulate OXPHOS and mitochondrial structural integrity, and have also been described in neurodegenerative diseases with autosomal inheritance. Mitochondria may have an important role in ageing-related neurodegenerative disorders like Parkinson''s disease (PD), Alzheimer''s disease (AD), Huntington''s disease (HD) and amyotrophic lateral sclerosis (ALS). In primary mitochondrial and neurodegenerative disorders, there is strong evidence that mitochondrial dysfunction occurs early and has a primary role in pathogenesis. In the present review, we discuss several mitochondrial diseases as models of neurodegeneration. [Copyright &y& Elsevier]

Abstract: Cerebral microangiopathies are responsible of a great number of strokes. In the recent years advances in molecular genetics identified several monogenic conditions involving cerebral small vessels and predisposing to ischemic and/or hemorrhagic stroke and diffuse white matter disease leading to vascular dementia. Clinical features and diagnostic clues of these conditions, [cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL), COL4A1-related cerebral small vessel diseases, autosomal dominant retinal vasculopathy with cerebral leukodystrophy (AD-RVLC), and Fabry''s disease] are here reviewed. Albeit with variable phenotypes and with different defective genes, all these disorders produce arteriopathy and microvascular disintegration with changes in brain functions. Specific diagnostic tools are recommended, genetic analysis being the gold standard for the diagnosis. [Copyright &y& Elsevier]

Manganese is essential for several metabolic pathways but becomes toxic in excessive amounts. Manganese levels in the body are therefore tightly regulated, but the responsible protein(s) remain incompletely known. We studied two consanguineous families with neurologic disorders including juvenile-onset dystonia, adult-onset parkinsonism, severe hypermanganesemia, polycythemia, and chronic hepatic disease, including steatosis and cirrhosis. We localized the genetic defect by homozygosity mapping and then identified two different homozygous frameshift SLC30A10 mutations, segregating with disease. SLC30A10 is highly expressed in the liver and brain, including in the basal ganglia. Its encoded protein belongs to a large family of membrane transporters, mediating the efflux of divalent cations from the cytosol. We show the localization of SLC30A10 in normal human liver and nervous system, and its depletion in liver from one affected individual. Our in silico analyses suggest that SLC30A10 possesses substrate specificity different from its closest (zinc-transporting) homologs. We also show that the expression of SLC30A10 and the levels of the encoded protein are markedly induced by manganese in vitro. The phenotype associated with SLC30A10 mutations is broad, including neurologic, hepatic, and hematologic disturbances. Intrafamilial phenotypic variability is also present. Chelation therapy can normalize the manganesemia, leading to marked clinical improvements. In conclusion, we show that SLC30A10 mutations cause a treatable recessive disease with pleomorphic phenotype, and provide compelling evidence that SLC30A10 plays a pivotal role in manganese transport. This work has broad implications for understanding of the manganese biology and pathophysiology in multiple human organs. [ABSTRACT FROM AUTHOR]

Abstract: The paper deals with the theoretical prediction of the at-rest earth pressure coefficient for normally consolidated soils (k 0(NC)) using simple elasto-plastic constitutive models. In the first part of the work, the k 0(NC) expressions derived from critical state soil mechanics models are critically discussed. It is shown that, adopting typical values of the models parameters, the experimental k 0(NC) values are usually over-predicted. In the second part, a possible modification of the critical state model formulation, which allows to predict lower k 0(NC) values for a fixed angle of shearing resistance, is considered. Finally, a discussion of the results and some brief conclusions are proposed. [Copyright &y& Elsevier]

Leachate from vermicomposting contains large amounts of plant nutrients and can be used as liquid fertilizer, but normally diluted to avoid plant damage. The amount of nutrients applied is thus reduced so that an additional fertilizer is required. We investigated how dilution of vermicompost leachate combined with different concentrations of NPK triple 17 fertilizer, and polyoxyethylene tridecyl alcohol as dispersant and polyethylene nonylphenol as adherent to increase efficiency of fertilizer uptake, affected sorghum plant development. The vermicomposting leachate with pH 7.8 and electrolytic conductivity 2.6dSm−1, contained 834mg K+ l−1, 247mg l−1 and 168mg l−1, was free of pathogens and resulted in a 65 % germination index. Vermicompost leachate can be used as liquid fertilizer for the cultivation of sorghum without dilution and mixed with 140–170gl−1 of NPK triple 17 fertilizer and 2–3ml−1 of dispersant and 0–1mll−1 adherent. It was found that vermicompost leachate stimulated plant development, but fertilization with NPK was required for maximum growth. [Copyright &y& Elsevier]

In this report, we review the clinical, biochemical, pathophysiologic, and therapeutic aspects of cerebrotendinous xanthomatosis. We stress the importance of early diagnosis and treatment. In addition, we describe our experience in treating patients with chenodeoxycholic acid, an essential drug for this disorder that is no longer available. [ABSTRACT FROM AUTHOR]

Renowned for their role in haemostasis and thrombosis, platelets are also increasingly recognized for their contribution in innate immunity, immunothrombosis and inflammatory diseases. Platelets express a wide range of receptors, which allows them to reach a variety of activation endpoints and grants them immunomodulatory functions. Activated platelets release extracellular vesicles (PEVs), whose formation and molecular cargo has been shown to depend on receptor‐mediated activation and environmental cues. This study compared the immunomodulatory profiles of PEVs generated via activation of platelets by different receptors, glycoprotein VI, C‐type lectin‐like receptor 2 and combining all thrombin‐collagen receptors. Functional assays in vivo in zebrafish and in vitro in human macrophages highlighted distinct homing and secretory responses triggered by the PEVs. In contrast, omics analyses of protein and miRNA cargo combined with physicochemical particle characterization found only subtle differences between the activated PEV types, which were insufficient to predict their different immunomodulatory functions. In contrast, constitutively released PEVs, formed in the absence of an exogenous activator, displayed a distinct immunomodulatory profile from the receptor‐induced PEVs. Our findings underscore that PEVs are tunable through receptor‐mediated activation. To truly comprehend their role(s) in mediating platelet functions among immune cells, conducting functional assays is imperative. [ABSTRACT FROM AUTHOR]

Due to its insular condition, Gran Canaria operates an isolated energy system that requires high self-sufficiency in energy generation and that covers its energy need, 3,327,872.76 MWh/year. The future integration of the Chira-Soria Pumped Hydroelectric Power Plant, scheduled for 2030, is expected to radically transform the energy dynamics of Gran Canaria's electricity system, facing different challenges and opportunities. Challenges include the need for greater flexibility due to growing renewable energy sources, going from 381,000 MW of renewables today to an increase of more than 750,000 MW, environmental commitments, and the operation must address potential operational constraints related to downstream hydrological effects. On the other hand, the opportunities lie in the ability of the reversible hydropower plant to provide balancing services during off-peak hours, improving the integration of intermittent energy sources such as wind and solar power, in addition, the Pumped Hydroelectric (PHES) technology is recognized as mature and efficient for large-scale energy storage, contributing significantly to the integration of renewable energy sources. Implementing innovative approaches such as integrating Big Data into construction projects can also improve efficiency and decision-making in the project delivery process. This facility will facilitate energy storage through the pumping of water at high levels, allowing it to be subsequently turbined in periods of high demand, which will be essential to improve the management and efficiency of the island's energy system. This energy demand will be studied, which follows certain patterns according to the day of the week and, continuing with the line of research established in previous works, a detailed analysis of the existing system, simulation and algorithmic optimization of the integration of the Chira-Soria Pumped Hydroelectric Power Plant into the energy system of Gran Canaria in the year 2023 will be carried out, providing the expected results of such optimal integration by differentiating the demand patterns of each day of the week, previously establishing these annual representative days. [ABSTRACT FROM AUTHOR]

A pulsed microwave surfaguide-type discharge used for nitrogen fixation in N 2 –O 2 gas mixtures is characterized by optical emission spectroscopy. Results show that both rotational and vibrational temperatures are elevated in the active zone near the waveguide, decaying along the discharge tube in both upstream and downstream. The characteristic length of optical emission from NO(A - X) transition gets contracted when pressure increases, specifically at P ≥ 2 Torr. The degree of vibrational non-equilibrium (defined as the ratio between vibrational and rotational temperatures) is decreased by a factor of two when pressure changes from 0.6 to 10 Torr. Non-equilibrium likely disappears as the discharge pressure rises, resulting in a gas temperature elevation. A correlation between gas residence time, pulse duration, and characteristic times for different energy transfer channels is discussed. The rotational–vibrational dynamics differs for NO and N 2 during the pulse. Both species lose vibrational excitation at the beginning of the pulse, whereas N 2 gets re-excited again during the second half of the pulse, which may occur as a result of an efficient pumping-up effect through the vibrational–vibrational energy transfer. At the same time, vibrational relaxation of NO takes place primarily due to a strong vibrational–translational exchange via NO–O 2 and NO–O collisions. [ABSTRACT FROM AUTHOR]