IntroductionConcerns about ecological degradation and reduced biodiversity have intensified the search for sustainable solutions in agriculture. The use of plant growth-promoting bacteria (PGPB) offers a promising alternative to enhance soil quality and crop yield while reducing the consumption of chemical fertilizers.MethodsHere, we characterize the plant growth-promoting potential of Azotobacter chroococcum W5 through comparative genomics, in vitro experiments, and metabolomic analyses.ResultsComparative genomic analysis revealed plant growth-promoting traits, including phytohormone biosynthesis, nutrient acquisition, stress adaptation, and colonization in the A. chroococcum W5 strain. Experimental assays confirmed the production of auxin, gibberellic acid, phosphate solubilization, moderate nitrogen fixation, and growth on ACC. Wheat seed inoculation significantly enhanced germination metrics, seedling vigor, and altered carbohydrate metabolism in the seed endosperm. Under salt and osmotic stress, A. chroococcum W5 metabolomic profiling revealed adaptive responses, including elevated levels of osmoprotectants (proline, glycerol) and oxidative stress markers such as 2-hydroxyglutarate, while putrescine and glycine decreased.DiscussionOur results show that the A. chroococcum W5 strain has great potential for the development of novel formulations. More importantly, our results highlight the potential of using plant growth-promoting microorganisms for innovative, sustainable solutions in agriculture.

Biosynthetic gold nanoparticles (bAuNPs) present a promising avenue for enhancing bio-compatibility and offering an economically and environmentally responsible alternative to traditional production methods, achieved through a reduction in the use of hazardous chemicals. While the potential of bAuNPs as anticancer agents has been explored, there is a limited body of research focusing on the crucial physicochemical conditions influencing bAuNP production. In this study, we aim to identify the optimal growth phase of Pseudomonas aeruginosa cultures that maximizes the redox potential and coordinates the formation of bAuNPs with increased efficiency. The investigation employs 2,6-dichlorophenolindophenol (DCIP) as a redox indicator. Simultaneously, we explore the impact of temperature, pH, and incubation duration on the biosynthesis of bAuNPs, with a specific emphasis on their potential application as antitumor agents. Characterization of the resulting bAuNPs is conducted using ATR-FT-IR, TEM, and UV-Vis spectroscopy. To gain insights into the anticancer potential of bAuNPs, an experimental model is employed, utilizing both non-neoplastic (HPEpiC) and neoplastic (PC3) epithelial cell lines. Notably, P. aeruginosa cultures at 9 h/OD600 = 1, combined with biosynthesis at pH 9.0 for 24 h at 58 °C, produce bAuNPs that exhibit smaller, more spherical, and less aggregated characteristics. Crucially, these nanoparticles demonstrate negligible effects on HPEpiC cells while significantly impacting PC3 cells, resulting in reduced viability, migration, and lower IL-6 levels. This research lays the groundwork for the development of more specialized, economical, and ecologically friendly treatment modalities.

The mitochondrial outer membrane protein porin 1 (Por1), the yeast orthologue of mammalian voltage-dependent anion channel (VDAC), is the major permeability pathway for the flux of metabolites and ions between cytosol and mitochondria. In yeast, several Por1 phosphorylation sites have been identified. Protein phosphorylation is a major modification regulating a variety of biological activities, but the potential biological roles of Por1 phosphorylation remains unaddressed. In this work, we analysed 10 experimentally observed phosphorylation sites in yeast Por1 using bioinformatics tools. Two of the residues, T100 and S133, predicted to reduce and increase pore permeability, respectively, were validated using biological assays. In accordance, Por1T100D reduced mitochondrial respiration, while Por1S133E phosphomimetic mutant increased it. Por1T100A expression also improved respiratory growth, while Por1S133A caused defects in all growth conditions tested, notably in fermenting media. In conclusion, we found phosphorylation has the potential to modulate Por1, causing a marked effect on mitochondrial function. It can also impact on cell morphology and growth both in respiratory and, unpredictably, also in fermenting conditions, expanding our knowledge on the role of Por1 in cell physiology.

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Maize, a vital crop for human nutrition, livestock, and industrial development, faces increasingly severe climatic conditions that hinder its production capacity along with other strategic crops. Novel treatments based on microorganisms have demonstrated efficiency in enhancing plant development and responding to stress. The use of bacteria isolated from seeds is a novel approach for biotreatment, as recent studies point to a co-evolution process for their presence in seeds. This approach hypothesize a pre-adaptation to its host, which may lead to increased efficiency. However, several aspects of this approach remain understudied. In this study, we aimed to evaluate the potential of native maize seed microbiota in comparison to that isolated from other species to mitigate drought stress. For this we characterized seedborne microbiota of a common productive maize variety in Portugal, to use it as biotreatment in other two varieties (sensitive and resistant to drought), selecting the strain Pseudomonas fulva MB as the most promising candidate. Stenotrophomonas maltophilia MS-M1 strain, isolated from wild alfalfa seeds and previously characterized as a drought-tolerant enhancer, served as a non-native control strain. . According to the data, both varieties of maize demonstrated enhanced vegetative growth when treated with both individual strains, as well with the consortium, with an increase in plant height of 5-7% in full and medium irrigation, and 50-55% when not irrigated. This trend was also observed in plant weight, which increased by 13-23%, even under no irrigation. In addition, production in both varieties was positively impacted by these treatments, based on the amount of grain produced (by weight). The drought-sensitive variety experienced a 17% increase under full irrigation, while the most tolerant variety experienced a 25-40% increase. Under medium irrigation level, the increase was about 15% in both varieties, while a 100% and 140% increase was observed in each variety, respectively, when no irrigation was applied. The results suggest that native strain the P. fulva MB was slightly more efficient treatment, as it outperformed the non-native strain in terms of productivity in both varieties. However, the differences were not solid enough along all parameters to consistently asses such difference. The consortium treatment only showed better performance under full or medium irrigation conditions for some production traits. These findings support the use of seed microbiota as very efficient biotreatments, suggesting than even non-native strains have a remarkable beneficial impact (interspecific), expanding the possible of use of this kind of bioinoculants. Further test are required to optimize the use of seed-isolated strains as better adapted or tailor-made solutions for agriculture.

Saline and gypsic soils impede or condition the establishment of farms in many regions worldwide. Stress caused by the accumulation of sodium or calcium ions in the soil drastically limits plant growth and is a limiting factor in the production of many crops. For this reason, saline and gypsic soils were preferentially exploited for mineral extraction. However, nowadays, they can be a source of new biotechnological tools to help in the osmotic stress to which some crops are exposed. In these environments, despite being traditionally characterized by their low biodiversity, we can find well-adapted microbiota that may be able to interact with plants to deal with different environmental stresses. These mechanisms may consist of a very important contribution to the development of new osmotic stress-dealing bioinoculants. The present study sought to elucidate the diversity of the cultivable population of such environments and use them as regulators of soil nutrients and stress-relieving symbionts in plants under osmotic stress. Among the candidate strains selected to cover more scenarios, we found that the strains Stutzerimonas stutzeri A38 and Bacillus pumilus A49 were able to increase root size under osmotic stress in Medicago sativa and Medicago polymorpha plants. Moreover, Peribacillus frigoritolerans A70 and Bacillus licheniformis A46 also enhanced the performance in M. polymorpha, showing interesting potential for a future use in wasteland use for production to livestock feeding or other relevant industries.

Prostate cancer (PCa) is the second most common malignancy in men, and the fifth leading cause of death worldwide. Mesenchymal stromal/stem cells (MSC) have been identified in PCa, although contradictory effects in malignant transformation and tumor progression have been described. Since umbilical cord (UC) MSC and cord blood serum (CBS) are rich in numerous growth and anti-inflammatory factors, UC-MSC secretome and CBS are able to modulate tumor cell proliferation and survival as well as immunity and angiogenesis. In the present study, we address this relationship and investigate the influence of UC-MSC secretome and CBS on two human PCa cell lines (PC3 and LNCaP) and a normal epithelial prostate cell line (HPEpiC). Our results disclosed that upon exposure to UC-MSC-conditioned medium or CBS, both PC3 and LNCaP cells exhibited reduced viability, proliferation, and motility while non-malignant epithelial prostate cells were unaffected. These findings were corroborated by expression analysis of AKT/PI3K signaling pathway, p53 and interleukin genes. UC-MSC and CBS factors decreased the expression of growth-stimulating AKT and PI3K effectors and simultaneously up-regulated the expression of tumor-suppressor p53. Moreover, a more anti-inflammatory expression profile was found in both malignant PCa cell lines. Altogether, these results shed light into possible mechanisms by which UC-MSC and CBS reduce PCa progression, further reinforcing their potential use as novel therapeutic agents in PCa.

EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.

Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

The understanding of the molecular basis of cellular processes and ultimately disease, requires knowledge on protein structures, interactions, and functions. Protein–protein interaction data (PPI) is available in the publicly available main PPI databases that show little overlap due to the use of different criteria. Therefore, web platforms that aggregate the data from multiple sources, such as EvoPPI ( http://evoppi.i3s.up.pt ), where the existing databases have been updated and new ones were added, as here described, and APID ( http://cicblade.dep.usal.es:8080/APID/init.action ) are useful. Still, in both EvoPPI 1.0 and 2, here presented, we have made a special effort to make it flexible in what concerns the choice of the databases to be compared. Moreover, interacting protein pairs tend to be evolutionarily conserved, and thus the information available for one species might be used to predict the incompleteness of the network in another one, and identify putative missing interactions. This approach is now available in EvoPPI 2 for Homo sapiens and the model species Mus musculus, Caenorhabditis elegans, and Drosophila melanogaster, using either Ensembl ( https://www.ensembl.org ) or DIOPT Ortholog Finder ( https://www.flyrnai.org/cgi-bin/DRSC_orthologs.pl ) orthologies/paralogies. Moreover, since not all available PPI data is present in the main databases (e.g. PPI observed in patient tissues and mutant animal species, where PPI might be aberrant, are usually not included in the main databases, although in several studies this has been shown not to be the case), we provide the needed tools (including a Lubuntu-based virtual machine where all software is already installed and ready-to-run) to run a local EvoPPI 2 instance and create a custom database from the existing ones. This way the user can add new data for any species and from any source database, creating custom interactomes. Administrator tools are provided to help in the automatic processing and conversion of files from various sources into the custom EvoPPI database format.

Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, especially with the application of machine learning techniques to help identify risk levels of risk factors of a project before its development begins, with the goal of improving the likelihood of success of these projects. This paper presents the results of the application of machine learning techniques for risk assessment in software projects. A Python application was developed and, using Scikit-learn, two machine learning models, trained using software project risk data shared by a partner company of this project, were created to predict risk impact and likelihood levels on a scale of 1 to 3. Different algorithms were tested to compare the results obtained by high performance but non-interpretable algorithms (e.g., Support Vector Machine) and the ones obtained by interpretable algorithms (e.g., Random Forest), whose performance tends to be lower than their non-interpretable counterparts. The results showed that Support Vector Machine and Naive Bayes were the best performing algorithms. Support Vector Machine had an accuracy of 69% in predicting impact levels, and Naive Bayes had an accuracy of 63% in predicting likelihood levels, but the results presented in other evaluation metrics (e.g., AUC, Precision) show the potential of the approach presented in this use case.

2 Introduction Prostate carcinoma, an indolent cancer with a noninvasive nature, has the highest rate of neoplastic transformation in the human body, and related deaths seem to be due to failure to control metastatic disease. Although a diversity of treatment modalities is available to improve survival in patients with metastasis, consideration of stem cell therapy has not receivedmuch attention. Several studies have shown umbilical cord (UC) marked therapeutic potential in diverse pathologies, from hematology to immunology, tissue regeneration, and oncology, but the role of some UC components in the oncology field is not yet clear. UC is an easy-access source of stem cells and can be noninvasively obtained from a donor riskfree source. Among UC hematopoietic and mesenchymal stem cells, UC is also a source of cord blood serum (CBS), which has a high concentration of platelet products, essential growth factors, and cell adhesion molecules. Therefore, CBS has been investigated as a potential therapeutic agent for the treatment of a variety of conditions, including ocular surface disease and skin ulcers. In fact, CBS has several therapeutic advantages over adult platelet-rich plasma, considering that CBS contains higher levels of antiinflammatory molecules and platelet-rich plasma has more proinflammatory molecules, which can intensify certain conditions. Objective This work aimed to evaluate the effect of CBS on viability, proliferation, and migration of two human prostate cancer cell lines (PC3 and LNCaP). Methods CBS was obtained from the cord blood bank BebéVida after an automatic separation with the AutoXpress process. Both cell lines were incubated for 24 hours with 10%, 20%, and 25% of CBS, and the CBS effects on viability, proliferation, and motility were evaluated.

Superiority of long acting injectable antipsychotics (LAI) over oral antipsychotics remains controversial and dependent on study design and inclusion criteria. Meta-analysis of 21 RCTs demonstrated no difference in their effectiveness, but meta-analysis of 25 mirror-image studies did. None of these included paliperidone palmitate (PP). Methods: We challenged efficiency of PP in a multicentric mirror-image study. Primary outcome was total hospitalization days. Mirror periods were 365 days either side of the first injection in model-1, and either side of index admission in model-2. Inclusion criteria were: 18 to 65 years, schizophrenia spectrum disorder, ≥ 3 injections received, and oral antipsychotic prescriptions before PP trial. Exclusion criteria were: prior clozapine or LAI trial. Cost-effectiveness was calculated from a public payer's perspective.114 patients were recruited (77% males, mean 37 years, mean disease duration 10 years). Oral antipsychotics adherence was 43%. Mean PP treatment lasted 297 days (adherence 81%). Mean annual hospitalization days weren't significantly different in model-1 (45.8 days vs 38.5 days, p = 0.058), but were significantly lower in model-2, (14.4 days vs 24.2 days, p = 0.003). 1.9 admissions per patient-year fell to 0.64 on PP (p < 0.0001). PP was approximately cost-neutral: differences were -$326 and $1788 for model-1 and model-2.PP as a first LAI improved adherence, decreased hospital visits and duration was cost neutral. Drawbacks are the retrospective design and lack of comparator and safety data. Strengths are naturalistic design and adherence calculation. A subset of patients responds well to LAI, leading to meaningful reductions in hospital services requirements.

os pedidos de execução de aplicações na arquitetura cloud e no paradigma fog são geralmente heterogéneos e o escalonamento nessas arquiteturas é um problema de otimização com múltiplas restrições. Neste artigo, fizemos um levantamento sobre os trabalhos relacionados com o escalonamento na arquitetura cloud e no paradigma fog, identificamos as suas limitações, explorarmos perspetivas de melhorias e propomos um modelo de escalonamento sensíveis ao contexto para o paradigma fog. A solução proposta utiliza a normalização Min-Max, para resolver a heterogeneidade e normalizar os diferentes parâmetros de contexto. A prioridade dos pedidos é definida através da aplicação da técnica de análise de Regressão Linear Múltipla e o escalonamento é feito utilizando a técnica de Otimização de Programação Não Linear Multiobjetivo. Os resultados obtidos a partir de simulações no kit de ferramentas iFogSim, demonstram que a nossa proposta apresenta um melhor desempenho em comparação com as propostas não sensíveis ao contexto.

While schizophrenia (SCZ) has been traditionally described as a disorder manifesting during adolescence and early adulthood, disruption of early neurodevelopmental events is implicated in SCZ. Understanding how in utero disruption of brain development leads to complex symptoms of schizophrenia decades later is essential for identifying early interventions to possibly prevent the symptoms of SCZ. Recent studies have identified a convergence of SCZ associated genes in the frontal cortex during mid-fetal development, a crucial developmental period for the formation of neuronal circuits.

Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.

Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that lead us to question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and, in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a review of the literature on the main task scheduling algorithms in cloud and fog architecture; we studied and discussed their limitations, and we also explored and suggested some perspectives for improvement.

According to the author's knowledge task scheduling in fog paradigm is highly complex and in the literature there are still few studies on it. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique.