An IoT-fuzzy intelligent approach for holistic management of COVID-19 patients.

In this study, an internet of things (IoT)-enabled fuzzy intelligent system is introduced for the remote monitoring, diagnosis, and prescription of treatment for patients with COVID-19. The main objective of the present study is to develop an integrated tool that combines IoT and fuzzy logic to provide timely healthcare and diagnosis within a smart framework. This system tracks patients' health by utilizing an Arduino microcontroller, a small and affordable computer that reads data from various sensors, to gather data. Once collected, the data are processed, analyzed, and transmitted to a web page for remote access via an IoT-compatible Wi-Fi module. In cases of emergencies, such as abnormal blood pressure, cardiac issues, glucose levels, or temperature, immediate action can be taken to monitor the health of critical COVID-19 patients in isolation. The system employs fuzzy logic to recommend medical treatments for patients. Sudden changes in these medical conditions are remotely reported through a web page to healthcare providers, relatives, or friends. This intelligent system assists healthcare professionals in making informed decisions based on the patient's condition.

Specific Gene Expression in Pseudomonas Putida U Shows New Alternatives for Cadaverine and Putrescine Catabolism.

Pseudomonas putida strain U can be grown using, as sole carbon sources, the biogenic amines putrescine or cadaverine, as well as their catabolic intermediates, ɣ-aminobutyrate or δ-aminovalerate, respectively. Several paralogs for the genes that encode some of the activities involved in the catabolism of these compounds, such as a putrescine-pyruvate aminotransferase (spuC1 and spuC2 genes) and a ɣ-aminobutyrate aminotransferase (gabT1 and gabT2 genes) have been identified in this bacterium. When the expression pattern of these genes is analyzed by qPCR, it is drastically conditioned by supplying the carbon sources. Thus, spuC1 is upregulated by putrescine, whereas spuC2 seems to be exclusively induced by cadaverine. However, gabT1 increases its expression in response to different polyamines or aminated catabolic derivatives from them (i.e., ɣ-aminobutyrate or δ-aminovalerate), although gabT2 does not change its expression level concerning no-amine unrelated carbon sources (citrate). These results reveal differences between the mechanisms proposed for polyamine catabolism in P. aeruginosa and Escherichia coli concerning P. putida strain U, as well as allow a deeper understanding of the enzymatic systems used by this last strain during polyamine metabolism.

Molecular Identification and Acid Stress Response of an Acidithiobacillus thiooxidans Strain Isolated from Rio Tinto (Spain).

Acidithiobacillus thiooxidans is of paramount importance in the development of biomining technologies. Being widely recognized as an extreme acidophile, extensive research has been dedicated to understanding its significant role in the extraction of several ores in recent years. However, there still exist significant molecular uncertainties surrounding this species. This study focuses on developing a taxonomic assignment method based on the sequencing of the 16S-5S rRNA cluster, along with a qPCR-based technology enabling precise growth determination. Additionally, an approach to understanding its response to acid stress is explored through RT-PCR and MALDI-TOF analysis. Our findings indicate that when subjected to pH levels below 1, the cell inhibits central (carbon fixation and metabolism) and energy (sulfur metabolism) metabolism, as well as chaperone synthesis, suggesting a potential cellular collapse. Nevertheless, the secretion of ammonia is enhanced to raise the environmental pH, while fatty acid synthesis is upregulated to reinforce the cell membrane.

From Genes to Bioleaching: Unraveling Sulfur Metabolism in Acidithiobacillus Genus.

Sulfur oxidation stands as a pivotal process within the Earth's sulfur cycle, in which Acidithiobacillus species emerge as skillful sulfur-oxidizing bacteria. They are able to efficiently oxidize several reduced inorganic sulfur compounds (RISCs) under extreme conditions for their autotrophic growth. This unique characteristic has made these bacteria a useful tool in bioleaching and biological desulfurization applications. Extensive research has unraveled diverse sulfur metabolism pathways and their corresponding regulatory systems. The metabolic arsenal of the Acidithiobacillus genus includes oxidative enzymes such as: (i) elemental sulfur oxidation enzymes, like sulfur dioxygenase (SDO), sulfur oxygenase reductase (SOR), and heterodisulfide reductase (HDR-like system); (ii) enzymes involved in thiosulfate oxidation pathways, including the sulfur oxidation (Sox) system, tetrathionate hydrolase (TetH), and thiosulfate quinone oxidoreductase (TQO); (iii) sulfide oxidation enzymes, like sulfide:quinone oxidoreductase (SQR); and (iv) sulfite oxidation pathways, such as sulfite oxidase (SOX). This review summarizes the current state of the art of sulfur metabolic processes in Acidithiobacillus species, which are key players of industrial biomining processes. Furthermore, this manuscript highlights the existing challenges and barriers to further exploring the sulfur metabolism of this peculiar extremophilic genus.

RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics.

The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the next-generation sequencing of RNA (RNA-seq) has several advantages over other techniques, such as the capability of detecting all the transcripts in one assay over RT-qPCR, such as its higher accuracy and broader dynamic range over microarrays or the ability to detect novel transcripts, including non-coding RNA molecules, at nucleotide-level resolution over both techniques. Despite these advantages, many microbiology laboratories have not yet applied RNA-seq analyses to their investigations. The high cost of the equipment for next-generation sequencing is no longer an issue since this intermediate part of the analysis can be provided by commercial or central services. Here, we detail a protocol for the first part of the analysis, the RNA extraction and an introductory protocol to the bioinformatics analysis of the sequencing data to generate the differential expression results.

Bidimensional Analyses of the Intra- and Extracellular Proteomes of Steroid Producer Mycobacteria.

The importance of the pathogenic mycobacteria has mainly focused the omic analyses on different aspects of their clinical significance. However, those industrially relevant mycobacteria have received less attention, even though the steroid market sales in 2021 were estimated in $56.45 billion.The extracellular proteome, due to its relevance in the sterol processing and uptake, and the intracellular proteome, because of its role in steroids bioconversion, are the core of the present chapter. Both, monodimensional gels, as preparatory analysis, and bidimensional gels as proteome analysis are described. As a proof of concept, the protein extraction methods for both sub-proteomes of Mycobacterium are described. Thus, procedures and relevant key points of these proteome analyses are fully detailed.

Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery.

In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out. In the study, the length of stay of patients undergoing cardiac surgery, within the operating room, was used as the response variable. The obtained results show that the RSF model has less error rate for the training and testing data sets, at 23.55% and 20.31%, respectively, than the Weibull model, which has an error rate of 23.82%. Regarding the Harrell C-index, we obtain the values 0.76, 0.79, and 0.76, for the RSF and Weibull models, respectively. After the selection procedure, the Weibull model contains variables associated with the type of protocol and type of patient being statistically significant at 5%. The RSF model chooses age, type of patient, and type of protocol as relevant variables for prediction. We employ the randomForestSRC package of the R software to perform our data analysis and computational experiments. The proposal that we present has many applications in biology and medicine, which are discussed in the conclusions of this work.

Biotechnological Fungal Platforms for the Production of Biosynthetic Cannabinoids.

Cannabinoids are bioactive meroterpenoids comprising prenylated polyketide molecules that can modulate a wide range of physiological processes. Cannabinoids have been shown to possess various medical/therapeutic effects, such as anti-convulsive, anti-anxiety, anti-psychotic, antinausea, and anti-microbial properties. The increasing interest in their beneficial effects and application as clinically useful drugs has promoted the development of heterologous biosynthetic platforms for the industrial production of these compounds. This approach can help circumvent the drawbacks associated with extraction from naturally occurring plants or chemical synthesis. In this review, we provide an overview of the fungal platforms developed by genetic engineering for the biosynthetic production of cannabinoids. Different yeast species, such as Komagataella phaffii (formerly P. pastoris) and Saccharomyces cerevisiae, have been genetically modified to include the cannabinoid biosynthetic pathway and to improve metabolic fluxes in order to increase cannabinoid titers. In addition, we engineered the filamentous fungus Penicillium chrysogenum for the first time as a host microorganism for the production of Δ9-tetrahydrocannabinolic acid from intermediates (cannabigerolic acid and olivetolic acid), thereby showing the potential of filamentous fungi as alternative platforms for cannabinoid biosynthesis upon optimization.

Construction of a repetitive magic square with Ramanujan's number as its product.

In this article, we build a repetitive magic square by multiplying four elements. This square is a matrix with its corresponding elements. The elements of this matrix that take different values allow us to obtain Ramanujan's number 1729 as its multiplicative magic constant. The additive magic constant of the square is the number 40. The elements of these magic constants form an arithmetic progression. An algorithm to build magic squares is also proposed.

Penicillium chrysogenum as a fungal factory for feruloyl esterases.

Plant biomass is a promising substrate for biorefinery, as well as a source of bioactive compounds, platform chemicals, and precursors with multiple industrial applications. These applications depend on the hydrolysis of its recalcitrant structure. However, the effective biological degradation of plant cell walls requires several enzymatic groups acting synergistically, and novel enzymes are needed in order to achieve profitable industrial hydrolysis processes. In the present work, a feruloyl esterase (FAE) activity screening of Penicillium spp. strains revealed a promising candidate (Penicillium rubens Wisconsin 54-1255; previously Penicillium chrysogenum), where two FAE-ORFs were identified and subsequently overexpressed. Enzyme extracts were analyzed, confirming the presence of FAE activity in the respective gene products (PrFaeA and PrFaeB). PrFaeB-enriched enzyme extracts were used to determine the FAE activity optima (pH 5.0 and 50-55 °C) and perform proteome analysis by means of MALDI-TOF/TOF mass spectrometry. The studies were completed with the determination of other lignocellulolytic activities, an untargeted metabolite analysis, and upscaled FAE production in stirred tank reactors. The findings described in this work present P. rubens as a promising lignocellulolytic enzyme producer. KEY POINTS: • Two Penicillium rubens ORFs were first confirmed to have feruloyl esterase activity. • Overexpression of the ORFs produced a novel P. rubens strain with improved activity. • The first in-depth proteomic study of a P. rubens lignocellulolytic extract is shown.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients.

BACKGROUND: During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems. OBJECTIVE: To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. METHODOLOGY: We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal. RESULTS: Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments. CONCLUSION: Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Characterization of Microbial Diversity in Decayed Wood from a Spanish Forest: An Environmental Source of Industrially Relevant Microorganisms.

Rotting wood is inhabited by a large diversity of bacteria, fungi, and insects with complex environmental relationships. The aim of this work was to study the composition of the microbiota (bacteria and fungi) in decaying wood from a northwest Spanish forest as a source of industrially relevant microorganisms. The analyzed forest is situated in a well-defined biogeographic area combining Mediterranean and temperate macrobioclimates. Bacterial diversity, determined by metagenome analyses, was higher than fungal heterogeneity. However, a total of 194 different cultivable bacterial isolates (mainly Bacillaceae, Streptomycetaceae, Paenibacillaceae, and Microbacteriaceae) were obtained, in contrast to 343 fungal strains (mainly Aspergillaceae, Hypocreaceae, and Coniochaetaceae). Isolates traditionally known as secondary metabolite producers, such as Actinobacteria and members of the Penicillium genus, were screened for their antimicrobial activity by the detection of antibiotic biosynthetic clusters and competitive bioassays against fungi involved in wood decay. In addition, the ability of Penicillium isolates to degrade cellulose and release ferulic acid from wood was also examined. These results present decaying wood as an ecologically rich niche and a promising source of biotechnologically interesting microorganisms.

A New Wavelet-Based Privatization Mechanism for Probability Distributions.

In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide range of types of data, including audio signals and images often related to sensors, as unstructured data. Specifically, the cumulative wavelet integral function is defined to build the perturbation on a probability with the help of this function. We show that an arbitrary distribution function additively perturbed is still a distribution function, which can be seen as a privatized distribution, with the privatization mechanism being a wavelet function. Thus, we offer a mathematical method for choosing a suitable probability distribution for data by starting from some guessed initial distribution. Examples of the proposed method are discussed. Computational experiments were carried out using a database-sensor and two related algorithms. Several knowledge areas can benefit from the new approach proposed in this investigation. The areas of artificial intelligence, machine learning, and deep learning constantly need techniques for data fitting, whose areas are closely related to sensors. Therefore, we believe that the proposed privatization mechanism is an important contribution to increasing the spectrum of existing techniques.

Irisin injection mimics exercise effects on the brain proteome.

Physical inactivity can endanger human health and increase the incidence of neurodegenerative disease. Exercise has tremendous beneficial effects on brain health and cognitive function, especially in older adults. It also improves brain-related outcomes in depression, epilepsy and neurodegenerative disorders, such as Parkinson's disease and Alzheimer's disease. Irisin is a mediator of the beneficial effects of exercise. This study aimed to assess the proteome alterations in adult male National Maritime Research Institute (NMRI) mice brain tissue upon three different conditions including endurance exercise, resistance exercise and irisin injection. Quantification of irisin levels in blood was performed using irisin-ELISA Kit. Quantification and identification of proteins via two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)/MS showed the alteration of at least 21 proteins due to different treatments. Cellular pathway analysis revealed common beneficial effects of sole irisin treatment and different exercise procedures suggesting the capability of irisin injection to substitute the exercise when physical activity is not possible.

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data.

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

Using Rhizosphere Phosphate Solubilizing Bacteria to Improve Barley (Hordeum vulgare) Plant Productivity.

On average less than 1% of the total phosphorous present in soils is available to plants, making phosphorous one of the most limiting macronutrients for crop productivity worldwide. The aim of this work was to isolate and select phosphate solubilizing bacteria (PSB) from the barley rhizosphere, which has other growth promoting traits and can increase crop productivity. A total of 104 different bacterial isolates were extracted from the barley plant rhizosphere. In this case, 64 strains were able to solubilize phosphate in agar plates. The 24 strains exhibiting the highest solubilizing index belonged to 16 different species, of which 7 isolates were discarded since they were identified as putative phytopathogens. The remaining nine strains were tested for their ability to solubilize phosphate in liquid medium and in pot trials performed in a greenhouse. Several of the isolated strains (Advenella mimigardefordensis, Bacillus cereus, Bacillus megaterium and Burkholderia fungorum) were able to significantly improve levels of assimilated phosphate, dry weight of ears and total starch accumulated on ears compared to non-inoculated plants. Since these strains were able to increase the growth and productivity of barley crops, they could be potentially used as microbial inoculants (biofertilizers).

A Two-Stage Location Problem with Order Solved Using a Lagrangian Algorithm and Stochastic Programming for a Potential Use in COVID-19 Vaccination Based on Sensor-Related Data.

Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient's records in a data warehouse and generate 2S-SPLPO instances using sensors.

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data.

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.

Muscles proteome analysis; irisin administration mimics some molecular effects of exercise in quadriceps muscle.

Because of health-promoting effects, the adaptation of skeletal muscles to exercise is considered a therapeutic strategy for metabolic complications and musculoskeletal disabilities. Myokines display many beneficial effects of different exercise modalities. Among them, irisin is known as a systemic effector that positively influences several organs. There are a few studies about the effects of irisin on skeletal muscles, and irisin prosperities need to be well-defined for being an exercise mimetic. To aim this purpose, we assessed the proteome profile of mouse skeletal muscle after eight weeks of irisin injection comparing to resistance and endurance exercise treated groups. In the current study, two-dimensional gel electrophoresis was used to evaluate the protein content of the quadriceps muscle. The results were analyzed with Image Master 2D Platinum V6 software. Differentially expressed proteins were characterized by mass spectrometry (MALDI TOF/TOF) and interpreted using protein data banks and co-expression network. Irisin increases cellular ATP content by driving its overproduction through glycolysis and oxidative phosphorylation similar to two exercise protocols and as a specific property, decreases ATP consumption through creatine kinase downregulation. It also improves the microstructural properties of quadriceps muscle by increasing fiber proteins and might induce cellular proliferation and differentiation. Network analysis of differentially expressed proteins also revealed the co-expression of Irisin precursor with structural and metabolic-related proteins. The protein alterations after irisin administration display the potential of this myokine to mimic some molecular effects of exercise, suggesting it a promising candidate to improve muscle metabolism and structure.