Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

SeqKit2: A Swiss army knife for sequence and alignment processing.

In the era of ubiquitous high-throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user-friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error-tolerant parsing of sequence records. In addition, three subcommands designed for real-time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real-time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing-equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines.

Ultrafast Detection of Arsenic Using Carbon-Fiber Microelectrodes and Fast-Scan Cyclic Voltammetry.

Arsenic contamination poses a significant public health risk worldwide, with chronic exposure leading to various health issues. Detecting and monitoring arsenic exposure accurately remains challenging, necessitating the development of sensitive detection methods. In this study, we introduce a novel approach using fast-scan cyclic voltammetry (FSCV) coupled with carbon-fiber microelectrodes (CFMs) for the electrochemical detection of As3+. Through an in-depth pH study using tris buffer, we optimized the electrochemical parameters for both acidic and basic media. Our sensor demonstrated high selectivity, distinguishing the As3+ signal from those of As5+ and other potential interferents under ambient conditions. We achieved a limit of detection (LOD) of 0.5 µM (37.46 ppb) and a sensitivity of 2.292 nA/µM for bare CFMs. Microscopic data confirmed the sensor's stability at lower, physiologically relevant concentrations. Additionally, using our previously reported double-bore CFMs, we simultaneously detected As3+-Cu2+ and As3+-Cd2+ in tris buffer, enhancing the LOD of As3+ to 0.2 µM (14.98 ppb). To our knowledge, this is the first study to use CFMs for the rapid and selective detection of As3+ via FSCV. Our sensor's ability to distinguish As3+ from As5+ in a physiologically relevant pH environment showcases its potential for future in vivo studies.

Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study.

BACKGROUND: The HIV-1 molecular network is an innovative tool, using gene sequences to understand transmission attributes and complementing social and sexual network studies. While previous research focused on static network characteristics, recent studies' emphasis on dynamic features enhances our understanding of real-time changes, offering insights for targeted interventions and efficient allocation of public health resources. OBJECTIVE: This study aims to identify the dynamic changes occurring in HIV-1 molecular transmission networks and analyze the primary influencing factors driving the dynamics of HIV-1 molecular networks. METHODS: We analyzed and compared the dynamic changes in the molecular network over a specific time period between the baseline and observed end point. The primary factors influencing the dynamic changes in the HIV-1 molecular network were identified through univariate analysis and multivariate analysis. RESULTS: A total of 955 HIV-1 polymerase fragments were successfully amplified from 1013 specimens; CRF01_AE and CRF07_BC were the predominant subtypes, accounting for 40.8% (n=390) and 33.6% (n=321) of the specimens, respectively. Through the analysis and comparison of the basic and terminal molecular networks, it was discovered that 144 sequences constituted static molecular networks, and 487 sequences contributed to the formation of dynamic molecular networks. The findings of the multivariate analysis indicated that the factors occupation as a student, floating population, Han ethnicity, engagement in occasional or multiple sexual partnerships, participation in anal sex, and being single were independent risk factors for the dynamic changes observed in the HIV-1 molecular network, and the odds ratio (OR; 95% CIs) values were 2.63 (1.54-4.47), 1.83 (1.17-2.84), 2.91 (1.09-7.79), 1.75 (1.06-2.90), 4.12 (2.48-6.87), 5.58 (2.43-12.80), and 2.10 (1.25-3.54), respectively. Heterosexuality and homosexuality seem to exhibit protective effects when compared to bisexuality, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. Additionally, the National Eight-Item score and sex education experience were also identified as protective factors against dynamic changes in the HIV-1 molecular network, with OR values of 0.12 (95% CI 0.05-0.32) and 0.26 (95% CI 0.11-0.64), respectively. CONCLUSIONS: The HIV-1 molecular network analysis showed 144 sequences in static networks and 487 in dynamic networks. Multivariate analysis revealed that occupation as a student, floating population, Han ethnicity, and risky sexual behavior were independent risk factors for dynamic changes, while heterosexuality and homosexuality were protective compared to bisexuality. A higher National Eight-Item score and sex education experience were also protective factors. The identification of HIV dynamic molecular networks has provided valuable insights into the characteristics of individuals undergoing dynamic alterations. These findings contribute to a better understanding of HIV-1 transmission dynamics and could inform targeted prevention strategies.

Gold Nanoparticle-Modified Carbon-Fiber Microelectrodes for the Electrochemical Detection of Cd2+ via Fast-Scan Cyclic Voltammetry.

Neurotoxic heavy metals, such as Cd2+, pose a significant global health concern due to their increased environmental contamination and subsequent detrimental health hazards they pose to human beings. These metal ions can breach the blood-brain barrierblood-brain barrier, leading to severe and often irreversible damage to the central nervous system and other vital organs. Therefore, developing a highly sensitive, robust, and rapid in vivo detection method for these hazardous heavy metal ions is of the utmost importance for early detection, thus initiating timely therapeutics. Detecting ultra-low levels of toxic metal ions in vivo and obtaining accurate speciation information remains a challenge with conventional analytical techniques. In this study, we fabricated a novel carbon carbon-fiber microelectrode (CFM)-based sensor that can detect Cd2+ ions using fast-scan cyclic voltammetry by electrodepositing gold nanoparticles (AuNP). We optimized electrochemical parameters that generate a unique cyclic voltammogram (CV) of Cd2+ at a temporal resolution of 100 ms with our novel sensor. All our experiments were performed in tris buffer that mimics the artificial cerebellum fluid. We established a calibration curve resulting in a limit of detection (LOD) of 0.01 µM with a corresponding sensitivity of 418.02 nA/ µM. The sensor's selectivity was evaluated in the presence of other metal ions, and it was noteworthy to observe that the sensor retained its ability to produce the distinctive Cd2+ CV, even when the concentration of other metal ions was 200 times higher than that of Cd2+. We also found that our sensor could detect free Cd2+ ions in the presence of complexing agents. Furthermore, we analyzed the solution chemistry of each of those Cd2+-ligand solutions using a geochemical model, PHREEQC. The concentrations of free Cd2+ ions determined through our electrochemical data align well with geochemical modeling data, thus validating the response of our novel sensor. Furthermore, we reassessed our sensor's LOD in tris buffer based on the concentration of free Cd2+ ions determined through PHREEQC analysis, revealing an LOD of 0.00132 µM. We also demonstrated the capability of our sensor to detect Cd2+ ions in artificial urine samples, showcasing its potential for application in actual biological samples. To the best of our knowledge, this is the first AuNP-modified, CFM-based Cd2+ sensor capable of detecting ultra-low concentrations of free Cd2+ ions in different complex matrices, including artificial urine at a temporal resolution of 100 ms, making it an excellent analytical tool for future real-time, in vivo detection, particularly in the brain.

Nowcasting and forecasting the 2022 U.S. mpox outbreak: Support for public health decision making and lessons learned.

In June of 2022, the U.S. Centers for Disease Control and Prevention (CDC) Mpox Response wanted timely answers to important epidemiological questions which can now be answered more effectively through infectious disease modeling. Infectious disease models have shown to be valuable tools for decision making during outbreaks; however, model complexity often makes communicating the results and limitations of models to decision makers difficult. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the CDC Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases (early, exponential growth, peak, and decline) during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naïve Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.

Analysis of VOCs in Liquids through Vaporization in a Tubular Oven Monitored by Chemical Ionization Mass Spectrometry.

The analysis of chemical compounds present at trace levels in liquids is important not only for environmental measurements but also, for example, in the health sector. The reference technique for the analysis of Volatile Organic Compounds (VOCs) in liquids is GC, which is difficult to use with an aqueous matrix. In this work, we present an alternative technique to GC to analyze VOCs in water. A tubular oven is used to completely vaporize the liquid sample deposited on a gauze. The oven is heated in the presence of a dinitrogen flow, and the gas is analyzed at the exit of the oven by a chemical ionization mass spectrometer developed in our laboratory. It is a low magnetic field Fourier Transform Ion Cyclotron Resonance (FT-ICR) optimized for real-time analysis. The Proton Transfer Reaction (PTR) used during the Chemical Ionization event results in the selective ionization of the VOCs present in the gas phase. The optimization of the desorption conditions is described for the main operating parameters: temperature ramp, liquid quantity, and nitrogen flow. Their influence is studied using a 100 ppmv aqueous toluene solution. The analytical method is then tested on a mixture of seven VOCs.

Image-activated pico-injection for single-cell analysis.

The addition of reagents into preformed droplets is a crucial yet intricate task in droplet-based applications where sequential reactions is required. Pico-injection offers high throughput and robustness in accomplishing this task, but the existing pico-injection techniques work in an indiscriminate manner, making it difficult to target particular groups of droplets. Here we report image-activated pico-injection (imgPico) for label-free, on-demand reagent supplementation into droplets. The imgPico detects the droplets of interest by real-time image analysis and makes decisions for the downstream pico-injection operation. We studied the performance of different algorithms for the image analysis and optimized the experimental settings of the imgPico. In the validation experiment, the imgPico successfully injected fluorescent dyes into droplets encapsulating one, two, and three cells, respectively, as expected. We further demonstrated the utility of imgPico by targeting droplets encapsulating single cells in droplet-based single-cell RNA sequencing (scRNA-seq) using exceedingly high cell density, and the results showed that the imgPico effectively reduced the presence of doublets in the scRNA-seq data. With the merits of being label-free and versatile, the imgPico represents a technical advance with potential applications in single-cell analysis.

Real-time and quantitative protein detection via polyacrylamide gel electrophoresis and online intrinsic fluorescence imaging.

The detection of intrinsic protein fluorescence is a powerful tool for studying proteins in their native state. Thanks to its label-free and stain-free feature, intrinsic fluorescence detection has been introduced to polyacrylamide gel electrophoresis (PAGE), a fundamental and ubiquitous protein analysis technique, to avoid the tedious detection process. However, the reported methods of intrinsic fluorescence detection were incompatible with online PAGE detection or standard slab gel. Here, we fulfilled online intrinsic fluorescence imaging (IFI) of the standard slab gel to develop a PAGE-IFI method for real-time and quantitative protein detection. To do so, we comprehensively investigated the arrangement of the deep-UV light source to obtain a large imaging area compatible with the standard slab gel, and then designed a semi-open gel electrophoresis apparatus (GEA) to scaffold the gel for the online UV irradiation and IFI with low background noise. Thus, we achieved real-time monitoring of the protein migration, which enabled us to determine the optimal endpoint of PAGE run to improve the sensitivity of IFI. Moreover, online IFI circumvented the broadening of protein bands to enhance the separation resolution. Because of the low background noise and the optimized endpoint, we showcased the quantitative detection of bovine serum albumin (BSA) with a limit of detection (LOD) of 20 ng. The standard slab gel provided a high sample loading volume that allowed us to attain a wide linear range of 0.03-10 µg. These results indicate that the PAGE-IFI method can be a promising alternative to conventional PAGE and can be widely used in molecular biology labs.

Sample Injection for Real-Time Analysis (SIRTA) Using GC-MS with Cold EI.

There is a continual demand for advanced methods and instruments for real-time analysis (RTA). Most of the current RTA techniques based on MS involve ambient desorption ionization technology. However, flow injection of liquid extracted samples is another option without added modifications or cost to existing LC-MS instruments. In this work, we introduce a new RTA approach named sample injection for real-time analysis (SIRTA) using GC-MS with Cold EI. In SIRTA, the standard GC column is replaced with a 1 m long 0.1 mm I.D. fused silica capillary that connects the GC injector to the MS transfer-line of Cold EI. Thus, SIRTA with Cold EI imposes no need for any additional instrumentation; hence, it is characterized by zero added cost. Like in flow injection in MS of LC-MS, the sample is dissolved in ∼1 mL methanol or another solvent. Subsequently, the vial is placed in the GC-MS autosampler while using a standard syringe for injection without any GC separation. The analysis takes merely 0.2-0.7 min, ensuring rapid and consecutive analyses. Unlike standard EI, Cold EI enables SIRTA by taking advantage of its fly through open ion source to avoid overwhelming the ion source during the elution of solvents while still providing enhanced molecular ions for nearly all analytes. In this study, we demonstrated SIRTA Cold EI analysis of 12 compounds and 7 mixtures, including various prescription and illicit drugs, cannabis and petroleum samples, and other synthetic organic compounds including those with molecular weight up to 800 g/mol.

High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling.

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high-throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high-throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low-discrepancy sampling, including on-the-fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov-Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points.

Structural Health Monitoring Design and Performance Evaluation of a Middle-Span Bridge.

Structural health monitoring (SHM) has attracted significant attention over the past two decades due to its ability to provide real-time insight into the condition of structures. Despite the development of several SHM systems for long-span bridges, which play a crucial role in the assessment of these structures, studies focusing on short- or middle-span bridges remain scarce. This research paper presents an efficient and practical bridge monitoring and warning system established on a middle-span bridge, a key crossroad bridge located in Shenzhen. The monitoring system consists of sensors and measuring points that collect a substantial amount of data, enabling the close monitoring of various operational indicators to facilitate the early detection of threshold exceedances. Based on this system, the subtle condition of the bridge can be evaluated, and the operational condition of the bridge can be studied through the comparative analysis of the collected data. Over four months of monitoring, data including the strain and creep of the main beam, the strain and settlement of piers and the crack width of the bridge body are observed. Furthermore, the real-time operational status of the bridge is analyzed and evaluated through the combination of the collected data and the structural finite element model.

A stream processing abstraction framework.

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value.

Bio-engineered sensing of Atrazine by green CdS quantum dots: Evidence from electrochemical studies and DFT simulations.

The present investigation reports a comprehensible and responsive strategy for identifying atrazine in several conditions using an extensive electrochemical method. CdS Quantum dots were synthesized via a greener approach, and their formation was endorsed by numerous characterization techniques such as FTIR, SEM, Raman, UV-Vis, and XRD. Owing to the splendid electrocatalytic behavior, Green CdS quantum dots (QDs) of crystallite size ∼2 nm was opted as the sensor material and were, therefore, incorporated on the bare carbon paste electrode's surface. The developed sensor demonstrated an impressive outcome for atrazine sensing accompanied by superior selectivity and sensitivity. The lower detection limit (LLOD) of 0.53 µM was attained using the developed sensor in a linear concentration range of 10-100 µM. Furthermore, the practical pertinence of the developed sensor was examined on distilled water, wastewater, and fresh liquid milk, resulting in a tremendous retrieval of atrazine (91.33-99.8%).

Real-time monitoring of infant theta power during naturalistic social experiences.

Infant-directed speech and direct gaze are important social cues that shape infant's attention to their parents. Traditional methods for probing their effect on infant attention involve a small number of pre-selected screen-based stimuli, which do not capture the complexity of real-world interactions. Here, we used neuroadaptive Bayesian Optimization (NBO) to search a large 'space' of different naturalistic social experiences that systematically varied in their visual (gaze direct to averted) and auditory properties (infant directed speech to nonvocal sounds). We measured oscillatory brain responses (relative theta power) during episodes of naturalistic social experiences in 57 typically developing 6- to 12-month-old infants. Relative theta power was used as input to the NBO algorithm to identify the naturalistic social context that maximally elicited attention in each individual infant. Results showed that individual infants were heterogeneous in the stimulus that elicited maximal theta with no overall stronger attention for direct gaze or infant-directed speech; however, individual differences in attention towards averted gaze were related to interpersonal skills and greater likelihood of preferring speech and direct gaze was observed in infants whose parents showed more positive affect. Our work indicates NBO may be a fruitful method for probing the role of distinct social cues in eliciting attention in naturalistic social contexts at the individual level.

Effective voltammetric tool for Nano-detection of triazine herbicide (1-Chloro-3-ethylamino-5-isopropylamino-2,4,6-triazine) by naphthalene derivative.

The development and operation of a nanosensor for detecting the poisonous 1-chloro-3-ethylamino-5-isopropylamino-2,4,6-triazine (Atrazine) are described in this study for the first time. The carbon electrode (CE) surface was modified with cysteine-substituted naphthalene diimide to create this sensitive platform. The developed nanosensor (NDI-cys/GCE) was evaluated for its ability to sense Atrazine using differential pulse voltammetry and cyclic voltammetry. To achieve the best response from the target analyte, the effects of several parameters were examined to optimize the conditions. The cysteine-substituted naphthalene diimide significantly improved the signals of the Atrazine compared to bare GCE due to the synergistic activity of substituted naphthalene diimide and cysteine molecules. Under optimal conditions, atrazine detection limits at the (NDI-cys/GCE) were reported to be 94 nM with a linear range of 10-100 µM. The developed sensing platform also showed positive results when used to detect the atrazine herbicide in real tap water, wastewater, and milk samples. Furthermore, a reasonable recovery rate for real-time studies, repeatability, and stability revealed that the developed electrochemical platform could be used for sample analysis.

Direct mass spectrometry analysis of exhaled human breath in real-time.

The molecular composition of exhaled human breath can reflect various physiological and pathological conditions. Considerable progress has been achieved over the past decade in real-time analysis of exhaled human breath using direct mass spectrometry methods, including selected ion flow tube mass spectrometry, proton transfer reaction mass spectrometry, extractive electrospray ionization mass spectrometry, secondary electrospray ionization mass spectrometry, acetone-assisted negative photoionization mass spectrometry, atmospheric pressure photoionization mass spectrometry, and low-pressure photoionization mass spectrometry. Here, recent developments in direct mass spectrometry analysis of exhaled human breath are reviewed with regard to analytical performance (chemical sensitivity, selectivity, quantitative capabilities) and applications of the developed methods in disease diagnosis, targeted molecular detection, and real-time metabolic monitoring.

Development of two devices for high-throughput screening of ethanol-producing microorganisms by real-time CO2 production monitoring.

Bioethanol's importance as a renewable energy carrier led to the development of new devices for the high-throughput screening (HTS) of ethanol-producing microorganisms, monitoring ethanol production, and process optimization. This study developed two devices based on measuring CO2 evolution (an equimolar byproduct of microbial ethanol fermentation) to allow for a fast and robust HTS of ethanol-producing microorganisms for industrial purposes. First, a pH-based system for identifying ethanol producers (Ethanol-HTS) was established in a 96-well plate format where CO2 emission is captured by a 3D-printed silicone lid and transferred from the fermentation well to a reagent containing bromothymol blue as a pH indicator. Second, a self-made CO2 flow meter (CFM) was developed as a lab-scale tool for real-time quantification of ethanol production. This CFM contains four chambers to simultaneously apply different fermentation treatments while LCD and serial ports allow fast and easy data transfer. Applying ethanol-HTS with various yeast concentrations and yeast strains displayed different colors, from dark blue to dark and light green, based on the amount of carbonic acid formed. The results of the CFM device revealed a fermentation profile. The curve of CO2 production flow among six replications showed the same pattern in all batches. The comparison of final ethanol concentrations calculated based on CO2 flow by the CFM device with the GC analysis showed 3% difference which is not significant. Data validation of both devices demonstrated their applicability for screening novel bioethanol-producer strains, determining carbohydrate fermentation profiles, and monitoring ethanol production in real time.

Correlating nanoscale motion and ATP production in healthy and favism erythrocytes: a real-time nanomotion sensor study.

Introduction: Red blood cells (RBCs) are among the simplest, yet physiologically relevant biological specimens, due to their peculiarities, such as their lack of nucleus and simplified metabolism. Indeed, erythrocytes can be seen as biochemical machines, capable of performing a limited number of metabolic pathways. Along the aging path, the cells' characteristics change as they accumulate oxidative and non-oxidative damages, and their structural and functional properties degrade. Methods: In this work, we have studied RBCs and the activation of their ATP-producing metabolism using a real-time nanomotion sensor. This device allowed time-resolved analyses of the activation of this biochemical pathway, measuring the characteristics and the timing of the response at different points of their aging and the differences observed in favism erythrocytes in terms of the cellular reactivity and resilience to aging. Favism is a genetic defect of erythrocytes, which affects their ability to respond to oxidative stresses but that also determines differences in the metabolic and structural characteristic of the cells. Results: Our work shows that RBCs from favism patients exhibit a different response to the forced activation of the ATP synthesis compared to healthy cells. In particular, the favism cells, compared to healthy erythrocytes, show a greater resilience to the aging-related insults which was in good accord with the collected biochemical data on ATP consumption and reload. Conclusion: This surprisingly higher endurance against cell aging can be addressed to a special mechanism of metabolic regulation that permits lower energy consumption in environmental stress conditions.

MAIRA: Protein-based Analysis of MinION Reads on a Laptop.

Third-generation sequencing technologies are being increasingly used in microbiome research and this has given rise to new challenges in computational microbiome analysis. Oxford Nanopore's MinION is a portable sequencer that streams data that can be basecalled on-the-fly. Here we give an introduction to the MAIRA software, which is designed to analyze MinION sequencing reads from a microbiome sample, as they are produced in real-time, on a laptop. The software processes reads in batches and updates the presented analysis after each batch. There are two analysis steps: First, protein alignments are calculated to determine which genera might be present in a sample. When strong evidence for a genus is found, then, in a second step, a more detailed analysis is performed by aligning the reads against the proteins of all species in the detected genus. The program presents a detailed analysis of species, antibiotic resistance genes, and virulence factors.