Using AI and BES/MFC to decrease the prediction time of BOD5 measurement.

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD5 is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD5 measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD5 measurements were carried out within 6-24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD5 sensor in real-life scenarios.

Microbial fuel cell biosensor for the determination of biochemical oxygen demand of wastewater samples containing readily and slowly biodegradable organics.

OBJECTIVES: Single-chamber air cathode microbial fuel cells (MFCs) were applied as biosensors for biochemical oxygen demand (BOD) measurement of real wastewaters with considerable suspended and/or slowly biodegradable organic content. RESULTS: The measurement method consists of batch sample injection, continuous measurement of cell voltage and calculation of total charge (Q) gained during the biodegradation of organic content. Diverse samples were analyzed: acetate and peptone samples containing only soluble readily biodegradable substrates; corn starch and milk samples with suspended and colloidal organics; real domestic and brewery wastewaters. Linear regression fitted to the Q vs. BOD5 measurement points of the real wastewaters provided high (> 0.985) R2 values. Time requirement of the measurement varied from 1 to 4 days, depending on the composition of the sample. CONCLUSIONS: Relative error of BOD measured in the MFCs comparing with BOD5 was less than 10%, thus the method might be a good basis for the development of on-site automatic BOD sensors for real wastewater samples.

ASAR: visual analysis of metagenomes in R.

Motivation: Functional and taxonomic analyses are critical steps in understanding interspecific interactions within microbial communities. Currently, such analyses are run separately, which complicates interpretation of results. Here we present the ASAR interactive tool for simultaneous analysis of metagenomic data in three dimensions: taxonomy, function, metagenome. Results: An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available. Availability and implementation: Source code of the ASAR is accessible at GitHub (https://github.com/Askarbek-orakov/ASAR). Contact: askarbek.orakov@nu.edu.kz or goryanin@gmail.com.

Single chamber air-cathode microbial fuel cells as biosensors for determination of biodegradable organics.

OBJECTIVES: Single chamber air cathode microbial fuel cells (MFCs) were investigated with sodium-acetate and peptone as test substrates to assess the potential for application as biosensor to determine the concentration of biodegradable organics in water/wastewater samples. RESULTS: MFCs provided well-reproducible performance at high (> 2000 mg COD l-1-Chemical Oxygen Demand) acetate concentration values. Current in the cells proved to be steady from 25 to 35 °C, significant decrease was, however, revealed in the current below 20 °C. Direct calculation of non-toxic biodegradable substrate concentration in water/wastewater from the current in MFCs is possible only in the non-saturated substrate concentration range due to the Monod-like dependence of the current. This range was determined by a fitted and verified Monod-based kinetic model. Half saturation constant (KS) values were calculated at 30 °C applying different external resistance values (100 Ω, 600 Ω and 1000 Ω, respectively). In each case KS remained below 10 mg COD l-1. CONCLUSIONS: Biosensors with this particular MFC design and operation are potentially applicable for detecting as low as 5 mg COD l-1 readily biodegradable substrates, and measuring the concentration of these substances up to ~ 50-70 mg COD l-1.

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology.

SUMMARY: Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats. AVAILABILITY AND IMPLEMENTATION: All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.

Optimization of microbial fuel cell performance application to high sulfide industrial wastewater treatment by modulating microbial function.

Microbial fuel cells (MFCs) are innovative eco-friendly technologies that advance a circular economy by enabling the conversion of both organic and inorganic substances in wastewater to electricity. While conceptually promising, there are lingering questions regarding the performance and stability of MFCs in real industrial settings. To address this research gap, we investigated the influence of specific operational settings, regarding the hydraulic retention time (HRT) and organic loading rate (OLR) on the performance of MFCs used for treating sulfide-rich wastewater from a canned pineapple factory. Experiments were performed at varying hydraulic retention times (2 days and 4 days) during both low and high seasonal production. Through optimization, we achieved a current density generation of 47±15 mA/m2, a COD removal efficiency of 91±9%, and a sulfide removal efficiency of 86±10%. Microbiome analysis revealed improved MFC performance when there was a substantial presence of electrogenic bacteria, sulfide-oxidizing bacteria, and methanotrophs, alongside a reduced abundance of sulfate-reducing bacteria and methanogens. In conclusion, we recommend the following operational guidelines for applying MFCs in industrial wastewater treatment: (i) Careful selection of the microbial inoculum, as this step significantly influences the composition of the MFC microbial community and its overall performance. (ii) Initiating MFC operation with an appropriate OLR is essential. This helps in establishing an effective and adaptable microbial community within the MFCs, which can be beneficial when facing variations in OLR due to seasonal production changes. (iii) Identifying and maintaining MFC-supporting microbes, including those identified in this study, should be a priority. Keeping these microbes as an integral part of the system's microbial composition throughout the operation enhances and stabilizes MFC performance.

A semi-automated genome annotation comparison and integration scheme.

BACKGROUND: Different genome annotation services have been developed in recent years and widely used. However, the functional annotation results from different services are often not the same and a scheme to obtain consensus functional annotations by integrating different results is in demand. RESULTS: This article presents a semi-automated scheme that is capable of comparing functional annotations from different sources and consequently obtaining a consensus genome functional annotation result. In this study, we used four automated annotation services to annotate a newly sequenced genome--Arcobacter butzleri ED-1. Our scheme is divided into annotation comparison and annotation determination sections. In the functional annotation comparison section, we employed gene synonym lists to tackle term difference problems. Multiple techniques from information retrieval were used to preprocess the functional annotations. Based on the functional annotation comparison results, we designed a decision tree to obtain a consensus functional annotation result. Experimental results show that our approach can greatly reduce the workload of manual comparison by automatically comparing 87% of the functional annotations. In addition, it automatically determined 87% of the functional annotations, leaving only 13% of the genes for manual curation. We applied this approach across six phylogenetically different genomes in order to assess the performance consistency. The results showed that our scheme is able to automatically perform, on average, 73% and 86% of the annotation comparison and determination tasks, respectively. CONCLUSIONS: We propose a semi-automatic and effective scheme to compare and determine genome functional annotations. It greatly reduces the manual work required in genome functional annotation. As this scheme does not require any specific biological knowledge, it is readily applicable for genome annotation comparison and genome re-annotation projects.

FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models.

BACKGROUND: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. METHODS: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. RESULTS: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. CONCLUSIONS: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.

Diagnostic of Patients with COVID-19 Pneumonia Using Passive Medical Microwave Radiometry (MWR).

BACKGROUND: Chest CT is widely regarded as a dependable imaging technique for detecting pneumonia in COVID-19 patients, but there is growing interest in microwave radiometry (MWR) of the lungs as a possible substitute for diagnosing lung involvement. AIM: The aim of this study is to examine the utility of the MWR approach as a screening tool for diagnosing pneumonia with complications in patients with COVID-19. METHODS: Our study involved two groups of participants. The control group consisted of 50 individuals (24 male and 26 female) between the ages of 20 and 70 years who underwent clinical evaluations and had no known medical conditions. The main group included 142 participants (67 men and 75 women) between the ages of 20 and 87 years who were diagnosed with COVID-19 complicated by pneumonia and were admitted to the emergency department between June 2020 to June 2021. Skin and lung temperatures were measured at 14 points, including 2 additional reference points, using a previously established method. Lung temperature data were obtained with the MWR2020 (MMWR LTD, Edinburgh, UK). All participants underwent clinical evaluations, laboratory tests, chest CT scans, MWR of the lungs, and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2. RESULTS: The MWR exhibits a high predictive capacity as demonstrated by its sensitivity of 97.6% and specificity of 92.7%. CONCLUSIONS: MWR of the lungs can be a valuable substitute for chest CT in diagnosing pneumonia in patients with COVID-19, especially in situations where chest CT is unavailable or impractical.

Age-Related Changes in the Temperature of the Lumbar Spine Measured by Passive Microwave Radiometry (MWR).

A study was conducted to determine the age dependence of the temperature of the low back in the region of the five lumbar vertebrae by using passive microwave radiometry (MWR). The rationale for the study is that the infrared brightness on which the temperature measurement is based will be dependent upon blood circulation and thus on metabolic, vascular, and other regulatory factors. The brightness and infrared temperatures were determined in five zones above each of the medial, left, and right lateral projections of the vertebrae. A total of 115 healthy subjects were recruited, aged between 18 and 84 years. No significant differences in infrared temperature were detected. As predicted, brightness temperature increased until 25 years old and then gradually decreased. In subjects over 70 years of age, compared with those aged 60-70 years, there is a significant increase in brightness temperature at the level of 3-5 lumbar vertebrae by 0.3-0.7 °C. This is interpreted as indicating that individuals who have lived to an advanced age successfully maintain metabolic and regenerative processes. The benchmark data that has been obtained can be usefully employed in future studies of the aetiology of low back pain. In particular, the prospect exists for the technology to be used to provide a non-invasive biomarker to evaluate the effectiveness of antiaging therapies.

Correction of Local Brain Temperature after Severe Brain Injury Using Hypothermia and Medical Microwave Radiometry (MWR) as Companion Diagnostics.

The temperature of the brain can reflect the activity of its different regions, allowing us to evaluate the connections between them. A study involving 111 patients in a vegetative state or minimally conscious state used microwave radiometry to measure their cortical temperature. The patients were divided into a main group receiving a 10-day selective craniocerebral hypothermia (SCCH) procedure, and a control group receiving basic therapy and rehabilitation. The main group showed a significant improvement in consciousness level as measured by CRS-R assessment on day 14 compared to the control group. Temperature heterogeneity increased in patients who received SCCH, while remaining stable in the control group. The use of microwave radiometry to assess rehabilitation effectiveness and the inclusion of SCCH in rehabilitation programs appears to be a promising approach.

EnzML: multi-label prediction of enzyme classes using InterPro signatures.

BACKGROUND: Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. RESULTS: We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. CONCLUSIONS: InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN).

Monitoring Protein Denaturation of Egg White Using Passive Microwave Radiometry (MWR).

Passive microwave radiometry (MWR) is a measurement technique based on the detection of passive radiation in the microwave spectrum of different objects. When in equilibrium, this radiation is known to be proportional to the thermodynamic temperature of an emitting body. We hypothesize that living systems feature other mechanisms of emission that are based on protein unfolding and water rotational transitions. To understand the nature of these emissions, microwave radiometry was used in several in vitro experiments. In our study, we performed pilot measurements of microwave emissions from egg whites during denaturation induced by ethanol. Egg whites comprise 10% proteins, such as albumins, mucoproteins, and globulins. We observed a novel phenomenon: microwave emissions changed without a corresponding change in the water's thermodynamic temperature. We also found striking differences between microwave emissions and thermodynamic temperature kinetics. Therefore, we hypothesize that these two processes are unrelated, contrary to what was thought before. It is known that some pathologies such as stroke or brain trauma feature increased microwave emissions. We hypothesize that this phenomenon originates from protein denaturation and is not related to the thermodynamic temperature. As such, our findings could explain the reason for the increase in microwave emissions after trauma and post mortem for the first time. These findings could be used for the development of novel diagnostics methods. The MWR method is inexpensive and does not require fluorescent or radioactive labels. It can be used in different areas of basic and applied pharmaceutical research, including in kinetics studies in biomedicine.

Treatment and Companion Diagnostics of Lower Back Pain Using Self-Controlled Energo-Neuroadaptive Regulator (SCENAR) and Passive Microwave Radiometry (MWR).

Evaluation of the effectiveness of treatment of nonspecific lower back pain (LBP) is currently largely based on the patient's subjective feelings. The purpose of this study was to use passive microwave radiometry (MWR) as a tool for assessing the effectiveness of various treatment methods in patients with acute and subacute nonspecific LBP. Patients with a pain assessment on a visual analogue scale (VAS) of 6 to 10 points were divided into two groups: Group I included patients with pharmacological, syndrome-oriented treatment (n = 30, age 54.9 ± 2.3 years); Group II included a combination of pharmacotherapy with self-controlled energy-neuroadaptive regulation (SCENAR) (n = 25, age 52.8 ± 2.5 years). The analysis showed that the addition of SCENAR therapy (Group II) significantly potentiated the analgesic effect at the stages of treatment, and after 3 weeks, this had increased by more than two times, by 1.3 points on the VAS. There was also a significant decrease in the maximum internal temperature and normalization of the gradient of internal and skin temperatures, and a decrease in thermo-asymmetry, as assessed by temperature fields. Thermal asymmetry visualization allows the identification of the area of pathological muscle spasm and/or inflammation in the projection of the vertebral-motor segment for the possible targeted use of treatment methods such as percutaneous electro neurostimulation, massage, manual therapy, diagnostic and treatment blocks, etc. The MWR method also avoids unnecessary radiation exposure.

Novel species identification and deep functional annotation of electrogenic biofilms, selectively enriched in a microbial fuel cell array.

In this study, electrogenic microbial communities originating from a single source were multiplied using our custom-made, 96-well-plate-based microbial fuel cell (MFC) array. Developed communities operated under different pH conditions and produced currents up to 19.4 A/m3 (0.6 A/m2) within 2 days of inoculation. Microscopic observations [combined scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS)] revealed that some species present in the anodic biofilm adsorbed copper on their surface because of the bioleaching of the printed circuit board (PCB), yielding Cu2 + ions up to 600 mg/L. Beta- diversity indicates taxonomic divergence among all communities, but functional clustering is based on reactor pH. Annotated metagenomes showed the high presence of multicopper oxidases and Cu-resistance genes, as well as genes encoding aliphatic and aromatic hydrocarbon-degrading enzymes, corresponding to PCB bioleaching. Metagenome analysis revealed a high abundance of Dietzia spp., previously characterized in MFCs, which did not grow at pH 4. Binning metagenomes allowed us to identify novel species, one belonging to Actinotalea, not yet associated with electrogenicity and enriched only in the pH 7 anode. Furthermore, we identified 854 unique protein-coding genes in Actinotalea that lacked sequence homology with other metagenomes. The function of some genes was predicted with high accuracy through deep functional residue identification (DeepFRI), with several of these genes potentially related to electrogenic capacity. Our results demonstrate the feasibility of using MFC arrays for the enrichment of functional electrogenic microbial consortia and data mining for the comparative analysis of either consortia or their members.

Development of a New Hydrogel Anion Exchange Membrane for Swine Wastewater Treatment.

We developed a proprietary anion exchange membrane (AEM) for wastewater treatment as an alternative to commercial products. Following the successful development of a hydrogel cation exchange membrane on a porous ceramic support, we used the same approach to fabricate an AEM. Different positively charged monomers and conditions were tested, and all AEMs were tested for nitrate and phosphate anion removal from buffers by electrodialysis. The best AEM was tested further with real swine wastewater for phosphate removal by electrodialysis and nitrate removal in a bioelectrochemical denitrification system (BEDS). Our new AEM showed better phosphate removal compared with a commercial membrane; however, due to its low permselectivity, the migration of cations was detected while operating a two-chambered biocathode BEDS in which the membrane was utilized as a separator. After improving the permselectivity of the membrane, the performance of our proprietary AEM was comparable to that of a commercial membrane. Because of the usage of a porous ceramic support, our AEM is self-supporting, sturdy, and easy to attach to various frames, which makes the membrane better suited for harsh and corrosive environments, such as swine and other animal farms and domestic wastewater.

Diagnostics of Ovarian Tumors in Postmenopausal Patients.

Early diagnosis of ovarian cancer remains an urgent issue owing to the continuing trend towards increasing incidence along with only marginal improvements in mortality and 5-year survival rates. Furthermore, there is a lack of a clear formulation of the concept of pathogenesis. The diagnostic values of tumor markers, their potential advantages and disadvantages, and their combination with radiation imaging methods and transvaginal sonography are discussed. More advanced imaging techniques, such as computed tomography and magnetic resonance imaging have proven too expensive for widespread use. According to the World Health Organization, more than half of the world's population does not have access to diagnostic imaging. Consequently, there is high demand for a low-cost, reliable, and safe imaging system for detecting and monitoring cancer. Currently, there is no clear algorithm available for examining and accurately diagnosing patients with postmenopausal ovarian tumors; moreover, reliable criteria allowing dynamic observation and for determining surgical access and optimal surgical intervention measures in postmenopausal patients are lacking. Medical microwave radiometry shows promising results yielding an accuracy of 90%.

Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics.

BACKGROUND AND OBJECTIVE: Medical microwave radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. METHODS: We investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found. We evaluated and compared the model based on the F1 score, accuracy, precision, recall, and the number of connections. RESULTS: The experiments were conducted on a dataset of 4912 patients, classified as low or high risk for breast cancer. The weight agnostic BIPOP-CMA-ES model achieved the best average performance. It obtained an F1-score of 0.933, accuracy of 0.932, precision of 0.929, recall of 0.942, and 163 connections. CONCLUSIONS: The results of the model are an indication of the promising potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we can improve the overall performance.

Study of Brain Circadian Rhythms in Patients with Chronic Disorders of Consciousness and Healthy Individuals Using Microwave Radiometry.

The study of circadian rhythms in the human body using temperature measurements is the most informative way to assess the viability of the body's rhythm-organizing systems. Pathological processes can affect circadian rhythm dynamics in damaged organs. Severe brain damage that caused the development of disorders of consciousness (DOC) (strokes, traumatic brain injury) disrupts the activity of central oscillators, by directly damaging or destroying the periphery links, and the level of preservation of circadian rhythms and the dynamics of their recovery can be informative diagnostic criteria for patient's condition assessment. This study examined 23 patients with DOC by using a non-invasive method for obtaining body and cerebral cortex temperature to compare with healthy controls. Measurements were made with a 4 h interval for 52 h beginning at 08:00 on day 1 and ending at 08:00 on day 3. The profile of patients with DOC showed complete disruption compared to healthy controls with rhythmic patterns. The results indicate that the mechanisms for maintaining brain circadian rhythms are different from general homeostasis regulation of the body. Use of microwave radio thermometry for the identification of rehabilitation potential in patients with DOC is a promising area of investigation.

Using medical microwave radiometry for brain temperature measurements.

Brain temperature (BT) is a crucial physiological parameter used to monitor cerebral status. Physical activities and traumatic brain injuries (TBI) can affect BT; therefore, non-invasive BT monitoring is an important way to gain insight into TBI, stroke, and wellbeing. The effects of BT on physical performance have been studied at length. When humans are under extreme conditions, most of the energy consumed is used to maintain the BT. In addition, measuring the BT is useful for early brain diagnostics. Passive microwave radiometry (MWR) measures the intrinsic radiation of tissues in the 1-4 GHz range. It was shown that non-invasive passive MWR technology can successfully measure BT and identify even small TBIs. Here, we review the potential applications of MWR for assessing BT.