Effect of industrial wastewater treatment system upgrade on the composition of emitted odorants and volatile organic compounds from a cheese production facility.

This study investigates the rarely studied volatile organic compound emissions from a cheese production facility and the impact of its wastewater treatment system upgrade on the composition of emitted odorants. Wastewater grab samples were collected from six separate wastewater channels before (2019) and after (2021) the system upgrade and analyzed for volatile organic compounds, pH, total dissolved solids, and electrical conductivity. Results showed that the channel from hard cheese production in 2021 had the highest number of volatile organic compounds (35), followed by the fresh cheese production channel (22). Following the industrial wastewater treatment system upgrade, a mineral oil contamination occurred; however, the number of odorants with nasal impact frequency (NIF) ≥ 0.5 in the effluent decreased from 11 to 5. 2-Propenoic acid butyl ester (NIF 0.75) stood out as the most prominent compound, described as fruity, waxy, or green. After the industrial wastewater treatment system upgrades, we observed a decrease in the number of odorants. However other measures must be taken to ensure proper wastewater processing. PRACTITIONER POINTS: More than 60 VOCs were identified in 6 channels from the cheese production facility.15 odorants in cheese production wastewater were detected by SPME-GC-MS/O. The most potent odorants before and after the system upgrade were 1-octen-3-ol and 2-propenoic acid butyl ester, respectively. The upgrades of the industrial wastewater treatment system had a positive impact on reducing the number of odorants and their odor intensity.

In Vitro High-Throughput Genotoxicity Testing Using γH2AX Biomarker, Microscopy and Reproducible Automatic Image Analysis in ImageJ-A Pilot Study with Valinomycin.

(1) Background: The detection of DNA double-strand breaks in vitro using the phosphorylated histone biomarker (γH2AX) is an increasingly popular method of measuring in vitro genotoxicity, as it is sensitive, specific and suitable for high-throughput analysis. The γH2AX response is either detected by flow cytometry or microscopy, the latter being more accessible. However, authors sparsely publish details, data, and workflows from overall fluorescence intensity quantification, which hinders the reproducibility. (2) Methods: We used valinomycin as a model genotoxin, two cell lines (HeLa and CHO-K1) and a commercial kit for γH2AX immunofluorescence detection. Bioimage analysis was performed using the open-source software ImageJ. Mean fluorescent values were measured using segmented nuclei from the DAPI channel and the results were expressed as the area-scaled relative fold change in γH2AX fluorescence over the control. Cytotoxicity is expressed as the relative area of the nuclei. We present the workflows, data, and scripts on GitHub. (3) Results: The outputs obtained by an introduced method are in accordance with expected results, i.e., valinomycin was genotoxic and cytotoxic to both cell lines used after 24 h of incubation. (4) Conclusions: The overall fluorescence intensity of γH2AX obtained from bioimage analysis appears to be a promising alternative to flow cytometry. Workflow, data, and script sharing are crucial for further improvement of the bioimage analysis methods.

Adipose-Derived Stem Cells in Reinforced Collagen Gel: A Comparison between Two Approaches to Differentiation towards Smooth Muscle Cells.

Scaffolds made of degradable polymers, such as collagen, polyesters or polysaccharides, are promising matrices for fabrication of bioartificial vascular grafts or patches. In this study, collagen isolated from porcine skin was processed into a gel, reinforced with collagen particles and with incorporated adipose tissue-derived stem cells (ASCs). The cell-material constructs were then incubated in a DMEM medium with 2% of FS (DMEM_part), with added polyvinylalcohol nanofibers (PVA_part sample), and for ASCs differentiation towards smooth muscle cells (SMCs), the medium was supplemented either with human platelet lysate released from PVA nanofibers (PVA_PL_part) or with TGF-ß1 + BMP-4 (TGF + BMP_part). The constructs were further endothelialised with human umbilical vein endothelial cells (ECs). The immunofluorescence staining of alpha-actin and calponin, and von Willebrand factor, was performed. The proteins involved in cell differentiation, the extracellular matrix (ECM) proteins, and ECM remodelling proteins were evaluated by mass spectrometry on day 12 of culture. Mechanical properties of the gels with ASCs were measured via an unconfined compression test on day 5. Gels evinced limited planar shrinkage, but it was higher in endothelialised TGF + BMP_part gel. Both PVA_PL_part samples and TGF + BMP_part samples supported ASC growth and differentiation towards SMCs, but only PVA_PL_part supported homogeneous endothelialisation. Young modulus of elasticity increased in all samples compared to day 0, and PVA_PL_part gel evinced a slightly higher ratio of elastic energy. The results suggest that PVA_PL_part collagen construct has the highest potential to remodel into a functional vascular wall.

Chest area segmentation in 3D images of sleeping patients.

Although the field of sleep study has greatly developed over recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory. This examination measures several vital signals by polysomnograph during a full night's sleep using multiple sensors connected to the patient's body. Nevertheless, despite being the gold standard, the sensors and the unfamiliar environment's connection inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of accurate and affordable 3D sensing devices, new approaches for non-contact sleep study have emerged. These methods utilize different techniques to extract the same breathing parameters but with contactless methods. However, to enable reliable remote extraction, these methods require accurate identification of the basic region of interest (ROI), i.e., the patient's chest area. The lack of automated ROI segmenting of 3D time series is currently holding back the development process. We propose an automatic chest area segmentation algorithm that given a time series of 3D frames containing a sleeping patient as input outputs a segmentation image with the pixels that correspond to the chest area. Beyond significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can enable a more precise feature extraction. In addition, further tests of the algorithm on existing data demonstrate its ability to improve the sensitivity of a prior solution that uses manual ROI selection. The approach is on average 46.9% more sensitive with a maximal improvement of 220% when compared to manual ROI. All mentioned can pave the way for placing non-contact algorithms as leading candidates to replace existing traditional methods used today.

Nanoscale mapping of nuclear phosphatidylinositol phosphate landscape by dual-color dSTORM.

Current models of gene expression, which are based on single-molecule localization microscopy, acknowledge protein clustering and the formation of transcriptional condensates as a driving force of gene expression. However, these models largely omit the role of nuclear lipids and amongst them nuclear phosphatidylinositol phosphates (PIPs) in particular. Moreover, the precise distribution of nuclear PIPs in the functional sub-nuclear domains remains elusive. The direct stochastic optical reconstruction microscopy (dSTORM) provides an unprecedented resolution in biological imaging. Therefore, its use for imaging in the densely crowded cell nucleus is desired but also challenging. Here we present a dual-color dSTORM imaging and image analysis of nuclear PI(4,5)P2, PI(3,4)P2 and PI(4)P distribution while preserving the context of nuclear architecture. In the nucleoplasm, PI(4,5)P2 and PI(3,4)P2 co-pattern in close proximity with the subset of RNA polymerase II foci. PI(4,5)P2 is surrounded by fibrillarin in the nucleoli and all three PIPs are dispersed within the matrix formed by the nuclear speckle protein SON. PI(4,5)P2 is the most abundant nuclear PIP, while PI(4)P is a precursor for the biosynthesis of PI(4,5)P2 and PI(3,4)P2. Therefore, our data are relevant for the understanding the roles of nuclear PIPs and provide further evidence for the model in which nuclear PIPs represent a localization signal for the formation of lipo-ribonucleoprotein hubs in the nucleus. The discussed experimental pipeline is applicable for further functional studies on the role of other nuclear PIPs in the regulation of gene expression and beyond.

Sleep Apnea Detection with Polysomnography and Depth Sensors.

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

Analysis of Lipid Droplet Content in Fission and Budding Yeasts using Automated Image Processing.

Lipid metabolism and its regulation are of interest to both basic and applied life sciences and biotechnology. In this regard, various yeast species are used as models in lipid metabolic research or for industrial lipid production. Lipid droplets are highly dynamic storage bodies and their cellular content represents a convenient readout of the lipid metabolic state. Fluorescence microscopy is a method of choice for quantitative analysis of cellular lipid droplets, as it relies on widely available equipment and allows analysis of individual lipid droplets. Furthermore, microscopic image analysis can be automated, greatly increasing overall analysis throughput. Here, we describe an experimental and analytical workflow for automated detection and quantitative description of individual lipid droplets in three different model yeast species: the fission yeasts Schizosaccharomyces pombe and Schizosaccharomyces japonicus, and the budding yeast Saccharomyces cerevisiae. Lipid droplets are visualized with BODIPY 493/503, and cell-impermeable fluorescent dextran is added to the culture media to help identify cell boundaries. Cells are subjected to 3D epifluorescence microscopy in green and blue channels and the resulting z-stack images are processed automatically by a MATLAB pipeline. The procedure outputs rich quantitative data on cellular lipid droplet content and individual lipid droplet characteristics in a tabular format suitable for downstream analyses in major spreadsheet or statistical packages. We provide example analyses of lipid droplet content under various conditions that affect cellular lipid metabolism.

Antifungal Polyamides of Hydroxycinnamic Acids from Sunflower Bee Pollen.

The aim of the bioassay-guided fractionation was the selection of the most potent group of compounds responsible for the protection of sunflower bee pollen grains. Synthesis of prospective antifungal polyamides of hydroxycinnamic acids was based on previous structural elucidation of ethanol soluble fraction by 1H,1H-PFG-COSY, 1H,13C-HSQC, FT-IR, FT-Raman, and LC-MS experiments. The main compounds found were tri- p-coumaroylspermidines accompanied by other HCAA of spermidine and putrescine. Several model HCAA derivatives were prepared to test their antifungal activity against widespread spoilage fungi ( A. niger 42 CCM 8189, F. culmorum DMF 0103, and P. verrucosum DMF 0023). A. niger CCM 8189 and F. culmorum DMF 0103 exhibited higher resistance to the antifungal effects of hydroxycinnamic acid amides, whereas P. verrucosum DMF 0023 was the most sensitive strain. It has been discovered the effect of HCAA polarity on the role of secondary metabolites in the microbial protection of pollen grains. The combination of bioassay-guided fractionation, structural elucidation, selection of prospective compounds, and their synthesis to determine their antifungal properties could be considered as an original approach.

Mitotic defects in fission yeast lipid metabolism 'cut' mutants are suppressed by ammonium chloride.

Fission yeast 'cut' mutants show defects in temporal coordination of nuclear division with cytokinesis, resulting in aberrant mitosis and lethality. Among other causes, the 'cut' phenotype can be triggered by genetic or chemical perturbation of lipid metabolism, supposedly resulting in shortage of membrane phospholipids and insufficient nuclear envelope expansion during anaphase. Interestingly, penetrance of the 'cut' phenotype in mutants of the transcription factor cbf11 and acetyl-coenzyme A carboxylase cut6, both related to lipid metabolism, is highly dependent on growth media, although the specific nutrient(s) affecting 'cut' occurrence is not known. In this study, we set out to identify the growth media component(s) responsible for 'cut' phenotype suppression in Δcbf11 and cut6-621 cells. We show that mitotic defects occur rapidly in Δcbf11 cells upon shift from the minimal EMM medium ('cut' suppressing) to the complex YES medium ('cut' promoting). By growing cells in YES medium supplemented with individual EMM components, we identified ammonium chloride, an efficiently utilized nitrogen source, as a specific and potent suppressor of the 'cut' phenotype in both Δcbf11 and cut6-621. Furthermore, we found that ammonium chloride boosts lipid droplet formation in wild-type cells. Our findings suggest a possible involvement of nutrient-responsive signaling in 'cut' suppression.

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis.

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

Motion tracking and gait feature estimation for recognising Parkinson's disease using MS Kinect.

BACKGROUND: Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space. METHODS: The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data. RESULTS: The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications. CONCLUSIONS: Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.

Complex continuous wavelet coherence for EEG microstates detection in insight and calm meditation.

Complex continuous wavelet coherence (WTC) can be used for non-stationary signals, such as electroencephalograms. Areas of the WTC with a coherence higher than the calculated optimal threshold were obtained, and the sum of their areas was used as a criterion to differentiate between groups of experienced insight-focused meditators, calm-focused meditators and a control group. This method demonstrated the highest accuracy for the real WTC parts in the frontal region, while for the imaginary parts, the highest accuracy was shown for the frontal occipital pairs of electrodes. In the frontal area, in the broadband frequency, both types of experienced meditators demonstrated an enlargement of the increased coherence areas for the real WTC parts. For the imaginary parts unaffected by the volume conduction and global artefacts, the most significant increase occurred for the frontal occipital pair of electrodes.