Chemical Composition, Anti-bacterial Activity and Molecular Docking Studies of Essential Oil Isolated from Sa Sam Nam (Launaea sarmentosa).

Launaea sarmentosa, also known as Sa Sam Nam, is a widely used remedy in Vietnamese traditional medicine and cuisine. However, the chemical composition and bioactivity of its essential oil have not been elucidated yet. In this study, we identified 40 compounds (98.6% of total peak area) in the essential oil via GC-MS analysis at the first time. Among them, five main compounds including Thymohydroquinone dimethyl ether (52.4%), (E)-α-Atlantone (9.0%), Neryl isovalerate (6.6%), Davanol D2 (isomer 2) (3.9%), and trans-Sesquisabinene hydrate (3.9%) have accounted for 75.8% of total peak area. The anti-bacterial activity of the essential oil against 4 microorganisms including Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa has also investigated via agar well diffusion assay. The results showed that the essential oil exhibited a strong antibacterial activity against Bacillus subtilis with the inhibition zones ranging from 8.2 to 18.7 mm. To elucidate the anti-bacterial effect mechanism of the essential oil, docking study of five main compounds of the essential oil (Thymohydroquinone dimethyl ether, (E)-α-Atlantone, Neryl isovalerate, Davanol D2 (isomer 2), and trans-Sesquisabinene hydrate) against some key proteins for bacterial growth such as DNA gyrase B, penicillin binding protein 2A, tyrosyl-tRNA synthetase, and dihydrofolate reductase were performed. The results showed that the main constituents of essential oil were highly bound with penicillin binding protein 2A with the free energies ranging -27.7 to -44.8 kcal/mol, which suggests the relationship between the antibacterial effect of essential oil and the affinity of main compounds with penicillin binding protein. In addition, the free energies of main compounds of the essential oil with human cyclooxygenase 1, cyclooxygenase 2, and phospholipase A2, the crucial proteins related with inflammatory response were less than diclofenac, a non-steroidal antiinflammatory drug. These findings propose the essential oil as a novel and promising anti-bacterial and anti-inflammatory medicine or cosmetic products.

Precise cuts for tailoring chromene-phenyl COX inhibitors with Ligand Designer.

Cyclooxygenases 1 and 2 (COX-1/2) are enzymes renowned for inducing inflammatory responses through the production of prostaglandins. Thus, the development of COX inhibitors has been a promising approach for identifying compounds with anti-inflammatory potential. In this study, we designed 27 new compounds (1-27) based on the structure of a previously known COX inhibitor, using the Ligand Designer tool. Our aim was to improve the affinity of the compounds with COX enzymes by inducing interactions with residue Arg120 while retaining the good π-π stacking interactions of the chromene-phenyl scaffold. Through screening based on ligand-binding free energy defined by molecular docking simulations and MM/GBSA technique, compounds 9 and 10 were identified as having the highest ability to inhibit COX proteins. The binding affinities of the two compounds with COX-1/2 were superior to those of the original NAI10 compound and the reference drug indomethacin. Our virtual screening suggests that compounds 9 and 10 have a strong ability to inhibit COX-1/2 and thus could be promising candidates for further anti-inflammatory drug studies. In essence, our study underscores the pivotal role of the N-aryl iminocoumarin scaffold in shaping the future landscape of novel anti-inflammatory drug development.

Oscillatory differentiation dynamics fundamentally restricts the resolution of pseudotime reconstruction algorithms.

The challenge to understand differentiation and cell lineages in development has resulted in many bioinformatics software tools, notably those working with gene expression data obtained via single-cell RNA sequencing obtained at snapshots in time. Reconstruction methods for trajectories often proceed by dimension reduction, data clustering and then computation of a tree graph in which edges indicate closely related clusters. Cell lineages can then be deduced by following paths through the tree. In the case of multi-potent cells undergoing differentiation, this trajectory reconstruction involves the reconstruction of multiple distinct lineages corresponding to commitment to each of a set of distinct fates. Recent work suggests that there may be cases in which the cell differentiation process involves trajectories that explore, in a dynamic and oscillatory fashion, propensity to differentiate into a number of possible cell fates before commitment finally occurs. Here, we show theoretically that the presence of such oscillations provides intrinsic constraints on the quality and resolution of the trajectory reconstruction process, even for idealized noise-free data. These constraints point to inherent common limitations of current methodologies and serve both to provide additional challenge in the development of software tools and also may help to understand features observed in recent experiments.

ARGUS: Visualization of AI-Assisted Task Guidance in AR.

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi-year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine-tune the components of the AR assistant.

Performance and limitations of a supervised deep learning approach for the histopathological Oxford Classification of glomeruli with IgA nephropathy.

BACKGROUND AND OBJECTIVE: The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with an end-to-end deep learning pipeline that involves automatic glomerular segmentation followed by classification for mesangial hypercellularity (M), endocapillary hypercellularity (E), segmental sclerosis (S) and active crescents (C). METHODS: A total number of 1056 periodic acid-Schiff (PAS) whole slide images (WSIs), coming from 386 kidney biopsies, were annotated. Several detection models for glomeruli, based on the Mask R-CNN architecture, were trained on 587 WSIs, validated on 161 WSIs, and tested on 127 WSIs. For the development of segmentation models, 20,529 glomeruli were annotated, of which 16,571 as training and 3958 as validation set. The test set of the segmentation module comprised of 2948 glomeruli. For the Oxford classification, 6206 expert-annotated glomeruli from 308 PAS WSIs were labelled for M, E, S, C and split into a training set of 4298 glomeruli from 207 WSIs, and a test set of 1908 glomeruli. We chose the best-performing models to construct an end-to-end pipeline, which we named MESCnn (MESC classification by neural network), for the glomerular Oxford classification of WSIs. RESULTS: Instance segmentation yielded excellent results with an AP50 ranging between 78.2-80.1 % (79.4 ± 0.7 %) on the validation and 75.1-77.7 % (76.5 ± 0.9 %) on the test set. The aggregated Jaccard Index was between 73.4-75.9 % (75.0 ± 0.8 %) on the validation and 69.1-73.4 % (72.2 ± 1.4 %) on the test set. At granular glomerular level, Oxford Classification was best replicated for M with EfficientNetV2-L with a mean ROC-AUC of 90.2 % and a mean precision/recall area under the curve (PR-AUC) of 81.8 %, best for E with MobileNetV2 (ROC-AUC 94.7 %) and ResNet50 (PR-AUC 75.8 %), best for S with EfficientNetV2-M (mean ROC-AUC 92.7 %, mean PR-AUC 87.7 %), best for C with EfficientNetV2-L (ROC-AUC 92.3 %) and EfficientNetV2-S (PR-AUC 54.7 %). At biopsy-level, correlation between expert and deep learning labels fulfilled the demands of the Oxford Classification. CONCLUSION: We designed an end-to-end pipeline for glomerular Oxford Classification on both a granular glomerular and an entire biopsy level. Both the glomerular segmentation and the classification modules are freely available for further development to the renal medicine community.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

Enhancing the Biological Oxidation of H2S in a Sewer Pipe with Highly Conductive Concrete and Electricity-Producing Bacteria.

Hydrogen sulfide (H2S) generated in sewer systems is problematic to public health and the environment, owing to its corrosive consequences, odor concerns, and poison control issues. In a previous work, conductive concrete, based on amorphous carbon with a mechanism that operates as a microbial fuel cell was investigated. The objective of the present study is to develop additional materials for highly conductive concrete, to mitigate the concentration of H2S in sewer pipes. Adsorption experiments were conducted to elucidate the role of the H2S reduction. Additionally, electricity-producing bacteria (EPB), isolated from a municipal wastewater treatment plant, were inoculated to improve the H2S reduction. The experimental results showed that inoculation with EPB could decrease the concentration of H2S, indicating that H2S was biologically oxidized by EPB. Several types of new materials containing acetylene black, or magnetite were discovered for use as conductive concrete, and their abilities to enhance the biological oxidation of H2S were evaluated. These conductive concretes were more effective than the commercial conductive concrete, based on amorphous carbon, in decreasing the H2S concentration in sewer pipes.

The basic anatomy of the medial sural artery perforator flaps in Vietnamese adults.

Background: Both free medial sural artery perforator flaps and pedicled medial sural artery perforator flaps have been being effectively used in treatment of body defects especially in head and neck region by plastic surgeons worldwide. However, there is a lack of comprehensive studies on the anatomy of perforating artery branches in Vietnam. This study aims to describe anatomical vascular pedicles of medial sural artery perforator flap in Vietnamese adults . Methods: A descriptive cross-sectional study, dissected 62 lower limbs of 41 Vietnamese adult cadavers preserved by formalin in Department of Anatomy, Hanoi Medical University and Ho Chi Minh Medicine and Pharmacy University. Results: Origin of medial sural artery was branched constantly from popliteal artery. Common stem of artery was 8.39 ± 3.5 cm in mean length. The diameter of common stem, which was measured from origin, was 2.88 ± 0.98 mm averagely. The common stem of artery, which did not have any branch (15%), divided in to 2 branches (15%), 3 branches (30%), 4 branches (40%) before entering muscle. Medial sural artery had 1 to 5 branches perforating to the skin. The distance from perforating branch to the knee joint (popliteal crease) was 10.12 ± 3.7 cm, the distance from perforator branch to middle posterior leg was 1.6 ± 0.96 cm. Conclusions: The medial sural artery constantly originates from popliteal artery, supplies blood for medial gastrocnemius muscle. The skin area covering this muscle is nourished by one of five perforators of the medial sural artery. The perforating flaps can be created using medial sural artery perforating branches.

Phosphorus application enhances alkane hydroxylase gene abundance in the rhizosphere of wild plants grown in petroleum-hydrocarbon-contaminated soil.

This study assessed the ability of phosphorus (P) fertilizer to remediate the rhizosphere of three wild plant species (Banksia seminuda, a tree; Chloris truncata, a grass; and Hakea prostrata, a shrub) growing in a soil contaminated with total (aliphatic) petroleum hydrocarbon (TPH). Plant growth, photosynthesis (via chlorophyll fluorescence), soil microbial activity, alkane hydroxylase AlkB (aliphatic hydrocarbon-degrading) gene abundance, and TPH removal were evaluated 120 days after planting. Overall, although TPH served as an additional carbon source for soil microorganisms, the presence of TPH in soil resulted in decreased plant growth and photosynthesis. However, growth, photosynthesis, microbial activities, and AlkB gene abundance were enhanced by the application of P fertilizer, thereby increasing TPH removal rates, although the extent and optimum P dosage varied among the plant species. The highest TPH removal (64.66%) was observed in soil planted with the Poaceae species, C. truncata, and amended with 100 mg P kg-1 soil, while H. prostrata showed higher TPH removal compared to the plant belonging to the same Proteaceae family, B. seminuda. The presence of plants resulted in higher AlkB gene abundance and TPH removal relative to the unplanted control. The removal of TPH was associated directly with AlkB gene abundance (R2 > 0.9, p < 0.001), which was affected by plant identity and P levels. The results indicated that an integrated approach involving wild plant species and optimum P amendment, which was determined through experimentation using different plant species, was an efficient way to remediate soil contaminated with TPH.

Chemical composition and antibacterial activities of essential oils from rhizomes and aerial parts of Homalomena cochinchinensis (Araceae).

In this study, the phytochemical composition and antibacterial activity of the essential oils isolated from the rhizomes and the aerial parts of Homalomena cochinchinensis were analysed for the first time using gas chromatography-mass spectrometry (GC-MS) and disk diffusion method. Consequently, 36 constituents were reported from H. cochinchinensis rhizomes, including linalool (57.4%), terpinen-4-ol (10.6%), α-sabinene (4.2%) as major compounds. Meanwhile, 30 compounds have been identified in the essential oil of the aerial parts of H. cochinchinensis, which myrcene (41.1%), sabinene (8.2%), D-limonene (9.1%) were the main constituents. Furthermore, the antibacterial activity of the essential oil from the rhizomes of the studied species could inhibit the growth of all six tested bacterial strains, including Bacillus cereus, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Salmonella enteritidis and Salmonella typhimurium whereas the essential oil from the aerial parts was proved to be able to inhibit the growth of five bacterial strains except S. enteritidis.

Visualization and modeling of inhibition of IL-1ß and TNF-α mRNA transcription at the single-cell level.

IL-1ß and TNF-α are canonical immune response mediators that play key regulatory roles in a wide range of inflammatory responses to both chronic and acute conditions. Here we employ an automated microscopy platform for the analysis of messenger RNA (mRNA) expression of IL-1ß and TNF-α at the single-cell level. The amount of IL-1ß and TNF-α mRNA expressed in a human monocytic leukemia cell line (THP-1) is visualized and counted using single-molecule fluorescent in-situ hybridization (smFISH) following exposure of the cells to lipopolysaccharide (LPS), an outer-membrane component of Gram-negative bacteria. We show that the small molecule inhibitors MG132 (a 26S proteasome inhibitor used to block NF-κB signaling) and U0126 (a MAPK Kinase inhibitor used to block CCAAT-enhancer-binding proteins C/EBP) successfully block IL-1ß and TNF-α mRNA expression. Based upon this single-cell mRNA expression data, we screened 36 different mathematical models of gene expression, and found two similar models that capture the effects by which the drugs U0126 and MG132 affect the rates at which the genes transition into highly activated states. When their parameters were informed by the action of each drug independently, both models were able to predict the effects of the combined drug treatment. From our data and models, we postulate that IL-1ß is activated by both NF-κB and C/EBP, while TNF-α is predominantly activated by NF-κB. Our combined single-cell experimental and modeling efforts show the interconnection between these two genes and demonstrates how the single-cell responses, including the distribution shapes, mean expression, and kinetics of gene expression, change with inhibition.

Evaluating Asthma Mobile Apps to Improve Asthma Self-Management: User Ratings and Sentiment Analysis of Publicly Available Apps.

BACKGROUND: The development and use of mobile health (mHealth) apps for asthma management have risen dramatically over the past two decades. Asthma apps vary widely in their content and features; however, prior research has rarely examined preferences of users of publicly available apps. OBJECTIVE: The goals of this study were to provide a descriptive overview of asthma mobile apps that are publicly available and to assess the usability of asthma apps currently available on the market to identify content and features of apps associated with positive and negative user ratings. METHODS: Reviews were collected on June 23, 2020, and included publicly posted reviews until June 21, 2020. To characterize features associated with high or low app ratings, we first dichotomized the average user rating of the asthma app into 2 categories: a high average rating and a low average rating. Asthma apps with average ratings of 4 and above were categorized as having a high average rating. Asthma apps with average ratings of less than 4 were categorized as having a low average rating. For the sentiment analysis, we modeled both 2-word (bi-gram) and 3-word (tri-gram) phrases which commonly appeared across highly rated and lowly rated apps. RESULTS: Of the 10 apps that met the inclusion criteria, a total of 373 reviews were examined across all apps. Among apps reviewed, 53.4% (199/373) received high ratings (average ratings of 4 or 5) and 47.2% (176/373) received low ratings (average ratings of 3 or less). The number of ratings across all apps ranged from 188 (AsthmaMD) to 10 (My Asthma App); 30% (3/10) of apps were available on both Android and iOS. From the sentiment analysis, key features of asthma management that were common among highly rated apps included the tracking of peak flow readings (n=48), asthma symptom monitoring (n=11), and action plans (n=10). Key features related to functionality that were common among highly rated apps included ease of use (n=5). Users most commonly reported loss of data (n=14) and crashing of app (n=12) as functionality issues among poorly rated asthma apps. CONCLUSIONS: Our study results demonstrate that asthma app quality, maintenance, and updates vary widely across apps and platforms. These findings may call into question the long-term engagement with asthma apps, a crucial factor for determining their potential to improve asthma self-management and asthma clinical outcomes.

BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic framework for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty. Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere.

As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering. However, given the wide range of topics of Twitter users, tweets related to drug abuse are rare in most of the datasets. This imbalanced data remains a major issue in building effective tweet classifiers, and is especially obvious for studies that include abuse-related slang terms. In this study, we approach this problem by designing an ensemble deep learning model that leverages both word-level and character-level features to classify abuse-related tweets. Experiments are reported on a Twitter dataset, where we can configure the percentages of the two classes (abuse vs. non abuse) to simulate the data imbalance with different amplitudes. Results show that our ensemble deep learning models exhibit better performance than ensembles of traditional machine learning models, especially on heavily imbalanced datasets.

Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.

The finite state projection (FSP) approach to solving the chemical master equation has enabled successful inference of discrete stochastic models to predict single-cell gene regulation dynamics. Unfortunately, the FSP approach is highly computationally intensive for all but the simplest models, an issue that is highly problematic when parameter inference and uncertainty quantification takes enormous numbers of parameter evaluations. To address this issue, we propose two new computational methods for the Bayesian inference of stochastic gene expression parameters given single-cell experiments. We formulate and verify an adaptive delayed acceptance Metropolis-Hastings (ADAMH) algorithm to utilize with reduced Krylov-basis projections of the FSP. We then introduce an extension of the ADAMH into a hybrid scheme that consists of an initial phase to construct a reduced model and a faster second phase to sample from the approximate posterior distribution determined by the constructed model. We test and compare both algorithms to an adaptive Metropolis algorithm with full FSP-based likelihood evaluations on three example models and simulated data to show that the new ADAMH variants achieve substantial speedup in comparison to the full FSP approach. By reducing the computational costs of parameter estimation, we expect the ADAMH approach to enable efficient data-driven estimation for more complex gene regulation models.

An adaptive solution to the chemical master equation using tensors.

Solving the chemical master equation directly is difficult due to the curse of dimensionality. We tackle that challenge by a numerical scheme based on the quantized tensor train (QTT) format, which enables us to represent the solution in a compressed form that scales linearly with the dimension. We recast the finite state projection in this QTT framework and allow it to expand adaptively based on proven error criteria. The end result is a QTT-formatted matrix exponential that we evaluate through a combination of the inexact uniformization technique and the alternating minimal energy algorithm. Our method can detect when the equilibrium distribution is reached with an inexpensive test that exploits the structure of the tensor format. We successfully perform numerical tests on high-dimensional problems that had been out of reach for classical approaches.

Changes in Gait over a 30-min Walking Session in Obese Females.

PURPOSE: This study assessed the biomechanical gait changes in obese and normal-weight female adult subjects after a commonly recommended 30-min walking session. Hip and knee adduction and extensor moments, which are the primary modulators of frontal and sagittal plane load distribution, were hypothesized to increase in obese females after a 30-min walking period, resulting in more stress across the hip and knee joint. METHODS: Ten obese (37.7 ± 4.8 yr of age, body mass index [BMI] = 36.1 ± 4.2 kg·m) and 10 normal-weight control female subjects (38.1 ± 4.5 yr of age, BMI = 22.6 ± 2.3 kg·m) walked 30 min continuously on the treadmill at their self-selected speed. V˙O2max was estimated using Ebbeling protocol. A three-dimensional pre- and posttreadmill gait analysis was conducted using infrared markers and force plates to calculate hip and knee moments. RESULTS: Knee extensor moments increased in both obese, pretreadmill (0.54 ± 0.28 N·m·kg) to posttreadmill (0.78 ± 0.43 N·m·kg) (P = 0.01), and control subjects, pretreadmill (0.57 ± 0.34 N·m·kg) to posttreadmill (0.80 ± 0.49 N·m·kg) (P = 0.02). Hip extensor moments decreased for both obese and control subjects. Knee adduction moments did not change in either obese or control subjects. Knee extensor and adductor moments showed good to moderate relationships with V˙O2max, but not BMI or waist circumference. CONCLUSION: Obese and normal-weight subjects experienced an increase in knee extensor moments after 30 min of walking similarly; therefore, clinicians do not need special consideration for obese individuals when recommending 30-min walking sessions. Fitness may be the important factor in judging the implications of exercise on joint mechanics and parameters of a walking program.

Computational study of p53 regulation via the chemical master equation.

A stochastic model of cellular p53 regulation was established in Leenders, and Tuszynski (2013 Front. Oncol. 3 1-16) to study the interactions of p53 with MDM2 proteins, where the stochastic analysis was done using a Monte Carlo approach. We revisit that model here using an alternative scheme, which is to directly solve the chemical master equation (CME) by an adaptive Krylov-based finite state projection method that combines the stochastic simulation algorithm with other computational strategies, namely Krylov approximation techniques to the matrix exponential, divide and conquer, and aggregation. We report numerical results that demonstrate the extend of tackling the CME with this combination of tools.

Potential application of high pressure carbon dioxide in treated wastewater and water disinfection: Recent overview and further trends.

Recently emerging disadvantages in conventional disinfection have heightened the need for finding a new solution. Developments in the use of high pressure carbon dioxide for food preservation and sterilization have led to a renewed interest in its applicability in wastewater treatment and water disinfection. Pressurized CO2 is one of the most investigated methods of antibacterial treatment and has been used extensively for decades to inhibit pathogens in dried food and liquid products. This study reviews the literature concerning the utility of CO2 as a disinfecting agent, and the pathogen inactivation mechanism of CO2 treatment is evaluated based on all available research. In this paper, it will be argued that the successful application and high effectiveness of CO2 treatment in liquid foods open a potential opportunity for its use in wastewater treatment and water disinfection. The findings from models with different operating conditions (pressure, temperature, microorganism, water content, media …) suggest that most microorganisms are successfully inhibited under CO2 treatment. It will also be shown that the bacterial deaths under CO2 treatment can be explained by many different mechanisms. Moreover, the findings in this study can help to address the recently emerging problems in water disinfection, such as disinfection by-products (resulting from chlorination or ozone treatment).

A high-transparency, micro-patternable chip for X-ray diffraction analysis of microcrystals under native growth conditions.

Microcrystals present a significant impediment to the determination of macromolecular structures by X-ray diffraction methods. Although microfocus synchrotron beamlines and X-ray free-electron lasers (XFELs) can enable the collection of interpretable diffraction data from microcrystals, there is a need for efficient methods of harvesting small volumes (<2 µl) of microcrystals grown under common laboratory formats and delivering them to an X-ray beam source under native growth conditions. One approach that shows promise in overcoming the challenges intrinsic to microcrystal analysis is to pair so-called `fixed-target' sample-delivery devices with microbeam-based X-ray diffraction methods. However, to record weak diffraction patterns it is necessary to fabricate devices from X-ray-transparent materials that minimize background scattering. Presented here is the design of a new micro-diffraction device consisting of three layers fabricated from silicon nitride, photoresist and polyimide film. The chip features low X-ray scattering and X-ray absorption properties, and uses a customizable blend of hydrophobic and hydrophilic surface patterns to help localize microcrystals to defined regions. Microcrystals in their native growth conditions can be loaded into the chips with a standard pipette, allowing data collection at room temperature. Diffraction data collected from hen egg-white lysozyme microcrystals (10-15 µm) loaded into the chips yielded a complete, high-resolution (<1.6 Å) data set sufficient to determine a high-quality structure by molecular replacement. The features of the chip allow the rapid and user-friendly analysis of microcrystals grown under virtually any laboratory format at microfocus synchrotron beamlines and XFELs.