Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations

Nitrogen Dioxide (NO2 ) is a common air pollutant associated with several adverse health problems such as pediatric asthma, cardiovascular mortality,and respiratory mortality. Due to the urgent society's need to reduce pollutant concentration, several scientific efforts have been allocated to understand pollutant patterns and predict pollutants' future concentrations using machine learning and deep learning techniques. The latter techniques have recently gained much attention due it's capability to tackle complex and challenging problems in computer vision, natural language processing, etc. In the NO2 context, there is still a research gap in adopting those advanced methods to predict the concentration of pollutants. This study fills in the gap by comparing the performance of several state-of-the-art artificial intelligence models that haven't been adopted in this context yet. The models were trained using time series cross-validation on a rolling base and tested across different periods using NO2 data from 20 monitoring ground-based stations collected by Environment Agency- Abu Dhabi, United Arab Emirates. Using the seasonal Mann-Kendall trend test and Sen's slope estimator, we further explored and investigated the pollutants trends across the different stations. This study is the first comprehensive study that reported the temporal characteristic of NO2 across seven environmental assessment points and compared the performance of the state-of-the-art deep learning models for predicting the pollutants' future concentration. Our results reveal a difference in the pollutants concentrations level due to the geographic location of the different stations, with a statistically significant decrease in the NO2 annual trend for the majority of the stations. Overall, NO2 concentrations exhibit a similar daily and weekly pattern across the different stations, with an increase in the pollutants level during the early morning and the first working day. Comparing the state-of-the-art model performance transformer model demonstrate the superiority of ( MAE:0.04 (± 0.04),MSE:0.06 (± 0.04), RMSE:0.001 (± 0.01), R2 : 0.98 (± 0.05)), compared with LSTM (MAE:0.26 (± 0.19), MSE:0.31 (± 0.21), RMSE:0.14 (± 0.17), R2 : 0.56 (± 0.33)), InceptionTime (MAE: 0.19 (± 0.18), MSE: 0.22 (± 0.18), RMSE:0.08 (± 0.13), R2 :0.38 (± 1.35) ), ResNet (MAE:0.24 (± 0.16), MSE:0.28 (± 0.16), RMSE:0.11 (± 0.12), R2 :0.35 (± 1.19) ), XceptionTime (MAE:0.7 (± 0.55), MSE:0.79 (± 0.54), RMSE:0.91 (± 1.06), R2 : - 4.83 (± 9.38) ), and MiniRocket (MAE:0.21 (± 0.07), MSE:0.26 (± 0.08), RMSE:0.07 (± 0.04), R2 : 0.65 (± 0.28) ) to tackle this challenge. The transformer model is a powerful model for improving the accurate forecast of the NO2 levels and could strengthen the current monitoring system to control and manage the air quality in the region.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00754-z.

Systematically exploring repurposing effects of antihypertensives

With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.

State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study

The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into two steps. In the first step, the researcher selects some set of variables to adjust for. In the second step, the researcher implements a causal inference algorithm to adjust for the selected variables and estimate the average treatment effect. In this paper, we use a simulation study to explore the operating characteristics and robustness of state-of-the-art methods for step two (statistical adjustment for selected variables) when step one (variable selection) is performed in a realistically sub-optimal manner. More specifically, we study the robustness of a cross-fit machine learning based causal effect estimator to the presence of extraneous variables in the adjustment set. The take-away for practitioners is that there is value to, if possible, identifying a small sufficient adjustment set using subject matter knowledge even when using machine learning methods for adjustment.

Observation of the fastest chemical processes in the radiolysis of water

Elementary processes associated with ionization of liquid water provide a framework for understanding radiation-matter interactions in chemistry and biology. Although numerous studies have been conducted on the dynamics of the hydrated electron, its partner arising from ionization of liquid water, H2O+, remains elusive. We used tunable femtosecond soft x-ray pulses from an x-ray free electron laser to reveal the dynamics of the valence hole created by strong-field ionization and to track the primary proton transfer reaction giving rise to the formation of OH. The isolated resonance associated with the valence hole (H2O+/OH) enabled straightforward detection. Molecular dynamics simulations revealed that the x-ray spectra are sensitive to structural dynamics at the ionization site. We found signatures of hydrated-electron dynamics in the x-ray spectrum.

The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling

In many data classification problems, a number of methods will give similar accuracy. However, when working with people who are not experts in data science such as doctors, lawyers, and judges among others, finding interpretable algorithms can be a critical success factor. Practitioners have a deep understanding of the individual input variables but far less insight into how they interact with each other. For example, there may be ranges of an input variable for which the observed outcome is significantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm searches for a separation that optimizes the difference between separated areas while obtaining a high level of support. We evaluate its performance using six sample datasets and demonstrate that thresholds identified by the algorithm align well with published results and known physiological boundaries. We also introduce two classification approaches that use UFA and show that the performance attained on unseen test data is comparable to or better than traditional classifiers when confidence intervals are considered. We identify conditions under which UFA performs well, including applications with large amounts of missing or noisy data, applications with a large number of inputs relative to observations, and applications where incidence of the target is low. We argue that ease of explanation of the results, robustness to missing data and noise, and detection of low incidence adverse outcomes are desirable features for clinical applications that can be achieved with relatively simple classifier, like UFA.

Quantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface

Liver surface is covered by a collagenous layer called the Glisson's capsule. The structure of the Glisson's capsule is barely seen in the biopsy samples for histology assessment, thus the changes of the collagen network from the Glisson's capsule during the liver disease progression are not well studied. In this report, we investigated whether non-linear optical imaging of the Glisson's capsule at liver surface would yield sufficient information to allow quantitative staging of liver fibrosis. In contrast to conventional tissue sections whereby tissues are cut perpendicular to the liver surface and interior information from the liver biopsy samples were used, we have established a capsule index based on significant parameters extracted from the second harmonic generation (SHG) microscopy images of capsule collagen from anterior surface of rat livers. Thioacetamide (TAA) induced liver fibrosis animal models was used in this study. The capsule index is capable of differentiating different fibrosis stages, with area under receiver operating characteristics curve (AUC) up to 0.91, making it possible to quantitatively stage liver fibrosis via liver surface imaging potentially with endomicroscopy.

Abundant copy-number loss of CYCLOPS and STOP genes in gastric adenocarcinoma

BACKGROUND

Gastric cancer, a leading cause of cancer death worldwide, has been little studied compared with other cancers that impose similar health burdens. Our goal is to assess genomic copy-number loss and the possible functional consequences and therapeutic implications thereof across a large series of gastric adenocarcinomas.

METHODS

We used high-density single-nucleotide polymorphism microarrays to determine patterns of copy-number loss and allelic imbalance in 74 gastric adenocarcinomas. We investigated whether suppressor of tumorigenesis and/or proliferation (STOP) genes are associated with genomic copy-number loss. We also analyzed the extent to which copy-number loss affects Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS (CYCLOPS) genes-genes that may be attractive targets for therapeutic inhibition when partially deleted.

RESULTS

The proportion of the genome subject to copy-number loss varies considerably from tumor to tumor, with a median of 5.5 %, and a mean of 12 % (range 0-58.5 %). On average, 91 STOP genes were subject to copy-number loss per tumor (median 35, range 0-452), and STOP genes tended to have lower copy-number compared with the rest of the genes. Furthermore, on average, 1.6 CYCLOPS genes per tumor were both subject to copy-number loss and downregulated, and 51.4 % of the tumors had at least one such gene.

CONCLUSIONS

The enrichment of STOP genes in regions of copy-number loss indicates that their deletion may contribute to gastric carcinogenesis. Furthermore, the presence of several deleted and downregulated CYCLOPS genes in some tumors suggests potential therapeutic targets in these tumors.

Automated scoring of liver fibrosis through combined features from different collagen groups

Liver biopsy remains the gold standard in monitoring progression of liver fibrosis associated with an abnormal increase in collagen. Descriptive scoring systems are still being widely used to grade biopsy samples. In this study, we propose a new set of features by clustering collagen fibers into three groups first based on their localization and connectivity properties, and then by extracting morphological features of collagen fibers. The new feature set is compared to the earlier features used in classification of liver fibrosis, which were based on the total amount of collagen fibers. Our results show that new features lead to more accurate grading of liver fibrosis.

Correlation of cell membrane dynamics and cell motility

Background

Essential events of cell development and homeostasis are revealed by the associated changes of cell morphology and therefore have been widely used as a key indicator of physiological states and molecular pathways affecting various cellular functions via cytoskeleton. Cell motility is a complex phenomenon primarily driven by the actin network, which plays an important role in shaping the morphology of the cells. Most of the morphology based features are approximated from cell periphery but its dynamics have received none to scant attention. We aim to bridge the gap between membrane dynamics and cell states from the perspective of whole cell movement by identifying cell edge patterns and its correlation with cell dynamics.

Results

We present a systematic study to extract, classify, and compare cell dynamics in terms of cell motility and edge activity. Cell motility features extracted by fitting a persistent random walk were used to identify the initial set of cell subpopulations. We propose algorithms to extract edge features along the entire cell periphery such as protrusion and retraction velocity. These constitute a unique set of multivariate time-lapse edge features that are then used to profile subclasses of cell dynamics by unsupervised clustering.

Conclusions

By comparing membrane dynamic patterns exhibited by each subclass of cells, correlated trends of edge and cell movements were identified. Our findings are consistent with published literature and we also identified that motility patterns are influenced by edge features from initial time points compared to later sampling intervals.

Toward surface quantification of liver fibrosis progression

Monitoring liver fibrosis progression by liver biopsy is important for certain treatment decisions, but repeated biopsy is invasive. We envision redefinition or elimination of liver biopsy with surface scanning of the liver with minimally invasive optical methods. This would be possible only if the information contained on or near liver surfaces accurately reflects the liver fibrosis progression in the liver interior. In our study, we acquired the second-harmonic generation and two-photon excitation fluorescence microscopy images of liver tissues from bile duct-ligated rat model of liver fibrosis. We extracted morphology-based features, such as total collagen, collagen in bile duct areas, bile duct proliferation, and areas occupied by remnant hepatocytes, and defined the capsule and subcapsular regions on the liver surface based on image analysis of features. We discovered a strong correlation between the liver fibrosis progression on the anterior surface and interior in both liver lobes, where biopsy is typically obtained. The posterior surface exhibits less correlation with the rest of the liver. Therefore, scanning the anterior liver surface would obtain similar information to that obtained from biopsy for monitoring liver fibrosis progression.

Sub-population analysis based on temporal features of high content images

Background

High content screening techniques are increasingly used to understand the regulation and progression of cell motility. The demand of new platforms, coupled with availability of terabytes of data has challenged the traditional technique of identifying cell populations by manual methods and resulted in development of high-dimensional analytical methods.

Results

In this paper, we present sub-populations analysis of cells at the tissue level by using dynamic features of the cells. We used active contour without edges for segmentation of cells, which preserves the cell morphology, and autoregressive modeling to model cell trajectories. The sub-populations were obtained by clustering static, dynamic and a combination of both features. We were able to identify three unique sub-populations in combined clustering.

Conclusion

We report a novel method to identify sub-populations using kinetic features and demonstrate that these features improve sub-population analysis at the tissue level. These advances will facilitate the application of high content screening data analysis to new and complex biological problems.

Geometric confinement influences cellular mechanical properties I -- adhesion area dependence

Interactions between the cell and the extracellular matrix regulate a variety of cellular properties and functions, including cellular rheology. In the present study of cellular adhesion, area was controlled by confining NIH 3T3 fibroblast cells to circular micropatterned islands of defined size. The shear moduli of cells adhering to islands of well defined geometry, as measured by magnetic microrheometry, was found to have a significantly lower variance than those of cells allowed to spread on unpatterned surfaces. We observe that the area of cellular adhesion influences shear modulus. Rheological measurements further indicate that cellular shear modulus is a biphasic function of cellular adhesion area with stiffness decreasing to a minimum value for intermediate areas of adhesion, and then increasing for cells on larger patterns. We propose a simple hypothesis: that the area of adhesion affects cellular rheological properties by regulating the structure of the actin cytoskeleton. To test this hypothesis, we quantified the volume fraction of polymerized actin in the cytosol by staining with fluorescent phalloidin and imaging using quantitative 3D microscopy. The polymerized actin volume fraction exhibited a similar biphasic dependence on adhesion area. Within the limits of our simplifying hypothesis, our experimental results permit an evaluation of the ability of established, micromechanical models to predict the cellular shear modulus based on polymerized actin volume fraction. We investigated the "tensegrity", "cellular-solids", and "biopolymer physics" models that have, respectively, a linear, quadratic, and 5/2 dependence on polymerized actin volume fraction. All three models predict that a biphasic trend in polymerized actin volume fraction as a function of adhesion area will result in a biphasic behavior in shear modulus. Our data favors a higher-order dependence on polymerized actin volume fraction. Increasingly better experimental agreement is observed for the tensegrity, the cellular solids, and the biopolymer models respectively. Alternatively if we postulate the existence of a critical actin volume fraction below which the shear modulus vanishes, the experimental data can be equivalently described by a model with an almost linear dependence on polymerized actin volume fraction; this observation supports a tensegrity model with a critical actin volume fraction.

Krankheitskosten von COPD in Deutschland

The present cost-of-illness study is focused on the costs of COPD in Germany. In a pre-study, data on 814 randomly selected patients were collected to achieve reliable figures for the distribution of COPD severity grades and the frequencies of exacerbations. The main study was performed on 321 randomly selected patients from the pre-study. Data on resource use were collected in a face-to-face interview with the respective physicians using the patient records as a basis. Costs associated with resource consumption were weighted with the frequencies of COPD severity grades as assessed in the pre-study to determine the costs of COPD. Annual COPD-related costs per patient were 3,027 from the societal perspective. Main cost components were hospitalisations (26 %), medication (23 %) and early retirement (17 %). Annual COPD-related costs from the perspective of the German health insurance system (GKV) were 1,944 euros per patient.

Identification of a novel gene coding for neoxanthin synthase from Solanum tuberosum

The polymerase chain reaction analysis of potato plants, transformed with capsanthin capsorubin synthase ccs, revealed the presence of a highly related gene. The cloned cDNA showed at the protein level 89.6% identity to CCS. This suggested that the novel enzyme catalyzes a mechanistically similar reaction. Such a reaction is represented by neoxanthin synthase (NXS), forming the xanthophyll neoxanthin, a direct substrate for abscisic acid formation. The function of the novel enzyme could be proven by transient expression in plant protoplasts and high performance liquid chromatography analysis. The cloned NXS was imported in vitro into plastids, the compartment of carotenoid biosynthesis.

Regulation and activation of phytoene synthase, a key enzyme in carotenoid biosynthesis, during photomorphogenesis

During photomorphogenesis in higher plants, a coordinated increase occurs in the chlorophyll and carotenoid contents. The carotenoid level is under phytochrome control, as reflected by the light regulation of the mRNA level of phytoene synthase (PSY), the first enzyme in the carotenoid biosynthetic pathway. We investigated PSY protein levels, enzymatic activity and topological localization during photomorphogenesis. The results revealed that PSY protein levels and enzymatic activity increase during de-etiolation and that the enzyme is localized at thylakoid membranes in mature chloroplasts. However, under certain light conditions (e.g., far-red light) the increases in PSY mRNA and protein levels are not accompanied by an increase in enzymatic activity. Under those conditions, PSY is localized in the prolamellar body fraction in a mostly enzymatically inactive form. Subsequent illumination of dark-grown and/or in far-red light grown seedlings with white light causes the decay of these structures and a topological relocalization of PSY to developing thylakolds which results in its enzymatic activation. This light-dependent mechanism of enzymatic activation of PSY in carotenoid biosynthesis shares common features with the regulation of the NADPH:protochlorophyllide oxidoreductase, the first light-regulated enzyme in chlorophyll biosynthesis. The mechanism of regulation described here may contribute to ensuring a spatially and temporally coordinated increase in both carotenoid and chlorophyll contents.

Light-dependent regulation of carotenoid biosynthesis occurs at the level of phytoene synthase expression and is mediated by phytochrome in Sinapis alba and Arabidopsis thaliana seedlings

In chloroplasts, carotenoids are essential pigments involved in photosynthesis. During-photomorphogenesis, a coordinated increase in the amounts of chlorophylls and carotenoids, in conjugation with other components, leads to the formation of a functional photosynthetic apparatus. To investigate the regulation of carotenoid biosynthesis during this process at the molecular level, GGPS, PSY and PDS cDNAs have been cloned from white mustard (Sinapis alba L). GGPS encodes a key enzyme in plastid isoprenoid metabolism, while the products of PSY and PDS catalyse the subsequent steps in carotenoid biosynthesis. Due to the low mRNA levels of the genes involved, the use of a RT-PCR protocol was necessary to measure gene expression during photomorphogenesis. With light, there is an up-regulation of PSY expression, the first gene within the carotenoid biosynthetic pathway, while PDS and GGPS expression levels remain constant. Treatment with different light qualities reveals a phytochrome-mediated regulation of PSY expression in developing white mustard seedlings. To obtain more detailed information on the light-regulation, Arabidopsis thaliana wild-type and phytochrome mutants were utilized. Continuous far-red and red light both increase the expression of PSY in wild-type seedlings, demonstrating that both light-labile and light-stable phytochromes are involved in PSY regulation. The response to far-red light is completely abolished in the phyA mutant, showing that PHYA mediates the increase in PSY transcript levels under these light conditions. In the phyB mutant, the red light response is normal, indicating that PSY expression is not controlled by PHYB but by other light-stable phytochromes. Measurement of chlorophylls and carotenoids under the same light regimes shows that the up-regulation of PSY expression does not necessarily result in an increase of the carotenoid content. Only those light conditions which allow chlorophyll biosynthesis lead to a significant increase of the carotenoid content. Therefore, it is proposed that up-regulation of PSY mRNA levels leads to an increased capacity for the formation of carotenoids. However, this only takes place under light conditions leading to protochlorophyllide photoconversion.

Equipment configuration and procedures: preferences for interventional microtherapy

Magnetic resonance imaging (MRI), computed tomography, and electron beam tomography scanners are built for radiologic diagnosis. With increasing frequency they are being used in the field of interventional Microtherapy to permit transparent visualization of the therapeutic field. Each of these scanners can be combined with endoscopy, fluoroscopy/digital subtraction angiography, and ultrasound units for hybrid imaging techniques as well as with therapeutic systems like lasers or radiofrequency. MRI affords 3D localization without x-ray exposure. Open access and keyhole imaging allow nearly real time guidance of instruments. Minimally invasive techniques using endoscopes and hybrid tomographic guidance result in improved tip tracking of microinstruments and reduced complications. This safer access into the body will lead to interdisciplinary cooperation with the potential for large cost reductions. This report summarizes our experience regarding which of the hybrid imaging suites is best suited for procedures including among others drug instillations, prosthesis (stent) implantation, or microoperations (endoscopic diskectomy/sequestrectomy), and physiological measurements simultaneously.

Stability and self-assembly of the S-layer protein of the cell wall of Bacillus stearothermophilus

The surface layer of the cell envelope of Bacillus stearothermophilus consists of a regular array of protein subunits. As shown by dodecyl sulfate polyacrylamide gel-electrophoresis and ultracentrifugation, the fully solubilized S-layer protein represents a homogeneous entity with a subunit molecular mass of 115 +/- 5 kDa. Solubilization of the protein may be accomplished at acid pH, or using high concentrations of urea or guanidine X HCl. It is accompanied by (partial) denaturation, thus interfering with the characterization of the protein in its unperturbed native state. Removal of the solubilizing agent by dialysis or dilution allows the S-layer to be reassembled into two-dimensional crystalline lattices identical to those observed in intact cells. To determine the kinetics of association, optimum conditions are found to be rapid mixing with 0.1 M sodium phosphate pH 7.0, 20 degrees C, final protein concentration greater than 10 micrograms/ml. If the time course of the self-assembly is monitored by light scattering, as well as by chemical cross-linking with glutardialdehyde, multiphasic kinetics with a rapid initial phase and slow consecutive processes of higher than second-order are observed. The rapid phase may be attributed to the formation of oligomeric precursors (Mr greater than 10(6) ). Concentration-dependent light scattering measurements give evidence for a "critical concentration" of association, suggesting that patches of 12-16 protein subunits fuse and recrystallize into the final (native) S-layer structure. Recrystallization tends to be complete.