Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.

Background: The COVID-19 pandemic has had a profound impact globally, presenting significant social and economic challenges. This study aims to explore the factors affecting mortality among hospitalized COVID-19 patients and construct a machine learning-based model to predict the risk of mortality. Methods: The study examined COVID-19 patients admitted to Imam Reza Hospital in Tabriz, Iran, between March 2020 and November 2021. The Elastic Net method was employed to identify and rank features associated with mortality risk. Subsequently, an artificial neural network (ANN) model was developed based on these features to predict mortality risk. The performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The study included 706 patients with 96 features, out of them 26 features were identified as crucial predictors of mortality. The ANN model, utilizing 20 of these features, achieved an area under the ROC curve (AUC) of 98.8 %, effectively stratifying patients by mortality risk. Conclusion: The developed model offers accurate and precipitous mortality risk predictions for COVID-19 patients, enhancing the responsiveness of healthcare systems to high-risk individuals.

A new method to quantify the impacts of human activity on soil conservation service.

Human activities and climate change impact ecosystem services, thereby affecting economic and social sustainable development. Measuring the heterogeneity in space and time of how human activities affect ecosystem services poses a challenge for the sustainable management of land resources. Based on "human appropriation of net primary production (HANPP) - Fractional Vegetation Cover (FVC) - Soil Conservation Service (SCS)" cascading effect, first, a geographically and temporally weighted regression (GTWR) model was employed to assess the impact of HANPP in percent of potential NPP (hereafter HANPP%) on the FVC; second, changes in the FVC caused by human activities were quantified; and third, the potential soil conservation service (SCSp) and actual soil conservation service (SCSa) were estimated using the Revised Universal Soil Loss Equation (RUSLE) model, and the difference between them represented the changes in soil conservation service caused by human activities (SCSh). Taking the Qinghai-Tibet Plateau as a case study, we found that the GTWR model was well suited for analyzing the relationship between the HANPP% and the FVC (R2 = 0.897). The HANPP resulted in a decrease in the FVC from 0.222 in 2001 to 0.199 in 2019 and correspondingly resulted in a decrease in the ratio of SCSh to SCSp from 8.95% to 7.24%. This study provides a quantitative method that allows quantifying the influence of human activity on ecosystem services closely related to the FVC.

Discarded fishing net pollution in coastal areas of Bangladesh.

Discarded fishing nets, a major source of marine litter, significantly threaten the marine environment and contribute to plastic pollution due to the synthetic polymers they contain. Though Bangladesh is a maritime country with 0.5 million of fishers dependent on coastal and marine fishing, there have been no studies to date on the plastic pollution impact of fishing nets. This study demonstrates the fishing nets associated with marine litter in two coastal locations of Bangladesh, Charfesson and Cox's Bazar. Fishing net samples were collected from local net shops and semi-structured interviews were taken of the shop owners to gather information about available fishing nets. This was complemented by photo-quadrat surveys to document waste fishing net materials on the shore in both locations. Among the 17 net samples, there were 12 types of gill nets, which showed a wide range of variation in price, material types, and longevity. Through the FTIR analysis, we identified the presence of Nylon 6, Polyethylene, Polyvinyl chloride, Polypropylene and Polyethylene terephthalate in the collected fishing net samples. Photoquadrat surveys found **of fishing net related plastic pollution in coastal areas. This study addressed the knowledge gap regarding the diversity and chemical characteristics of fishing nets and the resulting litter in Bangladesh.

Role of ectomycorrhizal colonization in enhancement of nutrients for survival of plants collected from mountainous cold stress areas.

BACKGROUND: Ectomycorrhizal (ECM and ECM-like) structures associated with plant root systems are a challenge for scientists. The dispersion pattern of roots within the soil profile and the nutritional conditions are both favourable factors to motivate the plants to make ECM associations. RESULTS: This study discusses the colonization of mycorrhizal associations in Kobresia and Polygonum species including Polygonum viviparum, Kobresia filicina, K. myosuroides, Alnus nitida, Betula pendula, Pinus sylvestris, and Trifolium repens grown naturally in cold stressed soils of Gilgit-Baltistan (high-altitude alpine Deosai plains), Hazara, Swat, Dir, and Bajaur. Sieved soil batches were exposed to +5 °C (control), -10, -20, -30, -40, -50, -125 °C for 5 h, and selected plants were sown to these soils for 10 weeks under favourable conditions for ECM colonization. Ectomycorrhizal associations were examined in the above mentioned plants. Some ECM fungi have dark mycelia that look like the mantle and Hartig net. Examples of these are Kobresia filicina, K. myosuroides, and Polygonum viviparum. Findings of this study revealed that K. myosuroides excelled in ECM root tip length, dry mass, and NH4 concentration at -125 °C. Contrarily, A. nitida demonstrated the lower values, indicated its minimum tolerance. Notably, T. repens boasted the highest nitrogen concentration (18.7 ± 1.31 mg/g), while P. sylvestris led in phosphorus (3.2 ± 0.22 mg/g). The B. pendula showed the highest potassium concentration (9.4 ± 0.66 mg/g), emphasising species-specific nutrient uptake capabilities in extreme cold conditions. The PCA analysis revealed that the parameters, e.g., NH4 in soil mix (NH4), NO3 in soil mix (NO3), phosphorus in soil in species of Polygonum viviparum, Kobresia filicina, K. myosuroides, Alnus nitida, Betula pendula, Pinus sylvestris, and Trifolium repens are most accurately represented in cases of + 5 °C, -10 °C, and -20 °C temperatures. On the other hand, the parameters for ECM root tips (ECM) and Dry Mass (DM) are best described in -40 °C, -50 °C, and - 125 °C temperatures. All parameters have a strong influence on the variability of the system indicated the efficiency of ECM. The heatmap supported the nutrients positively correlated with ECM colonization with the host plants. CONCLUSION: At lower temperatures, hyphae and spores in roots were reduced, while soluble phosphorus concentrations of leaves were increased in cold stress soils. Maximum foliar nutrient concentrations were found in K. myosuroides at the lowest temperature treatments due to efficient functioning and colonization of ECM.

Psychosocial factors play a central role in determining SNAP utilization for farm workforce.

Introduction: Federal food safety net programs, like the Supplemental Nutrition Assistance Program (SNAP), may not reach vulnerable populations like rural residents, immigrants, and Latinx individuals. Because these groups are overrepresented among the farm workforce, exploring SNAP utilization among farm communities may clarify the role it plays in alleviating food insecurity. Methods: In-depth interviews were conducted with 31 farmworkers and farm owners. Patterns and predictors of SNAP utilization were organized using an adapted Andersen Behavioral Model of Health Service Utilization. Results: Psychosocial factors played the central role in participants' use of SNAP. Discussion: Opportunities to improve the design and delivery of SNAP include expanded eligibility cut-offs and targeted engagement mechanisms.

Closing the gaps in adolescent vaccinations: Rhode Island's Vaccinate Before You Graduate program as a model for other jurisdictions.

Objective: The northeastern state of Rhode Island (RI) has a Vaccinate Before You Graduate (VBYG) program that supplements the traditional primary care infrastructure by providing vaccines to adolescents while they are in school, with no out-of-pocket expenses. We analyzed data from RI's immunization registry to evaluate whether VBYG also reduces disparities in adolescent immunization rates. Methods: We identified adolescent and catch-up vaccines administered in RI to people who were aged 11-18 at any point during the 5-year study period of 2019-2023, and conducted bivariate and multivariate analyses of vaccine administration data by setting (VBYG clinics, community health centers [CHCs], all other primary care practices [oPCPs], other school-based clinics, and other sites) and adolescent demographics (racial and ethnic identity, insurance status, sex, and age at time of vaccine). Results: Of over 387,000 routine vaccines administered during the study period, 3.3 % were administered by a VBYG clinic despite significant declines during school closures associated with the early COVID-19 pandemic. VBYG-administered doses went to slightly older youth, and a higher proportion were catch-up doses (25.7 % versus 14.1 % for CHC doses and 6.5 % for oPCP). Youths received an average of 2.71 vaccines in VBYG clinics compared to 1.77 from oPCPs and 2.08 from CHCs. A higher proportion of vaccines administered by VBYG went to adolescents of color and those without private insurance than those administered by oPCPs. Conclusions: VBYG provides a model to other jurisdictions of a vaccine safety net for adolescents who may not otherwise receive recommended vaccines before exiting the school system.

A Neuroendocrine Tumor of Unknown Primary Origin: A Case Report and Review of the Literature.

Neuroendocrine tumors (NETs) are uncommon malignancies that develop from neuroendocrine cells which most commonly occur in the GI tract, lung, and pancreas. Treatment courses for these tumors are largely dictated by the primary origin site, which can present diagnostic and therapeutic challenges in NETs of unknown primary origin. Herein, we present a case of an NET of unknown primary origin with significant liver metastases. Our aim is to highlight the key components of the workup of NETs of unknown primary origin and detail the biochemical, histopathological, and imaging modalities as recommended by current literature. We highlight the importance of a multidisciplinary approach to both diagnosis and treatment of these patients as well as touch upon therapeutic options.

Comparing nnU-Net and deepflash2 for Histopathological Tumor Segmentation.

Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.

BFNet: a full-encoder skip connect way for medical image segmentation.

In recent years, semantic segmentation in deep learning has been widely applied in medical image segmentation, leading to the development of numerous models. Convolutional Neural Network (CNNs) have achieved milestone achievements in medical image analysis. Particularly, deep neural networks based on U-shaped architectures and skip connections have been extensively employed in various medical image tasks. U-Net is characterized by its encoder-decoder architecture and pioneering skip connections, along with multi-scale features, has served as a fundamental network architecture for many modifications. But U-Net cannot fully utilize all the information from the encoder layer in the decoder layer. U-Net++ connects mid parameters of different dimensions through nested and dense skip connections. However, it can only alleviate the disadvantage of not being able to fully utilize the encoder information and will greatly increase the model parameters. In this paper, a novel BFNet is proposed to utilize all feature maps from the encoder at every layer of the decoder and reconnects with the current layer of the encoder. This allows the decoder to better learn the positional information of segmentation targets and improves learning of boundary information and abstract semantics in the current layer of the encoder. Our proposed method has a significant improvement in accuracy with 1.4 percent. Besides enhancing accuracy, our proposed BFNet also reduces network parameters. All the advantages we proposed are demonstrated on our dataset. We also discuss how different loss functions influence this model and some possible improvements.

The spike-and-slab lasso and scalable algorithm to accommodate multinomial outcomes in variable selection problems.

Spike-and-slab prior distributions are used to impose variable selection in Bayesian regression-style problems with many possible predictors. These priors are a mixture of two zero-centered distributions with differing variances, resulting in different shrinkage levels on parameter estimates based on whether they are relevant to the outcome. The spike-and-slab lasso assigns mixtures of double exponential distributions as priors for the parameters. This framework was initially developed for linear models, later developed for generalized linear models, and shown to perform well in scenarios requiring sparse solutions. Standard formulations of generalized linear models cannot immediately accommodate categorical outcomes with > 2 categories, i.e. multinomial outcomes, and require modifications to model specification and parameter estimation. Such modifications are relatively straightforward in a Classical setting but require additional theoretical and computational considerations in Bayesian settings, which can depend on the choice of prior distributions for the parameters of interest. While previous developments of the spike-and-slab lasso focused on continuous, count, and/or binary outcomes, we generalize the spike-and-slab lasso to accommodate multinomial outcomes, developing both the theoretical basis for the model and an expectation-maximization algorithm to fit the model. To our knowledge, this is the first generalization of the spike-and-slab lasso to allow for multinomial outcomes.

The UCI Fluxtron: A versatile dynamic chamber and software system for biosphere-atmosphere exchange research.

Here we present the UCI Fluxtron, a cost-effective multi-enclosure dynamic gas exchange system that provides an adequate level of control of the experimental conditions for investigating biosphere-atmosphere exchange of trace gases. We focus on the hardware and software used to monitor, control, and record the air flows, temperatures, and valve switching, and on the software that processes the collected data to calculate the exchange flux of trace gases. We provide the detailed list of commercial materials used and also the software code developed for the Fluxtron, so that similar dynamic enclosure systems can be quickly adopted by interested researchers. Furthermore, the two software components -Fluxtron Control and Fluxtron Process- work independently of each other, thus being highly adaptable for other experimental designs. Beyond plants, the same experimental setup can be applied to the study of trace gas exchange by animals, microbes, soil, or any materials that can be enclosed in a suitable container.

MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network's perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People's Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.

Reduction of particle size of field peas (Pisum sativum L.) increases net energy and digestibility of starch when fed to growing pigs, but there is no difference in nutritional value between peas from the U.S. and peas from Canada.

Two experiments were conducted to test the hypothesis that particle size of field peas and location where peas are grown do not affect apparent total tract digestibility (ATTD) of nutrients and gross energy (GE), digestible energy (DE), metabolizable energy (ME), and net energy (NE), apparent ileal digestibility (AID) of starch, or standardized ileal digestibility (SID) of crude protein (CP) and amino acids (AA). In both experiments, three sources of field peas were used. One source was obtained from the U.S., and two sources were obtained from Canada (i.e., Canada 1, Canada 2). The U.S. field peas were ground to 678, 457, or 265 µm, whereas the two sources of Canadian peas were ground to 411 and 415 µm. Therefore, five batches of field peas were used in both experiments. A basal diet contained corn and soybean meal as the only source of energy, starch, and AA, and five diets containing corn and soybean meal and 50% of each source of field peas were also formulated. The ratio between corn and soybean meal was 1.92:1 in all diets. In Exp. 1, an N-free diet was also used to calculate basal endogenous losses of AA and CP, but in Exp. 2, no N-free diet was used. In Exp. 1, seven barrows (initial body weight = 60.6 ± 2.1 kg) that had a T-cannula installed in the distal ileum were allotted to a 7 × 7 Latin square design with seven diets and seven periods. In Exp. 2, twenty-four pigs (initial body weight = 30.8 ± 1.0 kg) were housed in six calorimeter chambers with four pigs per chamber. The six chambers were allotted to one of the six diets using a 6 × 6 Latin square design with six consecutive periods of 15 d. Results of Exp. 1 demonstrated that the SID of CP and AA was not influenced by the origin of the peas or by the particle size, but the AID of starch increased (linear, P < 0.001) as particle size was reduced from 678 µm to 457 or 265 µm. Results of Exp. 2 indicated that growing location did not affect concentrations of DE, ME, or NE of field peas, but concentrations of DE, ME, and NE increased (linear, P < 0.05) when the particle size was reduced from 678 µm to 457 or 265 µm. In conclusion, field peas grown in Canada or the U.S. have the same nutritional value, but starch digestibility and NE increase if the particle size of field peas is reduced.

The impact of renewable energy, eco-innovation, and GDP growth on CO2 emissions: Pathways to the UK's net zero target.

In May 2019, the Climate Change Committee (CCC) recommended that the UK adopt a net-zero target, aiming to reduce its greenhouse gas emissions (GHG) by 100% from the 1990s baseline by 2050. The government accepted the recommendation, and the UK became the first major economy to establish a net-zero emissions law. To progress towards its climate objectives, the government took several initiatives, such as increasing its reliance on renewable energy sources and investing in climate mitigation technologies, which are commonly referred to as process eco-innovation. This study examines the impact of eco-innovation, process eco-innovation, renewable energy consumption, and economic growth on CO2 emissions in the UK using data from 1988 to 2020. We used the ARDL bound test with an error correction model (ECM) to examine the long-run and short-run cointegration between the variables of concern. We found that eco-innovation, process eco-innovation, and renewable energy consumption have significant roles in mitigating CO2 emissions, while economic growth contributes to environmental degradation in the UK. We also found that the effect of eco-innovation on CO2 emissions abatement is stronger than that of process eco-innovation in the short and long-run. Our robustness tests have confirmed the accuracy of those findings. In addition, the results from the Toda-Yamamoto causality revealed a one-way causality from process eco-innovation to CO2, renewable energy to CO2, and eco-innovation to CO2 emissions. Further, a bidirectional causality was found between GDP and CO2 emissions. The evidence presented in this paper provides great insight for shaping the energy policy in the UK and for establishing the climate budget in line with the country's net-zero target.

Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.

To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method's Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.

Ownership and usage of insecticide-treated nets in Ghana: a scoping review of facilitators and barriers.

BACKGROUND: Insecticide-treated nets (ITNs) are pivotal tools for malaria prevention in endemic regions like Ghana. Understanding the protective factors and barriers influencing ITN ownership and usage is crucial for designing effective interventions. A scoping review was conducted to identify studies exploring protective factors and barriers related to ITN ownership and usage. METHODS: This review followed the guidelines by Askey and O'Malley. Search was done in four major databases including PubMed, Science Direct, PubMed CENTRAL, and JSTOR. Additional searches were done in Google Scholar and Google. Peer-reviewed and grey literature were included. RESULTS: A total of 24 papers met the eligibility criteria and were included in the review. Included studies found regional disparities in ITN ownership and usage. Furthermore, included studies reported ownership rates between 97.8 and 28% and usage rates between 94 and 20%. Protective factors facilitating ITN ownership were marital status, higher educational attainment, higher income levels, and being aged 25 years or older. In contrast, the factors for its use included community-level campaigns advocating for ITN use and awareness, individuals with secondary education or higher and those residing in urban areas. Missed opportunities in free distribution exercises and the unavailability of subsidized ITNs at health facilities were barriers to ownership. CONCLUSION: Understanding and addressing protective factors and barriers influencing ITN ownership and usage are crucial for enhancing malaria prevention strategies and achieving sustainable progress in combating malaria in endemic areas. Collaborative and evidence-based interventions are essential for addressing these challenges effectively.

Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on native T1 maps from cardiac MRI in both long-axis and short-axis orientations. Models were trained on native myocardial T1 maps from 50 healthy volunteers and 75 patients using manual segmentation as the reference standard. Based on a U-Net architecture, we systematically optimized the model design using two different training metrics (Sørensen-Dice coefficient = DSC and Intersection-over-Union = IOU), two different activation functions (ReLU and LeakyReLU) and various numbers of training epochs. Training with DSC metric and a ReLU activation function over 35 epochs achieved the highest overall performance (mean error in T1 10.6 ± 17.9 ms, mean DSC 0.88 ± 0.07). Limits of agreement between model results and ground truth were from -35.5 to + 36.1 ms. This was superior to the agreement between two human raters (-34.7 to + 59.1 ms). Segmentation was as accurate for long-axis views (mean error T1: 6.77 ± 8.3 ms, mean DSC: 0.89 ± 0.03) as for short-axis images (mean error ΔT1: 11.6 ± 19.7 ms, mean DSC: 0.88 ± 0.08). Fully automated segmentation and quantitative analysis of native myocardial T1 maps is possible in both long-axis and short-axis orientations with very high accuracy.

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model.

Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is commonly employed for screening, yet its effectiveness is hindered by challenges like weak contrast, speckle noise, and hazy boundaries in images. This study proposes an adaptive deep learning-based segmentation technique using a database of ovarian ultrasound cyst images. A Guided Trilateral Filter (GTF) is applied for noise reduction in pre-processing. Segmentation utilizes an Adaptive Convolutional Neural Network (AdaResU-net) for precise cyst size identification and benign/malignant classification, optimized via the Wild Horse Optimization (WHO) algorithm. Objective functions Dice Loss Coefficient and Weighted Cross-Entropy are optimized to enhance segmentation accuracy. Classification of cyst types is performed using a Pyramidal Dilated Convolutional (PDC) network. The method achieves a segmentation accuracy of 98.87%, surpassing existing techniques, thereby promising improved diagnostic accuracy and patient care outcomes.

Intercomparison of measured and modelled photochemical ozone production rates: Suggestion of chemistry hypothesis regarding unmeasured VOCs.

Ozone (O3) pollution is a severe environmental problem in China. The incomplete understanding of atmospheric photochemical reaction mechanisms prevents us from accurately understanding the chemistry of O3 production. Here, we used an improved dual-channel reaction chamber technique to measure net photochemical O3 production rate (P(O3)net) directly in Dongguan, a typical industrial city in China. The maximum P(O3)net was 46.3 ppbv h-1 during the observation period, which is at a relatively high level compared to previous observations under different environment worldwide. We employed an observation-based box model coupled with the state-of-the-art atmospheric chemical mechanism (MCM v3.3.1) to investigate the chemistry of O3 production. Under the base scenario, the modelling underestimates P(O3)net by ~30 %. Additionally considering HO2 uptake by ambient aerosols, inorganic deposition, and Cl chemistry only caused a small change (< 13 %) in the simulation of P(O3)net. Further analysis indicates that unmeasured reactive volatile organic compounds (VOCs), such as oxygenated VOCs and branched alkenes are potential contributors to the underestimation of P(O3)net. This study underscores the underestimation of P(O3)net in conventional atmospheric modelling setups, providing a crucial scientific foundation for further investigation aimed at promoting our understanding of photochemical O3 formation.

Semantic segmentation in skin surface microscopic images with artifacts removal.

Skin surface imaging has been used to examine skin lesions with a microscope for over a century and is commonly known as epiluminescence microscopy, dermatoscopy, or dermoscopy. Skin surface microscopy has been recommended to reduce the necessity of biopsy. This imaging technique could improve the clinical diagnostic performance of pigmented skin lesions. Different imaging techniques are employed in dermatology to find diseases. Segmentation and classification are the two main steps in the examination. The classification performance is influenced by the algorithm employed in the segmentation procedure. The most difficult aspect of segmentation is getting rid of the unwanted artifacts. Many deep-learning models are being created to segment skin lesions. In this paper, an analysis of common artifacts is proposed to investigate the segmentation performance of deep learning models with skin surface microscopic images. The most prevalent artifacts in skin images are hair and dark corners. These artifacts can be observed in the majority of dermoscopy images captured through various imaging techniques. While hair detection and removal methods are common, the introduction of dark corner detection and removal represents a novel approach to skin lesion segmentation. A comprehensive analysis of this segmentation performance is assessed using the surface density of artifacts. Assessment of the PH2, ISIC 2017, and ISIC 2018 datasets demonstrates significant enhancements, as reflected by Dice coefficients rising to 93.49 (86.81), 85.86 (79.91), and 75.38 (51.28) respectively, upon artifact removal. These results underscore the pivotal significance of artifact removal techniques in amplifying the efficacy of deep-learning models for skin lesion segmentation.