Computational Framework For Neuro-Optics Simulation And Deep Learning Denoising

The application of machine learning techniques in microscopic image restoration has shown superior performance. However, the development of such techniques has been hindered by the demand for large datasets and the lack of ground truth. To address these challenges, this study introduces a computer simulation model that accurately captures the neural anatomic volume, fluorescence light transportation within the tissue volume, and the photon collection process of microscopic imaging sensors. The primary goal of this simulation is to generate realistic image data for training and validating machine learning models. One notable aspect of this study is the incorporation of a machine learning denoiser into the simulation, which accelerates the computational efficiency of the entire process. By reducing noise levels in the generated images, the denoiser significantly enhances the simulation\u27s performance, allowing for faster and more accurate modeling and analysis of microscopy images. This approach addresses the limitations of data availability and ground truth annotation, offering a practical and efficient solution for microscopic image restoration. The integration of a machine learning denoiser within the simulation significantly accelerates the overall simulation process, while improving the quality of the generated images. This advancement opens new possibilities for training and validating machine learning models in microscopic image restoration, overcoming the challenges of large datasets and the lack of ground truth

BioRED: A Comprehensive Biomedical Relation Extraction Dataset

Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles. Further, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a comprehensive dataset can successfully facilitate the development of more accurate, efficient, and robust RE systems for biomedicine

Artificial Intelligence, 3D Documentation, and Rock Art - Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

Rock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art

Dr.Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods simulate bokeh effects without relying on a physical camera lens. However, in the realm of digital bokeh synthesis, the two main challenges for bokeh synthesis are color bleeding and partial occlusion at object boundaries. Our primary goal is to overcome these two major challenges using physics principles that define bokeh formation. To achieve this, we propose a novel and accurate filtering-based bokeh rendering equation and a physically-based occlusion-aware bokeh renderer, dubbed Dr.Bokeh, which addresses the aforementioned challenges during the rendering stage without the need of post-processing or data-driven approaches. Our rendering algorithm first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur. By softening non-differentiable operations, we make Dr.Bokeh differentiable such that it can be plugged into a machine-learning framework. We perform quantitative and qualitative evaluations on synthetic and real-world images to validate the effectiveness of the rendering quality and the differentiability of our method. We show Dr.Bokeh not only outperforms state-of-the-art bokeh rendering algorithms in terms of photo-realism but also improves the depth quality from depth-from-defocus

Trust as a mediator in the relationship between childhood sexual abuse and IL-6 level in adulthood

Childhood sexual abuse (CSA) has been shown to predict the coupling of depression and inflammation in adulthood. Trust within intimate relationships, a core element in marital relations, has been shown to predict positive physical and mental health outcomes, but the mediating role of trust in partners in the association between CSA and inflammation in adulthood requires further study. The present study aimed to examine the impact of CSA on inflammatory biomarkers (IL-6 and IL-1β) in adults with depression and the mediating role of trust. A cross-sectional survey data set of adults presenting with mood and sleep disturbance was used in the analysis. CSA demonstrated a significant negative correlation with IL-6 level (r = -0.28, p<0. 01) in adults with clinically significant depression, while trust showed a significant positive correlation with IL-6 level (r = 0.36, p < .01). Sobel test and bootstrapping revealed a significant mediating role for trust between CSA and IL-6 level. CSA and trust in partners were revealed to have significant associations with IL-6 level in adulthood. Counterintuitively, the directions of association were not those expected. Trust played a mediating role between CSA and adulthood levels of IL-6. Plausible explanations for these counterintuitive findings are discussed

Vitamin D receptor agonists increase klotho and osteopontin while decreasing aortic calcification in mice with chronic kidney disease fed a high phosphate diet

Vascular calcification is common in chronic kidney disease, where cardiovascular mortality remains the leading cause of death. Patients with kidney disease are often prescribed vitamin D receptor agonists (VDRAs) that confer a survival benefit, but the underlying mechanisms remain unclear. Here we tested two VDRAs in a mouse chronic kidney disease model where dietary phosphate loading induced aortic medial calcification. Mice were given intraperitoneal calcitriol or paricalcitol three times per week for 3 weeks. These treatments were associated with half of the aortic calcification compared to no therapy, and there was no difference between the two agents. In the setting of a high-phosphate diet, serum parathyroid hormone and calcium levels were not significantly altered by treatment. VDRA therapy was associated with increased serum and urine klotho levels, increased phosphaturia, correction of hyperphosphatemia, and lowering of serum fibroblast growth factor-23. There was no effect on elastin remodeling or inflammation; however, the expression of the anticalcification factor, osteopontin, in aortic medial cells was increased. Paricalcitol upregulated osteopontin secretion from mouse vascular smooth muscle cells in culture. Thus, klotho and osteopontin were upregulated by VDRA therapy in chronic kidney disease, independent of changes in serum parathyroid hormone and calcium

Japanese Encephalitis Viruses from Bats in Yunnan, China

Genome sequencing and virulence studies of 2 Japanese encephalitis viruses (JEVs) from bats in Yunnan, China, showed a close relationship with JEVs isolated from mosquitoes and humans in the same region over 2 decades. These results indicate that bats may play a role in human Japanese encephalitis outbreaks in this region

Measuring the Impact of Conservation : The Growing Importance of Monitoring Fauna, Flora and Funga

Many stakeholders, from governments to civil society to businesses, lack the data they need to make informed decisions on biodiversity, jeopardising efforts to conserve, restore and sustainably manage nature. Here we review the importance of enhancing biodiversity monitoring, assess the challenges involved and identify potential solutions. Capacity for biodiversity monitoring needs to be enhanced urgently, especially in poorer, high-biodiversity countries where data gaps are disproportionately high. Modern tools and technologies, including remote sensing, bioacoustics and environmental DNA, should be used at larger scales to fill taxonomic and geographic data gaps, especially in the tropics, in marine and freshwater biomes, and for plants, fungi and invertebrates. Stakeholders need to follow best monitoring practices, adopting appropriate indicators and using counterfactual approaches to measure and attribute outcomes and impacts. Data should be made openly and freely available. Companies need to invest in collecting the data required to enhance sustainability in their operations and supply chains. With governments soon to commit to the post-2020 global biodiversity framework, the time is right to make a concerted push on monitoring. However, action at scale is needed now if we are to enhance results-based management adequately to conserve the biodiversity and ecosystem services we all depend on.This paper was made possible by funding from the Swiss Network for International Studies to the University of Lausanne (L.F. and P.J.S.) and its partners under the project: "Unblocking the flow of biodiversity data for multi-stakeholder environmental sustainability management". The research was carried out, in part, by GNG at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). PAVB was supported by the project MACRISK-PTDC/BIA-CBI/0625/2021, through the FCT-FundacAo para a Ciencia e a Tecnologia. YNB acknowledges support from the Audemars-Watkins Foundation for the CBCR's protected area monitoring work featured in this paper.info:eu-repo/semantics/publishedVersio

BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers

Background: The K3326X variant in BRCA2 (BRCA2*c.9976A>T; p.Lys3326*; rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormone-related cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76 637 cancer case patients and 83 796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9x10- 6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8x10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor–negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4x10-5 and ORw = 1.50, 95% CI = 1.28 to 1.76, P = 4.1x10-5, respectively). For BRCA1 mutation carriers, there was a statistically significant inverse association of the K3326X variant with risk of ovarian cancer (HR = 0.43, 95% CI = 0.22 to 0.84, P = .013) but no association with breast cancer. No association with prostate cancer was observed. Conclusions: Our study provides evidence that the K3326X variant is associated with risk of developing breast and ovarian cancers independent of other pathogenic variants in BRCA2. Further studies are needed to determine the biological mechanism of action responsible for these associations