Month: April 2024

Abstract

Given an open book decomposition \((S,\mathfrak{h} )\) adapted to a closed, oriented 3-manifold \(M\) , we define a chain map \(\Phi \) from a certain Heegaard Floer chain complex associated to \((S,\mathfrak{h} )\) to a certain embedded contact homology chain complex associated to \((S,\mathfrak{h} )\) , as defined in (Colin et al. in Geom. Topol., 2024), and prove that it induces an isomorphism on the level of homology. This implies the isomorphism between the hat version of Heegaard Floer homology of \(-M\) and the hat version of embedded contact homology of \(M\) .

VitaDAO funds longevity research through a blockchain-based decentralized autonomous organization (DAO), showcasing the potential of collaborative, transparent and alternative systems while also highlighting the challenges of coordination, regulation, biases and skepticism in reshaping traditional research financing methods.

Abstract

Currently, the most widespread infectious illness in the world is the coronavirus (COVID-19). The original diagnosis of this illness presents the most obstacle in preventing subsequent infections and their transmission from one person to another. Therefore, it is crucial to employ both a clinical process and an automated diagnostic technology for the quick detection of COVID-19 to stop its spread. Chest X-ray (CXR) images from chest radiography could be used in artificial intelligence (AI) approaches to diagnose COVID-19 with excellent diagnostic accuracy. In this research, a new support vector machine kernel (SVM Kernel) and convolutional neural network (CNN) combination is suggested to classify COVID-19 using X-ray images. The fact that there are relatively few studies in the literature that provide novel solutions, particularly for regression issues, the goal of this study is to look into the creation of new SVM kernels. To categorize CXR pictures into the three categories of COVID-19, pneumonia, and normal utilizing pre-trained CNN models such as AlexNet, ResNet50, ResNet101, VGG-16, and VGG-19, this study proposes a revolutionary SVM Kernel. The results of the suggested approach show that the updated SVM Kernel may be used as a more effective forecasting tool. ResNet50 offers the greatest accuracy 96.2% and produces the best optimization results in a very short amount of time.

Abstract

Brain tumors are a threat to life for every other human being, be it adults or children. Gliomas are one of the deadliest brain tumors with an extremely difficult diagnosis. The reason is their complex and heterogenous structure which gives rise to subjective as well as objective errors. Their manual segmentation is a laborious task due to their complex structure and irregular appearance. To cater to all these issues, a lot of research has been done and is going on to develop AI-based solutions that can help doctors and radiologists in the effective diagnosis of gliomas with the least subjective and objective errors, but an end-to-end system is still missing. An all-in-one framework has been proposed in this research. The developed end-to-end multi-task learning (MTL) architecture with a feature attention module can classify, segment, and predict the overall survival of gliomas by leveraging task relationships between similar tasks. Uncertainty estimation has also been incorporated into the framework to enhance the confidence level of healthcare practitioners. Extensive experimentation was performed by using combinations of MRI sequences. Brain tumor segmentation (BraTS) challenge datasets of 2019 and 2020 were used for experimental purposes. Results of the best model with four sequences show 95.1% accuracy for classification, 86.3% dice score for segmentation, and a mean absolute error (MAE) of 456.59 for survival prediction on the test data. It is evident from the results that deep learning–based MTL models have the potential to automate the whole brain tumor analysis process and give efficient results with least inference time without human intervention. Uncertainty quantification confirms the idea that more data can improve the generalization ability and in turn can produce more accurate results with less uncertainty. The proposed model has the potential to be utilized in a clinical setup for the initial screening of glioma patients.

Abstract

This study focuses on how recent global crises such as the COVID-19 pandemic and the Russia–Ukraine war have affected the relationship between the U.S. and Chinese agricultural futures markets. By applying wavelet coherence analysis (WCA) and time-varying parameter vector autoregression (TVP-VAR), we obtain the following findings. First, both events have changed the correlation and lead–lag comovement between U.S. and Chinese soybean and corn futures returns but have little impact on the comovement between the two cotton futures returns. Second, U.S. agricultural markets transmit more volatility risk to Chinese markets than the risk spillover from the reverse direction. Third, the risk spillover enhancement effect from the war is stronger than that from the pandemic, which is obvious in both the soybean and corn futures markets but not in the cotton market. Our paper has implications for policy makers seeking to stabilize agricultural commodity prices during global crisis episodes and for designing strategies for cross-market hedging of spillover risks among commodity markets for international investors.

Abstract

Background

Near work is generally considered as a risk factor for myopia onset and progression. This study aimed to investigate the choroidal responses to a brief-period of near work in children and young adults.

Methods

Thirty myopic medical students (aged 18–28 years) and 30 myopic children (aged 8–12 years) participated in this study. The submacular total choroidal area (TCA), luminal area (LA), stromal area (SA), choroidal vascularity index (CVI) and choriocapillaris flow deficit (CcFD), as well as subfoveal choroidal thickness (SFCT) were measured with swept-source optical coherence tomography/optical coherence tomography angiography (SS-OCT/OCTA) before and immediately after 20 min, 40 min, 60 min of near work at a distance of 33 cm.

Results

In adults, 20 min of near work induced a significant reduction in SFCT (− 5.1 ± 6.5 μm), LA [(− 19.2 ± 18.6) × 10³ μm²], SA [(− 8.2 ± 12.6) × 10³ μm²] and TCA [(− 27.4 ± 24.9) × 10³ μm²] (all P < 0.01). After 40 min of near work, LA was still reduced [(− 9.4 ± 18.3) × 10³ μm²], accompanied with a decreased CVI (− 0.39% ± 0.70%) and an increased CcFD (0.30% ± 0.78%) (all P < 0.05). After 60 min of near work, CVI was still reduced (− 0.28% ± 0.59%), and CcFD was still increased (0.37% ± 0.75%) (all P < 0.05). In children, 20 min of near work induced a significant increase in CcFD (0.55% ± 0.64%), while 60 min of near work induced increases in SA [(7.2 ± 13.0) × 10³ μm²] and TCA [(9.7 ± 25.3) × 10³ μm²] and a reduction in CVI (− 0.28% ± 0.72%) (all P < 0.05). Children exhibited lower near work-induced LA and TCA reduction than adults, with a mean difference of − 0.86% and − 0.82%, respectively (all P < 0.05).

Conclusions

The temporal characteristics and magnitude of changes of choroidal vascularity and choriocapillaris perfusion during near work was not identical between children and adults. The initial response to near work was observed in choriocapillaris in children, whereas it was observed in the medium- and large-sized vessels in adults.

Trial registration: Clinical Trial Registry (ChiCTR), ChiCTR2000040205. Registered on 25 November 2020, https://www.chictr.org.cn/bin/project/edit?pid=64501.

Abstract

Background

Linear fuel breaks are being implemented to moderate fire behavior and improve wildfire containment in semiarid landscapes such as the sagebrush steppe of North America, where extensive losses in perennial vegetation and ecosystem functioning are resulting from invasion by exotic annual grasses (EAGs) that foster large and recurrent wildfires. However, fuel-break construction can also pose EAG invasion risks, which must be weighed against the intended fire-moderation benefits of the treatments. We investigated how shrub reductions (mowing, cutting), pre-emergent EAG-herbicides, and/or drill seedings of fire-resistant perennial bunchgrasses (PBGs) recently applied to create a large fuel-break system affected native and exotic plant abundances and their associated fuel loading and predicted fire behavior.

Results

In heavily EAG-invaded areas, herbicides reduced EAG and total herbaceous cover without affecting PBGs for 2–3 years and reduced predicted fire behavior for 1 year (from the Fuel Characteristic Classification System). However, surviving post-herbicide EAG cover was still > 30%, which was sufficient fuel to exceed the conventional 1.2-m-flame length (FL) threshold for attempting wildfire suppression with hand tools. In less invaded shrubland, shrub reduction treatments largely reduced shrub cover and height by ~ half without increasing EAGs, but then redistributed the wood to ground level and increased total herbaceous cover. Herbicides and/or drill seeding after shrub reductions did not affect EAG cover, although drill seedings increased PBG cover and exotic forbs (e.g., Russian thistle). Fire behavior was predicted to be moderated in only one of the many yearly observations of the various shrub-reduction treatment combinations. Over all treatments and years, FLs were predicted to exceed 1.2 m in 13% of simulations under average (11 km h⁻¹) or high (47 km h⁻¹) wind speed conditions and exceed the 3.4-m threshold for uncontrollable fire in 11% of simulations under high-wind speeds only.

Conclusions

Predicted fire-moderation benefits over the first 4 years of fuel break implementation were modest and variable, but, generally, increases in EAGs and their associated fire risks were not observed. Nonetheless, ancillary evidence from shrublands would suggest that treatment-induced shifts from shrub to herbaceous fuel dominance are expected to improve conditions for active fire suppression in ways not readily represented in available fire models.

Navigating China's 2024 Regulations on Cross-Border Data Transfers  Lexology

Why a USTR Report Represents Another Step Back for Digital Trade  Disruptive Competition Project

China Released New Regulations to Ease Requirements for Outbound Cross-Border Data Transfers  White & Case LLP