The Neural Networks Based Needle Detection for Medical Retinal Surgery

In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets

Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue

Tagged magnetic resonance imaging (MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues. Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.Comment: Accepted to MIDL 2023 (full paper

Effects of a Single Bout of Endurance Exercise on Brain-Derived Neurotrophic Factor in Humans: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

We aimed to investigate the impact of a single bout of endurance exercise on the brain-derived neurotrophic factor (BDNF) in humans and analyze how a single bout of endurance exercise impacts the peripheral BDNF types by age group. We performed a systematic literature review by searching PubMed, Elsevier, and Web of Science for studies that included a single bout of endurance exercise in the experimental group and other exercise types in the control group. Eight interventions were included in the study. Overall, a single bout of endurance exercise significantly increased BDNF expression (SMD = 0.30; 95% CI = [0.08, 0.52]; p = 0.001), which was confirmed in the serum BDNF (SMD = 0.30; 95% CI = [0.04, 0.55]; p p = 0.017). The serum and plasma BDNF levels significantly increased regardless of age (SMD = 0.35; 95% CI = [0.11, 0.58]; p = 0.004; I2 = 0%). In conclusion, a single bout of endurance exercise significantly elevates BDNF levels in humans without neurological disorders, regardless of age. The serum BDNF is a more sensitive index than the plasma BDNF in evaluating the impact of a single bout of endurance exercise on the BDNF

Design of a One-Stop Platform for on-Demand Supply of Biomass Pellet Fuel in China

China is currently rich in biomass resources, and is in a period of energy transition, the development of biomass is the general trend. However, the development of biomass pellet fuel is still immature and faces many obstacles. At this stage, the sales of biomass pellet fuel has problems such as unclear parameters or insufficient coverage. Therefore, it is proposed to build a one-stop platform to promote the development of biomass fuel industry by using modern means to widely collect information on pellet fuel, calculate the reduction potential of pellet fuel, and build an online platform to reduce the information difference. This paper constructs a database by obtaining a large amount of information on biomass fuels through questionnaires, telephone consultations and access to information on websites. Based on the established database, Jupyter was used to analyze the number of fuel types, the provincial contribution, the fuel price distribution and the calorific value distribution. Among them, Guangdong, Shandong, Jiangsu, Hebei and other large grain provinces have the highest contribution of biomass fuels; most of the fuel types are mainly wood fuels, with wood chips, pine and redwood being the main types; the calorific value of fuels is mainly concentrated in the range of 3800-4600kcal/kg; and the price of fuels is mainly concentrated in the range of 600-1000 RMB. In order to reflect the cleanliness of biomass fuels, the article accounts for the carbon emissions of biomass fuel substitutes at different stages of their life cycle, with the aim of providing a reference for users. Eventually a one-stop platform for supply and demand of biomass fuel was established based on a database and carbon accounting methodology. The idea is to import the database into the backend of the WeChat app for retrieval, display it on the front end and sort it according to the user's choice, and give an estimate of emission reductions based on the user's purchase volume and transport distance

Global Control of CH₃NH₃PbI₃ Formation with Multifunctional Ionic Liquid for Perovskite Hybrid Photovoltaics

Here, a simple strategy using 1-butyl-3-methylimidazolium iodide (BMII) as a multifunctional additive was employed to globally modify the two-step deposition process of the perovskite film. Morphological, structural, and spectral analyses showed that the BMII additive could coordinate with PbI₂ and thereby retarded the reaction of PbI₂ and CH₃NH₃I through the ionic exchange process. Moreover, the residual BMII provided a liquid domain to promote the coarsening of the perovskite crystal during the thermal annealing process. Thus, the obtained MAPbI₃ film preferred low PbI₂ residue, high-quality crystallization, and large-grained microstructure. Using films prepared with BMII additives, the maximum power conversion efficiency of the solar cells was improved from 12.6% of the reference cell to 15.6%. The present study gives a reproductive and facile strategy toward high quality of perovskite thin films and efficient solar cells

Mitochondrial Transfer Regulates Cell Fate Through Metabolic Remodeling in Osteoporosis

Abstract Mitochondria are the powerhouse of eukaryotic cells, which regulate cell metabolism and differentiation. Recently, mitochondrial transfer between cells has been shown to direct recipient cell fate. However, it is unclear whether mitochondria can translocate to stem cells and whether this transfer alters stem cell fate. Here, mesenchymal stem cell (MSC) regulation is examined by macrophages in the bone marrow environment. It is found that macrophages promote osteogenic differentiation of MSCs by delivering mitochondria to MSCs. However, under osteoporotic conditions, macrophages with altered phenotypes, and metabolic statuses release oxidatively damaged mitochondria. Increased mitochondrial transfer of M1‐like macrophages to MSCs triggers a reactive oxygen species burst, which leads to metabolic remodeling. It is showed that abnormal metabolism in MSCs is caused by the abnormal succinate accumulation, which is a key factor in abnormal osteogenic differentiation. These results reveal that mitochondrial transfer from macrophages to MSCs allows metabolic crosstalk to regulate bone homeostasis. This mechanism identifies a potential target for the treatment of osteoporosis

Integrating genetics and metabolomics from multi- ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk