Implication of Bariatric Surgery for Breastfeeding: Maternal Nutrition, Milk Composition, and Milk Production - A Care Guide Review for Registered Dietitians and Lactation Consultants

Breastfeeding is a norm for a woman who is generally healthy. According to WHO, CDC and AAP, exclusive breastfeeding is recommended for infants aged 0-6 months. Given the dramatically increased prevalence of obesity and subsequent bariatric surgery in the United States and the patients’ rising concerns to sustain breastfeeding, researchers identified maternal nutrition, breast milk production and its nutrient composition as the most concerning aspects, which will be covered in detail in the paper. Both obesity and bariatric surgery might be negatively associated with breastfeeding, yet a better lactation outcome is still achievable with professional assistance, particularly from RDs and IBCLCs. Yet there’s limited accessible compiled resources in the field, it’s imperative to have such guidelines to serve as a reference for healthcare professionals (RD, IBCLC, etc.) who are responsible for providing evidence-based nutrition support and lactation assistance for breastfeeding parents who have undergone bariatric surgery.Master of Public Healt

The Greenhouse Gas Emission from Portland Cement Concrete Pavement Construction in China.

This study proposes an inventory analysis method to evaluate the greenhouse gas (GHG) emissions from Portland cement concrete pavement construction, based on a case project in the west of China. The concrete pavement construction process was divided into three phases, namely raw material production, concrete manufacture and pavement onsite construction. The GHG emissions of the three phases are analyzed by a life cycle inventory method. The CO₂e is used to indicate the GHG emissions. The results show that for 1 km Portland cement concrete pavement construction, the total CO₂e is 8215.31 tons. Based on the evaluation results, the CO₂e of the raw material production phase is 7617.27 tons, accounting for 92.7% of the total GHG emissions; the CO₂e of the concrete manufacture phase is 598,033.10 kg, accounting for 7.2% of the total GHG emissions. Lastly, the CO₂e of the pavement onsite construction phase is 8396.59 kg, accounting for only 0.1% of the total GHG emissions. The main greenhouse gas is CO₂ in each phase, which accounts for more than 98% of total emissions. N₂O and CH₄ emissions are relatively insignificant

Generative Multi-Agent Behavioral Cloning

We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative, i.e., non-deterministic, multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces by introducing a hierarchy with macro-intent variables that encode long-term intent. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, and yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density

Generating Multi-Agent Trajectories using Programmatic Weak Supervision

We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts

Anti-VEGF medicine with PRP for neovascular glaucoma

AIM:To investigate the effect of anti-vascular endothelial growth factor(VEGF)combined with pan retinal photocoagulation(PRP)on neovascular glaucoma(NVG)and its effect on VEGF and platelet-derived growth factor-C(PDGF-C)in aqueous humor. METHODS: A total of 90 patients with NVG(93 eyes)who underwent examination and treatment in our hospital from November 2016 to November 2017 were randomly divided into control group and observation group. The control group was treated with PRP, and the observation group was treated with laser photocoagulation combined with vitreous injection of ranibizumab. The clinical efficacy, iris neovascularization and visual recovery were compared between the two groups after treatment. And we compared the retinal vein circulation time, intraocular pressure, retinal nerve fiber layer thickness, visual field defect value, VEGF and PDGF-C levels in aqueous humor and adverse reactions before and after treatment. RESULTS: At 1mo after treatment, the clinical efficacy, iris neovascularization and visual recovery were better than the control group(PPP>0.05). CONCLUSION: The use of PRP combined with anti-VRGF drugs for NVG can inhibit angiogenesis and restore retinal function more effectively, which may be better because the combination therapy has better down-regulation of VEGF and PDGF-C

Leveraging BEV Representation for 360-degree Visual Place Recognition

This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task

Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories

We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared to conventional text-based retrieval settings. Most notably, the data is fine-grained meaning that the specific location of agents is important in describing behavior. Additionally, the data often contains tracks of multiple agents (e.g., multiple players in a sports game), which generally leads to a permutational alignment problem when performing relevance estimation. Due to the frequent position swap of agents, it is difficult to maintain the correspondence of agents, and such issues make the pairwise comparison problematic for multi-agent spatiotemporal data. To address this issue, we propose a tree-based method to estimate the relevance between multi-agent spatiotemporal tracks. It uses a hierarchical structure to perform multi-agent data alignment and partitioning in a coarse-to-fine fashion. We validate our approach via user studies with domain experts. Our results show that our method boosts performance in retrieving similar sports plays -- especially in interactive situations where the user selects a subset of trajectories compared to current state-of-the-art methods