Efficient content distribution on source constraint networks: peer communication

Popular applications such as P2P file sharing, multiplayer gaming, videoconferencing, etc. rely on the efficiency of content distribution from a single source to multiple receivers. Most users of these applications are on the widely prevalent source constraint networks such as the Digital Subscriber Line (DSL) and wireless networks. Overlay multicast techniques are widely employed to address this issue. However, most approaches do not consider the source constraint nature of the underlying network nor do they achieve optimal system throughput in the overlay structure. We propose various topologies in which all participating nodes contribute their bandwidth, resulting in high overall system throughput. We implement Hypp, a hybrid P2P system, based on the proposed topology. Hypp consists of one or more Supernodes that perform mesh and node management functions, and participating Peers, connected together in a structured manner as directed by a Supernode. This work focuses on the Peer subsystem that is responsible for communication and actual content delivery. We present results of experiments conducted over PlanetLab nodes that prove Hypp is a scalable and high throughput system capable of synchronous and efficient content delivery

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Continuous Freezecasting

In electric vehicle manufacturing, battery electrodes are made using roll-to-roll manufacturing, a continuous process in which an electrode mixture is moved along a roller-based processing line. It is efficient and cost-effective. Freeze casting is a process used to manufacture ceramic materials by using a temperature gradient to cast. The gradient aligns the grains in such a way that makes the material very conductive. The goal of this project is to design a mechanism to allow continuous freeze casting to be used in electrode manufacturing.Wenda TanUM Mechanical Engineering departmenthttp://deepblue.lib.umich.edu/bitstream/2027.42/192012/1/UM_Tan_F23_Team08_Continous-Freeze-Casting.pd

EFFICIENT VIDEO DISSEMINATION IN STRUCTURED HYBRID P2P NETWORKS

In this paper, we propose a structured hybrid P2P Mesh for optimal video dissemination from a single source node to multiple receivers in a bandwidth-asymmetric network such as Digital Subscriber Line (DSL) access network. Our hybrid P2P structured mesh consists of one or more Supernodes responsible for node and mesh management and a large number of streaming nodes, Peers. The peers are interconnected in a special manner designed for streaming and realtime video dissemination and are responsible for the actual data delivery. Our proposed hybrid P2P structured mesh is designed to achieve scalability, low delay and high throughput. Our experimental Internet-wide system consisting of PlanetLab nodes demonstrates the aforementioned qualities. 2

Artificial Intelligence Guided De Novo Molecular Design Targeting COVID-19

An extensive search for active therapeutic agents against the SARS-CoV-2 is being conducted across the globe. Computational docking simulations have traditionally been used for in silico ligand design and remain popular method of choice for high-throughput screening of therapeutic agents in the fight against COVID-19. Despite the vast chemical space (millions to billions of biomolecules) that can be potentially explored as therapeutic agents, we remain severely limited in the search of candidate compounds owing to the high computational cost of these ensemble docking simulations employed in traditional in silico ligand design. Here, we present a de novo molecular design strategy that leverages artificial intelligence to discover new therapeutic biomolecules against SARS-CoV-2. A Monte Carlo Tree Search algorithm combined with a multi-task neural network (MTNN) surrogate model for expensive docking simulations and recurrent neural networks (RNN) for rollouts, is used to sample the exhaustive SMILES space of candidate biomolecules. Using Vina scores as target objective to measure binding of therapeutic molecules to either the isolated spike protein (S-protein) of SARS-CoV-2 at its host receptor region or to the S-protein:Angiotensin converting enzyme 2 (ACE2) receptor interface, we generate several (~100\u27s) new biomolecules that outperform FDA (~1000’s) and non-FDA biomolecules (~million) from existing databases. A transfer learning strategy is deployed to retrain the MTNN surrogate as new candidate molecules are identified - this iterative search and retrain strategy is shown to accelerate the discovery of desired candidates. We perform detailed analysis using Lipinski\u27s rules and also analyze the structural similarities between the various top performing candidates. We spilt the molecules using a molecular fragmenting algorithm and identify the common chemical fragments and patterns – such information is important to identify moieties that are responsible for improved performance. Although we focus on therapeutic biomolecules, our AI strategy is broadly applicable for accelerated design and discovery of any chemical molecules with user-desired functionality

OTONet: Deep Neural Network for Precise Otoscopy Image Classification

Otoscopy is a diagnostic procedure to visualize the external ear canal and eardrum, facilitating the detection of various ear pathologies and conditions. Timely otoscopy image classification offers significant advantages, including early detection, reduced patient anxiety, and personalized treatment plans. This paper introduces a novel OTONet framework specifically tailored for otoscopy image classification. It leverages octave 3D convolution and a combination of feature and region-focus modules to create an accurate and robust classification system capable of distinguishing between various otoscopic conditions. This architecture is designed to efficiently capture and process the spatial and feature information present in otoscopy images. Using a public otoscopy dataset, OTONet has reached a classification accuracy of 99.3% and an F1 score of 99.4% across 11 classes of ear conditions. A comparative analysis demonstrates that OTONet surpasses other established machine learning models, including ResNet50, ResNet50v2, VGG16, Dense-Net169, and ConvNeXtTiny, across various evaluation metrics. The research’s contribution to improved diagnostic accuracy reduced human error, expedited diagnostics, and its potential for telemedicine applications

Tetrahydrocannabinol Reduces Hapten-Driven Mast Cell Accumulation and Persistent Tactile Sensitivity in Mouse Model of Allergen-Provoked Localized Vulvodynia

Vulvodynia is a remarkably prevalent chronic pain condition of unknown etiology. An increase in numbers of vulvar mast cells often accompanies a clinical diagnosis of vulvodynia and a history of allergies amplifies the risk of developing this condition. We previously showed that repeated exposures to oxazolone dissolved in ethanol on the labiar skin of mice led to persistent genital sensitivity to pressure and a sustained increase in labiar mast cells. Here we sensitized female mice to the hapten dinitrofluorobenzene (DNFB) dissolved in saline on their flanks, and subsequently challenged them with the same hapten or saline vehicle alone for ten consecutive days either on labiar skin or in the vaginal canal. We evaluated tactile ano-genital sensitivity, and tissue inflammation at serial timepoints. DNFB-challenged mice developed significant, persistent tactile sensitivity. Allergic sites showed mast cell accumulation, infiltration of resident memory CD8+CD103+ T cells, early, localized increases in eosinophils and neutrophils, and sustained elevation of serum Immunoglobulin E (IgE). Therapeutic intra-vaginal administration of Δ9-tetrahydrocannabinol (THC) reduced mast cell accumulation and tactile sensitivity. Mast cell-targeted therapeutic strategies may therefore provide new ways to manage and treat vulvar pain potentially instigated by repeated allergenic exposures