Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss

The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been proposed to deal with long-tailed data or noisy data, while a few methods are developed to tackle long-tailed noisy data. To solve this, we propose a robust method for learning from long-tailed noisy data with sample selection and balanced loss. Specifically, we separate the noisy training data into clean labeled set and unlabeled set with sample selection, and train the deep neural network in a semi-supervised manner with a novel balanced loss based on model bias. Experiments on benchmarks demonstrate that our method outperforms existing state-of-the-art methods

Buccal Transmucosal Delivery System of Enalapril for Improved Cardiac Drug Delivery: Preparation and Characterization

Purpose: To prepare and characterize buccal transmucosal delivery system of enalapril maleate for overcoming its low bioavailability, and hence provide improved therapeutic efficacy and patient compliance.Methods: Transmucosal drug delivery systems of enalapril maleate were formulated as buccal films by solvent casting technique using polyvinylpyrrolidone K90, hydroxypropyl methylcellulose, sodium carboxymethylcellulose (high viscosity). The films were evaluated for film weight, thickness, folding endurance, drug content uniformity, surface pH, in vitro residence time, in vitro drug release and ex-vivo permeation.Results: All the formulations showed high drug content (96.45 to 98.49 %). Those with good swelling showed good residence time. In vitro drug release was highest for films prepared with high viscosity grade sodium carboxymethylcellulose (SCMC- HV,F2), releasing 92.24 % of drug in 1.5 h) followed by F4 (containing polyvinyl pyrrolidone K-90 1 % w/v and SCMC (HV) 1 % w/v). Ex-vivo drug permeation at the end of 10 h was 82.24 and 89.9 % for F2 and F4, respectively.Conclusion: Prompt drug release was obtained from the formulation (F2) containing SCMC 2 % w/v with 10 mg enalapril. However, on the basis of the highest swelling and residence time, and controlled drug release, formulation F4 (containing PVP K-90 and SCMC HV) would be suitable for the development of buccal film for effective therapy of cardiac diseases.Keywords: Cardiac disease, Transmucosal, Buccal films, Enalapril maleate, Drug release, Ex-vivo permeatio

The physical origin of the periodic activity for FRB 20180916B

Fast radio bursts (FRBs) are transient radio signals with millisecond-duration, large dispersion measure (DM) and extremely high brightness temperature. Among them, FRB 20180916B has been found to have a 16-day periodic activity. However, the physical origin of the periodicity is still a mystery. Here, we utilize the comprehensive observational data to diagnose the periodic models. We find that the ultra-long rotation model is the most probable one for the periodic activity. However, this model cannot reproduce the observed rotation measure (RM) variations. We propose a self-consistent model, i.e., a massive binary containing a slowly rotational neutron star and a massive star with large mass loss, which can naturally accommodate the wealth of observational features for FRB 20180916B. In this model, the RM variation is periodic, which can be tested by future observations.Comment: 12 pages, 8 figure

Interactions of Bacteria with Monolithic Lateral Silicon Nanospikes Inside a Microfluidic Channel

This paper presents a new strategy of integrating lateral silicon nanospikes using metal-assisted chemical etching (MacEtch) on the sidewall of micropillars for on-chip bacterial study. Silicon nanospikes have been reported to be able to kill bacteria without using chemicals and offer a new route to kill bacteria and can prevent the overuse of antibiotics to reduce bacteria. We demonstrated a new methodology to fabricate a chip with integrated silicon nanospikes onto the sidewalls of micropillars inside the microfluidic channel and attested its interactions with the representative gram-negative bacteria Escherichia coli. The results of colony-forming unit (CFU) calculation showed that 80% bacteria lost their viability after passing through the chip. Moreover, the results of adenosine triphosphate (ATP) measurement indicated that the chip with lateral silicon nanospikes could extract more than two times ATP contents compared with the chip without lateral silicon nanospikes, showing potential for using the chip with lateral silicon nanospikes as a bacterial lysing module

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction.Comment: Project page: https://lakonik.github.io/ssdner

Using LDDMM and a kinematic cardiac growth model to quantify growth and remodelling in rat hearts under PAH

Pulmonary arterial hypertension (PAH) is a rapidly progressive and fatal disease, with right ventricular failure being the primary cause of death in patients with PAH. This study aims to determine the mechanical stimuli that may initiate heart growth and remodelling (G&R). To achieve this, two bi-ventricular models were constructed: one for a control rat heart and another for a rat heart with PAH. The growth of the diseased heart was estimated by warping it to the control heart using an improved large deformation diffeomorphic metric mapping (LDDMM) framework. Correlation analysis was then performed between mechanical cues (stress and strain) and growth tensors, which revealed that principal strains may serve as a triggering stimulus for myocardial growth and remodelling under PAH. The growth tensors, estimated from in vivo images, could explain 84.3% of the observed geometrical changes in the diseased heart with PAH by using a kinematic cardiac growth model. Our approach has the potential to quantify G&R using sparse in vivo images and to provide insights into the underlying mechanism of triggering right heart failure from a biomechanical perspective