AU-PD: An Arbitrary-size and Uniform Downsampling Framework for Point Clouds

Point cloud downsampling is a crucial pre-processing operation to downsample the points in the point cloud in order to reduce computational cost, and communication load, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not perform arbitrary-size sampling directly. Moreover, their sampled results always comprise many overlapping points. In this paper, we introduce the AU-PD, a novel task-aware sampling framework that directly downsamples point cloud to any smaller size based on a sample-to-refine strategy. Given a specified arbitrary size, we first perform task-agnostic pre-sampling to sample the input point cloud. Then, we refine the pre-sampled set to make it task-aware, driven by downstream task losses. The refinement is realized by adding each pre-sampled point with a small offset predicted by point-wise multi-layer perceptrons (MLPs). In this way, the sampled set remains almost unchanged from the original in distribution, and therefore contains fewer overlapping cases. With the attention mechanism and proper training scheme, the framework learns to adaptively refine the pre-sampled set of different sizes. We evaluate sampled results for classification and registration tasks, respectively. The proposed AU-PD gets competitive downstream performance with the state-of-the-art method while being more flexible and containing fewer overlapping points in the sampled set. The source code will be publicly available at https://zhiyongsu.github.io/Project/AUPD.html

Potential Reductions in Greenhouse Gas and Fine Particulate Matter Emissions Using Corn Stover for Ethanol Production in China

Corn stover is an abundant raw material that can be used to produce ethanol and reduce air pollution. This paper studied the potential reductions in greenhouse gas (GHG) and fine particulate matter (PM2.5) emissions across China if corn stover was used for ethanol production. Field surveys in nine provincial regions were conducted. Life-cycle assessment (LCA) was used to assess the GHG and PM2.5 emissions from a corn stover based ethanol system. The LCA system boundaries included several process stages from corn planting to ethanol fuel used in vehicles. Corn stover geographical distributions and emission reduction factors were combined. Results showed that the total surplus quantity of corn stover in China was 86.2 million metric tons (Mt) in 2015. It was sufficient to reach the ethanol production target set by the Chinese government. In the scenario that 38.5 Mt or 44.6% of corn stover surplus were used for ethanol production, the total potential emission reductions were 36.5 Mt CO2-eq GHG and 450.9 kt PM2.5. Among the 31 provincial regions in China, the reduction potentials varied from 0.001 to 8.9 Mt CO2-eq for GHG and from 0.013 to 109.7 kt for PM2.5. This study provided useful information to policy makers, researchers and industry managers who work on environmental control and corn stover management

MicroRNA-96 Promotes Schistosomiasis Hepatic Fibrosis in Mice by Suppressing Smad7

Infection with Schistosoma causes aberrant expression of host microRNAs (miRNAs), and normalizing the levels of dysregulated miRNAs can attenuate pathology. Here, we show that the host miRNA, miR-96, is markedly upregulated during the progression of hepatic schistosomiasis. We demonstrate that elevation of miR-96 induces hepatic fibrosis in infected mice by suppressing the expression of its target gene, Smad7. We show that infection with Schistosoma induces the expression of transforming growth factor beta1 (TGF-beta1), which in turn upregulates the expression of miR-96 through SMAD2/3-DROSHA-mediated post-transcriptional regulation. Furthermore, inhibition of miR-96 with recombinant adeno-associated virus 8 (rAAV8)-mediated delivery of Tough Decoy RNAs in mice attenuated hepatic fibrosis and prevented lethality following schistosome infection. Taken together, our data highlight the potential for rAAV8-mediated inhibition of miR-96 as a therapeutic strategy to treat hepatic schistosomiasis

Impact of honey on radiotherapy-induced oral mucositis in patients with head and neck cancer: A systematic review and meta-analysis

© Annals of Palliative Medicine. Background: Oral mucositis is one of the most frequent, irreversible and distressing complications faced by head and neck cancer (HNC) patients undergoing radiotherapy. Several studies have investigated the role of honey in the prevention and alleviation of radiation-induced oral mucositis in HNC patients, however, a definitive conclusion has not yet been generated. We performed this updated systematic review and metaanalysis to determine whether honey can prevent and alleviate radiation-induced oral mucositis in HNC patients. Methods: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) through October 2019. We searched and selected literature, extracted data and assessed risk of bias accordingly, and then conducted statistical analyses with RevMan software version 5.3. Results: Seven trials involving 412 patients were included in the final analysis. Meta-analyses showed that honey did not decrease the incidence of radiation-induced oral mucositis [(relative risk (RR), 0.69; 95% confidence interval (CI), 0.40-1.18; P=0.18]; however, relieved the severity of oral mucositis (RR, 0.22; 95% CI, 0.13-0.38; P \u3c 0.001), maintained or increased weight (RR, 1.92; 95% CI, 1.33-2.77; P \u3c 0.001) and reduced the treatment interruption related to oral mucositis (RR, 0.13; 95% CI, 0.02-0.97; P=0.05). Qualitative analysis also revealed a decreased incidence of oral mucositis in the honey group. Conclusions: Based on limited evidence, honey may have a clinical benefit against radiation-induced oral mucositis in HNC patients. However, future trials with large-scale and rigorous methods are warranted to further establish the role of honey in the management of radiation-induced oral mucositis

Learning to Reweight with Deep Interactions

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.Comment: Accepted to AAAI-202

Preliminary investigation of the diagnosis and gene function of deep learning PTPN11 gene mutation syndrome deafness

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public’s view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children’s Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1–6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6–12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research

Dual-constraint coarse-to-fine network for camouflaged object detection

Camouflaged object detection (COD) is an important yet challenging task, with great application values in industrial defect detection, medical care, etc. The challenges mainly come from the high intrinsic similarities between target objects and background. In this paper, inspired by the biological studies that object detection consists of two steps, i.e., search and identification, we propose a novel framework, named DCNet, for accurate COD. DCNet explores candidate objects and extra object-related edges through two constraints (object area and boundary) and detects camouflaged objects in a coarse-to-fine manner. Specifically, we first exploit an area-boundary decoder (ABD) to obtain initial region cues and boundary cues simultaneously by fusing multi-level features of the backbone. Then, an area search module (ASM) is embedded into each level of the backbone to adaptively search coarse regions of objects with the assistance of region cues from the ABD. After the ASM, an area refinement module (ARM) is utilized to identify fine regions of objects by fusing adjacent-level features with the guidance of boundary cues. Through the deep supervision strategy, DCNet can finally localize the camouflaged objects precisely. Extensive experiments on three benchmark COD datasets demonstrate that our DCNet is superior to 12 state-of-the-art COD methods. In addition, DCNet shows promising results on two COD-related tasks, i.e., industrial defect detection and polyp segmentation