RGB-T salient object detection via fusing multi-level CNN features

RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast

Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program

BackgroundArtificial intelligence breast ultrasound diagnostic system (AIBUS) has been introduced as an alternative approach for handheld ultrasound (HHUS), while their results in BI-RADS categorization has not been compared.MethodsThis pilot study was based on a screening program conducted from May 2020 to October 2020 in southeast China. All the participants who received both HHUS and AIBUS were included in the study (N = 344). The ultrasound videos after AIBUS scanning were independently watched by a senior radiologist and a junior radiologist. Agreement rate and weighted Kappa value were used to compare their results in BI-RADS categorization with HHUS.ResultsThe detection rate of breast nodules by HHUS was 14.83%, while the detection rates were 34.01% for AIBUS videos watched by a senior radiologist and 35.76% when watched by a junior radiologist. After AIBUS scanning, the weighted Kappa value for BI-RADS categorization between videos watched by senior radiologists and HHUS was 0.497 (p < 0.001) with an agreement rate of 78.8%, indicating its potential use in breast cancer screening. However, the Kappa value of AIBUS videos watched by junior radiologist was 0.39, when comparing to HHUS.ConclusionAIBUS breast scan can obtain relatively clear images and detect more breast nodules. The results of AIBUS scanning watched by senior radiologists are moderately consistent with HHUS and might be used in screening practice, especially in primary health care with limited numbers of radiologists

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Image captioning with memorized knowledge

Image captioning, which aims to automatically generate text description of given images, has received much attention from researchers. Most existing approaches adopt a recurrent neural network (RNN) as a decoder to generate captions conditioned on the input image information. However, traditional RNNs deal with the sequence in a recurrent way, squeezing the information of all previous words into hidden cells and updating the context information by fusing the hidden states with the current word information. This may miss the rich knowledge too far in the past. In this paper, we propose a memory-enhanced captioning model for image captioning. We firstly introduce an external memory to store the past knowledge, i.e., all the information of generated words. When predicting the next word, the decoder can retrieve knowledge information about the past by means of a selective reading mechanism. Furthermore, to better explore the knowledge stored in the memory, we introduce several variants that consider different types of past knowledge. To verify the effectiveness of the proposed model, we conduct extensive experiments and comparisons on the well-known image captioning dataset MS COCO. Compared with the state-of-the-art captioning models, the proposed memory-enhanced captioning model shows a significant improvement in terms of the performance (improving 3.5% in terms of CIDEr). The proposed memory-enhanced captioning model, as demonstrated in the experiments, is more effective and superior to the state-of-the-art methods

Production of Ultra-high Molecular Weight Poly-gamma-Glutamic Acid with Bacillus licheniformis P-104 and Characterization of its Flocculation Properties

A novel strain of Bacillus licheniformis P-104 was isolated from Chinese soybean paste to produce a bioflocculant. The bioflocculant was confirmed as ultra-high molecular weight poly-gamma-glutamic acid (gamma-PGA) using Fourier transform infrared spectrum, high-performance liquid chromatography, and gel permeation chromatography with multi-angle laser light scattering. The production technology and flocculation properties of gamma-PGA were investigated. By fed-batch fermentation in a 7-L bioreactor, the maximum gamma-PGA yield reached 41.6 g L-1 with a productivity rate of 1.07 g L-1 h(-1). The flocculating activity of gamma-PGA for kaolin suspension was 33.5 +/- 1.6 1/OD under the optimized flocculation conditions (6 mM Ca2+, 1.5 mg L-1 gamma-PGA, and pH 6.0). The optimized dosage of gamma-PGA for flocculation was just about 30 % of that of reported gamma-PGA produced by other strains. Moreover, the flocculation activity of gamma-PGA produced by strain P-104 was much higher than commercial gamma-PGA with the molecular weight ranging 200-500 kDa and 1,500-2,500 kDa. This study provided a promising strain and an efficient method for production of ultra-high molecular weight gamma-PGA which could be used as a potential green bioflocculant

Chinese herbal medicine bi min fang for allergic rhinitis: protocol for a double-blind, double-dummy, randomized controlled trial

Abstract Background People with allergic rhinitis (AR) often seek help from Chinese medicine due to dissatisfaction with conventional treatments. Lung-spleen qi deficiency syndrome (LSQDS) is the most common type of AR, and the Chinese herbal medicine formula bi min fang (BMF) is commonly prescribed for AR patients with LSQDS. However, direct evidence supporting its efficacy and safety is not available, and its potential mechanism of action remains unclear. Methods/design This paper presents a double-blind, double-dummy, randomized controlled trial. After a 2-week run-in period, 80 AR patients with LSQDS will be recruited and randomly allocated to the BMF group or the control group in a 1:1 ratio. The patients in the BMF group will receive BMF and the placebo for levocetirizine hydrochloride orally, while the control group participants will receive levocetirizine hydrochloride and the placebo for BMF orally. All participants will receive 4 weeks of treatment and 12 weeks of follow-up. The primary outcome is a change in the Total Nasal Symptom Score (TNSS). Secondary outcomes include changes in scores for the standard version of the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ(S)), and visual analog scale (VAS); changes in serum levels of the cytokines interleukin-4, interferon-γ, transforming growth factor β-1, and interleukin-17; and changes in the gut microbiota composition in the stool. The TNSS, RQLQ(S), and VAS will be recorded at the beginning of, middle of and after the treatment period and at the end of each month in the 3-month follow-up period. Blood and stool samples will be collected at baseline and the end of the treatment. The aforementioned four cytokines will be detected in the serum using enzyme-linked immunosorbent assays, and the stool gut microbiota will be detected using 16S ribosomal ribonucleic acid sequencing. Any side effects of the treatment will be recorded. Discussion The results of this trial will provide consolidated evidence of the effect of BMF on AR and the potential mechanism by which BMF acts. This study will be the first to explore the mechanism of action of Chinese herbal medicine on the gut microbiota in AR. Trial registration Chinese Clinical Trial Registry, ChiCTR-IPR-17010970. Registered on 23 March 2017

Strain ultrasonic elastography imaging features of locally advanced breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival

Abstract Background Due to the highly heterogeneity of the breast cancer, it would be desirable to obtain a non-invasive method to early predict the treatment response and survival outcome of the locally advanced breast cancer (LABC) patients undergoing neoadjuvant chemotherapy (NAC). This study aimed at investigating whether strain elastography (SE) can early predict the pathologic complete response (pCR) and recurrence-free survival (RFS) in LABC patients receiving NAC. Methods In this single-center retrospective study, 122 consecutive women with LABC who underwent SE examination pre-NAC and after one and two cycles of NAC enrolled in the SHPD001(NCT02199418) and SHPD002 (NCT02221999) trials between January 2014 and August 2017 were included. The SE parameters (Elasticity score, ES; Strain ratio, SR; Hardness percentage, HP, and Area ratio, AR) before and during NAC were assessed. The relative changes in SE parameters after one and two cycles of NAC were describe as ΔA1 and ΔA2, respectively. Logistic regression analysis and Cox proportional hazards model were used to identify independent variables associated with pCR and RFS. Results Forty-nine (40.2%) of the 122 patients experienced pCR. After 2 cycles of NAC, SR2 (odds ratio [OR], 1.502; P = 0.003) and ΔSR2 (OR, 0.013; P = 0.015) were independently associated with pCR, and the area under the receiver operating characteristic curve for the combination of them to predict pCR was 0.855 (95%CI: 0.779, 0.912). Eighteen (14.8%) recurrences developed at a median follow-up of 60.7 months. A higher clinical T stage (hazard ratio [HR] = 4.165; P = 0.005.), a higher SR (HR = 1.114; P = 0.002.) and AR (HR = 1.064; P <  0.001.) values at pre-NAC SE imaging were independently associated with poorer RFS. Conclusion SE imaging features have the potential to early predict pCR and RFS in LABC patients undergoing NAC, and then may offer valuable predictive information to guide personalized treatment