Pork carcass composition, meat and belly qualities as influenced by feed efficiency selection in replacement boars from Large White sire and dam lines

This study evaluated carcass attributes, meat and belly qualities in finisher boars (n = 79) selected for feed efficiency (low, intermediate and high) based on estimated breeding value for feed conversion ratio within a Large White dam and sire genetic lines. The sire line had lower trimmed fat proportions and higher lean than the dam line (P < 0.01). Genetic lines expressed slight colour changes and drip losses (P < 0.05), with no differences in pH, marbling and cooking traits (P > 0.05). High-efficient animals presented the highest lean yield (P < 0.01), the lowest trimmed fat proportion (P < 0.01) and no effect on meat and belly quality attributes (P > 0.05) compared with other efficient groups. Interaction between efficiency group and genetic line was only detected for belly weight and thickness (P < 0.01). High-efficient animals offer a greater leanness level, with minimal impact on meat and belly quality traits.

Effect of contact precautions on preventing meticillin-resistant Staphylococcus aureus transmission in intensive care units: a review and modelling study of field trials

BACKGROUND

Contact precautions (CPs) have been widely implemented in hospitals, particularly in intensive care units (ICUs), as the standard approach for managing meticillin-resistant Staphylococcus aureus (MRSA). However, the effectiveness of CPs in preventing MRSA transmission remains a subject of debate.

AIM

To assess the effectiveness of CPs in reducing MRSA transmission within ICUs.

METHODS

A comprehensive analysis was conducted using data from 16 sets of parameters collected from 13 field studies investigating the effectiveness of CPs in ICUs, and an epidemiologic model was developed to simulate the dynamics of MRSA incidence in the hospital setting.

FINDINGS

The analysis demonstrated a mean reduction of 20.52% (95% confidence interval -30.52 to 71.60%) in the MRSA transmission rate associated with the implementation of CPs. Furthermore, reducing the time-delay of screening tests and increasing the percentage of patients identified with MRSA through screening at admission were found to contribute to the effectiveness of CPs.

CONCLUSION

Proper implementation of CPs, with a particular emphasis on early identification of MRSA-colonized/infected patients, demonstrated a strong association with reduced MRSA transmission within the hospital setting. However, further research is necessary to investigate the effectiveness and safety of decolonization and other interventions used in conjunction with CPs to mitigate the risk of infection among colonized patients.

USBDAN: Unsupervised Scale-aware and Boundary-aware Domain Adaptive Network for Gastric Tumor Segmentation

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.

Gated CNN-Transformer Network for Automatic Cardiovascular Diagnosis using 12-lead Electrocardiogram

12-lead electrocardiogram (ECG) is a widely used method in the diagnosis of cardiovascular disease (CVD). With the increase in the number of CVD patients, the study of accurate automatic diagnosis methods via ECG has become a research hotspot. The use of deep learning-based methods can reduce the influence of human subjectivity and improve the diagnosis accuracy. In this paper, we propose a 12-lead ECG automatic diagnosis method based on channel features and temporal features fusion. Specifically, we design a gated CNN-Transformer network, in which the CNN block is used to extract signal embeddings to reduce data complexity. The dual-branch transformer structure is used to effectively extract channel and temporal features in low-dimensional embeddings, respectively. Finally, the features from the two branches are fused by the gating unit to achieve automatic CVD diagnosis from 12-lead ECG. The proposed end-to-end approach has more competitive performance than other deep learning algorithms, which achieves an overall diagnostic accuracy of 85.3% in the 12-lead ECG dataset of CPSC-2018.

Adversarial learning-based multi-level dense-transmission knowledge distillation for AP-ROP detection

The Aggressive Posterior Retinopathy of Prematurity (AP-ROP) is the major cause of blindness for premature infants. The automatic diagnosis method has become an important tool for detecting AP-ROP. However, most existing automatic diagnosis methods were with heavy complexity, which hinders the development of the detecting devices. Hence, a small network (student network) with a high imitation ability is exactly needed, which can mimic a large network (teacher network) with promising diagnostic performance. Also, if the student network is too small due to the increasing gap between teacher and student networks, the diagnostic performance will drop. To tackle the above issues, we propose a novel adversarial learning-based multi-level dense knowledge distillation method for detecting AP-ROP. Specifically, the pre-trained teacher network is utilized to train multiple intermediate-size networks (i.e., teacher-assistant networks) and one student network by dense transmission mode, where the knowledge from all upper-level networks is transmitted to the current lower-level network. To ensure that two adjacent networks can distill the abundant knowledge, the adversarial learning module is leveraged to enforce the lower-level network to generate the features that are similar to those of the upper-level network. Extensive experiments demonstrate that our proposed method can realize the effective knowledge distillation from the teacher to student networks. We achieve a promising knowledge distillation performance for our private dataset and a public dataset, which can provide a new insight for devising lightweight detecting systems of fundus diseases for practical use.

Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the l_2,p norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.

[Intestinal microbe Prevotella in pulmonary tuberculosis research]

Tuberculosis is a major global infectious disease that seriously endangers human health. Studies have shown that there will be an imbalance of intestinal microecology after infection with Mycobacterium tuberculosis. And vise versa the imbalance of intestinal flora will also increase the susceptibility to Mycobacterium tuberculosis. Prevotella is a newly discovered intestinal microorganism closely related to inflammatory diseases, and its abundance changes significantly in patients with tuberculosis. Therefore, this paper reviews the correlation between intestinal microorganism Prevotella and pulmonary tuberculosis, in order to provide new ideas for the diagnosis and treatment of pulmonary tuberculosis.

The relationship between serum 25-hydroxyvitamin-D level and sweat function in patients with type 2 diabetes mellitus

AIMS

The objective of this study is to explore the relationship between serum 25-hydroxyvitamin-D(25-(OH)₂D₃) level and sweat function in patients with type 2 diabetes mellitus (T2DM).

METHODS

A cross-sectional study of 1021 patients with T2DM who underwent 25-(OH)₂D₃ level detections and sweat function tests was carried out. These individuals were divided into deficient groups (n = 154 cases), insufficient groups (n = 593 cases) and sufficient groups (n = 274 cases). Spearman correlation analysis and multivariate stepwise linear regression analysis were implemented to determine the association of 25-(OH)2D3 level and sweat function.

RESULTS

The total presence of sweating dysfunction was 38.59%. Patients with a lower level of serum 25-(OH)₂D₃ had more severe sweat secretion impairment (P < 0.05). As the decrease of serum 25-(OH)₂D₃ level, the presence of sweating dysfunction increased (P < 0.05). 25-(OH)₂D₃ level was positively correlated with sweat function parameters, age and duration of T2DM were negatively correlated with sweat function parameter (P < 0.05). Multivariate stepwise linear regression analysis explored a significant association between serum 25-(OH)₂D₃ level with sweat function (P < 0.05).

CONCLUSIONS

Serum 25-(OH)₂D₃ level was positively correlated with sweat function in patients with T2DM.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

Latent Space Learning and Feature Learning using Multi-template for Multi-classification of Alzheimer's Disease

Alzheimer's disease (AD) is a common brain disease in the elderly that leads to thinking, memory, and behavior disorders. As the population ages, the proportion of AD patients is also increasing. Accordingly, computer-aided diagnosis of AD attracts more and more attention recently. In this paper, we propose a novel model combining latent space learning and feature learning using features extracted from multiple templates for AD multi-classification. Specifically, latent space learning is employed to obtain the inter-relationship between multiple templates, and feature learning is performed to explore the intrinsic relation in feature space. Finally, the most discriminative features are selected to boost the multi-classification performance. Our proposed model uses the data from the Alzheimer's disease neuroimaging initiative dataset. Furthermore, a series of comparative experiments indicate that our proposed model is quite competitive.

Associations between gut microbiota and thyroidal function status in Chinese patients with Graves' disease

OBJECTIVE

The imbalance of gut microbiota has been linked to manifold endocrine diseases, but the association with Graves' disease (GD) is still unclear. The purpose of this study was to investigate the correlation between human gut microbiota and clinical characteristics and thyroidal functional status of GD.

METHODS

14 healthy volunteers (CG) and 15 patients with primary GD (HG) were recruited as subjects. 16SrDNA high-throughput sequencing was performed on IlluminaMiSeq platform to analyze the characteristics of gut microbiota in patients with GD. Among them, the thyroid function of 13 patients basically recovered after treatment with anti-thyroid drugs (oral administration of Methimazole for 3-5 months). The fecal samples of patients after treatment (TG) were sequenced again, to further explore and investigate the potential relationship between dysbacteriosis and GD.

RESULTS

In terms of alpha diversity index, the observed OTUs, Simpson and Shannon indices of gut microbiota in patients with GD were significantly lower than those in healthy volunteers (P < 0.05).The difference of bacteria species was mainly reflected in the genus level, in which the relative abundance of Lactobacillus, Veillonella and Streptococcus increased significantly in GD. After the improvement of thyroid function, a significant reduction at the genus level were Blautia, Corynebacter, Ruminococcus and Streptococcus, while Phascolarctobacterium increased significantly (P < 0.05). According to Spearman correlation analysis, the correlation between the level of thyrotropin receptor antibody (TRAb) and the relative abundance of Lactobacillus and Ruminococcus was positive, while Synergistetes and Phascolarctobacterium showed a negative correlation with TRAb. Besides, there were highly significant negative correlation between Synergistetes and clinical variables of TRAb, TPOAb and TGAb (P < 0.05, R <  - 0.6).

CONCLUSIONS

This study revealed that functional status and TRAb level in GD were associated with composition and biological function in the gut microbiota, with Synergistetes and Phascolarctobacterium protecting the thyroid probably, while Ruminococcus and Lactobacillus may be novel biomarkers of GD.

Unsupervised Domain Adaptation Based Image Synthesis and Feature Alignment for Joint Optic Disc and Cup Segmentation

Due to the discrepancy of different devices for fundus image collection, a well-trained neural network is usually unsuitable for another new dataset. To solve this problem, the unsupervised domain adaptation strategy attracts a lot of attentions. In this paper, we propose an unsupervised domain adaptation method based image synthesis and feature alignment (ISFA) method to segment optic disc and cup on the fundus image. The GAN-based image synthesis (IS) mechanism along with the boundary information of optic disc and cup is utilized to generate target-like query images, which serves as the intermediate latent space between source domain and target domain images to alleviate the domain shift problem. Specifically, we use content and style feature alignment (CSFA) to ensure the feature consistency among source domain images, target-like query images and target domain images. The adversarial learning is used to extract domain invariant features for output-level feature alignment (OLFA). To enhance the representation ability of domain-invariant boundary structure information, we introduce the edge attention module (EAM) for low-level feature maps. Eventually, we train our proposed method on the training set of the REFUGE challenge dataset and test it on Drishti-GS and RIM-ONE_r3 datasets. On the Drishti-GS dataset, our method achieves about 3% improvement of Dice on optic cup segmentation over the next best method. We comprehensively discuss the robustness of our method for small dataset domain adaptation. The experimental results also demonstrate the effectiveness of our method. Our code is available at https://github.com/thinkobj/ISFA.

Dual feedback based bipolar current source with high stability for driving voice coil motors in wide temperature ranges

Bipolar current sources with a stability better than 0.1% in the temperature range of -30 to +70 °C are demanded for driving voice coil motors applied in a new ultra-quiet satellite platform, but almost none of the existing designs satisfy the harsh requirements. This paper presents a possible solution, which is essentially a floating-load, bipolar current source circuit with a dual feedback path. The key circuit is a composite amplifier (co-amp) composed of a high precision amplifier for error correction and a high power amplifier for load driving. The first feedback path comprises a specially designed four-wire current-sense resistor for current-to-voltage conversion and a discrete instrumentation amplifier for amplifying the converted voltage and closing the loop. The second feedback path is a proposed compensation network for loop stability. Error budgets for evaluating current stability and choosing key components of the circuit are comprehensively studied based on a derived rigorous current equation. Loop-stability problems attributable to the inductive load and the high open-loop gain of the co-amp are analyzed, and the proposed dual feedback compensation method is verified by theory, simulation, and measurement. All these contributions are demonstrated by three implemented prototypes with an output of up to ±2 A. The measured results agree well with theoretical predictions. The best and the worst stability performances of the three prototypes at +2 and -2 A are, respectively, 394 and 986 ppm in the temperature range of -30 to +70 °C, which are close to the theoretical value of 776 ppm.

Cross-attention multi-branch network for fundus diseases classification using SLO images

Fundus diseases classification is vital for the health of human beings. However, most of existing methods detect diseases by means of single angle fundus images, which lead to the lack of pathological information. To address this limitation, this paper proposes a novel deep learning method to complete different fundus diseases classification tasks using ultra-wide field scanning laser ophthalmoscopy (SLO) images, which have an ultra-wide field view of 180-200˚. The proposed deep model consists of multi-branch network, atrous spatial pyramid pooling module (ASPP), cross-attention and depth-wise attention module. Specifically, the multi-branch network employs the ResNet-34 model as the backbone to extract feature information, where the ResNet-34 model with two-branch is followed by the ASPP module to extract multi-scale spatial contextual features by setting different dilated rates. The depth-wise attention module can provide the global attention map from the multi-branch network, which enables the network to focus on the salient targets of interest. The cross-attention module adopts the cross-fusion mode to fuse the channel and spatial attention maps from the ResNet-34 model with two-branch, which can enhance the representation ability of the disease-specific features. The extensive experiments on our collected SLO images and two publicly available datasets demonstrate that the proposed method can outperform the state-of-the-art methods and achieve quite promising classification performance of the fundus diseases.

Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from histological sections for mitosis screening. Specifically, our method exploits deep convolutional neural networks to extract high-level features of mitosis to detect mitotic candidates. Then, we use spatial attention modules to re-encode mitotic features, which allows the model to learn more efficient features. Finally, we use multi-branch classification subnets to screen the mitosis. Compared to existing related methods in literature, our method obtains the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition. Code has been made available at: https://github.com/liushaomin/MitosisDetection.

AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images

The scanning laser ophthalmoscopy (SLO) has become an important tool for the determination of peripheral retinal pathology, in recent years. However, the collected SLO images are easily interfered by the eyelash and frame of the devices, which heavily affect the key feature extraction of the images. To address this, we propose a generative adversarial network called AMD-GAN based on the attention encoder (AE) and multi-branch (MB) structure for fundus disease detection from SLO images. Specifically, the designed generator consists of two parts: the AE and generation flow network, where the real SLO images are encoded by the AE module to extract features and the generation flow network to handle the random Gaussian noise by a series of residual block with up-sampling (RU) operations to generate fake images with the same size as the real ones, where the AE is also used to mine features for generator. For discriminator, a ResNet network using MB is devised by copying the stage 3 and stage 4 structures of the ResNet-34 model to extract deep features. Furthermore, the depth-wise asymmetric dilated convolution is leveraged to extract local high-level contextual features and accelerate the training process. Besides, the last layer of discriminator is modified to build the classifier to detect the diseased and normal SLO images. In addition, the prior knowledge of experts is utilized to improve the detection results. Experimental results on the two local SLO datasets demonstrate that our proposed method is promising in detecting the diseased and normal SLO images with the experts labeling.

0075 Neural Correlates of Cognitive Fatigue in Parkinson Disease

Introduction

Parkinson’s disease (PD) is a common neurodegenerative disease affecting millions of people world-wide. Fatigue is a prevalent and debilitating non-motor symptom in PD. However, the neural correlates underlying cognitive fatigue are poorly understood. Our previous studies suggested that continuous performance of a simple but mentally demanding psychomotor vigilance task (PVT) induced cognitive fatigue, operationalized as subjective exhaustion and time-on-task performance decline. Here we used arterial spin labeling (ASL) perfusion fMRI to investigate regional cerebral blood flow (CBF) changes in PD patients during cognitive fatigue induced by continuous performance of 20-min PVT.

Methods

Twenty-one PD patients completed a 20-min PVT during the ASL scan and two additional 4-min resting-state ASL scans before and after PVT. Reaction times (RTs) and regional CBF changes throughout the PVT as well as during pre- and post-task resting baselines were measured. Cognitive fatigue was analyzed by dividing the entire PVT performance into five quintiles in addition to the immediate measurement of self-rated fatigue before and after PVT.

Results

PD patients demonstrated significantly increased self-reported fatigue ratings after the task (p &lt; 0.05) and progressively slower RTs across quintiles (p &lt; 0.05). Perfusion data showed that the PVT activates the right middle frontal cortex, right inferior parietal lobe, right insula, bilateral occipital cortex, and right cerebellum (FDR corrected). Moreover, the bilateral middle frontal gyri were less active during the post-task rest compared to the pre-task rest.

Conclusion

These results demonstrated that cognitive fatigue has an ongoing effect on brain activity after a period of continuous mental effort and supported the critical role of prefrontal cortex in mediating cognitive fatigue in PD. The findings also suggest the utility of continuous PVT as an appropriate paradigm to induce and examine cognitive fatigue in PD.

Support

Supported in part by Parkinson’s Foundation Translational Research Grant and NIH grants R01-MH107571, R21-AG051981, and P30-NS045839.

0294 Baseline Response Speed Predicts Locus Coeruleus Integrity Change After Sleep Deprivation

Introduction

Locus coeruleus (LC) is the major source of norepinephrine (NE) in the brain, which plays a key role in maintaining arousal and alertness. Sleep loss significantly impairs arousal and alertness. However, it is unknown whether sleep loss disrupts LC integrity, which can be measured non-invasively by diffusion tensor imaging (DTI). In the current study, we used DTI to examine the effects of one night of acute total sleep deprivation (TSD) on fractional anisotropy (FA), an index reflecting fiber density, axonal diameter and myelination.

Methods

We analyzed DTI and psychomotor vigilance test (PVT) data from N=54 health adults (23 females, age range 21–50 years) from a well controlled in-laboratory sleep deprivation study. Participants were randomized to either a TSD condition (n=40) without sleep on night 2, or a control condition (n=14) with no sleep loss. Standard DTI scans were conducted on the morning of day 2 and day 3 between 0700h-1000h. The PVT reciprocal response time (RRT) was used to measure individual’s response speed at baseline without sleep loss. LC regions-of-interest (ROI) were defined by standard templates from Keren et al. (2009). Imaging data were analyzed using FSL toolbox.

Results

For the whole TSD group, no differences were found in the LC FA values before and after sleep deprivation (p &gt; .2). However, when dividing the TSD group to a slow group and a fast group based on their baseline PVT response speed, significantly increased LC FA were found in the slow group (p = .007) but not in the fast group (p &gt; .4). The PVT RRT negatively correlated with LC FA value changes after TSD (r = -.44, p = .004). No correlations were found between the PVT RRT and LC FA changes in the control group.

Conclusion

Our results showed that baseline vigilance response speed correlated with LC integrity change after sleep deprivation, with slower response exhibiting greater changes in LC integrity. These findings support the key role of LC-NE system in the regulation of alertness and arousal.

Support

Supported in part by NIH grants R01-HL102119, R01-MH107571, R21-AG051981. CTRC UL1RR024134, and P30-NS045839.

0060 Morning Locus Coeruleus Activation During the PVT Predicts Later-Day Sleepiness

Introduction

The locus coeruleus (LC) plays a key role in the regulation of arousal and autonomic function. Homeostatic sleep pressure refers to the drive for sleep that increases as a saturating exponential when we stay awake and decreases exponentially when we sleep. The current study used arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI) to investigate the relationship between homeostatic sleep pressure (sleepiness) and LC activity during the psychomotor vigilance test (PVT).

Methods

We analyzed sleepiness and ASL imaging data from N=70 health adults (40 males, age range 21–50 years) who participated in a controlled in-laboratory sleep study. All participants were scanned at rest and during the PVT on the morning between 0700h-1000h after 9 hour time-in-bed (TIB) baseline sleep. LC regions-of-interest (ROI) were defined by standard templates from Keren et al. (2009). Sleepiness was assessed by the Karolinska Sleepiness Scale (KSS) every two hours from 10:30 am to 10:30 pm.

Results

Sleepiness scores gradually increased over wakefulness time and reached its peak in the evening at about 10:20pm. PVT-induced CBF changes did not correlate with sleepiness scores on the morning (p &gt; 0.05), but showed significant negative correlations with sleepiness scores on later day when sleep pressure became higher, especially during the night-time (r = -0.41, p &lt; 0.001). Specifically, LC CBF showed significant increases during the PVT scan as compared to the resting scan (p = 0.04) in individuals with less nigh-time sleepiness (KSS &lt; 4), but no differences (p &gt; 0.1) in individuals with greater nigh-time sleepiness (KSS ≥ 5). After controlling for age, gender, and total sleep time, PVT-induced regional CBF difference in the LC still negatively predicted sleepiness (β = -0.325, p = 0.005).

Conclusion

Our findings showed that individuals with greater LC CBF increases during the PVT were less sleepy during the night, supporting the key role of LC activity in promoting wakefulness and maintaining sleep homeostasis. PVT-induced LC activation may provide a non-invasive bio-marker of homeostatic sleep pressure in healthy adults.

Support

Supported in part by NIH grants R01-HL102119, R01-MH107571, R21-AG051981. CTRC UL1RR024134, and P30-NS045839.

An Improved Object Detection Method for Mitosis Detection

Breast cancer grading is important for patient prognosis, and the mitosis count is one of the most important indicators for breast cancer grading. Traditional methods use handcraft features and deep learning based methods to detect mitosis in a classified model. These methods are time-consuming and difficult for practical clinical practice application. For this reason, this paper proposes an improved object detection method for automatic mitosis detection from histological images. First, we use a convolutional neural network (CNN) to automatically extract mitosis features. Then, we use the region proposed network (RPN) to locate a set of class-agnostic mitosis proposals. Finally, we use the improved R-CNN subnet to screen for mitosis from these proposals. Our approach achieved the best results in the ICPR2012 mitosis detection competition test dataset. Additionally, our proposed method is fast enough to be potentially used in clinical and health centers.

Nanoscale extracellular vesicle-derived DNA is superior to circulating cell-free DNA for mutation detection in early-stage non-small-cell lung cancer

Background

The comparison between relatively intact nanoscale extracellular vesicle-derived DNA (nEV-DNA) and fragmented circulating cell-free DNA (cfDNA) in mutation detection among patients with non-small-cell lung cancer (NSCLC) has not been carried out yet, and thus deserves investigation.

Patients and methods

Both nEV-DNA and cfDNA was obtained from 377 NSCLC patients with known EGFR mutation status and 69 controls. The respective EGFRE19del/T790M/L858R mutation status was interrogated with amplification-refractory-mutation-system-based PCR assays (ARMS-PCR).

Results

Neither nEV-DNA nor cfDNA levels show a strong correlation with tumor volumes. There is no correlation between cfDNA and nEV-DNA levels either. The detection sensitivity of nEV-DNA and cfDNA using ARMS-PCR in early-stage NSCLC was 25.7% and 14.2%, respectively, with 96.6% and 91.7% specificity, respectively. In late-stage NSCLC, both nEV-DNA and cfDNA show ∼80% sensitivity and over 95% specificity.

Conclusions

nEV-DNA is superior to cfDNA for mutation detection in early-stage NSCLC using ARMS-PCR. However, the advantages vanish in late-stage NSCLC.

Real-time parallel 3D multiple particle tracking with single molecule centrifugal force microscopy

Real-time tracking of multiple particles is key for quantitative analysis of dynamic biophysical processes and materials science via time-lapse microscopy image data, especially for single molecule biophysical techniques, such as magnetic tweezers and centrifugal force microscopy. However, real-time multiple particle tracking with high resolution is limited by the current imaging processes or tracking algorithms. Here, we demonstrate 1 nm resolution in three dimensions in real-time with a graphics-processing unit (GPU) based on a compute unified device architecture (CUDA) parallel computing framework instead of only a central processing unit (CPU). We also explore the trade-offs between processing speed and size of the utilized regions of interest and a maximum speedup of 137 is achieved with the GPU compared with the CPU. Moreover, we utilize this method with our recently self-built centrifugal force microscope (CFM) in experiments that track multiple DNA-tethered particles. Our approach paves the way for high-throughput single molecule techniques with high resolution and efficiency. LAY DESCRIPTION: Particles are widely used as probes in life sciences through their motions. In single molecule techniques such as optical tweezers and magnetic tweezers, microbeads are used to study intermolecular or intramolecular interactions via beads tracking. Also tracking multiple beads' motions could study cell-cell or cell-ECM interactions in traction force microscopy. Therefore, particle tracking is of key important during these researches. However, parallel 3D multiple particle tracking in real-time with high resolution is a challenge either due to the algorithm or the program. Here, we combine the performance of CPU and CUDA-based GPU to make a hybrid implementation for particle tracking. In this way, a speedup of 137 is obtained compared the program before only with CPU without loss of accuracy. Moreover, we improve and build a new centrifugal force microscope for multiple single molecule force spectroscopy research in parallel. Then we employed our program into centrifugal force microscope for DNA stretching study. Our results not only demonstrate the application of this program in single molecule techniques, also indicate the capability of multiple single molecule study with centrifugal force microscopy.

Cohort Profile: The Chongqing Cancer Cohort Study (CCCS) of the Urban Population in Southwest China

Purpose: Urbanization is causing an increasingly negative effect on public health in China. This study was established to examine the associations between socio-economic and environmental exposures and the potential impact of gene-environment interactions and cancer risk of urban population in Chongqing, China. Participants: The cohort was established in Beibei District of Chongqing in southwest China. Between March 2016 and December 2016, we enrolled 57,332 adults who were 40-69 years of age, and collected baseline data on demographic information, socio-economic status, lifestyle, family and personal disease histories through face-to-face interviews using a standardized questionnaire. Regular follow-up including face-to-face interviews will take place every 5 years. Findings to date: Ninety-nine percent (56658/57332) of the participants completed the baseline assessment. The eligible participants had a mean age of 54.8 years, and 51.42% were females. Nearly three-fifths of participants having a normal BMI (18.5 to 23.9 kg/m2) and one-third being overweight (24.0 to 27.9 kg/m2). Among males, 29.58% were smokers and 21.08% were alcohol users. Among females, 1.49% were smokers and 1.66% were alcohol users. Among all participants, 7.03% of males and 9.08% of females reported their family history of cancer. Future plans: The relationships of modifiable risk factors with the cancer risk will be analyzed. Meanwhile, participants will be closely tracked to minimize loss to follow-up. We plan to construct a risk prediction model on cancer and verify the prediction model by genome-wide association studies (GWAS). The successful completion of this cohort study will allow for better targeting of cancer screening to those at highest risk in urban population of China and provide clinicians and policymakers with a practical predication rule.

Nitrate-induced and in situ electrochemical activation synthesis of oxygen deficiencies-rich nickel/nickel (oxy)hydroxide hybrid films for enhanced electrocatalytic water splitting

Hydrogen produced by electrochemical water splitting offers a hopeful and renewable solution for addressing the global energy crisis; however, development of highly efficient non-noble-metal electrocatalysts remains a big challenge. Herein, we report a facile strategy to fabricate oxygen deficiencies-rich nickel/nickel (oxy)hydroxide hybrid films as efficient electrocatalysts for water splitting by in situ oxygen evolution reaction (OER) activation. Under OER conditions, the originally deposited Ni films from the ethaline-based deep eutectic solvent (DES) undergo a structural rearrangement with a phase transformation in the oxidation state from Ni(ii) to Ni(iii) at the surface. The change is coupled with an increase in oxygen deficiencies and a pronounced defective precursor is induced by the addition of nitrate ions, providing structural disordering and boosting the intrinsic activity of the catalyst, which strongly enhances the water splitting performance.

Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.

Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.

p.R180C mutation of glycosyltransferase B leads to B subgroup, an in vitro and in silico study

BACKGROUND AND OBJECTIVES

Dysfunctional glycosyltransferase A or B may lead to incomplete glycosylation of H antigen and atypical ABO blood group with weak A or B phenotypes, posing challenges for blood typing for transfusion.

MATERIALS AND METHODS

Serological studies and ABO gene analysis were performed. Flow cytometry was performed on HeLa cells transfected glycosyltransferase B expressing plasmids. Agglutination of transfected cells and total glycosyltransferase B transfer capacity were examined. Molecular dynamics simulations were used to explore possible dynamic conformational changes around the binding pocket.

RESULTS

We identified a mutation c.538C>T (p. R180C) of B allele in a Chinese donor and his father with ABw phenotype. In vitro expression study showed that mutation p.R180C, although not affecting expression of glycosyltransferase B, impaired H to B antigen conversion. The in silico analyses found that the residue Arg180 on the internal loop next to the entry of the binding pocket may have its long side chain salt-bridged with the highly flexible C-terminal carboxyl and contribute to the catalysis of H to B antigen conversion.

CONCLUSION

The p.R180C mutation impairs the conversion from H to B antigen and leads to weak B phenotype. Dynamic interaction between Arg180 and C-terminal of glycosyltransferase B may stabilize its binding with UDP-galactose and facilitate H/B antigen conversion.

Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses

Identifying the exact transmission route(s) of infectious diseases in indoor environments is a crucial step in developing effective intervention strategies. In this study, we proposed a comparative analysis approach and built a model to simulate outbreaks of 3 different in-flight infections in a similar cabin environment, that is, influenza A H1N1, severe acute respiratory syndrome (SARS) coronavirus (CoV), and norovirus. The simulation results seemed to suggest that the close contact route was probably the most significant route (contributes 70%, 95% confidence interval [CI]: 67%-72%) in the in-flight transmission of influenza A H1N1 transmission; as a result, passengers within 2 rows of the index case had a significantly higher infection risk than others in the outbreak (relative risk [RR]: 13.4, 95% CI: 1.5-121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%-23%), 29% (95% CI: 27%-31%), and 50% (95% CI: 48%-53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%-87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2-77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes.

Joint regression and classification via relational regularization for Parkinson's disease diagnosis

It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.

Sparse feature learning for multi-class Parkinson's disease classification

This paper solves the multi-class classification problem for Parkinson's disease (PD) analysis by a sparse discriminative feature selection framework. Specifically, we propose a framework to construct a least square regression model based on the Fisher's linear discriminant analysis (LDA) and locality preserving projection (LPP). This framework utilizes the global and local information to select the most relevant and discriminative features to boost classification performance. Differing in previous methods for binary classification, we perform a multi-class classification for PD diagnosis. Our proposed method is evaluated on the public available Parkinson's progression markers initiative (PPMI) datasets. Extensive experimental results indicate that our proposed method identifies highly suitable regions for further PD analysis and diagnosis and outperforms state-of-the-art methods.

Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

Immunotolerant indoleamine-2,3-dioxygenase is increased in condyloma acuminata

BACKGROUND

The tryptophan-depleting enzyme indoleamine-2,3-dioxygenase (IDO) is critical for the regulation of immunotolerance and plays an important role in immune-associated skin diseases.

OBJECTIVES

To analyse the level of IDO in condyloma acuminata (CA) and its role in this condition.

METHODS

IDO expression was assessed in the skin and peripheral blood of healthy controls and patients with CA. To assess the role of skin IDO in immunity, the ability of isolated epidermal cells to metabolize tryptophan and the influence on polyclonal T-cell mitogen (PHA)-stimulated T-cell proliferation were explored.

RESULTS

IDO median fluorescence intensities in peripheral blood mononuclear cells from patients with CA were similar to those from healthy controls. Immunohistochemistry showed that IDO⁺ cells were rare in normal skin and the control skin of patients with CA, but were greatly accumulated in wart tissue. Most fluorescence signals of IDO⁺ cells did not overlap with those of CD1a⁺ Langerhans cells. Human papillomavirus (HPV) DNA probe in situ hybridization showed a large number of IDO⁺ cells in the HPV^- site. Keratinocytes in the skin of healthy controls and the circumcised skin of patients with CA could minimally transform tryptophan into kynurenine, but IDO-competent epidermal cells from warts could transform tryptophan. In addition, these IDO-competent epidermal cells could inhibit PHA-stimulated T-cell proliferation. The addition of an IDO inhibitor, 1-methyl-d-tryptophan, restored the inhibited T-cell proliferation.

CONCLUSIONS

Abnormally localized high IDO expression might be involved in the formation of a local immunotolerant microenvironment.

Hierarchical Saliency Detection via Probabilistic Object Boundaries

Though there are many computational models proposed for saliency detection, few of them take object boundary information into account. This paper presents a hierarchical saliency detection model incorporating probabilistic object boundaries, which is based on the observation that salient objects are generally surrounded by explicit boundaries and show contrast with their surroundings. We perform adaptive thresholding operation on ultrametric contour map, which leads to hierarchical image segmentations, and compute the saliency map for each layer based on the proposed robust center bias, border bias, color dissimilarity and spatial coherence measures. After a linear weighted combination of multi-layer saliency maps, and Bayesian enhancement procedure, the final saliency map is obtained. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed model outperforms eight state-of-the-art saliency detection models.