Thalassemia is a significant health problem worldwide. Prenatal diagnosis is the only effective way to prevent the birth of a fetus with severe thalassemias, which include hemoglobin Bart's hydrops ...fetalis and thalassemia major. However, accurate prenatal diagnosis depends on the comprehensive consideration of the molecular basis of thalassemias. To make a correct decision, the obstetrician should have a certain understanding of the genetics of thalassemias. Here we present a brief introduction of some fundamental genetic knowledge of thalassemias, including the production of hemoglobin, structure and location of globin genes, hemoglobin switch, epidemiology, clinical classification, molecular and cellular pathology, genotype–phenotype correlation, and genetic modifiers. Furthermore, some unusual clinical cases that cannot be explained by Mendel's laws are described. On the basis of a thorough understanding of the above information, clinicians should have the ability to precisely diagnose thalassemia patients and provide applicable genetic counselling to the affected families.
Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people's emotions, which makes the task of analyzing the ...sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.
GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is ...composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deliver a solution to the maximum correntropy criterion (MCC)-based low-rank decomposition problem. Experimental results show that GoDec+ is efficient and robust to different corruptions including Gaussian noise, Laplacian noise, salt & pepper noise, and occlusion on both synthetic and real vision data. We further apply GoDec+ to more general applications including classification and subspace clustering. For classification, we construct an ensemble subspace from the low-rank GoDec+ matrix and introduce an MCC-based classifier. For subspace clustering, we utilize GoDec+ values low-rank matrix for MCC-based self-expression and combine it with spectral clustering. Face recognition, motion segmentation, and face clustering experiments show that the proposed methods are effective and robust. In particular, we achieve the state-of-the-art performance on the Hopkins 155 data set and the first 10 subjects of extended Yale B for subspace clustering.
Recognizing complex human actions is very challenging, since training a robust learning model requires a large amount of labeled data, which is difficult to acquire. Considering that each complex ...action is composed of a sequence of simple actions which can be easily obtained from existing data sets, this paper presents a simple to complex action transfer learning model (SCA-TLM) for complex human action recognition. SCA-TLM improves the performance of complex action recognition by leveraging the abundant labeled simple actions. In particular, it optimizes the weight parameters, enabling the complex actions to be learned to be reconstructed by simple actions. The optimal reconstruct coefficients are acquired by minimizing the objective function, and the target weight parameters are then represented as a combination of source weight parameters. The main advantage of the proposed SCA-TLM compared with existing approaches is that we exploit simple actions to recognize complex actions instead of only using complex actions as training samples. To validate the proposed SCA-TLM, we conduct extensive experiments on two well-known complex action data sets: 1) Olympic Sports data set and 2) UCF50 data set. The results show the effectiveness of the proposed SCA-TLM for complex action recognition.
In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient ...generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.
Despite the presence of numerous inhibitory cell types, laminar excitatory input has only been characterized for limited identified types, and it is unknown whether there are differences between cell ...types in their laminar sources of inhibitory input. In the present study, we characterized sources of local input to nine distinct types of layer 2/3 inhibitory neurons in living slices of mouse somatosensory cortex. Whole-cell recordings from identified cell types, facilitated by use of transgenic mice expressing green fluorescent protein in limited inhibitory neuron populations, were combined with laser scanning photostimulation. We found that each inhibitory cell type received distinct excitatory and inhibitory laminar input patterns. Excitatory inputs could be grouped into three categories. All inhibitory cell types received strong excitation from layer 2/3, and for calretinin (CR)-positive Martinotti cells and burst-spiking interneurons, this was their dominant source of excitatory input. Three other cell types, including fast-spiking basket cells, CR-negative Martinotti cells, and bipolar interneurons, also received strong excitatory input from layer 4. The remaining four inhibitory cell types, including chandelier cells, neurogliaform cells, irregular spiking basket cells, and regular spiking presumptive basket cells, received strong excitatory input from layer 5A and not layer 4. Laminar sources of inhibitory input varied between cell types and could not be predicted from the sources of excitatory input. Thus, there are cell-type specific differences in laminar sources of both excitation and inhibition, and complementary input patterns from layer 4 versus layer 5A suggest cell type differences in their relationships to lemniscal versus paralemniscal pathways.
The high fall rate of the elderly brings enormous challenges to families and the medical system; therefore, early risk assessment and intervention are quite necessary. Compared to other sensor-based ...technologies, in-shoe plantar pressure sensors, effectiveness and low obtrusiveness are widely used for long-term fall risk assessments because of their portability. While frequently-used bipedal center-of-pressure (COP) features are derived from a pressure sensing platform, they are not suitable for the shoe system or pressure insole owing to the lack of relative position information. Therefore, in this study, a definition of "weak foot" was proposed to solve the sensitivity problem of single foot features and facilitate the extraction of temporal consistency related features. Forty-four multi-dimensional weak foot features based on single foot COP were correspondingly extracted; notably, the relationship between the fall risk and temporal inconsistency in the weak foot were discussed in this study, and probability distribution method was used to analyze the symmetry and temporal consistency of gait lines. Though experiments, foot pressure data were collected from 48 subjects with 24 high risk (HR) and 24 low risk (LR) ones obtained by the smart footwear system. The final models with 87.5% accuracy and 100% sensitivity on test data outperformed the base line models using bipedal COP. The results and feature space shown the novel features of wearable plantar pressure could comprehensively evaluate the difference between HR and LR groups. Our fall risk assessment models based on these features had good generalization performance, and showed practicability and reliability in real-life monitoring situations.
Viral tracers are important tools for neuroanatomical mapping and genetic payload delivery. Genetically modified viruses allow for cell-type-specific targeting and overcome many limitations of ...non-viral tracers. Here, we summarize the viruses that have been developed for neural circuit mapping, and we provide a primer on currently applied anterograde and retrograde viral tracers with practical guidance on experimental uses. We also discuss and highlight key technical and conceptual considerations for developing new safer and more effective anterograde trans-synaptic viral vectors for neural circuit analysis in multiple species.
Xu et al. review the viruses that have been used for neural circuit mapping and provide a primer on currently applied anterograde and retrograde viral tracers with practical guidance on experimental uses.
This paper presents a biologically-inspired saliency prediction method to imitate two main characteristics of the human perception process: focalization and orienting. The proposed network, named ...ACNet is composed of two modules. The first one is an essential concentrated module (CM), which assists the network to “see” images with appropriate receptive fields by perceiving rich multi-scale multi-receptive-field contexts of high-level features. The second is a parallel attention module (PAM), which explicitly guides the network to learn “what” and “where” is salient by simultaneously capturing global and local information with channel-wise and spatial attention mechanisms. These two modules compose the core component of the proposed method, named ACBlock, which is cascaded to progressively refine the inference of saliency estimation in a manner similar to that humans zoom in their lens to focus on the saliency. Experimental results on seven public datasets demonstrate that the proposed ACNet outperforms the state-of-the-art models without any prior knowledge or post-processing.
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