3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Effect of the Functional VP1 Unique Region of Human Parvovirus B19 in Causing Skin Fibrosis of Systemic Sclerosis.

Human parvovirus B19 (B19V) is a single-stranded non-enveloped DNA virus of the family Parvoviridae that has been associated with various autoimmune disorders. Systemic sclerosis (SSc) is an autoimmune connective tissue disorder with high mortality and has been linked to B19V infection. However, the precise mechanism underlying the B19V contribution to the development of SSc remains uncertain. This study investigated the impacts of the functional B19V-VP1 unique region (VP1u) in macrophages and bleomycin (BLE)-induced SSc mice. Cell experimental data showed that significantly decreased viability and migration of both B19V-VP1u-treated U937 and THP-1 macrophages are detected in the presence of celastrol. Significantly increased MMP9 activity and elevated NF-kB, MMP9, IL-6, TNF-α, and IL-1ß expressions were detected in both B19V-VP1u-treated U937 and THP-1 macrophages. Conversely, celastrol revealed an inhibitory effect on these molecules. Notably, celastrol intervened in this pathogenic process by suppressing the sPLA2 activity of B19V-VP1u and subsequently reducing the inflammatory response. Notably, the administration of B19V-VP1u exacerbated BLE-induced skin fibrosis in mice, with augmented expressions of TGF-ß, IL-6, IL-17A, IL-18, and TNF-α, ultimately leading to α-SMA and collagen I deposits in the dermal regions of BLE-induced SSc mice. Altogether, this study sheds light on parvovirus B19 VP1u linked to scleroderma and aggravated dermal fibrosis.

Deep Mobile Linguistic Therapy for Patients with ASD.

Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human-Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.

In recent years, much research has found that dysregulation of glutarylation is associated with many human diseases, such as diabetes, cancer, and glutaric aciduria type I. Therefore, glutarylation identification and characterization are essential tasks for determining modification-specific proteomics. This study aims to propose a novel deep neural network framework based on word embedding techniques for glutarylation sites prediction. Multiple deep neural network models are implemented to evaluate the performance of glutarylation sites prediction. Furthermore, an extensive experimental comparison of word embedding techniques is conducted to utilize the most efficient method for improving protein sequence data representation. The results suggest that the proposed deep neural networks not only improve protein sequence representation but also work effectively in glutarylation sites prediction by obtaining a higher accuracy and confidence rate compared to the previous work. Moreover, embedding techniques were proven to be more productive than the pre-trained word embedding techniques for glutarylation sequence representation. Our proposed method has significantly outperformed all traditional performance metrics compared to the advanced integrated vector support, with accuracy, specificity, sensitivity, and correlation coefficient of 0.79, 0.89, 0.59, and 0.51, respectively. It shows the potential to detect new glutarylation sites and uncover the relationships between glutarylation and well-known lysine modification.

[Advances in the study of ovarian dysfunction with aging].

Societal changes regarding the role of women have significant impacts on women's willingness and the timing of childbearing. Ovarian reserve in woman typically begins to decline at the age of 35, and it is primarily characterized by a reduction in the number of ovarian follicles and a decline in oocyte quality. The clinical diagnosis of ovarian insufficiency relies on multiple variables including changes of follicle stimulating hormone (FSH), serum anti-Müllerian hormone (AMH), inhibin B, antral follicle count, menstruation and age. It is proven that ovarian cells demonstrate dysfunction associated with aging including mitochondrial dysfunction, telomere shortening, impaired DNA repair, epigenetic changes and metabolic/energetic disorders. In this review, we introduce the clinical diagnosis and management of ovarian insufficiency. We mainly discuss the molecular mechanism and potential interventions. We are optimistic that this information and knowledge will inform the important decisions for women and society regarding childbearing.

Enhanced Two-Factor Authentication and Key Agreement Using Dynamic Identities in Wireless Sensor Networks.

Key agreements that use only password authentication are convenient in communication networks, but these key agreement schemes often fail to resist possible attacks, and therefore provide poor security compared with some other authentication schemes. To increase security, many authentication and key agreement schemes use smartcard authentication in addition to passwords. Thus, two-factor authentication and key agreement schemes using smartcards and passwords are widely adopted in many applications. Vaidya et al. recently presented a two-factor authentication and key agreement scheme for wireless sensor networks (WSNs). Kim et al. observed that the Vaidya et al. scheme fails to resist gateway node bypassing and user impersonation attacks, and then proposed an improved scheme for WSNs. This study analyzes the weaknesses of the two-factor authentication and key agreement scheme of Kim et al., which include vulnerability to impersonation attacks, lost smartcard attacks and man-in-the-middle attacks, violation of session key security, and failure to protect user privacy. An efficient and secure authentication and key agreement scheme for WSNs based on the scheme of Kim et al. is then proposed. The proposed scheme not only solves the weaknesses of previous approaches, but also increases security requirements while maintaining low computational cost.

A secure smart-card based authentication and key agreement scheme for telecare medicine information systems.

A smart-card based authentication scheme for telecare medicine information systems enables patients, doctors, nurses, health visitors and the medicine information systems to establish a secure communication platform through public networks. Zhu recently presented an improved authentication scheme in order to solve the weakness of the authentication scheme of Wei et al., where the off-line password guessing attacks cannot be resisted. This investigation indicates that the improved scheme of Zhu has some faults such that the authentication scheme cannot execute correctly and is vulnerable to the attack of parallel sessions. Additionally, an enhanced authentication scheme based on the scheme of Zhu is proposed. The enhanced scheme not only avoids the weakness in the original scheme, but also provides users' anonymity and authenticated key agreements for secure data communications.

An intelligent knowledge-based and customizable home care system framework with ubiquitous patient monitoring and alerting techniques.

This study develops and integrates an efficient knowledge-based system and a component-based framework to design an intelligent and flexible home health care system. The proposed knowledge-based system integrates an efficient rule-based reasoning model and flexible knowledge rules for determining efficiently and rapidly the necessary physiological and medication treatment procedures based on software modules, video camera sensors, communication devices, and physiological sensor information. This knowledge-based system offers high flexibility for improving and extending the system further to meet the monitoring demands of new patient and caregiver health care by updating the knowledge rules in the inference mechanism. All of the proposed functional components in this study are reusable, configurable, and extensible for system developers. Based on the experimental results, the proposed intelligent homecare system demonstrates that it can accomplish the extensible, customizable, and configurable demands of the ubiquitous healthcare systems to meet the different demands of patients and caregivers under various rehabilitation and nursing conditions.

A vision-based driver nighttime assistance and surveillance system based on intelligent image sensing techniques and a heterogamous dual-core embedded system architecture.

This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.

[In vivo identification of radix panacis quinquefolii by spectral imaging technology].

Radix Panacis quinquefolii pieces coming from different drug stores were tested by crystal liquid spectral imaging instrument, and a new method for quality control was presented. The spectral resolution is 5 nm, the spectral range is from 405 to 680 nm, and the spatial resolution is 50 lp x mm(-1). The characteristic spectrum from spectral cube was used to construct the fingerprint of Radix Panacis quinquefolii, and the fingerprint was analyzed by principal method to identify the counterfeit and to evaluate the quality. The result is fully consistent with biological character and chemical analysis result. So the technology is suitable for fingerprint construction and quality evaluation of traditional Chinese medicine. The whole testing process is noninvasive, fast and convenient.