Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio-Visual Emotion Recognition with Capsule-like Feature Representation and Model-Based Reinforcement Learning

This paper presents the techniques used in our contribution to Multimodal Emotion Recognition Challenge (MEC 2017). The purpose of the challenge is to classify the eight basic emotions (happy, sad, angry, worried, anxious, surprise, disgust and neutral) from Chinese Natural Audio-Visual Emotion Database (CHEAVD) 2.0 selected from Chinese movies and TV programs. As racial expressions are caused by the movement of racial features such as the mouth and eyebrows, a capsule like feature representation is proposed to captures not only the existences of static racial emotions in video frames but also the instantiation parameters. In order to further improve the performance of emotion classification accuracy, a model based reinforcement learning is proposed for audio-visual fusion method, which exploits feedbacks of submission on challenge testing dataset as rewards to learn the fusion model. The overall accuracy of proposed approach on test dataset is 52.3% and the macro average precision is 39.7%. The performance achieves the top 2 of the MEC2017 audio-visual sub challenge.
Chronic Pain in Breast Cancer Survivors: Nociceptive, Neuropathic, or Central Sensitization Pain?

The differentiation between acute and chronic pain can be insufficient for appropriate pain management. The aim of this study was to evaluate the prevalence of the predominant pain type (nociceptive, neuropathic, or central sensitization [CS] pain) in breast cancer survivors (BCS) with chronic pain. The secondary aims were to examine (1) differences in health‐related quality of life (HRQoL) between the different pain groups; and (2) the associations between patient‐, disease‐, and treatment‐related factors and the different pain types.
Serum‐Free Medium Enhances the Immunosuppressive and Antifibrotic Abilities of Mesenchymal Stem Cells Utilized in Experimental Renal Fibrosis

Serum used in culture medium brings risks of immune reactions or infections and thus may hinder using ex vivo expanded mesenchymal stem cells (MSCs) for medical treatment. Here, we cultured MSCs in a serum‐free medium (SF‐MSCs) and in a medium containing 10% fetal bovine serum (10%MSCs) and investigated their effects on inflammation and fibrosis. MSC‐conditioned medium suppressed transforming growth factor‐β1–induced phosphorylation of Smad2 in HK‐2 cells, with no significant difference between the two MSCs. This finding suggests that the direct antifibrotic effect of SF‐MSCs is similar to that of 10%MSCs. However, immunohistochemistry revealed that renal fibrosis induced by unilateral ureteral obstruction in rats was more significantly ameliorated by the administration of SF‐MSCs than by that of 10%MSCs. Coculture of MSCs and monocytic THP‐1 cell‐derived macrophages using a Transwell system showed that SF‐MSCs significantly induced polarization from the proinflammatory M1 to the immunosuppressive M2 phenotype macrophages, suggesting that SF‐MSCs strongly suppress the persistence of inflammation. Furthermore, the gene expression of tumor necrosis factor‐α–induced protein 6 (TSG‐6), which inhibits the recruitment of inflammatory cells, was higher in SF‐MSCs than in 10%MSCs, and TSG‐6 knockdown in SF‐MSCs attenuated the anti‐inflammatory responses in unilateral ureteral obstruction rats. These findings imply that SF culture conditions can enhance the immunosuppressive and antifibrotic abilities of MSCs and the administration of ex vivo expanded SF‐MSCs has the potential to be a useful therapy for preventing the progression of renal fibrosis. Stem Cells Translational Medicine 2018;7:893–905
A Range-Based Adaptive Target Localization Method in Wireless Sensor Networks With Mobile Anchors

T360 paper proposes a range-based adaptive target localization method in wireless sensor networks with mobile anchors. In the proposed method, we assume that the communication between the mobile anchor nodes occurs in a Hamilton cycle, where every mobile anchor access only once to its immediate neighbor anchor at each iteration time. The positions of unknown nodes are estimated by using the adaptive incremental LMS (Least Mean Squared) algorithm implemented on the mobile anchors based on the sensed range information including the measured distance and direction between the anchor and the target. Due to the adaptation of LMS algorithm, the time-variations of mobile anchors can be tracked to reflect the changing statistical profile of the range. Simulation results show that the proposed method can locate the unknown target with a satisfied accuracy. Further, we evaluate the performance of the proposed method under the different moving speed and noise level.
The Synergy of Aging and LPS Exposure in a Mouse Model of Parkinson’s Disease

Aging is an inevitable physiological challenge occurring in organisms over time, and is also the most important risk factor of neurodegenerative diseases. In this study, we observed cellular and molecular changes of different age mice and LPS-induced Parkinson disease (PD) model. The results showed that behavioral performance and dopaminergic (DA) neurons were declined, accompanied by increased expression of pro-inflammatory factors (TLR2, p-NF-kB-p65, IL-1β and TNF-α), as well as pro-oxidative stress factor gp91phox in aged mice compared with young mice. Aging exaggerated inflammatory M1 microglia, and destroyed the balance between oxidation and anti-oxidation. The intranasal LPS instillation induced PD model in both young and aged mice. The poor behavioral performance and the loss of DA neurons as well as TLR2, p-NF-kB-p65, IL-1β, TNF-α, iNOS and gp91phox were further aggravated in LPS-aged mice. Interestingly, the expression of Nrf2 and HO-1 was up-regulated by LPS only in young LPS-PD mice, but not in aged mice. The results indicate that the synergy of aging process and LPS exposure may prominently aggravate the DA neurons loss caused by more serious neuroinflammation and oxidative stress in the brain.
Hepatitis B Virus Infection: Fatigue-Associated Illness Experiences Among Koreans

Fatigue is one of the most common patient-reported symptoms and can result in poor quality of life in patients with the hepatitis B virus (HBV). Little is known about the characteristics of fatigue in Korean patients with HBV. This study's purpose was to examine fatigue severity and explore health issues associated with fatigue in Korean patients with HBV. A mixed-methods research design was used. Quantitative data on fatigue severity were measured using the revised Piper Fatigue Scale (PFS) with 147 participants. Qualitative data on health issues induced by fatigue were collected via interviews with11 participants. Content analysis revealed three categories of health issues (physical, psychological, and social) and eight subcategories. Participants' overall fatigue level was moderate. The cognitive/mood dimension on the PFS had a significant negative correlation with age. Emotional and psychosocial issues associated with HBV-related fatigue may be more prevalent than physical issues in Korean patients.
Ghrelin protects the myocardium with hypoxia/reoxygenation treatment through upregulating the expression of growth hormone, growth hormone secretagogue receptor and insulin-like growth factor-1, and promoting the phosphorylation of protein kinase B

Ghrelin is an endogenous ligand of growth hormone (GH) secretagogue receptor (GHSR) and has a number of biological effects, including heart protection. The present study aimed to reveal the positive effect of ghrelin on myocardium with hypoxia/reoxygenation (H/R) treatment and the involved molecular mechanisms. Successful construction of lentiviral expression vector (ghrelin-pLVX-Puro) was confirmed by colony polymerase chain reaction (PCR) verification. Primary rat cardiac myocytes were isolated and identified by immunofluorescence staining. Existence of red fluorescence of α-sarcomeric actinin indicated the successful isolation. Following ghrelin transfection and H/R treatment, primary cells were divided into four groups: Control, H/R, empty (empty pLVX-Puro + H/R) and ghrelin (ghrelin-pLVX-Puro + H/R). Cell viability and apoptosis were evaluated by Cell Counting Kit-8 (CCK-8) and Hoechst staining, respectively. The cell viability in the ghrelin group was significantly higher than that in the empty control group (P<0.05). The apoptosis rate in the ghrelin group was significantly lower than that in the empty control group (P<0.05). An ex vivo rat cardiac perfusion model was established. Following ghrelin incubation and H/R treatment, ex vivo myocardium was divided into four groups: Control, sham, H/R and ghrelin (ghrelin + H/R). Immunohistochemical analysis demonstrated that ghrelin increased the integrity of cardiac myocytes, and decreased shrinkage and apoptosis. mRNA and protein expression levels of GH, GHSR, insulin-like growth factor-1 (IGF-1), protein kinase B (Akt), phosphorylated Akt (p-Akt) were determined by reverse transcription (RT)-PCR, western blot analysis and immunohistochemical analysis. Ghrelin upregulated the mRNA and protein expression levels of GH, GHSR and IGF-1, and increased the ratio of p-Akt to Akt protein level (p-Akt/Akt) in cardiac myocytes and myocardial tissues with H/R treatment. In conclusion, ghrelin protected the myocardium with H/R treatment through upregulating the expression of GH, GHSR and IGF-1, and promoting the phosphorylation of Akt. This would provide promising insights into the treatment of hypoxic myocardial injury by ghrelin.
Magnetic Resonance Imaging Findings in Viral Encephalitis: A Pictorial Essay

ABSTRACT Viral encephalitis by definition is the result of human virus affecting the brain and sparing the meninges. The other nervous system manifestations are meningitis, meningoencephalitis, encephalomyelitis, and encephalomyeloradiculitis. Encephalitis can involve any age group from children to old people. The severity of the disease depends on the viral agent and the host immune system. The patient can present with fever, headache, seizure, neurological deficit, or altered sensorium. Laboratory investigations, imaging, and cerebrospinal fluid analysis are crucial in the diagnosis of encephalitis. Magnetic resonance imaging (MRI) findings may be nonspecific or specific and plays a major role in the diagnosis of encephalitis and predicting the possible cause. This pictorial essay reviews the MRI findings of common types of viral encephalitis.
Adaptive trajectories and early risk factors in the autism spectrum: A 15‐year prospective study

Little is known about long‐term outcomes. We investigate the adaptive trajectories and their risk factors in ASD. Data were obtained from 281 children prospectively followed untill adulthood. The final sample consisted of 106 individuals. Vineland scores were collected at baseline (T1), 3 (T2), 10 (T3), and 15 (T4) years later. A group‐based method was used to identify homogeneous patterns of adaptive skills trajectories. Results show that among the children initially categorized as autistic, 82.6% remained over the ADOS diagnostic threshold, 11.9% converted to atypical autism, and 5.4% fell under the ADOS threshold. Most atypical autism diagnoses were unstable. Most (81.7%) autistic participants had an ID at inclusion. At T1, 59.3% were nonverbal, but only 39% at T4. Most changes occurred between 4 and 8 years of age. Approximately 25% of participants exhibited a “high” growth trajectory, in which progress continues throughout adolescence, and 75% a “low” growth trajectory, characterized by greater autistic symptoms, intellectual disability, and lower language abilities reflected by high CARS scores, low apparent DQ, and speech difficulties, which mostly, but not always, predicted low trajectories. Our findings suggest that the adaptive prognosis of autism is mostly poor in this cohort, biased toward intellectual disability. However, changes in diagnostic, speech, and adaptive status are not uncommon, even for indivduals with low measured intelligence or apparent intellectual disability, and are sometimes difficult to predict. Autism Research 2018, 11: 1455–1467. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Ensemble System for Multimodal Emotion Recognition Challenge (MEC 2017)

Speech emotion recognition (SER) is a challenging task with many problems unsolved, such as extracting representative features and dealing with imbalanced training data. For the past few decades, much research has been done in this area, but the performance is still far from satisfying. In this paper, we propose an Ensemble System which fuses four different subsystems. TDNN (Time Delay Neural Network) System uses a neural network with p-norm and time delay as the classifier. i-vector/SVM (Support Vector Machine) System learns the acoustic feature from i-vector space. Simple Late Fusion System fuses different features on the decision level while Balanced Late Fusion System introduces a data rebalance module to rebalance the class distribution of the training samples. The overall Ensemble System takes the advantages of each subsystem on the decision level. Experiments are conducted on the CHEAVD 2.0 database which is provided in the Multimodal Emotion Recognition Challenge. The results of the Simple Late Fusion System on the test set outperforms the baseline system by 3.9% and 6.9% on Accuracy (ACC) and Macro Average Precision (MAP), respectively. Our results indicate that the Simple Late Fusion System is more effective on ACC and MAP while the Balanced Late Fusion System outperforms other systems on Macro Average Recall and Macro Average F1.
Development and decline of the hippocampal long-axis specialization and differentiation during encoding and retrieval of episodic memories

Change in hippocampal function is a major factor in lifespan development and decline of episodic memory. Evidence indicates a long-axis specialization where anterior hippocampus is more engaged during encoding and posterior during retrieval. We tested the lifespan trajectory of hippocampal long-axis episodic memory-related activity and functional connectivity (FC). 496 participants (6.8-80.8 years) were scanned with functional MRI while encoding and retrieving associative memories. We found clear evidence for a long-axis encoding-retrieval specialization. These long-axis effects declined linearly during development and aging, eventually vanishing in the older adults. This was mainly driven by age effects on retrieval. Retrieval was associated with gradually lower activity from childhood to adulthood, followed by positive age-relationships until 70 years. Interestingly, this pattern characterized task engagement regardless of memory success or failure. Children engaged posterior hippocampus more than anterior, while anterior hippocampus was more activated relative to posterior already in teenagers. Intra-hippocampal connectivity increased during task, and this increase declined with age. In sum, the results suggest that hippocampal long-axis differentiation and communication during episodic memory tasks develop rapidly during childhood and adolescence, are markedly different in older compared to younger adults, and are related to task engagement, not the successful completion of the task.
De-Noising Method Based on Dual-Tree Complex Wavelet Packet Transform and Principal Manifold and its Application for Fault Diagnosis

In order to extract the week fault features contained in the vibration signal of the mechanical equipment, a new de-nosing method based on dual-tree complex wavelet packet transform (DTCWPT) and principal manifold was proposed. Firstly, the vibration signals were decomposed into several sub-frequency bands by DTCWPT, the Shannon entropy was used to seek the best basis of DTCWPT, and a new adaptive threshold function was employed to denoise the wavelet packet coefficients on the best basis of the real part and imaginary part of DTCWPT via the de-noising criterion, then the wavelet packet coefficients were reconstructed into a high dimensional space. Secondly, t-distributed stochastic neighbor embedding (t-SNE) was performed to extract a low dimensional manifold, the proposed threshold function was further applied to process the low dimensional manifold, aiming at separate the signal and noise, the principal manifold was reconstructed by the method of spectral regression analysis, thus, the signals were reconstructed back into one dimensional time series after eliminating the noise. Finally, a simulated signal and a real bearing fault signal were used to validate the proposed method, the results have demonstrated that the proposed method has a good performance of nonlinear noise reduction, and can extract fault features of rolling bearing effectively.
Analysis of Bidding Strategies in the German Control Reserve Market

This paper analyzes bidding strategies in the German control reserve market, a multi-part pay-as-bid auction. We present a methodology to investigate multivariate time series under consideration of exogenous factors and apply it to real data. As results, we examine exogenous drivers of control reserve power prices, identify the efficient set of bidding strategies and analyze the serial correlation of applied bids in the period 2015 to 2017. The efficient set consists of two clusters - increased capacity price bid with moderate energy price bid and increased energy price bid with moderate capacity price. Combined with game-theoretic rationales, we are able to explain the price development and draw conclusions on how to prevent the application of bidding strategies in the control reserve market.
Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.
On Combination of Fuzzy Memberships for Medical Image Fusion using NSST based Fuzzy-PCNN

In this article the proposed Medical Image Fusion (MIF) technique uses combination of fuzzy membership val- ues as input to Pulse Coupled Neural Network (PCNN). We named the used PCNN model as Fuzzy-PCNN as the inputs are fuzzy in nature. The spatially co-registered medical images are multi-modal in nature. After decomposing the source medical images using Non-Subsampled Shearlet Transform (NSST) low frequency subbands (LFSs) are fused using the $\displaystyle \max$-selection rule. To fuse the high frequency subbands (HFSs), fuzzy member- ships using multiple membership functions are generated from a specific local-region of the HFSs’ coefficients. Then an L2- norm based ensembling operation is applied to find out the resultant of them. These resultant fuzzy memberships are used as input of PCNN. Inverse NSST (INSST) is applied to the fused coefficients to get the fused image. Visual and quantitative analysis and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme.
Applications des ultrasons de puissance en industries alimentaires

Cet article est une synthese des connaissances de base sur les ultrasons, en particulier la definition, les mecanismes d'action ainsi que les applications des ultrasons de puissance en industries alimentaires. A ce jour, les ultrasons de puissance sont utilises dans des domaines de production et de recherche varies, avec des applications plus ou moins directes. Ces applications sont: - optimiser des reactions chimiques - degazer des solutions ou les boissons - nettoyer des surfaces inertes des equipements agroalimentaires ou des matieres premieres (fruits ou legumes) - steriliser des objets ou des fluides en association avec d'autres technologies (ozone, micro-ondes.) - extraire des huiles, des jus de fruits, des substances toxiques, des parfums. - produire des emulsions ou des homogeneisations (mayonnaise, ketchup, salade, sauce.) - modifier des activites enzymatiques (augmenter l'efficacite des reactions enzymatiques recherchees ou inhiber des reactions enzymatiques indesirables) - depolymeriser des polymeres, par exemple l'amidon. D'autres applications sont listees dans cet article. Mais en raison des brevets concernant de nombreuses applications, on note un manque d'informations techniques.
The unlikely partnership between LRRK2 and α‐synuclein in Parkinson's disease

Our understanding of the mechanisms underlying Parkinson's disease, the once archetypical nongenetic neurogenerative disorder, has dramatically increased with the identification of α‐synuclein and LRRK2 pathogenic mutations. While α‐synuclein protein composes the aggregates that can spread through much of the brain in disease, LRRK2 encodes a multidomain dual‐enzyme distinct from any other protein linked to neurodegeneration. In this review, we discuss emergent datasets from multiple model systems that suggest these unlikely partners do interact in important ways in disease, both within cells that express both LRRK2 and α‐synuclein as well as through more indirect pathways that might involve neuroinflammation. Although the link between LRRK2 and disease can be understood in part through LRRK2 kinase activity (phosphotransferase activity), α‐synuclein toxicity is multilayered and plausibly interacts with LRRK2 kinase activity in several ways. We discuss common protein interactors like 14‐3‐3s that may regulate α‐synuclein and LRRK2 in disease. Finally, we examine cellular pathways and outcomes common to both mutant α‐synuclein expression and LRRK2 activity and points of intersection. Understanding the interplay between these two unlikely partners in disease may provide new therapeutic avenues for PD.
A Composite Control Strategy for Suppressing the Current Harmonic at the Grid Side of V2G Charger

T307 V2G (Vehicle to Grid) charger is an important physical interface for EVs to provide V2G services to the grid. It not only has the charging function, but also has the function of feeding the power grid. The bi-directional charger with V2G service has a wide range of output power. The current ripple proportion at light-load becomes larger and the current response speed becomes slower, which will cause the zero-crossing distortion. And the traditional controller steady-state error will also increase, so that the grid side current harmonic proportion becomes larger lead to deteriorate in power quality. In this paper, a composite control strategy based on quasi proportional resonant (QPR) control and the weighted current (WC) control is presented. By simulation and experimental results, it is proved that this strategy has better harmonic suppression performance than traditional PI control methods, limited to 3.2%, which is conducive to the realization of electric vehicles feeding the smart grid cleanly.