Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyclic Strain and Electrical Co-stimulation Improve Neural Differentiation of Marrow-Derived Mesenchymal Stem Cells

The current study investigated the combinatorial effect of cyclic strain and electrical stimulation on neural differentiation potential of rat bone marrow-derived mesenchymal stem cells (BMSCs) under epidermal growth factor (EGF) and fibroblast growth factor 2 (FGF2) inductions in vitro. We developed a prototype device which can provide cyclic strain and electrical signal synchronously. Using this system, we demonstrated that cyclic strain and electrical co-stimulation promote the differentiation of BMCSs into neural cells with more branches and longer neurites than strain or electrical stimulation alone. Strain and electrical co-stimulation can also induce a higher expression of neural markers in terms of transcription and protein level. Neurotrophic factors and the intracellular cyclic AMP (cAMP) are also upregulated with co-stimulation. Importantly, the co-stimulation further enhances the calcium influx of neural differentiated BMSCs when responding to acetylcholine and potassium chloride (KCl). Finally, the phosphorylation of extracellular-signal-regulated kinase (ERK) 1 and 2 and protein kinase B (AKT) was elevated under co-stimulation treatment. The present work suggests a synergistic effect of the combination of cyclic strain and electrical stimulation on BMSC neuronal differentiation and provides an alternative approach to physically manipulate stem cell differentiation into mature and functional neural cells in vitro.
Traumatic Brain Injury Exposure Lowers Age of Cognitive Decline in AD and Non-AD Conditions

We aimed to detect the possible accelerating role of previous traumatic brain injury (TBI) exposures on the onset of later cognitive decline assessed across different brain diseases. We analyzed data from the National Alzheimer's Coordinating Center (NACC), which provide information on history of TBI and longitudinal data on cognitive and non-cognitive domains for each available subject. At the time of this investigation, a total of 609 NACC subjects resulted to have a documented history of TBI. We compared subjects with and without a history of previous TBI (of any type) at the time of their first cognitive decline assessment, and termed them, respectively, TBI+ and TBI– subjects. Three hundred and sixty-one TBI+ subjects (229 male/132 female) and 248 TBI– subjects (156 male/92 female) were available. The analyses included TBI+ and TBI– subjects with a clinical diagnosis of Mild Cognitive Impairment, Alzheimer's disease, Dementia with Lewy bodies, Progressive supranuclear palsy, Corticobasal degeneration, Frontotemporal dementia, Vascular dementia, non-AD Impairment, and Parkinson's disease. The data showed that the mean age of TBI+ subjects was lower than TBI– subjects at the time of their first cognitive decline assessment (71.6 ± 11.2 vs. 74.8 ± 9.5 year; p < 0.001). Moreover, the earlier onset of cognitive decline in TBI+ vs. TBI– subjects was independent of sex, race, attained education, APOE genotype, and importantly, clinical diagnoses. As for specific cognitive aspects, MMSE, Trail Making Test part B and WAIS-R scores did not differ between TBI+ and TBI– subjects, whereas Trail Making Test part A (p = 0.013) and Boston Naming test (p = 0.008) did. In addition, data showed that neuropsychiatric symptoms [based on Neuropsychiatry Inventory (NPI)] were much more frequent in TBI+ vs. TBI– subjects, including AD and non-AD neurodegenerative conditions such as PD. These cross-sectional analyses outcomes from longitudinally-assessed cohorts of TBI+ subjects that is, subjects with TBI exposure before the onset of cognitive decline in the contest of different neurodegenerative disorders and associated pathogenetic mechanisms, are novel, and indicate that a previous TBI exposure may act as a significant “age-lowering” factor on the onset of cognitive decline in either AD and non-AD conditions independently of demographic factors, education, APOE genotype, and current or upcoming clinical conditions.
Assessing Phonological Profiles in Children and Adolescents With Down Syndrome: The Effect of Elicitation Methods

In the context of comparing linguistic profiles across neurodevelopmental disorders, Down syndrome (DS) has captured growing attention for its uneven profile. Although specific weaknesses in grammatical and phonological processing have been reported, research evidence on phonological development remains scarce, particularly beyond early childhood. The purpose of this study was to explore the phonological profiles of children and adolescents with Down syndrome. The profiles were based on the frequency and relative proportion of the processes observed by classes, and they were compared to those of typically developing preschool children of similar verbal age. A complementary goal was to assess the effect of two different methods of elicitation: a test of articulation and spontaneous speech sampling. Finally, intergroup and intragroup differences in full match percentages between three positions at syllable-level (complex onset, medial coda, and final coda) were assessed. The results of the present study confirmed that the frequency of phonological processes in children and adolescents with DS is atypically high and is above what is expected for lexical age and at the same level as grammatical age. Highly increased frequency of processes, consistent in all kinds of processes and positions at the syllable-level, and asynchronous with verbal age and mental age suggest atypical developmental trajectories of phonological development in the Down syndrome population.
Social Media Exposure, Psychological Distress, Emotion Regulation, and Depression During the COVID-19 Outbreak in Community Samples in China

The outbreak of coronavirus disease 2019 (COVID-19) has been a global emergency, affecting millions of individuals both physically and psychologically. The present research investigated the associations between social media exposure and depression during the COVID-19 outbreak by examining the mediating role of psychological distress and the moderating role of emotion regulation among members of the general public in China. Participants (N = 485) completed a set of questionnaires online, including demographic information, self-rated physical health, and social media exposure to topics related to COVID-19. The Impact of Event Scale-Revised (IES-R), the Beck Depression Inventory-II (BDI-II), and the Emotion Regulation Questionnaire (ERQ) were utilized to measure psychological distress about COVID-19, depression, and emotion regulation strategies, respectively. Results found that older age and greater levels of social media exposure were associated with more psychological distress about the virus (r = 0.14, p = 0.003; r = 0.22, p < 0.001). Results of the moderated mediation model suggest that psychological distress mediated the relationship between social media exposure and depression (β = 0.10; Boot 95% CI = 0.07, 0.15). Furthermore, expressive suppression moderated the relationship between psychological distress and depression (β = 0.10, p = 0.017). The findings are discussed in terms of the need for mental health assistance for individuals at high risk of depression, including the elderly and individuals who reported greater psychological distress and those who showed preference usage of suppression, during the COVID-19 crisis.
Agarose, Alginate and Chitosan Nanostructured Aerogels for Pharmaceutical Applications: A Short Review

In this short review, drug delivery systems, formed by polysaccharide-based (i.e., agarose, alginate, and chitosan) aerogels, are analyzed. In particular, the main papers, published in the period 2011–2020 in this research field, have been investigated and critically discussed, in order to highlight strengths and weaknesses of the traditional production techniques (e.g., freeze-drying and air evaporation) of bio-aerogels with respect to supercritical CO2 assisted drying. Supercritical CO2 assisted drying demonstrated to be a promising technique to produce nanostructured bio-aerogels that maintain the starting gel volume and shape, when the solvent removal occurs at negligible surface tension. This characteristic, coupled with the possibility of removing also cross-linking agent residues from the aerogels, makes these advanced devices safe and suitable as carriers for controlled drug delivery applications.
A Meta-Analysis of the Effects of Mental Contrasting With Implementation Intentions on Goal Attainment

Mental contrasting with implementation intentions (MCII) is a self-regulation strategy that enhances goal attainment. This meta-analysis evaluated the efficacy of MCII for goal attainment and explored potential moderators. A total of 21 empirical studies with 24 independent effect sizes (15,907 participants) were included in the analysis. Results showed that MCII to be effective for goal attainment with a small to medium effect size (g = 0.336). The effect was mainly moderated by intervention style. Specifically, studies with interventions based on interactions between participants and experimenters (g = 0.465) had stronger effects than studies with interventions based on interactions between participants and documents (g = 0.277). The results revealed that MCII is a brief and effective strategy for goal attainment with a small to moderate effect; however, because of some publication bias, the actual effect sizes may be smaller. Due to small number of studies in this meta-analysis, additional studies are needed to determine the role of moderator variables.
Do You Get What I Mean?!? The Undesirable Outcomes of (Ab)Using Paralinguistic Cues in Computer-Mediated Communication

The shift to working from home, which has intensified due to Covid-19, increased our reliance on communication technology and the need to communicate effectively via computer-mediated communication and especially via text. Paralinguistic cues, such as repeated punctuation, are used to compensate for the lack of non-verbal cues in text-based formats. However, it is unclear whether these cues indeed bridge the potential gap between the writer’s intentions and the reader’s interpretations. A pilot study and two experiments investigated the effect of using repeated punctuation on behavioral intention to assist an email writer in a work-related situation. Findings demonstrate that while the intentions behind using repeated punctuation relate to signaling situational importance or affective state, behavioral intentions are driven by dispositional rather than situational attributions. Specifically, the use of repeated punctuation reduces perceived competence of the message writer and consequently decreases positive behavioral intentions. Overall, the study challenges the simplified view of paralinguistic cues as communication facilitators, highlighting their potential harmful effects on impression formation and behavioral intentions in the digital age.
Alterations of Serum Metabolites and Fecal Microbiota Involved in Ewe Follicular Cyst

While the interactions of the gut microbiome and blood metabolome have been widely studied in polycystic ovary disease in women, follicular cysts of ewes have been scarcely investigated using these methods. In this study, the fecal microbiome and serum metabolome were used to compare between ewes diagnosed with ovarian cystic follicles and ewes with normal follicles, to investigate alterations of the fecal bacterial community composition and metabolic parameters in relation to follicular cystogenesis. Ewes from the same feeding and management system were diagnosed with a follicular cyst (n = 6) or confirmed to have normal follicles (n = 6) by using a B-mode ultrasound scanner. Blood serum and fresh fecal samples of all ewes were collected and analyzed. The α-diversity of fecal microbiome did not differ significantly between follicular cyst ewes and normal follicle ewes. Three genera (Bacteroides, Anaerosporobacter, and Angelakisella) were identified and their balance differentiated between follicular cyst and normal follicle ewes. Alterations of several serum metabolite concentrations, belonging to lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, benzenoids, phenylpropanoids and polyketides, and organoheterocyclic compounds, were associated with the presence of a follicular cyst. Correlation analysis between fecal bacterial communities and serum metabolites indicated a positive correlation between Anaerosporobacter and several fatty acids, and a negative correlation between Bacteroides and L-proline. These observations provide new insights for the complex interactions of the gut microbiota and the host serum lipid profiles, and support gut microbiota as a potential strategy to treat and prevent follicular cysts in sheep.
Research on Market Stock Index Prediction Based on Network Security and Deep Learning

As one of the most popular financial management methods, stocks have attracted more and more investors to participate. The risks of stock investment are relatively high. How to reduce risks and increase profits has become the most concerned issue for investors. Traditional stock forecasting models use forecasting models based on stock time series analysis, but time series models cannot consider the influence of investor sentiment on stock market changes. In order to use investor sentiment information to make more accurate stock market forecasts, this paper establishes a stock index forecast and network security model based on time series and deep learning. Based on the time series model, it is proposed to use CNN to extract in-depth emotional information to replace the basic emotional features of the emotional extraction level. At the data source level, other information sources, such as basic features, are introduced to further improve the predictive performance of the model. The results show that the algorithm is feasible and effective and can better predict the changes in the market stock index. This also proves that multiple information sources can improve the accuracy of model prediction more effectively than a single information source.
Integrated communication and localization in millimeter-wave systems

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.
Hierarchical Latent Words Language Models for Automatic Speech Recognition

: This paper presents hierarchical latent words language models (h-LWLMs) for improving automatic speech recognition (ASR) performance in out-of-domain tasks. Language models called h-LWLM are an advanced form of LWLM that are one one hopeful approach to domain robust language modeling. The key strength of the LWLMs is having a latent word space that helps to e ﬃ ciently capture linguistic phenomena not present in a training data set. However, standard LWLMs cannot consider that the function and meaning of words are essentially hierarchical. Therefore, h-LWLMs employ a multiple latent word space with hierarchical structure by estimating a latent word of a latent word recursively. The hierarchical latent word space helps us to ﬂexibly calculate generative probability for unseen words. This paper provides a deﬁnition of h-LWLM as well as a training method. In addition, we present two implementation methods that enable us to introduce the h-LWLMs into ASR tasks. Our experiments on a perplexity evaluation and an ASR evaluation show the e ﬀ ectiveness of h-LWLMs in out-of-domain tasks.
A New Hybrid LGPMBWM-PIV Method for Automotive Material Selection

Efforts are continuously being made by researchers to improve fuel efficiency and to reduce CO 2 emissions from the passenger cars. To achieve these goal, recent trend is to make the cars components light in weight for which manufacturing car roofs using natural fiber reinforced composites (NFCs) is one of the method. Several natural fibers (NFs)are available as alternative reinforcements for the fabrication of NFCs. Different NFs possess different properties and therefore, it is necessary to select the most appropriate natural fiber for fabrication of the composites which in turn will lead to the desired performance of the vehicle. Selection of the optimal natural fiber, amongst the several alternatives, is basically a multi criteria decision making (MCDM) problem as selection is based on the evaluation of several conflicting criteria. In this study, twelve alternative natural fibers (Flax, Hemp, Jute, Kenaf, Ramie, Okra, PALF, Coir, Isora, Cotton, Banana and Sisal) and six evaluation criteria (Tensile strength, Stiffness, Failure strain, Density, Degradation temperature and Moisture gain) are considered and selection of the optimal NF is made using a newly developed hybrid MCDM method i.e. Linear goal programming model for Best-Worst method (LGPMBWM) and Proximity index value method (PIV). Results of the study reveal that among all considered natural fibers, Ramie fiber is the most suitable alternative for the fabrication of composites and coir fiber is the worst candidate for the same.
Millimeter-wave wireless communications for home network in fiber-to-the-room scenario

Millimeter-wave (mmWave) technology has been well studied for both outdoor long-distance transmission and indoor short-range communication. In the recently emerging fiber-to-the-room (FTTR) architecture in the home network of the fifth generation fixed networks (F5G), mmWave technology can be cascaded well to a new optical network terminal in the room to enable extremely high data rate communication (i.e., >10 Gb/s). In the FTTR+mmWave scenario, the rapid degradation of the mmWave signal in long-distance transmission and the significant loss against wall penetration are no longer the bottlenecks for real application. Moreover, the surrounding walls of every room provide excellent isolation to avoid interference and guarantee security. This paper provides insights and analysis for the new FTTR+mmWave architecture to improve the customer experience in future broadband services such as immersive audiovisual videos.
N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection

Mobile malware poses a great challenge to mobile devices and mobile communication. With the explosive growth of mobile networks, it is significant to detect mobile malware for mobile security. Since most mobile malware relies on the networks to coordinate operations, steal information, or launch attacks, evading network monitor is difficult for the mobile malware. In this paper, we present an N-gram, semantic-based neural modeling method to detect the network traffic generated by the mobile malware. In the proposed scheme, we segment the network traffic into flows and extract the application layer payload from each packet. Then, the generated flow payload data are converted into the text form as the input of the proposed model. Each flow text consists of several domains with 20 words. The proposed scheme models the domain representation using convolutional neural network with multiwidth kernels from each domain. Afterward, relationships of domains are adaptively encoded in flow representation using gated recurrent network and then the classification result is obtained from an attention layer. A series of experiments have been conducted to verify the effectiveness of our proposed scheme. In addition, to compare with the state-of-the-art methods, several comparative experiments also are conducted. The experiment results depict that our proposed scheme is better in terms of accuracy.
Target Recognition of SAR Images Based on Azimuthal Constraint Reconstruction

A synthetic aperture radar (SAR) target classification method has been developed, in the study, based on dynamic target reconstruction. According to SAR azimuthal sensitivity, the truly useful training samples for the reconstructing the test sample are those with approaching azimuths and same labels. Hence, the proposed method performs linear presentation of the test sample on the local dictionary established by several training samples selected from each class under the azimuthal correlation. By properly adjusting the azimuthal correlation constraint, the test sample can be reconstructed at different levels by different scales of training samples. During the classification phase, the reconstruction error vectors from different levels are combined by linear fusion and the label of the test sample is determined based on the fused errors. Experimental conditions are setup on the moving and stationary target acquisition and recognition (MSTAR) dataset to evaluate the proposed method. The results confirm the effectiveness of the proposed method.
OTFS modulation performance in a satellite-to-ground channel at sub-6-GHz and millimeter-wave bands with high mobility

Orthogonal time frequency space (OTFS) modulation has been widely considered for high-mobility scenarios. Satellite-to-ground communications have recently received much attention as a typical high-mobility scenario and face great challenges due to the high Doppler shift. To enable reliable communications and high spectral efficiency in satellite mobile communications, we evaluate OTFS modulation performance for geostationary Earth orbit and low Earth orbit satellite-to-ground channels at sub-6-GHz and millimeter-wave bands in both line-of-sight and non-line-of-sight cases. The minimum mean squared error with successive detection (MMSE-SD) is used to improve the bit error rate performance. The adaptability of OTFS and the signal detection technologies in satellite-to-ground channels are analyzed. Simulation results confirm the feasibility of applying OTFS modulation to satellite-to-ground communications with high mobility. Because full diversity in the delay-Doppler domain can be explored, different terminal movement velocities do not have a significant impact on the performance of OTFS modulation, and OTFS modulation can achieve better performance compared with classical orthogonal frequency division multiplexing in satellite-to-ground channels. It is found that MMSE-SD can improve the performance of OTFS modulation compared with an MMSE equalizer.