Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Chinese Square Dance Exercise on Cognitive Function in Older Women With Mild Cognitive Impairment: The Mediating Effect of Mood Status and Quality of Life

The present study aimed to assess the effects of square dance exercise on the cognitive function and quality of life in older women with mild cognitive impairment and to investigate the mediating role of a depressed mood and reduced quality of life in the relationship between square dance exercise and cognition. The study design was a single-blind whole-group controlled trial. A total of 136 eligible participants were divided according to their nursing home into either an experimental or control group. The nursing home grouping was determined by the drawing of lots. The Montreal Cognitive Assessment (MoCA), Quality of Life (SF-12) and Geriatric Depression Scale (GDS-15) were used to assess participants at baseline, week 9, and week 18, respectively. Generalized estimating equations (GEE) were used to compare the results at baseline with mid-test and post-test changes in cognitive function and quality of life. Maximum likelihood estimation (ML) and robust standard errors were used to perform the mediation model. The study results indicated that the experimental group (compared to the control group) had a significant improvement in cognitive function, quality of life, and mood state at baseline in the mid-test and post-test results. The results of this 18-week experiment showed that the exercise–cognition relationship was significantly mediated by a reduction in depressive symptoms (indirect effect: β = −0.375; 95% CI = −0.864 to −0.069) and an improvement in quality of life (indirect effect: β = −0.678; 95% CI = −1.222 to −0.290). This study revealed the effects of moderate-intensity square dance exercise on cognitive function and quality of life in older Chinese women with mild cognitive impairment and explored the potential mediating mechanisms. These findings can be used to inform the development of public health policies to promote brain health in older adults with mild cognitive impairment.
Antineutrophil Cytoplasmic Antibody-Associated Vasculitis With Acute Kidney Injury: Short-Term Recovery Predicts Long-Term Outcome

Background Kidney involvement is common in antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV). It tends to be aggressive, and in some patients, the kidney involvement may reach the criteria of acute kidney injury (AKI). Here, we aim to describe the clinical characteristics of these patients and find risk factors for poor outcomes. Methods Patients diagnosed with AAV in our hospital from February 2003 to February 2017 were included. Those who reached the KDIGO AKI criteria were reclassified according to the KDIGO AKI stage. The clinical features of these patients were analyzed. Also, according to the variation of serum creatinine 3 months after AKI episode, patients were further divided into two groups: patients whose serum creatinine (Scr) level at the third month decreased by 30% or more from the peak Scr level was classified into G1 and others were classified into G2. Long-term renal and survival outcomes of these patients were analyzed with a Cox model. The renal endpoint was reaching end-stage renal disease (ESRD), and the survival endpoint was death. Nomograms were built based on cox models. Results Of 141 AAV patients included, during the median follow-up period of 64.0 (IQR 34.8, 85.4) months, 36 (25.5%) patients reached renal endpoints, and 22 (15.6%) patients died. The median renal survival time was 35.9 (IQR 21.3, 72.6) months and the median survival time was 48.4 (IQR 26.8, 82.8) months. Multivariate analysis showed that poor recovery of Scr level at 90 days (P < 0.001, RR = 9.150, 95%CI 4.163–20.113), BVAS score (P = 0.014, RR = 1.110, 95% CI1.021–1.207), and AKI stage 3 (P = 0.012 RR = 3.116, 95%CI 1.278–7.598) were independent risk factors for renal endpoints; poor recovery of Scr level at 90 days (P = 0.010, RR = 3.264, 95%CI 1.326–8.035), BVAS score (P = 0.010, RR = 1.171, 95%CI 1.038–1.320) and age (P = 0.017, RR = 1.046, 95%CI 1.008–1.086) were independent risk factors for all-cause death. The c-index of nomograms is 0.830 for the renal outcome and 0.763 for the survival outcome. Conclusion KDIGO AKI stage 3 is the risk factor for ESRD in AAV patients with AKI. The BVAS score and level of kidney function recovery at 90 days are the independent risk factors for both ESRD and all-cause death and are of predictive value for the outcome.
Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. The molecular profiles of immune components and related genes are of tremendous value for the study of primary resistance to immune checkpoint blockers (ICBs) for gastric cancer (GC) and serve as prognostic biomarkers to predict GC survival. Recent studies have revealed that tumor immune cell infiltration (ICI) is an indicator of the survival and responsiveness to chemotherapy in GC patients. Here, we describe the immune cell landscape based on the ESTIMATE and CIBERSORT algorithms to help separate GC into 3 ICI clusters using the unsupervised clustering method. Further in-depth analyses, such as differential expression gene (DEG) analysis and principal component analysis (PCA), help to establish an ICI scoring system. A low ICI score is characterized by an increased tumor mutation burden (TMB). The combination of the ICI score and TMB score better predicts the survival of GC patients. Analyses based on public and our own database revealed that the ICI scoring system could also help predict the survival and chemotherapy responsiveness of GC patients. The present study demonstrated that the ICI score may be an effective prognostic biomarker and predictive indicator for chemotherapy and immunotherapy.
Benefits Beyond Borders: Assessing Landowner Willingness-to-Accept Incentives for Conservation Outside Protected Areas

Unplanned land-use change surrounding protected areas (PAs) can lead to degradation and fragmentation of wildlife habitats, thereby placing tremendous pressure on PAs especially in tropical countries. Incentivizing the expansion of habitats beyond PAs will not only benefit wildlife but also has the potential to create livelihood opportunities for marginalized communities living adjacent to PAs. Our study explored landowners’ willingness to participate in an incentive-based, wildlife-friendly land-use program using a discrete choice modeling approach. We surveyed 699 landowners living in 287 villages within a five-kilometer buffer around Nagarahole and Bandipur National Parks in India. We found that landowners preferred wildlife-friendly land-use over their ongoing farming practices. Landowners preferred short-term programs, requiring enrolling smaller parcels of land for wildlife-friendly land-use, and offering higher payment amounts. Landowners with larger landholdings, a longer history of living next to the PA, and growing fewer commercial crops were more likely to prefer enrolling large parcels of land. Landowners who grew more commercial crops were likely to prefer long term programs. We also estimated the average monetary incentive to be INR 64,000 (US$ 914) per acre per year. Wildlife-friendly land use, in developing economies like India with shrinking wildlife habitats and expanding infrastructural developments, could supplement rural incomes and potentially expand habitat for wildlife, thereby being a promising conservation strategy.
Associations Between Child Maltreatment and Depressive Symptoms Among Chinese College Students: An Analysis of Sex Differences

Background: Depressive symptoms and child maltreatment are both global public health problems among young adults. This study aimed to investigate the associations between five types of child maltreatment and depressive symptoms among Chinese college students, with a focus on potential sex differences. Methods: A cross-sectional study of a nationally representative sample of Chinese college students was conducted from March to June 2019 with a multistage, stratified cluster, random sampling method. In total, 30,179 college students from 60 colleges of 10 Chinese province-level regions completed standard questionnaires, including a history of child maltreatment and current depressive symptoms. Results: The prevalence of depressive symptoms among college students in China was 7.3%. After adjusting for control variables, physical abuse (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI] = 1.17–1.23), emotional abuse (aOR = 1.21, 95% CI = 1.19–1.23), sexual abuse (aOR = 1.19, 95% CI = 1.16–1.22), physical neglect (aOR = 1.14, 95% CI = 1.12–1.16) and emotional neglect (aOR = 1.08, 95% CI = 1.07–1.09) were all positively associated with depressive symptoms. Notably, a cumulative effect of child maltreatment on depressive symptoms among Chinese college students was observed. Moreover, sex differences in the associations of emotional abuse, emotional neglect, and the number of maltreatment types with depressive symptoms were statistically significant (P < 0.05). Further stratification analyses showed that female students who experienced emotional abuse and emotional neglect had a higher risk of depressive symptoms than male students, and the cumulative effect of maltreatment types was stronger for females than males. Conclusion: Five types of child maltreatment and their co-occurrence were associated with an increased risk of depressive symptoms among college students. Furthermore, the effects of emotional abuse, emotional neglect and the number of maltreatment types on depressive symptoms were stronger for females than for males. These findings can promote understanding of the effects of child maltreatment on depressive symptoms, and prevention and intervention strategies for depressive symptoms should consider the type of child maltreatment and sex differences.
Improved Speech in Noise Perception in the Elderly After 6 Months of Musical Instruction

Understanding speech in background noise poses a challenge in daily communication, which is a particular problem among the elderly. Although musical expertise has often been suggested to be a contributor to speech intelligibility, the associations are mostly correlative. In the present multisite study conducted in Germany and Switzerland, 156 healthy, normal-hearing elderly were randomly assigned to either piano playing or music listening/musical culture groups. The speech reception threshold was assessed using the International Matrix Test before and after a 6 month intervention. Bayesian multilevel modeling revealed an improvement of both groups over time under binaural conditions. Additionally, the speech reception threshold of the piano group decreased during stimuli presentation to the left ear. A right ear improvement only occurred in the German piano group. Furthermore, improvements were predominantly found in women. These findings are discussed in the light of current neuroscientific theories on hemispheric lateralization and biological sex differences. The study indicates a positive transfer from musical training to speech processing, probably supported by the enhancement of auditory processing and improvement of general cognitive functions.
Serglycin-Deficiency Causes Reduced Weight Gain and Changed Intestinal Cytokine Responses in Mice Infected With Giardia intestinalis

The proteoglycan serglycin (SG) is expressed by different innate and adaptive immune cells, e.g. mast cells, macrophages, neutrophils, and cytotoxic T lymphocytes, where SG contributes to correct granule storage and extracellular activity of inflammatory mediators. Here the serglycin-deficient (SG−/−) mouse strain was used to investigate the impact of SG on intestinal immune responses during infection with the non-invasive protozoan parasite Giardia intestinalis. Young (≈11 weeks old) oral gavage-infected congenic SG−/− mice showed reduced weight gain as compared with the infected SG+/+ littermate mice and the PBS-challenged SG−/− and SG+/+ littermate mice. The infection caused no major morphological changes in the small intestine. However, a SG-independent increased goblet cell and granulocyte cell count was observed, which did not correlate with an increased myeloperoxidase or neutrophil elastase activity. Furthermore, infected mice showed increased serum IL-6 levels, with significantly reduced serum IL-6 levels in infected SG-deficient mice and decreased intestinal expression levels of IL-6 in the infected SG-deficient mice. In infected mice the qPCR analysis of alarmins, chemokines, cytokines, and nitric oxide synthases (NOS), showed that the SG-deficiency caused reduced intestinal expression levels of TNF-α and CXCL2, and increased IFN-γ, CXCL1, and NOS1 levels as compared with SG-competent mice. This study shows that SG plays a regulatory role in intestinal immune responses, reflected by changes in chemokine and cytokine expression levels and a delayed weight gain in young SG−/− mice infected with G. intestinalis.
Gestational Weight Gain and Its Effects on Maternal and Neonatal Outcome in Women With Twin Pregnancies: A Systematic Review and Meta-Analysis

Background: The incidence of twin pregnancies has risen recently. Such pregnancies are associated with an increased risk for poor maternal and infant outcomes. Gestational weight gain, particularly in singleton pregnancies, has been well-linked with maternal and infant outcomes. The aim of the current meta-analysis was to evaluate the effects of gestational weight gain on maternal and fetal outcomes in women with twin pregnancies. Methods: A systematic search was conducted using the PubMed, Scopus, and Google Scholar databases. Studies, either retrospective or prospective in design, evaluating the effects of gestational weight gain (defined using Institute of Medicine (IOM) guidelines) maternal and/or fetal/neonatal outcomes in women with twin pregnancies were included. Statistical analysis was performed using STATA software. Results: Eleven studies were included in the meta-analysis. Mothers with inadequate weight gain had increased risk for gestational diabetes mellitus (OR 1.19; 95% CI: 1.01, 1.40) and decreased risk for gestational hypertension (OR 0.58; 95% CI: 0.49, 0.68) and cesarean section (OR 0.94; 95% CI: 0.93, 0.96). Neonates born to mothers with inadequate weight gain were susceptible to increased risk for preterm delivery (OR 1.17; 95% CI: 1.03, 1.34), very preterm delivery (gestational age <32 weeks) (OR 1.84; 95% CI: 1.36, 2.48), small for gestational age status (OR 1.41; 95% CI: 1.15, 1.72), low birth weight status (<2,500 g) (OR 1.27; 95% CI: 1.17, 1.38), and neonatal intensive care unit (NICU) admission (OR 1.16; 95% CI: 1.08, 1.24). The pooled findings indicate an increased risk for gestational hypertension (OR 1.82; 95% CI: 1.60, 2.06) and cesarean section (OR 1.07; 95% CI: 1.05, 1.08) among mothers with excessive weight gain. Neonates born to mothers with excessive weight gain were susceptible to increased risk for preterm delivery and very preterm delivery, but were associated with a decreased risk for low birth weight status and small for gestational age status. Conclusions: Gestational weight gain in twin pregnancy, either lower or higher than IOM recommended guidelines, is associated with poor maternal and neonatal outcomes. Our findings call for incorporating counseling on optimal weight gain during pregnancy as part of routine antenatal visits.
Exploiting Non-Negative Matrix Factorization for Binaural Sound Localization in the Presence of Directional Interference

This study presents a novel solution to the problem of binaural localization of a speaker in the presence of interfering directional noise and reverberation. Using a state-of-the-art binaural localization algorithm based on a deep neural network (DNN), we propose adding a source separation stage based on non-negative matrix factorization (NMF) to improve the localization performance in conditions with interfering sources. The separation stage is coupled with the localization stage and is optimized with respect to a broad range of different acoustic conditions, emphasizing a robust and generalizable solution. The machine listening system is shown to greatly benefit from the NMF-based separation stage at low target-to-masker ratios (TMRs) for a variety of noise types, especially for non-stationary noise. It is also demonstrated that training the NMF algorithm on anechoic speech provides better performance than using reverberant speech, and that optimizing the source separation stage using a localization metric rather than a source separation metric substantially increases the system performance.
An Adaptive Discriminant and Sparsity Feature Descriptor for Finger Vein Recognition

The use of finger-vein (FV) trait for the purpose of identity authentication has attracted much attention in recent years. However, most of the conventional FV recognition methods are hand-crafted by design and require strong prior knowledge, which are ineffective at expressing the distinctiveness of the FV images. In this paper, we propose an adaptive discriminant and sparsity feature descriptor (DSFD) for FV feature extraction and recognition. Specifically, we first form a direction difference vector (DDV) to better represent the direction feature of the FV images. Afterwards, the DSFD adaptively projects the DDVs into a feature space with discriminative binary codes in which the distance of the within-class samples is minimized and simultaneously the distance of the between-class samples is maximized. Lastly, we concatenate the block-wise histograms into a global histogram as the final feature descriptor for FV recognition. Experimental results on two publicly available FV databases demonstrate the effectiveness of the proposed method.
Multi-Scale and Multi-Region Facial Discriminative Representation for Automatic Depression Level Prediction

Physiological studies have shown that differences in facial activities between depressed patients and normal individuals are manifested in different local facial regions and the durations of these activities are not the same. But most previous works extract features from the entire facial region at a fixed time scale to predict the individual depression level. Thus, they are inadequate in capturing dynamic facial changes. For these reasons, we propose a multi-scale and multi-region fa-cial dynamic representation method to improve the prediction performance. In particular, we firstly use multiple time scales to divide the original long-term video into segments containing different facial regions. Secondly, the segment-level feature is extracted by 3D convolution neural network to characterize the facial activities with different durations in different facial regions. Thirdly, this paper adopts eigen evolution pooling and gradient boosting decision tree to aggregate these segment-level features and select discriminative elements to generate the video-level feature. Finally, the depression level is predicted using support vector regression. Experiments are conducted on AVEC2013 and AVEC2014. The results demonstrate that our method achieves better performance than the previous works.
Patch Decoder-Side Depth Estimation In Mpeg Immersive Video

This paper presents a new approach for achieving bitrate and pixel rate reduction in the MPEG immersive video coding setting. We demonstrate that it is possible to avoid the transmission of some depth information in the Test Model for Immersive Video (TMIV) by estimating it at the receiver's side. Although the transmitted information in TMIV is considered as non-redundant, we show that it is possible to improve this algorithm. This method provides 3.4%, 9.0%, and 12.1% average BD-rate gain for natural content on high, medium, and low bitrate, respectively, with up to respectively 12.3%, 16.0%, and 18.4% peak reductions. Moreover, it preserves the perceptual quality as measured with MS-SSIM and VMAF metrics. Additionally, it decreases the pixel rate by 8.3% for each test sequence.
Acoustic Reflectors Localization from Stereo Recordings Using Neural Networks

Acoustic room geometry estimation is often performed in ad hoc settings, i.e., using multiple microphones and sources distributed around the room, or assuming control over the excitation signals. We propose a fully convolutional network (FCN) that localizes reflective surfaces under the relaxed assumptions that (i) a compact array of only two microphones is available, (ii) emitter and receivers are not synchronized, and (iii) both the excitation signals and the impulse responses of the enclosures are unknown. Our FCN is trained in a supervised fashion to predict the likelihood of reflective surfaces at specific distances and directions-of-arrival (DOA). When a single reflective surface is present, up to 80% of real and virtual sources are detected, while this figure approaches 50% in rectangular rooms. Experiments on real-world recordings report similar accuracy as with artificially reverberated speech signals, validating the generalization capabilities of the framework.
Non-Intrusive Binaural Prediction of Speech Intelligibility Based on Phoneme Classification

In this study, we explore an approach for modeling speech intelligibility in spatial acoustic scenes. To this end, we combine a non-intrusive binaural frontend with a deep neural network (DNN) borrowed from a standard automatic speech recognition (ASR) system. The DNN estimates phoneme probabilities that degrade in the presence of noise and reverberation, which is quantified with an entropy-based measure. The model output is used to predict speech recognition thresholds, i.e., signal-to-noise ratio with 50% word recognition accuracy. It is compared to measured data obtained from eight normal-hearing listeners in acoustic scenarios with varying positions of localized maskers, different rooms and reverberation times. The model is non-intrusive; yet it produces a root mean squared error in the range of 0.6-2.1 dB, which is similar to results obtained with a reference model (0.3-1.8 dB) that uses oracle knowledge both in the frontend and in the backend stage.
Unsupervised Stacked Capsule Autoencoder for Hyperspectral Image Classification

Since CapsNet [1] shattered all previous records of algorithms for image recognition, the capsule's conception has attracted bright attention. It interprets an object by the geometrical arrangement of parts. We think it can be transferred to hyperspectral images. In a hyperspectral data cube, each pixel spectrum can be regarded as a continuous curve representing its inherent properties. In the spatial domain, there are various spatial distributions in different positionsand there is usually a specific structural relationship between adjacently distributed categories. Based on HSI data's aforementioned structural characteristics, combined with the stacked capsule autoencoder, we propose our model to achieve an unsupervised HSI classification. In our model, the ConvLSTM is employed to discover part capsules of HSI, and we utilize Set Transformer to encode relations among all parts and indicate object capsules. The decoders of both phases use Gaussian mixture models to reconstruct specific information. Experimental results of the Pavia Center dataset show the exceptional of our model.
Efficient Adversarial Audio Synthesis VIA Progressive Upsampling

This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform. Progressive upsampling GAN (PUGAN) leverages the progressive generation of higher-resolution output by stacking multiple encoder-decoder architectures. Compared to an existing state-of-the-art model called WaveGAN, which uses a single decoder architecture, our model generates audio signals and converts them to a higher resolution in a progressive manner, while using a significantly smaller number of parameters, e.g., 3.17x smaller for 16 kHz output, than WaveGAN. Our experiments show that the audio signals can be generated in real time with a comparable quality to that of WaveGAN in terms of the inception scores and human perception.