Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Phagocytic Properties of CD45low Microglia and CD45high Brain Mononuclear Phagocytes—Activation and Age-Related Effects

In the central nervous system (CNS), microglia are innate immune mononuclear phagocytes (CNS MPs) that can phagocytose infectious particles, apoptotic cells, neurons, and pathological protein aggregates, such as Aβ in Alzheimer’s disease (AD). While CD11b+CD45low microglia account for the majority of CNS MPs, a small population of CD11b+CD45high CNS MPs is also recognized in AD that surround Aβ plaques. These transcriptionally and pathologically unique CD45high cells have unclear origin and undefined phagocytic characteristics. We have comprehensively validated rapid flow cytometric assays of bulk-phase and amyloid β fibril (fAβ) phagocytosis and applied these to study acutely isolated CNS MPs. Using these methods, we provide novel insights into differential abilities of CD11b+ CD45low and CD45high CNS MPs to phagocytose macroparticles and fAβ under normal, acute, and chronic neuroinflammatory states. CD45high CNS MPs also highly upregulate TREM2, CD11c, and several disease-associated microglia signature genes and have a higher phagocytic capacity for Aβ as compared to CD45low microglia in the 5xFAD mouse model of AD that becomes more apparent with aging. Our data suggest an overall pro-phagocytic and protective role for CD11b+CD45high CNS MPs in neurodegeneration, which if promoted, could be beneficial.
Effects on Subclinical Heart Failure in Type 2 Diabetic Subjects on Liraglutide Treatment vs. Glimepiride Both in Combination with Metformin: A Randomized Open Parallel-Group Study

Objective We aimed to investigate the effect of liraglutide treatment on heart function in type 2 diabetes (T2D) patients with subclinical heart failure. Methods Randomized open parallel-group trial. 62 T2D patients (45 male) with subclinical heart failure were randomized to either once daily liraglutide 1.8 mg, or glimepiride 4 mg, both add on to metformin 1 g twice a day. Mitral annular systolic (s′) and early diastolic (e′) velocities were measured at rest and during bicycle ergometer exercise, using tissue Doppler echocardiography. The primary endpoint was 18-week treatment changes in longitudinal functional reserve index (LFRIdiastolic/systolic). Results Clinical characteristics between groups (liraglutide = 33 vs. glimepiride = 29) were well matched. At baseline left ventricle ejection fraction (53.7 vs. 53.6%) and global longitudinal strain (−15.3 vs. −16.5%) did not differ between groups. There were no significant differences in mitral flow velocities between groups. For the primary endpoint, there was no treatment change [95% confidence interval] for: LFRIdiastolic (−0.18 vs. −0.53 [−0.28, 2.59; p = 0.19]), or LFRIsystolic (−0.10 vs. −0.18 [−1.0, 1.7; p = 0.54]); for the secondary endpoints, there was a significant treatment change in respect of body weight (−3.7 vs. −0.2 kg [−5.5, −1.4; p = 0.001]), waist circumference (−3.1 vs. −0.8 cm [−4.2, −0.4; p = 0.019]), and heart rate (HR) (6.3 vs. −2.3 bpm [−3.0, 14.2; p = 0.003]), with no such treatment change in hemoglobin A1c levels (−11.0 vs. −9.2 mmol/mol [−7.0, 2.6; p = 0.37]), between groups. Conclusion 18-week treatment of liraglutide compared with glimepiride did not improve LFRIdiastolic/systolic, but however increased HR. There was a significant treatment change in body weight reduction in favor for liraglutide treatment.
Effects of Dietary Cholesterol and Its Oxidation Products on Pathological Lesions and Cholesterol and Lipid Oxidation in the Rabbit Liver

This study was conducted to determine the effects of dietary cholesterol (CHO) and cholesterol oxidation products (COPs) on the induction of pathological lesions in rabbit liver tissues. Liver lesions were induced only when the levels of CHO and COPs in the diet were very high. The amount of CHO measured in the liver increased when dietary CHO was increased; by comparison, dietary COPs affected liver CHO amounts to a lesser extent. The TBARS (thiobarbituric acid reactive substances) value measured for the liver samples also increased when dietary CHO and COP levels were elevated, and the TBARS value was more strongly affected by the amount of COPs in the diet than by the amount of CHO. At 6 and 12 weeks, COP levels were the highest in the group that received 1.2 g CHO + 0.8 g COPs, followed by the 0.5 g CHO + 0.5 g COPs and 1.6 g CHO + 0.4 g COPs groups; the control (0 g) group showed the lowest COP levels among all groups. In this study, we found that not only dietary CHO but also COPs were involved in hypercholesterolemia induced liver lesions when the amount of CHO and COPs was high.
Syntax and Semantics of Italian Poetry in the First Half of the 20th Century

In this paper we study, analyse and comment rhetorical figures present in some of most interesting poetry of the first half of the twentieth century. These figures are at first traced back to some famous poet of the past and then compared to classical Latin prose. Linguistic theory is then called in to show how they can be represented in syntactic structures and classified as noncanonical structures, by positioning discontinuous or displaced linguistic elements in Spec XP projections at various levels of constituency. Then we introduce LFG (Lexical Functional Grammar) as the theory that allows us to connect syntactic noncanonical structures with informational structure and psycholinguistic theories for complexity evaluation. We end up with two computational linguistics experiments and then evaluate the results. The first one uses best online parsers of Italian to parse poetic structures; the second one uses Getarun, the system created at Ca Foscari Computational Linguistics Laboratory. As will be shown, the first approach is unable to cope with these structures due to the use of only statistical probabilistic information. On the contrary, the second one, being a symbolic rule based system, is by far superior and allows also to complete both semantic an pragmatic analysis.
Alteration in follistatin gene expression detected in prenatally androgenized rats

Abstract Impaired ovarian follicle development, the hallmark of polycystic ovarian syndrome (PCOS), is believed to be due to the changes in expression of related genes such as follistatin (FST). Expression of FST gene and methylation level of its promoter in theca cells from adult female rats, prenatally exposed to androgen excess, during different phases of the estrus cycle was determined and compared with controls. Eight pregnant Wistar rats (experimental group) were treated by subcutaneous injection of 5 mg free testosterone on day 20 of pregnancy, while controls (n = 8) received 500 ml solvent. Based on observed vaginal smear, adult female offspring of mothers were divided into three groups. Levels of serum steroidogenic sexual hormones and gonadotropins, expression and promoter methylation of the FST gene were measured using ELISA, cyber-green real-time PCR and bisulfite sequence PCR (BSP), respectively. Compared to controls, the relative expression of FST gene in the treated group decreased overall by 0.85 fold; despite significant changes in different phases, but no significant differences in methylation of FST promoter. Our results reveal that manifestation of PCOS-like phenotype following prenatal exposure to excess androgen is associated with irregularity in expression of the FST gene during the estrus cycle.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. They are also at the core of discrete choice models. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally expensive, as the complexity scales linearly with the number of outcomes. To address this problem, we propose augment and reduce (A&R), a method to alleviate the computational complexity. A&R uses two ideas: latent variable augmentation and stochastic variational inference. It maximizes a lower bound on the marginal likelihood of the data. Unlike existing methods which are specific to softmax, A&R is more general and is amenable to other categorical models, such as multinomial probit. On several large-scale classification problems, we show that A&R provides a tighter bound on the marginal likelihood and has better predictive performance than existing approaches.
A Validation Framework for Visible Light Positioning in Mobile Robotics

Visible Light Positioning (VLP) is emerging as a solution for indoor localization. Interest on VLP has risen, amongst other reasons, as a result of the dissemination of LED illumination. This paper proposes a VLP system based on photo-diode receivers arranged in a regular pattern over a hemispherical dome to detect the position of a light source. Additionally, it proposes a 3D test environment, based on Gazebo, allowing the simulation and validation of our system and its application to mobile robot systems. The results show that the proposed system is capable of estimating the location of a mobile robot with an error of a few centimetres in a 3D space of 20×20×5m.
OFDM-Based Interference Alignment in Single-Antenna Cellular Wireless Networks

Interference alignment (IA) is widely regarded as a promising interference management technique in wireless networks. Despite its rapid advances in cellular networks, most results of IA are limited to information-theoretic exploration or physical-layer signal design. Little progress has been made so far to advance IA in cellular networks from a networking perspective. In this paper, we aim to fill this gap by studying IA in large-scale cellular networks. For the uplink, we propose an OFDM-based IA scheme and prove its feasibility at the physical layer by showing that all data streams in the IA scheme can be transported free of interference. Based on the IA scheme, we develop a cross-layer IA optimization framework that can fully translate the benefits of IA to throughput gain in cellular networks. Furthermore, we show that the IA optimization problem in the downlink can be solved in the exactly same way as that in the uplink. Simulation results show that our OFDM-based IA scheme can significantly increase the user throughput and the throughput gain increases with user density in the network.
Passive extended double-sided two-way ranging algorithm for UWB positioning

Wireless sensor networks gain ground rapidly due to their potentially low cost. These networks, including variants with UWB (ultra-wideband) physical layer, can be utilized extremely well for positioning because of the technological capabilities. However, the accuracy of localization based on the measurement of time depends on the usage of timestamps provided by certain elements of the network. This article proposes a method to use time of arrival (ToA) also known as time of flight (ToF) measurements in an UWB technology-based passive asynchronous localization strategy (which provides low-power and quick positioning) in combination with extended two-way ranging to improve accuracy named by the authors as PE-ranging. The proposed method makes the positioning system more resistant to clock drift, while requires fewer messages due to the passive nature of the solution. First, the traditional variants of two-way ranging (TWR) are introduced, contributing the foundation of the proposed strategy, then the so-called PE-ranging is explained, which is a localization strategy based on indirect distance-measurement.
Effects of recommendations to follow the Dietary Approaches to Stop Hypertension (DASH) diet v. usual dietary advice on childhood metabolic syndrome: a randomised cross-over clinical trial

The effects of the Dietary Approaches to Stop Hypertension (DASH) eating plan on childhood metabolic syndrome (MetS) and insulin resistance remain to be determined. The present study aimed to assess the effects of recommendations to follow the DASH diet v. usual dietary advice (UDA) on the MetS and its features in adolescents. In this randomised cross-over clinical trial, sixty post-pubescent adolescent girls with the MetS were randomly assigned to receive either the recommendations to follow the DASH diet or UDA for 6 weeks. After a 4-week washout period, the participants were crossed over to the alternate arm. The DASH group was recommended to consume a diet rich in fruits, vegetables and low-fat dairy products and low in saturated fats, total fats and cholesterol. UDA consisted of general oral advice and written information about healthy food choices based on healthy MyPlate. Compliance was assessed through the quantification of plasma vitamin C levels. In both the groups, fasting venous blood samples were obtained at baseline and at the end of each phase of the intervention. The mean age and weight of the participants were 14·2 (sd 1·7) years and 69 (sd 14·5) kg, respectively. Their mean BMI and waist circumference were 27·3 kg/m2 and 85·6 cm, respectively. Serum vitamin C levels tended to be higher in the DASH phase than in the UDA phase (860 (se 104) v. 663 (se 76) ng/l, respectively, P= 0·06). Changes in weight, waist circumference and BMI were not significantly different between the two intervention phases. Although changes in systolic blood pressure were not statistically significant between the two groups (P= 0·13), recommendations to follow the DASH diet prevented the increase in diastolic blood pressure compared with UDA (P= 0·01). We found a significant within-group decrease in serum insulin levels (101·4 (se 6·2) v. 90·0 (se 5·5) pmol/l, respectively, P= 0·04) and a non-significant reduction in the homeostasis model assessment for insulin resistance score (P= 0·12) in the DASH group. Compared with the UDA group, the DASH group experienced a significant reduction in the prevalence of the MetS and high blood pressure. Recommendations to follow the DASH eating pattern for 6 weeks among adolescent girls with the MetS led to reduced prevalence of high blood pressure and the MetS and improved diet quality compared with UDA. This type of healthy diet can be considered as a treatment modality for the MetS and its components in children.
Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.
A new look at nursing home residents’ depressive symptoms: the role of basic versus expanded everyday competence

ABSTRACT Background: Depressive symptoms are highly prevalent in nursing home (NH) residents. The relationship between depressive symptoms and everyday competence in terms of basic (BaCo) and expanded everyday competence (ExCo; see Baltes et al., 2001) in the NH setting is, however, not clear. Applying Lewinsohn's depression model, we examined how residents’ BaCo and ExCo relate to their depressive symptoms. Furthermore, we investigated the mediating role of perceived control. Methods: Cross-sectional data from 196 residents (M age = 83.7 years, SD = 9.4 years) of two German NHs were analyzed. Study variables were assessed by the Geriatric Depression Scale-Residential (GDS-12R), maximal gait speed (BaCo), proxy ratings of residents’ in-home activity participation, and self-initiated social contact done by staff (ExCo). Structural equation modeling (SEM) was used and a simulation study was included to determine power and potential estimation bias. Results: At the descriptive level, one quarter of the residents showed symptoms of depression according to the GDS-12R cut-off criterion. Residents’ BaCo and ExCo were independently and equally strongly associated with their depressive symptoms in the SEM analysis. These findings were affected neither by cognitive impairment, sex, nor age. Perceived control mediated between BaCo but not ExCo and depressive symptoms. Conclusion: Future research needs to follow the connection between residents’ everyday competence and their depressive symptoms longitudinally to better understand the underlying mechanisms.
Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification

In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ2,1-norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation. The proposed algorithm not only exploits the discrimination ability of multiple features but also greatly reduces the interference of atoms that are irrelevant to the test sample, thus effectively improving classification performance. Conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) public SAR database, experimental results show that the proposed approach is effective and superior to many state-of-the-art methods.
A Learning-Based Framework to Utilize E-HowNet Ontology and Wikipedia Sources to Generate Multiple-Choice Factual Questions

This paper proposes a framework that automatically generates multiple-choice questions. Unlike most other similar works that focus on generating questions for English proficiency tests, this paper provides a framework to generate factual questions in Chinese. We have decomposed this problem into several sub-tasks: a) the identification of sentences that contain factual knowledge, b) the identification of the query term from each factual sentence, and c) the generation of distractors. Learning-based approaches are applied to address the first two problems. We then propose a way to generate distractors by using E-How Net ontology database and Wikipedia sources. The system was evaluated through user study and test theory, and achieved a satisfaction rate of up to 70.6%.
The Impact of Advanced Age on the Outcomes of Primary Total Hip and Knee Arthroplasty for Osteoarthritis: A Systematic Review

Background: Elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) are effective options for the management of osteoarthritis that has not responded to conservative therapy, and they are increasing in utilization worldwide. Therefore, we sought to investigate how older age influences the outcomes of THA and TKA through a review of the current literature. Methods: The Embase and MEDLINE databases were examined for relevant studies that assessed the role of older age in determining the outcomes of THA and TKA. All studies except for systematic reviews and case reports were considered for inclusion. Studies were excluded if they included data on procedures other than THA and TKA or if they focused on the effect of surgical techniques and rehabilitation programs rather than age. We excluded studies if they included data on total joint arthroplasty (TJA) performed for indications other than osteoarthritis unless osteoarthritis was the diagnosis for >90% of the patient cohort. Journal references were manually searched to identify any additional articles. Results: Thirty-two articles were identified in this review. The most elderly groups in these studies were shown to experience higher mortality rates, more postoperative complications, and a longer stay in the hospital. Perioperative mortality was reported to be between 2.6% and 2.9% for nonagenarians and 1.09% and 1.54% for octogenarians. The increase in length of stay for the elderly following TJA was reported to be 0.6 to 3.1 days. Despite the increased rate of adverse events, there were gains in terms of pain relief and ability to perform activities of daily living, and overall the most elderly groups were satisfied with the THA or TKA. However, there was considerable inconsistency among studies with respect to the methodology for evaluating health-related quality of life and the level of functional ability postoperatively. Conclusions: Although TJA is considered an effective surgery for the elderly, clinicians should be aware that the elderly are at a higher risk of greater length of stay, complications, and mortality. However, the available literature contained poor data and heterogeneous patient cohorts, and reported on a wide variety of outcomes. Further research assessing the effect of older age on TJA is warranted.
Transfer learning for sequential recommendation model

In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or rank items precisely. Recently, transfer learning techniques are applied to address this problem. Although the lack of data in target domain may result in underfitting, data from auxiliary domains can be utilized to assist model training. Most of recommendation systems combined with transfer learning aim at the rating prediction problem whose user feedback is explicit and not sequential. In this paper, we apply transfer learning techniques to a model utilizing user preference and sequential information. To the best of our knowledge, no previous works have addressed the problem. Experiments on realworld datasets are conducted to demonstrate our framework is able to improve prediction accuracy by utilizing auxiliary data.