Miracle-dz/UniFAR-FTUs huggingface dataset:
Menoprogen, a TCM Herbal Formula for Menopause, Increases Endogenous E2 in an Aged Rat Model of Menopause by Reducing Ovarian Granulosa Cell Apoptosis

The effect of Menoprogen (MPG) on ovarian granulosa cell (GC) apoptosis was investigated in vitro and in vivo in an aged rat model of menopause. Intragastric administration of Menoprogen or estradiol valerate to 14-month-old senile female rats for eight weeks increased plasma E2 levels, as well as the weight of both ovarian and uterine tissues. Flow cytometric (FCM) analysis of isolated GCs from MPG-treated aged rats showed reductions in the G0/G1 ratio and apoptotic peaks. Isolated GCs also exhibited an increase in cell size and the number of cytoplastic organelles and intracellular gap junctions, the reappearance of secretory granules, and a lack of apoptotic bodies as determined by TEM. Results from a TdT-mediated dUTP nick end-labeling (TUNEL) assay revealed a reduction in TUNEL-positive GCs after MPG treatment. Immunohistochemical analysis showed a downregulation of proapoptotic Bax proteins and an upregulation of antiapoptotic Bcl-2 proteins. The addition of MPG-medicated serum to the media of cultured GCs also reduced cadmium chloride-induced apoptosis and downregulated caspase-3 protein expression. This work demonstrates that Menoprogen inhibits GC apoptosis in aged female rats and thereby increases E2 production. This represents a novel mechanism of action for this herbal medicine in the treatment of menopausal symptoms.
Indexing Consistency and its Implications for Information Architecture : A Pilot Study

Consistency in the assignment of indexing terms has been studied on the small scale for many years. As opportunities increase for large numbers of people to contribute to indexing of public documents on the World Wide Web, consistency on the large scale becomes problematic. This pilot study examines inter-indexer consistency on a larger scale than other studies. Consistency in the assignment of indexing terms to a document follows an inverse shape, although it is not strictly power law-based, as has been observed for many social phenomena. The authors discuss the challenges and potential benefits of having this diversity of indexing access points to documents and their implications for effective information architecture.
State of the art for mechatronic design concepts

Nowadays the trend toward multi-disciplinary and ever complex system development is becoming more and more essential to understand and improve engineering approaches for such systems in industrial product engineering and with that also poses questions to solve for research on about how to efficiently and successfully handle such systems development. Since the application area for the development of such systems is very broad, e.g. including automotive, aeronautics, robotics or consumer products and much more, on top the need for flexible and adaptable methods for the development of such systems arises as well. Such complex, interdisciplinary systems are also called mechatronic systems, referring to the synergistic integration of software, electronics and mechanics. To approach the complexity inherent in the discipline-spanning aspect, different development and integrations methods and techniques coming from the involved disciplines are applied. Especially the interdisciplinary aspect of mechatronics, makes it difficult to handle such development and still there is no such thing as a promoted unified mechatronic modeling and design language, which is capable of describing a joint model with sufficient details to manage the large number of involved system-aspects included in each of the representative domains-specific disciplines. In order to obtain a coherent model from the entire mechatronic system, an integrated model framework capable to manage various requirements and modeling specifications present in each discipline is needed. This paper will provide a comprehensive review regarding the evolution of design methodologies for mechatronic systems, investigate recent research approaches in the realm of model-based design, and also point out the problems and limitations inherent in the different concepts and finally summarize the most promising research directions.
Predicting ASR errors by exploiting barge-in rate of individual users for spoken dialogue systems

We exploit the barge-in rate of individual users to predict automatic speech recognition (ASR) errors. A barge-in is a situation in which a user starts speaking during a system prompt, and it can be detected even when ASR results are not reliable. Such features not using ASR results can be a clue for managing a situation in which user utterances cannot be successfully recognized. Since individual users in our system can be identified by their phone numbers, we accumulate how often each user barges in and use this rate as a user profile for determining whether a current “barge-in” utterance should be accepted or not. We furthermore set a window that reflects the temporal transition of the user’s behavior as they get accustomed to the system. Experimental results show that setting the window improves the prediction accuracy of whether the utterance should be accepted or not. The experiments also clarify the minimum window width for improving accuracy. Index Terms: spoken dialogue system, user modeling, barge-in
Two Extremal Problems in Graph Theory

We consider the following two problems. (1) Let $t$ and $n$ be positive integers with $n\geq t\geq 2$. Determine the maximum number of edges of a graph of order $n$ that contains neither $K_t$ nor $K_{t,t}$ as a subgraph. (2) Let $r$, $t$ and $n$ be positive integers with $n\geq rt$ and $t\geq 2$. Determine the maximum number of edges of a graph of order $n$ that does not contain $r$ disjoint copies of $K_t$. Problem 1 for $n < 2t$ is solved by Turan's theorem and we solve it for $n=2t$. We also solve Problem 2 for $n=rt$.
Analytical Evaluation of Timing Offset Error in OFDM System

This paper proposes an idea of developing a model of estimating timing offset error of OFDM (Orthogonal Frequency Division Multiplexing) system without the use of additional pilots relying on inherent characteristics of the OFDM signal. An analytical expression has been derived and formulated to analyze the effect of bit error rate (BER) due to timing offset introduced by the transmission channel. This will help in determining the exact length of cyclic prefix to be added to each OFDM symbol to avoid misinterpretation by the receiver. The performances have also been evaluated under coded (convolution) and uncoded systems. The introduction of channel coding decreases this basic impairment of OFDM systems significantly. Simulated results show that the symbol error rate (SER) linearly depends on timing offset. It is expected that further works on the proposed estimated method will lead to a standard model so that at the receiving end the effect of timing offset can be eliminated totally.
Size-exclusion chromatography-based enrichment of extracellular vesicles from urine samples

Renal biopsy is the gold-standard procedure to diagnose most of renal pathologies. However, this invasive method is of limited repeatability and often describes an irreversible renal damage. Urine is an easily accessible fluid and urinary extracellular vesicles (EVs) may be ideal to describe new biomarkers associated with renal pathologies. Several methods to enrich EVs have been described. Most of them contain a mixture of proteins, lipoproteins and cell debris that may be masking relevant biomarkers. Here, we evaluated size-exclusion chromatography (SEC) as a suitable method to isolate urinary EVs. Following a conventional centrifugation to eliminate cell debris and apoptotic bodies, urine samples were concentrated using ultrafiltration and loaded on a SEC column. Collected fractions were analysed by protein content and flow cytometry to determine the presence of tetraspanin markers (CD63 and CD9). The highest tetraspanin content was routinely detected in fractions well before the bulk of proteins eluted. These tetraspanin-peak fractions were analysed by cryo-electron microscopy (cryo-EM) and nanoparticle tracking analysis revealing the presence of EVs. When analysed by sodium dodecyl sulphate–polyacrylamide gel electrophoresis, tetraspanin-peak fractions from urine concentrated samples contained multiple bands but the main urine proteins (such as Tamm–Horsfall protein) were absent. Furthermore, a preliminary proteomic study of these fractions revealed the presence of EV-related proteins, suggesting their enrichment in concentrated samples. In addition, RNA profiling also showed the presence of vesicular small RNA species. To summarize, our results demonstrated that concentrated urine followed by SEC is a suitable option to isolate EVs with low presence of soluble contaminants. This methodology could permit more accurate analyses of EV-related biomarkers when further characterized by -omics technologies compared with other approaches.
microRNAs Involved in Regulating Spontaneous Recovery in Embolic Stroke Model

To date, miRNA expression studies on cerebral ischemia in both human and animal models have focused mainly on acute phase of ischemic stroke. In this study, we present the roles played by microRNAs in the spontaneous recovery phases in cerebral ischemia using rodent stroke models. Brain tissues were harvested at different reperfusion time points ranging from 0–168 hrs after middle cerebral artery occlusion using homologous emboli. MiRNA and mRNA expression profiles were investigated by microarray followed by multiple statistical analysis. Candidate transcripts were also validated by quantitative RT-PCR. Three specific groups of miRNAs were observed among a total of 346 differentially expressed miRNAs. miRNAs, miR-21, -142-3p, -142-5p, and -146a displayed significant upregulation during stroke recovery (48 hrs to 168 hrs) compared with those during acute phases (0 hrs to 24 hrs). On the other hand, an opposite trend was observed in the expression of miR-196a/b/c, -224 and -324-3p. Interestingly, miR-206, -290, -291a-5p and -30c-1*, positively correlated with the infarct sizes, with an initial increase up to 24hrs followed by a gradual decrease from 48 hrs to 168 hrs (R = 0.95). Taken together with the expression levels of corresponding mRNA targets, we have also found that Hedgehog, Notch, Wnt and TGF-β signaling pathways could play significant roles in stroke recovery and especially in neuronal repair.
Mean Field Games: Numerical Methods for the Planning Problem

Mean field games describe the asymptotic behavior of differential games in which the number of players tends to $+\infty$. Here we focus on the optimal planning problem, i.e., the problem in which the positions of a very large number of identical rational agents, with a common value function, evolve from a given initial spatial density to a desired target density at the final horizon time. We propose a finite difference semi-implicit scheme for the optimal planning problem, which has an optimal control formulation. The latter leads to existence and uniqueness of the discrete control problem. We also study a penalized version of the semi-implicit scheme. For solving the resulting system of equations, we propose a strategy based on Newton iterations. We describe some numerical experiments.
Neural correlates of focused attention in patients with mild Alzheimer’s disease

Alzheimer’s Disease (AD) is characterized by an early and significant memory impairment, and progresses to affect other cognitive domains. Impairments in Focused Attention (FA) have been observed in patients diagnosed with mild AD. A functional magnetic resonance imaging (fMRI) Stroop paradigm with verbal responses was used to investigate the neural correlates of FA in AD patients. Twenty-one patients diagnosed with mild AD performed a verbal Stroop—fMRI paradigm. Colour words were printed in an incongruent ink colour. Series 1 consisted of four blocks “Read the word” followed by four blocks “Say the colour of the ink”; Series 2 alternated between the two conditions. Functional data were analyzed using SPM5 to detect anatomical areas with significant signal intensity differences between the conditions. Within-group analyses of the colour minus word contrast yielded significant activation in the following left hemisphere regions: precentral gyrus, inferior frontal gyrus, fusiform gyrus and supplementary motor area (p < 0.05, uncorrected). Relative to cognitively normal older adults who underwent the same experimental task, Stroop performance was significantly worse in AD patients. The fMRI task yielded similar activated brain regions between the two groups. The use of verbal responses in this novel fMRI Stroop task avoids the confusion and memorizing of button locations seen with the manual response modality, allowing the neural correlates of FA to be investigated in AD patients.
Calibration, terrain reconstruction and path planning for a planetary exploration system

In this paper, three important preparation tasks of a planetary exploration system are described, namely calibration, terrain reconstruction and path planning. The calibration is retrieved from the images of the planetary terrain (the same images from which the 3D measurements of the terrain are obtained). Once the system has been calibrated, the same images can be used to estimate a digital elevation map of the environment around the lander. These images are r st processed pairwise using a stereo algorithm yielding sub-pixel disparity maps. In order to perform the path planning a digital elevation map is required. An algorithm is described which can generate a regular digital elevation map at any desired resolution (interpolating if necessary) and easily takes occlusions into account. The results of the calibration and reconstruction are then used by the path planning module which is capable of computing the optimal path between points of interest of the terrain.
Towards understanding SNS fatigue: exploration of social experience in the Virtual World

The rise of social network services (SNSs) has brought an emerging need of understanding the virtual world from social contexts. The stay time of SNSs is increasing worldwide. Billion-scale virtual world social interactions are realized and has impacted multiple aspects of human lives. One notable negative aspect of SNSs is SNS fatigue. People are heavily involved in SNSs and some show SNS fatigue or SNS burnouts. The author compiles the reasons that users left SNSs. Then, the author discusses the three factors that impact forming attitudes, a two-dimensional classification of SNS departures in different contexts, and a tree-diagram view that parses the process of SNS departures.
Portable and Performant Userspace SCTP Stack

One of only two new transport protocols introduced in the last 30 years is the Stream Control Transmission Protocol (SCTP). SCTP enables capabilities like additional throughput and fault tolerance for multihomed hosts. An SCTP implementation is included with the Linux kernel and another implementation called sctplib functions successfully in userspace on several platforms but unfortunately neither of these implementations have all of the latest features nor do they perform as well as the FreeBSD kernel implementation of SCTP. We were motivated to produce a portable implementation of the FreeBSD kernel SCTP stack that operates in userspace of any system because of both our desires to obtain a higher performance SCTP stack for Linux as well as to exploit recent developments in hardware virtualization and transport protocol onloading. Unlike any other userspace transport implementation for TCP or SCTP, our userspace SCTP stack simultaneously achieves similar throughput and latency as the Linux kernel TCP stack, without compromising on any of the transport's features as well as maintaining true portability across multiple operating systems and devices. We create a callback API and implement a threshold to control its usage; our userspace SCTP stack with these optimizations obtains higher throughput than the Linux kernel implementation of SCTP. We describe our userspace SCTP stack's design and demonstrate how it gives similar throughput and latency on Linux as the kernel TCP implementation, with the benefits of the new features of SCTP.
TARDIS - A simulation platform with an affective virtual recruiter for job interviews

The number of young people not in employment, education or training is increasing across Europe. These youngsters often lack self-conﬁdence and the essential social skills needed to seek and secure employment. The TARDIS project aims to build a scenario-based serious-game simulation platform for young people at risk of exclusion to improve their social skills. This paper intends to propose a model for a socio-emotionally realistic virtual agent in the context of job interview simulations. Our model of aﬀect is composed of emotions, moods, social attitudes and personality that intends to create a realistic virtual recruiter
Improved Otsu segmentation based on sobel operator

The classical maximum variance algorithm which could be called Otsu is stable, however, the threshold calculation is rather low which leads to an undesirable performance in terms of segmenting the target from the background during the strip detection process. The poor segmentation process will have negative impact for the subsequent steps, such as the target extraction and the target recognition. In order to overcome this disadvantage, we proposed an improved thresholding segmentation approach based on the maximum variance algorithm. The proposed approach consists of two major steps. The first is to adapt the Sobel operator to extract the grayscale information at the edge. The second is to apply Otsu on the extracted grayscale information. According to our experimental results, the proposed approach achieved the desirable segmentation performance.
Interactive Construction of Visual Concepts for Image Annotation and Retrieval

This paper describes a web-based interactive approach for building visual vocabularies in support of image annotation and retrieval. Under the proposed formulation a semantic concept is associated with a specific visual representation, thereby, explicitly modeling the ‘semantic gap’. Visual concepts obtained in this way can be used for image annotation/retrieval, object recognition and detection for both images and video and for building visual resources like visual dictionaries. A distinctive feature of our approach is that it can be used with any collection of images, without requiring the users to provide training data. Also, the proposed formulation allows prototype-based classification which can be useful for real-time applications. We performed preliminary experiments on image annotation using two different databases. Experimental results show that the proposed approach is a promising solution to bridge the semantic gap. However, some aspects of the proposed formulation still need to be improved.
Simicry : A mimicry-feedback loop for second language learning

This paper introduces the concept of Simicry, defined as similarityof mimicry, for the purpose of second language acquisition.We apply this method using a computer assisted languagelearning system called Ville on foreign students learningSwedish. The system deploys acoustic similarity measures betweennative and non-native pronunciation, derived from durationsyllabicity and pitch. The system uses these measures togive pronunciation feedback in a mimicry-feedback loop exercisewhich has two variants: a ’say after me’ mimicry exercise,and a ’shadow with me’ exercise.The answers of questionnaires filled out by students afterseveral training sessions spread over a month, show that thelearning and practicing procedure has a promising potential beingvery useful and fun.
The Relationship between Intramuscular Adipose Tissue, Functional Mobility, and Strength in Postmenopausal Women with and without Type 2 Diabetes

Objectives. To determine (1) whether intramuscular adipose tissue (IntraMAT) differs between women with and without type 2 diabetes and (2) the association between IntraMAT and mobility and strength. Methods. 59 women ≥ 65 years with and without type 2 diabetes were included. A 1-Tesla MRI was used to acquire images of the leg. Timed-up-and-go (TUG) and grip strength were measured. Regression was used to determine associations between the following: (1) type 2 diabetes and IntraMAT (covariates: age, ethnicity, BMI, waist : hip ratio, and energy expenditure), (2) IntraMAT and TUG (covariates: diabetes, age, BMI, and energy expenditure), and (3) IntraMAT and grip strength (covariates: diabetes, age, height, and lean mass). Results. Women with diabetes had more IntraMAT. After adjustment, IntraMAT was similar between groups (diabetes mean [SD] = 13.2 [1.4]%, controls 11.8 [1.3]%, P = 0.515). IntraMAT was related to TUG and grip strength, but the relationships became nonsignificant after adjustment for covariates (difference/percent IntraMAT [95% CI]: TUG = 0.041 seconds [−0.079–0.161], P = 0.498, grip strength = −0.144 kg [−0.335–0.066], P = 0.175). Conclusions. IntraMAT alone may not be a clinically important predictor of functional mobility and strength; however, whether losses in functional mobility and strength are promoted by IntraMAT accumulation should be explored.
Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.
Managing Responsibility and Information Flow in Dynamic Team Decision-Making

Abstract : We propose a method to allocate decision responsibility and arrange information flow dynamically within a team of decision-makers for command and control. A model of decision making which relates the decision load to decision accuracy is proposed and employed as an atomic building block to create predictive models of team decision making. An optimization problem is then proposed for a given set of forecast decision requirements in which the information flow between the atomic decision-making models is varied so as to maximize an aggregate measure of decision accuracy. A small-scale MATLAB Simulink implementation is presented as well as the outline of current work in which a genetic algorithm is employed to perform the optimization. Preliminary results indicate the technique improves the decision-making performance measure. We conclude with a discussion of implementation issues in a larger C2 context.