Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncoupling protein 2 protects dopaminergic neurons from acute 1,2,3,6‐methyl‐phenyl‐tetrahydropyridine toxicity

Oxidative stress is implicated in the death of dopaminergic neurons in sporadic forms of Parkinson's disease. Because oxidative stress can be modulated endogenously by uncoupling proteins (UCPs), we hypothesized that specific neuronal expression of UCP2, one member of the UCP family that is rapidly induced in the CNS following insults, could confer neuroprotection in a mouse model of Parkinson's disease. We generated transgenic mice overexpressing UCP2 in catecholaminergic neurons under the control of the tyrosine hydroxylase promoter (TH‐UCP2). In these mice, dopaminergic neurons of the substantia nigra showed a twofold elevation in UCP2 expression, elevated uncoupling of their mitochondria, and a marked reduction in indicators of oxidative stress, an effect also observed in the striatum. Upon acute exposure to 1,2,3,6‐methyl‐phenyl‐tetrahydropyridine, TH‐UCP2 mice showed neuroprotection and retention of locomotor functions. Our data suggest that UCP2 may represent a drug target for slowing the progression of Parkinson's disease.
GUI creation for removal of motion artifact in PPG signals

Photoplethysmography(PPG) signals provide a valuable insight to the cardiac functioning non-invasively. Analysis of PPG signals is of great challenge as the Finger PPG sensors are subjected to various noises like motion artifact, noises from the ambient sunlight and also flow of blood in the terminal arterioles. To obtain the reliable measures from PPG signals, filtering the PPG signal from various noises becomes important. Various methods such as periodic moving average filter, SVD method, Wavelet Filters, FFT Filter, Adaptive Filters, have been proposed to remove motion artifact of PPG signals by various authors. All the filtering methods are carried out individually as a signal processing application. It would be helpful to compare the different filtering methods if all this filtering method are done under single GUI, this also helps the researcher to filter the motion artifact in the PPG signals with ease. In this work a code has been written in MATLAB for different filtering methods such as periodic moving average filter, SVD method, Wavelet Filters, FFT Filter and Adaptive Filters. The different filtering methods have been compared using SNR (Signal to Noise Ratio) values. The SNR values of SVD and AS-LMS algorithm were 2.24 and 2.39 respectively and were found to be the best signal processing methods for removal of motion artifacts in PPG signals. This new GUI has been conceived for filtering PPG signals using different signal processing methods and in making the filtering process more users friendly.
QoS-Guaranteed Scheduling and Resource Allocation Algorithm for IEEE 802.16 OFDMA System

For IEEE 802.16 broadband wireless access (BWA) system, the support of QoS is of prime importance for providing various traffic profiles in wireless links. Scheduling and resource allocation play the key roles in providing QoS as well as making better use of limited radio resources. This paper proposes an efficient scheduling algorithm for IEEE 802.16 OFDMA system with adaptive allocation of time-frequency resources. We utilize a user and connection based scheme to offer more efficient and QoS-guaranteed performance. The users' signal-to-noise ratio (SNR) on subchannels and their distribution characteristics are also taken into consideration. A new allocation scheme based on maximum deviation channel first (MDCF) is proposed to use the resources more efficiently. The fundamental of allocation scheme is to satisfy the QoS requirements of users as much as possible first, and to maximize system throughput thereafter. The simulation results show that the proposed scheduling and resource allocation algorithm can provide diverse QoS guarantees and use resources efficiently.
Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.
Single-Cell Data Analysis Using MMD Variational Autoencoder for a More Informative Latent Representation

Variational Autoencoder (VAE) is a generative model from the computer vision community; it learns a latent representation of images and generates new images in an unsupervised way. Recently, Vanilla VAE has been applied to single-cell data analysis, in the hope of harnessing the representation power of latent space to evade the “curse of dimensionality” of the original dataset. However, Vanilla VAE is suffering from the issue of less informative latent space, which raises a question concerning the reliability of Vanilla VAE latent space in representing the high-dimensional single-cell datasets. Therefore I set up such a study to examine this issue from the multiple perspectives. This paper confirms the issue of Vanilla VAE by comparing it with MMD-VAE, a variant of VAE which has claimed to have overcome this issue based on image data, across a series of single-cell RNAseq and mass cytometry datasets. The result indicates that MMD-VAE is superior to Vanilla VAE in retaining the information not only in the latent space but also the reconstruction space, which suggests that MMD-VAE be a better option for single-cell data analysis than Vanilla VAE.
Study of steady-state feature extraction algorithm based on EMD

The accuracy of appliance identification is mainly determined by the selection of features in Non-Intrusive Load Monitoring (NILM). The time domain features extracted based on the appliance steady-state depends highly on the stable voltage. This paper proposes a frequency domain features extraction method, which applies the empirical mode decomposition (EMD) principle. The combination of frequency domain features and time domain features can effectively reduce the impact of voltage fluctuations on the accuracy of electrical identification. Through experimental comparison, it is proved that this method can solve time domain features overlapping, with higher anti-interference ability and robustness, especially for the non-resistive electronic loads.
Nutrition and Iron Status of 1-Year Olds following a Revision in Infant Dietary Recommendations

A previous study showed low iron status in 12-month-old Icelandic infants associated most strongly with cow's milk intake and growth. Infant dietary recommendations were revised in 2003. This study investigated nutrition and iron status in a new infant cohort. Subjects/Methods. Randomly selected infants were prospectively investigated for diet, anthropometry, and iron status (n = 110–141). Results. Breastfeeding initiation rate was 98%; 38% of 5-month olds were exclusively and 20% of 12-month olds partially breastfed. Formula was given to 21% of 6-month olds and 64% of 12-month olds, but cow's milk to 2.5% and 54.4% of 6- and 12-month olds, respectively. Iron depletion (serum ferritin < 12 μg/L) affected 5.8%, 1.4% were also iron deficient (MCV < 74 fl), and none were anemic (Hb < 105 g/l). Iron status associated negatively with growth and breastfeeding duration and positively with meat and formula intake at 9–12 months, but not with cow's milk. Conclusion. Improved iron status might be explained by a shift from cow's milk to formula in the diet of Icelandic 6–12-month olds. Dietary changes altered associations between foods and iron status.
An extensible toolkit for neural network language modeling

We present a new tool for training neural network language models (NNLMs), scoring sentences, and generating text. The tool has been written using Python library Theano, which al-lows researcher to easily extend it and tune any aspect of the training process. Regardless of the ﬂexibility, Theano is able to generate extremely fast native code that can utilize a GPU or multiple CPU cores in order to parallelize the heavy numerical computations. The tool has been evaluated in difﬁcult Finnish and English conversational speech recognition tasks, and signiﬁ-cant improvement was obtained over our best back-off n-gram models. The results that we obtained in the Finnish task were compared to those from existing RNNLM and RWTHLM toolkits, and found to be as good or better, while training times were an order of magnitude shorter.
Treatment for relapsed acute myeloid leukemia: what is new?

Purpose of reviewDespite enormous progress in the understanding of leukemia pathophysiology and novel transplantation protocols, the prognosis following acute myeloid leukemia (AML) relapse is still uniformly poor. In the current review, advances in risk stratification, protocols involving novel agents and allogeneic stem cell transplantation (ASCT) will be discussed in light of the vision of personalized therapy. Recent findingsThe role of ASCT in relapsed/refractory AML is well established and has been recently confirmed as mandatory for cure. Retrospective observations of different large cohorts categorized patients with early relapse, poor cytogenetics or fms-like tyrosine kinase receptor-3 internal tandem duplication mutation as the most challenging population. Multiple novel agents have been studied with various promising results; however, these agents can only serve as a bridge to transplantation. If ASCT is not an option, therapy should focus on prolongation of patient's life at its best possible quality. Accumulated molecular data open new horizons for personalizing therapy and assigning each patient to the drug or protocol from which the patient will benefit most. SummaryRelapsed/refractory AML is a heterogeneous disease and no uniform protocol will provide cure to all patients. Molecular tests may contribute to future personalizing therapy resulting in improved outcome. Meanwhile, novel and more effective induction and postremission protocols are warranted to lower the relapse rate.
Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences

We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.
Discordance between HCV RNA Assays for Week 24 HCV RNA Determination during Pegylated Interferon-α/Ribavirin Treatment for Chronic Hepatitis C

Background The development of more sensitive HCV RNA assays might necessitate re-evaluation of the rules for stopping treatment (for example, HCV RNA negativity at week 24 during treatment with pegylated interferon-α and ribavirin for chronic hepatitis C). The aim of this study was to assess discordance between the week 24 HCV RNA test results of two PCR-based assays (Amplicor and Taq-Man) and the transcription-mediated amplification (TMA) HCV RNA qualitative assay. Methods A total of 89 week 24 samples that were negative using PCR-based assays during treatment were retested with the TMA qualitative assay to investigate discordance between tests results. All week 24 samples were HCV RNA negative by Amplicor or by TaqMan. Results Of the 89 patients, 46 (52%) achieved sustained virological response (SVR). Viral breakthrough or relapse occurred in 43 patients (48%). All 89 HCV RNA negative week 24 samples were retested with the qualitative TMA assay. Eleven out of 89 samples had detectable HCV RNA (12%). All patients with detectable HCV RNA experienced breakthrough or relapse (negative predictive value 100%). Of the 78 patients with undetectable HCV RNA at week 24 using the TMA assay, 46 achieved SVR. This resulted in a positive predictive value (PPV) of 59% for the TMA assay compared with a PPV of 52% for the PCR-based assays. Conclusions All patients with detectable HCV RNA at week 24 using the TMA assay eventually relapsed. On the basis of these results, the use of this more sensitive HCV RNA assay could lead to the prevention of unnecessary treatment.
A machine learning-based system to normalise gene mentions to unique database identifiers

In this paper, we propose an integrated Gene normaliser (GNer) to assign a unique database identifier for each recognised gene mention in biological literature. The GNer combines Support Vector Machines (SVMs) and some rule-base components. First, we construct a dictionary from EntrezGene and BioThesaurus. Then we reduce variations and ambiguities of synonyms based on a designed pre-processor. Finally, a SVM-based disambiguation filter is developed to eliminate the ambiguity of exact matching. From the experimental results, the proposed GNer can achieve a fairly good performance, which can achieve the precision 80.5%, the recall 86.4% and the F(beta=1) measure 83.4.
COHORT PROFILE Cohort Profile : The Bambuı́ ( Brazil ) Cohort Study of Ageing

Ageing of the population is the most important demographic change facing many countries around the world. The speed of demographic ageing in Latin American and the Caribbean, however, will be unprecedented in comparison with Western European and North American countries. This demographic change will generate populations with large numbers of elderly who at some time in their lives have been exposed to infectious diseases. Infections may affect ageing in different ways. Most important postulated mechanisms include enhanced inflammation, pathogen-dependent tissue destruction or accelerated cellular ageing through increased turnover. Most epidemiological studies of ageing have, understandably, focused on non-communicable diseases and related conditions. To our knowledge, no previous population-based cohort study had examined the consequences of the double burden of non-communicable diseases and a parasitic chronic infection in old age. Chagas disease, which is caused by the protozoan Trypanosoma cruzi, is endemic in Latin American countries, with about 8 million people infected. As a consequence of immigration from endemic countries, Chagas disease is an emerging issue in North America and Europe, and an example in the era of globalization of how infectious diseases extend beyond endemic areas. Chagas disease is related to poor socio-economic circumstances in early life. In endemic areas, the main source of infection is a bloodsucking triatomine insect that colonizes poor households. Most individuals in these areas acquire the infection at <20 years of age, and 30% develop chronic chagasic cardiomyopathy. The ageing of the population, together with a cohort effect observed in endemic areas where the household insect transmission has been interrupted, will lead to increases in the number of older adults who are already infected by T. cruzi. The main objective of the Bambui Cohort Study of Ageing is to examine the separate and joint effects of chronic T. cruzi infection and non-communicable diseases on health outcomes in old age.
The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data

ABSTRACT Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies.
Explorer Continuously tempered Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables. In common with many MCMC methods, however, the standard HMC approach performs poorly in distributions with multiple isolated modes. We present a method for augmenting the Hamiltonian system with an extra continuous temperature control variable which allows the dynamic to bridge between sampling a complex target distribution and a simpler unimodal base distribution. This augmentation both helps improve mixing in multimodal targets and allows the normalisation constant of the target distribution to be estimated. The method is simple to implement within existing HMC code, requiring only a standard leapfrog integrator. We demonstrate experimentally that the method is competitive with annealed importance sampling and simulating tempering methods at sampling from challenging multimodal distributions and estimating their normalising constants.
DI_GA: A Heuristic Mapping Algorithm for Heterogeneous Network-on-Chip

Heterogeneous multi-core processors have become the forefront of processor development due to their advantages in system throughput and execution efficiency, but they also bring many new challenges to system design. The mapping of heterogeneous Network-on-Chip (NoC) is challenging. To solve this problem, this paper proposes a heterogeneous multi-core processor task mapping algorithm based on an improved genetic algorithm. By constructing a good initial population method to improve the initial population quality, a dual population genetic mechanism is used in the iteration process. The algorithm can make tasks more reasonably distributed to various network nodes, and has high efficiency for optimizing network power consumption on heterogeneous multi-cores.
TotalCOW: Unleash the Power of Copy-On-Write for Thin-provisioned Containers

Modern file systems leverage the Copy-on-Write (COW) technique to efficiently create snapshots. COW can significantly reduce demand on disk space and I/O bandwidth by not duplicating entire files at the time of making the snapshots. However, memory space and I/O requests demanded by applications cannot benefit from this technique. In existing systems, a disk block shared by multiple files due to COW would be read from the disk multiple times. Each block in the reads is treated as an independent one in different files and is cached as a sperate block in memory. This issue is due to the fact that current file access and caching are based on logic file addresses. It poses a significant challenge on the emerging light-weight container virtualization techniques, such as Linux Container and Docker, which rely on COW to quickly spawn a large number of thin-provisioned container instances. We propose a lightweight approach to address this issue by leveraging knowledge about files produced by COW. Experimental results show that a prototyped system using the approach, named TotalCOW, can significantly remove redundant disk reads and caching without compromising efficiency of accessing COW files.
Semantic parser enhancement for dialogue domain extension with little data

Statistical semantic parser trained on sufficient in-domain data has shown robustness to speech recognition errors in end-to-end spoken dialogue systems. However, when the dialogue domain is extended, due to the introduction of new semantic slots, values and unknown speech pattern, the parsing performance may significantly degrade. Effective re-training of statistical semantic parser is therefore important. This paper describes a novel semantic parser enhancement approach for domain extension with very little new data. It employs automatic pseudo-data generation for parser re-training and domain independent rescoring to further improve parsing performance. The approach was evaluated on the DSTC3 (the third Dialog State Tracking Challenge) data corpus. Experiments showed that the proposed approach can yield consistent and significant improvements across all metrics of semantic parsing and dialog state tracking.
Massively parallel nearest neighbors searches in dynamic point clouds on GPU

This dissertation introduces a GPU grid-based data structure for massively parallel nearest neighbor queries for dynamic point clouds. The implementation provides real-time performance and it is executed on GPU, both grid construction and nearest neighbors (approximate or exact) searches. This minimizes the memory transfer between device and system memories, improving overall performance. The proposed algorithm may be used across different applications with static and dynamic scenarios. Moreover, the data structure supports tri-dimensional point clouds and given its dynamic nature, the user can change the data structure’s parameters at runtime. The same applies to the number of neighbors to be found. A CPU reference was implemented and performance comparisons justify the use of GPUs as massive parallel processors. Furthermore, the proposed data structure’s performance is related to previous CPU and GPU implementations. Finally, a real-time Point-Based Rendering application was developed in order to verify the data structure’s potential.
A Simple Radioimmunoassay for Plasma Cortisol: Comparison with the Fluorimetric Method of Determination

A quick and simple method for the radioimmunoassay of plasma cortisol is described. The mean morning plasma cortisol concentration in 43 normal subjects was 9.8 ± 3.1 (S.D.) μg/100 ml with a range of 5.0–19.5 μg/100 ml. Mean midnight concentration in 24 normal subjects was 4.3 ± 2.3 (S.D.) μg/100 ml with a range of 1.4–9.6 μg/100 ml. When compared with the fluorimetric method the mean results by radioimmunoassay of 154 routine specimens were 23% lower. In samples from unstimulated patients, regression analysis of results obtained by the two methods gave a correlation coefficient (r) of 0.93, regression line slope of 1.1, and intercept of 1.4 μg. Mean radioimmunoassay results were 15% lower. When plasma cortisol concentration was above the normal range (>30 μg/100 ml) the regression line slope was 0.87, the intercept 17.9 μg, r = 0.87 and mean radioimmunoassay results were 37% lower. Plasma cortisol concentration in patients after insulin or Synacthen stimulation exhibited similar responses when measured by either method. Plasma cortisol concentration in normal subjects given metyrapone was lower when measured by radioimmunoassay (mean ± S.D. = 8.7 ± 2.7 μg/100 ml) than when measured by fluorimetry (18.5 ± 10.8 μg/100 ml). The diagnostic usefulness of the two methods, ease of assay, and costs are compared.