Miracle-dz/UniFAR-FTUs huggingface dataset:
TPH, SLC6A2, SLC6A3, DRD2 and DRD4 Polymorphisms and Neuroendocrine Factors Predict SSRIs Treatment Outcome in the Chinese Population with Major Depression

Abstract Objective: This study was intended to determine whether antidepressant outcome to SSRIs was associated with catecholamine gene polymorphisms and neuroendocrine factors in patients of Chinese Han ethnicity with MDD. Method: A total of 290 MDD patients were recruited and received a 6-week randomized double-blinded treatment. Cortisol, adrenocorticotropic hormone (ACTH), thyroid-stimulating hormone (TSH), 3′-triiodothyronine (T3), thyroxine (T4), free triiodothyronine (fT3), and free thyroxine (fT4) levels were measured at the baseline. Allele, genotype, and haplotype frequencies of catecholamine genes were compared between responders and non-responders, remission and non-remission groups respectively. Results: We found that genotype frequency of the rs1800544 polymorphism in the DRD4 gene was significantly different between responders and non-responders (P<0.05). Also the frequency of the rs1800544 CG genotype was significantly higher (P<0.05) in responders (51.4%) than that in non-responders (35.8%). No significant difference was found between responders and non-responders, remission and non-remission groups in the SNPs polymorphisms in the TPH, SLC6A2, SLC6A3, or DRD2 genes. The combination of all neuroendocrine factors, clinical characteristics and gene polymorphisms predicted 74.8% of the variation in SSRI response and 65.5% in SSRI remission. Conclusion: Polymorphisms of the DRD4 gene were associated with SSRI response in Chinese Han MDD patients. A combination of clinical characteristics, neuroendocrine factors, and gene polymorphisms might be able to predict the outcome to SSRIs.
Ultrasonic curing of one-part epoxy system

Ultrasonic curing of one-part epoxy system was studied using differential scanning calorimetry (DSC) analysis. During both isothermal oven curing and ultrasonic curing of this system, an induction period was observed. For the rapid progression of polymerization reaction in ultrasonic curing, a preheating temperature of 100°C was found to be sufficient. However, degree of curing due to the nonthermal effect of ultrasonic curing was found to be maximal at a preheating temperature of 80°C. It was noticed that the ultrasonic curing reduced the time of curing by a factor of four, and the curing level from pure thermal effects could be determined by incorporating the curing temperature history into the autocatalytic chemical model. The degree of curing from the experimental results of isothermal heating in DSC and the one predicted by the autocatalytic chemical model exhibited a good agreement.
Optimal Short-Term Population Coding: When Fisher Information Fails

Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate fmax (power constraint), and the maximal average rate fmax (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (fmin T, fmax T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.
Cognitive Radio Wireless Mesh Networks : A Survey on Routing Protocols

Cognitive radio (CR) technology enables the opportunistic use of the vacant licensed frequency bands and henceforth improving the spectrum utilization. Cognitive radio networks (CRNs) are composed of cognitive, spectrum-agile devices which are capable of changing their configurations depending upon the spectral environment which they use. This capability opens up the possibility of designing flexible and dynamic spectrum access strategies with the purpose of opportunistically reusing portions of the spectrum temporarily vacated by licensed primary users. On the other hand, the flexibility in the spectrum access phase comes with an increased complexity in the design of communication protocols at different layers. An extensive overview of the research in the field of routing for Cognitive radio networks differentiating the two main categories: approaches based on a full spectrum knowledge, and approaches that consider only local spectrum knowledge obtained via distributed procedures and protocols is been provided in this report.
Human motion: modeling and recognition of actions and interactions

Processing of image sequences has progressed from simple structure from motion paradigm to the recognition of actions/interactions as events. Understanding human activities in video has many potential applications including automated surveillance, video archival/retrieval, medical diagnosis, sports analysis, and human-computer interaction. Understanding human activities involves various steps of low-level vision processing such as segmentation, tracking, pose recovery, and trajectory estimation as well as high-level processing tasks such as body modeling and representation of action. While low-level processing has been actively studied, high-level processing is just beginning to receive attention. This is partly because high-level processing depends on the results of low-level processing. However, high-level processing also requires some independent and additional approaches and methodologies. We focus on the following aspects of high-level processing: (1) human body modeling, (2) level of detail needed to understand human actions, (3) approaches to human action recognition, and (4) high-level recognition schemes with domain knowledge. The review is illustrated by examples of each of the areas discussed, including recent developments in our work on understanding human activities.
Estimation of Human Body Orientation Using Histogram of Oriented Gradients

Estimation of human orientation is considered a key configuration to study and understand human behaviours. There have been many works focusing on this. However, most of previous works are sensor-based technologies but visual-based technologies. In this paper, we proposed a method to estimate the orientation of human in a given image patch. We have observed the performance of a feature called Histogram of Oriented Gradients (HOG) on human detection and found the sensitivity of this feature in in-plane rotation. It is so sensitive that it could be used in classifying images of rotated and non-rotated human body. Therefore we have drawn an idea to exploit a modified HOG descriptor with ’bin shifting’ technique to estimate the orientation of human. The results show that the proposed method can effectively estimate the orientation of human pose inside an image with fast and simple additional operations required.
Deriving robust biomarkers from multi-site resting-state data: An Autism-based example

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropathologies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks.
Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework

Multi-task learning seeks to improve the generalization performance by sharing common information among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not hold in many real-world applications. Existing techniques, which attempt to address this issue, aim to identify groups of related tasks using group sparsity. In this paper, we propose a probabilistic tree sparsity (PTS) model to utilize the tree structure to obtain the sparse solution instead of the group structure. Specifically, each model coefficient in the learning model is decomposed into a product of multiple component coefficients each of which corresponds to a node in the tree. Based on the decomposition, Gaussian and Cauchy distributions are placed on the component coefficients as priors to restrict the model complexity. We devise an efficient expectation maximization algorithm to learn the model parameters. Experiments conducted on both synthetic and real-world problems show the effectiveness of our model compared with state-of-the-art baselines.
Reassessment of biases in predicted nitrogen flows to the duodenum by NRC 2001.

The appearance of numerous plots in recent literature from which the residuals are plotted against observed values (Y) to assess a model's potential bias raises this question: should residuals be regressed against Y or against predicted values (Y)? The answer requires knowing the expected relationship under the assumption of an unbiased model. The objectives of this paper are: 1) to derive the expected relationship between residuals, Y, and Y; 2) to determine whether Y or Y should be used for the assessment of bias; and 3) to reassess the extent of mean and linear bias in the prediction of N flows to the duodenum by the NRC (2001). In the simplest case, we can assume a true model of the form Y = Xbeta + epsilon. This model is estimated by Y = Xb + e, and Y = Xb. The correlation between the residual vector e and the vector of observations Y can easily be derived. The numerator of the correlation coefficient is shown to be equal to e'e, the residual sum of squares. The denominator of this correlation is equal to the square root of e'e multiplied by the total sum of squares. Algebraic simplifications show that the correlation between e and Y is equal to the square root of (1-R2). That is, under the assumption of an unbiased model, the residuals are correlated with the observed values and the slope of e regressed on Y is equal to (1-R2). Thus, a graph of e versus Y will show a positive slope between e and Y unless the model is a perfect predictor (i.e., R2 is equal to 1.0). Significant slopes linking e to Y have been erroneously interpreted as evidence of biased models in the NRC (2001). Conversely, the slope of e regressed on Y is expected to be zero under the assumption of an unbiased model. Therefore, residuals should be regressed against Y and not Y. When Y, as opposed to Y, was used to assess biases in the prediction of flows to the duodenum of microbial N, nonammonia-nonmicrobial N and nonammonia N in NRC (2001), mean biases became nonsignificant and linear biases over the range of predicted values are of the same magnitude or smaller than the standard errors of measurements reported in literature. Thus, although N flow predictions from NRC (2001) may not be precise, they appear to have insignificant and inconsequential biases.
A study of the structure and vulnerabilities of metropolitan area networks

In recent years, there have been numerous geographically localized disruptions in Internet connectivity, as a result of malicious or natural causes. Though Internet-wide vulnerabilities have been studied extensively, the robustness of metropolitan area networks is still not well understood. In this paper, we study structural properties and vulnerabilities in metropolitan area networks, and develop techniques to monitor failures by small detection sets. By combining geolocation techniques with traceroute based measurements, we collect a large data set for 12 of the 25 most populated metropolitan regions in the US. Our analysis reveals that the structure of these paths gives superior insights into the robustness of metropolitan area networks as compared to the topological structure of the graph induced by these paths, which has been traditionally studied.
In Vitro and In Vivo Effects of Metformin on Osteopontin Expression in Mice Adipose-Derived Multipotent Stromal Cells and Adipose Tissue

Metformin is applied not only as antidiabetic drug, but also in the treatment of obesity or as antiaging drug. The first part of the research discussed the effect of metformin at concentrations of 1 mM, 5 mM, and 10 mM on the morphology, ultrastructure, and proliferation potential of mice adipose-derived multipotent mesenchymal stromal cells (ASCs) in vitro. Additionally, we determined the influence of metformin on mice adipose tissue metabolism. This study has shown for the first time that metformin inhibits the proliferative potential of ASCs in vitro in a dose- and time-dependent manner. In addition, we have found a significant correlation between the activity of ASCs and osteopontin at the mRNA and protein level. Furthermore, we have demonstrated that 5 mM and 10 mM metformin have cytotoxic effect on ASCs, causing severe morphological, ultrastructural, and apoptotic changes. The reduced level of OPN in the adipose tissue of metformin-treated animals strongly correlated with the lower expression of Ki67 and CD105 and increased caspase-3. The metformin influenced also circulating levels of OPN, which is what was found with systemic and local action of metformin. The results are a valuable source of information regarding the in vitro effect of metformin on adipose-derived stem cells.
Estimation on effective modulus of polycrystalline aggregates in various geometric configurations

i Acknowledgements . iii List of Figures . ix List of Tables . xiii List of Symbols . xvii Chapter 1 Introduction . 1 1.1 Thin film applications . 1 1.2 Motivation . 2 1.3 Description of geometric configurations . 6 1.4 Objective and outline . 8 Chapter 2 Literature Review . 11 2.1 Thin film experimental techniques . 11 2.1.1 Experimental techniques for free-standing thin film . 12 2.1.2 Experimental techniques for substrate-attached thin film . 16 2.1.3 Summary for experimental observations . 18 2.2 Analytical modeling . 22 2.2.1 Bound methods . 22 2.2.2 Approximate methods . 26 2.3 Numerical simulation . 29 2.3.1 Reconstruction technique . 30 2.3.2 Computer-generated microstructure . 33 Chapter 3 Analytical Considerations . 35 3.1 Elastic anisotropy . 35 3.2 Effective elastic properties of polycrystalline aggregates . 39 3.2.1 Revisiting Voigt and Reuss approximations . 42
A Bayesian Perspective on Sensory and Cognitive Integration in Pain Perception and Placebo Analgesia

The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.
MEASURING THE SOURCES OF THE INTERGALACTIC IONIZING FLUX

We use a wide-field (0.9 deg2) X-ray sample with optical and Galaxy Evolution Explorer (GALEX) ultraviolet observations to measure the contribution of active galactic nuclei (AGNs) to the ionizing flux as a function of redshift. Our analysis shows that the AGN contribution to the metagalactic ionizing background peaks at around z = 2. The measured values of the ionizing background from the AGNs are lower than previous estimates and confirm that ionization from AGNs is insufficient to maintain the observed ionization of the intergalactic medium (IGM) at z > 3. We show that only X-ray sources with broad lines in their optical spectra have detectable ionizing flux and that the ionizing flux seen in an AGN is not correlated with its X-ray color. We also use the GALEX observations of the GOODS-N region to place a 2σ upper limit of 0.008 on the average ionization fraction fν(700 Å)/fν(1500 Å) for 626 UV selected galaxies in the redshift range z = 0.9–1.4. We then use this limit to estimate an upper bound to the galaxy contribution in the redshift range z = 0–5. If the z ∼ 1.15 ionization fraction is appropriate for higher-redshift galaxies, then contributions from the galaxy population are also too low to account for the IGM ionization at the highest redshifts (z > 4).
Novel architectures for P2P applications: the continuous-discrete approach

We propose a new approach for constructing P2P networks based on a dynamic decomposition of a continuous space into cells corresponding to processors. We demonstrate the power of these design rules by suggesting two new architectures, one for DHT (Distributed Hash Table) and the other for dynamic expander networks. The DHT network, which we call Distance Halving allows logarithmic routing and load, while preserving constant degrees. It offers an optimal tradeoff between the degree and the dilation in the sense that degree d guarantees a dilation of O(log d n). Another advantage over previous constructions is its relative simplicity. A major new contribution of this construction is a dynamic caching technique that maintains low load and storage even under the occurrence of hot spots. Our second construction builds a network that is guaranteed to be an expander. The resulting topologies are simple to maintain and implement. Their simplicity makes it easy to modify and add protocols. A small variation yields a DHT which is robust against random faults. Finally we show that, using our approach, it is possible to construct any family of constant degree graphs in a dynamic environment, though with worst parameters. Therefore we expect that more distributed data structures could be designed and implemented in a dynamic environment.
Boosting BOLD fMRI by K-Space Density Weighted Echo Planar Imaging

Functional magnetic resonance imaging (fMRI) has become a powerful and influential method to non-invasively study neuronal brain activity. For this purpose, the blood oxygenation level-dependent (BOLD) effect is most widely used. T2* weighted echo planar imaging (EPI) is BOLD sensitive and the prevailing fMRI acquisition technique. Here, we present an alternative to its standard Cartesian recordings, i.e. k-space density weighted EPI, which is expected to increase the signal-to-noise ratio in fMRI data. Based on in vitro and in vivo pilot measurements, we show that fMRI by k-space density weighted EPI is feasible and that this new acquisition technique in fact boosted spatial and temporal SNR as well as the detection of local fMRI activations. Spatial resolution, spatial response function and echo time were identical for density weighted and conventional Cartesian EPI. The signal-to-noise ratio gain of density weighting can improve activation detection and has the potential to further increase the sensitivity of fMRI investigations.
CRT based somewhat homomorphic encryption over the integers

Over the last decade, the demand for privacy and data confidentiality in communication and storage processes have increased exponentially. Cryptography can be the solution for this demand. However, the critical issue occurs when there is a need for computing publicly on sensitive information or delegating computation to untrusted machines. This must be done in such a way that preserves the information privacy and accessibility. For this reason, we need an encryption algorithm that allows computation on information without revealing details about them. In 1978 Rivest, Adleman and Dertouzos [1] raised a crucial question: can we use a special privacy homomorphism to encrypt the data and do an unlimited computations on it while it remains encrypted without the necessity of decrypting it? Researchers made extensive efforts to achieve such encryption algorithm. In this paper, we introduce the implementation of the CRT-based somewhat homomorphic encryption over the integers scheme. The main goal is to provide a proof of concept of this new and promising encryption algorithm.
Practical multi-candidate election system

The aim of electronic voting schemes is to provide a set of protocols that allow voters to cast ballots while a group of authorities collect the votes and output the final tally. In this paper we describe a practical multi-candidate election scheme that guarantees privacy of voters, public verifiability, and robustness against a coalition of malicious authorities. Furthermore, we address the problem of receipt-freeness and incoercibility of voters. Our new scheme is based on the Paillier cryptosystem and on some related zero-knowledge proof techniques. The voting schemes are very practical and can be efficiently implemented in a real system.