Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolomic biomarkers from patients with Barth syndrome treated with elamipretide: insights from the TAZPOWER study

Background Barth syndrome is an inherited disorder that results from pathogenic mutations in TAZ, the gene responsible for encoding tafazzin, an enzyme that remodels the mitochondrial phospholipid cardiolipin. Barth syndrome is characterized by heart and skeletal muscle myopathy, growth delay, and neutropenia among other features. The TAZPOWER clinical trial investigated the effects of the mitochondria-targeting peptide elamipretide in patients with Barth syndrome. Methods and findings TAZPOWER included a randomized, double-blind, crossover study of 12 weeks treatment with elamipretide or placebo in 12 patients with Barth syndrome. A broad spectrum of plasma and urine metabolites were measured using liquid chromatography-tandem mass spectrometry at baseline and after 12 weeks treatment with elamipretide or placebo. Of 51 energy-linked metabolites analyzed, we highlight here the effects of elamipretide on the plasma and urinary concentrations of several metabolites previously observed to be abnormal in patients with Barth syndrome. Elamipretide treatment was associated with significantly lowered medium- and short-chain acylcarnitines in plasma and urine, respectively (p < 0.05). Acetylcarnitine, 3-hydroxybutyrate, and 3-methylglutaconate trended to decrease after 12 weeks of elamipretide, but these trends did not reach statistical significance. After 12 weeks of treatment, elamipretide had no discernible effect on four amino acids previously characterized as having abnormal concentrations in patients with Barth syndrome. Lastly, elamipretide caused a significant rise in plasma taurine concentrations, an amino acid which has been observed to be decreased in patients with Barth syndrome. Conclusions As evidenced by reduced plasma and urinary content of acylcarnitines and trends for lowered ketone body 3-hydroxybutyrate, fat metabolism in Barth syndrome appears to be modified after 12 weeks of elamipretide treatment. Overall, these data are consistent with the improved mitochondrial function that may precede functional benefits with a longer duration of therapy with elamipretide in patients with Barth syndrome.
FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network

The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable private and sensitive data. Huge amount of private data are being generated from personal devices such as smart phone and wearable in recent years. Being able to utilize these personal data will bring big opportunities to design personalized products, conduct precision healthcare and many other tasks that were impossible in the past. However, due to privacy, safety and regulation reasons, it is often difficult to transfer or store data in its original form while keeping them safe. Building a secure data transfer and storage infrastructure to preserving privacy is costly in most cases and there is always a concern of data security due to human errors. In this study, we propose a method, named FakeSafe, to provide human level data protection using generative adversarial network with cycle consistency and conducted experiments using both benchmark and real world data sets to illustrate potential applications of FakeSafe.
Protein phosphatase 2A, complement component 4, and APOE genotype linked to Alzheimer disease using a systems biology approach

Background: Recent reports suggest that the rare apolipoprotein E (APOE) Christchurch mutation and {varepsilon}2 allele protect against Alzheimer disease (AD) pathology by reducing the burden of tau pathology. However, the mechanism(s) underlying the {varepsilon}2 protective effect linking to tau is largely unknown. Methods: The role of the {varepsilon}2 allele in AD was investigated a genome-wide association study (GWAS) for AD among 2,120 {varepsilon}2 carriers from the Alzheimer Disease Genetics Consortium (ADGC), and then prioritized by gene network analysis, differential gene expression analysis at tissue- and cell-levels as well as methylation profiling of CpG sites, in prefrontal cortex tissue from 761 brains of the Religious Orders Study and Memory and Aging Project (ROSMAP) and the Framingham Heart Study (FHS), Boston University Alzheimer Disease Center (BUADC). The levels of two catalytic subunit proteins from protein phosphatase 2A (PPP2CA and PPP2CB) were validated in prefrontal cortex area of 193 of the FHS/BUADC brains. The findings from human autopsied brains were further validated by a co-culture experiment of human isogenic APOE induced pluripotent stem cell (iPSC) derived neurons and astrocytes. Results: Of the significantly associated loci with AD among APOE {varepsilon}2 carriers (P<10-6), PPP2CB (P=1.1x10-7) was the key node in the APOE {varepsilon}2-related gene network and contained the most significant CpG site (P=7.3x10-4) located 2,814 base pair upstream of the top-ranked GWAS variant. Among APOE {varepsilon}3/{varepsilon}4 subjects, the level of A{beta}42 was negatively correlated with protein levels of PPP2CA (P=9.9x10-3) and PPP2CB (P=2.4x10-3), and PPP2CA level was correlated with the level of pTau231 level (P=5.3x10-3). Significant correlations were also observed for PPP2CB with complement 4B (C4B) protein levels (P=3.3x10-7) and PPP2CA with cross reactive protein (CRP) levels (P=6.4x10-4). C1q level was not associated with A{beta}42, pTau231, PPP2CB, or C4B levels. We confirmed the significant correlation of PPP2CB expression with pTau231/tTau ratio (P=0.01) and C4A/B (P=2.0x10-4) expression observed in brain tissue in a co-culture experiment of iPSC derived neurons and astrocytes. Conclusion: We demonstrated for the first time a molecular link between a tau phosphatase and the classical complement pathway, especially C4, and AD-related tau pathology.
Controlling Symmetries and Clustered Dynamics of Complex Networks

Symmetries are an essential feature of complex networks as they regulate how the graph collective dynamics organizes into clustered states. We here show how to control network symmetries, and how to enforce patterned states of synchronization with nodes clustered in a desired way. Our approach consists of perturbing the original network connectivity, either by adding new edges or by adding/removing links together with modifying their weights. By solving suitable optimization problems, we guarantee that changes made on the existing topology are minimal. The conditions for the stability of the enforced pattern are derived for the general case, and the performance of the method is illustrated with paradigmatic examples. Our results are relevant to all the practical situations in which coordination of the networked systems into diverse groups may be desirable, such as for teams of robots, unmanned autonomous vehicles, power grids and central pattern generators.
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights

The quality grading of mangoes is a crucial task for mango growers as it vastly affects their profit. However, until today, this process still relies on laborious efforts of humans, who are prone to fatigue and errors. To remedy this, the paper approaches the grading task with various convolutional neural networks (CNN), a tried-and-tested deep learning technology in computer vision. The models involved include Mask R-CNN (for background removal), the numerous past winners of the ImageNet challenge, namely AlexNet, VGGs, and ResNets; and, a family of self-defined convolutional autoencoder-classifiers (ConvAE-Clfs) inspired by the claimed benefit of multi-task learning in classification tasks. Transfer learning is also adopted in this work via utilizing the ImageNet pretrained weights. Besides elaborating on the preprocessing techniques, training details, and the resulting performance, we go one step further to provide explainable insights into the model’s working with the help of saliency maps and principal component analysis (PCA). These insights provide a succinct, meaningful glimpse into the intricate deep learning black box, fostering trust, and can also be presented to humans in real-world use cases for reviewing the grading results.
Sparse sketches with small inversion bias

For a tall $n\times d$ matrix $A$ and a random $m\times n$ sketching matrix $S$, the sketched estimate of the inverse covariance matrix $(A^\top A)^{-1}$ is typically biased: $E[(\tilde A^\top\tilde A)^{-1}]\ne(A^\top A)^{-1}$, where $\tilde A=SA$. This phenomenon, which we call inversion bias, arises, e.g., in statistics and distributed optimization, when averaging multiple independently constructed estimates of quantities that depend on the inverse covariance. We develop a framework for analyzing inversion bias, based on our proposed concept of an $(\epsilon,\delta)$-unbiased estimator for random matrices. We show that when the sketching matrix $S$ is dense and has i.i. d. sub-gaussian entries, then after simple rescaling, the estimator $(\frac m{m-d}\tilde A^\top\tilde A)^{-1}$ is $(\epsilon,\delta)$-unbiased for $(A^\top A)^{-1}$ with a sketch of size $m=O(d+\sqrt d/\epsilon)$. This implies that for $m=O(d)$, the inversion bias of this estimator is $O(1/\sqrt d)$, which is much smaller than the $\Theta(1)$ approximation error obtained as a consequence of the subspace embedding guarantee for sub-gaussian sketches. We then propose a new sketching technique, called LEverage Score Sparsified (LESS) embeddings, which uses ideas from both data-oblivious sparse embeddings as well as data-aware leverage-based row sampling methods, to get $\epsilon$ inversion bias for sketch size $m=O(d\log d+\sqrt d/\epsilon)$ in time $O(\text{nnz}(A)\log n+md^2)$, where nnz is the number of non-zeros. The key techniques enabling our analysis include an extension of a classical inequality of Bai and Silverstein for random quadratic forms, which we call the Restricted Bai-Silverstein inequality; and anti-concentration of the Binomial distribution via the Paley-Zygmund inequality, which we use to prove a lower bound showing that leverage score sampling sketches generally do not achieve small inversion bias.
Strong detection of the CMB lensing and galaxy weak lensing cross-correlation from ACT-DR4, Planck Legacy, and KiDS-1000

We measured the cross-correlation between galaxy weak lensing data from the Kilo Degree Survey (KiDS-1000, DR4) and cosmic microwave background (CMB) lensing data from the Atacama Cosmology Telescope (ACT, DR4) and the Planck Legacy survey. We used two samples of source galaxies, selected with photometric redshifts, (0.1 < zB < 1.2) and (1.2 < zB < 2), which produce a combined detection significance of the CMB lensing and weak galaxy lensing cross-spectrum of 7. 7σ. With the lower redshift galaxy sample, for which the cross-correlation was detected at a significance of 5. 3σ, we present joint cosmological constraints on the matter density parameter, Ωm, and the matter fluctuation amplitude parameter, σ8, marginalising over three nuisance parameters that model our uncertainty in the redshift and shear calibration as well as the intrinsic alignment of galaxies. We find our measurement to be consistent with the best-fitting flat ΛCDM cosmological models from both Planck and KiDS-1000. We demonstrate the capacity of CMB weak lensing cross-correlations to set constraints on either the redshift or shear calibration by analysing a previously unused high-redshift KiDS galaxy sample (1.2 < zB < 2), with the cross-correlation detected at a significance of 7σ. This analysis provides an independent assessment for the accuracy of redshift measurements in a regime that is challenging to calibrate directly owing to known incompleteness in spectroscopic surveys.
An Empirical Study on Failed Error Propagation in Java Programs with Real Faults

During testing, developers can place oracles externally or internally with respect to a method. Given a faulty execution state, i.e., one that differs from the expected one, an oracle might be unable to expose the fault if it is placed at a program point with no access to the incorrect program state or where the program state is no longer corrupted. In such a case, the oracle is subject to failed error propagation. We conducted an empirical study to measure failed error propagation on Defects4J, the reference benchmark for Java programs with real faults, considering all 6 projects available (386 real bugs and 459 fixed methods). Our results indicate that the prevalence of failed error propagation is negligible when testing is performed at the unit level. However, when system-level inputs are provided, the prevalence of failed error propagation increases substantially. This indicates that it is enough for method postconditions to predicate only on the externally observable state/data and that intermediate steps should be checked when testing at system level.
Spreading of correlations and entanglement in the long-range transverse Ising chain

Whether long-range interactions allow for a form of causality in non-relativistic quantum models remains an open question with far-reaching implications for the propagation of information and thermalization processes. Here, we study the out-of-equilibrium dynamics of the one-dimensional transverse Ising model with algebraic long-range exchange coupling. Using a state of the art tensor-network approach, complemented by analytic calculations and considering various observables, we show that a weak form of causality emerges, characterized by non-universal dynamical exponents. While the local spin and spin correlation causal edges are sub-ballistic, the causal region has a rich internal structure, which, depending on the observable, displays ballistic or super-ballistic features. In contrast, the causal region of entanglement entropy is featureless and its edge is always ballistic, irrespective of the interaction range. Our results shed light on the propagation of information in long-range interacting lattice models and pave the way to future experiments, which are discussed.
Origin and binary evolution of millisecond pulsars

We summarize the channels formation of neutron stars (NS) in single or binary evolution and the classic recycling scenario by which mass accretion by a donor companion accelerates old NS to millisecond pulsars (MSP). We consider the possible explanations and requirements for the high frequency of the MSP population in Globular Clusters. Basics of binary evolution are given, and the key concepts of systemic angular momentum losses are first discussed in the framework of the secular evolution of Cataclysmic Binaries. MSP binaries with compact companions represent end-points of previous evolution. In the class of systems characterized by short orbital period and low companion mass, we may instead be catching the recycling phase `in the act'. These systems are in fact either MSP, or low mass X--ray binaries (LMXB), some of which accreting X-ray MSP (AMXP), or even `transitional' systems from the accreting to the radio MSP stage. The donor structure is affected by irradiation due to X-rays from the accreting NS, or by the high fraction of MSP rotational energy loss emitted in the gamma rays range of the energy spectrum. X-ray irradiation leads to cyclic LMXB stages, causing super-Eddington mass transfer rates during the first phases of the companion evolution, and, possibly coupled with the angular momentum carried away by the non-accreted matter, helps to explain the high positive orbital period derivatives of some LMXB systems and account for the (apparently) different birthrates of LMXB and MSP. Irradiation by the MSP may be able to drive the donor to a stage in which either radio-ejection (in the redbacks) or mass loss due to the companion expansion, and `evaporation' may govern the evolution to the black widow stage and to the final disruption of the companion.
Sensing Ambiguity in Henry James' "The Turn of the Screw"

Fields such as the philosophy of language, continental philosophy, and literary studies have long established that human language is, at its essence, ambiguous and that this quality, although challenging to communication, enriches language and points to the complexity of human thought. On the other hand, in the NLP field there have been ongoing efforts aimed at disambiguation for various downstream tasks. This work brings together computational text analysis and literary analysis to demonstrate the extent to which ambiguity in certain texts plays a key role in shaping meaning and thus requires analysis rather than elimination. We revisit the discussion, well known in the humanities, about the role ambiguity plays in Henry James' 19th century novella, The Turn of the Screw. We model each of the novella's two competing interpretations as a topic and computationally demonstrate that the duality between them exists consistently throughout the work and shapes, rather than obscures, its meaning. We also demonstrate that cosine similarity and word mover's distance are sensitive enough to detect ambiguity in its most subtle literary form, despite doubts to the contrary raised by literary scholars. Our analysis is built on topic word lists and word embeddings from various sources. We first claim, and then empirically show, the interdependence between computational analysis and close reading performed by a human expert.
Time Series Data Imputation: A Survey on Deep Learning Approaches

Time series are all around in real-world applications. However, unexpected accidents for example broken sensors or missing of the signals will cause missing values in time series, making the data hard to be utilized. It then does harm to the downstream applications such as traditional classification or regression, sequential data integration and forecasting tasks, thus raising the demand for data imputation. Currently, time series data imputation is a well-studied problem with different categories of methods. However, these works rarely take the temporal relations among the observations and treat the time series as normal structured data, losing the information from the time data. In recent, deep learning models have raised great attention. Time series methods based on deep learning have made progress with the usage of models like RNN, since it captures time information from data. In this paper, we mainly focus on time series imputation technique with deep learning methods, which recently made progress in this field. We will review and discuss their model architectures, their pros and cons as well as their effects to show the development of the time series imputation methods.
On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems

Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. The structure of such mathematical models relies on a set of hysical hypotheses that are assumed to be fulfilled by the system within a certain range of environmental conditions. A new perspective is now raising that uses physical knowledge to inform the data prediction capability of artificial neural networks. A particular extension of this data-driven approach is Physically-Guided Neural Networks with Internal Variables (PGNNIV): universal physical laws are used as constraints in the neural network, in such a way that some neuron values can be interpreted as internal state variables of the system. This endows the network with unraveling capacity, as well as better predictive properties such as faster convergence, fewer data needs and additional noise filtering. Besides, only observable data are used to train the network, and the internal state equations may be extracted as a result of the training processes, so there is no need to make explicit the particular structure of the internal state model. We extend this new methodology to continuum physical problems, showing again its predictive and explanatory capacities when only using measurable values in the training set. We show that the mathematical operators developed for image analysis in deep learning approaches can be used and extended to consider standard functional operators in continuum Physics, thus establishing a common framework for both. The methodology presented demonstrates its ability to discover the internal constitutive state equation for some problems, including heterogeneous and nonlinear features, while maintaining its predictive ability for the whole dataset coverage, with the cost of a single evaluation.
Effect of Additional Pain Neuroscience Education in Interdisciplinary Multimodal Pain Therapy on Current Pain. A Non-Randomized, Controlled Intervention Study

Purpose Interdisciplinary multimodal pain therapy (IMPT) programs for chronic back pain are effective and recommended. The patient-centered and biopsychosocial nature of IMPT is grounded in contemporary understanding that chronic pain states reflect heightened sensitization of the nervous system rather than an issue in the tissue. Teaching patients about pain is part of IMPT programs, though a clinical guideline is lacking. This study aims to answer the following question: Does the addition of an evidence-based pain neuroscience education (PNE) lecture for patients, into an IMPT program, produce superior results than the IMPT program itself? Patients and Methods A non-randomized, controlled intervention study was performed with 179 back pain patients indicated for IMPT. Intervention group (N=102) received a four-week IMPT program, which contained 4 one-hour sessions PNE. Control group (N=77) received the same IMPT without the additional PNE. Primary outcome was current pain after intervention. Pain knowledge, physical function, depression, anxiety, stress, quality of life and fear-avoidance were analyzed as secondary outcomes. Outcomes were defined as change of the parameter measured before and immediately after the four-week IMPT. For each outcome, linear regression models were used to estimate the raw and adjusted (sex, age and BMI) effect of additional PNE. Results Despite improvement in all outcomes for both groups during the treatment phase, the implemented PNE did not result in additional pain reduction (regression coefficient for PNE effect on pain level 0.34; 95% CI −6.23–6.97). Between-group differences could only be shown for pain-related knowledge in favor for the intervention group (0.78; 95% CI 0.35–1.20). Conclusion The additional PNE lecture did not lead to pain reduction beyond the usual IMPT. However, the PNE did increase pain-related knowledge and, therefore, might be helpful in coping with pain after the IMPT program.