Miracle-dz/UniFAR-FTUs huggingface dataset:
Can transcranial electric stimulation with multiple electrodes reach deep targets?

To reach a deep target in the brain with transcranial electric stimulation (TES), currents have to pass also through the cortical surface. Thus, it is generally thought that TES cannot achieve focal deep brain stimulation. Recent efforts with interfering waveforms and pulsed stimulation have argued that one can achieve deeper or more intense stimulation in the brain. Here we argue that conventional transcranial stimulation with multiple current sources is just as effective as these new approaches. The conventional multi-electrode approach can be numerically optimized to maximize intensity or focality at a desired target location. Using such optimal electrode configurations we find in a detailed and realistic head model that deep targets may in fact be strongly stimulated, with cerebro-spinal fluid guiding currents deep into the brain. Highlights Deep targets can be reached with intensities comparable to the cortical surface. Multi-electrode montages increase intensity with the same current limits per electrode. High-definition and intersectional pulsed stimulation are largely equivalent. Interferential stimulation is generally weaker than conventional stimulation.
17β-Estradiol Enhances Schwann Cell Differentiation via the ERβ-ERK1/2 Signaling Pathway and Promotes Remyelination in Injured Sciatic Nerves

Remyelination is critical for nerve regeneration. However, the molecular mechanism involved in remyelination is poorly understood. To explore the roles of 17β-estradiol (E2) for myelination in the peripheral nervous system, we used a co-culture model of rat dorsal root ganglion (DRG) explants and Schwann cells (SCs) and a regeneration model of the crushed sciatic nerves in ovariectomized (OVX) and non-ovariectomized (non-OVX) rats for in vitro and in vivo analysis. E2 promoted myelination by facilitating the differentiation of SCs in vitro, which could be inhibited by the estrogen receptors (ER) antagonist ICI182780, ERβ antagonist PHTPP, or ERK1/2 antagonist PD98059. This suggests that E2 accelerates SC differentiation via the ERβ-ERK1/2 signaling. Furthermore, E2 promotes remyelination in crushed sciatic nerves of both OVX and non-OVX rats. Interestingly, E2 also significantly increased the expression of the lysosome membrane proteins LAMP1 and myelin protein P0 in the regenerating nerves. Moreover, P0 has higher degree of colocalization with LAMP1 in the regenerating nerves. Taking together, our results suggest that E2 enhances Schwann cell differentiation and further myelination via the ERβ-ERK1/2 signaling and that E2 increases the expression of myelin proteins and lysosomes in SCs to promotes remyelination in regenerating sciatic nerves.
An Effective Approach for Routing the Bug Reports to the Right Fixers

Routing the bug reports to potential fixers (i.e., bug triaging), is an integral step in software development and maintenance. However, manually inspecting and assigning bug reports is tedious and time-consuming, especially in those software projects that have a large amount of bug reports and developers. To make bug triaging more efficient, many machine learning and information retrieval based approaches have been proposed to automatically assign bug reports for suitable developers to fix. However, these techniques typically ignore two important facts in bug fixing. First, for some bug reports, the bug reporter himself/herself is one of the developers in the project, and he/she is likely to fix his/her reported bugs in the future. Second, for some bug reports, there may be a tossing sequence which contains several developers from the first potential fixer to the last actual fixer. Such tossing sequences encode valuable information such as the dependency of developers for the bug triaging task. To make use of the above facts, we propose a sequence to sequence model named SeqTriage to automatically route a given bug report to its responsible fixer. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches (20% at best and 5% at least).
State of the Art Enamel Remineralization Systems: The Next Frontier in Caries Management

The principles of minimally invasive dentistry clearly dictate the need for clinically effective measures to remineralize early enamel caries lesions. While fluoride-mediated remineralization is the cornerstone of current caries management philosophies, a number of new remineralization strategies have been commercialized or are under development that claim to promote deeper remineralization of lesions, reduce the potential risks associated with high-fluoride oral care products, and facilitate caries control over a lifetime. These non-fluoride remineralizing systems can be broadly categorized into biomimetic enamel regenerative technologies and the approaches that repair caries lesions by enhancing fluoride efficacy. This paper discusses the rationale for non-fluoride remineralization and the mechanism of action, challenges, and evidence behind some of the most promising advances in enamel remineralization therapies.
Assessing the effect of data transformations on test suite compilation

Background. The requirements and responsibilities assumed by software has increasingly rendered it to be large and complex. Testing to ensure that software meets all its requirements and is free from failures is a difficult and time-consuming task that necessitates the use of large test suites, containing many tests. Large test suites result in a corresponding increase in the size of the test code that sets up, exercises and verifies the tests. Time needed to compile and optimise the test code becomes prohibitive for large test code sizes. Aims. In this paper we demonstrate for the first time optimisations to speedup compilation of test code. Reducing the compilation time of test code for large and complex systems will allow additional tests to be compiled and executed, while also enabling more frequent and rigorous testing. Methods. We propose transformations that reduce the number of instructions in the test code, which in turn reduces compilation time. Using two well known compilers, GCC and Clang, we conduct empirical evaluations using subject programs from industry standard benchmarks and an industry provided program. We evaluate compilation speedup, execution time, scalability and correctness of the proposed test code transformation. Results. Our approach resulted in significant compilation speedups in the range of 1.3X to 69X. Execution of the test code was just as fast with our transformation when compared to the original while also preserving correctness of execution. Finally, our experiments show that the gains in compilation time allow significantly more tests to be included in a single binary, improving scalability of test code compilation. Conclusions. The proposed transformation results in faster test code compilation for all the programs in our experiment, with more significant speedups for larger case studies and larger numbers of tests. As systems get more complex requiring frequent and extensive testing, we believe our approach provides a safe and efficient means of compiling test code.
Observations on the Structure and Development of Bone

The structure and development of bone is a subject which has occupied the attention of physiologists both of present and past times, and holds a prominent place in their writings. From the time of Clopton H avers, who was the first to point out the vascular canals in bone, and whose name they have since borne, to the year 1850, when M. Kolliker’s elaborate work on Structural Anatomy appeared, physiological writers have dwelt at considerable length on this branch of their subject. All have, however, concluded with the admission that more remained to be learned before this important structure could be regarded as fully understood*. It is the purpose of the authors in this communication, to lay before the Royal Society the results of an extended series of observations on the structure and development of bone. Such of these results as are novel might, perhaps, be described without reference to those points which are already well known. It will, however, be seen, in the subsequent pages, that they are in themselves, and in their relations to osseous tissue and its development, as well as to structural anatomy generally, of such a nature, as would hardly admit of an intelligible description apart from some consideration of the whole subject. Hence it will be necessary to point out the relations between that which is already known, and that which it is the special purpose of this paper to communicate, and it is proposed to consider the details of the subject as they present themselves in the progress of investigation with but little reference to authorities, adding in the form of notes, those statements which require acknowledgement, either as agreeing with, or as being opposed to, the views advanced in this paper.
Evaluation of a Sign Language Support System for Viewing Sports Programs

As information support to deaf and hard of hearing people who are viewing sports programs, we have developed a sign language support system. The system automatically generates Japanese Sign Language (JSL) computer graphics (CG) animation and subtitles from prepared templates of JSL phrases corresponding to fixed format game data. To verify the system's performance, we carried out demonstration experiments on the generation and displaying of contents using real-time match data from actual games. From the experiment results we concluded that the automatically generated JSL CG is practical enough for understanding the information. We also found that among several display methods, the one providing game video and JSL CG on a single tablet screen was most preferred in this small-scale experiment.
The Curses of Blockchain Decentralization

Decentralization, which has backed the hyper growth of many blockchains, comes at the cost of scalability. To understand this fundamental limitation, this paper proposes a quantitative measure of blockchain decentralization, and discusses its implications to various trust models and consensus algorithms. Further, we identify the major challenges in blockchain decentralization. Our key findings are that true decentralization is hard to achieve due to the skewed mining power and that a fully decentralized blockchain inherently limits scalability as it incurs a throughput upper bound and prevents scaling smart contract execution. To address these challenges, we outline three research directions to explore the trade-offs between decentralization and scalability.
Time-Message Trade-Offs in Distributed Algorithms

This paper focuses on showing time-message trade-offs in distributed algorithms for fundamental problems such as leader election, broadcast, spanning tree (ST), minimum spanning tree (MST), minimum cut, and many graph verification problems. We consider the synchronous CONGEST distributed computing model and assume that each node has initial knowledge of itself and the identifiers of its neighbors - the so-called KT1 model - a well-studied model that also naturally arises in many applications. Recently, it has been established that one can obtain (almost) singularly optimal algorithms, i.e., algorithms that have simultaneously optimal time and message complexity (up to polylogarithmic factors), for many fundamental problems in the standard KT0 model (where nodes have only local knowledge of themselves and not their neighbors). The situation is less clear in the KT1 model. In this paper, we present several new distributed algorithms in the KT1 model that trade off between time and message complexity. Our distributed algorithms are based on a uniform approach which involves constructing a sparsified spanning subgraph of the original graph - called a danner - that trades off the number of edges with the diameter of the sparsifier. In particular, a key ingredient of our approach is a distributed randomized algorithm that, given a graph G and any delta in [0, 1], with high probability constructs a danner that has diameter Otilde(D + n^(1 - delta)) and Otilde(min{m, n^(1 + delta)}) edges in Otilde(n^(1 - delta)) rounds while using Otilde(min{m, n^(1 + delta)}) messages, where n, m, and D are the number of nodes, edges, and the diameter of G, respectively. Using our danner construction, we present a family of distributed randomized algorithms for various fundamental problems that exhibit a trade-off between message and time complexity and that improve over previous results.
Idle stripes shirt: ambient wearable display for activity tracking

We present the design and prototype of the Idle Stripes shirt, which is an aesthetic, clothing-integrated display, reflecting the wearer's physical activity in an ambient manner. The design is targeted to be smart business-wear for an office worker, which creates awareness of immobility periods during typical sitting-intensive office work. Long periods of such sitting are known to be health risks. The Idle Stripes shirt promotes healthy working, encouraging the wearer to break up their office desk work with walking breaks. The design prototype is constructed of a fabric with integrated optical fibers, which are illuminated based on the sitting time detected by an app running on the wearer's mobile phone.
A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism

Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction. We utilize stack autoencoder to explore high-order feature interactions and use improved FM for low-order feature interactions to portray the nonlinear associated relationship of features. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.
Blood-based biomarkers for Alzheimer’s disease: mapping the road to the clinic

1. AXA Research Fund & Sorbonne University Chair, Paris, France 2. Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, F-75013, Paris, France 3. Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France 4. Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France 5. University of North Texas Health Science Center, Fort Worth, Texas, USA 6. Barcelonaβeta Brain Research Centre, Pasqual Maragall Foundation, Barcelona, Spain 7. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden 8. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden 9. Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, UK 10. UK Dementia Research Institute at UCL, London, UK 11. The Florey Institute, The University of Melbourne, Melbourne, Australia 12. Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK 13. MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK 14. Roche Diagnostics International, Rotkreuz, Switzerland
Dense Multimodal Fusion for Hierarchically Joint Representation

Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Previous methods mainly focus on fusing the shallow features or high-level representations generated by unimodal deep networks, which only capture part of the hierarchical correlations across modalities. In this paper, we propose to densely integrate the representations by greedily stacking multiple shared layers between different modality-specific networks, which is named as Dense Multimodal Fusion (DMF). The joint representations in different shared layers can capture the correlations in different levels, and the connection between shared layers also provides an efficient way to learn the dependence among hierarchical correlations. These two properties jointly contribute to the multiple learning paths in DMF, which results in faster convergence, lower training loss, and better performance. We evaluate our model on audiovisual speech recognition and cross-modal retrieval. The noticeable performance demonstrates that our model can learn more effective joint representation.
Highly-Optimizing and Multi-Target Compiler for Embedded System Models: C++ Compiler Toolchain for the Component and Connector Language EmbeddedMontiArc

Component and Connector (C&C) models, with their corresponding code generators, are widely used by large automotive manufacturers to develop new software functions for embedded systems interacting with their environment; C&C example applications are engine control, remote parking pilots, and traffic sign assistance. This paper presents a complete toolchain to design and compile C&C models to highly-optimized code running on multiple targets including x86/x64, ARM and WebAssembly. One of our contributions are algebraic and threading optimizations to increase execution speed for computationally expensive tasks. A further contribution is an extensive case study with over 50 experiments. This case study compares the runtime speed of the generated code using different compilers and mathematical libraries. These experiments showed that programs produced by our compiler are at least two times faster than the ones compiled by MATLAB/Simulink for machine learning applications such as image clustering for object detection. Additionally, our compiler toolchain provides a complete model-based testing framework and plug-in points for middleware integration. We make all materials including models and toolchains electronically available for inspection and further research.
Keystone Species in Pregnancy Gingivitis: A Snapshot of Oral Microbiome During Pregnancy and Postpartum Period

It is well known that pregnancy is under the constant influence of hormonal, metabolic and immunological factors and this may impact the oral microbiota toward pregnancy gingivitis. However, it is still not clear how the oral microbial dysbiosis can modulate oral diseases as oral microbiome during pregnancy is very poorly characterized. In addition, the recent revelation that placental microbiome is akin to oral microbiome further potentiates the importance of oral dysbiosis in adverse pregnancy outcomes. Hence, leveraging on the 16S rRNA gene sequencing technology, we present a snapshot of the variations in the oral microbial composition with the progression of pregnancy and in the postpartum period and its association with pregnancy gingivitis. Despite the stability of oral microbial diversity during pregnancy and postpartum period, we observed that the microbiome makes a pathogenic shift during pregnancy and reverts back to a healthy microbiome during the postpartum period. Co-occurrence network analysis provided a mechanistic explanation of the pathogenicity of the microbiome during pregnancy and predicted taxa at hubs of interaction. Targeting the taxa which form the ecological guilds in the underlying microbiome would help to modulate the microbial pathogenicity during pregnancy, thereby alleviating risk for oral diseases and adverse pregnancy outcomes. Our study has also uncovered the possibility of novel species in subgingival plaque and saliva as the key players in the causation of pregnancy gingivitis. The keystone species hold the potential to open up avenues for designing microbiome modulation strategies to improve host health during pregnancy.
Speech Recognition via fNIRS Based Brain Signals

In this paper, we present the first evidence that perceived speech can be identified from the listeners' brain signals measured via functional-near infrared spectroscopy (fNIRS)—a non-invasive, portable, and wearable neuroimaging technique suitable for ecologically valid settings. In this study, participants listened audio clips containing English stories while prefrontal and parietal cortices were monitored with fNIRS. Machine learning was applied to train predictive models using fNIRS data from a subject pool to predict which part of a story was listened by a new subject not in the pool based on the brain's hemodynamic response as measured by fNIRS. fNIRS signals can vary considerably from subject to subject due to the different head size, head shape, and spatial locations of brain functional regions. To overcome this difficulty, a generalized canonical correlation analysis (GCCA) was adopted to extract latent variables that are shared among the listeners before applying principal component analysis (PCA) for dimension reduction and applying logistic regression for classification. A 74.7% average accuracy has been achieved for differentiating between two 50 s. long story segments and a 43.6% average accuracy has been achieved for differentiating four 25 s. long story segments. These results suggest the potential of an fNIRS based-approach for building a speech decoding brain-computer-interface for developing a new type of neural prosthetic system.
Molecular Mechanisms of Transdifferentiation of Adipose-Derived Stem Cells into Neural Cells: Current Status and Perspectives

Neurological diseases can severely compromise both physical and psychological health. Recently, adult mesenchymal stem cell- (MSC-) based cell transplantation has become a potential therapeutic strategy. However, most studies related to the transdifferentiation of MSCs into neural cells have had disappointing outcomes. Better understanding of the mechanisms underlying MSC transdifferentiation is necessary to make adult stem cells more applicable to treating neurological diseases. Several studies have focused on adipose-derived stromal/stem cell (ADSC) transdifferentiation. The purpose of this review is to outline the molecular characterization of ADSCs, to describe the methods for inducing ADSC transdifferentiation, and to examine factors influencing transdifferentiation, including transcription factors, epigenetics, and signaling pathways. Exploring and understanding the mechanisms are a precondition for developing and applying novel cell therapies.