Large-scale extraction of brain connectivity from the neuroscientific literature

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against invivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists. Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

A probabilistic design for practical homomorphic majority voting with intrinsic differential privacy

As machine learning (ML) has become pervasive throughout various fields (industry, healthcare, social networks), privacy concerns regarding the data used for its training have gained a critical importance. In settings where several parties wish to collaboratively train a common model without jeopardizing their sensitive data, the need for a private training protocol is particularly stringent and implies to protect the data against both the model's end-users and the other actors of the training phase. In this context of secure collaborative learning, Differential Privacy (DP) and Fully Homomorphic Encryption (FHE) are two complementary countermeasures of growing interest to thwart privacy attacks in ML systems. Central to many collaborative training protocols, in the line of PATE, is majority voting aggregation. Thus, in this paper, we design SHIELD, a probabilistic approximate majority voting operator which is faster when homomorphically executed than existing approaches based on exact argmax computation over an histogram of votes. As an additional benefit, the inaccuracy of SHIELD is used as a feature to provably enable DP guarantees. Although SHIELD may have other applications, we focus here on one setting and seamlessly integrate it in the SPEED collaborative training framework from \cite{grivet2021speed} to improve its computational efficiency. After thoroughly describing the FHE implementation of our algorithm and its DP analysis, we present experimental results. To the best of our knowledge, it is the first work in which relaxing the accuracy of an algorithm is constructively usable as a degree of freedom to achieve better FHE performances

Silicon on Nothing Mems Electromechanical Resonator

The very significant growth of the wireless communication industry has spawned tremendous interest in the development of high performances radio frequencies (RF) components. Micro Electro Mechanical Systems (MEMS) are good candidates to allow reconfigurable RF functions such as filters, oscillators or antennas. This paper will focus on the MEMS electromechanical resonators which show interesting performances to replace SAW filters or quartz reference oscillators, allowing smaller integrated functions with lower power consumption. The resonant frequency depends on the material properties, such as Young's modulus and density, and on the movable mechanical structure dimensions (beam length defined by photolithography). Thus, it is possible to obtain multi frequencies resonators on a wafer. The resonator performance (frequency, quality factor) strongly depends on the environment, like moisture or pressure, which imply the need for a vacuum package. This paper will present first resonator mechanisms and mechanical behaviors followed by state of the art descriptions with applications and specifications overview. Then MEMS resonator developments at STMicroelectronics including FEM analysis, technological developments and characterization are detailed.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

New Concepts in Oxidation Processes

This Special Issue of Catalysts aims to cover the recent progress and novel trends in the field of catalytic oxidation reaction. Topics addressed in this special issue concern the influence of different parameters on catalytic activity at various scales (atomic, laboratory, pilot, or industrial scales), the development of new catalytic materials of environmental or industrial importance, as well as the development of new methods, both microscopic and spectroscopic, to analyze oxidation processes

DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion

We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE. We show that diffusion models enhance the accuracy, robustness, and coherence of human pose estimations. We introduce DiffHPE, a novel strategy for harnessing diffusion models in 3D-HPE, and demonstrate its ability to refine standard supervised 3D-HPE. We also show how diffusion models lead to more robust estimations in the face of occlusions, and improve the time-coherence and the sagittal symmetry of predictions. Using the Human\,3.6M dataset, we illustrate the effectiveness of our approach and its superiority over existing models, even under adverse situations where the occlusion patterns in training do not match those in inference. Our findings indicate that while standalone diffusion models provide commendable performance, their accuracy is even better in combination with supervised models, opening exciting new avenues for 3D-HPE research.Comment: Accepted to 2023 International Conference on Computer Vision Workshop (Analysis and Modeling of Faces and Gestures

Large-scale extraction of brain connectivity from the neuroscientific literature

In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity

Putting up the swiss army knife of homomorphic calculations by means of TFHE functional bootstrapping

In this work, we first propose a new functional bootstrapping with TFHE for evaluating any function of domain and codomain the real torus T by using a small number of bootstrappings. This result improves some aspects of previous approaches: like them, we allow for evaluating any functions, but with better precision. In addition, we develop more efficient multiplication and addition over ciphertexts building on the digit-decomposition approach. As a practical application, our results lead to an efficient implementation of ReLU, one of the most used activation functions in deep learning. The paper is concluded by extensive experimental results comparing each building block as well as their practical relevance and trade-offs

Irreversible transformation of ferromagnetic ordered stripe domains in single-shot IR pump - resonant X-ray scattering probe experiments

The evolution of a magnetic domain structure upon excitation by an intense, femtosecond Infra-Red (IR) laser pulse has been investigated using single-shot based time-resolved resonant X-ray scattering at the X-ray Free Electron laser LCLS. A well-ordered stripe domain pattern as present in a thin CoPd alloy film has been used as prototype magnetic domain structure for this study. The fluence of the IR laser pump pulse was sufficient to lead to an almost complete quenching of the magnetization within the ultrafast demagnetization process taking place within the first few hundreds of femtoseconds following the IR laser pump pulse excitation. On longer time scales this excitation gave rise to subsequent irreversible transformations of the magnetic domain structure. Under our specific experimental conditions, it took about 2 nanoseconds before the magnetization started to recover. After about 5 nanoseconds the previously ordered stripe domain structure had evolved into a disordered labyrinth domain structure. Surprisingly, we observe after about 7 nanoseconds the occurrence of a partially ordered stripe domain structure reoriented into a novel direction. It is this domain structure in which the sample's magnetization stabilizes as revealed by scattering patterns recorded long after the initial pump-probe cycle. Using micro-magnetic simulations we can explain this observation based on changes of the magnetic anisotropy going along with heat dissipation in the film.Comment: 16 pages, 6 figure

NFE2-Related transcription factor 2 coordinates antioxidant defense with thyroglobulin production and iodination in the thyroid gland

Background: The thyroid gland has a special relationship with oxidative stress. While generation of oxidative substances is part of normal iodide metabolism during thyroid hormone synthesis, the gland must also defend itself against excessive oxidation in order to maintain normal function. Antioxidant and detoxification enzymes aid thyroid cells to maintain homeostasis by ameliorating oxidative insults, including during exposure to excess iodide, but the factors that coordinate their expression with the cellular redox status are not known. The antioxidant response system comprising the ubiquitously expressed NFE2-related transcription factor 2 (Nrf2) and its redox-sensitive cytoplasmic inhibitor Kelch-like ECH-associated protein 1 (Keap1) defends tissues against oxidative stress, thereby protecting against pathologies that relate to DNA, protein, and/or lipid oxidative damage. Thus, it was hypothesized that Nrf2 should also have important roles in maintaining thyroid homeostasis. Methods: Ubiquitous and thyroid-specific male C57BL6J Nrf2 knockout (Nrf2-KO) mice were studied. Plasma and thyroids were harvested for evaluation of thyroid function tests by radioimmunoassays and of gene and protein expression by real-time polymerase chain reaction and immunoblotting, respectively. Nrf2-KO and Keap1-KO clones of the PCCL3 rat thyroid follicular cell line were generated using CRISPR/Cas9 technology and were used for gene and protein expression studies. Software-predicted Nrf2 binding sites on the thyroglobulin enhancer were validated by site-directed in vitro mutagenesis and chromatin immunoprecipitation. Results: The study shows that Nrf2 mediates antioxidant transcriptional responses in thyroid cells and protects the thyroid from oxidation induced by iodide overload. Surprisingly, it was also found that Nrf2 has a dramatic impact on both the basal abundance and the thyrotropin-inducible intrathyroidal abundance of thyroglobulin (Tg), the precursor protein of thyroid hormones. This effect is mediated by cell-autonomous regulation of Tg gene expression by Nrf2 via its direct binding to two evolutionarily conserved antioxidant response elements in an upstream enhancer. Yet, despite upregulating Tg levels, Nrf2 limits Tg iodination both under basal conditions and in response to excess iodide. Conclusions: Nrf2 exerts pleiotropic roles in the thyroid gland to couple cell stress defense mechanisms to iodide metabolism and the thyroid hormone synthesis machinery, both under basal conditions and in response to excess iodide.Fil: Ziros, Panos G. Lausanne University; SuizaFil: Habeos, Ioannis. Patras University; GreciaFil: Chartoumpekis, Dionysios V. University of Pittsburgh; Estados UnidosFil: Ntalampyra, Eleni. Universite de Lausanne; SuizaFil: Somm, Emmanuel. Universite de Lausanne; SuizaFil: Renaud, Cédric O.. Universite de Lausanne; SuizaFil: Bongiovanni, Massimo. Institute Of Pathology Locarno; SuizaFil: Trougakos, Ioannis P. Universidad Nacional y Kapodistríaca de Atenas; GreciaFil: Yamamoto, Masayuki. University Of Tohoku; JapónFil: Kensler, Thomas W.. University of Pittsburgh at Johnstown; Estados UnidosFil: Santisteban, Pilar. Universidad Autónoma de Madrid; EspañaFil: Carrasco, Nancy. University of Yale. School of Medicine; Estados UnidosFil: Ris Stalpers, Carrie. Academic Medical Center; Países BajosFil: Amendola, Elena. Universidad de Nápoles; ItaliaFil: Liao, Xiao-Hui. University of Chicago; Estados UnidosFil: Rossich, Luciano Esteban. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Thomasz, Lisa. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Juvenal, Guillermo Juan. Comisión Nacional de Energía Atómica de Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Refetoff, Samuel. University of Chicago; Estados UnidosFil: Sykiotis, Gerasimos P.. Universite de Lausanne; Suiz