Miracle-dz/UniFAR-FTUs huggingface dataset:
Expansion microscopy of Plasmodium gametocytes reveals the molecular architecture of a bipartite microtubule organisation centre coordinating mitosis with axoneme assembly

Transmission of malaria-causing parasites to mosquitoes relies on the production of gametocyte stages and their development into gametes. These stages display various microtubule cytoskeletons and the architecture of the corresponding microtubule organisation centres (MTOC) remains elusive. Combining ultrastructure expansion microscopy (U-ExM) with bulk proteome labelling, we first reconstructed in 3D the subpellicular microtubule network which confers cell rigidity to Plasmodium falciparum gametocytes. Upon activation, as the microgametocyte undergoes three rounds of endomitosis, it also assembles axonemes to form eight flagellated microgametes. U-ExM combined with Pan-ExM further revealed the molecular architecture of the bipartite MTOC coordinating mitosis with axoneme formation. This MTOC spans the nuclear membrane linking cytoplasmic basal bodies to intranuclear bodies by proteinaceous filaments. In P. berghei, the eight basal bodies are concomitantly de novo assembled in a SAS6- and SAS4-dependent manner from a deuterosome-like structure, where centrin, γ-tubulin, SAS4 and SAS6 form distinct subdomains. Basal bodies display a fusion of the proximal and central cores where centrin and SAS6 are surrounded by a SAS4-toroid in the lumen of the microtubule wall. Sequential nucleation of axonemes and mitotic spindles is associated with a dynamic movement of γ-tubulin from the basal bodies to the intranuclear bodies. This dynamic architecture relies on two non-canonical regulators, the calcium-dependent protein kinase 4 and the serine/arginine-protein kinase 1. Altogether, these results provide insights into the molecular organisation of a bipartite MTOC that may reflect a functional transition of a basal body to coordinate axoneme assembly with mitosis.
Immune Mechanisms of Plaque Instability

Inflammation crucially drives atherosclerosis from disease initiation to the emergence of clinical complications. Targeting pivotal inflammatory pathways without compromising the host defense could compliment therapy with lipid-lowering agents, anti-hypertensive treatment, and lifestyle interventions to address the substantial residual cardiovascular risk that remains beyond classical risk factor control. Detailed understanding of the intricate immune mechanisms that propel plaque instability and disruption is indispensable for the development of novel therapeutic concepts. In this review, we provide an overview on the role of key immune cells in plaque inception and progression, and discuss recently identified maladaptive immune phenomena that contribute to plaque destabilization, including epigenetically programmed trained immunity in myeloid cells, pathogenic conversion of autoreactive regulatory T-cells and expansion of altered leukocytes due to clonal hematopoiesis. From a more global perspective, the article discusses how systemic crises such as acute mental stress or infection abruptly raise plaque vulnerability and summarizes recent advances in understanding the increased cardiovascular risk associated with COVID-19 disease. Stepping outside the box, we highlight the role of gut dysbiosis in atherosclerosis progression and plaque vulnerability. The emerging differential role of the immune system in plaque rupture and plaque erosion as well as the limitations of animal models in studying plaque disruption are reviewed.
Characterization of the complete plastome of Spiraea trilobata (Rosaceae), a perennial shrub

Abstract The complete plastome of Spiraea trilobata, a shrub, is determined. The plastome is 155,981 bp in length and comprises a large single-copy region (84,417 bp), a small single-copy region (18,878 bp), and a pair of inverted repeats regions (26,343 bp). A total of 113 unique genes are annotated for the plastome of S. trilobata, containing 79 protein coding genes (PCGs), 30 tRNAs, and four rRNAs. The GC content of this plastome is 36.8%. Phylogenomic analysis based on 17 plastomes reveals that S. trilobata is sister to Spiraea blumei. Compared with S. blumei, S. trilobata lacks both trnM-CAU and ycf1. In addition, the GC content of both species is the same.
The association of Schistosoma and geohelminth infections with β-cell function and insulin resistance among HIV-infected and HIV-uninfected adults: A cross-sectional study in Tanzania

Objectives Data on the role of helminths on diabetes in Africa are limited. We investigated whether Schistosoma and geohelminth infections are associated with β-cell function and insulin resistance among adults. Methods A cross-sectional study was conducted among adults during 2016–2017. Demography, Schistosoma and geohelminth infections, HIV and insulin data were collected. Insulin during an oral glucose tolerance test (fasting, 30, and 120-min), overall insulin secretion index, insulinogenic index, HOMA-β, and HOMA-IR were main outcome measures for β-cell function and insulin resistance, respectively. Generalized estimating equations and generalized linear models assessed the association of Schistosoma and geohelminth infections with outcome measures separately by HIV status. Outcomes were presented as marginal means with 95% CI. Results Data were obtained for 1718 participants. Schistosoma infection was associated with higher 30-min insulin (24.2 mU/L, 95% CI: 6.9, 41.6) and overall insulin secretion index (13.3 pmol/L/mmol/L; 3.7, 22.9) among HIV-uninfected participants but with lower fasting insulin (-0.9 mU/L; -1.6, -0.2), 120-min insulin (-12.0 mU/L; -18.9, -5.1), and HOMA-IR (-0.3 mmol/L; -0.6, -0.05) among HIV-infected participants not yet on antiretroviral therapy (ART). Among HIV-infected participants not on ART, geohelminth infection was associated with lower fasting insulin (-0.9 mU/L; -1.6, -0.2), 120-min insulin (-9.1 mU/L; -17.3, -1.0), HOMA-β (-8.9 mU/L)/(mmol/L; -15.3, -2.6) and overall insulin release index (-5.1 pmol/L/mmol/L; -10.3, 0.02), although this was marginally significant. There was no association among those on ART. Conclusions Schistosoma infection was associated with higher β-cell function among HIV-uninfected participants whereas Schistosoma and geohelminth infections were associated with reduced β-cell function among HIV-infected participants not on ART.
A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems

Deploying 3D single-photon Lidar imaging in real world applications presents multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based frameworks. Statistical methods provide rich information about the inferred parameters but are limited by the assumed model correlation structures, while deep learning methods show state-of-the-art performance but limited inference guarantees, preventing their extended use in critical applications. This paper unrolls a statistical Bayesian algorithm into a new deep learning architecture for robust image reconstruction from single-photon Lidar data, i.e. the algorithm’s iterative steps are converted into neural network layers. The resulting algorithm benefits from the advantages of both statistical and learning based frameworks, providing best estimates with improved network interpretability. Compared to existing learning-based solutions, the proposed architecture requires a reduced number of trainable parameters, is more robust to noise and mismodelling of the system impulse response function, and provides richer information about the estimates including uncertainty measures. Results on synthetic and real data show competitive results regarding the quality of the inference and computational complexity when compared to state-of-the-art algorithms.
Meta-learning spiking neural networks with surrogate gradient descent

Adaptive ‘life-long’ learning at the edge and during online task performance is an aspirational goal of artificial intelligence research. Neuromorphic hardware implementing spiking neural networks (SNNs) are particularly attractive in this regard, as their real-time, event-based, local computing paradigm makes them suitable for edge implementations and fast learning. However, the long and iterative learning that characterizes state-of-the-art SNN training is incompatible with the physical nature and real-time operation of neuromorphic hardware. Bi-level learning, such as meta-learning is increasingly used in deep learning to overcome these limitations. In this work, we demonstrate gradient-based meta-learning in SNNs using the surrogate gradient method that approximates the spiking threshold function for gradient estimations. Because surrogate gradients can be made twice differentiable, well-established, and effective second-order gradient meta-learning methods such as model agnostic meta learning (MAML) can be used. We show that SNNs meta-trained using MAML perform comparably to conventional artificial neural networks meta-trained with MAML on event-based meta-datasets. Furthermore, we demonstrate the specific advantages that accrue from meta-learning: fast learning without the requirement of high precision weights or gradients, training-to-learn with quantization and mitigating the effects of approximate synaptic plasticity rules. Our results emphasize how meta-learning techniques can become instrumental for deploying neuromorphic learning technologies on real-world problems.
Invariant Filtering for Legged Humanoid Locomotion on a Dynamic Rigid Surface

State estimation for legged locomotion over a dynamic rigid surface (DRS), which is a rigid surface moving in the world frame (e.g., ships, aircraft, and trains), remained an underexplored problem. This article introduces an invariant extended Kalman filter that estimates the robot’s pose and velocity during DRS locomotion by using common sensors of legged robots [e.g., inertial measurement units (IMUs), joint encoders, and RDB-D camera]. A key feature of the filter lies in that it explicitly addresses the nonstationary surface–foot contact point and the hybrid robot behaviors. Another key feature is that in the absence of IMU biases, the filter satisfies the attractive group affine and invariant observation conditions, and is thus provably convergent for the deterministic continuous phases. The observability analysis is performed to reveal the effects of DRS movement on the state observability, and the convergence property of the hybrid deterministic filter system is examined for the observable state variables. Experiments of a Digit humanoid robot walking on a pitching treadmill validate the effectiveness of the proposed filter under large estimation errors and moderate DRS movement. The video of the experiments can be found at: https://youtu.be/ScQIBFUSKzo.
Tracking on the Web, Mobile and the Internet-of-Things

‘Tracking’ is the collection of data about an individual’s activity across multiple distinct contexts and the retention, use, or sharing of data derived from that activity outside the context in which it occurred. This paper aims to introduce tracking on the web, smartphones, and the Internet of Things, to an audience with little or no previous knowledge. It covers these topics primarily from the perspective of computer science and humancomputer interaction, but also includes relevant law and policy aspects. Rather than a systematic literature review, it aims to provide an overarching narrative spanning this large research space. Section 1 introduces the concept of tracking. Section 2 provides a short history of the major developments of tracking on the web. Section 3 presents research covering the detection, measurement and analysis of web tracking technologies. Section 4 delves into the countermeasures against web tracking and mechanisms that have been proposed to allow users to control and limit tracking, as well as studies into end-user perspectives on tracking. Section 5 focuses on tracking on ‘smart’ devices including smartphones and the internet of things. Section 6 covers emerging issues affecting the future of tracking across these different platforms.
The Norwegian Parliamentary Speech Corpus

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a “democratizing” effects in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.
Joint Liver and Hepatic Lesion Segmentation using a Hybrid CNN with Transformer Layers

—Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the ﬁeld of medical image segmentation have been developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions. This lead to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was applied to clinical liver MRI, as well as to the publicly available CT data of the liver tumor segmentation (LiTS) challenge. Additionally, multiple state-of-the-art networks were implemented and applied to both datasets, ensuring a direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various inﬂuencing factors on the segmentation accuracy of our presented method. With Dice similarity scores of averaged 98 ± 2 % for liver and 81 ± 28 % lesion segmentation on the MRI dataset and 97 ± 2 % and 79 ± 25 % , respectively on the CT dataset, the proposed SWTR-Unet outperforms each of the additionally implemented state-of-the-art networks. The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.
Wetting of an elastic sheet subject to external tension

Recent studies of elasto-capillary phenomena have triggered interest in a basic variant of the classical YoungLaplace-Dupré (YLD) problem: The interaction between a liquid drop and a thin solid sheet of low bending stiffness. Here we consider a two-dimensional model where the sheet is subjected to an external tensile load and assume that the solid surface is characterised by a well-defined Young’s contact angle θY . Using a combination of numerical, variational, and asymptotic techniques, we discuss wetting as a function of the applied tension. We find that, for wettable surfaces, with 0 < θY < π/2, complete wetting is possible below a critical applied tension thanks to the deformation of the sheet. Conversely, for very large applied tensions, the sheet becomes flat and the classical YLD situation of partial wetting is recovered. At intermediate tensions, a vesicle forms in the sheet, which encloses most of the fluid and we provide an accurate asymptotic description of this vesicle. We show that bending stiffness, however small, affects the entire shape of the vesicle. Rich bifurcation diagrams involving partial wetting and “vesicle” solution are found. For moderately small bending stiffnesses, partial wetting can coexist both with the vesicle solution and complete wetting.
A LIMITATION ON PROVING THE EXISTENCE OF SMALL GAPS BETWEEN ZETA-ZEROS

Abstract. We assume the Riemann Hypothesis (RH) in this paper. The existence of Landau–Siegel zeros (or the Alternative Hypothesis) implies that there are long ranges where the zeros of the Riemann zeta-function are always spaced no closer than one half of the average spacing. However, numerical evidence strongly agrees with the GUE model where there are a positive proportion of consecutive zeros within any small multiple of the average spacing. Currently, assuming RH, the best result known produces infinitely many consecutive zeros within 0.515396 times the average spacing. This is obtained using the Montgomery–Odlyzko (M-O) method. It is also known that the M-O method fails to prove the existence of consecutive zeros closer than 1/2 times the average spacing. It is a tantalizing hope that the M-O method could still obtain infinitely many consecutive zeros arbitrarily close to 1/2 times the average spacing. We prove however that the M-O method can never find infinitely many consecutive zeros within 0.5042 times the average spacing.
FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control

Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if any. We propose the self-supervised description-to-sequence task, which allows for ﬁne-grained controllable generation on a global level. We do so by extracting high-level features about the target sequence and learning the conditional distribution of sequences given the corresponding high-level description in a sequence-to-sequence modelling setup. We train FIGARO (FIne-grained music Generation via Attention-based, RObust control) by applying description-to-sequence modelling to symbolic music. By combining learned high level features with domain knowledge, which acts as a strong inductive bias, the model achieves state-of-the-art results in controllable symbolic music generation and generalizes well beyond the training distribution.
Twitter-Demographer: A Flow-based Tool to Enrich Twitter Data

Twitter data have become essential to Natural Language Processing (NLP) and social science research, driving various scientific discoveries in recent years. However, the textual data alone are often not enough to conduct studies: especially social scientists need more variables to perform their analysis and control for various factors. How we augment this information, such as users' location, age, or tweet sentiment, has ramifications for anonymity and reproducibility, and requires dedicated effort. This paper describes Twitter-Demographer, a simple, flow-based tool to enrich Twitter data with additional information about tweets and users. Twitter-Demographer is aimed at NLP practitioners and (computational) social scientists who want to enrich their datasets with aggregated information, facilitating reproducibility, and providing algorithmic privacy-by-design measures for pseudo-anonymity. We discuss our design choices, inspired by the flow-based programming paradigm, to use black-box components that can easily be chained together and extended. We also analyze the ethical issues related to the use of this tool, and the built-in measures to facilitate pseudo-anonymity.
RBO Hand 3: A Platform for Soft Dexterous Manipulation

In this article, we present the RBO Hand 3, a highly capable and versatile anthropomorphic soft hand based on pneumatic actuation. The RBO Hand 3 is designed to enable dexterous manipulation, to facilitate transfer of insights about human dexterity, and to serve as a robust research platform for extensive real-world experiments. It achieves these design goals by combining many degrees of actuation with intrinsic compliance, replicating relevant functioning of the human hand, and by combining robust components in a modular design. The RBO Hand 3 possesses 16 independent degrees of actuation, implemented in a dexterous opposable thumb, two-chambered fingers, an actuated palm, and the ability to spread the fingers. In this article, we derive the design objectives that are based on experimentation with the hand’s predecessors, observations about human grasping, and insights about principles of dexterity. We explain in detail how the design features of the RBO Hand 3 achieve these goals and evaluate the hand by demonstrating its ability to achieve the highest possible score in the Kapandji test for thumb opposition, to realize all 33 grasp types of the comprehensive GRASP taxonomy, to replicate common human grasping strategies, and to perform dexterous in-hand manipulation.
Task-Oriented Semantic Communication Systems Based on Extended Rate-Distortion Theory

—Considering the performance of intelligent task during signal exchange can help the communication system to automatically select those semantic parts which are helpful to perform the target task for compression and reconstruction, which can both greatly reduce the redundancy in signal and ensure the performance of the task. The traditional communication system based on rate-distortion theory treats all the information in the signal equally, but ignores their different importance to accomplish the task, which leads to waste of communication resources. In this paper, combined with the information bottleneck method, we present an extended rate-distortion theory which considers both concise representation and semantic distortion. Based on this theory, a task-oriented semantic image communication system is proposed. In order to verify that the proposed system can achieve performance improvement on different intelligent tasks, we apply the basic system trained with classiﬁcation task to the system with object detection as the target task. The experimental results demonstrate that the proposed method outperforms the traditional and multi-task based communication system in terms of task performance at the same signal compression degree and noise interference degree. Furthermore, it is necessary to consider a compromise between rate-distortion theory and information bottleneck theory by comparing the pure rate-distortion scheme and the pure IB scheme.
Confidence intervals for the Cox model test error from cross‐validation

Cross‐validation (CV) is one of the most widely used techniques in statistical learning for estimating the test error of a model, but its behavior is not yet fully understood. It has been shown that standard confidence intervals for test error using estimates from CV may have coverage below nominal levels. This phenomenon occurs because each sample is used in both the training and testing procedures during CV and as a result, the CV estimates of the errors become correlated. Without accounting for this correlation, the estimate of the variance is smaller than it should be. One way to mitigate this issue is by estimating the mean squared error of the prediction error instead using nested CV. This approach has been shown to achieve superior coverage compared to intervals derived from standard CV. In this work, we generalize the nested CV idea to the Cox proportional hazards model and explore various choices of test error for this setting.