Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterisation of the Toxoplasma gondii tyrosine transporter and its phosphorylation by the calcium‐dependent protein kinase 3

Toxoplasma gondii parasites rapidly exit their host cell when exposed to calcium ionophores. Calcium‐dependent protein kinase 3 (TgCDPK3) was previously identified as a key mediator in this process, as TgCDPK3 knockout (∆cdpk3) parasites fail to egress in a timely manner. Phosphoproteomic analysis comparing WT with ∆cdpk3 parasites revealed changes in the TgCDPK3‐dependent phosphoproteome that included proteins important for regulating motility, but also metabolic enzymes, indicating that TgCDPK3 controls processes beyond egress. Here we have investigated a predicted direct target of TgCDPK3, ApiAT5‐3, a putative transporter of the major facilitator superfamily, and show that it is rapidly phosphorylated at serine 56 after induction of calcium signalling. Conditional knockout of apiAT5‐3 results in transcriptional upregulation of most ribosomal subunits, but no alternative transporters, and subsequent parasite death. Mutating the S56 to a non‐phosphorylatable alanine leads to a fitness cost, suggesting that phosphorylation of this residue is beneficial, albeit not essential, for tyrosine import. Using a combination of metabolomics and heterologous expression, we confirmed a primary role in tyrosine import for ApiAT5‐3. However, no significant differences in tyrosine import could be detected in phosphorylation site mutants showing that if tyrosine transport is affected by S56 phosphorylation, its regulatory role is subtle.
SlicerITKUltrasound: A 3D Slicer extension for scan conversion of B-mode and next-generation ultrasound imaging modalities

A 3D Slicer (Fedorov et al. 2012) extension for ultrasound scan conversion during 2D and 3D image generation for traditional B-mode imaging (Matthew M McCormick 2010), but also next-generation ultrasound image modalities like ultrasound spectroscopy (S. R. Aylward et al. 2016), acoustic radiation force imaging (ARFI) (Palmeri et al. 2016), acoustic radiation force shear wave imaging (ARFI-SWEI) (S. J. Rosenzweig 2014), and others. Interfaces are built off the ITKUltrasound library (Matthew Michael McCormick et al. 2014).
Object Detection for High-Resolution Sar Images Under the Spatial Constraints of Optical Images

With the rapid development of sensor technology, we pay more attention to object detection for high-resolution SAR images. Besides, the traditional object detection methods which only use one SAR image to accomplish detection aren't enough appreciate for some cases that the background around the object is complex. In the paper, we propose an object detection method for high-resolution SAR images under the spatial constraints of optical images. It consists of three main steps: Establishment of the spatial relation between optical and SAR images, spatial constraints projection from optical images and detection under the spatial constraints in high-resolution SAR images. At the end of the paper, we take the inshore ship detection as an example to show that the proposed method can greatly improve the accuracy and efficiency of object detection against the traditional SAR object detection methods in conditions that background around the object is complex.
Radar Forward-Looking Superresolution Imaging for SEA-Surface Targets Using Bayesian Method

This paper presents an angular superresolution method based on maximum a posterior (MAP) criterion to improve the azimuthal resolution of forward-looking scanning radar in the background of sea clutter. Firstly, in consideration of the statistical property of sea clutter, the Rayleigh distribution is employed to express the likelihood function. And then, the generalized Gaussian constraint is used as prior information about targets for better property of noise suppression and positional accuracy. Finally, the iterative expression is derived to recover the scattering coefficient of original sea-surface targets. Compared to the conventional Bayesian approaches, The results of simulation experiment are given to verify the superior performance of proposed method.
A Design Pattern Approach for Multi-Game Level Generation

Existing approaches to multi-game level generation rely upon level structure to emerge organically via level fitness. In this paper, we present a method for generating levels for games in the GVGAI framework using a design pattern-based approach, where design patterns are derived from an analysis of the existing corpus of GVGAI game levels. We created two new generators: one constructive, and one search-based, and compared them to a prior existing search-based generator. Results show that our generator is comparable, even preferred, over the prior generator, especially among players with existing game experience. Our search-based generator also outperforms our constructive generator in terms of player preference.
A Pronoun Test Suite Evaluation of the English–German MT Systems at WMT 2018

We evaluate the output of 16 English-to-German MT systems with respect to the translation of pronouns in the context of the WMT 2018 competition. We work with a test suite specifically designed to assess system quality in various fine-grained categories known to be problematic. The main evaluation scores come from a semi-automatic process, combining automatic reference matching with extensive manual annotation of uncertain cases. We find that current NMT systems are good at translating pronouns with intra-sentential reference, but the inter-sentential cases remain difficult. NMT systems are also good at the translation of event pronouns, unlike systems from the phrase-based SMT paradigm. No single system performs best at translating all types of anaphoric pronouns, suggesting unexplained random effects influencing the translation of pronouns with NMT.
Standards for Major Internet Disruptors: Blockchain, Intents, and Related Paradigms

The six articles in this special section address major issues concerned with evolving standardization processes and underlying technologies in the context of Blockchain for different application domains, showing how distributed ledger technologies can be so disruptive that they can change the Internet as we know it. Software is envisioned to play an unprecedented dominant role in future communication paradigms, with user-defined applications capable of not only communicating with, but also controlling some features of the underlying communication infrastructure in a secure and trustworthy manner. This (r)evolutionary trend toward virtualized and “softwarized” networks enable Internet-based service users and providers, as well as the related stakeholders, to take advantage of resource abstractions and to adopt a programmatic approach to service deployment. In a “game-changing” scenario, a user’s needs can be decoupled and expressed independently of the specific underlying implementable technology, but yet the requested service must meet the expected mutual trustiness and security levels. This trend opens a number of disruptive solutions that can change the Internet as we know it.
Redox poise and metabolite changes in bread wheat seeds are advanced by priming with hot steam.

Fast and uniform germination is key to agricultural production and can be achieved by seed 'priming' techniques. Here, we characterised the responses of bread wheat (Triticum aestivum L.) seeds to a hot steam treatment ('BioFlash'), which accelerated water uptake, resulting in faster germination and seedling growth, typical traits of primed seed. Before the completion of germination, metabolite profiling of seeds revealed advanced accumulation of several amino acids (especially cysteine and serine), sugars (ribose, glucose), and organic acids (glycerate, succinate) in hot steam-treated seeds, whereas sugar alcohols (e.g. arabitol, mannitol) and trehalose decreased in all seeds. Tocochromanols (the 'vitamin E family') rose independently of the hot steam treatment. We further assessed shifts in the half-cell reduction potentials of low-molecular-weight (LMW) thiol-disulfide redox couples [i.e. glutathione disulfide (GSSG)/glutathione (GSH) and cystine/cysteine], alongside the activities of the reactive oxygen species (ROS)-processing enzyme superoxide dismutase, catalase, ascorbate peroxidase, and glutathione reductase. Upon the first 4 h of imbibition, a rapid conversion of LMW disulfides to thiols occurred. Completion of germination was associated with a re-oxidation of the LMW thiol-disulfide cellular redox environment, before more reducing conditions were re-established during seedling growth, accompanied by an increase in all ROS-processing enzyme activities. Furthermore, changes in the thiol-disulfide cellular redox state were associated to specific stages of wheat seed germination. In conclusion, the priming effect of the hot steam treatment advanced the onset of seed metabolism, including redox shifts associated with germination and seedling growth.
A 20-ch TDC/ADC Hybrid Architecture LiDAR SoC for 240  $\times$  96 Pixel 200-m Range Imaging With Smart Accumulation Technique and Residue Quantizing SAR ADC

This paper presents a time-to-digital converter/analog-to-digital-converter (TDC/ADC) hybrid LiDAR system-on-chip (SoC) to realize reliable self-driving systems. The smart accumulation technique (SAT) is proposed to achieve both 200-m and high-pixel-resolution range imaging, which was untrodden with conventional LiDARs. The “smart” accumulation is realized by a simple object recognition strategy with small circuit overhead. When compared to conventional accumulations, the LiDAR range is enhanced without degrading the pixel resolution. Moreover, a TDC/ADC hybrid architecture is proposed to achieve a wide-distance-range LiDAR with a small silicon area and short-range precision. To minimize the ADC cost, a residue-quantizing noise-shaping (RQNS) SAR ADC is proposed. The prototype LiDAR SoC is fabricated in the 28-nm CMOS technology and integrated into the silicon photomultiplier (SiPM)-based LiDAR system. LiDAR measured with 240 <inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 96 pixels at 10 frames/s achieves a measurement range of 200 m with a 70-klx direct sunlight: the measurement range is 2<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> longer than conventional designs. Furthermore, our LiDAR achieves 4<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> higher effective pixel resolution compared to conventional designs using simple accumulation. A 3-D point-cloud image acquired with a real-life environment is presented.
Detecting Adaptation with Genome-Scale Molecular Evolutionary Analysis: An Educational Primer for Use with “RNA Interference Pathways Display High Rates of Adaptive Protein Evolution in Multiple Invertebrates”

Hosts and pathogens impose coevolutionary pressure on each other as pathogens strive to establish themselves and hosts seek to suppress infection. RNA interference (RNAi) is a mechanism by which cells repress viruses and transposable elements, thereby serving as a form of immune defense. Previous studies have shown that antiviral RNAi genes evolve extraordinarily quickly in the fruit fly Drosophila melanogaster, suggesting that they may adaptively coevolve with viruses and transposable elements. An article by Palmer and colleagues extends this observation to nematodes and multiple insects. Their article can be combined with this Primer to demonstrate the use of comparative genomics and molecular evolutionary analyses in the measurement of natural selection. Related article in GENETICS: Palmer, W. H. , J. D. Hadfield, and D. J. Obbard, 2018 RNA-Interference pathways display high rates of adaptive protein evolution in multiple invertebrates. Genetics 208: 1585–1599.
Comparison of Resting-State Brain Activation Detected by BOLD, Blood Volume and Blood Flow

Resting-state brain activity has been widely investigated using blood oxygenation level dependent (BOLD) contrast techniques. However, BOLD signal changes reflect a combination of the effects of cerebral blood flow (CBF), cerebral blood volume (CBV), as well as the cerebral metabolic rate of oxygen (CMRO2). In this study, resting-state brain activation was detected and compared using the following techniques: (a) BOLD, using a gradient-echo echo planar imaging (GE-EPI) sequence; (b) CBV-weighted signal, acquired using gradient and spin echo (GRASE) based vascular space occupancy (VASO); and (c) CBF, using pseudo-continuous arterial spin labeling (pCASL). Reliable brain networks were detected using VASO and ASL, including sensorimotor, auditory, primary visual, higher visual, default mode, salience and left/right executive control networks. Differences between the resting-state activation detected with ASL, VASO and BOLD could potentially be due to the different temporal signal-to-noise ratio (tSNR) and the short post-labeling delay (PLD) in ASL, along with differences in the spin-echo readout of VASO. It is also possible that the dynamics of spontaneous fluctuations in BOLD, CBV and CBF could differ due to biological reasons, according to their location within the brain.
A Mixed Signal Architecture for Convolutional Neural Networks

Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. This article presents the design and evaluation of an accelerator for CoNNs. The system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly layers to reduce computational overheads typically associated with a CoNN, (iii) a mixed-signal CeNN architecture that performs CoNN computations in the analog and mixed signal domain, and (iv) design space exploration that identifies what CeNN-based algorithm and architectural features fare best compared to existing algorithms and architectures when evaluated over common datasets—MNIST and CIFAR-10. Notably, the proposed approach can lead to 8.7× improvements in energy-delay product (EDP) per digit classification for the MNIST dataset at iso-accuracy when compared with the state-of-the-art DNN engine, while our approach could offer 4.3× improvements in EDP when compared to other network implementations for the CIFAR-10 dataset.
Taking the "Eco" Seriously: A Multilevel Model of Digital Innovation Ecosystems

As organizations innovate by increasingly opening their boundaries, resources and actors in innovation ecosystems are interacting to shape these innovations. Our review of the literature on digital innovation and ecosystems suggests that our efforts to develop, understand, and utilize digital innovations may benefit from taking the ecological perspective seriously, not just as metaphors, but as a modeling strategy. Specifically, the concept of ecosystem can be applied to multiple levels of the digital innovation landscape, linking diverse entities such as processes, organizations, industries, communities, and society, as they draw on resources, including attention, knowledge, technologies, and capabilities, to create, distribute, and realize the value of digital innovations. This multilevel, ecological model can help revitalize existing theories, resolve theoretical puzzles, and break new ground in the research both on digital innovation and on innovation ecosystems.
Effect of Electropulsing Treatment on the Fatigue Crack Growth Behavior of Copper

Crack propagation was quantitatively evaluated to investigate the effect of electropulsing treatment (EPT) on fatigue crack growth of copper specimens. Varying fatigue cycles were obtained under six different load levels. The crack lengths were measured under two load levels to examine the effect of cyclic stress. The microhardness was measured around the vicinity of the crack tip. Furthermore, the fracture surface was observed by scanning electron microscopy. Results show that EPT with electric current density of 150 A/mm2 enhances the high-cycle fatigue life, and the effect tends to increase with the decrease in cyclic stress. Vickers microhardness (HV) near the crack tip decreases to normal levels after treatment, and the approaching cracks on two sides can be observed. Local annealing and recrystallization occur around the fatigue crack tip. Accordingly, crack propagation can be delayed, and fatigue life can be prolonged by EPT.
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection

Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing. Audio Word2Vec previously proposed was shown to be able to represent audio segments for spoken words as such vectors carrying information about the phonetic structures of the signal segments. However, each linguistic unit (word, syllable, phoneme in text form) corresponds to unlimited number of audio segments with vector representations inevitably spread over the embedding space, which causes some confusion. It is therefore desired to better cluster the audio embeddings such that those corresponding to the same linguistic unit can be more compactly distributed. In this paper, inspired by Siamese networks, we propose some approaches to achieve the above goal. This includes identifying positive and negative pairs from unlabeled data for Siamese style training, disentangling acoustic factors such as speaker characteristics from the audio embedding, handling unbalanced data distribution, and having the embedding processes learn from the adjacency relationships among data points. All these can be done in an unsupervised way. Improved performance was obtained in preliminary experiments on the LibriSpeech data set, including clustering characteristics analysis and applications of spoken term detection.
Computational methods for small molecule identification

Abstract Identification of small molecules remains a central question in analytical chemistry, in particular for natural product research, metabolomics, environmental research, and biomarker discovery. Mass spectrometry is the predominant technique for high-throughput analysis of small molecules. But it reveals only information about the mass of molecules and, by using tandem mass spectrometry, about the mass of molecular fragments. Automated interpretation of mass spectra is often limited to searching in spectral libraries, such that we can only dereplicate molecules for which we have already recorded reference mass spectra. In my thesis “Computational methods for small molecule identification” we developed SIRIUS, a tool for the structural elucidation of small molecules with tandem mass spectrometry. The method first computes a hypothetical fragmentation tree using combinatorial optimization. By using a Bayesian statistical model, we can learn parameters and hyperparameters of the underlying scoring directly from data. We demonstrate that the statistical model, which was fitted on a small dataset, generalizes well across many different datasets and mass spectrometry instruments. In a second step the fragmentation tree is used to predict a molecular fingerprint using kernel support vector machines. The predicted fingerprint can be searched in a structure database to identify the molecular structure. We demonstrate that our machine learning model outperforms all other methods for this task, including its predecessor FingerID. SIRIUS is available as commandline tool and as user interface. The molecular fingerprint prediction is implemented as web service and receives over one million requests per month.
Petrobactin Protects against Oxidative Stress and Enhances Sporulation Efficiency in Bacillus anthracis Sterne

Bacillus anthracis causes the disease anthrax, which is transmitted via its dormant, spore phase. However, conversion from bacillus to spore is a complex, energetically costly process that requires many nutrients, including iron. B. anthracis requires the siderophore petrobactin to scavenge iron from host environments. We show that, in the Sterne strain, petrobactin is required for efficient sporulation, even when ample iron is available. The petrobactin biosynthesis operon is expressed during sporulation, and petrobactin is biosynthesized during growth in high-iron sporulation medium, but instead of being exported, the petrobactin remains intracellular to protect against oxidative stress and improve sporulation. It is also required for full growth and sporulation in blood (bovine), an essential step for anthrax transmission between mammalian hosts. ABSTRACT Bacillus anthracis is a Gram-positive bacillus that under conditions of environmental stress, such as low nutrients, can convert from a vegetative bacillus to a highly durable spore that enables long-term survival. The sporulation process is regulated by a sequential cascade of dedicated transcription factors but requires key nutrients to complete, one of which is iron. Iron acquisition by the iron-scavenging siderophore petrobactin is required for vegetative growth of B. anthracis under iron-depleted conditions and in the host. However, the extent to which petrobactin is involved in spore formation is unknown. This work shows that efficient in vitro sporulation of B. anthracis requires petrobactin, that the petrobactin biosynthesis operon (asbA to -F) is induced prior to sporulation, and that the siderophore itself associates with spores. Petrobactin is also required for oxidative stress protection during late-stage growth and for wild-type levels of sporulation in sporulation medium. Sporulation in bovine blood was found to be petrobactin dependent. Collectively, the in vitro contributions of petrobactin to sporulation as well as growth imply that petrobactin may be required for B. anthracis transmission via the spore during natural infections, in addition to its key known functions during active anthrax infections. IMPORTANCE Bacillus anthracis causes the disease anthrax, which is transmitted via its dormant, spore phase. However, conversion from bacillus to spore is a complex, energetically costly process that requires many nutrients, including iron. B. anthracis requires the siderophore petrobactin to scavenge iron from host environments. We show that, in the Sterne strain, petrobactin is required for efficient sporulation, even when ample iron is available. The petrobactin biosynthesis operon is expressed during sporulation, and petrobactin is biosynthesized during growth in high-iron sporulation medium, but instead of being exported, the petrobactin remains intracellular to protect against oxidative stress and improve sporulation. It is also required for full growth and sporulation in blood (bovine), an essential step for anthrax transmission between mammalian hosts.
Comparison of Gaofen-3 and Radarsat-2 Data for Polarimetric Sar Image Classification

On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. The GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric SAR can fully characterize the backscatter property of targets, and thus it is of great interest to explore the physical scattering mechanisms of terrain types, which is very important in interpreting polarimetric SAR imagery and for its further usages in Earth observation. Both Radarsat-2 and Gaofen-3 satellites operate in C-band and are designed for similar missions. In this study, we compare the performances of fully polarimetric Gaofen-3 and Radarsat-2 data in representation of terrain types. By using several classic polarimetric features and the iterative Wishart-distribution based classifier, Gaofen-3 and Radarsat-2 data are compared in the aspects of polarimetric property preservation and capability of terrain classification.