Miracle-dz/UniFAR-FTUs huggingface dataset:
High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI

High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3–30 ms) and short acquisition windows (<1 ms). The T2*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.
Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model

Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.
Silent Circulation of the Saint Louis Encephalitis Virus among Humans and Equids, Southeast Brazil

Saint Louis encephalitis virus (SLEV) is a mosquito-borne flavivirus that occurs throughout the Americas, and is considered a public health threat. In Brazil, SLEV has been detected from human cases associated with dengue-like disease, but no neurological symptoms were reported. Furthermore, the epidemiology of SLEV in human populations is still poorly explored in the country. We reported serological and molecular detection of SLEV in a healthy population of equids and humans from rural areas in Southeast Brazil. A plaque reduction neutralization test was applied, and neutralizing antibodies were detected in 11 individuals (4.6%) and 60 horses (21.5%). A qPCR targeting the 5′UTR region and reverse transcription-PCR (RT-PCR) targeting the non-structural protein (NS5) gene were performed and three individuals tested positive in both assays. Subsequent phylogenetic analysis confirmed SLEV circulation and its findings suggest the occurrence of an asymptomatic or subclinical presence in human and animal cases, correlating with the risks for outbreaks and consequently burden of SLEV infections to public health. Preventive strategies should include improved surveillance in regions with a high probability of SLEV occurrence, improvement in diagnostic methods, and evaluation of exposure/risk factors that can favor SLEV emergence.
Implications of the putamen in pain and motor deficits in complex regional pain syndrome.

Complex regional pain syndrome (CRPS) develops after limb injury, with persistent pain and deficits in movement frequently co-occurring. The striatum is critical for mediating multiple mechanisms that are often aberrant in CRPS, which includes sensory and pain processing, motor function and goal-directed behaviors associated with movement. Yet much remains unknown with regards to the morphological and functional properties of the striatum and its sub-regions in this disease. Thus, we investigated 20, patients (15 female, age 58 ± 9 years, right-handed) diagnosed with chronic (6+ months of pain duration) CRPS in the right hand and 20 matched, healthy controls with anatomical and resting-state, functional magnetic resonance imaging (fMRI). In addition, a comprehensive clinical and behavioral evaluation was performed, where each participant's pain, motor function and medical history were assessed. CRPS patients harbored significant abnormalities in hand coordination, dexterity and strength. These clinical pain and movement-related findings in CRPS patients were concomitant with bilateral decreases in gray matter density in the putamen as well as functional connectivity increases and decreases amongst the putamen and pre-/postcentral gyri and cerebellum, respectively. Importantly, higher levels of clinical pain and motor impairment were associated with increased putamen-pre-/postcentral gyri functional connectivity strengths. Collectively, these findings suggest that putaminal alterations, specifically the functional interactions with sensorimotor structures, may underpin clinical pain and motor impairment in chronic CRPS patients.
Noninvasive Helicobacter pylori Diagnostic Methods in Indonesia

Although the prevalence of Helicobacter pylori infection in Indonesia is lower than that in other countries, H. pylori is still an essential pathogen associated with severe gastric mucosal damage and dyspeptic symptoms. Invasive diagnostic methods are not ideal due to the lack of endoscopic centers and high costs without full coverage by social insurance. Among the noninvasive methods, the urea breath test is widely available in Indonesia and has been suggested as the primary option to ensure the successful eradication of H. pylori. There has been no local validation for the urea breath test utilizing 13C or 14C. The stool antigen test is inexpensive and suitable for use in active infections before and after eradication; however, customs and habits are obstacles to delivering fresh stool on time. Only polyclonal antibodies and qualitative stool antigen test kits with low sensitivity are available. Serology is a widely validated method and has good accuracy, but it cannot distinguish between active and inactive infections. According to our observations, serology is the main choice of experts and patients, as it is simple, inexpensive and widely known. The urine test is an alternative for reducing costs and endoscopic workload, with high accuracy but low sensitivity. Further studies are necessary to prove the validity of the urine test to be used throughout Indonesia, especially in areas with a low prevalence of H. pylori infection. In conclusion, the validated urea breath test and the stool antigen test are considered noninvasive practical approaches for the detection of H. pylori infection in Indonesia, with serological and urine tests as alternatives.
ScaleCheck: A Single-Machine Approach for Discovering Scalability Bugs in Large Distributed Systems

We present SCALECHECK, an approach for discovering scalability bugs (a new class of bug in large storage systems) and for democratizing large-scale testing. SCALECHECK employs a program analysis technique, for finding potential causes of scalability bugs, and a series of colocation techniques, for testing implementation code at real scales but doing so on just a commodity PC. SCALECHECK has been integrated to several large-scale storage systems, Cassandra, HDFS, Riak, and Voldemort, and successfully exposed known and unknown scalability bugs, up to 512-node scale on a 16-core PC.
Spatial coordinate transforms linking the allocentric hippocampal and egocentric parietal primate brain systems for memory, action in space, and navigation

A theory and model of spatial coordinate transforms in the dorsal visual system through the parietal cortex that enable an interface via posterior cingulate and related retrosplenial cortex to allocentric spatial representations in the primate hippocampus is described. First, a new approach to coordinate transform learning in the brain is proposed, in which the traditional gain modulation is complemented by temporal trace rule competitive network learning. It is shown in a computational model that the new approach works much more precisely than gain modulation alone, by enabling neurons to represent the different combinations of signal and gain modulator more accurately. This understanding may have application to many brain areas where coordinate transforms are learned. Second, a set of coordinate transforms is proposed for the dorsal visual system/parietal areas that enables a representation to be formed in allocentric spatial view coordinates. The input stimulus is merely a stimulus at a given position in retinal space, and the gain modulation signals needed are eye position, head direction, and place, all of which are present in the primate brain. Neurons that encode the bearing to a landmark are involved in the coordinate transforms. Part of the importance here is that the coordinates of the allocentric view produced in this model are the same as those of spatial view cells that respond to allocentric view recorded in the primate hippocampus and parahippocampal cortex. The result is that information from the dorsal visual system can be used to update the spatial input to the hippocampus in the appropriate allocentric coordinate frame, including providing for idiothetic update to allow for self‐motion. It is further shown how hippocampal spatial view cells could be useful for the transform from hippocampal allocentric coordinates to egocentric coordinates useful for actions in space and for navigation.
Decreasing Uncertainty for Improvement of Relevancy Prediction

As one of the key techniques of Information Retrieval (IR) and Information Filtering (IF), Text Classiﬁca-tion focuses on classifying textual documents into pre-deﬁned categories through relative classiﬁers learned from labelled or unlabelled training samples. Binary text classiﬁers is the main branch of Text Classiﬁca-tion, involving the relevance prediction of documents to users or categories. However, the current binary text classiﬁers cannot clearly describe the diﬀerence between relevant and irrelevant information because of knowledge uncertainty owing to the imperfection of the knowledge mining techniques and the limitation of feature selection methods. This paper proposes a relevance prediction model by decreasing the relative uncertainty to improve the performance of binary text classiﬁcation. It tries to form and train the decision boundary through partitioning the training samples into three regions (the positive, boundary and negative regions) to assure the discrimination of extracted knowledge for describing relevant and irrelevant information. It then produces six decision rules corresponding with six diﬀerent situations of the related objects to help make relevance predications for those objects. A large number of experiments have been conducted on two standard datasets including RCV1 and Reuters21578. The experiment results show that the proposed model has signiﬁcantly improved the performance of binary text classiﬁcation, thus proved to be eﬀective and promising.
Replenishment of Hepatitis B Virus cccDNA Pool Is Restricted by Baseline Expression of Host Restriction Factors In Vitro

Background: Covalently closed circular DNA (cccDNA) of hepatitis B virus (HBV) is the major cause of viral persistence in patients with chronic HBV infection. Understanding the mechanisms underlying stability and persistence of HBV cccDNA in hepatocytes is critical for developing novel therapeutics and managing chronic hepatitis B. In this study, we observed an unexpected increase in HBV cccDNA levels upon suppression of transcription by de novo DNA methyltransferase DNMT3A and uncovered additional mechanisms potentially involved in HBV cccDNA maintenance. Methods: HBV-expressing cell lines were transfected with a DNMT3A-expressing plasmid. Real-time PCR and HBsAg assays were used to assess the HBV replication rate. Cell cycling was analyzed by fluorescent cell sorting. CRISPR/Cas9 was utilized to abrogate expression of APOBEC3A and APOBEC3B. Alterations in the expression of target genes were measured by real-time PCR. Results: Similar to previous studies, HBV replication induced DNMT3A expression, which in turn, led to reduced HBV transcription but elevated HBV cccDNA levels (4- to 6-fold increase). Increased levels of HBV cccDNA were not related to cell cycling, as DNMT3A accelerated proliferation of infected cells and could not contribute to HBV cccDNA expansion by arresting cells in a quiescent state. At the same time, DNMT3A suppressed transcription of innate immunity factors including cytidine deaminases APOBEC3A and APOBEC3B. CRISPR/Cas9-mediated silencing of APOBEC3A and APOBEC3B transcription had minor effects on HBV transcription, but significantly increased HBV cccDNA levels, similar to DNMT3A. In an attempt to further analyze the detrimental effects of HBV and DNMT3A on infected cells, we visualized γ-H2AX foci and demonstrated that HBV inflicts and DNMT3A aggravates DNA damage, possibly by downregulating DNA damage response factors. Additionally, suppression of HBV replication by DNMT3A may be related to reduced ATM/ATR expression. Conclusion: Formation and maintenance of HBV cccDNA pools may be partially suppressed by the baseline expression of host inhibitory factors including APOBEC3A and APOBEC3B. HBV inflicts DNA damage both directly and by inducing DNMT3A expression.
SIMON: A Simple and Scalable Method for Sensing, Inference and Measurement in Data Center Networks

It is important to perform measurement and monitoring in order to understand network performance and debug problems encountered by distributed applications. Despite many products and much research on these topics, in the context of data centers, performing accurate measurement at scale in near real-time has remained elusive. There are two main approaches to network telemetry–switch-based and end-host-based–each with its own advantages and drawbacks. In this paper, we attempt to push the boundary of edge-based measurement by scalably and accurately reconstructing the full queueing dynamics in the network with data gathered entirely at the transmit and receive network interface cards (NICs). We begin with a Signal Processing framework for quantifying a key trade-off: reconstruction accuracy versus the amount of data gathered. Based on this, we propose S IMON , an accurate and scalable measurement system for data centers that reconstructs key network state variables like packet queuing times at switches, link utilizations, and queue and link compositions at the ﬂow-level. We use two ideas to speed up S IMON : (i) the hierarchical nature of data center topologies, and (ii) the function approximation capability of multi-layered neural networks. The former gives a speedup of 1,000x while the latter implemented on GPUs gives a speedup of 5,000x to 10,000x, enabling S IMON to run in real-time. We deployed S IMON in three testbeds with different link speeds, layers of switching and number of servers. Evaluations with NetFPGAs and a cross-validation technique show that S IMON reconstructs queue-lengths to within 3-5 KBs and link utilizations to less than 1% of actual. The accuracy and speed of S IMON enables sensitive A/B tests
Towards Non-Visual Web Search

Speech-based user interfaces and, in particular, voice-activated digital assistants are gaining popularity. Assistants provide their users with an opportunity for hands-free interaction, and present an additional accessibility level for people who are blind. According to prior research, informational searches form a noticeable fraction of user interactions with the assistants. All major commercially available assistants handle factoid questions well by providing an answer that is quick, concise, and to-the-point. However, for complex information seeking intents, when a deeper exploration and multi-turn interaction may be required, the assistants often do not produce the desired results. One of the main challenges for designing a voice-based web search system is the higher cognitive load for audio perception compared to visual perception. Additionally, close attention should be paid at differences in designing for different user groups, as their information seeking styles and design needs and may differ. In this work, we discuss the challenges of designing systems for non-visual ad-hoc web search and exploration and outline a set of proposed experiments tackling various aspects of non-visual web search.
Response to Comment on “Protein assemblies ejected directly from native membranes yield complexes for mass spectrometry”

Hirst et al. claim that proteins ejected directly from mitochondrial membranes in our study are degraded, are incorrectly assigned, lack lipids, and show discrepancies with “native states” mostly obtained in detergent micelles. Here, we add further evidence in full support of our assignments and show that all complexes are either ejected intact or in known intermediate states, with core subunit interactions maintained. None are degraded or rearranged.
Shinjuku: Preemptive Scheduling for μsecond-scale Tail Latency

The recently proposed dataplanes for microsecond scale applications, such as IX and ZygOS, use nonpreemptive policies to schedule requests to cores. For the many real-world scenarios where request service times follow distributions with high dispersion or a heavy tail, they allow short requests to be blocked behind long requests, which leads to poor tail latency. Shinjuku is a single-address space operating system that uses hardware support for virtualization to make preemption practical at the microsecond scale. This allows Shinjuku to implement centralized scheduling policies that preempt requests as often as every 5µsec and work well for both light and heavy tailed request service time distributions. We demonstrate that Shinjuku provides significant tail latency and throughput improvements over IX and ZygOS for a wide range of workload scenarios. For the case of a RocksDB server processing both point and range queries, Shinjuku achieves up to 6.6× higher throughput and 88% lower tail latency.
Performance Contracts for Software Network Functions

Software network functions (NFs), or milddleboxes, promise ﬂexibility and easy deployment of network services, but face the serious challenge of unexpected performance behaviour. We propose the notion of a performance contract , a construct formulated in terms of performance critical variables , that provides a precise description of NF performance. Performance contracts enable ﬁne-grained prediction and scrutiny of NF performance for arbitrary workloads, without having to run the NF itself. We describe B OLT , a technique and tool for computing such performance contracts for the entire software stack of NFs written in C, including the core NF logic, DPDK packet processing framework, and NIC driver. B OLT takes as input the NF implementation code and outputs the corresponding contract. Under the covers, it combines pre-analysis of a library of stateful NF data structures with automated symbolic execution of the NF’s code. We evaluate B OLT on four NFs—a Maglev-like load balancer, a NAT, an LPM router, and a MAC bridge—and show that its performance contracts predict the dynamic instruction count and memory access count with a maximum gap of 7% between the real execution and the conservatively predicted upper bound. With further engineering, this gap can be reduced.
Understanding Context for Tasks and Activities

Human activity is one of the most important pieces of context affecting an individual's information needs. Understanding the relationship between activities, time, location, and other contextual features can improve the quality of various intelligent systems, including contextual search engines, task managers, digital personal assistants, chat bots, and recommender systems. In this work, we propose a method for extraction of an extensive set of open-vocabulary activities from social media. In particular, we derive tens of thousands of ongoing activities from Twitter, where people share information about their past, present, and future events and, using attached metadata, we establish spatiotemporal models of these activities at the time of posting. While public Twitter content is subject to self-censorship (not all activities are tweeted about), we compare extracted data with unbiased survey data (ATUS) and show evidence that for activities which are tweeted about, the underlying spatiotemporal profiles correctly capture their real distributions of activity conditioned on time and location. Next, to better understand the set of activities present in this dataset (and what role self-censorship may play), we perform a qualitative analysis to understand the activities, locations, and their temporal properties. Finally, we go on to solve predictive tasks centered on the relationship between activity and spatiotemporal context that are aimed at supporting an individual's information needs. Our predictive models, which incorporate text, personal history and temporal features, show a significant performance gain over a strong frequency-based baseline.
Design Issues in Automatically Generated Persona Profiles: A Qualitative Analysis from 38 Think-Aloud Transcripts

Increased access to data and computational techniques enable innovations in the space of automated customer analytics, for example, automatic persona generation. Automatic persona generation is the process of creating data-driven representations from user or customer statistics. Even though automatic persona generation is technically possible and provides advantages compared to manual persona creation regarding the speed and freshness of the personas, it is not clear (a) what information to include in the persona profiles and (b) how to display that information. To query into these aspects relating information design of personas, we conducted a user study with 38 participants. In the findings, we report several challenges relating to the design of automatically generated persona profiles, including usability issues, perceptual issues, and issues relating to information content. Our research has implications for the information design of data-driven personas.