Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Generation of Posterior Motor Neurons from Human Pluripotent Stem Cells

The ability to generate spinal cord motor neurons from human pluripotent stem cells (hPSCs) is of great use for modelling motor neuron–based diseases and cell‐replacement therapies. A key step in the design of hPSC differentiation strategies aiming to produce motor neurons involves induction of the appropriate anteroposterior (A‐P) axial identity, an important factor influencing motor neuron subtype specification, functionality, and disease vulnerability. Most current protocols for induction of motor neurons from hPSCs produce predominantly cells of a mixed hindbrain/cervical axial identity marked by expression of Hox paralogous group (PG) members 1‐5, but are inefficient in generating high numbers of more posterior thoracic/lumbosacral Hox PG(8‐13)+ spinal cord motor neurons. Here, we describe a protocol for efficient generation of thoracic spinal cord cells and motor neurons from hPSCs. This step‐wise protocol relies on the initial generation of a neuromesodermal‐potent axial progenitor population, which is differentiated first to produce posterior ventral spinal cord progenitors and subsequently to produce posterior motor neurons exhibiting a predominantly thoracic axial identity. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.
Overview of Nonhuman Primate Models of SARS-CoV-2.

COVID-19, the disease caused by the SARS-CoV-2 betacoronavirus, was declared a pandemic by the World Health Organization on March 11, 2020. Since then, SARS-CoV-2 has triggered a devastating global health and economic emergency.In response, a broad range of preclinical animal models have been used to identify effective therapies and vaccines. Current animal models do not express the full spectrum of human COVID-19 disease and pathology, with most exhibiting mild to moderate disease without mortality. NHPs are physiologically, genetically, and immunologically more closely related to humans than other animal species; thus, they provide a relevant model for SARS-CoV-2 investigations. This overview summarizes NHP models of SARS-CoV-2 and their role in vaccine and therapeutic development.
Decommissioning procedure and induced activation levels, calculations and measurements in an 18 MeV medical cyclotron

The present article describes the decommissioning of a self-shielded 18 MeV medical cyclotron IBA Cyclone 18/9 after 14 years of operation. A Monte Carlo simulation of the possible nuclear reactions was performed in order to plan the decommissioning activities. During the cyclotron dismantling, the activities of the cyclotron components, concrete wall and floor samples were measured. Residual activities were analysed by means of an HPGe detector and liquid scintillation counting, and compared with simulation data. Dosimetry of the staff involved in the decommissioning procedure was monitored by individual TL dosimeters and/or digital dosimeter. The cyclotron component analysis confirmed the presence of gamma and pure beta emitters, 22Na, 54Mn, 60Co, 65Zn, 207Bi, 55Fe, 63Ni at different values of specific activity, depending on the positioning of the sample point and on the alloy of the sampled part. In these components the presence of gamma and pure beta emitters was measured 5 years after the shutdown at levels far above clearance limits as defined by the ‘Recommended radiological protection criteria for the recycling of metals from the dismantling of nuclear installations’ (RP89) guidelines. The simulation, carried out by FLUKA Code (version 2020.0.5) on the cyclotron components, provided good agreement with measurements, with a maximum discrepancy of the same order as the uncertainties. Four engineers of the cyclotron maintenance staff were involved in the dismounting of the hottest components and rigging of the cyclotron in the deposit 6 months after shutdown and two engineers were involved during the drilling phase 3.5 years after shutdown. The measured dose from external exposure of the involved staff was lower than 100 μSv person−1 during the first phase and lower than 20 μSv person−1 during the final drilling phase. Measured doses from intake were negligible. In conclusion, the decommissioning of the 18 MeV cyclotron does not represent a risk for the involved staff, but, due to the presence of long-lived radioisotopes, the cyclotron components are to be treated as low level radioactive waste, and stored in an authorised storage area for at least 25 years after shutdown.
Time-restricted eating for the prevention and management of metabolic diseases.

Time-restricted feeding (TRF, animal-based studies) and time-restricted eating (TRE, humans) are an emerging behavioral intervention approach based on the understanding of the role of circadian rhythms in physiology and metabolism. In this approach, all calorie intake is restricted within a consistent interval of less than 12 hours without overtly attempting to reduce calories. This article will summarize the origin of TRF/TRE starting with concept of circadian rhythms and the role of chronic circadian rhythm disruption in increasing the risk for chronic metabolic diseases. Circadian rhythms are usually perceived as the sleep-wake cycle and dependent rhythms arising from the central nervous system. However, the recent discovery of circadian rhythms in peripheral organs and the plasticity of these rhythms in response to changes in nutrition availability raised the possibility that adopting a consistent daily short window of feeding can sustain robust circadian rhythm. Preclinical animal studies have demonstrated proof of concept and identified potential mechanisms driving TRF-related benefits. Pilot human intervention studies have reported promising results in reducing the risk for obesity, diabetes, and cardiovascular diseases. Epidemiological studies have indicated that maintaining a consistent long overnight fast, which is similar to TRE, can significantly reduce risks for chronic diseases. Despite these early successes, more
Is the COVID‐19 pandemic impacting on the risk of African Swine Fever virus (ASFV) introduction into the United States? A short‐term assessment of the risk factors

Abstract African swine fever (ASF) is a contagious disease with high mortality in domestic and feral swine populations. Although it is not a zoonosis, its spread may have severe socio‐economic and public health consequences. The activities of veterinary services are essential for controlling ASF outbreaks within a country, but also for diminishing its threat of spread to neighbouring countries, and for recognizing its entry into countries that are currently free. ASF requires quick responses and permanent monitoring to identify outbreaks and prevent spread, and both aspects can be heavily undercut during the COVID‐19 pandemic. This paper analyses changing patterns of the main drivers and pathways for the potential introduction of ASFV into the United States during the COVID‐19 pandemic, including international movements of people, swine products and by‐products. Data on commercial flights and merchant ships was used as a proxy to indirectly assess the flow of illegal products coming from ASF affected countries. Results from this study highlight a decreasing trend in the legal imports of swine products and by‐products from ASF affected countries (Sen's slope = −99, 95% CI: −215.34 to −21.26, p‐value < 0.05), while no trend was detected for confiscations of illegal products at ports of entry. Additionally, increasing trends were detected for the monthly number of merchant ships coming from ASF affected countries (Sen's slope = 0.46, 95%CI 0.25–0.59), the monthly value of imported goods ($) through merchant ships (Sen's slope = 1513196160, 95%CI 1072731702–1908231855), and the monthly percentage of commercial flights (Sen's slope = 0.005, 95%CI 0.003–0.007), with the majority of them originating from China. Overall, the findings show an increased connection of the United States with ASF affected countries, highlighting the risk posed by ASF during a global public health crisis.
Evaluating the Difficulty involved in Designing Small Molecule Drugs to Inhibit Protein-Protein Interactions

The targeting of drugs to block protein-protein interactions (PPIs) has attracted great interest over recent years. Such targets, however, have been held to be difficult to inhibit using low molecular weight compounds, and as a consequence they are often branded as “undruggable”. This is partly because the interfaces involved are seen to be large, and the fact that they are generally regarded as being too smooth and too flat. In the work reported here, a series of quantitative systematic studies have been performed to determine the molecular area, roughness, curvature, and amino acid composition of the interfacial surfaces of PPIs, to determine the feasibility of designing small molecule drugs to inhibit these interactions. The X-ray crystal structures are analysed for a set of 48 PPIs involving G-protein, membrane receptor extracellular domain, and enzyme-inhibitor complexes. The protein partners involved in these PPIs are shown to have much larger interfacial areas than those for protein-small molecule complexes (≥ 900 A2 vs ~250 A2, respectively), and they have interfaces that are fairly smooth (with fractal dimensions close to 2) and quite flat (with mean surface curvatures in the order of ± 0.1 A-1). The mean interfacial surface curvatures of the PPI protein partners, however, are seen to change upon complexation, some very significantly so. Despite the fact that the amino acid compositions of the PPI interface surfaces are found to be significantly different from that of the average protein surface (with variations according to the type of PPI), it is concluded that the prospects for designing low molecular weight PPI inhibitors that act in an orthosteric manner remain rather limited. HIGHLIGHTS •Mean interfacial surface curvatures have been determined for protein-protein interaction (PPI) partners in their complexed and uncomplexed states. •Mean interfacial surface roughnesses have been determined for protein-protein interaction (PPI) partners in their complexed and uncomplexed states. •Amino acid compositions have been determined for PPI interface surfaces and these compared with that for the average protein surface. •Quantification of the PPI interfacial surface properties is used to assess the druggability of these targets.
Karawun: assisting evaluation of advances in multimodal imaging for neurosurgical planning and intraoperative neuronavigation

Purpose: To introduce a tool allowing neurosurgeons to evaluate the results of research tractography workflows for presurgical planning and intraoperative image-guidance, using standard neurosurgical navigation platforms. Theory and Methods: Improving communication between neurosurgeons and researchers developing new image acquisition and processing methods is critical for rapid translation of research to surgical practice. Presenting research outputs within existing clinical workflows is one approach that can assist such interdisciplinary communication. Neurosurgical navigation platforms can display and manipulate a wide range of medical image data and associated delineations and thus allow clinicians to evaluate the impact of new imaging research on their work. Currently, it is extremely difficult to integrate research-based image processing outputs into standard neurosurgical navigation platforms. Results: In this note we introduce Karawun, an open-source software tool for converting outputs from research imaging pipelines, especially diffusion MRI tractography reconstructions using advanced methodologies currently unavailable on commercial navigation platforms, into forms that can be imported into the Brainlab neurosurgical navigation platform (Brainlab AG, Munich, Germany). The externally created tractography images and delineations can be viewed and manipulated as if they were created by Brainlab. We illustrate how two surgical workups, created using open-source tools and different processing choices can be presented to the neurosurgeon who can evaluate the impact of the differences between the two workups on surgical decisions. Conclusion: Karawun allows researchers developing novel imaging methodologies to display their results in environments that are familiar to clinical end-users, especially neurosurgeons, thus assisting translation of research into clinical practice.
Cortisol dysregulation among American Sign Language interpreters in different work settings: Confirmation of occupational health risks

Cumulative motion injuries and burnout contribute to an international shortage of signed language interpreters (SLIs). Studies using the Job Content Questionnaire, a well-validated measure of occupational health risks, indicate that risk levels differ among SLI work settings. Specifically, video relay service (VRS) and primary and secondary educational settings (K–12) are associated with greater health risks. The current study validated those results by analysing the diurnal rhythm of salivary cortisol levels. Anomalies in diurnal cortisol rhythms are associated with an array of health problems. Our full-time SLI participants included VRS workers (N = 20), K–12 workers (N = 18), and SLIs who worked in other settings (N = 23). Non-interpreters using sign language daily at work (N = 18) comprised a fourth group. Saliva samples were collected four times daily over 2 working days. The VRS and K–12 SLIs demonstrated lower morning peak cortisol levels and flatter diurnal cortisol slopes from morning peak to subsequent diminution. Both findings are associated with chronic stress and greater health risks. The VRS and K–12 groups’ diurnal cortisol patterns closely resembled each other, while the Other SLI group’s curve closely resembled that of the Non-Interpreter group. Implications for SLI health, education, and job redesign are discussed in the context of the demand control schema.
Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities

In the Internet of Things (IoT) environment, detecting a faulty device is crucial to guarantee the reliable execution of IoT services. To detect a faulty device, existing schemes trace a series of events among IoT devices within a certain time window, extract correlations among them, and find a faulty device that violates the correlations. However, if a few users share the same IoT environment, since their concurrent activities make non-correlated devices react together in the same time window, the existing schemes fail to detect a faulty device without differentiating the concurrent activities. To correctly detect a faulty device in the multiple concurrent activities, this work proposes a new precise correlation extraction scheme, called PCoExtractor. Instead of using a time window, PCoExtractor continuously traces the events, removes unrelated device statuses that inconsistently react for the same activity, and constructs fine-grained correlations. Moreover, to increase the detection precision, this work newly defines a fine-grained correlation representation that reflects not only sensor values and functionalities of actuators but also their transitions and program states such as contexts. Compared to existing schemes, PCoExtractor detects and identifies 40.06% more faults for 4 IoT services with concurrent activities of 12 users while reducing 80.3% of detection and identification times.
RT-ZooKeeper: Taming the Recovery Latency of a Coordination Service

Fault-tolerant coordination services have been widely used in distributed applications in cloud environments. Recent years have witnessed the emergence of time-sensitive applications deployed in edge computing environments, which introduces both challenges and opportunities for coordination services. On one hand, coordination services must recover from failures in a timely manner. On the other hand, edge computing employs local networked platforms that can be exploited to achieve timely recovery. In this work, we first identify the limitations of the leader election and recovery protocols underlying Apache ZooKeeper, the prevailing open-source coordination service. To reduce recovery latency from leader failures, we then design RT-Zookeeper with a set of novel features including a fast-convergence election protocol, a quorum channel notification mechanism, and a distributed epoch persistence protocol. We have implemented RT-Zookeeper based on ZooKeeper version 3.5. 8. Empirical evaluation shows that RT-ZooKeeper achieves 91% reduction in maximum recovery latency in comparison to ZooKeeper. Furthermore, a case study demonstrates that fast failure recovery in RT-ZooKeeper can benefit a common messaging service like Kafka in terms of message latency.
Exploiting Activation Sparsity for Fast CNN Inference on Mobile GPUs

Over the past several years, the need for on-device deep learning has been rapidly increasing, and efficient CNN inference on mobile platforms has been actively researched. Sparsity exploitation has been one of the most active research themes, but the studies mostly focus on weight sparsity by weight pruning. Activation sparsity, on the contrary, requires compression at runtime for every input tensor. Hence, the research on activation sparsity mainly targets NPUs that can efficiently process this with their own hardware logic. In this paper, we observe that it is difficult to accelerate CNN inference on mobile GPUs with natural activation sparsity and that the widely used CSR-based sparse convolution is not sufficiently effective due to the compression overhead. We propose several novel sparsification methods that can boost activation sparsity without harming accuracy. In particular, we selectively sparsify some layers with an extremely high sparsity and adopt sparse convolution or dense convolution depending on the layers. Further, we present an efficient sparse convolution method without compression and demonstrate that it can be faster than the CSR implementation. With ResNet-50, we achieved 1.88 speedup compared to TFLite on a Mali-G76 GPU.
Workshop for designing biofeedback of driver’s state and emotion in automated vehicles

Different drivers’ states and emotions can affect negatively the driving performance. Recent advances in affective computing now give the opportunity to measure the users’ state or emotions using various sources of data such as physiological signals or voice samples. Conveying biofeedback in the car could help to make roads safer and improve users’ health and mental state during a ride in an autonomous car. This workshop aims at selecting the drivers’ hazardous states and emotions that are crucial to be assessed, as well as how to convey the appropriate biofeedback to the driver, using multimodal interaction in the car.
AliceVision Meshroom: An open-source 3D reconstruction pipeline

This paper introduces the Meshroom software and its underlying 3D computer vision framework AliceVision. This solution provides a photogrammetry pipeline to reconstruct 3D scenes from a set of unordered images. It also features other pipelines for fusing multi-bracketing low dynamic range images into high dynamic range, stitching multiple images into a panorama and estimating the motion of a moving camera. Meshroom's node-graph architecture allows the user to customize the different pipelines to adjust them to their domain specific needs. The user can interactively add other processing nodes to modify a pipeline, export intermediate data to analyze the result of the algorithms and easily compare the outputs given by different sets of parameters. The software package is released in open source and relies on open file formats. These features enable researchers to conveniently run the pipelines, access and visualize the data at each step, thus promoting the sharing and the reproducibility of the results.
Exploring Efficient Architectures on Remote In-Memory NVM over RDMA

Efficiently accessing remote file data remains a challenging problem for data processing systems. Development of technologies in non-volatile dual in-line memory modules (NVDIMMs), in-memory file systems, and RDMA networks provide new opportunities towards solving the problem of remote data access. A general understanding about NVDIMMs, such as Intel Optane DC Persistent Memory (DCPM), is that they expand main memory capacity with a cost of multiple times lower performance than DRAM. With an in-depth exploration presented in this paper, however, we show an interesting finding that the potential of NVDIMMs for high-performance, remote in-memory accesses can be revealed through careful design. We explore multiple architectural structures for accessing remote NVDIMMs in a real system using Optane DCPM, and compare the performance of various structures. Experiments are conducted to show significant performance gaps among different ways of using NVDIMMs as memory address space accessible through RDMA interface. Furthermore, we design and implement a prototype of user-level, in-memory file system, RIMFS, in the device DAX mode on Optane DCPM. By comparing against the DAX-supported Linux file system, Ext4-DAX, we show that the performance of remote reads on RIMFS over RDMA is 11.44 higher than that on a remote Ext4-DAX on average. The experimental results also show that the performance of remote accesses on RIMFS is maintained on a heavily loaded data server with CPU utilization as high as 90%, while the performance of remote reads on Ext4-DAX is significantly reduced by 49.3%, and the performance of local reads on Ext4-DAX is even more significantly reduced by 90.1%. The performance comparisons of writes exhibit the same trends.
Securing the wireless emergency alerts system

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the U.S., prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using commercially available hardware and modified open source software. Our attack can be performed using a commercially available software-defined radio, and our modifications to the open source software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The real impact of such an attack would, of course, depend on the density of cellphones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cellphone manufacturers. To seed this effort, we also propose three mitigation solutions to address this threat.