Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA sequencing reveals the emerging role of bronchoalveolar lavage fluid exosome lncRNAs in acute lung injury

Background Bronchoalveolar lavage fluid (BALF) exosomes possess different properties in different diseases, which are mediated through microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), among others. By sequencing the differentially expressed lncRNAs in BALF exosomes, we seek potential targets for the diagnosis and treatment of acute lung injury (ALI). Methods Considering that human and rat genes are about 80% similar, ALI was induced using lipopolysaccharide in six male Wistar rats, with six rats as control (all weighing 200 ± 20 g and aged 6–8 weeks). BALF exosomes were obtained 24 h after ALI. The exosomes in BALF were extracted by ultracentrifugation. The differential expression of BALF exosomal lncRNAs in BALF was analyzed by RNA sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to predict the functions of differentially expressed lncRNAs, which were confirmed by reverse transcription–polymerase chain reaction. Results Compared with the control group, the ALI group displayed a higher wet/dry ratio, tumor necrosis factor-α levels, and interleukin-6 levels (all P < 0.001). The airway injection of exosomes in rats led to significant infiltration by neutrophils. A total of 2,958 differentially expressed exosomal lncRNAs were identified, including 2,524 upregulated and 434 downregulated ones. Five lncRNAs confirmed the reliability of the sequencing data. The top three GO functions were phagocytic vesicle membrane, regulation of receptor biosynthesis process, and I-SMAD binding. Salmonella infection, Toll-like receptor signaling pathway, and osteoclast differentiation were the most enriched KEGG pathways. The lncRNA–miRNA interaction network of the five confirmed lncRNAs could be predicted using miRDB. Conclusions BALF-derived exosomes play an important role in ALI development and help identify potential therapeutic targets related to ALI.
Estimation with Heisenberg-Scaling Sensitivity of a Single Parameter Distributed in an Arbitrary Linear Optical Network

Quantum sensing and quantum metrology propose schemes for the estimation of physical properties, such as lengths, time intervals, and temperatures, achieving enhanced levels of precision beyond the possibilities of classical strategies. However, such an enhanced sensitivity usually comes at a price: the use of probes in highly fragile states, the need to adaptively optimise the estimation schemes to the value of the unknown property we want to estimate, and the limited working range, are some examples of challenges which prevent quantum sensing protocols to be practical for applications. This work reviews two feasible estimation schemes which address these challenges, employing easily realisable resources, i.e., squeezed light, and achieve the desired quantum enhancement of the precision, namely the Heisenberg-scaling sensitivity. In more detail, it is here shown how to overcome, in the estimation of any parameter affecting in a distributed manner multiple components of an arbitrary M-channel linear optical network, the need to iteratively optimise the network. In particular, we show that this is possible with a single-step adaptation of the network based only on a prior knowledge of the parameter achievable through a “classical” shot-noise limited estimation strategy. Furthermore, homodyne measurements with only one detector allow us to achieve Heisenberg-limited estimation of the parameter. We further demonstrate that one can avoid the use of any auxiliary network at the price of simultaneously employing multiple detectors.
Short-Circuit Protection for Low-Voltage DC Distribution Systems Based on Solid-State Circuit Breakers

Proper short-circuit protection in dc distribution systems has provided an austere challenge to researchers as the development of commercially-viable equipment providing fast operation, coordination and reliability still continues. The objective of this thesis is to analyze issues associated with short-circuit protection of low-voltage dc (LVDC) distribution systems and propose a short-circuit protection methodology based on solid-state circuit breakers (SSCBs) that provides fault-current limiting (FCL). Simulation results for a simplified notional 1-kVdc distribution system, performed in MATLAB/SIMULINK, would be presented to illustrate that SSCB solutions based on reverse-blocking integrated gate-commutated thyristors (RB-IGCT) are feasible for low-voltage dc distribution systems but requires connecting several devices in parallel to open fast-rising fault currents. To validate the implementation of the FCL function, the coordination between upstream and downstream SSCBs during a fault at different operating conditions of the system is presented. In addition, several fault-detection techniques would be compared by means of the let-through energies, and the impact of FCL on the thermal handling requirements of the RB-IGCT would also be discussed. ©2017 by Sharthak Munasib All Rights Reserved
Aviation contrail climate effects in the North Atlantic from 2016 to 2021

Abstract. Around 5 % of anthropogenic radiative forcing (RF) is attributed to aviation CO2 and non-CO2 impacts. This paper quantifies aviation emissions and contrail climate forcing in the North Atlantic, one of the world's busiest air traffic corridors, over 5 years. Between 2016 and 2019, growth in CO2 (+3.13 % yr−1) and nitrogen oxide emissions (+4.5 % yr−1) outpaced increases in flight distance (+3.05 % yr−1). Over the same period, the annual mean contrail cirrus net RF (204–280 mW m−2) showed significant inter-annual variability caused by variations in meteorology. Responses to COVID-19 caused significant reductions in flight distance travelled (−66 %), CO2 emissions (−71 %) and the contrail net RF (−66 %) compared with the prior 1-year period. Around 12 % of all flights in this region cause 80 % of the annual contrail energy forcing, and the factors associated with strongly warming/cooling contrails include seasonal changes in meteorology and radiation, time of day, background cloud fields, and engine-specific non-volatile particulate matter (nvPM) emissions. Strongly warming contrails in this region are generally formed in wintertime, close to the tropopause, between 15:00 and 04:00 UTC, and above low-level clouds. The most strongly cooling contrails occur in the spring, in the upper troposphere, between 06:00 and 15:00 UTC, and without lower-level clouds. Uncertainty in the contrail cirrus net RF (216–238 mW m−2) arising from meteorology in 2019 is smaller than the inter-annual variability. The contrail RF estimates are most sensitive to the humidity fields, followed by nvPM emissions and aircraft mass assumptions. This longitudinal evaluation of aviation contrail impacts contributes a quantified understanding of inter-annual variability and informs strategies for contrail mitigation.
Mitigation of Subsynchronous Oscillations by 48-Pulse VSC STATCOM Using Remote Signal

Series compensated power system may lead to a very unusual problem known as Subsynchronous Resonance(SSR). Flexible AC Transmission Systems(FACTS) controllers are widely applied to mitigate subsynchronous oscillations.The Static Synchronous Compensator(STATCOM) is a shunt device of the FACTS family using power electronics to control power flow and improve transient stability on power grids.With the advent of Wide Area Measurement(WAM) technology,it is possible to measure the states of a large interconnected power system with synchronized Phasor Measurement Units(PMU). In this paper the concept of using remote signals acquired through PMU has been proposed to damp SSR.An auxiliary subsynchronous damping controller(SSDC) for a STATCOM using the remote accelerating power of generator signal as the stabilizing signal has been designed to damp subsynchronous oscillations.The IEEE Second Benchmark(SBM) model is considered for the analysis and the complete digital simulation of the STATCOM within the power system is performed in the MATLAB/Simulink environment using the Power System Blockset(PSB).
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation. Swift: Adaptive Video Streaming with Layered Neural Codecs

Layered video coding compresses video segments into layers (additional code bits). Decoding with each additional layer improves video quality incrementally. This approach has po-tential for very fine-grained rate adaptation. However, layered coding has not seen much success in practice because of its cross-layer compression overheads and decoding latencies. We take a fresh new approach to layered video coding by exploiting recent advances in video coding using deep learning techniques. We develop Swift , an adaptive video streaming system that includes i) a layered encoder that learns to encode a video frame into layered codes by purely encoding residuals from previous layers without introducing any cross-layer compression overheads, ii) a decoder that can fuse together a subset of these codes (based on availability) and decode them all in one go, and, iii) an adaptive bit rate (ABR) protocol that synergistically adapts video quality based on available network and client-side compute capacity. Swift can be integrated easily in the current streaming ecosystem without any change to network protocols and applications by simply replacing the current codecs with the proposed layered neural video codec when appropriate GPU or similar accelerator functionality is available on the client side. Extensive evaluations reveal Swift ’s multi-dimensional benefits over prior video streaming systems.
A Simplified and Stubborn Approach to CTL Model Checking of Petri Nets

We present an interpretation of stubborn sets, described using labelled transitions systems. The approach is applied in the context of Petri nets with inhibitor arcs. We extend reachability preserving stubborns sets to include preservation of fireability, cardinality, and deadlock properties, and base it on generating an interesting set of transitions for a given formula. We introduce an interpretation for deciding the siphontrap property for Petri nets using integer linear programming. Based on the theory of state equations, we construct a CTL formula simplification technique that removes trivially verifiable subformulae. We implement and benchmark the individual and combined techniques against the state of the art verification tools in TAPAAL and LoLA. The integer linear programming interpretation of the siphon-trap property shows potential in analysis of deadlock freedom, but the implementation performs worse than the Boolean satisfiability representation implemented in LoLA. Our experiments show that the combination of structural reduction and stubborn reduction generally yields considerable performance improvements, but that the combination conflicts on some models. Formula simplification is able to reduce a significant number of formulae to questions that are trivially answered, and the simplified formulae improve the stubborn and structural reduction techniques. Issues with simplification of fireability formulae were discovered. On all formula categories combined, we provide more exclusive answers and consume less memory while LoLA perform faster verification and generally explore fewer states. All mentioned techniques have been implemented in the open-source Petri net verification tool TAPAAL.
Research on the Visual Elements of VR game <The Elder Scrolls V : skyrim VR>

In recent years, various virtual reality games are becoming commercialized. A variety of large 3D online games popular with users has begun to transform into VR games. < the Elder Scrolls V: skyrim VR > as a derivative of large-scale 3D online games < the Elder Scrolls V: skyrim special edition >, Since its launch on the steam game platform in April 2018, because of its original UI design, maximizes the user's immersive experience and firmly maintains the leading position of VR games. Although the visual similarities between < the Elder Scrolls V: Skylim VR > and < the Elder Scrolls V: Skylim Special Edition > are high, but the visual elements in the UI are quite different. This paper studies characters, scenes, operating interfaces, colors and fonts five visual UI elements that enhance the immersion in <the Elder Scrolls V: Skyrim VR> game. It provided a unique interface design reference to immersive experience of new VR games in the future.
DIAGNOSIS OF BEARING DEFECTS BY ANFIS IN THE INDUCTION MOTOR

In this paper, we developed a robust technique for fault classification in the induction motor, which was used by the Adaptive Network Reference Information System (ANFIS). A robustness and precision test of the proposed method was performed on a small database. To speed up the response and minimize the calculation time, the most sensitive indicators have been used to ensure the learning of the classifier. The database used was carried out at the University of Case Western, this database has two modes of healthy operation and with rolling defects located side coupling. The objective of the technique used in this paper is to extract the indicators and to test them to select the most sensitive to the apparition of defects. The obtained results show the effectiveness and sensitivity of the proposed approach to identify the nature of the defect even at the birth stage.
Three-dimensional Map Construction of Indoor Environment Based on RGB-D SLAM Scheme

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.
Dissecting the Function of Biological Machines Using Fleezers

Optical tweezer has been a classic method widely applied in the biophysical study for decades. Optical forces originate from the momentum exchange when light interacts with an object[1], and the forces include the gradient force, which is produced by the polarization of an object in a non-uniform electronic field, and the scattering force, which is produced by the absorption and the scattering of light. Stable optical trappings are achieved when the gradient force is balanced with the scattering force[1]. The detection of gradient and scattering forces on micron-sized particles was first reported by Professor Arthur Ashkin in 1970[2] and was applied to the cooling and trapping of neutral atoms by Steven Chu in 1986[3]. The standard optical tweezers yield rich information on the mechanical behaviors of the molecules by real-time recordings of force and extension[1]. However, there are still some limitations. For example, only with the standard optical tweezers, it’s not easy to measure the position and the orientation of single biomolecules, as well as their conformational fluctuations. Now this limitation can be overcome by the combination with fluorescence imaging. The hybrid method enables measurements of mechanical signals and forces simultaneously with fluorescence imaging of the same system[1]. The property of real-time measurement and imaging is ideal for singlemolecule tracking, and thus for studying the activity of molecular motors and the dynamics of biochemical processes.
Probing Word Syntactic Representations in the Brain by a Feature Elimination Method

Neuroimaging studies have identified multiple brain regions that are associated with semantic and syntactic processing when comprehending language. However, existing methods cannot explore the neural correlates of fine-grained word syntactic features, such as part-of-speech and dependency relations. This paper proposes an alternative framework to study how different word syntactic features are represented in the brain. To separate each syntactic feature, we propose a feature elimination method, called Mean Vector Null space Projection (MVNP). This method can remove a specific feature from word representations, resulting in one-feature-removed representations. Then we respectively associate one-feature-removed and the original word vectors with brain imaging data to explore how the brain represents the removed feature. This paper for the first time studies the cortical representations of multiple fine-grained syntactic features simultaneously and suggests some possible contributions of several brain regions to the complex division of syntactic processing. These findings indicate that the brain foundations of syntactic information processing might be broader than those suggested by classical studies.
Aggregation Detection in CSV Files

Aggregations are an arithmetic relationship between a single number and a set of numbers. Tables in raw CSV files often include various types of aggregations to summarize data therein. Identifying aggregations in tables can help understand file structures, detect data errors, and normalize tables. However, recognizing aggregations in CSV files is not trivial, as these files often organize information in an ad-hoc manner with aggregations appearing in arbitrary positions and displaying rounding errors. We propose the three-stage approach AggreCol to recognize aggregations of five types: sum, difference, average, division, and relative change. The first stage detects aggregations of each type individually. The second stage uses a set of pruning rules to remove spurious candidates. The last stage employs rules to allow individual detectors to skip specific parts of the file and retrieve more aggregations. We evaluated our approach with two manually annotated datasets, showing that AggreCol is capable of achieving 0.95 precision and recall for 91.1% and 86.3% of the files, respectively. We obtained similar results on an unseen test dataset, proving the generalizability of our proposed techniques.
Specialised Neural Network for Learning Word Frequency Measures in Large Document Collections

For very large document collections or high volume streams of documents, finding relevant documents is a major information filtering problem. One of the main types of information retrieval systems produces a word frequency measure estimated by some important parts of the document using neural network approaches. This paper reports a new network structure for this task. It is specialised considering the main difficulties of these kinds of applications, namely, the calculation time complexity. It will be pointed out that the calculation, hence, the learning time is much reduced applying the new algorithm, however, the result is significantly improved compared to the former approaches, which offer a possibility to increase the number of considered words, hence, improve the
F1 style MGU-H applied to the turbocharger of a gasoline hybrid electric passenger car

Abstract We consider a turbocharged gasoline direct injection (DI) engine featuring a motor-generator-unit (MGU-H) fitted on the turbocharger shaft. The MGU-H receives or delivers energy to the same energy storage (ES) of the hybrid power unit that comprises a motor-generator unit on the driveline (MGU-K) in addition to the internal combustion engine (ICE). The energy supply from the ES is mostly needed during sharp accelerations to avoid turbo-lag, and to boost torque at low speeds. At low speeds, it also improves the ratio of engine crankshaft power to fuel flow power, as well as the ratio of engine crankshaft plus turbocharger shaft power to fuel flow power. The energy supply to the ES is possible at high speeds and loads, where otherwise the turbine could have been waste gated, and during decelerations. This improves the ratio of engine crankshaft plus turbocharger shaft power to fuel flow power.
SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization

Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods.
Efficacy and safety of acupuncture combined with rehabilitation in the treatment of strephenopodia after stroke

Abstract Background: Strephenopodia is a common complication after stroke, which is easily neglected in the early stage of the disease and seriously affects the rehabilitation process of patients’ limbs, and brings huge security risks and family burden. A large number of studies have confirmed that acupuncture combined with rehabilitation (ACR) has a significant effect on strephenopodia after stroke (SAS), but there is still a lack of systematic scientific evidence to support this argument. In this systematic review, we aimed to evaluate the efficacy and safety of ACR in the treatment of SAS, to provide evidence-based medical evidence for the clinical treatment of the disease. Methods: We will search the following databases of 8 electronic databases from inception to January 2022: PubMed, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, China National Knowledge infrastructure (CNKI), Technology Periodical Database (VIP), WanFang Data, and China Biology Medicine (CBM). All relevant randomized controlled trials (RCTs) focus on ACR in the treatment of strephenopodia after stroke will be included. The primary outcome will be the Measurement of strephenopodia angle and Clinical Spasm Index Scale (CSI). The Secondary outcomes will include Holden Functional Walking Classification (FAC), Berg Balance Scale (BBS), and Modified Barthel Index Score (MBI). Two reviewers will independently conduct the Study selection and data extraction. The risk of bias will be evaluated according to the Cochrane tool. Risk ratio and 95% confidence intervals will be used to estimate the efficacy of treatment, and the grading of recommendations, assessment, development, and evaluation approach to rate the certainty of evidence. The data analysis will be analyzed using by RevMan5.4. Result: This study will provide a comprehensive evaluation of the efficacy and safety of ACR in the treatment of SAS, with a view of providing more reliable evidence-based solutions for SAS. Conclusions: The conclusion of this study will provide evidence to judge whether ACR is effective and safe in treating SAS. PROSPERO registration number: CRD42021290960.