Miracle-dz/UniFAR-FTUs huggingface dataset:
A sparse principal component analysis of Class III malocclusions.

OBJECTIVES: To identify the most characteristic variables out of a large number of anatomic landmark variables on three-dimensional computed tomography (CT) images. A modified principal component analysis (PCA) was used to identify which anatomic structures would demonstrate the major variabilities that would most characterize the patient. MATERIALS AND METHODS: Data were collected from 217 patients with severe skeletal Class III malocclusions who had undergone orthognathic surgery. The input variables were composed of a total of 740 variables consisting of three-dimensional Cartesian coordinates and their Euclidean distances of 104 soft tissue and 81 hard tissue landmarks identified on the CT images. A statistical method, a modified PCA based on the penalized matrix decomposition, was performed to extract the principal components. RESULTS: The first 10 (8 soft tissue, 2 hard tissue) principal components from the 740 input variables explained 63% of the total variance. The most conspicuous principal components indicated that groups of soft tissue variables on the nose, lips, and eyes explained more variability than skeletal variables did. In other words, these soft tissue components were most representative of the differences among the Class III patients. CONCLUSIONS: On three-dimensional images, soft tissues had more variability than the skeletal anatomic structures. In the assessment of three-dimensional facial variability, a limited number of anatomic landmarks being used today did not seem sufficient. Nevertheless, this modified PCA may be used to analyze orthodontic three-dimensional images in the future, but it may not fully express the variability of the patients.
Connecting the dots in the mechanism of action of Cucurbitacin E (CurE) – path analysis and steered molecular dynamics reveal the precise site of entry and the passage of CurE in filamentous actin

Abstract Cucurbitacin E (CurE) modulates actin cytoskeleton by forming an irreversible covalent bond with Cys257 of actin. The reported binding conformation of CurE is deeply buried in the subdomain 4 of actin and is closely situated to the ATP-binding site. The entry and the path taken by CurE to reach this buried site remain a mystery. In this study, steered molecular dynamics (SMD) simulations were conducted to delineate the diffusion of CurE to its binding site. SMD simulations reveal that the distinctive entry site of CurE found in subdomain 4 is by itself a closed and compact region of two loops lying beside each other like a closed door and CurE induces it to open. From this point, CurE moves toward its binding site through a path facilitated by Thr188, Leu261, Ile267, Ile309, Tyr306. This study is also an insight into how CurE distinctly differentiates its tunnel to the binding site from the ATP-binding site. The conformational changes of CurE along the path to the binding site are surprisingly very minimal and closely resemble the attack conformation at the end of simulation. The study reveals that the little energy spent by the molecule is compensated by the enthalpic contribution to binding-free energy barrier making it undoubtedly the most preferred path of CurE. This study is the first of its kind in which the SMD was used to derive the complete and continuous translocation of one of the most potent phytochemicals, CurE through the binding site gorge of actin. Communicated by Ramaswamy H. Sarma
Ramond–Ramond fields and twisted differential K-theory

We provide a systematic approach to describing the Ramond-Ramond (RR) fields as elements in twisted differential K-theory. This builds on a series of constructions by the authors on geometric and computational aspects of twisted differential K-theory, which to a large extent were originally motivated by this problem. In addition to providing a new conceptual framework and a mathematically solid setting, this allows us to uncover interesting and novel effects. Explicitly, we use our recently constructed Atiyah-Hirzebruch spectral sequence (AHSS) for twisted differential K-theory to characterize the RR fields and their quantization, which involves interesting interplay between geometric and topological data. We illustrate this with the examples of spheres, tori, and Calabi-Yau threefolds.
Data Forensic Determination of the Accuracy of International COVID-19 Reporting: Using Zipf's Law for Pandemic Investigation

Severe outbreaks of infectious disease occur throughout the world with some reaching the level of an international pandemic: Coronavirus (COVID-19) is the most recent to do so. As such pandemics cause extensive loss of lives, hamper industrial operations, and cause economic losses in both developing and developed countries, it is critical to establish common standards of accuracy in the determination and reporting of cases. In particular, there are current concerns that countries are hiding or incorrectly reporting cases of COVID-19. In this paper, we set out a mechanism for using Zipf's law to establish the accuracy of international reporting of COVID-19 cases via a determination of whether an individual country's COVID-19 reporting follows a power-law for confirmed, recovered, and death cases. We observe that the probability of Zipf's law (P-values) for COVID-19 confirmed cases show that Uzbekistan has the highest P-value of 0.940, followed by Belize (0.929), and Qatar (0.897). For COVID-19 recovered cases, Iraq had the highest P-value of 0.901, followed by New Zealand (0.888), and Austria (0.884). Furthermore, for COVID-19 death cases, Bosnia and Herzegovina had the highest P-value of 0.874, followed by Lithuania (0.843), and Morocco (0.825). China, where the COVID-19 pandemic began, is a significant outlier in recording P-values lower than 0.1 for the confirmed, recovered, and death cases. This raises important questions, not only for China but also any country whose data exhibits P-values below this threshold. The main application of this work is to serve as an early warning for the World Health Organization (WHO) and other health regulatory bodies to perform more investigations in countries where COVID-19 datasets deviate significantly from Zipf's law. To this end, we also provide a tool for illustrating Zipf's law P-values on a global map in order to report anomalies.
CRAFT: A multifunction online platform for speech prosody visualisation

There are many research tools which are also used for teaching the acoustic phonetics of speech rhythm and speech melody. But they were not purpose-designed for teaching-learning situations, and some have a steep learning curve. CRAFT (Creation and Recovery of Amplitude and Frequency Tracks) is custom-designed as a novel flexible online tool for visualisation and critical comparison of functions and transforms, with implementations of the Reaper, RAPT, PyRapt, YAAPT, YIN and PySWIPE F0 estimators, three Praat configurations, and two purpose-built estimators, PyAMDF, S0FT. Visualisations of amplitude and frequency envelope spectra, spectral edge detection of rhythm zones, and a parametrised spectrogram are included. A selection of audio clips from tone and intonation languages is provided for demonstration purposes. The main advantages of online tools are consistency (users have the same version and the same data selection), interoperability over different platforms, and ease of maintenance. The code is available on GitHub.
β-estradiol alleviates hypertension- and concanavalin A-mediated inflammatory responses via modulation of connexins in peripheral blood lymphocytes

Gap junctions (GJs) formed by connexins (Cxs) in T lymphocytes have been reported to have important roles in the T lymphocyte-driven inflammatory response and hypertension-mediated inflammation. Estrogen has a protective effect on cardiovascular diseases, including hypertension and it attenuates excessive inflammatory responses in certain autoimmune diseases. However, the mechanisms involved in regulating the pro-inflammatory response are complex and poorly understood. The current study investigated whether β-estradiol suppresses hypertension and pro-inflammatory stimuli-mediated inflammatory responses by regulating Cxs and Cx-mediated GJs in peripheral blood lymphocytes. Male, 16-week-old spontaneously hypertensive rats (SHR) and Wistar-Kyoto rats (WKY) rats were randomly divided into the following three groups: WKY rats, vehicle (saline)-treated SHRs, and β-estradiol (20 µg/kg/day)-treated SHRs. β-estradiol was administered subcutaneously for 5 weeks. Hematoxylin and eosin staining was performed to evaluate target organ injury. Flow cytometry and ELISA were used to measure the populations of T lymphocyte subtypes in the peripheral blood, and expression of Cx40/Cx43 in T cell subtypes, and pro-inflammation cytokines levels, respectively. ELISA, a dye transfer technique, immunofluorescence and immunoblotting were used to analyze the effect of β-estradiol on pro-inflammatory cytokine secretion, Cx-mediated GJs and the expression of Cxs in concanavalin A (Con A)-stimulated peripheral blood lymphocytes isolated from WKY rat. β-estradiol significantly decreased blood pressure and inhibited hypertension-induced target organ injury in SHRs. Additionally, β-estradiol treatment significantly improved the immune homeostasis of SHRs, as demonstrated by the decreased percentage of cluster of differentiation (CD)4+/CD8+ T-cell subset ratio, reduced serum levels of pro-inflammatory cytokines and increased the percentage of CD4+CD25+ T cells. β-estradiol also markedly reduced the expression of Cx40/Cx43 in T lymphocytes from SHRs. In vitro, β-estradiol significantly suppressed the production of pro-inflammatory cytokines, reduced communication via Cx-mediated gap junctions and decreased the expression of Cx40/Cx43 in Con A-stimulated lymphocytes. These results indicate that β-estradiol attenuates inflammation and end organ damage in hypertension, which may be partially mediated via downregulated expression of Cxs and reduced function of Cx-mediated GJ.
Robust Contraction Analysis of Nonlinear Systems via Differential IQC

We present a new approach to verifying contraction and L2-gain of uncertain nonlinear systems, extending the well-known method of integral quadratic constraints. We consider the uncertain system consisting of a feedback interconnection of a nonlinear nominal system and uncertainties satisfying differential integral quadratic constraints. A pointwise linear matrix inequality condition is formulated to verify the closed-loop differential L2 gain, which can lead to global reference-independent L2 gain performance of the nonlinear uncertain system. For a polynomial nominal system, the convex verification conditions can be solved via sum-of-squares programming. A simple computational example based on jet-engine surge with input delays illustrates the approach.
Sources of solar energetic particles

Solar energetic particles are an integral part of the physical processes related with space weather. We present a review for the acceleration mechanisms related to the explosive phenomena (flares and/or coronal mass ejections, CMEs) inside the solar corona. For more than 40 years, the main two-dimensional cartoon representing our understanding of the explosive phenomena inside the solar corona remained almost unchanged. The acceleration mechanisms related to solar flares and CMEs also remained unchanged and were part of the same cartoon. In this review, we revise the standard cartoon and present evidence from recent global magnetohydrodynamic simulations that support the argument that explosive phenomena will lead to the spontaneous formation of current sheets in different parts of the erupting magnetic structure. The evolution of the large-scale current sheets and their fragmentation will lead to strong turbulence and turbulent reconnection during solar flares and turbulent shocks. In other words, the acceleration mechanism in flares and CME-driven shocks may be the same, and their difference will be the overall magnetic topology, the ambient plasma parameters, and the duration of the unstable driver. This article is part of the theme issue ‘Solar eruptions and their space weather impact’.
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning

Convolutional neural networks (CNN) have shown state-of-the-art results for low-level computer vision problems such as stereo and monocular disparity estimations, but still, have much room to further improve their performance in terms of accuracy, numbers of parameters, etc. Recent works have uncovered the advantages of using an unsupervised scheme to train CNN’s to estimate monocular disparity, where only the relatively-easy-to-obtain stereo images are needed for training. We propose a novel encoder-decoder architecture that outperforms previous unsupervised monocular depth estimation networks by (i) taking into account ambiguities, (ii) efficient fusion between encoder and decoder features with rectangular convolutions and (iii) domain transformations between encoder and decoder. Our architecture outperforms the Monodepth baseline in all metrics, even with a considerable reduction of parameters. Furthermore, our architecture is capable of estimating a full disparity map in a single forward pass, whereas the baseline requires two passes. We perform extensive experiments to verify the effectiveness of our method on the KITTI dataset.
Admittance-Based Controller Design for Physical Human–Robot Interaction in the Constrained Task Space

In this article, an admittance-based controller for physical human–robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners—This work is motivated by the neglect of safety in existing controller design in physical human–robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human–robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: a differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: a learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector.
Widespread Molecular Outflows in the Infrared Dark Cloud G28.37+0.07: Indications of Orthogonal Outflow-filament Alignment

We present ALMA CO(2-1) observations toward a massive infrared dark cloud G28.37+0.07. The ALMA data reveal numerous molecular (CO) outflows with a wide range of sizes throughout the cloud. Sixty-two 1.3 mm continuum cores were identified to be driving molecular outflows. We have determined the position angle in the plane-of-sky of 120 CO outflow lobes and studied their distribution. We find that the distribution of the plane-of-sky outflow position angles peaks at about 100°, corresponding to a concentration of outflows with an approximately east–west direction. For most outflows, we have been able to estimate the plane-of-sky angle between the outflow axis and the filament that harbors the protostar that powers the outflow. Statistical tests strongly indicate that the distribution of outflow-filament orientations is consistent with most outflow axes being mostly orthogonal to their parent filament in three dimensions. Such alignment may result from filament fragmentation or continuous mass transportation from the filament to the embedded protostellar core. The latter is suggested by recent numerical studies with moderately strong magnetic fields.
Representations of Real Numbers Induced by Probability Distributions on ℕ

Abstract We observe that a probability distribution supported by ℕ, induces a representation of real numbers in [0, 1) with digits in ℕ. We first study the Hausdorff dimension of sets with prescribed digits with respect to these representations. Then, we determine the prevalent frequency of digits and the Hausdorff dimension of sets with prescribed frequencies of digits. As examples, we consider the geometric distribution, the Poisson distribution and the zeta distribution.
Cave Actinobacteria as Producers of Bioactive Metabolites

Recently, there is an urgent need for new drugs due to the emergence of drug resistant pathogenic microorganisms and new infectious diseases. Members of phylum Actinobacteria are promising source of bioactive compounds notably antibiotics. The search for such new compounds has shifted to extreme or underexplored environments to increase the possibility of discovery. Cave ecosystems have attracted interest of the research community because of their unique characteristics and the microbiome residing inside including actinobacteria. At the time of writing, 47 species in 30 genera of actinobacteria were reported from cave and cave related habitats. Novel and promising bioactive compounds have been isolated and characterized. This mini-review focuses on the diversity of cultivable actinobacteria in cave and cave-related environments, and their bioactive metabolites from 1999 to 2018.
Fault-Tolerant Nanosatellite Computing on a Budget

We present an on-board computer architecture designed for small satellites (< 50kg), which exploits software-fault-tolerance to achieve strong fault coverage with commodity hardware. Micro- and nanosatellites have become popular platforms for a variety of commercial and scientific applications, but today are considered suitable mainly for short and low-priority space missions due to their low reliability. In part, this can be attributed to their reliance upon cheap, low-feature size, COTS components originally designed for embedded and mobile-market applications, for which traditional hardware-voting concepts are ineffective. Software-fault-tolerance has been shown to be effective for such systems, but have largely been ignored by the space industry due to low maturity, as most have only been researched in theory. In practice, designers of payload instruments and miniaturized satellites are usually forced to sacrifice reliability in favor of delivering the level of performance necessary for cutting-edge science and innovative commercial applications. Thus, we developed a set of software measures facilitating fault tolerance based upon thread-level coarse-grain lockstep, which we validated through fault-injection. To offer strong long-term fault coverage, our architecture is implemented as tiled MPSoC on an FPGA, utilizing partial reconfiguration, as well as mixed criticality. This architecture can satisfy the high performance requirements of current and future scientific and commercial space missions at very low cost, while offering the strong fault-coverage guarantees necessary for platform control even for missions with a long duration. This architecture was developed for a 4-year ESA project. Together with two industrial partners, we are developing a prototype to then undergo radiation testing.
Affordance Learning In Direct Perception for Autonomous Driving

Recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles are using mediated perception approach for path planning and control, which highly rely on high-definition 3D maps and real time sensors. Recent research efforts aim to substitute the massive HD maps with coarse road attributes. In this paper, we follow the direct perception based method to train a deep neural network for affordance learning in autonomous driving. Our goal in this work is to develop the affordance learning model based on freely available Google Street View panoramas and Open Street Map road vector attributes. Driving scene understanding can be achieved by learning affordances from the images captured by car-mounted cameras. Such scene understanding by learning affordances may be useful for corroborating base maps such as HD maps so that the required data storage space is minimized and available for processing in real time. We compare capability in road attribute identification between human volunteers and our model by experimental evaluation. Our results indicate that this method could act as a cheaper way for training data collection in autonomous driving. The cross validation results also indicate the effectiveness of our model.