Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenoflow: Portable Workflow-based Phenotype Definitions

Clinical phenotyping is an effective way to identify patients with particular characteristics within a population. In order to enhance the portability of a phenotype, it is often defined abstractly, with users expected to realise the phenotype computationally before executing it against a local dataset. However, complex definitions, which also provide little information about how best to implement a phenotype in practice, mean that this process is often not easy. To address this issue, we propose a new multi-layer model for a phenotype definition, which is realised as a workflow, and can be combined with different implementation units in order to produce a computable form. A novel authoring architecture, Phenoflow, supports the generation of these structured definitions. To illustrate the utility of our approach, we re-author a diabetes phenotype definition, and then compare its portability to the original definition, in the context of a population of 26,406 patients at Northwestern University.
Zak's Phase in Non-Symmetric One-Dimensional Crystals

In this work, we derive some analytical properties of Berry’s phase in one-dimensional quantum and classical crystals, also named Zak’s phase, when computed with a Fourier basis. We show that Zak’s phase can be divided in two terms: a global phase required to make the Bloch wave periodic in the Brillouin zone and an internal phase which measures the relative delay of the different Fourier terms within the Brillouin zone. While the former phase is dependent on the origin of coordinates of the unit cell, the latter is independent of it, so that it can be interpreted as an internal property of the band itself. We show that this internal phase is always zero for a symmetric crystal while it can take any value when this symmetry is broken, showing therefore that it can be interpreted as a measure of the assymetry of the band. Since for a symmetric crystal Zak’s phase is entirely determined by the global part, we show that this can be easily calculated by means of the parity of the Fourier terms at the center and edge of the Brillouin zone, being therefore unnecessary the integration of the modes through the unit cell and the entire Brillouin zone. We provide numerical examples analyzing the internal part for both electronic and classical waves (acoustic or photonic). We analyze the weakest electronic potential capable of presenting asymmetry, as well as the doubleDirac delta potential, and in both examples it is found that the internal phase varies continuously as a function of a symmetry-control parameter, but it is zero when the crystal is symmetric. For classical waves, the layered material is analyzed. Although Zak’s phase has been mainly studied in connection with the existence of edge states in finite crystals, we consider that the study of the internal phase can be more relevant to understand bulk properties of quantum and classical crystals.
An asymptotic analysis of separating pointlike and $C^{\beta}$-curvelike singularities

In this paper, we present a theoretical analysis of separating images consisting of pointlike and $C^{ \beta}$-curvelike structures, where $\beta \in (1,2] $. Our approach is based on $l_1$-minimization, in which the sparsity of the desired solution is exploited by two sparse representation systems. It is well known that for such components wavelets provide an optimally sparse representation for point singularities, whereas $\alpha$-shearlet type with $\alpha$=$\frac{2}{\beta}$ might be best adapted to the $C^{\beta}$-curvilinear singularities. In our analysis, we first propose a reconstruction framework with a theoretical guarantee on convergence, which is extended to use general frames instead of Parseval frames. We then construct a dual pair of bandlimited $\alpha$-shearlets which possesses a good time and frequency localization. Finally, we apply the result to derive an asymptotic accuracy of the reconstructions. In addition, we show that it is possible to separate these two components as long as $\alpha<2$, i.e., bandlimited $\alpha$-shearlets which range from wavelet to shearlet type do not coincide with wavelets in the sense of isotropic fashion.
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography

Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised learning (SSL) has shown its effectiveness in medical image analysis with limited training samples. However, the network model sometimes cannot be well pre-trained in the conventional SSL framework due to the limitation of the pretext task and fine-tuning mechanism. In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD. In particular, a new gray-scale image mapping (GSIM) is designed as the pretext task, which embeds the class label information of mammograms into the image restoration task to improve discriminative feature representation. The proposed TSBN then innovatively integrates different network architecture, including the image restoration network and the classification network, into a unified SSL framework. It jointly trains the bi-channel network models and collaboratively transfers the knowledge from the pretext task network to the downstream task network with improved diagnostic accuracy. The proposed TSBN is evaluated on a public INbreast mammogram dataset. The experimental results indicate that it outperforms the conventional SSL and multi-task learning algorithms for diagnosis of breast cancers with limited samples.
The impact of environmental turbulence on the strategic decision-making process in Irish quantity surveying (QS) professional service firms (PSFs)

Abstract Construction firms operate within a business environment characterized by uncertainty and a lack of predictability, increasing the complexity of strategic decision-making. Construction contractor firms’ strategic response to environmental turbulence is appropriately documented but evidence regarding Construction Professional Service Firms (CPSF’S) remains scarce. CPSF’s are characteristically different from contractor organizations due to the intangibility of services and high knowledge intensity. The purpose of the study is to ascertain the impact of environmental turbulence on strategic decision-making process characteristics in CPSF’s, specifically Irish Quantity Surveying (QS) practices. Using a mixed-methods research strategy, data collected over two dissimilar stages on the economic cycle is presented. A comparative analysis over time exposes the varied impact of environmental turbulence on strategy process characteristics, however, a notable shift in the strategic choice is evident. An emergent approach to strategizing coupled with a move from written strategic plans is evident, while competitor analysis remains superficial. A taxonomy of the strategic decision-making process is derived from the empirical data which uniquely highlights the role of path dependence for CPSFs. The paper provides theoretical advancement in the discipline of CPSF strategy and also identifies a crucial component for consideration in driving transformational change required across the sector.
Tactile stimulus is essential to increase motivation for touch interaction in virtual environment

This paper reports the effectiveness of a tactile stimulus in a virtual environment to increase people's motivations during a monotonous task by comparing a touch with only visual stimuli and another with both visual and tactile stimuli. Although touch interaction with robots showed various positive effects such as improved motivation, visual and haptic stimuli were not separated due to the form of the touch in physical environments. Virtual environments enable us to investigate such effects by separating the modality of touch: a visual-only-touch and a visual-tactile touch. We experimented in a virtual environment where participants did a monotonous task after experiencing either a visual-only-touch or a visual-tactile-touch by an agent to compare them in the context of motivation improvements and the perceived impressions. The experimental results showed that a visual-tactile touch significantly increases the motivations of the participants for monotonous tasks. On the other hand, their likability of the agents is not significantly changed by the touch modalities. GRAPHICAL ABSTRACT
Perspective: Stabilizing the Microbiome Skin-Gut-Brain Axis with Natural Plant Botanical Ingredients in Cosmetics

The microbiome of the gut and skin have recently been shown to have a strong connection through the host immune system. Various skin and gut inflammatory conditions are interrelated and connected through intricate immune pathways that affect the host barrier functions both in the skin and the gut. Microbiome ‘dysbiosis’ of the skin and gut leads to various alterations in host immune pathways that can alter the barrier and lead to disease. In this perspective article, we discuss the role of plant botanicals in cosmetics and their effect on the skin-gut-brain axis.
Hydroxyl-Containing Aromatic Polyimides for Carbon Dioxide Removal from Natural Gas

Hydroxyl-Containing Aromatic Polyimides for Carbon Dioxide Removal from Natural Gas Nasser Yahya Alaslai King Abdullah University of Science and Technology, 2017 Advisor: Ingo Pinnau Natural gas is among the most dominant resources to provide energy supplies and Saudi Arabia ranks among the top 5 producers worldwide. However, prior to use of methane, natural gas has to be treated to remove other feed gas components, such as H2O, CO2, H2S, N2 and C2+ hydrocarbons. Most NG fields in KSA contain about 10 mol% carbon dioxide that has to be reduced to less than 2 mol% for pipeline delivery. The conventional unit operations for natural gas separations, that is, molecular sieves, amine absorption, cryogenic distillation, and turbo expansion exhibit some disadvantages in terms of economics, operational flexibility or system footprint. One of the most attractive alternative is membrane technology in either standaloneor hybrid system configuration. Currently, the only two membrane materials used in industrial natural gas applications are cellulose acetate and polyimide, which have moderate permeability and fairly low selectivity when tested under realistic industrial conditions. The goal for future research is to develop unique polymeric membranes, which can at least partially replace conventional gas processing in future natural gas projects. This will support global economics and specifically the economy of Saudi Arabia. Newly developed polymeric materials must meet certain criteria to be used on a commercial scale. These criteria include: (i) high permeability and selectivity, (ii) processability into thin
Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

Abstract Land use and land cover (LULC) mapping is a basic research topic in geography. In deep-learning (DL)-based LULC mapping, there are primarily the following issues: training and testing samples for DL are typically annotated by indoor visual interpretation without field surveys; remotely sensed scene classification based on DL typically lacks fine geometric boundaries for ground objects; historical big data (HBD) (e.g., vector data) are underutilized in DL; studies of large-scale remote sensing mapping (LSRSM) using DL are rare. To solve above issues, this paper proposes an object-oriented (i.e., polygon-based) and DL-based (OODLB) image classification method assisted by HBD for LSRSM to serve for monitoring the soil erosion and water loss (SEWL) in the Yangtze River Basin that includes the following steps: (1) using HBD and OpenStreetMap data, ground objects are vectorized; (2) a remote sensing interpretation key database is established by field surveys and data augmentation; (3) object-oriented (i.e., polygon-based) and Inception-ResNet-V2-based LULC mapping is performed; (4) DL-based classification results are updated by man-machine mutual verification. The experimental results of one county of the Yangtze River Basin show state-of-the-art performance with an overall accuracy of 90.20% in comparison of 75.60% for eCognition. It provides an excellent framework for OODLB large-scene mapping.
Erfassung geometrischer Daten des Schmelzbades zur Regelung eines WAAM-Prozesses

Zusammenfassung Als Möglichkeit individuelle oder komplexe Produkte aus unterschiedlichen Materialien automatisiert, schnell und kostengünstig zu fertigen, gewinnt der 3D-Druck immer mehr an Bedeutung. Unter 3D-Druck werden mehre Verfahren zusammengefasst. Ein weit verbreitetes Verfahren ist das additive Aufbringen von Kunststoffschichten, um beispielsweise komplexe Modelle zu „drucken“. Dabei wird das erzeugte Produkt nicht durch subtraktive Fertigungsverfahren, wie Drehen oder Fräsen, erzeugt. Eine Möglichkeit Metalle additiv zu fertigen ist das „Wire and Arc Additive Manufacturing (WAAM)“ zur additiven Erzeugung einer Struktur. Die Anwendung von Lichtbogenschweißen als additives Verfahren stellt besondere Anforderungen an die Mess- und Regelungstechnik. Hier wird ein Messsystem vorgestellt, mit dem die Breite des Schmelzbades innerhalb einer Frequenz von 40 Hz bei einer Latenz von 25 ms erfasst wird und an einen Regler weitergegeben wird.
Transient DUX4 expression induces blastomere-like expression program that is marked by SLC34A2

DUX4 has recently been recognized as a key regulator in human embryonic genome activation (EGA). The exact role of DUX4 in human embryo is still elusive, partly due to the cytotoxicity of persistent DUX4 expression in cellular models. We report here that a transient DUX4 expression in human embryonic stem cells (hESCs) retains cell viability while inducing an EGA-like expression program in a subpopulation of the cells. These cells showed resemblance to 8-cell stage blastomeres and were thus named induced blastomere-like (iBM) cells. Trajectory inference from the single-cell RNA-seq data suggested that the expression profile of these cells progressed in a manner similar to the morula to blastocyst transition in human embryo. Finally, viable iBM cells could be enriched using an antibody against NaPi2b (SLC34A2), paving the way for further experimental approaches. The iBM cells can become a powerful tool to model transcriptional dynamics and regulation during early human embryogenesis.
An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.
Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The ’curse of dimensionality’ phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimen-sion reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.
Comparative Analysis on the Estimation of Diurnal Solar-Induced Chlorophyll Fluorescence Dynamics for a Subtropical Evergreen Coniferous Forest

Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF687: R2 = 0.53, p < 0.001; SIF760: R2 = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.
Real-Time Estimation of GPS-BDS Inter-System Biases: An Improved Particle Swarm Optimization Algorithm

The restart of the receiver will lead to the change in the non-overlapping frequency inter-system biases (ISB), which will make it difficult to apply the tightly combined RTK method of pre-calibrating ISB to the actual scene. Particle swarm optimization (PSO) algorithm can be used to estimate the fractional part of the inter-system phase bias (F-ISPB) in real time, which is not affected by the receiver restart. However, the standard PSO can easily fall into local optimum and cannot accurately estimate the value of F-ISPB. In this contribution, based on the characteristics of F-ISPB, we propose an improved PSO with adaptive search space and elite reservation strategy to estimate the F-ISPB in real time. When the value of F-ISPB is close to the boundary of the search space, the improved PSO will transform the search space so that F-ISPB will be located near the central region of the new search space, which will greatly reduce the situation of the standard PSO easily falling into local optimum. Since F-ISPB is very stable, an elite retention strategy will help us to estimate F-ISPB faster and more accurately. Three sets of short baseline static data were selected for testing. The results show that the inter-system differenced model based on the improved PSO has a higher ambiguity fixed rate and positioning accuracy than the inter-system differenced model based on the standard PSO and the classical intra-system differenced model, and the fewer the number of satellites, the more obvious the effect.
From Alpha to Zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and sub-types of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy — the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (< 1% of the sequences) Beta (South Africa), Epsilon (California), Gamma and Zeta (Brazil) variants in the GISAID dataset. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large datasets.