Miracle-dz/UniFAR-FTUs huggingface dataset:
StereoSiTE: A framework to spatially and quantitatively profile the cellular neighborhood organized iTME

With emerging of Spatial Transcriptomics (ST) technology, a powerful algorithmic framework to quantitatively evaluate the active cell-cell interactions in the bio-function associated iTME unit will pave the ways to understand the mechanism underlying tumor biology. This study provides the StereoSiTE incorporating open source bioinformatics tools with the self-developed algorithm, SCII, to dissect a cellular neighborhood (CN) organized iTME based on cellular compositions, and to accurately infer the functional cell-cell communications with quantitatively defined interaction intensity in ST data. We applied StereoSiTE to deeply decode ST data of the xenograft models receiving immunoagonist. Results demonstrated that the neutrophils dominated CN5 might attribute to iTME remodeling after treatment. To be noted, SCII analyzed the spatially resolved interaction intensity inferring a neutrophil leading communication network which was proved to actively function by analysis of Transcriptional Factor Regulon and Protein-Protein Interaction. Altogether, StereoSiTE is a promising framework for ST data to spatially reveal tumoribiology mechanisms.
A clinically useful and biologically informative genomic classifier for papillary thyroid cancer

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.
Object detection and measurements in medical images via geodesic deformable contours

A novel scheme for performing detection and measurements in medical images is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric is defined by the image content. Measurements are performed after the object is detected. Due to the high accuracy achieved by the proposed geodesic approach, it is natural to use it to compute area or length of the detected object, which are of extreme value for diagnosis. Open curves with fix boundaries are computed as well. This addition to the deformable model adds flexibility, allowing the user to choose guiding points in the image or to select regions for measurements. Experimental results of applying the scheme to real medical images demonstrate its potential. The results may be extended to 3D object segmentation as well.
A model explaining environmental stiffness-dependent migration of fibroblasts with a focus on maturation of intracellular structures

Cell migration is fundamental to many biological processes, while it remains elusive how cells modulate their migration upon different environmental stiffness. In this work, we focus on the structural maturity of actin stress fibers to explain the substrate stiffness-dependent emergence of different cell migration velocity. We demonstrate that fibroblasts migrate longer distances on softer elastic substrates, and the distance is increased by lowering the myosin-driven contractile force. Stress fibers, the major intracellular structure to generate and sustain contractile forces, were found to be less mature in structure on soft substrate than on stiff substrate. Based on these experimental results, we present a minimal mathematical model to capture the salient features of how the substrate stiffness alters the migration velocity. Specifically, the ability of cells to generate large contractile forces is limited on soft substrate according to the Hooke’s law. The inverse relationship between the cellular force and migration velocity is described by the Hill’s muscle equation. These mathematical descriptions suggest that the migration velocity is raised on softer substrate where cells exert a lower magnitude of contractile forces. Cells undergoing faster movement make stress fibers less mature in structure as mathematically described by the maturation model, thereby limiting the ability to sustain the force and in turn allowing for consistent increase in cell migration velocity on soft substrate again according to the Hooke’s law and Hill’s muscle equation, respectively. Thus, our model, reproducing the basic trend of the experimental results, provides insights into the mechanisms of environmental cue-dependent migratory behavior of cells.
Diversity and evolution of cerebellar folding in mammals

The process of brain folding is thought to play an important role in the development and organisation of the cerebrum and the cerebellum. The study of cerebellar folding is challenging due to the small size and abundance of its folia. In consequence, little is known about its anatomical diversity and evolution. We constituted an open collection of histological data from 56 mammalian species and manually segmented the cerebrum and the cerebellum. We developed methods to measure the geometry of cerebellar folia and to estimate the thickness of the molecular layer. We used phylogenetic comparative methods to study the diversity and evolution of cerebellar folding and its relationship with the anatomy of the cerebrum. Our results show that the evolution of cerebellar and cerebral anatomy follows a stabilising selection process. Ancestral estimations indicate that size and folding of the cerebrum and cerebellum increase and decrease concertedly through evolution. Our analyses confirm the strong correlation between cerebral and cerebellar volumes across species, and show that large cerebella are disproportionately more folded than smaller ones. Compared with the extreme variations in cerebellar surface area, folial wavelength and molecular layer thickness varied only slightly, showing a much smaller increase in the larger cerebella. These findings provide new insights into the diversity and evolution of cerebellar folding and its potential influence on brain organisation across species.
High-sensitivity high-resolution refractometry with twin turn-around-point long-period gratings in a photonic crystal fiber

We present the numerical design of a turn-around-point long-period grating in a photonic crystal fiber (TAP PCFLPG) for high-sensitivity, high-resolution refractometry of gases. High refractive-index sensitivity is achieved by operating LPGs in the vicinity of the dispersion turning point of the optimized PCF. Despite the resonant wavelength of the optimized PCF-LPG is highly sensitive to the refractive index of analytes, its large shifts could be monitored with a reduced resolution because the resonance dip in the TAP LPG transmission spectrum is broad. To provide also high refractive-index resolution, twin TAP-LPGs have been proposed to be used as 3 dB broadband mode converters in the interferometric scheme. The first LPG couples a portion of the light in the core mode to a forward propagating cladding mode and the second LPG couples the light back to the core mode. The resulting interference fringes within the envelope of LPG attenuation dip provide a means for higher resolution sensing. Instead of monitoring the wavelength shift as a result of a refractive index change, the transmission spectrum can also be analyzed in terms of the shift in phase suffered by the fringe pattern. This is a more accurate way of interpreting the interferometric sensor measurements, since the phase shift is a direct result of an analyte-induced change in optical path length.
A versatile semiautomated image analysis workflow for time-lapsed camera trap image classification

Camera trap arrays can generate thousands to millions of images that require exorbitant time and effort to classify and annotate by trained observers. Computer vision has evolved as an automated alternative to manual classification. The most popular computer vision solution is the supervised Machine Learning technique, which uses labeled images to train automated classification algorithms. We propose a multi-step semi-automated workflow that consists of (1) identifying and separating bad-from good-quality images, (2) parsing good images into animals, humans, vehicles, and empty, and (3) cropping animals from images and classifying them into species for manual inspection. We trained, validated, and evaluated this approach using 548,627 images from 46 cameras in two regions of the Arctic (northeastern Norway, and Yamal Peninsula, Russia). We obtained an accuracy of 0.959 for all three steps combined with the complete year test data set at Varanger and 0.922 at Yamal, reducing the number of images that required manual inspection to 7.9% of the original set from Varanger and 3.2% from Yamal. Researchers can modify this multi-step process to meet their specific needs for monitoring and surveying wildlife, providing greater flexibility than current options available for image classification.
Acidophilic nitrification alleviates phosphorus deficiency in hydroponics using biogas digestates

Biogas digestates can be applied to hydroponics via nitrification. However, the low solubility of phosphorus in digestates can cause phosphorus deficiency in plants. Here, we hypothesized that acidophilic nitrification might prevent this deficiency by dissolving phosphorus in the digestate. Acidophilic and neutrophilic nitrification were conducted at a pH of 3.27 and 6.25 using biogas digestates from food wastes. Acidophilic nitrification dissolved about 3.5 times more phosphorus than neutrophilic nitrification, but the increased acidity also reduced the nitrification rate, resulting in residual ammonium. We then grew lettuce hydroponically with filtrates of these digestates. The growth performance suggested that the increased phosphorus improved growth and that the residual ammonium did not inhibit it. Acidophilic nitrification was shown to be effective for use in hydroponics, particularly to alleviate phosphorus deficiency. These findings should provide new insights into resource recycling, which is essential in both urban and space environments. Graphical abstract
Alternative Polyadenylation alters UBASH3B-ZNF652 competition to involve in Triple-Negative Breast Cancer

Alternative polyadenylation (APA) is an RNA-processing mechanism which may affect gene expression. Its involvements in cancers including the triple-negative breast cancer (TNBC) have been demonstrated in previous studies. Given the lack of biomarkers for TNBC, in this study, we were committed to finding novel biomarkers related to TNBC prognosis from the perspective of APA-mediated microRNA regulation. For this goal, raw bulk RNA sequencing data was collected for the identification of nine survival-related APA events by multivariate Cox regression analysis and forward-selected Likelihood Ratio Test. They showed good prognosis ability on TNBC risk. Of them, the APA event in UBASH3B was proposed as a novel potential biomarker. This event disturbed microRNA regulation on its host gene and other competing tumor genes to possibly involve in the pathogenesis of TNBC.
Mapping Tree Cover Expansion in Montana, U.S.A. Rangelands Using High-Resolution Historical Aerial Imagery

Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts to ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 ± 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was greater than 91%, but producer accuracy was lower than user accuracy (0.60 vs. 0.88). Nonetheless, the omission errors were not spatially correlated, indicating that the method is reliable for estimating the spatial extent of tree cover expansion. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.
Absorbance spectroscopy and identification of valence subband transitions in type-II InAs/GaSb superlattices

We describe the molecular-beam epitaxy growth, as well as both the structural and optical characterization of a set of InAs/GaSb type-II strained-layer superlattice samples, in which the GaSb layer thickness is systematically increased. Absorbance spectroscopy measurements show well-defined features associated with transitions from the various valence subbands to the lowest conduction subband, and also a significant blueshift of the band edge when the GaSb layers thickness is increased. Empirical pseudopotential method calculations are shown to successfully predict the blueshift and help identify the higher-energy transitions.
Bayesian Analysis of Zero-Inflated Distributions

Abstract In this paper zero-inflated distributions (ZID) are studied from the Bayesian point of view using the data augmentation algorithm. This type of discrete model arises in count data with excess of zeros. The zero-inflated Poisson distribution (ZIP) and an illustrative example via MCMC algorithm are considered.
ANALYSIS OF THE PICO DOS DIAS SURVEY HERBIG Ae/Be CANDIDATES

A large sample of Herbig Ae/Be (HAeBe) candidates, distributed in different Galactic regions south to declination +30°, were identified by the Pico dos Dias Survey (a search for young stellar objects based on IRAS colors). Most of the candidates are nearby or associated with star-forming clouds, but several others are considered isolated objects. Aiming to verify the young nature of 93 HAeBe candidates, we searched for additional information that could be useful to confirm if they are pre-main-sequence (PMS) stars or evolved objects, which coincidentally show similar IRAS colors. By adopting a spectral index that is related to the amount of infrared excess and the shape of the spectral energy distribution, we have classified the sample according to three groups, which are analyzed on the basis of (1) circumstellar luminosity; (2) spatial distribution; (3) optical polarization; (4) near-infrared colors; (5) stellar parameters (mass, age, effective temperature); and (5) intensity of emission lines. Our analysis indicates that only 76% of the studied sample, mainly the group with intermediate to low levels of circumstellar emission, can be more confidently considered PMS stars. The nature of the remaining stars, which are in the other group that contains the highest levels of infrared excess, remains to be confirmed. They share the same characteristics of evolved objects, requiring complementary studies in order to correctly classify them. At least seven objects show characteristics typical of post-asymptotic giant branch or proto-planetary nebulae.
Traits: Composing Classes from Behavioral Building Blocks

Inheritance is well-known and accepted as a fundamental mechanism for reuse in object-oriented languages. Unfortunately, the main variants — single inheritance, multiple inheritance, and mixin inheritance — all suffer from conceptual and practical problems related to software reuse and robustness with respect to changes. In a first part of this thesis, we identify and illustrate these problems. To overcome these problems, we then present trai s, a simple compositional model that extends single inheritance. A trait is essentially a (parameterized) set of methods; it serves as a behavioral building block for classes and is the primitive unit of code reuse. We develop a formal model of traits that establishes how traits can be composed to form other traits or classes, and we describe how we implemented traits in Squeak Smalltalk by bootstrapping a new language kernel. We present our experimental validation in which we apply traits to refactor parts of the Smalltalk kernel and library, and we develop a programming methodology around the usage of traits and the trait browser, the tool that we implemented to take full advantage of the availability of traits in the Squeak programming environment.
Spectral Properties in Modon Stability Theory

Spectral properties of an invariant functional, denoted by H, for the linear stability equation associated with the modon, or solitary drift vortex, solutions of the quasi‐geostrophic equivalent barotropic potential vorticity, or Charney–Hasegawa–Mima (CHM), equation are investigated. It is shown that H, which is the only known quadratic invariant in modon stability theory, is identical in form to the second variation of a “Benjamin‐like” variational principle for solitary vortices. However, such a principle does not exist for the modon. The discrete spectrum of the “form operator” in H contains two simple negative eigenvalues and the simple zero eigenvalue. For the leftward‐traveling solution there are only a finite number of positive eigenvalues. For the rightward‐traveling solution, there are a countable infinity of positive eigenvalues. A sharp lower bound on the spectrum, for both the rightward‐ and leftward‐traveling solutions, and a sharp upper bound for the leftward traveling solution, is determined. For the leftward‐traveling solutions, the eigenfunctions span a finite‐dimensional vector space and are orthogonal with respect to an inner product which is valid for all of L2. For the rightward‐traveling solutions, the eigenfunctions span an infinite‐dimensional Hilbert space, but are orthogonal with respect to an inner product, which is not valid for all of L2.
Surface roughness effects on the turbulence statistics in a low Reynolds number channel flow

A high-resolution particle image velocimetry was used to characterize a low Reynolds number turbulent flow in a channel. Experiments were conducted over a sand grain-coated surface of large relative roughness, and the results were compared with measurements over a smooth surface. The roughness perturbation significantly modified the outer layer. Even though the streamwise Reynolds stress shows less sensitivity in the outer layer to the boundary condition, significant enhancements were observed in the wall-normal Reynolds stress and the Reynolds shear stress. These modifications were considered as footprints of the larger-scale eddies transporting intense wall-normal motions away from the rough wall. A quadrant decomposition shows that strong and more frequent ejections are responsible for the larger values of the mean Reynolds shear stress over the rough wall. The results also indicate that spanwise vortex cores with mean vorticity of the same sign as the mean shear are the dominant smaller-scale vortical structures over the smooth and rough walls. A linear stochastic estimation-based analysis shows that the average larger-scale structure associated with these vortices is a shear layer that strongly connects the outer layer flow to the near-wall flow. A proper orthogonal decomposition of the flow suggests that the large-scale eddy is more energetic for the rough wall, and contributes more significantly to the resolved turbulent kinetic energy and the Reynolds shear stress than the smooth wall.
Learning Controllable Representations for Image Synthesis

In this thesis, our focus is learning a controllable representation and applying the learned controllable feature representation on images synthesis, video generation, and even 3D reconstruction. We propose different methods to disentangle the feature representation in neural network and analyze the challenges in disentanglement such as reference ambiguity and shortcut problem when using the weak label. We use the disentangled feature representation to transfer attributes between images such as exchanging hairstyle between two face images. Furthermore, we study the problem of how another type of feature, sketch, works in a neural network. The sketch can provide shape and contour of an object such as the silhouette of the side-view face. We leverage the silhouette constraint to improve the 3D face reconstruction from 2D images. The sketch can also provide the moving directions of one object, thus we investigate how one can manipulate the object to follow the trajectory provided by a user sketch. We propose a method to automatically generate video clips from a single image input using the sketch as motion and trajectory guidance to animate the object in that image. We demonstrate the efficiency of our approaches on several synthetic and real datasets.
Heterogeneous Consumers, Segmented Asset Markets, and the Real Effects of Monetary Policy

This article proposes a novel mechanism by which changes in the distribution of money holdings have real aggregate effects. I develop a flexible-price model of segmented asset markets in which monetary policy influences the aggregate demand elasticity via heterogenous money holdings. Because varieties of consumption bundles are purchased sequentially, newly injected money disseminates slowly throughout the economy via second-round effects. The model predicts a short-term inflation-output trade-off, a liquidity effect, countercyclical markups, and pro-cyclical wages after monetary shocks. Among other correlations of financial variables, it also reproduces the empirical, negative relationship between changes in the money supply and markups.
Einstein observations of EF Eridani (2A 0311-227) - The textbook example of AM Herculis-type systems

Hard and soft X-ray spectra of the AM Herculis star EF Eridani obtained with the Einstein Observatory are found to be fitted by a thermal bremsstrahlung spectrum with kT of about 20 keV, and a blackbody spectrum with kT of between 16 and about 33 eV, respectively. EF Eri is shown to behave almost exactly as predicted by simple theory, with its soft X-ray luminosity not exceeding the hard X-ray luminosity by more than a factor of three, and both spectra originating from the same active pole of the accreting white dwarf. The data are consistent with a model involving an accretion stream penetrating deep into the magnetosphere of the white dwarf, such that accretion occurs along nonpolar field lines, and an 'X-ray auroral oval' emission is formed on the surface of the white dwarf. 52 references.