Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination of Gene Loss in the DNA Mismatch Repair Pathway and Its Mutational Consequences in a Fungal Phylum

The DNA mismatch repair (MMR) pathway corrects mismatched bases produced during DNA replication and is highly conserved across the tree of life, reflecting its fundamental importance for genome integrity. Loss of function in one or a few MMR genes can lead to increased mutation rates and microsatellite instability, as seen in some human cancers. While loss of MMR genes has been documented in the context of human disease and in hypermutant strains of pathogens, examples of entire species and species lineages that have experienced substantial MMR gene loss are lacking. We examined the genomes of 1,107 species in the fungal phylum Ascomycota for the presence of 52 genes known to be involved in the MMR pathway of fungi. We found that the median ascomycete genome contained 49 / 52 MMR genes. In contrast, four closely related species of obligate plant parasites from the powdery mildew genera Erysiphe and Blumeria, have lost between 5 and 21 MMR genes, including MLH3, EXO1, and DPB11. The lost genes span MMR functions, include genes that are conserved in all other ascomycetes, and loss of function of any of these genes alone has been previously linked to increased mutation rate. Consistent with the hypothesis that loss of these genes impairs MMR pathway function, we found that powdery mildew genomes with higher levels of MMR gene loss exhibit increased numbers of mononucleotide runs, longer microsatellites, accelerated sequence evolution, elevated mutational bias in the A|T direction, and decreased GC content. These results identify a striking example of macroevolutionary loss of multiple MMR pathway genes in a eukaryotic lineage, even though the mutational outcomes of these losses appear to resemble those associated with detrimental MMR dysfunction in other organisms. Significance Statement The DNA mismatch repair pathway corrects nucleotide base errors that occur during the replication of DNA; loss of these genes leads to cancer. We examined the conservation of the DNA mismatch repair pathway’s genes across more than 1,000 species in a fungal phylum and found a lineage of powdery mildews, a group of fungi that infect the leaves of plants, which have experienced extensive loss of multiple, otherwise highly conserved, genes. The genomes of these powdery mildews show elevated rates of diverse types of mutation, raising the hypothesis that these organisms have diversified while lacking genes thought to be essential for the accurate replication of DNA.
Spontaneous Visual Preference for Face-Like Stimuli Is Impaired in Newly-Hatched Domestic Chicks Exposed to Valproic Acid During Embryogenesis

One of the most fascinating properties of the human brain is the capacity of newborn babies to recognize and orient to faces and simple schematic face-like patterns since the first hours of life. A striking feature of these social orienting mechanisms is their transversal appearance in remarkably diverse vertebrate species. Similar to newborn babies, also non-human primates and domestic chicks have been shown to express orienting responses to faces and schematic face-like patterns. More importantly, existing studies have hypothesized that early disturbances of these mechanisms represent one of the earliest biomarkers of social deficits in autism spectrum disorders (ASD). Recent data suggest that newborns at high risk for the disorder express altered responses to schematic face-like configurations. Here we modeled ASD in domestic chicks using the anticonvulsant valproic acid (VPA), and tested the animals for their predisposed preference towards schematic face-like configuration stimuli. We found that VPA impairs the chicks’ preference responses to the social stimuli. Based on the results shown here and in previous studies, we propose the domestic chicks as elective animal models to study early-emerging neurobehavioural markers and to investigate the biological mechanisms underlying face processing deficits in ASD.
P5A-ATPase controls the ER translocation of Wnt in neuronal migration

Wnt family are conserved secretory proteins required for developmental patterning and tissue homeostasis. The mechanisms underlying intracellular maturation and intercellular signal transduction of Wnt proteins have been extensively studied. However, how Wnt is targeted to the endoplasmic reticulum (ER) for processing and secretion remains elusive. Here we report that CATP-8/P5A-ATPase directs neuronal migration non-cell autonomously in C. elegans by regulating EGL-20/Wnt biogenesis. CATP-8 functions as a translocase to translocate EGL-20/Wnt nascent polypeptide into the ER by interacting with the hydrophobic core region of EGL-20 signal sequence. Such regulation of Wnt biogenesis by P5A-ATPase is conserved in human cells. These findings reveal physiological roles of P5A-ATPase in neural development and identify Wnt proteins as direct substrates of P5A-ATPase for ER translocation.
Multipotent progenitors and hematopoietic stem cells arise independently during the endothelial to hematopoietic transition in the early mouse embryo

During embryogenesis, waves of hematopoietic progenitors develop from hemogenic endothelium (HE) prior to the emergence of self-renewing hematopoietic stem cells (HSC). Although previous studies have shown that yolk sac-derived erythromyeloid progenitors and HSC emerge from distinct populations of HE, it remains unknown whether the earliest lymphoid-competent progenitors, multipotent progenitors, and HSC originate from common HE. Here we demonstrate by clonal assays and single cell transcriptomics that rare HE with functional HSC potential in the early murine embryo are distinct from more abundant HE with multilineage hematopoietic potential that fail to generate HSC. Specifically, HSC-competent HE are characterized by expression of CXCR4 surface marker and by higher expression of genes tied to arterial programs regulating HSC dormancy and self-renewal. Together, these findings suggest a revised model of developmental hematopoiesis in which the initial populations of multipotent progenitors and HSC arise independently from HE with distinct phenotypic and transcriptional properties.
High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat

Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest Mexico over four consecutive years. Hyperspectral images were acquired through 27 airborne flight campaigns. At harvest, GY, biomass, GNC and N output were determined. Spectral exploratory analysis was used to identify the best wavelength combinations, the most suitable vegetation indices (VIs) and the best growth stages to assess the agronomic variables. The relationship between the spectral information and the agronomic measurements was evaluated by the coefficient of determination (R2) and the root mean square error (RMSE). The ability of the indices to guide fertilizer recommendation was assessed through an error analysis based on the N sufficiency index. GY was better assessed from the end of flowering to the early milk stage by VIs based on the combination of bands from near infrared radiation/visible and from near infrared radiation/red-edge regions (R2 > 0.6; RMSE < 700 kg ha−1). N output was efficiently assessed by a combination of bands from near infrared radiation/red-edge at booting (R2 > 0.7; RMSE < 9 kg N ha−1). The GNC was better estimated by VIs combining bands in near infrared radiation/red-edge at early milk, but with great variability among the years studied. Some VIs were promising for guiding fertilizer recommendation for increasing GNC, but there was not a single index providing reliable recommendations every year. This study highlights the potential of remote sensing imagery to assess GY and N output in spring wheat, but the identification of GNC responsive sites needs to be improved.
Numerical Modelling of Liquid Metal Flows Interacting with Strongly Inhomogeneous Magnetic Fields

﻿Das Ziel der vorliegenden Arbeit ist die Erweiterung der Lorentzkraft-Anemometrie (LKA oder LFV) um die Geschwindigkeitsmessung mit kleinen Permanentmagneten, die sich durch stark inhomogene Magnetfelder auszeichnen. Trotz ihres unendlich ausgedehnten Feldes liefern die kleinen Magnete Informationen ueber das stroemungsfeld in ihrer unmittelbaren Umgebung (Einflussbereich). Die Untersuchung stuetzt sich hierbei auf die numerische Modellierung der magnetohydrodynamischen Interaktion des Magnetwuerfels mit einer Metallstroemung, welche durch einen Kanal mit quadratischem Querschnitt fliest. Die bei der Wechselwirkung entstehende Lorentzkraft wird durch eine Kombination der Simulationsprogramme COMSOL und FLUENT ermittelt. Der den Programmen zugrundeliegende Code wird in dieser Arbeit verifiziert und durch Laborexperimente bestaetigt. Anschliesend wird mit einem RANS (Reynolds-Averaged Navier Stokes)-Turbulenzmodell der Einfluss verschiedener Geometrien sowie verschiedener elektromagnetischer Kontrollparameter auf die Lorentzkraft geprueft. Die Untersuchungen beziehen sich hierbei auf drei spezielle Stroemungsregimes. Das erste ist das kinematische Regime, in dem die Rueckwirkung der Lorentzkraft auf die Stroemung vernachlaessigbar ist. In diesem Regime wurde eine universelle Abhaengigkeit der Kraft vom Magnetabstand gefunden. Die beiden anderen Regimes sind dynamische Regimes bei sehr niedrigen sowie hohen Reynolds-Zahlen. In diesen veraendert der Magnet das Stroemungsfeld. Die dynamischen Untersuchungen zeigen eine gute Uebereinstimmung mit den durchgefuehrten Experimenten. Der letzte Teil der vorliegenden Arbeit beschaeftigt sich mit LES (Large Eddy Simulations): (i) Es werden die Fehler in den verschiedenen LES-Modellen analysiert und mit denen der unteraufgeloesten DNS (direkten numerischen Simulation) verglichen. Die numerische Dissipation zeigt ein aehnliches Verhalten wie die subskaligen Spannungen, was den Einsatz spezifischer LES-Modelle ueberfluessig macht. (ii) Die Korrelation zwischen den Fluktuationen der Lorentzkraft und der Stroemungsgeschwindigkeit legt eine starke Kausalitaet zwischen den beiden Groesen nahe. Dies koennte fuer zukuenftige Arbeiten auf dem Gebiet der lokalen Geschwindigkeitsmessung mittels Lorentzkraft-Anemometrie relevant sein.
Merkle2: A Low-Latency Transparency Log System

Transparency logs are designed to help users audit untrusted servers. For example, Certificate Transparency (CT) enables users to detect when a compromised Certificate Authority (CA) has issued a fake certificate. Practical state-of-the-art transparency log systems, however, suffer from high monitoring costs when used for low-latency applications. To reduce monitoring costs, such systems often require users to wait an hour or more for their updates to take effect, inhibiting low-latency applications. We propose Merkle2, a transparency log system that supports both efficient monitoring and low-latency updates. To achieve this goal, we construct a new multi-dimensional, authenticated data structure that nests two types of Merkle trees, hence the name of our system, Merkle2. Using this data structure, we then design a transparency log system with efficient monitoring and lookup protocols that enables low-latency updates. In particular, all the operations in Merkle2 are independent of update intervals and are (poly)logarithmic to the number of entries in the log. Merkle2 not only has excellent asymptotics when compared to prior work, but is also efficient in practice. Our evaluation shows that Merkle2 propagates updates in as little as 1 second and can support 100× more users than state-of-the-art transparency logs.
ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.
High-Dimensional Satellite Image Compositing and Statistics for Enhanced Irrigated Crop Mapping

Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of high-dimensional geometric median composites (geomedians) and high-dimensional statistics of time-series may simplify classification models and enhance accuracy. High-dimensional statistics for temporal variation, such as the spectral median absolute deviation, indicate spectral variability within a period contributing to a geomedian. The Ord River Irrigation Area was used to validate Digital Earth Australia’s annual geomedian and temporal variation products. Geomedian composites and the spectral median absolute deviation were then calculated on Sentinel-2 images for three smallholder irrigation schemes in Matabeleland, Zimbabwe, none of which were classified as areas equipped for irrigation in AQUASTAT’s Global Map of Irrigated Areas. Supervised random forest classification was applied to all sites. For the three Matabeleland sites, the average Kappa coefficient was 0.87 and overall accuracy was 95.9% on validation data. This compared with 0.12 and 77.2%, respectively, for the Food and Agriculture Organisation’s Water Productivity through Open access of Remotely sensed derived data (WaPOR) land use classification map. The spectral median absolute deviation was ranked among the most important variables across all models based on mean decrease in accuracy. Change detection capacity also means the spectral median absolute deviation has some advantages for cropland mapping over indices such as the Normalized Difference Vegetation Index. The method demonstrated shows potential to be deployed across countries and regions where smallholder irrigation schemes account for large proportions of irrigated area.
Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada

Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data.
Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.
The halting problem and security's language-theoretic approach: Praise and criticism from a technical historian

The term ‘Halting Problem’ arguably refers to computer science’s most celebrated impossibility result and to the core notion underlying the language-theoretic approach to security. Computer professionals often ignore the Halting Problem however. In retrospect, this is not too surprising given that several advocates of computability theory implicitly follow Christopher Strachey’s alleged 1965 proof of his Halting Problem (which is about executable – i.e., hackable – programs) rather than Martin Davis’s correct 1958 version or his 1994 account (each of which is solely about mathematical objects). For the sake of conceptual clarity, particularly for researchers pursuing a coherent science of cybersecurity, I will scrutinize Strachey’s 1965 line of reasoning – which is widespread today – both from a charitable, historical angle and from a critical, engineering perspective.
Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.
Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.