Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of a pentose as a cytosine nucleobase modification in Shewanella phage Thanatos-1 genomic DNA mediating enhanced resistance towards host restriction systems

Co-evolution of bacterial defense systems and phage counter defense mechanisms has resulted in an intricate biological interplay between bacteriophages and their prey. To evade nuclease-based mechanisms targeting the DNA, various bacteriophages modify their nucleobases, which impedes or even inhibits recognition by endonucleases. We found that Shewanella phage Thanatos-1 DNA is insensitive to multiple restriction enzymes and, partially, also to Cas I-Fv and Cas9 cleavage. Furthermore, the phage genome shows strongly impaired basecalling with nanopore sequencing. We characterised the phage adenine methyltransferase TH1_126 in methylase-free E. coli ER3413 and derived and confirmed its recognition motif 5’-ATC-3’. Moreover, the data pointed to an additional, much more substantial nucleobase modification. Using LC-MS, we identified a deoxypentose of unknown configuration attached to cytosine as a yet undiscovered phage DNA modification, which is present in Thanatos-1 genomic DNA, likely mediates the observed resistance to restriction endonucleases, as well as a strong reduction in Cas nuclease activity. To elucidate the underlying enzyme functions, we determined structural homologs of Thanatos-1 proteins among known glycosyltransferase folds and experimentally proved a UDP-xylose pyrophosphorylase function of phage protein TH1_063 by in vitro enzyme assays.
Computation or Weight Adaptation? Rethinking the Role of Plasticity in Learning

The human brain is an adaptive learning system that can generalize to new tasks and unfamiliar environments. The traditional view is that such adaptive behavior requires a structural change of the learning system (e.g., via neural plasticity). In this work, we use artificial neural networks, specifically large language models (LLMs), to challenge the traditional view about the role of plasticity in learning and suggest that such an adaptive behavior can be achieved solely through computation if the learning system is sufficiently trained. We focus on statistical learning paradigms. These require identifying underlying regularities in seemingly arbitrary word sequences and are largely considered to require neural plasticity. LLMs can capture arbitrary structures without weight adaptation despite the divergence from their natural language training data. Our work provides novel insights into the role of plasticity in learning, showing that sufficiently trained learning systems are highly flexible, adapting to new tasks and environments solely through computation, much more than previously acknowledged. Furthermore, our work opens the door for future research to use deep learning models to conjure hypotheses about the brain.
Changes of urinary proteome in rats after intragastric administration of calcium gluconate

Calcium is an essential element for maintaining the normal physiological function of organisms. In this study, 3225 mg/kg/d calcium gluconate (equivalent to 300 mg/kg/d calcium) was intragastrically administered to rats for 4 days, and the urine proteome of rats was analyzed. Many differential proteins have been reported to be calcium related, such as Regucalcin (2.6 times higher after gavage than before gavage, p = 0.022), transmembrane protein 132A (8.2 times higher after gavage than before gavage, p = 0.009), creatine kinase (17.5 times higher before gavage than after gavage, p = 0.006), and claudin-3 (13.3 times higher before gavage than after gavage, p = 0.037). Differential protein enriched KEGG pathways included calcium signaling pathways, and biological processes and molecular functions also showed correlation with calcium. In this study, from the perspective of urine proteomics to explore the overall impact of calcium on the body, it is helpful to deeply understand the biological function of calcium and broaden the application potential of urine proteomics.
Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.
Improvement of Optical Character Recognition on Scanned Historical Documents Using Image Processing

As an effort to improve accessibility to historical documents, digitization of historical archives has been an ongoing process at many institutions since the origination of Optical Character Recognition. The old, scanned documents can contain deteriorations acquired over time or caused by old printing methods. Common visual attributes seen on the documents are variations in style and font, broken characters, ink intensity, noise levels and damage caused by folding or ripping and more. Many of these attributes are disfavoring for modern Optical Character Recognition tools and can lead to failed character recognition. This study approaches stated problem by using image processing methods to improve the result of character recognition. Furthermore, common image quality characteristics of scanned historical documents with unidentifiable text are analyzed. The Optical Character Recognition tool used to conduct this research was the open-source Tesseract software. Image processing methods like Gaussian lowpass filtering, Otsu’s optimum thresholding method and morpholog ical operations were used to prepare the historical documents for Tesseract. Using the Precision and Recall classification method, the OCR output was evaluated, and it was seen that the recall improved by 63 percentage points and the precision by 18 percentage points. This shows that using image pre-processing methods as an approach to increase the readability of historical documents for Optical Character Recognition tools is effective. Further it was seen that common characteristics that are especially disadvantageous for Tesseract are font deviations, occurrence of non-belonging objects,
General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path

General circulation models’ (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs’ inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the 5 dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in simulated liquid water path due to the param-10 eterized precipitation-suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree.
NUDGING FOR SUSTAINABILITY: INNOVATIVE PRACTICES IN SUSTAINABLE TOURISM AND PUBLIC POLICY

This study delves into the dynamic intersection of behavioral economics and sustainable tourism, focusing on the innovative use of nudging techniques to promote responsible and eco-friendly behaviors among travelers. Nudging, a concept rooted in behavioral science, is harnessed to design interventions that gently steer tourists toward more sustainable choices, without imposing restrictions or heavy-handed regulations. This research explores a spectrum of creative nudging strategies, from digital applications to environmental messaging all tailored to the unique demands of the tourism industry. These innovative practices not only enhance the visitor experience but also contribute to the preservation of natural and cultural assets. This paper demonstrates the real-world impact of nudging in public policies of governments especially through their Behavioural Insight Teams (BIT) called nudge units and the sustainable nudging practices in the tourism sector with a few cases of tourist destinations. These innovative nudging practices can inspire new approaches which can help in both environmental conservation and instill a sense of social responsibility amongst tourists.
Notes on Progress in Geophysics Palaeomagnetic Directions and Pole Positions-XI1

Columns 2, 3 and 4. Identification numbers are entered in column 2. Information about the rock units studied is given in columns 3 and 4. The symbols given in column 3 refer to the age of the rock unit: Quaternary Q, Tertiary T, Cretaceous K, Jurassic J, Triassic Tr, Permian P, Carboniferous C, Devonian D, Ordovician 0, Cambrian € and Pre-Cambrian Pc. Subdivisions of periods from Cambrian to Cretaceous, where recognised, are denoted by u, m or 1. Epochs within the Tertiary (Palaeocene, etc.) are denoted Tpa, Te, To, Tm and Tp, and within the Quaternary (Pleistocene and Recent) are denoted by Qp and Qr. Subdivision of the PreCambrian is not attempted.
AssemblyNet: A Point Cloud Dataset and Benchmark for Predicting Part Directions in an Exploded Layout

Exploded views are powerful tools for visualizing the assembly and disassembly of complex objects, widely used in technical illustrations, assembly instructions, and product presentations. Previous methods for automating the creation of exploded views are either slow and computationally costly or compromise on accuracy. Therefore, the construction of exploded views is typically a manual process. In this paper, we propose a novel approach for automatically predicting the direction of parts in an exploded view using deep learning. To achieve this, we introduce a new dataset, AssemblyNet, which contains point cloud data sampled from 3D models of real-world assemblies, including water pumps, mixed industrial assemblies, and LEGO models. The AssemblyNet dataset includes a total of 44 assemblies, separated into 495 subassemblies with a total of 5420 parts. We provide ground truth labels for regression and classification, representing the directions in which the parts are moved in the exploded views. We also provide performance benchmarks using various state-of-the-art models for shape classification on point clouds and propose a novel two-path network architecture. Project page available at https: /
On the numbers of radial orderings of planar point sets

Given a set S of n points in the plane, a radial ordering of S with respect to a point p (not in S) is a clockwise circular ordering of the elements in S by angle around p. If S is two-colored, a colored radial ordering is a radial ordering of S in which only the colors of the points are considered. In this paper, we obtain bounds on the number of distinct non-colored and colored radial orderings of S. We assume a strong general position on S, not three points are collinear and not three lines 14;each passing through a pair of points in S 14;intersect in a point of ℝ2 S. In the colored case, S is a set of 2n points partitioned into n red and n blue points, and n is even. We prove that: the number of distinct radial orderings of S is at most O(n4) and at least Ω(n3); the number of colored radial orderings of S is at most O(n4) and at least Ω(n); there exist sets of points with Θ(n4) colored radial orderings and sets of points with only O(n2) colored radial orderings.
of Machine-Learning Based Species Identiﬁcation and Species Distribution Prediction

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the diﬃculty of identifying plants, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms is almost impossible for the general public and is often diﬃcult even for professionals and naturalists. Bridging this gap is a key step towards enabling eﬀective biodiversity monitoring systems. The LifeCLEF campaign, presented in
The Principle and Value of the European Multi Lake Survey

On‐going global warming and eutrophication are expected to promote cyanobacterial dominance worldwide. Although increased lake temperature and nutrients are well‐established drivers of blooms, the mechanisms that determine cyanobacterial biomass are complex, with potentially direct, indirect, and interactive effects. Cyanobacteria can produce toxins that constitute a considerable risk for animal and human health and thus a substantial economic cost if we are to ensure safe drinking water. Such global range phenomena should be studied at a wide spatial scale, to directly compare phytoplankton response in different lake types across contrasting climatic zones. The European Multi Lake Survey (EMLS) sought to harness the power of group science in order to sample lakes across Europe and disentangle the effect of environmental stressors on potentially toxic cyanobacterial blooms. The first EMLS results showed that the distribution of cyanobacterial toxins and the toxic potential in lakes will be highly dependent on direct and indirect effects of temperature. If nutrients are not regulated, then they may interact synergistically with increased lake temperatures to promote cyanobacterial growth more than that of other phytoplankton taxa. Providing continental scale evidence is highly significant for the development of robust models that could predict cyanobacterial or algal response to environmental change.
Edinburgh Research Explorer Early experiences matter

Early life experiences can be important in determining offspring phenotypes and may influence interaction with the environment and hence health, welfare, and productivity. The prenatal environment of poultry can be divided into the pre-lay environment and the egg storage/incubation environment, both of which can affect offspring outcomes. The ability to separate maternal and egg/incubation effects makes birds well suited to this type of research. There are many factors, including feeding and nutrition, environmental conditions, husbandry practices, housing system, social environment, infectious environment, and maternal health status, that can influence both the health and performance and behavior and cognition of the offspring. There are some aspects of the environments that can be changed to produce beneficial effects in the offspring, like addition of certain additives to feed or short changes in incubation temperatures, while other aspects should be avoided to reduce negative effects, such as unpredictable feeding and lighting regimens. Measures of offspring characteristics may prove to be a useful method of assessing parent stock welfare if known stressors result in predictable offspring outcomes. This has the advantage of assessing the parent environment without interfering with the animals and possibly affecting their responses and could lead to improved welfare for the animals.
Stratospheric gravity waves excited by Hurricane Joaquin in 2015: 1 3-D characteristics and the correlation with hurricane intensification

16 Despite progress, accurately forecasting tropical cyclone (TC) intensity, especially rapid intensification, remains a 17 significant challenge. The correlations between the stratospheric gravity waves (GWs) excited by TCs and TC intensity have 18 been recognized. However, partly due to the limitations of conventional analysis methods and observational filters of current 19 satellite instruments, the characteristics of stratospheric GWs that indicate TC intensification remain unclear. This study 20 examined the specific characteristics of GWs and their linkage to hurricane intensification by high-resolution, realistic model 21 simulations and 3-D wave analysis method. First, the stratospheric GWs excited by Hurricane Joaquin in 2015 were simulated 22 using the Advanced Weather Research and Forecasting (WRF) model. Then, the GW characteristics were analyzed using the 23 novel 3-D Stockwell transform method. The GWs excited by Hurricane Joaquin are in the mid-frequency range and propagate 24 outward from the hurricane center counterclockwise while moving upward in a spiral. A high-level time-lagged correlation 25 exists between the intensities of the hurricane and stratospheric GWs during hurricane intensification, making it possible to 26 detect an increase in hurricane intensity by observing an increase in stratospheric GW intensities. Compared to the weakening 27 period, the stratospheric GWs excited during hurricane intensification exhibit relatively higher frequencies, shorter horizontal 28 wavelengths, and longer vertical wavelengths, with this contrast particularly evident near the center of the hurricane. This 29 study provides further knowledge for potentially monitoring hurricane intensification by observing stratospheric GWs using 30 satellite instruments in the infrared and microwave bands when it is difficult to use other measurement techniques. 31
A Sequential Learning-based Approach for Monocular Human Performance Capture

Human performance capture from RGB videos in un-constrained environments has become very popular for applications that require generating virtual avatars or digital actors. SOTA methods use neural network (NN) techniques to estimate the shape directly from photos, yielding a simpliﬁed model of the human body. While effective, NN techniques frequently fail under challenging poses and do not preserve temporal consistency. On the other hand, optimization-based methods like shape-from-silhouette can produce more precise reconstruction; however, they typically require a good initialization and are computationally more intensive than NN. To address issues of previous meth-ods, this work proposes a learning-based approach for optimizing ﬁne-grained shape representation from a monocular RGB video. Our main idea is to sequentially recover different shape details ( i.e . average shape, clothing, wrinkles) using separate neural networks. At each level, our network takes the sparse/noisy gradients of body mesh vertices w.r. t. the shape, and predicts dense gradients to update the body shape. Despite being trained on synthetic data, these networks have surprisingly good generalization to real images. Experimental validation shows that our approach outperforms NN approaches in recovering shape details while also being an order of magnitude faster than optimization-based methods and robust across varied poses and novel views.
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting

We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaussian Splatting. The Gaussians are defined by barycentric coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted optimization to simultaneously optimize the parameters of the Gaussians while walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual humans where the mesh represents low-frequency motion and surface deformation, while the Gaussians take over the high-frequency geometry and detailed appearance. Unlike existing deformation methods that rely on an MLP-based linear blend skinning (LBS) field for motion, we control the rotation and translation of the Gaussians directly by mesh, which empowers its compatibility with various animation techniques, e.g., skeletal animation, blend shapes, and mesh editing. Trainable from monocular videos for both full-body and head avatars, SplattingAvatar shows state-of-the-art rendering quality across multiple datasets.
Optical Screening of Citrus Leaf Diseases Using Label-Free Spectroscopic Tools: A Review

Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.