Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate biogeography of Arabidopsis thaliana: linking distribution models and individual variation

AIM Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. However, it is challenging to gather data on individual-level variation at large spatial scales. Model organisms are potentially important systems for biogeographical studies, given the available range-wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity. LOCATION Global TAXON Arabidopsis thaliana (“Arabidopsis”) METHODS We fit occurrence records to climate data, and then projected the distribution of Arabidopsis under last glacial maximum, current, and future climates. We confronted model predictions with individual performance measured on 2,194 herbarium specimens, and we asked whether predicted suitability was associated with life-history and genomic variation measured on ∼900 natural accessions. RESULTS The most important climate variables constraining the Arabidopsis distribution were winter cold in northern and high elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of Arabidopsis is constrained by climate-associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late-flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the center of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well-predicted by our native-range model applied to certain regions but not others, suggesting it has colonized novel climates. MAIN CONCLUSIONS Integration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.
The phonological status of initial peaks in European Portuguese

This paper investigates the phonology of initial peaks in European Portuguese intonation. Evidence is presented for two types of peaks: an accentual peak, that is consistently aligned relative to the first stressed syllable; and a phrasal peak, that is aligned relative to the left-edge of the intonational phrase and within the limits of the first prosodic word. The former is best represented as an H* accent, whereas the latter is a phrasal H tone with a peripheral association to the left-edge of the I-phrase and a secondary association with the first u. This secondary attachment precludes the phrasal H and a pitch accent from co-occurring at the left periphery of the contour. By offering a new account of initial peaks, the present study contributes to a better understanding of the options available at the left periphery of the Portuguese intonational system.
A Self-Supervised Model Based on CutPaste-Mix for Ductile Cast Iron Pipe Surface Defect Classification

Online surface inspection systems have gradually found applications in industrial settings. However, the manual effort required to sift through a vast amount of data to identify defect images remains costly. This study delves into a self-supervised binary classification algorithm for addressing the task of defect image classification within ductile cast iron pipe (DCIP) images. Leveraging the CutPaste-Mix data augmentation strategy, we combine defect-free data with enhanced data to input into a deep convolutional neural network. Through Gaussian Density Estimation, we compute anomaly scores to achieve the classification of abnormal regions. Our approach has been implemented in real-world scenarios, involving equipment installation, data collection, and experimentation. The results demonstrate the robust performance of our method, in both the DCIP image dataset and practical field application, achieving an impressive 99.5 AUC (Area Under Curve). This presents a cost-effective means of providing data support for subsequent DCIP surface inspection model training.
Crystal Structure of DNA Replication Protein SsbA Complexed with the Anticancer Drug 5-Fluorouracil

Single-stranded DNA-binding proteins (SSBs) play a crucial role in DNA metabolism by binding and stabilizing single-stranded DNA (ssDNA) intermediates. Through their multifaceted roles in DNA replication, recombination, repair, replication restart, and other cellular processes, SSB emerges as a central player in maintaining genomic integrity. These attributes collectively position SSBs as essential guardians of genomic integrity, establishing interactions with an array of distinct proteins. Unlike Escherichia coli, which contains only one type of SSB, some bacteria have two paralogous SSBs, referred to as SsbA and SsbB. In this study, we identified Staphylococcus aureus SsbA (SaSsbA) as a fresh addition to the roster of the anticancer drug 5-fluorouracil (5-FU) binding proteins, thereby expanding the ambit of the 5-FU interactome to encompass this DNA replication protein. To investigate the binding mode, we solved the complexed crystal structure with 5-FU at 2.3 Å (PDB ID 7YM1). The structure of glycerol-bound SaSsbA was also determined at 1.8 Å (PDB ID 8GW5). The interaction between 5-FU and SaSsbA was found to involve R18, P21, V52, F54, Q78, R80, E94, and V96. Based on the collective results from mutational and structural analyses, it became evident that SaSsbA’s mode of binding with 5-FU diverges from that of SaSsbB. This complexed structure also holds the potential to furnish valuable comprehension regarding how 5-FU might bind to and impede analogous proteins in humans, particularly within cancer-related signaling pathways. Leveraging the information furnished by the glycerol and 5-FU binding sites, the complexed structures of SaSsbA bring to the forefront the potential viability of several interactive residues as potential targets for therapeutic interventions aimed at curtailing SaSsbA activity. Acknowledging the capacity of microbiota to influence the host’s response to 5-FU, there emerges a pressing need for further research to revisit the roles that bacterial and human SSBs play in the realm of anticancer therapy.
EXPLORING THE MODIFIED GAMMA FRAILTY DISTRIBUTION: AN OPTIMAL DESIGN APPROACH USING PYTHON

A suction/injection controlled mixed convection flow of an incompressible and viscous fluid in a vertical SurvivalAnalysis is pivotal in understanding the effects of covariates on potentially censored failure times and in the joint modelling of clustered data. It is used in the context of incomplete repeated measures and failure times in longitudinal studies. Survival data are often subject to right censoring and to a subsequent loss of information about the effect of explanatory variables. Frailty models are one common approach to handle such data.Three frailty models are used to analyze bivariate time-to-event data. All approaches accommodate right censored lifetime data and account for heterogeneity in the study population. A Modified Gamma Frailty Model is compared with two existing Frailty Models. The survival-analysis was performed using the Python.The newly derived MGF was analyzed using Python which is more robust when sample size is more than forty.The MGF model performs better than the existing models in the presence of clustering. However the CGF is preferable in the absence of clusters in a given data set.
Configurable 2D–3D CNNs Accelerator for FPGA-Based Hyperspectral Imagery Classification

Convolutional neural network (CNN) is used efficiently for the classification of hyperspectral imagery (HSI). Both 3-D CNN and hybrid 2D–3D CNN have better performance due to the full extraction of joint spatial-spectral features. Most of previous acceleration research on field-programmable gate array (FPGA) has concentrated on 2-D CNN, few of which were made for 3-D CNN acceleration. More importantly, it is notable that the CNN models for HSI classification have some unique characteristics of computation and memory, different from those for object detection. Thus, the conventional accelerating approaches may be not applicable. To address this problem, we propose a dynamic configurable accelerator architecture suitable for both 2-D and 3-D CNN, ensuring fast development for various networks. The multiple nested loops are optimized delicately and the convolutional layers are fully parameterized, making it easy to scale up the network. Furthermore, we develop the parallelism-oriented memory pattern and data access strategy to optimize the data path and the local buffer. Finally, we implement the proposed architecture for 2-D, 3-D, and hybrid CNNs, validating its effectiveness and reconfigurability. To demonstrate its extensibility, we also prototype the accelerator on two FPGA platforms. We achieve the average and maximum throughput of 18.2/166.18 giga operations per second (GOPS) for HybridSN. To compare with previous FPGA accelerators, a typical baseline 3-D model is also implemented with the average and maximum throughput 160.25/225.87 GOPS, and the performance efficiency achieves up to 1.23 with 10.7% power efficiency gain.
Recent Foraminiferal Communities in Makassar Strait

Makassar strait is the very unique waters, the bottom topography was steep and complex, the waters mass was also exceptional since Indonesian through flow carried flavor water from Pacific warm pool to the area. To describe the exceptional of Makassar Strait hydrological characteristics through recent foraminiferal community structure as a proxy, Oceanographical Expedition in Makassar Strait was conducted in October 2004. Research Vessel of Baruna Jaya VII was used to carry out the research activities. Sediment layers are an essential tool on recording hydrological information in such period of time, and each certain layer preserved certain information as a bench mark data at a time period. The result shows that there are 5 genera of planktonic foraminifera which generated a bulk in sediment surface. Deep chlorophyll maximum in oceanic region of Makassar Strait was prominent factor generated living planktonic foraminifera in research location. Benthic community was formed by 164 species from 78 genera. Water depth was affected to benthic foraminiferal diversity, distribution and species diversity, which reveal that water depth was limiting factor to benthic foraminiferal assemblage. Uvigerinid was the important taxa which make up 46 to 57 % of the benthic specimen, particularly in river input yield adjacent area. Uvigerina asperula may considered as an indicator of OMZ (Oxygen minimum zone) in Makassar Strait.
Integrating Solar Resources and Topology Estimation Modules in Industrial ADMS Environment

In contrast with traditional utility monitoring and operational tools, advanced distribution management systems (ADMS) provide modern operational features to monitor, secure and operate the distribution system in an integrated manner. Large-scale adoption of ADMS at utilities is in the early stages since advanced applications within ADMS environments are still evolving. The topology estimation module is one of the important and challenging ADMS applications with enhanced automation. This article integrates the optimization-based topology estimation module with the load/photovoltaics (PV) estimation module. Validation of the feeder connectivity information is the major challenge faced during the integration process. The statistical analysis of the error of the load/PV estimation module shows that the error does not follow the Gaussian distribution. To accurately capture the uncertainty in the error of the load/PV estimation module, the error is modeled with the Gaussian mixture model (GMM). The study using the actual utility feeder data and the ADMS platform shows that in an integrated operation of the load/PV estimation module and topology estimation module, the accuracy of the topology estimation module is improved when the estimation error of the load/PV estimation module is modeled with GMM. The methodology outlined in this article serves as the proof-of-concept and framework for integration and validation of advanced applications such as load/PV estimation and topology estimation modules in the ADMS environment.
On the origin and nature of the interannual variability of the winter stratospheric circulation in the northern hemisphere

During the northern hemisphere winter, the stratospheric circulation exhibits quite large interannual variability. In the present study, the nature and origin of the interannual variation in the winter stratosphere is studied with two different approaches: one is composite analysis assuming a priori four possible causes: solar activity, equatorial quasi-biennial oscillation (QBO), volcanic aerosols, and unknown cause producing trends, while the other is an extended empirical orthogonal function analysis (E-EOF). The results of composite analysis show that in November, anomalous zonal-mean zonal winds and meridional temperature gradients are found in accordance with the assumed forcings in different regions in the stratosphere. As winter progresses, however, anomalous patterns in zonal wind become similar by forming a dipole-type pattern and extend down into the troposphere. The common features of the evolution of the anomalies during the winter can be well explained by the first mode of the E-EOF. The results of the present study suggest that interannual variability in the winter stratosphere in the northern hemisphere can be considered an internal mode of variability triggered by changes in forcing, such as solar activity, equatorial QBO, and volcanic aerosols. Although overall features of the evolution of zonal-mean zonal wind associated with trends are similar, the cause of the trends is still undetermined.
V2V-ESP: Vehicle-to-Vehicle Energy Sharing Privacy Protection Scheme Based on SDP Algorithm

Insufficient availability of electric vehicle (EV) charging infrastructure is a pressing issue, and vehicle-to-vehicle (V2V) energy trading offers a potential solution. V2V power transactions provide advantages like location flexibility and cost-effectiveness, but also entail privacy risks. Addressing user privacy concerns is a critical challenge. In this work, we propose a V2V energy sharing privacy protection scheme based on shuffle differential privacy (V2V-ESP). Our scheme utilizes a skeletal framework of critical locations to depict trajectories, extracting key positions that optimize computational efficiency while preserving essential trajectory features. By shuffle differential privacy method, we achieve a balance between data privacy and utility, effectively severing associations between messages and their owners through shuffling mechanisms. Additionally, we introduce a position perturbation algorithm that enhances data privacy protection and utility optimization by analyzing the context of EV route preservation. To validate V2V-ESP's effectiveness, we evaluate the scheme using real traffic trajectories from Beijing taxis. Results demonstrate the solution's independence from trusted third parties and effectively thwarts attacks involving multi-round location attack, global attack, trajectory inference attack and uniform attack. V2V-ESP scheme provides robust technical support for the future development and widespread adoption of EVs.
The Interaction of Mechanics and the Hippo Pathway in Drosophila melanogaster

Simple Summary Control of tissue growth is an important component of cancer treatment, but understanding how to do this effectively is still in the early stages. Tissue growth typically involves both complex interacting signal transduction networks as well as mechanical interactions within and between cells, and how they interact to control growth is an open problem. Herein we begin the process of how to understand the interactions by focusing on tissue growth in the fruit fly Drosophila melanogaster. A model of the Hippo pathway, which is one of the primary growth-control pathways, is integrated with a description of the mechanical behavior of cells in wing-disc tissue to predict how mechanics and signaling interact, and a number of significant insights and predictions have emerged from the analysis of the model. Abstract Drosophila melanogaster has emerged as an ideal system for studying the networks that control tissue development and homeostasis and, given the similarity of the pathways involved, controlled and uncontrolled growth in mammalian systems. The signaling pathways used in patterning the Drosophila wing disc are well known and result in the emergence of interaction of these pathways with the Hippo signaling pathway, which plays a central role in controlling cell proliferation and apoptosis. Mechanical effects are another major factor in the control of growth, but far less is known about how they exert their control. Herein, we develop a mathematical model that integrates the mechanical interactions between cells, which occur via adherens and tight junctions, with the intracellular actin network and the Hippo pathway so as to better understand cell-autonomous and non-autonomous control of growth in response to mechanical forces.
Spatial coherence of variations in seasonal extreme precipitation events over Northwest Arid Region, China

Daily precipitation data covering the period of 1961–2009 from 72 stations in Northwest Arid Region, China, are analysed to investigate coherent subregions of variations in extreme precipitation events. First, four extreme precipitation indices, i.e. number of heavy precipitation days (DPh), precipitation intensity above heavy precipitation (IPh), number of torrential precipitation days (DPt), and precipitation intensity above torrential precipitation (IPt), were defined and analysed seasonally. Then rotated empirical orthogonal function (REOF) technique is used to identify the coherent subregions of these seasonal extreme precipitation indices and related trends. The results indicate that the number of coherent subregions of DPh, IPh, DPt, and IPt is 6, 5, 14, and 10 for spring; 4, 18, 6, and 6 for summer; 15, 8, 10, and 8 for autumn; and 7, 7, 2, and 5 for winter, respectively. Attribute shifts of underlying surfaces should be an important driving factor behind changes of extreme precipitation events. Intensity variations of heavy and torrential precipitation are the main reason behind significant changes of extreme precipitation, especially intensity variations of the heavy precipitation. The highest increasing magnitude of IPh is 584% (10a)−1 which is found in summer in the west parts of the Southern Xinjiang, the middle Tianshan Mountains, and the west parts of the Junggar Basin in the Northern Xinjiang. The findings of this study are potentially relevant for the development of human understanding of hydrological cycle responses in arid regions to regional and global climate changes, particularly in terms of extreme weather extremes.
Stochastic Time-Domain Mapping for Comprehensive Uncertainty Assessment in Eye Diagrams

The eye diagram is one of the most common tools used for quality assessment in high-speed links. This article proposes a method of predicting the shape of the inner eye for a link subject to uncertainties. The approach relies on machine learning regression and is tested on the very challenging example of flexible link for smart textiles. Several sources of uncertainties are taken into account related to both manufacturing tolerances and physical deformation. The resulting model is fast and accurate. It is also extremely versatile: rather than focusing on a specific metric derived from the eye diagram, its aim is to fully reconstruct the inner eye and enable designers to use it as they see fit. This article investigates the features and convergence of three alternative machine learning algorithms, including the single-output support vector machine regression, together with its least squares variant, and the vector-valued kernel ridge regression. The latter method is arguably the most promising, resulting in an accurate, fast and robust tool enabling a complete parametric stochastic map of the eye.
A global land primary productivity and phytogeography model

A global primary productivity and phytogeography model is described. The model represents the biochemical processes of photosynthesis and the dependence of gas exchange on stomatal conductance, which in turn depends on temperature and soil moisture. Canopy conductance controls soil water loss by evapotranspiration. The assignment of nitrogen uptake to leaf layers is proportional to irradiance, and respiration and maximum assimilation rates depend on nitrogen uptake and temperature. Total nitrogen uptake is derived from soil carbon and nitrogen and depends on temperature. The long-term average annual carbon and hydrological budgets dictate canopy leaf area. Although observations constrain soil carbon and nitrogen, the distribution of vegetation types is not specified by an underlying map. Variables simulated by the model are compared to experimental results. These comparisons extend from biochemical processes to the whole canopy, and the comparisons are favorable for both current and elevated CO2 atmospheres. The model is used to simulate the global distributions of leaf area index and annual net primary productivity. These distributions are sufficiently realistic to demonstrate that the model is useful for analyzing vegetation responses to global environmental change.