dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, the local R package version provides the advantage of not requiring a server to run analyses, thus eliminating computational constraints that may arise when working with large cohorts like the TCGA-COADREAD (n=632 samples from RNAseq, n=484 from WES). GEGVIC offers various advantages over the current methods aimed at simplifying bioinformatics analysis. First, it allows for simultaneous analysis of genomic and transcriptomic data, making it suitable for investigating biological questions of different nature. Additionally, it provides greater flexibility in terms of input restrictions, statistical parameters and output customization. Moreover, GEGVIC automatically adapts its workflow to analyze mouse data with different versions of genomic annotations, without requiring additional interventions.
The package also includes gene information from the Biomart database  (12) , which eliminates the need for users to convert gene identifiers to gene symbols as required in some steps of the pipeline. In terms of the scope of the proposed analyses, it is important to note that GEGVIC offers complementary analytical approaches at each module, allowing users to adopt different methodologies and establish a more solid basis to obtain meaningful results. For example, to perform pathway enrichment analysis, both GSEA  (14)  and GSVA  (15)  are proposed, facilitating the comparative analysis from different statistical perspectives  (46, 47). GSEA is the most commonly used method to compare a set of differentially expressed genes (supervised approach), but GSVA offers an alternative strategy that involves characterizing the degree of expression enrichment of gene sets in each sample, enabling an unsupervised comparison of gene sets activity between different samples from the groups of interest.
In the context of immunological dissection of the TME, while both the immunodeconv and immunophenoscore strategies rely on assessing the expression levels of markers associated with distinct immune cells, utilizing multiple methods may enable to obtain more accurate and reproducible results, which can strengthen the interpretation and confidence of the analysis. Additionally, the IPS provides information not only about the anti-tumor immune infiltrates but also about the presence of immune suppressive cells (Tregs and MDSCs), which are indicative of tumor immune evasion  (48, 49). Finally, the Genomic Variation module offers, on the one hand, an overview of the mutational landscape identified in the studied dataset and comparison groups, including the identification of oncogenic 'driver' events, i.
Furthermore, the lack of clear consensus regarding the best analytical practices makes choosing the appropriate method for analysis a challenging task. We chose DESeq2 for DGE analysis over other popular options like edgeR (55) and limma-Voom (56), although no clear consensus exists regarding the optimal choice  (52, 57). In any case, due to the modular architecture of GEGVIC, incorporation of new features, methods and/or additional customization of the visualizations can be easily implemented to further improve and broaden the scope of the tool. Figure  1. GEGVIC modular framework. GEGVIC is divided in three main analytical modules that function independently and include commonly used R packages (red and purple). Gene expression data can be used for Gene Expression (GE) and Immune Composition (IC) modules, while genomic data (MAF file) is exclusively for the Genomic Variation (GV) module.
Availability and requirements Project name: GEGVIC Project home page: https://github. com/oriolarques/GEGVIC, https://github. com/oriolarques/GEGVICshine, https://gegvic. vhio. net/ Operating system(s): Platform independent Programming language: R and Shiny Other requirements: R 2. 10 or higher, but up to 4. 1. 3 or developmental version of R (4. 3. 0) License: MIT Any restrictions to use by non-academics: None.
Statistical analyses The effects of Amistar treatment on bumblebee performance in the learning phase and in the memory tests were analysed using generalized linear mixed models (GLMM). The analyses were conducted using R 4. 1. 1 software  [31] , and the models were fitted using the glm-mTMB function of the glmmTMB package  [6]. The models were fitted with a binomial distribution, and the regression lines were extracted using the effects package  [18]. Individual bees were considered as the unit of replication and the colony was used as a random effect in the models to control for potential pseudoreplication. The relative influence of each observation was adjusted in the models by using the 'weights' function. We performed residual diagnostics and checked the dispersion of our models using the DHARMa package in R  [21].
The UN 2030 Agenda for Sustainable Development provides a shared blueprint for peace and prosperity for people and the planet. Consisting of 17 Sustainable Development Goals (SDGs), it calls for action in a global partnership. Geographical Information Systems (GIS) have been proposed as an advanced tool for managing variability in commercial vineyards, gaining a high-resolution spatial perspective on crop performance distribution and its underpinning reasons. Recent evolution of open-source GIS software democratized access and provided an easy and affordable way for vineyard managers to combine a «big picture» with zooming in at each individual plant, in representing diverse datasets and performing complex calculations in geographic representation. Newly available sets of climate, soil, water, ecosystem and biodiversity data are contributing to the capacity to integrate conservation management in commercial vineyards, making geomatic technology an essential tool to measure and manage sustainable development indicators.
Climate data Climate data were obtained from a single location within the vineyard. The automated weather station comprised sensors (Campbell Scientific, Logan UT, USA) for temperature, relative humidity, precipitation, wind speed and direction, solar radiation and leaf wetness. Sensors were powered by a local solar panel, a battery conserving excess energy for night-time operation. Data were recorded every 15 minutes and wirelessly transmitted to a central server for unsupervised and supervised validation and restitution through a proprietary web interface (INEGI, Porto, Portugal). Sensor maintenance and, where appropriate calibration, was done four times every year.
Soil data Soil data were obtained by performing a microzoning study of the whole property. On a first approach a soil electric conductivity/resistivity survey was conducted by using an EM38 sensor (Geonics Ltd. , Mississauga ONT, Canada) at 0. 5 and 1. 0 metre depths. The Basic Terroir Unit method  [10, 11]  was applied by taking soil samples using an auger at 211 locations (approximately 2 samples per hectare) and down to a depth of 1. 2 metres. The samples were observed and characterized in terms of texture, soil depth, drainage, and geological facies. The site of each sampling was further characterized in terms of topography, slope rate, and aspect, landscape overture. These data were combined to produce a model of 22 spatialized terroir units. For each unit, a 2-meter-deep pit was dug to make a detailed description of the soil profile, horizon organization, and soil aptitudes and constraints (regarding rooting, chlorosis, water availability, plant vigour).
Geomorphology data High-resolution orthophotos were obtained by dronebased visible and near-infrared imagery at a resolution of 10 cm/pixel, planimetric resolution of 30 cm and absolute accuracy of 1. 5 m. The obtained images were processed to produce a digital elevation model (DEM) of the property. Geomatic algorithms were applied to the DEM to derive elevation isoline shapefiles as well as altitude, slope rate and slope aspect raster maps  [12].
Water and erosion data A map of maximum soil water holding capacity was obtained by processing soil data as described above. Further, models of surface and subsurface hydrological processes, linear drainage erosion, and slope instability were created using high-resolution DEM (40 cm/pixel). Correlations between lithology and electrical resistivity were analysed and models SIMWE (Simulated Water Erosion)  [13]  and SHALSTAB (Shallow Stability Model)  [14]  were applied. The Topographic Wetness Index  [15]  weighed by electrical resistivity (TWIw) was used to quantify the trend of water distribution, as influenced by the topography of the property. Raster files of surface drainage flow, maximum saturated hydraulic conductivity, TWI w and sediment flow were produced. Combining these data with geological and orography interpretation, risk areas were mapped.
Crop data Normalized difference vegetation index (NDVI)  [16]  and normalized difference red edge (NDRE)  [17]  were computed from five spectral band images (red, green, blue, infrared and red edge) after processing by software, using vine and row spacing matrices, into a point shapefile where every point represented a grapevine. Spectral images were obtained from a S20 micro-drone (Spin. Works, Lisboa, Portugal).
Biodiversity and ecosystem data Biodiversity and ecosystem data were surveyed during three field missions scheduled for Spring (April), Summer (June) and Fall (October). A first field reconnaissance (Spring) surveyed the main ecosystem features and mapped areas for further, detailed, data gathering. The two following missions (Summer and Fall) were dedicated to surveying systematic inventories of flora and fauna to establish an ecosystem status baseline against which a matrix of indicators and risks could be established, and an ecosystem and agroecology management plan would be developed. Additionally, specific situations requiring detailed approaches were identified and mapped, and land use cartography was validated.
Hardware A HP Z420 workstation (Hewlett-Packard, Palo Alto CA, USA) was dedicated to run the geographical information system software featuring an Intel(R) Xeon(R) CPU E5-1620-0 running at 3. 60 GHz with a x64-based processor using 4 cores and 8 logical processors, 16 GB RAM, 500 GB SSD drive, a NVIDIA Quadro K2000 2 GB graphics card and running Windows 10 Enterprise.
Critical areas were identified relating to potential soil saturation in water, erosion or drought. These areas boundaries were further visually confirmed by using historical satellite imagery available in Google Earth (Google LLC, Dublin, Ireland) since June 2002. Mapping the high-resolution data obtained from SIMWE model allowed for more detailed assessment of water flow along the slopes (Fig. 2 ) and the identification of trend water distribution (Fig. 3 ), while providing essential information on water-driven sediment flow responsible for erosion (Fig. 4 ). To preserve soil, management of these areas required specifically designed drainage systems able to drain rainwater while retaining soil matter. The gauge of drainage piping and canals was adapted considering values of water flow in each place.
Conclusions Available open-source GIS software has the power to accurately represent and perform geomatic calculations of large, diversified and complex datasets of soil, geomorphology, hydrology, and biodiversity of vineyards. They provide farmers with the ability to identify, map, segment, and analyse specific features and characteristics relevant for their viticultural management, allowing for general overviews of the whole property and the analysis of highly detailed representations that may attain the resolution of individual grapevines. The usage of such tools enables both smallholders and large landowners to assess the state of their properties on cultivated and non-cultivated areas, providing tools to assess, measure, and communicate their performance towards the United Nations' Sustainable Development Goals.
These tools may thus become valuable additions for vineyard managers, winemakers and marketeers alike, raising the world's awareness for this sector's commitment for overall sustainability. Further research, however, is needed to measure the level of adoption of these technologies by grape and wine farmers and identify barriers and incentives to adoption, the existence of gender, age or other gaps and define training programs, for both students and professionals with the goal of promoting their proficiency in geomatic analysis and representation. Findings can be used to improve both the available open-source geomatic softwares and public funding programs promoting training, high-specialized precision agroecological management and networks enabling growing contributions for the UN SDGs.
These approaches can either follow a supervised  [Litany et al. 2017] , unsupervised  [Cao et al. 2023; Halimi et al. 2019 ] or a self-supervised  [Cao and Bernard 2023]  paradigm depending on the availability and the diversity of annotated datasets. Supervised approaches are naturally data-dependent, and they require large-scale datasets with different classes of shapes for strong generalization. On the other hand, unsupervised alternatives are class-agnostic and do not require annotated data, but they still lag behind their supervised counterparts in terms of performance  [Halimi et al. 2019]. Moreover, the majority of these aforementioned approaches attempt to match near-isometric shapes of the same class, with less focus on non-isometric shapes (e. g. , human v. s. animal). This is mainly caused by the lack of datasets with inter-class shape pairs and the complexity of matching dissimilar shapes.
To achieve a deeper understanding of 3D shapes and their relationships, it is desirable to develop methods that can generalize well both to isometric and non-isometric shape matching. Recently, several large-scale models were introduced for different modalities such as language (e. g. , GPT3  [Brown et al. 2020] , Bloom  [Scao et al. 2022] ), and vision (e. g. , StableDiffusion  [Rombach et al. 2022] , DALLE-2  [Ramesh et al. 2022] ). These models are usually referred to as foundation models, and they have a broad knowledge of their domains since they were trained on large amounts of data. There are even ongoing efforts to connect these models to build a bridge between different modalities, such as Visual ChatGPT  [Wu et al. 2023] , and MiniGPT-4  [Zhu et al. 2023 ]. Unfortunately, it is still challenging to build similar models for modalities with limited amounts of data, such as 3D shapes.
Therefore, a plausible approach is to employ existing models for language and vision to solve problems for other data-limited modalities. Motivated by this, we attempt to exploit existing foundation models to perform zero-shot shape correspondence with no additional training or finetuning. To address this problem, we identified the following three key problems. First, we would like to predict the class of each of the two shapes in question given only their 3D meshes. We achieve this through zero-shot shape classification by feeding rendered views of the two shapes into a language-visual model, BLIP2  [Li et al. 2023] , to obtain object class proposals. Then, we use ChatGPT to merge these proposals into a single class per object. Second, we produce a semantic region set per shape, and a semantic mapping between the sets by exploiting the in-context learning  [Brown et al.
Large-Scale Models Several models that are trained on large-scale datasets were introduced recently for different modalities, given the advances in deep architectures design and computational capabilities. For instance, Large-Scale Language Models (LLMs) such as T5  [Raffel et al. 2020] ,  BLOOM [Scao et al. 2022] , GPT-3  [Brown et al. 2020] , and Instruct-GPT  [Ouyang et al. 2022] ; vision models StableDiffusion  [Rombach et al. 2022] , and DALLE-2  [Ramesh et al. 2022] ). LLMs have outstanding capabilities in understanding textual data, but they lack any understanding of natural images. Recent methods try to build cross-modal vision-language models that incorporate the capacities of both models. Visual-ChatGPT  [Wu et al. 2023 ] is one example that combines ChatGPT with many vision foundation models that are managed using a prompt manager that allows better combination and interaction.
MiniGPT-4  [Zhu et al. 2023 ] pursues a similar endeavor by attempting to align frozen LLM with a visual encoder through a projection layer. To further improve language coherence, they finetune the model on a well-aligned dataset using a conversational template. BLIP2  [Li et al. 2023 ] bootstraps vision-language models through efficient pre-training from off-the-shelf models. We employ these models to achieve zero-shot 3D shape classification and to generate shape-specific semantic regions that can then be utilized to perform zero-shot 3D semantic segmentation to find shape correspondences.
Strongly Non-Isometric Shapes Dataset (SNIS) Existing shape correspondence datasets  [Bogo et al. 2014; Zuffi et al. 2017]  usually encompass a single category of objects (e. g. , humans or animals), and they employ template models to derive dense correspondences to alleviate annotation workload. To facilitate the development of approaches that can generalize to non-isometric shape matching, we introduce a new dataset with mixed shape pairs from existing isometric datasets, e. g. , FAUST  [Bogo et al. 2014 ] (humans), SMAL  [Zuffi et al. 2017 ] (animals), and DeformingThings4D  [Li et al. 2021 ] (humanoid objects). For each pair of shapes, we annotate 34 keypoint correspondences between the shapes as well as a dense segmentation map. Figure  4  shows an illustration for these annotations. For the FAUST dataset  [Bogo et al.
2014] , we use a similar approach of annotation as described in  [Abdelreheem et al. 2023 ] that includes segmentation maps for all the available 100 shapes. Our SNIS dataset includes 250 shape pairs, where the first shape is either from FAUST or DeformingThings4D, and the second is from SMAL. The included classes are: {"cougar", "cow", "dog", "fox", "hippo", "horse", "lion", "person", "wolf"}. Note that it is desirable to include other categories of objects from diverse datasets such as SHREC09  [Godil et al. 2009 ]. Unfortunately, we find that this demands significant manual annotation effort, particularly with point-to-point dense annotation, which is time-consuming and labor-intensive. However, we demonstrate the generalization capabilities of our approach by showing some qualitative examples from SHREC09 in section 4.
Metrics For the final dense shape correspondence map, we use the standard average geodesic error as in  [Halimi et al. 2019; Litany et al. 2017 ]. We describe the newly proposed metrics below. Zero-Shot Object Classification Accuracy (ZSClassAcc). To evaluate if the predicted object class in 3. 1 is accurate, we compare it against the ground-truth shape label. However, since LLM-based approaches can predict several synonyms for each class (e. g. , human and person), the standard classification accuracy becomes infeasible. Therefore, we propose to generate a set of synonyms for each object class in the dataset from WordHoard 1. Whenever a class prediction matches any of the synonyms, it is counted as a correct prediction. Eventually, the accuracy is calculated as a standard binary classification accuracy.
Semantic Regions Generation F1-Score (SRGen-F1). Similar to the previous metric, we evaluate the generated semantic regions as a multi-class classification problem. Regions that are matched with the ground truth count as True Positives (TP), ground truth regions that were not predicted count as False Negative (FN), and predicted regions that do not exist in the ground truth count as False Positives (FP). Finally, a standard F1-Score is calculated as: SRGen-F1 = 2 • TP 2 • TP + FP + FN (2) Semantic Regions Prediction IoU (SRIoU). To evaluate the quality of semantic segmentation for different semantic regions in 𝑅 1 and 𝑅 2 , we calculate the average intersection-over-union over different regions and shapes as follows: 𝐼 12 = 𝐼 1 + 𝐼 2 2 (3) Figure  4 : Keypoint correspondences and segmentation maps for the proposed Strongly Non-Isometric Shapes (SNIS) dataset.
We provide 34 keypoint annotations, but we only show a few here for clarity. 𝐼 𝑖 = 1 |𝑅 𝑖 | |𝑅 𝑖 | ∑︁ 𝑟 =1 𝐼𝑜𝑈 𝑖 𝑟 (4) where 𝐼𝑜𝑈 𝑖 𝑟 is the intersection-over-union for region 𝑟 in shape 𝑖 compared to the groundtruth segmentation. Keypoint Label Matching Accuracy (KPLabelAcc). Since we provide keypoint annotations in our proposed SNIS dataset, we can evaluate the shape matching accuracy at these keypoints. For each shape 𝑖, we define keypoint indices vector 𝑃 𝑖 ∈ R 34×1 , which stores vertex indices from 𝑉 𝑖 for the annotated keypoints. Given faces labels 𝐹 𝐿 𝑖 from (1), we can generate labels for vertices as well that lie on these faces, and we denote them 𝑉 𝐿 𝑖. The keypoint label matching accuracy is then calculated between 𝑉 𝐿 1 , 𝑉 𝐿 2 , and the groundtruth labels 𝑉 𝐿 𝐺𝑇 as: KPLabelAcc = 1 |𝑃 | |𝑃 | ∑︁ 𝑗=1 𝑉 𝐿 1 [𝑃 1 𝑗 ] ∧ 𝑉 𝐿 2 [𝑃 2 𝑗 ] ∧ 𝑉 𝐿 𝐺𝑇 [𝑃 𝐺𝑇 𝑗 ] (5) where 𝑗 refers to elements in the vector and ∧ is the semantic AND operator, which means the three integers should share the same semantic label.
Generalization to Other Datasets To examine if our proposed approach generalizes to other datasets with highly unrelated shapes, we include some objects from the SHREC09  [Godil et al. 2009] , and 3D-CoMPaT  [Li et al. 2022b]  datasets. We form pairs of shapes where the first item is from SNIS, and the second is from SHREC09 or 3D-CoMPaT. Figures  10  and 11  show these examples. Our method was able to produce plausible results when matching a human with a chair, where the legs were matched correctly, and the seat was matched to the rest of the human body. A horse was also matched to a tricycle, where the horse limbs were matched to the wheels, the head to the handle, and the tail to the seat. We show in Figure  9  examples where the pairs are from daily objects  [Chang et al. 2015; Xiang et al.
CONCLUSION We proposed a novel zero-shot approach for 3D shape correspondence. Our approach exploited the capabilities of recently emerged language and vision foundation models to match challenging nonisometric shape pairs. There are two key differences in our work to traditional co-segmentation. First, we do not require the region   names to be known in advance. Second, our approach does not require a mutual set of semantic regions and generates shape-specific sets, and a semantic mapping between them instead, enabling it to match diverse shape pairs. We also introduced a new dataset for strongly non-isometric shapes (SNIS) as well as evaluation metrics for each stage in our pipeline to facilitate the development and evaluation of future methods. Limitations and Future Work Our approach can match coarse semantic regions such as main body parts (e.
Relatively few studies have taken advantage of the transcriptomic approach to examine the homoeolog expression bias in nascent wheat allohexaploids. Hao et al. [29]  reported that the D-subgenome is massively affected by the transcriptomic shock, with downregulation being the dominant change (32. 6% of D-homoeologs downregulated), while changes on A and B are infrequent. It was also established that the transcriptomic shock is not caused by the whole-genome duplication per se, but rather occurs at the allotriploid stage after the interploidy cross between T. turgidum and Ae. tauschii, and the transcriptional remodelling is partially reversed in the first allohexaploid generation  [29]. Ramirez-Gonzales et al. [10]  have analysed this dataset further and found no relationship between the presence of TEs in promoter regions and altered expression patterns between homoeologs in dominant and suppressed triads.