dataesr/scientific-paragraphs-categorization huggingface dataset:
Contrary to the gastric-induced DH, it was not possible to identify any significant correlations between the DH, and the mechanical (i. e. hardness, stiffness) and microstructural (porosity, cryogel vessel thickness) properties of the cryogels. It is therefore assumed that the complete disintegration of the residual cryogels during the early stages of the intestinal digestion facilitates the cleavage of the peptide bonds. To assess any supplementary health-benefitting effects, apart from the biological role of the living LGG cells, the peptide profile (500-3000 Da) of the gastric and intestinal phases was determined using nano-LC-MS/MS. The peptidome data were represented in two forms: a) average number of peptides detected on the y-axis versus their amino acids sequence length on the x-axis (Fig.
6% agarose) medium and covered with water for imaging. Meristems were imaged in water using a 20X long-distance water-dipping objective mounted either on a LSM880 (Zeiss; www. zeiss. com) or a SP8 (Leica; www. zeiss. com) confocal microscope. Z-stacks of 1-2 µm spacing were taken and the spacing was kept constant within a single experiment. Confocal Z stacks were analyzed using the ImageJ software (https://fiji. sc/) and a custommade code written in Matlab (Mathworks Inc. , Natick, MA). ImageJ was used for generating the panel figures showing the fluorescence reporters in the SAMs. To generate the panels, zprojections (sum slices) of meristems expressing TFP, GFP or VENUS were performed, and the Fire color code was used to represent the signal. The expression levels of the different fluorescence reporters were analyzed by using the Matlab code (see https://gitlab.
Funding This Work was supported by the  Centre National de la Recherche Scientifique (CNRS)  and by  HFSP  grant  2018 , RGP, 009. The study was partially supported by the labex " Who AM I? ", labex  ANR-11-LABX-0071 , as well as the  Université Paris Cité ,  Idex   ANR-18-IDEX-0001 , funded by the  French Government  through its " Investments for the Future" program  and also by " Mecha-Nuc " project  ANR-20CE13-0025-03.
Ticks are important vectors of many pathogens in Europe, where the most impactful species is Ixodes ricinus. Recently, the geographical distribution of this tick species has been expanding, resulting in an increased risk of human exposure to tick bites. With the present study, we aimed to screen 350 I. ricinus specimens collected from humans and wild animals (mainly ungulates), to have a broader understanding of the tick-borne pathogens circulating in the Lombardy region, in northern Italy. To do so, we took advantage of a high-throughput real-time microfluidic PCR approach to screen ticks in a cost-effective and time-saving manner. Molecular analysis of the dataset revealed the presence of four genera of bacteria and two genera of protozoa: in ungulates, 77 % of collected ticks carried Anaplasma phagocytophilum, while the most common pathogen species in ticks removed from humans were those belonging to Borrelia burgdorferi sensu lato group (7.
Validation of the results by PCR and DNA sequencing Following  Michelet et al. (2014) , in order to evaluate and possibly refine the results obtained with the BioMark™ system, conventional PCRs were performed on a subset of representative positive samples, in order to validate the results obtained with the BioMark™ system. This representative subset included at least 20 % of all positives to each pathogen. Different primers were used for each microorganism compared to those in the microfluidic real-time PCR, following published thermal protocols (Supplementary Table  2 ). The amplicons were bidirectionally sequenced by Eurofins (Germany), and then sequencing results were inspected and processed using the BioEdit software (Ibis Biosciences, Carlsbad). The results were compared with the NCBI nucleotide database using BLASTN.
, 2021; Pistone et al. , 2010; Zanzani et al. , 2019)  but in line with studies on ticks collected from humans  (Beltrame et al. , 2018; Garcia-Vozmediano et al. , 2021; Otranto et al. , 2014). Our results confirm previous reports on I. ricinus infected with B. garinii and B. afzelii, indicating that these are the most common genospecies in Italy  (Strnad et al. , 2017). In our dataset, B. burgdorferi sensu lato-positive ticks were collected from humans and unknown wild animals. In contrast, we did not detect any LB spirochetes in ticks removed from ungulates (n = 113). This statistically significant difference is consistent with previous studies  (Díaz-Cao et al. , 2022; Kazimírová et al. , 2018; Michalski et al. , 2021)  that demonstrated the borreliacidal effect of serum from wild ruminants and thus resulting in the loss of B.
ricinus collected from wild animals  (Michalski et al. , 2021; Rocha et al. , 2022). A complex and still not well understood interplay between various factors, related to ticks and reservoir hosts, determines the occurrence and prevalence of co-infections. In our dataset, we did not observe statistically significant differences in frequency of co-infections among the main pathogens investigated. The only significant exception pertains to the pairing of B. burgdorferi sensu lato and A. phagocytophilum, which is significantly less common than expected. However, this seems likely linked to their different vertebrate host tropism, in particular clearance of Borrelia by ungulates, rather than to direct interactions between these bacteria. In any case, we underline that co-infections are relevant for their potential consequences on public health, e.
Acknowledgements This research was supported by  EU  funding within the  NextGenerationEU-MUR PNRR Extended Partnership initiative on   Emerging Infectious Diseases  (project no. PE00000007 ,  INF-ACT ) to DS and MC, and funded by the  Italian Ministry of Health, Directorate -General for animal health and veterinary medicinal products (DGSAF) , grant number  IZS LER 2021/014. We would also like to thank  Claudio Marco Lodola  and  Giuseppe Luca Polonini  for technical support in DNA extraction, and  Celine Ann Grech  for English language proofreading.
Acknowledgements We are grateful to  Olga Soutourina  ( University Paris-Saclay, France ) and  Yu Matsuura  ( University of the Ryukyus, Japan ) for critical reading of the manuscript and constructive comments. This work benefited from financial support by  Saclay Plant Sciences-SPS , by the  ANR  grant  ANR-19-CE20-0007 , and by a  JSPS-CNRS   Bilateral Open Partnership Joint Research Project  ( 18KK0211 ) and a  CNRS International Research  Project to Y. K. and P. M. Y. K. was supported by the  MEXT KAKENHI  ( 21K18241 ,  22H05068 ). J. L. , G. L. and R. J. were supported by Ph. D. fellowships from the  French Ministry of Higher Education, Research, and Innovation  and A. B. benefited from a PhD contract in the frame of the  CNRS 80|PRIME -2021 program. This work was supported by  JSPS   Research Fellowships for Young Scientist  to S.
J. ( 21F21090 ), K. I. ( 22KJ0057 ) and T. O ( 14J03996 ,  20170267  and  19J01106 ). Tn-seq sequencing and data treatment were performed by the I2BC high-throughput sequencing facility, supported by  France Génomique  (funded by the  French National   Program Investissement d'Avenir   ANR-10-INBS-09 ). This work has benefited from the facilities and expertise of  MIMA2  ( Université Paris-Saclay ,  INRAE, AgroParisTech , 78350,  Jouy-en-Josas, France ).
The dataset obtained with edgeR showed sample counts for 27 607 genes. Of the 27 607 genes expressed, after sorting weakly expressed genes, 15 700 were conserved for differential gene expression. The Multidimensional Scaling (MDS) plot and correlation plots separate the samples with and without CD14, while the second axis separates the three independent experiments (Figure  3  and Figure  4 ). The MDS plot clearly discriminated the different conditions, supporting the hypothesis that the 15 700 genes allowed discrimination of samples. Global analysis of differentially expressed genes. In downstream analyses, genes were considered as differentially expressed between two conditions when the absolute value of the fold-change was above 1. 5 and the FDR was below 0. 01. Differentially expressed genes (DEG) were analyzed for each condition relative to the unstimulated cells.
Cluster 3 genes (336 genes) show intermediate levels of FC. Overall, this analysis indicates that most DEG were observed when cells were stimulated in the presence of CD14 with LPSR or LPSS, with more genes being deregulated when LPSR was used. CD14 alone did not signi cantly modify the transcriptomic pro le of PS cells. LPSS alone was a weak stimulus compared to LPSR, and the addition of CD14 markedly enhanced MEC responses highlighting the importance of CD14 for a full response of MEC. For each of the 4 clusters identi ed, we performed an analysis of enriched pathways using the Reactome database and a transcription factor (TF) enrichment analysis (TFEA) in order to identify TFs potentially responsible for the observed changes in gene expression (Figure  8 ). The "Signaling by interleukins" pathway is enriched in both clusters 3 and 4.
CD14 is essential for full activation of Myd88-independent pathway by smooth LPS A comparison of responses triggered by rough (LPSR) and smooth (LPSS) LPS in the absence of CD14 showed that, in these conditions, a total of 503 genes were found to be differentially expressed including 12 long non-coding RNA and 1 miRNA (miR-147) (Supplementary Figure  2  and Supplementary Table  2 ). Pathway analyses were performed against the KEGG pathway database using SPIA and against the Reactome database using ReactomePA. These analyses showed the "Cytokine-cytokine receptor interaction" and "Toll-like receptor signaling" pathways and more speci cally the "MyD88-independent TLR4 cascade" as being differently regulated between LPSR and LPSS in the absence of CD14 (Table  1 ). This rst analysis clearly shows that, in the absence of CD14, the response of PS cells to LPSR is more pro-in ammatory that the one observed with LPSS.
miRNA and lncRNA genes regulated by stimulation of MEC by LPS Our analyses evidenced a number of miRNA and genes annotated as lncRNA whose expression is altered upon stimulation of MEC with the different forms of LPS in the presence or not of CD14. As mentioned above, only a few miRNA genes could be identi ed as differentially expressed: this low number might stem from the RNA extraction and processing which was initially designed to have a global overview of the MEC transcription mRNA pro le and is very likely to have missed a signi cant number of small miRNA. Concerning lncRNA, we initially used ENSEMBL annotations and con rmed these loci were indeed lncRNA by using the FEELnc pipeline  27  : this led to a total of 940 FEELnc con rmed lncRNA, out of 1480 annotated lncRNA in the ENSEMBL database.
Among these lncRNA, 25 were signi cantly differentially expressed compared to the unstimulated cells in at least one condition. Interestingly, among these DEG lncRNA, none of them was signi cantly differentially expressed in LPSS-NONE samples indicating that CD14 was strictly required for their deregulation (Figure  12 ). One of these lncRNA, ENSBTAG00000054337, is located immediately downstream of the mi-RNA let7C-201 that has been involved in the negative regulation of TLR4-mediated LPS response in murine macrophages  28.
A global analysis of genes deregulated in at least one of the conditions tested identi ed four different clusters of genes with different expression patterns. Cluster 1 encompasses genes whose expression is reduced upon stimulation with LPS while the genes in the three other clusters showed induction of expression upon stimulation, the three clusters being differentiated by the level of induction observed. Each cluster was also enriched in genes belonging to different pathways from the REACTOME database. In particular, cluster 3 genes were enriched in genes from the "Interferon signaling" pathway and the IRF7 transcription factor was, among others, identi ed by CHEA3 as one of the transcription factors for which targets are enriched in cluster 3. Cluster 3 genes are characterized by a signi cant induction when PS cells are stimulated with smooth LPS in the presence of CD14 but not in the absence of CD14: the detailed comparison of LPSS-CD14 and LPSS-NONE samples identi ed a large number of DEG with an enrichment for genes of the KEGG "Cytokine-cytokine receptor interaction" pathway.
Among the genes investigated by the authors, we observed induction of Nrf2 and HMOX1 but not of NQO1, nor GCLM. A more speci c analysis of genes belonging to this pathway annotated in the Reactome database showed induction of only some of the associated genes (Supplementary gure 5). Expression of lncRNA upon LPS stimulation has recently been studied in MAC-T cells, another mammary epithelial cell line  39. Unfortunately, we were not able to con rm the results presented in this report with the one we obtained. A potential explanation is differences in annotations since this report used a previous annotation that contained only 210 lncRNA and 2000 new lncRNA not annotated in ENSEMBL. Among the lncRNA which we identi ed as differentially expressed, one was located in the close vicinity of the let-7c-201 miRNA.
The relevance of this lncRNA is illustrated by the fact that it is partly conserved in the human genome, the rst 519 nucleotide of ENSBTAG00000054337 showing 88% identity at the DNA level with the 5' region of the Homo sapiens mir-99a-let-7c cluster host gene (MIR99AHG) (Supplementary Figure  6 ). Interestingly, this lncRNA has been identi ed as a positive regulator of in ammation and macrophage polarization to promote growth of Mycobacterium tuberculosis  40. Whether this lncRNA plays a role in the response of MEC to E. coli infection will have to be deciphered in more details. Altogether, our results demonstrate that the presence of CD14 is required for a full-scale response of MEC to stimulation by LPSS. O-antigen is present on most, if not all, E. coli strains causing mastitis as its absence causes strains to be susceptible to complement mediated lysis in milk and thus unable to grow in milk.
This approach enables the identification of cleavage sites introduced by proteases and the resulting novel N-termini (neo-N-termini) formed in vivo upon proteolysis (fig. S3c, d ). COFRADIC analyses, however, did not reveal any of the enriched proteins at the ER as direct targets of MCA-IIs, as these proteins were not enriched in COFRADIC datasets from WT seeds (fig. S3e  and Data S1; i. e. ,). Hence, we examined the alternative possibility that mca-II-KOc seeds indirectly accumulate the identified ER proteins, by mounting ER stress that is known to activate UPR associated with the accumulation of BiP and PDI (4). However, RNA-seq experiments in seeds failed to identify the known transcriptional signatures which associate with UPR in mca-II-KOc seeds (fig. S4a-c  and Data S2; ref. (2) ). Taken together, these results imply that although mca-II-KOc seeds show ER stress, they do not activate a constitutive UPR.
S7ac ; ref. (30) ). AAA ATPase Cell Division Cycle 48 (CDC48, VCP in vertebrates) is a major ERAD component exporting proteins from the ER for proteasome delivery and also regulates the proteasome, functioning as an "unfoldase/segregase"  (26). Since CDC48 homologs were enriched 4. 1-fold in our proteome dataset (Data S1), we tested whether MCA-IIs regulate it. Accordingly, in budding yeast, the sole type I MCA (known as "Mca1") interacts with CDC48 to regulate protein homeostasis  (31). We focused on the specialized type of ERAD pathway named "lipid dropletsassociated degradation" (LDAD) that removes ubiquitinated oleosins from seed lipid droplets, facilitating their breakdown and remobilization during seed germination  (32). LDAD regulates oleosin levels at the lipid droplets, while in oleosin mutants, lipid droplets fuse with one another to form larger droplets  (33).
Acknowledgments We would like to acknowledge  Ikuko Hara-Nishimura  and  Hannele Tuominen  for sharing materials with us. Future Leader Fellowship from the  Biotechnology and Biological Sciences Research Council (BBSRC )  BB/R012172/1  (PD)  European Union  and Greek national funds through the  Operational Program Competitiveness, Entrepreneurship, and Innovation ,  Τ2ΕΔΚ-00597  under the call  RESEARCH-CREATE-INNOVATE  (" BIOME ";  PNM )  European Union   Horizon 2020   Marie Curie-RISE PANTHEON  grant  872969  (PNM)  Hellenic Foundation of Research and Innovation  grant  1426   NESTOR-Theodoros Papazoglou-Always Strive for Excellence  (PNM)  Hellenic Foundation  of Research and  Innovation  fellowship  5947  (IHH) The  Swedish Research Council  (VR)  2019-04250  (PVB,  PNM )  Carl Trygger Foundation  (CTS)  22 :2025 (PVB)  Knut and Alice Wallenberg Foundation   2018.
Nonetheless, to fully benefit from deep learning algorithms and exploit their potential, large labeled datasets are required, which is considered as a problem for agricultural applications because of the di culties related to the labeling process. Conversely, unlabeled data could be acquired in large quantities with relative simplicity. One of the challenges to overcome in semi-supervised learning is not only to be able to achieve a good performance as the one we can reach using large enough fully annotated data with the same model but, going further, to be able to perform similarly or better than state-of-the-art supervised models on datasets with a limited amount of labeled data. One way to do that is to solve the dilemma between using an accurate model with a su cient number of parameters, knowing that, only few labeled data will be available to train it.
• Extensive experiments on three public datasets, for a rigorous evaluation and assessment of the developed deep semi-supervised model, to demonstrate the e↵ectiveness of our proposed method. The remaining of this paper is structured as follows: The next section, presents the related work. The subsequent section is devoted to in-depth description of our proposed method. The forth section, describes at length the conducted experiments, as well as the obtained results in comparison to other state-of-the-art deep learning models. Finally, our conclusions and perspectives, regarding future work, are given in the last section.
Ke et al. (2019)  presented their approach called dual student, which focused also on the structure and proposed a sample stability constraint with respect to the unlabeled data to mitigate the e↵ect of confirmation bias. In order to overcome the confirmation bias, we propose an approach with two joint models. The formulation of the semi-supervised learning criterion is based on consistency regularization. The latter depends on minimizing the distance between reconstructed output images and clean output images, to provide accurate learning signals for more focus on incorporating reliable information about data structure during the training process. Moreover, the above-mentioned consistency-based methods were applied to general-purpose datasets, such as CIFAR-10 and CIFAR-100  (Krizhevsky, 2009) , the development of our method was driven by the goal of accurately recognizing and identifiying weed species, from images procured under real-world conditions.
ConvNeXt Encoder ConvNeXt is a family of pure convolutional neural networks, developed through the progressive process of modernizing standard resnets  (He et al. , 2015) , toward the design of transformers by exploring the design spaces and limits that can be reached by relying only on convnet modules  (Liu et al. , 2022). The series of steps adopted during the modernization process can be summarized in the following way: First a change of the compute stage ratio, meaning changing the computation distribution across stages, by specifying the number of blocks that will be attributed to each stage. In a multi-stage design the feature map resolution changes depending on the stage. The second step consists in replacing the standard resnet stem cell, with a 4⇥4 non-overlapping convolution layer called patchify stem.
Experiments and results This section first introduces the experimental setup and the key aspects of the evaluation procedure, then comprehensively assess the performance of the proposed semi-supervised approach for plant classification, particularly focusing on weed datasets. We compare our method with state-of-the-art supervised models, that employ a similar range of number of parameters to show the benefit of the semi-supervised approach. Additionally, we conduct experiments to compare the proposed method with similar semi-supervised techniques on CIFAR10 dataset. In addition to the ConvNeXt-Base supervised model, we have also chose two types of models belonging to two di↵erent families of neural networks, based on their performance and the availability of weights pre-trained on the large dataset ImageNet-22K, which is a superset of ImageNet-1K, and it is comprised of 14,197,122 labeled images divided into 21841 ⇡ 22K classes.
Using these pretrained weights as a starting point for training on the target datasets (DeepWeeds and 4-Weeds), is beneficial in terms of performance and reducing the required time to train the models, compared to the method initializing the weights randomly. These two models are E cientnet-V2-L  (Tan and Le, 2021) and ViT-B-16  (Dosovitskiy et al. , 2020) , the first type is part of the improved models of the family E cientnet-V2, that are optimized to have a faster training speed and e cient parameters, and the second type part of the vision transformer family.
With regard to the use of pre-trained weights, the dense ImageNet-22k layer was replaced by a layer containing a number of units corresponding to the classes specific to the dataset used during training (9 units for DeepWeeds, 4 units for 4-Weeds). With regard to the characteristics of the gaussian noise added during the semi-supervised training of the ConvNeXt-Base models, the standard deviation was set to 0. 1, while the mean µ was set to zero, and to initialize the decoders, pre-trained weights on ImageNet-1K were used. As for the hyperparameter that balances the unsupervised and supervised terms of the objective function, it was set to 1. For generating unlabeled data, a single transformation was applied to the 60% labeled portion of the training subset, involving rotations ranging from -180°to +180°.
Its goal is to provide an accurate estimate of how well a given model will perform in practice on new unseen data samples. Stratified sampling was used to ensure that each fold will be approximately representative of the original dataset in terms of class distribution. We initially partitioned the datasets (DeepWeeds and 4-Weeds) in the following manner: 60% for training, 20% for validation and the remaining 20% for testing, with five rounds of cross-validation. This means that for each model type, a total number of five models are trained on di↵erent partitions of the data, and we have used the validation subset for the selection of the optimal hyperparameters. During the learning process, no data augmentation techniques were employed to increase the size of the labeled portion. On the contrary, as mentioned previously, we avoided such augmentation in order to accurately assess its impact on model performance.