dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular Properties and Caco-2 MERGED Data Figure  1c  shows the distribution of the count of failures of the extended rule-of-five (Ro5) on the MERGED data. Following this rule of thumb, more than 60% of the MERGED data satisfied the extended Ro5 guidelines for oral bioavailability and the rest failed in at least one condition  [31, 32]. The ranking of molecular properties by increasing number of failures was as follows: number of hydrogen bond donors (HBD), calculated partition coefficient (slogP), number of hydrogen bond acceptors (HBA), number of rotatable bonds (RBN), and molecular weight (MW). Supplementary Figure  S1  shows the frequency distribution histograms of all these properties on the MERGED dataset. The analysis of the dataset showed a slight trend of increasing permeability when decreasing HBD, HBA, MW, and RBN.
The opposite effect occurred with the slogP lipophilicity descriptor. Although the correlations of all these properties with the log P app values in our data were not very strong (Pearson correlation coefficient <0. 4), their impact on the permeation rate has been widely described  [10, 25, 33]. Lipophilicity measured as logP is a structural parameter encoding intermolecular forces (electrostatic and hydrophobic) and intramolecular interactions. This metric represents the ratio at the equilibrium of the concentration of two phases (oil and aqueous phases)  [34]. High values are associated with more lipophilic compounds that are more capable to cross lipidic layers via a passive diffusion  [35]. The impact of the hydrogen bonds' acceptor/donor count on the interaction between the molecule and potential targets as the glycoprotein P has been widely recognized  [36].
We split the dataset at 500 g/mol and then, we divided it into four RBN ranges: [0,5],  (5, 10] ,  (10, 15] , and  (15, 62]. The choice of this molecular weight threshold follows the Lipinski threshold recommendation for drug-likeness. Molecular weight equal to or less than 500 g/mol and RBN ≤ 5 appears to be a better indicator of high permeability. More than 60% of the molecules in this group showed permeability values above 10 × 10 -6 cm/s (cut-off associated with good oral absorption), in contrast to 14% of molecules in this RBN range but with a molecular weight above 500 g/mol. Similar behavior was observed in the following range of RBN: molecules with lower molecular weight are more likely to exhibit high permeation rates in the Caco-2 cell line when RBN is higher than 5 or equal to or lower than 10.
These five variables were used to construct a new lowerdimensional model. This time, RMSE values ranged from 0. 46-0. 53, and r 2 values were 0. 5 (0. 01), 0. 56 (0. 01), and 0. 56 (0. 02) for the cross-validation, reliable, and external sets, respectively. These results highlight the consistency of the model and demonstrate that the recursive variable selection algorithm is useful in identifying the minimum number of variables good enough to predict the target property. The predictive ability of a QSPR model must be evaluated according to the experimental variability of the in vitro test. Note that the validation metric of the reliable set presents the best coefficient of determination and the lowest root mean squared error. However, if we select a 15% random set of molecules (size of the reliable set) from the whole MERGED Caco-2 dataset, the statistics are very close to the results of the cross-validation set.
Applicability Domain The reliability of the model predictions was evaluated by the combination of two approaches. The first approach consisted of estimating the prediction error of new samples through the five nearest neighbors (5-NN) algorithm based on the Euclidean distance. The estimated prediction error of the new samples was computed as follows: Estimated Prediction Error (%) = ( ∑ 5 1 RPE NN 5 ), where RPE NN is the recursive prediction error percentage computed for the nearest neighbors during the recursive data cleaning algorithm. The second approach consisted of comparing the prediction variance computed from random forest individual predictions with a predefined optimized threshold of 0. 3. New samples with highly variable predictions (prediction variance >0. 3) and with an estimated prediction error greater than 10% (RMSE > 0.
For molecules substrates of efflux mechanisms, it was ensured to take the value corresponding to the assay without the presence of any efflux inhibitor. In relation to the experimental conditions, assays with extreme values of stirring rate and pH of transport buffer were not considered. The standard deviation distribution of the experimental values collected from the literature for the 32 reference drugs are depicted in Figure  4d. Note that the major number of molecules is distributed in the range 0. 2-0. 6 standard deviation. These results are consistent with the high variability reported in other studies. The median value of all experimental measurements was adopted as the final value to evaluate the model performance. To implement this task, the model was retrained on the whole MERGED dataset and only those molecules belonging to the new external set were removed to ensure a blind prediction.
Model predictions for these drugs are shown in Table  3. distributed in the range 0. 2-0. 6 standard deviation. These results are consistent with the high variability reported in other studies. The median value of all experimental measurements was adopted as the final value to evaluate the model performance. To implement this task, the model was retrained on the whole MERGED dataset and only those molecules belonging to the new external set were removed to ensure a blind prediction. Model predictions for these drugs are shown in Table  3. The regression and the residual plots are depicted in Figure  4a ,b, respectively. In correspondence with the 10 × 10 -6 cm/s (-5 log units) cut-off for high permeability, the model allows us to correctly identify 100% of the molecules with moderate-low permeability and 89% of the molecules with high permeability.
For BDDCS, the optimal threshold to define the boundary between high and low permeability was selected using a dataset of 679 drugs as reported by Brocattelli and coworkers  [65]. The BDDCS classifications were updated according to the new classifications provided in the latest article by Bocci et al. [8]. Permeability predictions were calculated for this set, previously ensuring that any overlap between this set and our MERGED set was removed. From the initial BDDCS dataset, 70% was randomly chosen as the training set. The remainder was used as the test set. Receiver operating characteristic (ROC) analysis on the BDDCS training set suggested that an in silico log Papp permeability cut-off of 6. 3 × 10 -6 cm/s can be used to discriminate between classes 1-2 (high permeability rate) and 3-4 (low permeability rate).
By setting this threshold, the in silico permeability data were able to correctly identify 80% of test set drugs in class 1-2 drugs and 70% in class 3-4. If low confidence samples were removed, the accuracy reached 81% for 191 molecules (94% of the test set). In the case of the ICH list, 26 drugs out of a total of 32 were correctly classified in classes 1-2 or 3-4, for an accuracy of 81%. To compare our results with previous in silico BCS/BDDCS, the ICH list was used as the reference set. Only purely in silico studies in which model predictions were publicly In the context of drug discovery, early prediction of the BCS or BDDCS class for new molecular entities plays a critical role  [8]. Provisional classification in both systems can provide an early estimation of in vivo drug performance, transport mechanisms, and drug disposition  [56].
It is important to note that this provisional classification is limited to drug permeability/the extent of metabolism. For BCS provisional classification, the cut-off for in silico permeability was fixed to 10 × 10 -6 cm/s, as used for other studies  [66]. For the ICH list, BCS classifications were compiled from the literature  [8]. Among the 21 drugs on the ICH list with known and unambiguous classification, 18 were correctly predicted with an accuracy of 86%. For BDDCS, the optimal threshold to define the boundary between high and low permeability was selected using a dataset of 679 drugs as reported by Brocattelli and coworkers  [65]. The BDDCS classifications were updated according to the new classifications provided in the latest article by Bocci et al. [8]. Permeability predictions were calculated for this set, previously ensuring that any overlap between this set and our MERGED set was removed.
From the initial BDDCS dataset, 70% was randomly chosen as the training set. The remainder was used as the test set. Receiver operating characteristic (ROC) analysis on the BDDCS training set suggested that an in silico log P app permeability cut-off of 6. 3 × 10 -6 cm/s can be used to discriminate between classes 1-2 (high permeabil-ity rate) and 3-4 (low permeability rate). By setting this threshold, the in silico permeability data were able to correctly identify 80% of test set drugs in class 1-2 drugs and 70% in class 3-4. If low confidence samples were removed, the accuracy reached 81% for 191 molecules (94% of the test set). In the case of the ICH list, 26 drugs out of a total of 32 were correctly classified in classes 1-2 or 3-4, for an accuracy of 81%. To compare our results with previous in silico BCS/BDDCS, the ICH list was used as the reference set.
The use of a large and diverse dataset and the combination of regional and global regression models allowed us to cover a broad spectrum of chemical diversity and provide a comprehensive applicability domain. The mathematical treatment to take relevant information from this data is novel and the results showed a good correlation in the context of experimental variability. The usefulness of this system lies in its simplicity, since no experimental properties are used as input features, and it is only based on easily calculated 0-2D physicochemical and structural descriptors.
Conclusions A novel methodology was applied to model Caco-2 permeability on a reasonably large dataset. This approach combines recursive algorithms for variable selection and data cleaning with a conditional consensus model (CCM) made of regional and global random forest models. We presented one of the most extensive and fully public datasets used for Caco-2 modeling. Even when experimental conditions (pH, stirring rate, cell passage number, temperature, and cell culture conditions) were not available, the methodology was able to achieve good performance on a very heterogeneous dataset. The validation statistics of the model improved with the quality of validation sets in terms of the standard deviation of molecules with multiple measurements. The practical use of the model was confirmed with the blind prediction of a reliable validation set and two commercially available drug datasets, reaching RMSE values between 0.
43 and 0. 51 log units. The results obtained are consistent with the experimental variability of the modeling dataset. The methodology is based on a reduced number of basic molecular properties or 0-2D descriptors and is easily reproducible and applicable. Considerable curation efforts were made before modeling to avoid overestimation of the QSPR models and to ensure the validity of the structure-permeability relationship model. The model will be updated with new data to incorporate more chemical information and provide an extensive applicability domain. From a practical perspective of the Caco-2 regression model, the workflow for prediction is publicly available and includes as outputs the numeric value of apparent permeability on Caco-2 and the corresponding confidence level of the estimators to facilitate a Caco-2 prediction tool for the medicinal chemistry community.
In addition, the model proves to be useful for discriminating between classes 1-2 and 3-4 of the BCS and BDDCS. Finally, we consider that the present in silico model is a reliable tool to identify the most promising compounds with high intestinal permeability during the early stages of drug discovery and could be applied in the development of further provisional BCS or BDDCS classification systems. The performance of these new QSPR models is also expected to improve, as more and higher-quality data with corresponding experimental conditions are released in the future. The construction of QSPR models based on well-characterized Caco-2 data will be a step forward in the prediction of this task since individual models could be built for each range of experimental conditions without combining measurements from different laboratories.
Data Availability Statement: The training data and validation datasets are supplied in an XLSX file as part of the supplemental information. SMILES codes, log P app experimental value, log P app computational value, and RDKit descriptors are provided for each molecule. The open-source software KNIME version 4. 4. 2 (https://www. knime. com/download-previous-versions, accessed on 16 September 2022) was used for reading, analysis, modeling, and visualization of the data. Other extensions, such as "RDKit KNIME Integration" and "Indigo KNIME Integration" were downloaded directly from KNIME using the following update sites: https://update. knime. com/ community-contributions/trusted/4. 4 (accessed on 16 September 2022) and https://update. knime. com/community-contributions/4. 4 (accessed on 16 September 2022).
2021, to overcome  biases in event detection because of the varying levels of heterozygosities (Figure  S3 ). LOH event rates per line were calculated as N/D, where N= number of events (Interstitial, Terminal, or total events) and D=total number of divisions/ bottlenecks which we assumed to be 25, based on previous literature. All sequenced MA lines listed in Table  S6  are available upon request. In addition, sequence data are available from National Centre for Biotechnology Information Sequence Read Archive under accession number: PRJEB45355.
ACKNOWLEDGMENTS We thank  Andrew Wong  from  Mechanobiology Institute (MBI)  science communication core for editing the manuscript and MBI computational core for supporting us about computer-related research activities. We also appreciate  Yuting Lou  for valuable discussions. This research was supported by  Seed fund of Mechanobiology Institute  (to JP, TH) and  Singapore Ministry of Education Tier 3 grant ,  MOET32020-0001  (to GVS, JP, TH) and  JSPS KAKENHI  No. JP18H05529  and  JP21H05759  from  MEXT, Japan  (to TS).
arcgis. com/en/system-requirements/latest/arcgis-desktop-systemrequirements. htm). The distribution information of the Cixiidae in China was imported into ArcGIS Version 10. 8 software, the latitude and longitude of the distribution sites were set as the coordinate attribute elements, and the symbols in the map were set to different colors for distinguishing different genera of the tribes, and finally the maps of the distribution of the tribes and species were exported. Presence/absence matrices for species and for genera were built for each of the 10 OGUs (physiographical regions as operative geographical units, Crovello 1981). Similarity coefficients use binary data to measure association between OGU. On the basis of a review of similarity coefficients (Shi 1993), the Jaccard's coefficient in NTSYS Version 2.
Conclusions This study is the first zoogeographic analysis based on grid cells of Cixiidae in China and adjacent areas, including all the available data for the family. However this dataset has is own limits: 1) the stronger collecting efforts into southern China and taxonomic studies clearly advanced in the Taiwan region because of studies by Tsaur over the past three decades  (Tsaur and Lee 1987 , Tsaur et al. 1988 , Tsaur 1989a , Tsaur 1989b , Tsaur 1990a , Tsaur 1990b , Tsaur et al. 1991a , Tsaur et al. 1991b , Tsaur and Hsu 2003 , Tsaur 2009) , 2) the limited to very limited knowledge of Cixiidae in countries adjacent to China, despite studies by  Distant (1911 ), Emeljanov (1974 ), Fennah (1978) ,  Anufriev (1987) ,  Anufriev and Emeljanov (1988 ), Hoch (2013 ), Anonymous (2015) , 3) it does not take into account hostplants, which are however key factors also affecting the distribution of these obligatory, phytophagous planthoppers, although host-plants and the planthopper species complex are also together affected by other complex topographic and climatic factors embedded in a long dynamic geological process.
Acknowledgements We wish to express our sincere thanks to  Prof. Dr. W. H. Reissig  ( New York State Agriculture Station, Cornell University, USA ) for his critical comments on an earlier version of this manuscript as well as to our reviewers. The principal phase of this research was supported by the  Pilot Project of Standardized Curation, Data Integration and Resource Sharing of Zoological Collections  ( 2015FY210300 ) by the  Ministry of Science and Technology of China and China Scholarship Council  ( 201906300092 ).
In Fig. 4  the influence of the position of the taper tip on the coupling efficiency and reflections are considered by simulating the coupling using 3D FDTD software (Lumerical). Here the HMWG is modelled as a perfect electrical conductor, while in reality it is made of aluminium which introduces extra losses. Horizontal (y-axis) misalignment will not result in an excitation of the higher order mode (Fig. 2 ) because the waveguide's width at 1 mm insertion is single mode and does not support that mode. The alignment is performed by using translation stages. What is not shown in Fig. 4  is the reflection, the same simulations show that this fluctuates between -13 and -9 dB for all positions considered. Before moving on to the presented structure we first discuss the naive circular bend and the losses associated with it.
In the middle we plot the loss associated with the mode mismatch between the modes in the straight and bent section. On the right is the propagation loss due to material absorption (κ Si = 2. 07 × 10 -4 ) for the whole bending section. This data was extracted from 2D finite difference eigenmode simulations by Lumerical for a frequency of 650 GHz. We will now discuss different schemes, most of these were originally designed for optical wavelengths (more commonly used in integrated photonics) but the same techniques are valid for larger wavelengths. The first one we discuss aims to tackle the mode mismatch by shifting the circular waveguide slightly inward by such an amount that the mode overlap is maximal  [26]. For telecommunication wavelengths this can be an issue because it requires very precise fabrication  [27].
Lastly a novel technique was used in Ref. [30]  where they used a relatively sharp bend for a wide multimode waveguide and then define pixels (of a predetermined size) in the waveguide that are etched. The amount of etched pixels and their position follows from an optimization algorithm and simulation results. It should be noted that one can't apply semi-analytical techniques as were used to determine the loss of the naive circular bend. Instead one has to rely fully on 3D simulations. This is also true for our bends which should not be modelled as having a propagating mode, which is why we can also call it corners. Instead its performance has to be simulated in 3D (we used Lumerical's FDTD software) where the straight waveguide mode is launched into the corner and the resulting transmission (into the fundamental mode) is recorded after the corner.
[32]. The geometry is explained in Fig. 8. The facet is replaced by an ellipse section defined by focal points located in the center of the in and outgoing waveguides, separated at a distance of 2p. This shape increases the amount of terahertz waves which will be reflected at the outer edge by total internal reflection. The ellipse is shifted along the bisector of the two waveguides over a distance of s to account for the Goos-Hänchen effect  [33]. Lastly the inner corner is also rounded, circularly this time, with a radius of f. Now we have three parameters describing the structure. Since this structure cannot be understood using the same analysis used for most bends discussed in Sec. 4, we have to rely on 3D simulations using Lumerical's FDTD (finite-difference time-domain) software to explore the (p, s, f ) parameter space.
A least squares linear regression is performed to determine the three sources of loss: the insertion loss, the propagation loss and the loss per 90 • -bend. This is done through the statmodels package in python inputting a matrix whose columns are the number of bends, straight section length, the number of tapers (2) for the x-values and the measured loss as the y-value. The results of which are shown in Fig. 17 , together with the extracted standard errors. Averaging these out over the 600-750 GHz frequency range results in an average insertion loss and standard deviation of 0. 582±0. 117 dB per taper, a propagation loss of 0. 272±0. 006 dB/cm and a loss of 0. 141±0. 016 dB per 90 • -bend. The difference between the simulated and measured transmission spectra are attributed to fabrication imperfections.
Data Analysis Raw files were analyzed using MaxQuant software (version 1. 6. 10 and 1. 6. 14) and the MS/MS spectra were searched against the human FASTA file from the UniProt database (version 08/17), using a reverse decoy database and common contaminants (a list of 245 entries). The peptide search included cysteine carbamidomethylation as a fixed modification, and N-terminal acetylation and methionine oxidation as variable modifications. Trypsin was selected as the specified protease, and a maximum of two missed cleavages was allowed. The minimal peptide length was set to six amino acids, and the "match between runs" feature was enabled. A false discovery rate cutoff of 1% was applied at both the protein and PSM identification levels. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE  [65]  partner repository with the dataset identifier PXD022218 and PXD022256.
Bioinformatic Analysis All bioinformatics analyses were performed with the Perseus software  [66]. Protein groups were filtered to require 70% valid values in at least one experimental group. The label-free intensities were expressed as base log2, and empty values were imputed with random numbers from a normal distribution for each column to best simulate low abundance values close to the noise level. A group was created for each clinical condition, and a t-tests were performed, with an s0 of 0. 1 and a required a p-value of 0. 05. To visualize the profile of the experiment, hierarchical clustering of the resulting proteins was performed with log2 intensities after z-score normalization of the data for each cell line, using Euclidean distances. The Venn diagram of identified proteins was calculated using the online tool jVenn  [67].
Conclusions In conclusion, proteomic analysis of the AH of 47 RB samples and 6 controls allowed for the identification of a significant number of proteins in RB at various stages of the disease. This work provides a novel insight into the use of AH as a source of tumor biomarkers in this specific type of tumor. To our knowledge, this is the first proteomic study performed on AH from conservatively treated RB at different stages. The characterization of the mechanisms underlying the clonal selection of the tumor cells and the subsequent seeding development still requires considerable study. However, this approach, among other liquid biopsy analyses, will undeniably contribute to unravel the disease tumorigenesis process and guide patient management in the future. tools. Nucleic Acids Res] partner repository with the dataset identifier PXD022218 and PXD022256.