dataesr/scientific-paragraphs-categorization huggingface dataset:
What we call a transition is a set of consecutive points starting in the on zone (resp. off zone) and ending in the off zone (resp. on zone). One of our goals is to estimate the committor function using as little data as possible. It is thus interesting to study how the performance of each method scales with the amount of training data. To do so, we generate several training sets, having an increasing number of transitions N T that span a large interval:  10, 20, 30, 50, 75, 100, 150, 200, 300, 400, 500. In practice, to ensure meaningful comparison between these training sets, we only generate the longest one, with N T = 500. The shorter ones simply consist of the N T first transitions of this very long trajectory. N T = In the case of the AMOC model, generating a trajectory with 500 transitions is not possible without S transitions occurring, which we want to avoid.
AMC is fairly easy to optimize, since it relies on a single parameter: the number of analogs K. We tested different values of K, ranging from K = 5 to K = 250, but we retained only the results for K = 25 because they gave the best results, both in terms of logarithm and difference score. For the largest training dataset, we find a mean normalized logarithm score of 0. 708 for K = 250 up to 0. 718 for K = 25. DGA has an even better score than AMC for N T ≤ 50, although the relative difference of both scores, of less than 9 %, is largely within both error bars. The gap between the 5th and 95th percentile of DGA is also slightly larger than for AMC (respectively, 0. 337 against 0. 331) because it is more skewed towards larger scores. It shows that DGA may tend to produce sharper transitions in the committor between the on states and off states.
722 for DGA against 0. 729 for FFNN). The performance of the FFNN (orange) is exactly the expected one, as it is well known that machine learning poorly performs when trained with too little data. Here, FFNN yields a normalized logarithm score of 0. 026 when trained with the smallest dataset, which is equivalent to predicting the climatology everywhere. Moreover, the committor estimates are very inconsistent, heavily depending on the trajectory: the 5th percentile is -0. 862 and the 95th percentile is 0. 516. But as the size of the training set increases, the FFNN can extract more complex features from the data. As a result, the score increases fast and the error bars shrink. For N T ≥ 200, FFNN performs at least as well as AMC and keeps improving, although this difference is negligible compared to error bars.
-Training DGA consists in two steps: computing a diffusion map kernel matrix (O(N 2 )) and computing the modes as eigenvectors (O(N 3 )). -The training time of FFNN mostly depends on the architecture of the neural network. The only dependence on N comes from the sequential use of the training samples. So, the training time only scales as O(N). -For RC, the training time heavily depends on the size of the reservoir, which does not depend on N, and the training samples are seen sequentially. If we call R the dimension of the features vector, then the training time scales as O(N R + R 2 ) + O(R 3 ) (linear regression + computation of the pseudo-inverse). R depends on the dimension of the system, and this contribution may outweigh that of the size of the training set. As for the testing times, if we call M the number of samples in the test dataset, for each method they scale as follows: -To test AMC, we need to build a KD tree using the training set, which is about O(N log N).
5a , we see that AMC is no longer one of the best-performing methods. It is even the worstperforming method on average for N T > 100. For N T ≤ 100, its score increases from -0. 430 up to 0. 032. It is thus only for N T ≥ 100 that AMC can perform at least as well as the climatology. Moreover, its maximum normalized logarithm score is only 0. 167 for N T = 200. Furthermore, the distribution of scores of AMC exhibits a huge spread: the difference between its 95th percentile and its 5th percentile is 1. 702 for N T = 20, down to 0. 746 for N T = 200. For such a large dataset, it corresponds to the largest spread of all methods. Moreover, the optimal value of K here is K = 15, instead of K = 25 for the AMOC model. When increasing the value of K, the normalized logarithm score dramatically decreases: it is on average -0.
012 for K = 20, -0. 083 for K = 25, and keeps decreasing. In other words, for K ≥ 20, AMC performs worse than the climatology. This may be explained by the more complex structure of the phase space of the double-gyre model (see Fig. 4b ). Here, FFNN performs even more poorly as in the AMOC model when trained with too little data (score of -0. 690 for N T = 10). FFNN is even the poorest-performing method up to N T = 100 and only manages to give a better estimate of the committor than AMC for N T ≥ 150. Its maximum score, for N T = 500, is 0. 360. Moreover, the gap between the 5th and 95th percentiles of this score is huge for N T ≤ 75 (more than 1. 89, systematically larger than that of AMC) but quickly shrinks down to 0. 6 on average for N T ≥ 150. From N T = 75 to N T = 150, the average normalized logarithm score of FFNN also increases from -0.
Its training time is multiplied by 242 from N T = 10 (0. 22 s) to N T = 200 (52. 26 s). For N T = 200, the number of samples N in the dataset is twice as large for the AMOC model with N T = 500. Due to the O(N 2 ) time complexity of the iterative eigensolver, it becomes difficult to train this method in a reasonable time. As a comparison, between N T = 10 and N T = 200, the training time of FFNN is only multiplied by 38 and that of RC by 8. For N T ≤ 20, DGA is faster to train than FFNN, with a training time of less than 10 s. However, for N T ≥ 30, it becomes the slowest method of all by far, with its training time ranging from 23 to 627 s for N T = 100. Between N T = 10 and N T = 100, this training time is multiplied by 263, so its scaling with the amount of data is even worse than for AMC, due to the complexity in O(N 3 ) of the modes computation.
Although efficient in the AMOC model, AMC is very slow and not so robust in more complex settings such as the double-gyre model. The sampling of the phase space indeed becomes difficult when it displays complex structures. This result may be related to what  Lucente et al. (2022a)  observed in the Jin-Timmermann model: there are certain zones of the phase space where the committor function displays a complex, fine-scale structure, which we cannot expect AMC to predict accurately due to its analog approximation. Even the testing phase that uses a search through a KD tree quickly becomes very computationally expensive with dimensionality and requires a lot of training data. FFNN proves a very robust method that can adapt to complex phase spaces. Its main drawback is the time it takes to be trained and the amount of data needed to obtain an adequate estimate of the committor.
RC may also be interesting because at its core lies a simple linear regression. It is thus the only method that makes no assumption on transitions: the training committor will be fitted whatever the value of the probabilities. The problem, on the other hand, is that we need to compute the committor for training, which is what we are trying to avoid because of its cost. It might then be interesting to use a combined approach: for instance, compute a first estimate of the committor using DGA and use it to train RC. Another important question that arises is how the computation time of these methods scale with an increasing number of dimensions. This problem is equally as important as the scaling of the computation time with the size of the dataset in the (realistic) case where we have a model with very high dimension because it often implies we cannot compute long trajectories.
AMC involves the model dimension during the computation of the analogs (for the train and test phase). Then the using a KD tree does not show significant improvement over a brute force search. But after this step, AMC only deals with the number assigned to every analog and thus only depends on the size of the dataset. DGA only consists of multiplying the vectors that contain the modes, which take their values in R; hence its complexity only depends on the size of the dataset. However, the model dimension plays an important role when it comes to computing the modes. Indeed, the method provided by  Thiede et al. (2019)  implies computing the distance between every sample, which is increasingly expensive as the dimension of the state space grows larger. For both FFNN and RC, the model dimension is an important bottleneck.
Acknowledgements. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 956170. The work of  Freddy Bouchet  was supported by the  ANR  grant  SAMPRACE , project  ANR-20-CE01-0008-01. Financial support. This research has been supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  (grant no. 956170 ). Review statement. This paper was edited by  Stéphane Vannitsem  and reviewed by two anonymous referees.
For future therapeutic decisions, PGx data should be included in the patients' electronic health records. Currently, only few in vitro diagnostic tests are commercially available for PGx analyses. The majority of medical diagnostic laboratories that offer PGx panel diagnostics use lab developed assays, which may vary in gene selection, variants detected, and nomenclature for phenotype description. This may lead to discrepancies in the test results between laboratories and the need for standardizing the minimal requirements for a diagnostic PGx test. Markus Paulmichl (Salzburg, Austria) closed the session with a presentation on "Standardization in PGx diagnostics". George Dagher (Paris, France) and Sofia Siest (Nancy, France) introduced the keynote lecture on "Advances in Cancer Detection" presented by Nickolas Papadopoulos (Baltimore, United States).
Several studies presented were supported by the CANCER-ID consortium (including Roche and Agena Bioscience) and unrestricted research grants of Bristol Myers Squibb, Bio-Rad, Biocartis, and Agena Bioscience. Various studies were based on collaborations with MUG Graz (E Heitzer), Imagenome Montpellier (P-J Lamy) and UKE Hamburg (H Wikman). The session was concluded by the talk from Oellerich et al. (Göttingen, Germany), on "Donor-derived cell-free DNA testing in organ transplantation: a value proposition". Dr Oellerich outlined a value proposition for donor-derived cell-free DNA (dd-cfDNA) testing in organ transplantation. There is a need to improve personalized immunosuppression in organ transplantation to reduce premature graft loss. Biomarkers are needed to better detect rejection, asymptomatic graft injury, and underimmunosuppression.
The second presenter of this session was Ekaterini Chatzaki (Alexandroupoli, Greece), describing her recent work on "Biomarker discovery in the era of automated machine learning: from targeted to data-driven approaches". Biomarkers are the cornerstone of precision medicine: identified as a measurable indicator of some biological state or condition, they promise to offer solutions for accurate diagnosis, prognosis and therapeutic monitoring. Dr Chatzaki and her team have been studying methylation in liquid biopsy material in different pathological conditions such as cancer and diabetes. The team has moved gradually from hypothesis-driven to (big) data-driven approaches, as modern -omics technologies lead the accumulation of large precious multi-parametric biological datasets. They employed ad hoc auto machine-learningtools for data extrapolation, delivering low-feature validated models/classifiers and suggest that this approach can have unprecedented added value in different medical conditions.
Based on these results, the CEN/TC 140 for "in-vitro diagnostic medical devices" had released first 9 European Technical Specifications for preanalytical workflows addressing different blood, other body fluids and tissue based molecular applications. In 2018 and 2019 they progressed to International Standards at the ISO/TC 212 for "clinical laboratory testing and in vitro diagnostic test systems". The successor EU SPIDIA4P consortium project (2017-2021), supported by a large international network, has broadened to a final portfolio of 22 pre-analytical CEN and ISO Standards intending to improve in vitro diagnostics and biomedical research, has developed corresponding External Quality Assurance (EQA) and is driving international implementation. The SPIDIA project received funding from the EU's FP7 under grant agreement no.
Session VI-first part-genetic screening and clinical applications The session was chaired by Guillaume Paré, Hamilton Canada and George Dedoussis, Athens, Greece Georges V Dedoussis (Athens, Greece) presented recent work on the "Omics and Mastiha treatment in NAFLD -The EU Mast4Health program". Non-alcoholic fatty liver disease (NAFLD) is a major public health concern in both industrialized and developing nations, with an estimated global incidence of 25% in the general population and with limited treatment approaches. The Mast4Health consortium investigated the effect of the nutraceutical Mastiha supplement in the omics profile of patients with NAFLD, within a multicenter, randomized, double-blinded, and placebocontrolled clinical trial design. Based on the results, there was an improvement in liver inflammation and fibrosis (as assessed by MRI and the use of the sensitive LiverMultiScan software).
It describes also the lack of computer competency or self-efficacy and/or the lack of communications infrastructure. This situation can impact negatively the use of the above described technologies in many areas of our world. This can be partially compensated using specialized applications off-line on computers or mobile devices. Ivan Brandslund (Odense, Denmark) continued the session with a talk on "AI in cancer, emergency and COVID-19". Artificial Intelligence (AI) is a promising technology to use in analysis of numerous data and in diagnostics. Based on the assumption that the effect of any disease would change the pattern of laboratory test results and thus a possibility that analysis of these patterns could evaluate absolute risk for specific diseases, Dr Brandlund et al. have tested the ability of AI to predict risk of cancer in patients consulting their general practitioner, the predictive values for sepsis, death and specific diseases in emergency received patients as well as analyzed consecutive data from patients admitted with severe disease caused by COVID-19.
Otoferlin also has a FerA domain, a four-helix bundle fold with its own Ca 2+ -dependent phospholipid-binding activity, suggesting that the interaction of this domain with the membrane is enhanced by the presence of Ca 2+  (Harsini et al. , 2018). Otoferlin is produced mainly in the brain and inner ear (cochlea and vestibule), and in smaller amounts in other organs: the heart, liver, pancreas, kidney, and skeletal muscle  (Yasunaga et al. , 2000). Otoferlin appears as a globular protein when using the AlphaFold protein structure prediction software  (Jumper et al. , 2021 ; Figure  4 ). In humans, a long and short isoform of otoferlin have been identified  (Yasunaga et al. , 1999 (Yasunaga et al. , , 2000)) , with the long one comprising all six C2 domains and a C-terminal trans-membrane domain (TMD) (1997 aa), while the short one expresses only the last three C-terminal C2 domains (C2-DEF) with the TMD (1230 aa).
Genetic mutations and DFNB9 Approximately 220 mutations causing DFNB9-type deafness have been identified, some of which cause deafness during a febrile episode  (Vona et al. , 2020; Azaiez et al. , 2021). Interestingly, there is no described pathological mutation of the C2A domain (Figure  5 ;  Stenson et al. , 2003). All other areas of the protein can be affected. According to the Human Gene Mutation Database, 41% of the variants are missense, 22% nonsense, 15% deletions, 14% splicing, 6% insertions, and 2% copy number variants. The missense variants alter the folding, stability, or function of the protein. Truncating variants (nonsense, deletion, splicing, insertion, and copy number variation) result in a missing or truncated non-functional protein. Both types of variants result in severe to profound hearing loss.
In mRNA with exon-47 (neuronal otoferlin), translation terminates in that exon, leaving exon-48 untranslated. (D) Exon-47 of the brain isoform and exon-48 of the cochlear isoform specify the C-terminal TMD 60 amino acids of otoferlin. The amino acid sequences of the two isoforms differ essentially in the predicted transmembrane sequence (underlined). (E) Analysis of protein structure with AlphaFold software online (developed by DeepMind and EMBL-EBI) predicts a different angle of insertion of cochlear TMD1 and neuronal TMD2 otoferlin isoforms within phospholipid membranes. The use of a specific TMD is likely important for targeting the protein to specific subcellular locations, such as particular glutamatergic excitatory presynaptic active zones, and/or for specifying the interaction with ligands.
(F) TMD structure analysis and representation using Protter software  (Omasits et al. , 2014)  predicts a 21 aa TMD1 for the cochlear isoform (gi| 154240702| ref| NP_001093865. 1| otoferlin [Mus musculus]) and a 23 aa for the brain isoform (gi| 154240679| ref| NP_114081. 2| otoferlin [Mus musculus]).
, 2010; Michalski et al. , 2017). Another mouse model, Pachanga, carrying a missense mutation in the C2-F domain of otoferlin, was obtained by ENU (N-ethyl-N-nitrosourea) mutagenesis  (Schwander et al. , 2007). Pachanga mice have profound hearing loss, but unlike Otof -/-mice, IHCs still weakly express otoferlin and maintain rapid fusion of the pool of fusogenic vesicles, forming the RRP (for "readily releasable pool of vesicles")  (Pangrsic et al. , 2010). However, IHC exocytosis cannot be sustained upon repeated stimulations in these Pachanga mutant mice, indicating impaired recruitment of vesicles to IHC fusion sites. These results suggested that  Tissue and brain expression of otoferlin. (A) Tissue specificity of otoferlin RNA expression (data from the Human Protein Atlas). (B) Otoferlin expression (mRNA in situ hybridization) in the mouse brain thalamus and cortex (cortical subplate) from the Allen Brain Atlas portal.
Variation in the voltage-gating and spatial The resting size of the hair cells was measured using whole-cell patch clamp recordings of electrical capacitance in absence of stimulation. Data were well fitted with a linear correlation (R 2 = 0. 91). Note that hair cells of Otof-KO mice have a decreased number of synaptic ribbons and proportionally smaller cell sizes as compared to WT mice  (Vincent et al. , 2014 (Vincent et al. , , 2017;; Tertrais et al. , 2019) , suggesting otoferlin as essential for the maintenance of both ribbons and cell sizes, possibly by regulating intracellular hydrostatic pressure and the F-actin membrane network  (Vincent et al. , 2015). organization of Ca V 1. 3 calcium channels at each presynaptic active zone has been proposed to determine the firing specificity of the auditory nerve fibers  (Özçete and Moser, 2021).
The weak restoration of wave-1 amplitude during AAV gene therapy is likely due to an uncomplete rescue of the loss of ribbon synapses in the absence of functional otoferlin  (Stalmann et al. , 2021). Note that a reduction in spiral ganglion neurons with apoptosis is also a secondary defect caused by a lack of otoferlin in IHCs  (Tsuzuki et al. , 2023). Currently, several biotech companies (SENSORION, NCT05402813 and AKOUOS-NCT05572073) are in the preclinical phase for the development of gene therapy for otoferlin mutations in humans.
(D) Exon-47 of the brain isoform and exon-48 of the cochlear isoform specify the C-terminal TMD 60 amino acids of otoferlin. The amino acid sequences of the two isoforms differ essentially in the predicted transmembrane sequence (underlined). (E) Analysis of protein structure with AlphaFold software online (developed by DeepMind and EMBL-EBI) predicts a different angle of insertion of cochlear TMD1 and neuronal TMD2 otoferlin isoforms within phospholipid membranes. The use of a specific TMD is likely important for targeting the protein to specific subcellular locations, such as particular glutamatergic excitatory presynaptic active zones, and/or for specifying the interaction with ligands. (F) TMD structure analysis and representation using Protter software (Omasits et al. , 2014)  predicts a 21 aa TMD1 for the cochlear isoform (gi| 154240702| ref| NP_001093865.
Single Crystal X-ray Study The X-ray diffraction data for LiPr9Mo16O35 and MgPr9Mo16O35 were collected on a Nonius Kappa CCD diffractometer using graphite-monochromated Mo-K radiation ( = 0. 71073 Å) at room temperature. The COLLECT program package  [11]  was employed to establish the angular scan conditions ( and scans) used in the data collection. The data set was processed using EvalCCD  [12]  for the integration procedure. An absorption correction was applied using the description of the crystal faces and the analytical method described by de Meulenaar and Tompa  [13]. Analysis of the data revealed that the systematic absences were consistent with the monoclinic space group C2/m. The structure was solved with the direct methods program SIR97  [14]  and refined using SHELXL97  [15]. Crystallographic data and X-ray structural analysis for both compound are summarized in Table  1 , and selected interatomic distances are listed in Table  2.
Further details of the crystal structure investigation can be obtained from the Fachinformationszentrum Karlsruhe, 76344 Eggenstein-Leopoldshafen, Germany, (fax: (49) 7247-808-666; e-mail: crysdata@fiz. karlsruhe. de) on quoting the depository number CSD-419443. https://doi. org/10. 26434/chemrxiv-2023-fspxm ORCID: https://orcid. org/0000-0003-4778-5581 Content not peer-reviewed by ChemRxiv. License: CC BY-NC-ND 4. 0 Magnetic susceptibility measurements. Susceptibility data were collected on cold pressed powder samples (ca. 100 mg) using a Quantum Design SQUID magnetometer between 2 K and 400 K and at an applied field of 0. 1 T.
Research questions were identified through a 3-step process. (1) Using an online questionnaire (available in Supp. materials), delivered through the Survey monkey platform, participants were asked to respond to a number of questions regarding a wide range of issues relating to salt marsh ecosystem services and management. An initial research prioritisation process was carried out through an open question where experts were asked to list 10 priority research questions concerning saltmarsh ecosystem functioning and ecosystem services, with a total of 191 research questions returned. (2) Analysis of the questions resulted in the identification of 15 research themes, with each theme containing between 5 and 33 questions. Data analysis involved thematic coding of the individual responses, using standard qualitative analysis techniques and data reduction processes carried out by three of the research team to ensure the consistency of the thematic coding.
Volume reconstruction from electron microscopy datasets is a tool increasingly used to study the ultrastructure of the synapse in the broader context of neuronal network and brain organization. Fine modifications of synapse structure, such as activity-dependent dendritic spine enlargement and changes in the size and shape of the postsynaptic density, occur upon maturation and plasticity. The lack of structural plasticity or the inability to stabilize potentiated synapses are associated with synaptic and neuronal functional impairment. Mapping these rearrangements with the high resolution of electron microscopy proved to be essential in order to establish precise correlations between the geometry of synapses and their functional states. In this review we discuss recent discoveries on the substructure of the postsynaptic compartment of central excitatory synapses and how those are correlated with functional states of the neuronal network.