dataesr/scientific-paragraphs-categorization huggingface dataset:
The data from the two studies demonstrated that domestic cats are susceptible to experimental infection with SARS-CoV, that the virus could be transmitted to other cats, and that clinical signs and pathology were similar to those in humans  [32, 33]. Natural infection in field cats was also described during the first SARS outbreak  [27, 28, 31, 35, 36]. Domestic cats living in an apartment block in Hong Kong where 100 people contracted SARS-CoV infection also tested RT-PCR-positive. However, final proof of direct transmission from humans to cats was lacking  [32, 37, 38]. Oropharyngeal and rectal swabs were collected from cats from a multiple cat household and from two dogs in this apartment block over 14 days, after their owners were diagnosed with SARS; eight cats and one of the dogs tested RT-PCR-positive.
Any cat from a COVID-19 household should not be taken into another household. The American Veterinary Medical Association (AVMA) has developed detailed protocols that could be implemented to protect staff when they are exposed to high-risk situations, such as when entering an infected person's home or coming into proximity with a sick person. AVMA recommends that procedures should be consistent with the most up-to-date guidance from the relevant public health authorities (AVMA, 2020). This guideline will continue to be updated regularly on the ABCD homepage (www. abcdcatsvets. org) as new data become available. In addition, ABCD has given answers to common questions (see Appendix A); these are also available on the ABCD homepage. Pet owners should always maintain good hygiene practices and under no circumstances should cats be abandoned.
Related Work Trainability of a Neural Network. Training neural networks can be seen as a non convex optimization problem. However because of the absence of poor local minima, the trainability of a Deep Neural Network is proven to be possible  [15]. Recent results theoretically prove that gradient descent can find a global minimum for nonlinear deep neural networks of sizes commonly encountered in practice  [17]. In fact a linear increase of the number of trainable parameters as the size of the dataset increases is sufficient to find a global minimum. Such a network generalizes well on unseen test samples. Growing Neural Networks. The properties of growing networks, notably with regard to training convergence, are more and more investigated. The authors of  [16]  for example add a special neuron by output unit in order to eliminate all suboptimal local minima of any deep neural network.
This idea of a network adapting its architecture to the dataset it is trained on is also found in automated Neural Architecture Search (NAS, see  [8] ). Typical works in this field are  [4]  and  [5] , where the authors use a grow-and-prune training paradigm to iteratively add and remove units in order to achieve more compact networks with state of the art performances. The idea of progressive growing of models has also been explored in the context of Generative Adversarial Networks (GAN)  [10]  to increasingly add new details as the training progresses. In  [14] , the authors start with low-resolution images, and then progressively increase the resolution by adding layers to the networks. This allows the model to learn increasingly finer scale detail, instead of having to learn all scales simultaneously.
In particular, this procedure, called Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss and present empirical results showing model generalization improvement across a variety of benchmark datasets. In order to correctly evaluate the flatness of minima, measures invariant to the rescaling issue pointed out in  [7]  now exist  [31, 32]  and can be used to evaluate the difference of flatness in minima between models of the same size. The goal of our work is to investigate whether growing a feedforward neural network (FNN) from scratch throughout the learning phase yields better loss surface properties at minima than learning a standard FNN, both having ultimately the same number of parameters, using the measure developed in  [32]. 3 Model Description.
Experiments with Small Models We implement our model in PyTorch and evaluate it on AGNews and MNIST  [19]. AGNews : AG News (AG's News Corpus) is a sub-dataset of A. Gulli's corpus of news articles constructed by assembling titles and description fields of articles from the 4 largest classes ("World", "Sports", "Business", "Sci/Tech") of AG's Corpus. The AG News contains 30,000 training and 1,900 test samples per class. MNIST : The handwritten digit benchmark MNIST is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. We compare our model with a fully connected network with the same number of parameters as the final number of parameters of our model (after insertions). Our objective is to study the properties of the final loss surface when adding neurons one by one, all other hyper-parameters being equal (number of parameters, activation functions, weight initialization, accuracy, batch size, SGD algorithm,.
). In order to study the loss surface, we use the metric proposed in  [32] , and more precisely the scale invariant measure to compare the flatness of the minima found by our different models. This Hessian-based measure for flatness is invariant to rescaling as the authors use a metric on a quotient manifold structure that captures the rescaling that is natural to the space of parameters of neural networks with positively homogeneous activations. As done in  [32] , we train each network architecture and dataset up to 100% of training accuracy with stochastic gradient descent (SGD). We test two different batch sizes of 10 and 100 samples for MNIST, and 1 and 16 samples for AGNews. No pretrained word embeddings are used for AGNews. For both datasets we use the categorical cross entropy loss.
The classification head is classically composed of a two-layers feed-forward network with 1024 hidden neurons. We thus propose next to replace this classification head with a bare one-layer feed-forward network, and to grow it until it reaches the same number of parameters as the reference classification layer. The rest of the pre-trained Roberta large model, i. e. , the self-attention layers, stays the same. We use the standard CoLA benchmark  [37]  to train and evaluate our growing model against RoBERTa. The CoLA dataset is composed of 9594 training and 1063 test sentences. The objective is to classify each sentence into two classes: grammatically correct and incorrect English sentences. In order to study the impact of growing the network, we insert neurons progressively throughout the whole learning process, which consists in 10 epochs in the original RoBERTa experiment.
Conclusion and Future Work Our main contribution is the proposal of a growing neural network approach, which we experimentally validate in several conditions on three tasks and with small and large deep learning models. The results show that the resulting loss function has flatter minima than with the traditional training procedure on the full network. We further show that such flatter minima improves the generalization capability of the trained models when they do not rely on complex embeddings. These results tend to show that the paradigm of growing neural networks during the learning phase intrisically leads to flatter minima, which is an interesting observation, although these results have to be confirmed on different datasets, potentially with transfer learning experiments in order to better assess the generalization performances across related tasks.
MMN in schizophrenic/Alzheimer's patients Participants Eligible patient and healthy control participants were identified via patient cohort registers and clinical programs affiliated with the participating centers. The study has been conducted at three research sites in The Netherlands (University Medical Center Utrecht, VU University Medical Center Amsterdam, and Leiden University Medical Center), and two research sites in Spain (both located in Madrid: Hospital General Universitario Gregorio Marañón and Hospital Universitario de La Princesa). In light of data suggesting that impairment of MMN response, as well as social withdrawal, could be observed both in SZ and AD patients, it was hypothesized that MMN response may correlate with the level of social withdrawal, regardless of the diagnosed disease  8, 12, 16.
EEG recording and processing EEG signals were collected using EEG caps with the position of the 64 EEG electrodes following the international 10-20 Jasper system (eego system from ANT; pass band: 0. 1-70 Hz; sampling rate: 2048 Hz). Impedances of all electrodes were kept lower than 20 kΩ. In order to improve artifact rejection, two electro-oculographic (EOG) channels were simultaneously recorded using silver cup electrodes to monitor ocular movements and eye blinks. In order to reduce electromagnetic noise due to the main power supply, a digital narrow 50-Hz-centered notch filter was applied to data. During data acquisition, subjects were seated on an armchair bed in a quiet environment and watched a short silent video (in order to provide a visual distraction). None of the participants exhibited hearing problems as checked by a simplified tonal audiometric evaluation performed on study subjects at screening.
EEG data were first preprocessed by performing filtering, epoching (i. e. , [-200 + 500] ms around the stimuli), and various steps of automatic artifact reduction and rejection  [21] [22] [23] [24]. Artifact rejection and ERP constructions were carried out using Matlab. Next, the clean EEG was used to extract biomarkers  25. Epochs were then grouped based on their corresponding stimuli type (i. e. , standard or deviant). For each electrode, the MMN ERPs were obtained by subtraction of the average ERP of standard stimuli from the average ERP of deviant stimuli, while the parameters extracted from the MMN difference wave were • Peak amplitude (µV): amplitude of the negative peak in time window [90, 290] ms after the stimulus. • Peak latency (ms): latency of the above peak in time window [90, 290] ms after the stimulus.
Auditory mismatch negativity (Flip-Flop) procedure All of the electrophysiological recordings were performed 7 days after the last day of PCP administration and took place between 9 am and 1. 30 pm in the home-cage environment. The telemetry data were collected using an acquisition system from Data Sciences International (St Paul, MN, USA). During the experimental session, the animals were individually housed in cages placed on the receiver panel. EEG signal was monitored and stored on the hard drive of a personal computer using HEM software (version 4. 3, Notocord France). The stimulation protocol used is illustrated in Fig. 1b  and has been adapted from the protocol used in a clinical trial by BIOTRIAL described above, from previously published work  27  and from preliminary experiments performed in the lab.
Briefly, EEG was continuously recorded during a 25-min session of acoustic stimulation using an oddball flip-flop paradigm. During the first 12 min (flip), animals were exposed to approximately 2000 tones with 90% of sounds at 6 kHz (standard sound) and 10% at 8 kHz (deviant sound) with a fixed interstimuli interval of 350 ms. After a 1-min pause (silent), another 12 min of acoustic stimulation was performed (flop) with approximately 2000 stimuli with 90% of sounds at 8 kHz (standard sound) and 10% at 6 kHz (deviant sound) and a fixed interstimuli interval of 350 ms. Auditory stimuli were delivered by sound card controlled by ad hoc scripts running under E-Prime v2 software (Psychology Software Tools, Sharpsburg, PA, USA). The same script sends synchronization triggers to the acquisition system in order to allow EEG epochs averaging to obtain ERPs.
Sound stimuli were delivered through a speaker mounted above the experimental cage. Each stimulus was calibrated as follows: using a sound calibrator (Laserliner SoundTest Master, Germany) prior to the test: 75 dB, 50 ms (fade-in: 5 ms, fade-out: 5 ms). Signal processing was performed using ad hoc Matlab scripts. Continuous EEG was split into epochs surrounding each auditory stimulus. EEG epochs start 50 ms prior to stimulus onset and last 350 ms. Low-pass filter was applied on each epoch. This filter is a zero-phase-shift Butterworth filter admitting a 50-Hz cut-off frequency and a 22-order to remove high-frequency components that are noisy or irrelevant for the current study. The average value prior stimulus onset was subtracted from the whole epoch to achieve a baseline correction. Automatic epoch rejection relies on the threshold method: if the amplitude of any sample of an epoch exceeded 300 µV, this epoch was rejected from the analysis.
Linear detrending was performed on each remaining epoch. Epoch set was slipped into four subsets matching with the two types of stimulus and the probability of occurrence (4 classes: 6 Hz or 8 Hz during flip or flop). Averaging all remaining epochs within each subset was performed to obtain a single ERP for each class and each recording. The difference between ERP obtained from deviant stimuli minus standard stimuli leads to new ERPs where MMN can be measured: the MMN was estimated by calculating the integral on the [65-105]-ms interval. Parameters were extracted from those individual ERPs. Grand average ERPs are the mean of individual ERPs for each condition. To counterbalance the individual variability previously observed in our setting (preliminary experiments, data not shown), the quantitative comparison between vehicle-and PCP-treated rats was made after baseline normalization of the MMN area (i.
Auditory mismatch negativity (Flip-Flop) procedure After a recovery period of 2 weeks, a baseline EEG was recorded using the active two-electrode differential amplifier (Biosemi, Netherlands) for 20 min without acoustic stimuli. Auditory stimuli were generated with a custom program written in Labview and sound stimuli were delivered through a speaker mounted above the floor of the experimental box. For each box, sound-pressure level and frequencies were checked with a high-quality sound meter (Brüel & Kjar 2270-S, HBK) according to a standardized protocol. The EEG signals underlying the auditory-evoked potentials were digitized at 512 Hz, band-pass filtered between 0. 16 Hz and 100 Hz. Vigilance states were identified using EEG amplitude and spectra records reconstituted on the computer screen.
A series of four randomized sequences of 20 min were presented at 3-s interstimulus intervals, with the low-pitch tone of 6 kHz serving as the frequent (STD, probability 80%) and the high-pitch tone of 8 kHz as the infrequent oddball (DEV, probability 20%), and vice versa as a means to control for sound frequency (i. e. , STD is delivered as DEV: flip-flop design). This protocol was determined during a harmonization effort, based on data collected from experiments performed in multiple sites involved in the IMI PRISM consortium. The peak amplitudes N1 or P2 components of the ERPs were extracted from 20 to 60 ms or 50 to 125 ms after stimulus presentation, for each mouse strain, and averaged across genotype groups.
4b ). AEP responses to the first standard after the deviant (non-or less-adapted response), which might be regarded as a control and the last standard before the next deviant (fully adapted response), were extracted and were compared to responses elicited by all standard stimuli. The amplitude of the ERPs to standard stimuli did not reveal the qualitative difference, suggesting that no habituation to the continuously displayed tones was present over the time of the experiment (data not shown). An MMN-like potential response peaking at 50-125 ms was observed in both genotypes for the high deviant tone versus the low standard tone (Fig. 4b ) but not for the reversed contrast (not shown). We performed an iterative approach to determine the most significant difference between the AUC (WT vs. APP-PS1 mice) in different time windows and to determine the most interesting time window for analysis.
The upper and lower panels illustrate ERPs recorded via Channel#1 and Channel#2, respectively. The results are expressed as means. BL baseline. b The left panel illustrates MMN area (calculated with the 6-kHz flip-flop method) expressed in change from baseline (CBL) with EEG recording on Channel#1 (n = 8-12). An unpaired Student's t test revealed significant impairment in PCP-treated rats compared to control (t 18 = 2. 496; *P = 0. 0225). Individual data are represented along with means ± SEM. The right panel shows MMN area expressed in change from baseline (CBL) with EEG recording on Channel#2 (n = 8-12). An unpaired Student's t test revealed no significant impairment in PCP-treated rats compared to control (t 18 = 1. 145; P = 0. 2671). Individual data are represented along with means ± SEM.
Discussion In this study, we used a circuit-based transnosographic approach providing new insight into the cross-disease evaluation of the MMN response. We report clinical data that we back-translated into animal disease models. Our study strongly supports the translational potential of MMN and emphasizes its tremendous interest as efficacy biomarker for neuropsychiatry drug development. 6] [37]  ) and associated animal models  28, 35, 38, 39  as well as in AD patients (for review see refs. 12, 13, 36 ), a more limited amount of MMN data is available for AD animal models  32. While we confirm the sensitivity of MMN observation in schizophrenic patients and preclinical PCP models, the present study discloses novel MMN results on the IMI PRISM dataset and the APP/PS1-21 mouse model of amyloidosis.
This effect of PCP on MMN is also in line with previous data reporting impaired MMN in rats under chronic PCP treatment  28  or repeatedly exposed to PCP only during neonatal period  38. Interestingly, the effect of PCP withdrawal on the MMN response was much clearer when EEG was recorded with the active electrode onto the frontal cortex (Channel#1), whereas variability was much higher when the recording was made from a more posterior area (i. e. , via Channel#2 that corresponded to the active electrode onto the auditory cortex). This finding is consistent with the data obtained in human studies suggesting a more clear difference in the frontal area, while occipital areas seemed to be less impacted (Fig. 2a, b ). Subchronic PCP treatment has been widely used to model schizophrenia in rodents as it is thought to some extent to resemble the symptomatology and neuropathology of the disease  29, 53, 54.
Acknowledgements The clinical data presented here were generated in the framework of the  PRISM  project (http://www. prism-project. eu),which received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement number:  115916. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. The authors would like to thank  Ria Biermans  for excellent technical assistance in conducting the mouse experiments and  Damien Maurice  for reviewing the M&M section.
Statistical Analysis In Experiment I, milk production (average value from week 3 to 11), milk fat and protein production, BCS (average value from week 3 to 11), and bodyweight (average value from week 3 to 11), were analyzed using analysis of co-variance by PROC Mixed SAS Institute  [49]  following the model below: Yijkl = µ + SLi + Pj + HAk + STl + HAk × STl + SLi × HAk + SLi × STl + b × Xijkl + eijkl where Yijkl represents the analyzed variable, µ is the overall mean, SLi is the fixed effect of the lactation stage group (i = 1-2), Pj is the fixed effect of the parity (j = 1-2), HAk is the fixed effect of the herbage allowance (k = 1-2), STl is the fixed effect of the supplement type (l = 1-2), HAk × STl is the interaction between HA and ST, SLi × HAk is the interaction between lactation stage group and HA, SLi × STl is the interaction between lactation stage group and ST, b × Xijkl is linear effect of the pre-experimental centered covariate for each experimental variable (pre-experimental milk production was used as the covariate for milk production, milk fat production, and milk protein production), and eijkl is the residual error term.
All other animal variables, for which covariates were not available (milk composition, blood metabolites, behaviors, and DM intakes), were analyzed using analysis of variance by PROC Mixed SAS  [49]  following the same model but without the effects of the covariates. The effect (p-values) of the lactation stage and the number of parity (primiparous or multiparous) are presented in the tables but the means are not shown (these factors were only included in the statistical model to account for the variation attributable to them). In Experiment II, ruminal fermentation parameters were analyzed according to a 4 × 4 Latin square design using the GLM procedure of SAS  [49]  with the following model: Yijkl = µ + Pi + Cj +HAk + STl + HAk × STl + eijkl where Yijkl, µ, Pi, Cj, HAk, STl, HAk × STl and eijkl represent the analyzed variable, the overall mean, the random effect of period (i = 1-4), the random effect of a cow (j = 1-4), the fixed effect of herbage allowance (k = 1-2), the fixed effect of supplement type (l = 1-2), the interaction between herbage allowance and supplement type, and the residual error term, respectively.
The molar % of free phenolic groups in b-O-4 linked G or S units, referred to as %GOH or %SOH, is calculated according to the outlined formula. The data represent mean (SD) values from biological triplicates. Different letters in columns indicate significant differences (Duncan test, P < 0. 01). (3 þ 4)/(1 þ 2) %GOH %SOH 100 3 1/(1 þ 2) 100 3 3/(3 þ Figure  4  Relationships between the pCA amounts in poplar CWs and the percentage of G lignin units with free phenolic groups (%GOH, black circles, full line) or the percentage of S lignin units with free phenolic groups (%SOH, white circles, dotted lines). The lignin structural traits %GOH and %SOH are evaluated by thioacidolysis of permethylated samples for BdPMT1-OE poplars and their WT controls percentage of the total area of the main dimers (set to 100; Supplemental Table  S2 ), the relative amount of dimers with biphenyl or biphenyl ether bonds was not increased by the presence of BdPMT1 and suggests that BdPMT1-OE/WT line 17 does not contain lignins more branched than the WT ones.
The percentage of the alkali-soluble lignin (%Alk-L) was calculated from the SR recovery yield and the lignin amount of the CW and SR samples. From the data of Table  6 , we can see that the lignins from the AtF5H-OE control samples are more alkali-soluble than the lignins from the WT samples. This result is consistent with the higher frequency of terminal units with free phenolic groups in the AtF5H-OE control samples as compared to the WT samples (Table  5 ). Whatever the genetic background, the percentage of the alkali-soluble lignin (%Alk-L) revealed that the BdPMT1-OE lines are more easily delignified by the employed mild alkaline treatment compared with their control lines. Whereas 15%-20% of the lignin polymers were solubilized by cold alkali for the controls, the %Alk-L was substantially increased in the BdPMT1-OE lines (up to 26%-28% in lines 9, 17, and 21, Table  6 ).
Acknowledgments We thank  Armelle Delile ,  Orlane Touzet  ( INRAE Biofora ) and  Anita Rinfray  (GBFOR,  INRAE ,  Forest Genetics  and  Biomass Facility , https://doi. org/10. 15454/1. 5572308287502317E12 )for their technical contribution to produce and characterize the plant material. Likewise, the technical assistance of Frederic Legee for the determination of KL is gratefully acknowledged. The production of transgenic material has benefited from the  LICA (Laboratoire d'Ingenierie Cellulaire de l'Arbre)  equipments.