dataesr/scientific-paragraphs-categorization huggingface dataset:
Pre-treatment Genetic, Clinical, Biochemical, and Histopathological Findings in Individuals with FLAD1 VariantsThe American Journal of Human Genetics 98, 1130-1145, June 2, 2016 1135 (p. Arg530Cys), affecting the FADS domain and a frameshift variant in exon 2 on the other allele. The four additional individuals were all found to carry biallelic frameshift variants in exon 1 or 2. Carrier testing showed that each parent analyzed was heterozygous for one allele (Figure1A). None of the predicted LOF variants was present in an in-house database with WES data from approximately 6,000 individuals or in >120,000 alleles from the Exome Aggregation Consortium (ExAC) Browser (accessed January 2016). Only variants c. 1588C>T (p. Arg530Cys) and c. 508T>C (p. Phe170Leu) were recorded in the ExAC Browser; c.
Acknowledgments We thank all affected individuals and their families for their participation and for providing important samples for the present research study. We thank  Dr. Francesco De Cillis  for his participation in the early stages of this work,  Dr. James Pitt  for biochemical evaluation of individuals S1a and S1b,  Dr. Norma Romero  for muscle histology analysis of individual  S7 , and  Dr. Maria Teresa Balsimelli ,  Dr. Carla Cozzolino ,  Prof. Vincenzo Nigro , and  Dr. Filippo Maria Santorelli  for their clinical care and biochemical evaluation of individual  S6. The study was supported by the  Aarhus County Research Initiative , the  Danish Council of Independent Medical Research  ( 4004-00548  to R. K. J. O. ), the  John and Birthe Meyer Foundation  (N. G. ), the  European Commission   FP7-PEOPLE-ITN Mitochondrial European Educational Training Project  ( GA 317433  to R.
1588C>T was recorded as heterozygous three times in 121,410 alleles, and c. 508T>C was recorded as heterozygous two times in 121,408 alleles. Of note, the affected individuals can be distinguished by their symptoms and the identified FLAD1 variants (Table1and the Supplemental Note). Whereas subjects S1b, S2, and S3 Muscle Investigations Other FLAD1 Variants FADS1 Variants Increased Respiratory- Mitochondrial (GenBank: (GenBank: Affected Age of Responsive Blood Elevated Chain Enzyme ID Sex Consanguineous NM_025207. 4) NP_079483. 3) Domain Onset Status to Riboflavin Acylcarnitines a Organic Acids a Activities b Activities Histology S1a F yes c. [1484_1486delCCT]; p. [Ser495del]; FADS 32 hr died at NT C5, C8, C14, ethylmalonic, within NR vacuoles in [1484_1486delCCT] [Ser495del] 3 days C5-DC adipic, suberic, control muscle fibers and range (EM not done) dehydrosebacic acids, hexanoylglycine S1b M yes c.
Furthermore, using the same isolation procedure, there was a substantial difference in total protein concentration between mitochondria isolated from young and aged rats. Mitochondria isolated from aged rats contained almost half the amount of protein concentration than those from young rats. This is not surprising since the number and mass of mitochondria is known to decrease with age, which alters the quantification of protein between both groups of animals. Therefore, to obtain a more accurate estimation of mitochondrial content, we used cytochrome c oxidase (COX) activity to normalize the data obtained from AT1 and AT2 receptor expression determined by WB (Figure  3i ). 15 fect of mitochondrial angiotensin receptors on mitochondrial respiration. Bioenergetic studies were carried out using mitochondria isolated from the nigral region of rat, and confirmed with mitochondria isolated from the whole brain.
Real-time PCR was used to examine the relative levels of TH, angiotensinogen, AT1 and AT2 mRNA. A real-time iCycler PCR platform (Bio Rad, Hercules, CA, USA) and IQ SYBR Green Supermix kit (Bio Rad) were used. β-Actin was used as a housekeeping gene and the data were evaluated by the delta-delta Ct method (2 -ΔΔCt), where Ct is the cycle threshold. Finally, the PCR products were loaded on a 2% agarose gel with SYBR Safe stain (Invitrogen) and separated by electrophoresis. Primer sequences were as follows: for AT1, forward 5′-TTCAACCTCTACGCCAGTGTG-3′, reverse 5′-GCCAAGCCAGCCATCAGC-3′; for AT2, forward 5′-AACATCTGCTGAAGACCAATAG-3′, reverse 5′-AGAAGGTCAGAA CATGGAAGG-3′; for angiotensinogen, forward 5′-GAGTGAGGCAAGAGGTGTA-3′, reverse 5′-TCCAACGATCCAAGGTAGAA-3′; for TH, forward 5′-GGCTTCTCTGAC CAGGTGTATCG-3′, reverse 5′-GCAATCTCTTCCGCTGTGTATTCC-3′; and for β-actin, forward 5′-TCGTGCGTGACATTAAAGAG-3′, reverse 5′-TGCCACAGGATT CCATACC-3′.
5 mM EGTA; pH 7. 4) using a substrate-uncouplerinhibitor titration protocol (SUIT), as previously described. 39 The substrates used to fuel the ETS were 2 mM malate+5 mM pyruvate (MP) for evaluating complex I respiration. The OXPHOS capacity, which is similar to State 3, was measured in the presence of substrates with saturating ADP (2. 5 mM). The maximum ETS capacity (ETS max ; i. e. non-coupled respiration) was evaluated using carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP; 0. 5-1. 5 μM) as the uncoupler. The integrity and function of our mitochondrial preparations were confirmed by addition of exogenous cytochrome c, which did not enhance mitochondrial respiration, indicating nonsignificant damage to the outer mitochondrial membrane during isolation procedures (data not shown).
Respiration rates (O 2 flux) were calculated as the negative time derivative of oxygen concentration, and Datlab software (OROBOROS Instruments) was used for both data acquisition and analysis. Respiration rates were normalized to the amount of mitochondrial protein to allow comparison between groups without the influence of differences in the amount of mitochondria loaded into the chamber. Mitochondrial membrane potential. Membrane potential of mitochondria isolated from rat brain was measured using the sensitive lipophilic cationic fluorescent probe 5,5′,6,6′-tetrachoro-1,1′,3,3′-tetraethylbenzimidazolyl-carbocyanine iodide (JC-1; Sigma) as previously described. 40 Isolated mitochondria were treated with different compounds and respiratory substrates for 10 min at 37 °C. Mitochondria were treated with AII (1 nM) and the AT2 antagonist PD123,319 (2 μM) or the AT1 antagonist losartan (3 μM) to investigate the effect of mitochondrial AT1 and AT2 receptors on mitochondrial membrane potential.
Nitrate was then converted to nitrite by the nitrate reductase enzyme, and the total nitrite concentration was measured as nitrite reacted with the fluorescent probe DAN (2,3-diaminonaphthalene), which is proportional to the total nitric oxide production. LDH and COX activities. LDH is a cytosolic enzyme predominantly associated with contaminating synaptosomes; thus, it was used to assess the purity of the mitochondrial fraction. LDH activity was measured in 2. 5 μg isolated mitochondria and whole homogenate protein (n = 6), according to the manufacturer's specifications (Roche 04744926001). The micromol/min/mg was calculated using LDH enzyme (Sigma, L-3916) as a reference standard. The COX activity was used to normalize the data obtained from AT1 and AT2 receptor expression performed by WB in aged rats, as this provides the most accurate estimation of mitochondrial content.
COX activity was measured in isolated mitochondria using a commercial kit according to the manufacturer's specifications (KC310100; Biochain, Newark, CA, USA). COX activity was measured in a 96-well plate by detecting the decrease in absorbance of ferrocytochrome c at 550 nm, as it is oxidized to ferricytochrome c by COX. Ten micrograms of isolated mitochondria protein with 0. 1% of DTT were assayed adding ferrocytochrome c substrate and reading the absorption at 550 nm immediately for 55 s. The COX activity (unit/ml) of each sample was calculated using the molar absorbance coefficient 21. 84 for ferrocytochrome c and ferricytochrome c at 550 nm Statistical analysis. All data were obtained from at least three independent experiments and were expressed as mean values ± S. E. M. Two-group comparisons were analyzed by a Student's t-test and multiple comparisons were analyzed by one-way ANOVA followed by a post hoc Bonferroni test.
Acknowledgements. We thank  Dr. Wei-Dong Le  for providing the MES 23. 5 dopaminergic cell line. We thank  Pilar Aldrey ,  Iria Novoa ,  Begoña Gonzalez  and  Jose Trillo  for their technical assistance. Grant sponsors of this work are  Spanish Ministry of Economy and Competitiveness  ( BFU2012-37087 ),  Spanish Ministry of Health  ( RD12/0019/0020  and CIBERNED),  Galician Government (XUGA)  and  FEDER (Regional European Development Fund).
Statistical analysis All analyses were performed using the R software, version 3. 0. 2 [31] , with the lme4 package  [32]. We analyzed upwind flight and localization using binomial logistic mixed-effect models. The day of release was set as random intercept because releases performed on a same day might not be independent and because all three genotypes have not been tested each day. The kdr genotypes (SS, RS or RR), the different odor sources (none, Rabbit without ITN, and Rabbit+ITN) and interactions between them were included in the models as explanatory variables. Upwind flight (UF) and localization (L) models were written as follow: logit(=> @A B CDE ) = F G + F C H#IJ45%# + F D KLJM + F C H#IJ45%# ×F D KLJOM + P E , where UF or L ijk is the proportion UF or L recorded for genotype i with odor source j on day k, F C H#IJ45%# denotes the effect on the logit of the classification in category i (SS, RS or RR) of Genotype; F D KLJM denotes the effect of the classification in category j of Odor source: Empty (experiment 1), Rabbit without ITN (experiment 2 and 3), or Rabbit+ITN (experiment 4); and d k represents the random intercept for day k.
The increment in cleared volume was calculated by dividing the amount of compound in the receiver compartment by the drug concentration in the donor compartment. The volume cleared was plotted versus time and the slope estimated by linear regression analysis. Then the average cleared volume was plotted versus time, and permeability x surface area product value for endothelial monolayer (PSe) was calculated as follows: 1 PSendothelial ¼ 1 PStotal À 1 PSinsert Where PS total corresponded to the experimental data and PS insert corresponded to the insert without endothelial cells (insert with only C6). PS endothelial divided by the surface area (A) in cm 2 (0. 33cm 2 for a 24-transwell) generated the endothelial permeability coefficient (Pe in 10 -6 cm. s -1 ) and calculated as follows: Pe ¼ PSendothelial A Whole cell ELISA of Pgp, MRP1, ZO-1 and occludin Cocultured cells were washed with 1% BSA in PBS at pH 7.
Electrophysiological recordings and data analysis Electrodes were pulled from GB150F-8P glass capillaries (Science Products, Hofheim, Germany) using a P-97 puller (Sutter Instrument, Novato, CA, USA). They had a tip diameter of 1 to 2. 5 µm and were filled with PS. The reference electrode was introduced through the soft articular membrane between the telson and the abdomen. The recording electrode was inserted with a NMM-25 micromanipulator (Narishige, London, UK) in the base, middle or apex region of the flagellum area bearing the aesthetascs, between two aesthetasc rows (Fig. 1B ). Signals were amplified (×100) and filtered (0. 1-1000 Hz) using an EX1 amplifier with a 4002 headstage (Dagan, Minneapolis, MN, USA), and digitized at 2 kHz by a 16-bit acquisition board (Digidata 1440A) under Clampex 10.
Construction of an articulatory-based speech synthesizer In a first step, we designed an intelligible articulatory-based speech synthesizer converting the trajectories of the main speech articulators (tongue, lips, jaw, and velum) into speech (see  Fig 1). For this purpose, we first built a large articulatory-acoustic database, in which articulatory data from a native French male speaker was recorded synchronously with the produced audio speech signal. Then computational models based on DNNs were trained on these data to transform articulatory signals into acoustic speech signals (i. e. articulatory-to-acoustic mapping). When considering articulatory synthesis using physical or geometrical models, the articulatory data obtained by EMA can be mapped to the geometrical parameters of the model  [39].
Here we consider a machine-learning approach in which the articulatory data obtained by EMA is directly mapped to the acoustic parameters of a vocoder. Articulatory data acquisition and parameterization. The articulatory data was recorded using the electromagnetic articulography (EMA) NDI Wave system (NDI, Ontario, Canada), which allows three-dimensional tracking of the position of small coils with a precision of less than a millimeter. Nine such 3D coils were glued on the tongue tip, dorsum, and back, as well as on the upper lip, the lower lip, the left and right lip corners, the jaw and the soft palate  (Fig 2A). This configuration was chosen for being similar to the ones used in the main publicly available databases, such as MOCHA (http://www. cstr. ed. ac. uk/research/projects/artic/mocha.
First, the tongue and soft palate sensors were glued onto small pieces of silk in order to increase contact surface, and second, the tongue, soft palate and jaw surfaces were carefully dried using cottons soaked with 55% green Chartreuse liquor. The recorded sequences of articulatory coordinates were down-sampled from 400 Hz to 100 Hz. Finally, in order to assess the effect of the number of articulatory parameters on the intelligibility of synthesized signals, four different parameterizations of the articulatory data were used: using the raw 3D data of the 9 sensors (27 parameters), projecting the data in the midsagittal Acoustic data acquisition, parameterization and synthesis. The speech signal was recorded at 22,050 Hz synchronously with the articulatory data. Its spectral content was parameterized by 25 mel-cepstrum (MEL) coefficients (Fig  2C ) computed every 10 ms (hence a 100 Hz sampling matching the articulatory data acquisition frequency) from a 23-ms (512 samples) sliding window using the Speech Processing ToolKit (SPTK, http://sp-tk.
Using such an excitation signal results in an unnatural (''robotic") speech sound in which all the phonemes are voiced, and have the same pitch. Transcription of the audio signals was first done manually in naturally written text, then translated into phone sequences using LLiaPhon phonetizer (https://gna. org/projects/lliaphon) [49] , and finally manually corrected. Phone sequences were then automatically aligned on audio files using a standard speech recognition system (based on a set of tied-state context-dependent phonetic hidden Markov models (HMM) trained using the HTK toolkit http://htk. eng. cam. ac. uk/)) and a forced-alignment procedure. Reference articulatory-acousticdatabase (the BY2014 corpus). For this specific study, we recorded a large articulatory-acoustic database, named BY2014, containing more than 45 minutes of speech after removing the periods of silence.
This database was composed of 712 items of variable length, ranging from isolated vowels, vowel-consonant-vowel sequences (VCVs), phonetically balanced sentences, and other sentences extracted from French newspaper "Le Monde". This resulted in 18,828 phones in total. The distribution of the 34 different phonetic classes used to describe French language in this corpus is shown in Fig 3A. Phone frequency ranged from 1,420 occurrences for the phone /a/ to 27 occurrences for /η/. The distribution of the articulatory data points in the midsagittal plane is represented in Fig 3B. The velum was the articulator with the smallest movement amplitude (less than 1cm), followed by the jaw and the upper lip (about 1cm), the lower lip (about 2cm), and finally the tongue had the highest amplitude of movement (about 3cm for each sensor).
In the following, this dataset is referred to as the reference data, and the subject from whom this data was recorded is referred to as the reference speaker. This dataset is available for download at https://zenodo. org/record/154083 (doi: 10. 5281/zenodo. 154083). Articulatory-to-acoustic mapping using Deep Neural Networks (DNN). The articulatory-to-acoustic mapping was performed using a deep neural network (DNN) trained on the reference data (see  Fig 1). A DNN is an artificial neural network with more than one or two hidden layers, which can be used to address complex (and highly non-linear) classification and regression problems. More details can be found in  [50]. The choice for using a DNN-based mapping was motivated by previous results  [35, 51]  showing that such mapping was more robust to noisy input than a state-of-the-art mapping based on Gaussian Mixture Model (GMM) such as the one proposed by Toda et al.
Dividing into batches allowed more efficient computation than when using single samples  [52]. The training was made by randomly selecting 90% of the articulatory-acoustic database items, while half of the remaining 10% were used for early stopping of the training (the training was stopped when the error on this validation set did not improve over 20 epochs) and the other half for testing. The whole training was done using custom-made optimization tools written in C++, the conjugate gradient code being adapted from the Matlab implementation of the DRToolBox (http://lvdmaaten. github. io/drtoolbox/). Four different DNNs were trained, one for each of the four different parametrizations of the articulatory data (with 27, 14, 10 and 7 articulatory parameters).
To achieve this calibration, we acquired articulatory data from the new speaker that corresponded to known reference articulatory data. This calibration model was then applied in real time to incoming articulatory trajectories of each silent speaker to produce continuous input to the speech synthesizer. Since the subjects were in silent speech and thus no glottal activity was available, we chose to perform the synthesis using the fixed-pitch template-based excitation, and in order to reduce the number of control parameters, we chose the synthesis model using 14 articulatory parameters since results showed that it was able to produce fully intelligible speech (see first part of the Results section). Fig 4C summarizes the whole experimental protocol, which is detailed in the following sections.
Subjects and experimental design. Four subjects (1 female, 3 males) controlled the synthesizer in real time. The reference speaker was one of them (Speaker 1), i. e. he was also used as a test subject, but with data from a different session than the reference data session. The articulatory data was recorded using the NDI Wave system in similar conditions as for acquiring the reference data (recording at 400Hz and down-sampling to 100Hz), except that only 6 sensors were used to record the articulatory movements of the lower and upper lips, the tongue tip, dorsum and back, and the jaw. This was due to the fact that the NDI Wave system was limited to 6 sensors when retrieving the articulatory data in real-time (while it was possible to use more sensors in offline mode). The soft palate sensor was one of the discarded sensor because most subjects (3 out of 4) were very uncomfortable with keeping the soft palate sensor for a long duration.
The corner lip sensors were also discarded because they were the least informative for the synthesis. While the coils were positioned at the same anatomical locations, no particular attention was given to place them at very precise locations. Articulatory-to-articulatory mapping. In order to estimate the articulatory-to-articulatory mapping, it was necessary to obtain articulatory data from the new speakers in synchrony with articulatory data from the reference speaker when articulating the same sounds. The new speakers were thus asked to silently repeat a subset of 50 short sentences (about 4 words each), extracted from the reference articulatory-acoustic database, in synchrony with the corresponding audio presented through earphones. Each sentence was first displayed on the screen during one second, then after a visual countdown, it was played three times at a fixed pace, so that the speaker could adapt to the reference speaker rate and way of speaking.
During this closedloop situation, each speaker was asked to silently articulate a set of test items while given the synthesized auditory feedback through amagnetic Nicolet TIP-300 insert earphones (Nicolet Biomedical, Madison, USA) ensuring no interference with the magnetic field of the NDI Wave system. The auditory feedback was recorded for further intelligibility evaluation. Subjects were allowed to adapt to the closed-loop situation for at least 20 minutes. Then, they were asked to pronounce a set of test items, which were not part of the datasets used to train the articulatory-toacoustic and the articulatory-to-articulatory mappings. The test set consisted of the 7 isolated vowels /a/, /e/, /i/, /o/, /u/, /oe/, and /y/, and 21 vowel-consonant-vowel (VCV) pseudo-words made by the 7 consonants /b/, /d/, /g/, /l/, /v/, /z/ and /ʒ/, in /a/, /i/ and /u/ context (e.
Evaluation protocol The quality of open and closed-loop speech synthesis was assessed in two ways. We first carried out a qualitative evaluation, in which the acoustic signals obtained by offline synthesis from the reference database corpus (reference offline synthesis) or during closed-loop experiments (closed-loop synthesis) were compared with the original signals processed through analysissynthesis. Analysis-synthesis was performed by converting the audio signals into mel-cepstrum (MEL) coefficients, which were then directly converted back into audio signals using the MLSA filter and template-based excitation. Such signal is referred here to as the 'anasynth signal'. This conversion is not lossless, though it represents what would be the best achievable quality for the synthetic speech signal in the present context.
Multiple comparisons were made using contrasts according to  [56]. For the sentences, a paired Student test was performed to compare the results. All the tests were made using the R software, and packages lme4, multcomp and lsmeans. Analysis of the articulatory-to-articulatory mapping. For the articulatory-to-articulatory mapping, mean distance between predicted articulatory trajectories and reference articulatory trajectories was computed for each item of the test corpus and each speaker. A two-factor ANOVA with repeated measures was performed using the following model: Distance ~Sensor Ã RefSpeaker + Error (Item / (Sensor Ã RefSpeaker)), where Distance is the mean distance between predicted and reference trajectories, RefSpeaker has two levels indicating if it was the reference speaker (Speaker 1) or another speaker (Speaker 2, 3 or 4), Item corresponds to the identifier of the tested item, and Sensor has 7 levels corresponding to the different EMA sensor positions to be predicted (upper lip, lower lip, jaw, tongue tip, tongue dorsum, tongue back and velum).
Multiple comparisons were made using contrasts according to  [56]. All the tests were made using the R software, and packages lme4, and multcomp. Analysis of the real-time closed-loop synthesis. As for offline reference synthesis, statistical analysis of the real-time closed-loop synthesis results was performed using mixed logistic regression. The following model was used: Result ~(Segment + RefSpeaker) ^2 + (1 | Listener), where Result is the binary answer (equals 0 if the item was wrongly identified, otherwise 1), Segment has two levels corresponding to the type of item (vowel or VCV), RefSpeaker has two levels indicating if it was the reference speaker (Speaker 1) or another speaker (Speaker 2, 3 or 4), and Listener has 12 levels corresponding to each listener that participated in the listening test.