dataesr/scientific-paragraphs-categorization huggingface dataset:
The phase 1b clinical study (NCT02829827) reported here, involved several centers in Europe. The study, however, was terminated because of the challenge in recruiting a sufficient number of patients within a reasonable time frame. Among the three infants enrolled to the study, one patient became spasm-free, and two patients had clinical improvement followed by a worsening after radiprodil discontinuation. This first clinical use of radiprodil in infants indicates the compound was generally well tolerated. There were no treatment related serious adverse events, no sedation, and no behavioral changes observed. In all three patients, the pharmacokinetic data were closely consistent with the predicted exposures from the PBPK modeling. A number of factors contributed to the difficulty in recruiting infants into the study.
Acknowledgements The authors thank the  French Agence Nationale de la Recherche for funding  (grant  ANR-16CE29-0008-01 , project  BIOMAGNET ). This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the program " Investissements d'Avenir " supervised by the  Agence Nationale de la Recherche.
After washing with TBST, membranes were incubated with HRP-conjugated secondary antibodies diluted in TBST containing 3% BSA for 60 min, washed with TBST and then developed using the G:BOX gel imaging system (Syngene). The appropriate exposure time of the digital camera for acquisition of chemiluminescent signals from immunoblots was adapted to avoid saturated pixels and expression levels were estimated by Image J software (NIH, Bethesda, MD; http://rsb. info. nih. gov/ij/).
Relative Quantitative Expression of mRNA transcripts Expressions of Gli1 and Ptch1 mRNA in the electroporated regions of the somatosensory cortex were measured by real-time quantitative RT-PCR. Total RNAs were isolated from cerebral cortices (P15 rats) using Mini RNeasy kit (Qiagen) then converted to cDNA using 1 µg RNA and a QuantiTect Reverse Transcription kit (Qiagen) according to manufacturer's instructions. PCR was carried out with the LightCycler 480 SYBR Green I Master (Roche Applied Science) with 1 µL cDNA using the following oligonucleotides (QuantiTect Primer Assays, Qiagen): Gli1 (Gli1; QT01290324), Ptch1 (QT01579669), KCC2 (Slc12a5; QT00145327) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; QT001199633). Relative mRNA values were calculated using the LC480 software and GAPDH as the housekeeping gene.
Spiking activity and data analysis Extracellular field potential recordings were performed from P14, P20 and P30 electroporated somatosensory cortical slices. Extracellular tungsten electrodes of 50µm diameter (California Fine Wire, Grover Beach, CA, USA) were positioned in the V/VI cortical pyramidal cell layer of the transfected area to record the Multi Unit Activity (MUA). Field potentials were recorded using a DAM80 Amplifier (World Precision Instruments, Sarasota, FL, USA) using a 1-3 Hz bandpass filter and analyzed off-line with the Axon package MiniAnalysis program (Jaejin Software, Leonia, NJ, USA). To determine the developmental changes in the GABAA signaling, we used isoguvacine, a potent and selective GABAA receptor agonist  (Krogsgaard-Larsen and Johnston, 1978). We determined the effect of isoguvacine on MUA as a ratio of the spiking frequency at the peak of the isoguvacine response to the spiking frequency in control.
2. Isoguvacine (30 µM) was dissolved in an external solution and focally applied to recorded cells through a micropipette connected to a Picospritzer (General Valve Corporation, pressure 5 p. s. i. ). Recordings were done using an Axopatch-200A amplifier and pCLAMP acquisition software (Molecular Devices) in voltage-clamp mode. Data were low pass filtered at 2 kHz and acquired at 10 kHz. Isoguvacine responses were recorded at voltages -110, -90, -70 and -50 mV or at -70, -50, -30 and -10 mV depending on neuron GABA inversion potential. A linear regression was used to calculate the best-fit line of the voltage dependence of the isoguvacine responses.
The cluster properties and fluorescence intensities of each cell were analyzed with Metamorph software. First, we used the logical "NOT" conversion of pairs of Alexa-647 and CY3 images to isolate in each focal plane the Alexa-647 signal that was not overlapping with CY3 fluorescence restricted to plasma membrane. This gave rise to additional images reflecting the fluorescence of the internalized pool of labeled clusters, called thereafter "NOT-conversion". Second, the arithmetic summation for each Z-stack and channel was performed to collect the whole fluorescence of the different signals (Alexa-488 = total protein fluorescence; CY3 = plasma membrane restricted fluorescence; NOT-conversion = internalized restricted fluorescence; Alexa-647 = all surface labeled fluorescence). Third, a binary mask was created for each cell from Alexa-488 image to isolate the signal coming from the transfected neuron, and the fluorescence parameters (total fluorescence, single cluster fluorescence as well as density and brightness of clusters) were analyzed for each channel (Alexa-488, CY3, NOT-conversion and Alexa-647) in regions overlapping with the binary mask.
Statistical analysis No statistical methods were used to predetermine sample sizes. To ensure the consistency and reproducibility of our results, we conducted repeated trials in different cell cultures and acute brain slices prepared from at least three different animals for each experimental condition. If not stated otherwise, statistics are presented as the mean ± the standard deviation (SD) for normally distributed data and as the median only for non-normally distributed data. Experiments with control and Smo electroporated animals were processed at the same time to ensure homogeneity of experimental conditions. Statistical analyses and assessment of normal distribution (Shapiro-Wilk test) were performed with GraphPad Prism (GraphPad software 5. 01). For data showing normal distribution, ANOVA and the post hoc Tukey test were used for multiple comparisons between groups, and a paired t-test was used to compare paired data.
Funding: This work was supported by The  National Institute of Health and Medical Research (INSERM) , the  National Center for Scientific Research (CNRS) , the  National Agency for Research (ANR , grant number  R07066AS 2008-2011  to CP and IM and the A* MIDEX  project (n°  ANR-11-IDEX-0001-02 ) funded by the  « Investissements d'Avenir » French Government program , managed by the  French National Research Agency (ANR). IMERA/INSERM Grant, Marie Curie Prestige/ERC Grant  ( PRESTIGE-2016-3-0025 ) and  Brain & Behavior Research Foundation (NARSAD)  Young investigator grant (# 25356 ) to YHB. QD and MH were supported by a Doctoral fellowship obtained by from  Aix-Marseille University.
Data The dataset represents the raw data (supplementary Table ), frequencies of alleles and genotypes for five single nucleotide polymorphisms (SNPs) of 5 genes, rs1514175 TNNI3K, rs713586 RBJ, rs887912 FANCL, rs2241423 MAP2K5, and rs12444979 GPRC5B in Russian women (Table  1 ). These SNPs were associated with body mass index (BMI) in previously published genome-wide and candidate gene association studies  [1e10]. The dataset frequencies of the SNP alleles and genotypes were divided into five groups according to the BMI of the participants: all samples, underweight (BMI<18. 50), normal weight (18. 50e24. 99), overweight (25. 00e29. 99), and obese (>30. 00). No significant differences in the allele and genotype frequencies between the groups with underweight (BMI<18. 50), normal weight (18.
This is where the use of bioprinting has the 15 major impact: recapitulating PR sheet from rare, mature and freshly isolate cell. For sake of 16 comparison, PRs were also bioprinted on bare TCP surface. In that case, the bioprinted drops did 17 not spread on the surface of the underneath RPE layer, leading to non-homogeneous distribution 18 of the photoreceptors (data not show). which ng/mL levels of hVEGF were found in the RPE and RPE/PR constructs. As a control, 2 hVEGF production from PR was shown to be negligible. 3 Then, the distribution and the organization of the actin filaments within the RPE/PR constructs 4 were evaluated through phalloidin staining and laser confocal microscopy, 3 days after PR 5 bioprinting. As can be seen in Figure  3 -C and 3-D, a dense network of actin filaments was 6 evidenced with a clear identification of large and spread RPE cells (green asterisk) together with 7 small and compact PR cells (white arrows).
Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website.
Descriptive Statistics We created a list of all known investigators who had attempted optogenetics in NHPs around the world and searched for their contact information on personal and institutional websites. Through the published literature and referrals, 66 labs were identified in the United States, Canada, United Kingdom, Germany, France, Belgium, Switzerland, China, and Japan. Each lab was sent an invitation to share published and unpublished data with the initiative, and 52 labs responded positively (Figure  2 ). Of those that responded positively, 45 contributed data, and 32 provided unpublished data. As of November 2019, 1,042 viral vector injections in NHPs were included in the database. Of those, 552 entries were unpublished results, effectively doubling the number of injection cases in the literature.
Each entry in the database consists of a single injection of a viral vector. Multiple entries could be generated from the same animal. Injections at different depths along the same needle penetration tract were grouped into a single entry. The database includes experiments conducted in 6 different NHP species (Figure  3A ). Although most injection experiments were done in rhesus macaques (71%), many were conducted in cynomolgus macaques (11%), Japanese macaques (8%), marmosets (6%), and squirrel monkeys (3%). The database accepts entries from all NHP species and welcomes new contributions from investigators. One entry corresponds to one injection. Variants with less than 10 entries were grouped in the category "Other. " "Macaque" refers to entries where the species of macaque was not specified.
The most widely used opsin was the humanized ChR2 with the H134R mutation for optogenetic activation through cation conductance (hChR2(H134R); 39%). Some experiments used inhibitory opsins (25% of experiments), including ArchT, an opsin acting as an outward proton pump, used in 6% of experiments, and Jaws, a halorhodopsin that acts through a chloride pump, also used in 6% of injections. A large number of tests logged in the database (20%) did not include an opsin gene (Figure  3D ). These experiments included only a reporter gene (e. g. , green fluorescent protein  [GFP] ) to test the efficacy and spread of viral transduction in preparation for a later optogenetic experiment. To visualize the spread and efficiency of transduction in the targeted brain areas, most experiments included a fluorescent reporter gene in their cassette (Figure  3E ).
AAV Nomenclature Clarification The nomenclature used for AAVs can be confusing. For example, when an investigator reports using an AAV5, is s/he referring to serotype AVV5/5 with rep and cap genes from AAV5 or to AAV2/5 with the rep genes of AAV2 and the cap genes of AAV5? Following consultations with the Penn Vector Core, the UNC Vector Core, and the Stanford Vector Core, it was confirmed that all cores use the AAV2 rep genes for recombinant AAV production and that only the capsid genes are swapped to create a specific serotype. In other words, AAV1 is really AAV2/1, AAV2 is really AAV2/2, AAV9 is really AAV2/9, and so on. Therefore, in the database, AAV5 and AAV2/5 were grouped together into a single category of  AAV (i. e. , AAV5) , and the same for AAV2/1 and AAV1 (i. e. , AAV1), etc. In this article, when entries list "AAV2; AAV5," it refers to two independent viral vectors (AAV2 and AAV5) mixed in the same viral solution before injection.
Outcome measures Investigators included outcome measures to describe the success or failure of an optogenetic experiment in NHPs. These outcomes were grouped into three broad categories: (1) anatomy, referring to results involving examination of tissue using histology (in vivo observation of epifluorescence is also included in this category); (2) physiology, referring to assessments of the functionality of the opsin and its effect on neural activity, whether using electrophysiology, imaging, or other means; and (3) behavior, where behavioral variables, ranging from simple muscle contractions to complex decision making, were assessed in response to optogenetic stimulation. Contributing investigators had to include one answer for each of these outcome categories for each test they included in the database.
(A) Proportion of injections that led to a strong physiological effect, a weak or mixed effect, or no effect. A weak or mixed effect was defined according to the investigators reporting a weak, unreliable, or mixed physiological response. White numbers represent absolute numbers of injection tests. (B and C) Same as in (A) but for anatomical and behavioral analyses, respectively. (D) Left: proportion of injections that led to at least one strong outcome across all three possible outcome modalities (physiology, anatomy, or behavior). Right: this bar also includes injections that led to at least one weak or mixed outcome (or better) across all three outcome modalities. We sought to estimate the success rate of all injection experiments reported in the database. Because different experiments may seek different outcome measures (e.
g. , one experiment may measure physiology but not anatomy and another one the reverse), we needed to choose a criterion for "success" that would be flexible enough to encompass all database entries. For the sake of analysis, the success of an optogenetic experiment with NHPs was defined by the presence of at least one outcome measure demonstrating a strong effect, whether anatomical, physiological, or behavioral. For example, an experiment that only tested physiology and obtained the expected positive results could be considered a successful injection experiment. We quantified the number of experiments that led to at least one strong effect or at least one weak or mixed effect across the three possible outcomes (Figure  4D ). 76% of all experiments reported at least one strong effect, and 91% reported at least one weak or mixed effect or a strong effect across all outcome categories.
Outcome per Viral Construct To identify important factors that may influence the success of optogenetic experiments in NHPs, we examined the success rate of experiments as a function of specific methods and tools. For this analysis, "success" is defined (as above) as having at least one strong effect among the three possible outcome measures (anatomy, physiology, and behavior), which was the case in 76% of the tests reported (Figure  4D ). We looked at each type of viral vector, promoter, opsin gene, and reporter protein and calculated the percentage of tests that were successful when using each variant. Because some variants were used infrequently in the database, we flagged those with fewer than 20 data entries or those tested in fewer than five different animals (dotted bars in Figure  5 ).
We warn readers to be careful when interpreting the success rate of variants that have a low sample size (dotted bars). We hope that future additions to the database will improve the reliability of these interpretations. Importantly, all reported success rates are univariate analyses that need to be interpreted carefully in consideration of all other parameters that might influence the success of an injection experiment. Please refer directly to the database for a full list of the more than 30 parameters listed for each injection experiment (e. g. , species, brain region, injection volume, immunosuppressive drug, etc. ). When examining the success rates of all experiments as a function of NHP species, we found that the highest success rates were achieved in cynomolgus monkeys (88%; 110 injection tests) (Figure  5A ).
Japanese macaques show the lowest success rates with 64% (75 injection tests). The database listed a total of 24 viral vectors that have been used across all injection experiments in the database (Table  1 ). In Figure  5B , only those with more than 10 entries were included (N = 13). Of those vectors with adequate sample sizes, we found that AAV9 and AAV8M(Y733F) yielded the highest success rates, with 93% and 88%, respectively. At the other end of the spectrum, we find that CAV2 (58%) and AAV5 (71%) have the lowest success rates across species. Overall, investigators tended to purchase their viral vectors either from the University of North Carolina at Chapel Hill (UNC) Vector Core (42%) or the University of Pennsylvania (UPenn) Vector Core (25%), with a total of 26 vector cores serving the international community (Figure  5C ).
The hChR2(H134R) opsin, the most widely used in the community with 397 injections, produced an 85% success rate. The K176R mutation to Chrimson (ChrimsonR) had the lowest yield, with 55%. Finally, investigators used several different reporter genes as a proxy to visualize expression of the opsin (Table  1 ). The most widely used, eYFP, is also one with a high success rate at 86% (Figure  5F ). The mCherry reporter yielded the lowest rates of success, with 54%. Again, all reported success rates are to be interpreted carefully in consideration of other parameters that might also influence the success of an injection experiment. Please refer directly to the database for a full list of the more than 30 parameters listed for each injection.
Viral Delivery Methods Injection of viral vectors into the brain parenchyma can be performed using several different surgical techniques (Figure  6A ). For example, investigators have used an existing recording chamber to lower the injection needle through a grid under control of a micromanipulator (63% of database entries, 78% success rate). This technique has the advantage of using existing hardware to access the brain and may be performed without general anesthesia. It has the caveat of offering no visual access to the cannula's penetration of the brain tissue, which is helpful to confirm that the cannula penetrated the cortex and is not merely creating a dent in the pliant cortical surface. However, this limitation is surmountable with an "injectrode" that allows simultaneous injection and recording of neural activity to ascertain cannula placement (84 injections used an injectrode).
The injection of the viral solution can be made at various infusion speeds. When selecting a speed, the goal is to minimize trauma to the tissue and maximize the efficiency of delivery while minimizing the time required to perform the injection. Injection speed may also affect the size of the transduced area in a virus-specific manner  (Lerchner et al. , 2014). It is generally assumed that slower injection rates minimize trauma to the brain tissue. However, the database reports successful experiments with injection rates ranging from 6 nL/min to 5,000 nL/min, with a median injection rate of 200 nL/min (Figure  6B ). The higher end of this spectrum falls into the category of convection-enhanced delivery (CED) techniques, pioneered by Krystof Bankiewicz (UCSF) for gene therapy applications and refined for use in NHP optogenetics by Dr.
NAbs can be tested in serum and in cerebrospinal fluid (CSF), with important distinctions (see R. C. 's advice in the STAR Methods). Only 4. 7% of all entries in the database reported having tested for NAbs to guide this decision. Unfortunately, the sample size was too low to determine whether this approach yielded better success rates. Finally, several labs used immunosuppressing drugs during the incubation period to dampen the innate and adaptive immune response to the transgene. Around 20% of experiments used the steroidal drug dexamethasone after injection or before and after injection of the viral vector. However, in the sample of entries where immunosuppressing drug use was reported (N = 594), there was no difference in success rates between those who used (70. 5%) and those who did not use such drugs (73.
6%, chi-square test, p = 0. 42). The choice of immunosuppressing drugs and drug regimens can affect those results and was not accounted for in this preliminary analysis. Moreover, systemic immunosuppressing drugs may have other effects in the host, such as making it more vulnerable to local or systemic opportunistic infections  (Youssef et al. , 2016). Whether these drugs may be used locally at the site of injection has so far not been tested. As the database continues to grow, we hope that it will be possible to parse out those effects and provide better guidance regarding the best immunosuppression strategies.
Discussion The NHP Optogenetics Open Database is accessible for everyone to contribute to and consult on the Open Science Framework website (https://osf. io/mknfu/). It continues to accept new contributions from investigators who would like to share positive and negative results with the community (instructions are included on the website), and it encourages sharing information on a broad range of variables, which could be expanded as the community agrees in the future to include more parameters. As the database continues to accept new entries from contributing laboratories, the data will be curated and quality checked by S. T. Any laboratories wishing to contribute are encouraged to log on to the Open Science Framework (OSF) website or to contact S. T. directly by email (strem@pennmedicine.
upenn. edu). This resource has already generated important information that can be used to inform new optogenetics approaches in NHPs. With more data, more factors influencing the success and failure of optogenetics experiments in NHPs can be identified, and interaction effects between multiple factors can be modeled. We encourage investigators around the world to continue expanding and relying on this public resource, including those working with rodents and humans. In addition to presenting the database, the current manuscript includes a series of comments with advice from contributing laboratories tailored to investigators who wish to undertake optogenetics experiments in their lab. This advice was provided to answer the following question: "If there was one thing you wish you had known before starting your optogenetics experiments, what would that be?" The goal of this exercise was to identify challenges and caveats investigators experience in practice and that may not always be included in published articles.