dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Enzalutamide plus androgen deprivation therapy (ADT) improved radiographic progression-free survival versus ADT alone in patients with metastatic hormone-sensitive prostate cancer (mHSPC) in ARCHES (NCT02677896). While health-related quality of life (HRQoL) was generally maintained in the intent-to-treat population, we further analyzed patient-reported outcomes (PROs) in defined subgroups.
| INTRODUCTION 2] [3] [4] [5]  Treatment selection must consider efficacy but also health-related quality of life (HRQoL) and toxicity. Patient-reported outcomes (PROs) are especially important when selecting treatment in patients with advanced disease. Enzalutamide is an oral AR inhibitor, either approved or under regulatory consideration to treat patients with castration-resistant prostate cancer, irrespective of the presence of metastases, and also patients with mHSPC, globally. 6 In mHSPC, enzalutamide added to ADT facilitated improved radiographic progression-free survival (rPFS) versus ADT alone in the ARCHES trial (NCT02677896)  1  and improved overall survival (OS) versus nonsteroidal antiandrogen therapy plus ADT in the ENZAMET trial (NCT02446405)  2  ; this was accompanied by significant delays in symptomatic skeletal-related events and castration resistance versus ADT alone in ARCHES and prostate-specific antigen progression and clinical progression over time versus ADT and nonsteroidal antiandrogens in ENZAMET.
As observed in the main ITT analysis, mean FACT-P total, BPI-SF worst pain scores, mean EORTC QLQ-PR25 modified urinary symptoms scores, and EQ-5D-5L visual analog scale (VAS) scores through Week 73 remained relatively unchanged, with few differences observed (data not shown). Therefore, this analysis focused on clinically meaningful differences between subgroups using TTFD and TTFCD, which both use prespecified thresholds to indicate minimal clinically meaningful within-patient deterioration. Here, we report PROs for subgroups according to prognostic risk (per the LATITUDE study [NCT01715285] definition),  4  local therapy, and baseline pain/HRQoL (Table  1 ). Pain subgroups were identified using a similar approach to that used in TITAN (NCT02489318)  4  and were as described previously (Table  1 ).
| Statistical analysis Study size and completion rate calculations were previously defined  7  ; PRO analyses were conducted on the ITT population using observed data. Missing data were assumed to be missing at random as previously described. 7 an domain scores were reported by visit. Changes from baseline were assessed for patients with baseline and ≥1 postbaseline score using a mixed model for repeated measures approach, controlling for baseline covariates. Distribution of time to deterioration was assessed by the Kaplan-Meier product limit method and HRs, with 95% confidence intervals (CIs) calculated using a stratified Cox proportional hazards model. Data analyses, processing, and summarization were conducted with SAS software (version 9. 3 or higher of the SAS System for Windows, copyright 2012; SAS Institute Inc.
Therefore, it is encouraging that delays in radiographic progression with enzalutamide in patients in the ARCHES study are accompanied by general maintenance of HRQoL, health status, and well-being versus placebo. In studies with abiraterone and apalutamide (LATITUDE and TITAN studies),  3, 4  HRQoL was maintained over time in patients with mHSPC. In LATITUDE, abiraterone delayed deterioration of pain and HRQoL (FACT-P total score). 4 However, unlike LATITUDE, ARCHES included patients with prior docetaxel, prostatectomy, and/or RT, patients with high-volume and low-volume disease, as well as patients with both de novo and recurrent mHSPC who may have a different disease course than the patients with de novo high-volume metastatic disease included in LATITUDE. In the CHAARTED study (NCT00309985), adding docetaxel to ADT only improved HRQoL versus ADT alone after 12 months, possibly reflecting short-term, chemotherapy-induced adverse effects.
ACKNOWLEDGMENT This study was funded by Astellas Pharma Inc. and Pfizer Inc. , the codevelopers of enzalutamide. Medical writing support was provided by Tom Lavelle, PhD, and Paul Littlebury, PhD, from Bioscript and editorial assistance by Beatrice Vetter-Ceriotti, PhD, and Lauren Smith, BA (Hons), from Complete HealthVizion, all funded by the study sponsors. The sponsors had a role in study design, data analysis and interpretation, and writing of the report. All authors had full access to all data and the corresponding author had final responsibility for the decision to submit for publication. The manuscript was written with editorial support from medical writers, funded by the study sponsors. The authors developed the analysis plan and all stages of the manuscript in collaboration with Astellas Pharma Inc.
Population We analyzed the data from all patients with acute stroke referred for a mechanical thrombectomy procedure at Besançon University Hospital between 1st of January 2015 and 31st of December 2020. We excluded patients who had not undergone a T2 * -weighted MRI sequence in the acute phase, or whose imaging contained artifacts that made the images prohibitively difficult to interpret. We also excluded any patients whose symptom onset time was not recorded in the patient record.
Clinical Data Demographic data had been collected prospectively by the stroke unit and was obtained from the patient records for our study. The available data included the patient's age, sex, and treatment with an antiplatelet drug, anticoagulant, or both at admission. Data concerning the acute phase of the stroke had been collected prospectively by the on-call neurology team. This included the National Institute of Health Stroke Score (NIHSS) at arrival  (23). Two neurologists (MD, GC) used the modified Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification  (24, 25)  to retrospectively categorize the stroke etiology based on the patient record. We also identified patients with cardioembolic stroke for whom a previously undiagnosed atrial fibrillation was discovered during their hospitalization.
Time Data The SO time was recorded prospectively by the on-call neurology team. When the exact time was uncertain (e. g. , wake-up stroke or daytime-unwitnessed stroke), the team recorded the time at which the patient had last been seen well. We defined patients with unknown SO time as those who were last seen well more than 15 min prior. The other patients were considered to have a known onset time. We obtained the time of the first MRI sequence from the Besançon University Hospital image archiving and transfer system. The SO-imaging time was calculated in minutes.
Radiological Data We performed a retrospective analysis of the MRI data obtained in the acute phase. The exact MRI sequences and protocols used were at the discretion of the radiology teams. Every patient included had undergone a T2 standard gradient echo. We defined the presence of SVS as a hypointense signal on the T2 * images at the site of the occluded artery, for which the signal exceeded the diameter of the contralateral artery  (28). The presence or absence of SVS was established visually by a junior neurologist (MD) and a senior interventional neuroradiologist (GC) as a single reading. For images that were difficult to classify, a consensus decision was made by the two readers. Doubtful cases were classified as absence of SVS. The site of the occlusion was defined using either the 3D-TOF sequence of the MRI, or the diagnostic angiography from the mechanical thrombectomy procedure.
We calculated the infarct volume manually by drawing an outline on the diffusion sequence images. We also used the software Carestream (Carestream Health, Rochester, USA) to perform a semi-automatic calculation. The presence of a symptomatic hemorrhagic transformation on the CT scan at 24 h following the mechanical thrombectomy procedure was defined as an intraparenchymal hemorrhage with a volume 30% greater than the ischemic lesion, associated with an increase of 4 points on the NIHSS score, in accordance with the Heidelberg classification  (29). A malignant infarction was defined retrospectively as a large lesion observed at the CT scan 24 h after the mechanical thrombectomy procedure, associated with cerebral herniation and clinical deterioration.
Data Related to Mechanical Thrombectomy The use of a stent retriever during the mechanical thrombectomy procedure was recorded either prospectively by the on-call neurology team, or retrospectively using the operative report. We used the Modified Treatment in Cerebral Ischemia score (mTICI)  (30)  to evaluate the level of brain tissue reperfusion following the procedure. The mTICI scores were reviewed by a junior neurologist (MD) and a senior interventional neuroradiologist (GC).
Analysis of the Subgroup With Exact Known Symptom Onset Time (KSO) The demographic, radiological and follow-up data of the KSO subgroup is presented in Table  1. This subgroup included 221 patients (56. 9% of the whole study population). Of these, 173 presented an SVS (78. 3%). SVS-patients had a significantly shorter mean SO-imaging time than SVS+ patients (p < 0. 001). A graph showing mean SO-imaging time and 95% confidence interval depending on SVS in this subgroup is presented in Figure  2. SVS was associated with stroke etiology (p < 0. 01). For SVS-patients, stroke etiology was more often other determined etiology than SVS+ patients. Among other determined etiology in this subgroup, neoplasia was the most common (5 patients), followed by cervical artery dissection (3 patients). We found no significant differences for the other variables analyzed.
Protein is biology workhorse. Since the recent breakthrough of novel folding methods, the amount of available structural data is increasing, closing the gap between data-driven sequence-based and structure-based methods. In this work, we focus on the inverse folding problem that consists in predicting an amino-acid primary sequence from protein 3D structure. For this purpose, we introduce a simple Transformer model from Natural Language Processing augmented 3D-structural data. We call the resulting model PeTriBERT: Proteins embedded in tridimensional representation in a BERT model. We train this small 40-million parameters model on more than 350 000 proteins sequences retrieved from the newly available AlphaFoldDB database. Using PetriBert, we are able to in silico generate totally new proteins with a GFP-like structure.
I. INTRODUCTION Proteins are at the heart of cellular life and therefore are the center of numerous medical and chemical applications. They are macro molecules that are built as various length chains composed as combinations of the 20 amino acids (also called residues) found in nature. Proteins fold into 3D conformations depending on their primary sequence and post translational modifications, which define most of their chemical and mechanical properties  [1]. Decoding the relationship between the primary sequence of proteins and their structural functions is an important topic of computational biology and has been a long standing problem that was traditionally approached via physical modeling  [2]. However, thanks to the recent advent of deep learning and to the growth of both labeled and unlabeled protein databanks, a new range of algorithms arose that operated a paradigm shift in computational biology, leading to unprecedented results  [3].
Mostly leveraging natural language processing methods  [9] -  [12] , this setting was able to provide task-agnostic representations that can capture important inherent properties to be further finetuned to a wide variety of tasks. As a matter of fact, it appears that solving the protein folding problem was the missing link between the structurebased and sequence-based paradigms and was hence considered as one key problem to be solved in computational biology. To a large extent, this long-expected question can now be considered solved, since the groundbreaking introduction of AlphaFold 2  [13]  and RoseTTAFold  [14]. They both use a dual path transformer architecture that combines evolutionary clustered data (embodied as Multiple Segment Alignment) and graph embedding of the residues interactions constrained by geometrical properties to iteratively solve this complex task.
AlphaFold in particular exhibited unrivaled performances on this task at the 14th Critical Assessment of protein Structure Prediction (CASP14) challenge, outperforming its competitors by a large margin. AlphaFold is an adhoc model that was crafted specifically for this task and its very specific architecture allows it to achieve much better performance than other out-of-the-box architectures. Since this achievement, Deepmind has publicly released the AlphaFold database  [15] , which freely offers AlphaFold estimates for the structures of a daily-increasing number of proteins (350,000 at the time we started our experiments). This highly remarkable contribution opens new perspectives in the computational protein field. In particular, we are interested here in leveraging this very valuable data to investigate a problem that appears as very important in practice in biology, which is inverse folding.
A. Sequence-based methods As primary sequences of amino acids are believed to fully encode the information of proteins, sequenced-based machine learning methods arose naturally, relying notably on Natural Language Processing (NLP) frameworks and self-supervision techniques. Self-supervision has recently become a main topic in artificial intelligence research, allowing to benefit from huge amounts of unlabeled data. In NLP, models are trained on proxy tasks such as predicting missing words in a sentence or predicting the next sentence when input some text. They are then finetuned on smaller datasets for specialization on the target task  [24]. This particular type of transfer learning was transposed in biology where early works on this topic  [25] -  [28]  were inspired by the well-known word2vec model  [24]  and where primary sequences of proteins are simply viewed as sentences.
As new and more expressive deep neural architectures arose in the general AI community  [16] ,  [29] -  [33]  and with the exponential growth of protein sequence databases, computational biology has recently strongly benefited from this new paradigm. Recurrent neural networks for proteins modeling were investigated by works such as Heinzinger et al. [34]  who adapted the ELMo model  [35]  derived from an LSTM network and showed that NLP is a viable setting for capturing biological properties. Rives et al. [9]  explored the use of a high capacity transformer network on 250 M of proteins that is the whole known proteome and outperformed models such as LSTM. While learned purely from sequences, they have shown that their model can generalize and actually captures some of the biochemical properties, biological variations, homology et alignment with protein family.
They have shown that using MSA allows transformer to outperform single sequence methods and demonstrated the limitations of the latter to accurately encode evolutionary data. Other MSA-based works have also been proposed  [43] ,  [44]. The advantage of exploiting MSA features was also illustrated by AlphaFold  [13]  and RoseTTAFold  [14]  that achieved unprecedented results using dual-path transformer-based neural networks architecture where one of the paths uses MSA inputs. However, while MSA-based model have shown superiority to single-sequence based models, some other works such as Elnaggar et al. [11]  showed that MSA features may very well be dropped in some near feature. They compared several stateof-the-art auto-encoders and auto-regressive models and were able to obtain comparable performance to MSA-based model for the first time.
2) We extract rotation parameters for each residue. These parameters are obtained from the matrix O made of the orthogonal and normal eigenvectors of the spatial covariance matrix of each residue atomic data and verifies: Cov (i∈I) (c i ) = O T DO (2) where D is the diagonal matrix of eigenvalues of Cov (i∈I) (c i ) To further compress the rotation data, we use a parametrization of the matrix O by solving: min a ∥R(a) -O∥ 2 s. t a 2 1 + a 2 2 = 1 a 2 3 + a 2 4 = 1 a 2 5 + a 2 6 = 1            (3) Where a = a 1 , a 2 , a 3 , a 4 , a 5 , a 6 T is the vector of parameters of the rotations of the residue and R(a) is a rotation matrix parametrized over a. The rotation matrix R(a) is parametrized as a product of 3 rotation matrices, each being a rotation around a different axis: R(a) def =   1 0 0 0 a 1 -a 2 0 a 2 a 1   ×   a 3 0 -a 4 0 1 0 a 4 0 a 3   ×   a 5 -a 6 0 a 6 a 5 0 0 0 1   =   a 3 a 5 -a 3 a 6 -a 4 a 1 a 6 -a 2 a 4 a 5 a 1 a 5 + a 2 a 4 a 6 -a 2 a 3 a 2 a 6 + a 1 a 4 a 5 a 2 a 5 -a 1 a 4 a 6 a 1 a 3  .
IV. TRAINING A. Setting 1) Hardware: Training was performed on one computing node with 2 Intel Cascade Lake 6226 processors (2 x 12 cores at 2. 7 GHz) and 8 GPU Nvidia Tesla V100 SXM2 32Go during 70 hours for every variant of the model with a batch size of 128 sequences (sub-batch of 16 sequences per GPU). 2) Dataset: We trained our model using the AlphaFold Dataset who was made of about 360 000 proteins at the time of our experiment. We used 80% for training (about 290 000 proteins), 10 % for validation (about 36 500 proteins) and 10 % for test (about 36 500 proteins). The AlphaFoldDB dataset was pre-processed according to section III-B. Within the model, input sequences are truncated or padded to a length of 1024 and tokenized according to section III-A. 3) Model setting and Hyper-parameters: We use a small BERT configuration of 5 layers, 12 attention heads and query dimensions of 64.
• rot in the variant name: variants where the 6-dimensional positional embedding of the rotations of the residues (eigen vectors cosine and sin angles of the coordinates of the atoms of residues, see section III-B) is used. • MLP in the variant name: an small multi-layer perceptron is added behind the residues coordinates embedding module or residues rotation embedding module if present in the variant. We tried the 9 following variants: (1) uni, (2) tri + rot + MLP, (3) tri + rot, (4) tri + MLP, (5) tri, (6) uni + tri + rot + MLP,  (7)  uni + tri + rot, (8) uni + tri + MLP, (9) uni + tri. Figures 2, 3 and 4 respectively show the train loss, the validation loss and the validation accuracy observed during training. Table  I  provides the loss and accuracy of the test dataset for the 9 variants.
Compared to our work, this model is auto-regressive (our is an autoencoder), is based on a complex adhoc architecture  [6] , uses more parameters (142 M for the largest version against 40 for our) and learned on much more Data (12M proteins agains around 350 000 for our). At the opposite, our model is more simple, relates to the well-know NLP framework and easy to implement as it relies on a simple trick that consists in i) using a tridimensional encoding of a transformer encoder ii) data augmentation to train the network to be rotation and translation invariant. As such, we hope our model can give some insights on a pratical way to incorporate the structural data of recent forlding technics to transformers-based network. Nevertheless, it appears that such models could highly benefit from using more parameters and larger datasets, which will be feasible as the part of the proteome that provide structure data will very likely grow over the few next years.
ACKNOWLEDGMENT This work was granted access to the  HPC resources of IDRIS  under the allocation  20XX-101719  made by  GENCI. This research is supported by  MUSE University of Montpellier  research grant ( AI3P :  Artificial Intelligence to Predict Phenotypes in Plants) , the  ANR  (the  French National Research Agency)  under the " Investissements d'avenir " programme with the reference  ANR-16-IDEX-0006  to GK.
Data were reported as mean ± standard deviation for at least three replications. Total flavonoid content (TFC) Aluminium chloride was used to determine total flavonoids according to the protocol of Abdel-Hameed, (2009) with a few changes. Quercetin was used as a reference compound. Briefly, 1 mL of each sample solution (0. 3 mg/mL) was combined with an equal amount of aluminum trichloride (2%) and a droplet of acetic acid. This was followed by adding methanol to the mixture to make it 5 mL. After 40 min, the absorption was determined at 415 nm. Blank was prepared from all reagents without the samples. Concentrations of flavonoids were deduced from the prepared standard curve (y = 0. 0159x + 0. 0052; R 2 = 0. 999) and expressed as mg quercetin equivalent per gram dry weight (QE/g DW). All determinations were carried-out in triplicates.
Statistical Analysis All the statistics were performed using the XLSTAT software for Windows. Mean values obtained for the variables studied in the different groups were compared by one-way ANOVA, assuming that there were significant differences among them when the statistical comparison gives P<0. 05. To correctly assess the correlation between the contents of phytochemicals and the antioxidant activity estimated by the parameters SC50 and EC50, the constants KSC50 and KEC50, which are the inverted fraction of SC50 and EC50, were calculated and used in the correlation study (Table  3 ).
Acknowledgement The authors thank go to the  General Directorate of Forests  and all forestry officers and agents of Jijel conservation, circumscription, and the forest districts of Mechat and  Bordj- Ali  for their assistance and guidance in the field. This work was funded in the frame of the national research fund, project of  socio-economic impact  (contract No. 446/12/18 ) with support from the  General Directorate for Scientific and Technological Development of the Algerian Ministry of Higher Education and Scientific Research.
Characterization In order to see the suitability of the grown single crystal for its nonlinear optical properties, many analyses have been realized. To determine the basic composition of the crystal, we used an energydispersive X-ray (EDX) analysis using an analytical scanning electron microscopic (SEM) system (JOEL JSM -6380 LA). Furthermore, the grown crystal has been subjected to UV-visible spectrum using the Perkin Elmer Lambda 35 UV-vis spectrometer in the wavelength region 200-400 nm. Its structure was assessed by single crystal X-ray diffraction data using a Bruker Axs Kappa APEXII CCD area detector diffractometer. The thermal analysis (TGA) was determined using a Perkin Elmer DIAMOND with a heating rate of 10 • C/min in the temperature range from 30 • C to 600 • C in an air atmosphere.
Single crystal X-ray diffraction We used the Burker Axs Kappa APEXII CCD to study the x-ray diffraction of the compound. The size of the crystal was 0. 52 × 0. 3 × 0. 06 mm 3 and the temperature was 150. 03 K. As for the resolution and the refinement of the structure, they were performed using the SHELXT 2014  [19]  and SHELXL 2014 program  [20] , respectively. Moreover, the PLATON software was used to study the structure solution and the refinement during the measurements. In order to determine the position of hydrogen, a Fourier difference was performed by using residual electronic densities. It is trusty to note that the crystal system was monoclinic with a Cm space group. Its lattice parameters were as follow: The volume was found to be 953. 76A 3. These results are collected in Table  2.
Acknowledgments The authors would like to acknowledge  Imperial College Healthcare NHS Trust ,  Zoe Brown ,  Lucio D'Anna ,  Gavin Fatania ,  Charles Hall ,  Omid Halse ,  Sohaa Jamil ,  Harri Jenkins ,  Dheeraj Kalladka ,  Joseph Kwan ,  Abid Malik ,  Maneesh Patel ,  Neil Rane ,  Abhinav Singh ,  Eleanor Taylor , and  Marius Venter.