dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioavailability of Aplysinopsin Analogs According to Lipinski's Rule of Five Next, we examined the drug-likeness of aplysinopsin  (10)  and its analogs 4b, 4c, 4 5a, and 5b according to Lipinski's rule of five using the free tool SwissADME [27] (Tab 1). The induction of apoptosis by the five most active analogs led to investigation of interaction with the target anti-apoptotic protein BCL2 using molecular docking. Accordingly, BCL2 crystal structure was obtained from the protein data bank. Its native ligand fundamental poses were employed as references for validating the simulation process and determining the grid box dimensions and positioning. It was noticed that the native ligand exhibit numerous hydrogen bonds interaction with the target including ASP103, GLY145, ASN143, and GLN99, besides Pi-Pi and Pi-alkyl interactions (Figure  3 ).
Molecular Docking of Aplysinopsin The induction of apoptosis by the five most active analogs led to investigation teraction with the target anti-apoptotic protein BCL2 using molecular docking. A ingly, BCL2 crystal structure was obtained from the protein data bank. Its native fundamental poses were employed as references for validating the simulation proce determining the grid box dimensions and positioning. It was noticed that the native exhibit numerous hydrogen bonds interaction with the target including ASP103, GL ASN143, and GLN99, besides Pi-Pi and Pi-alkyl interactions (Figure  3 ).
Assessment of the Binding Mode of the Studied compounds with BCL2 (PDB ID: 6O0K) The docking result of all studied compounds indicated that all of them were superimposable to LBM. In addition, they showed good binding mode with the active pocket through the formation of multiple interactions with the key amino acids of BCL2 in a similar manner as LBM (Table  S6 , Figure  4 ). Moreover, they revealed good binding energy towards the binding site of BCL2 (PDB ID: 6O0K) with values ranging from -9. 2 to -8. 3 kcal/mol (Table  S6 ). The most promising compounds showing the lowest binding energy and establishing good binding interactions with both the binding pocket and the allosteric site of the BCL2 followed the order: 4e (-9. 2Kcal/mol) > 5a (-8. 9 Kcal/mol) > 4c and aplysinopsin (10) (-8. 7 Kcal/mol) > 5b (-8.
5 Kcal/mol) > 4b (-8. 3 Kcal/mol). The docking result of all studied compounds indicated that all of them were superimposable to LBM. In addition, they showed good binding mode with the active pocket through the formation of multiple interactions with the key amino acids of BCL2 in a similar manner as LBM (Table  S6 , Figure  4 ). Moreover, they revealed good binding energy towards the binding site of BCL2 (PDB ID: 6O0K) with values ranging from -9. 2 to -8. 3 kcal/mol (Table  S6 ). The most promising compounds showing the lowest binding energy and establishing good binding interactions with both the binding pocket and the allosteric site of the BCL2 followed the order: 4e (-9. 2Kcal/mol) > 5a (-8. 9 Kcal/mol) > 4c and aplysinopsin (10) (-8. 7 Kcal/mol) > 5b (-8. 5 Kcal/mol) > 4b (-8. 3 Kcal/mol).
We propose that the formed hydrogen bonds, attractive charge, pi-pi bond, and pialkyl modulate these analogs mode of action. Generally, the thiophene, indole and cyclohexane moieties are susceptible to play the role of the aromatic heterocyclic moiety in the native ligands (LBM) (Figures  5  and 6 ). We propose that the formed hydrogen bonds, attractive charge, pi-pi bond, and pi-alkyl modulate these analogs mode of action. Generally, the thiophene, indole and cyclohexane moieties are susceptible to play the role of the aromatic heterocyclic moiety in the native ligands (LBM) (Figures  5  and 6 ). through the formation of multiple interactions with the key amino acids of BCL2 in a si ilar manner as LBM (Table  S6 , Figure  4 ). Moreover, they revealed good binding ener towards the binding site of BCL2 (PDB ID: 6O0K) with values ranging from -9.
2 tokcal/mol (Table  S6 ). The most promising compounds showing the lowest binding ener and establishing good binding interactions with both the binding pocket and the alloste site of the BCL2 followed the order: 4e (-9. 2Kcal/mol) > 5a (-8. 9 Kcal/mol) > 4c and a lysinopsin (10) (-8. 7 Kcal/mol) > 5b (-8. 5 Kcal/mol) > 4b (-8. 3 Kcal/mol). We propose that the formed hydrogen bonds, attractive charge, pi-pi bond, and alkyl modulate these analogs mode of action. Generally, the thiophene, indole and cyc hexane moieties are susceptible to play the role of the aromatic heterocyclic moiety in native ligands (LBM) (Figures  5  and 6 ). 10 ) and its analogs 4b, 4c, 4e, 5a, and 5b inside binding pocket of BCl2 (PDB: 6O0K). Figure  6. The 2D interaction of aplysinopsin (  10 ) and its analogs 4b, 4c, 4e, 5a, and 5b within binding pocket of BCl2 (PDB: 6O0K) illustrating the formed hydrogen bonds, attractive charge, pi bond, and pi-alkyl.
Therefore, BCL2 was down-regulated in prostate can cells treated with the most active five aplysinopsin analogs that we developed. Along w these results, other recently discovered indole derivatives (indole-1,2,4-triazoles) also duced their anti-proliferative activity through the gradual decrease of Bcl2 levels  [41]. addition, we analyzed the expression levels of pro-apoptotic genes such as Bax a Caspase3. Bax causes the assembly of a channel in the mitochondrial outer membra leading to its permeabilization and the release of cytochrome c which triggers apopto  [42]. We found that Bax was upregulated in PC-3 cells treatment with aplysinopsin an   10 ) and its analogs 4b, 4c, 4e, 5a, and 5b within the binding pocket of BCl2 (PDB: 6O0K) illustrating the formed hydrogen bonds, attractive charge, pi-pi bond, and pi-alkyl.
Determination of IC 50 Values Active hits possessing cytotoxicity ≥ 60 % on different cancer cell lines were selected for dose-response studies at different concentrations. The final tested concentrations were 100, 50, 25, 12. 5, and 6. 25 µM in triplicates. The IC 50 values were calculated using the concentration-response curve fit to the non-linear regression model using Graph Pad Prism®v6. 0 (GraphPad Software Inc. , San Diego, CA, USA).
Cell Cycle Analysis by Flow Cytometry Assay Annexin-V-FITC Apoptosis Detection Kit (BIPEC, USA) was used to determine apoptotic cells. PI staining was used to reveal cell cycle stage. Briefly, cells treated with each analog were resuspended with 400 µl binding buffer, labeled with annexin-V-FITC for 15min and PI for another 5min at 4 • C. The fluorescence of the stained cells was analyzed using flow cytometry by by FACSCalibur (BD Biosciences, Mountain View, CA). The data were analyzed using the CellQuest software (BD Biosciences).
Molecular Docking Molecular docking studies of active aplysinopsin analogs together with anti-apoptotic protein BCL2 was performed using PyRx tools Autodock Vina (version 1. 1. 2) [56]. The crystal structure of BCL2 complexed with the inhibitor (LBM), 4-{4-[(4 -chloro-5,5-dimethyl  [3, 4, 5  The native ligand and the water molecules were removed from the protein using VEGA ZZ 2. 3. 2 tool followed by adding polar hydrogen and Kollman charges and then converted to PDBQT format by Autodock Vina tools. All the molecules were constructed with the ChemDraw ultra 10. 0 and saved as mol file, then protonated, minimized and converted to pdb file by Open Babel software. The created pdb file was submitted to Autodock Vina tools to set a number of torsion and for pdbqt file construction. AutoGrid was used with a grid box to create the grid map.
Statistical Analysis Comparisons between multiple treatments were made by either one-way or two-way ANOVA, followed by Dunnett's multiple comparison test; * p < 0. 05, ** p < 0. 01, *** p < 0. 001, **** p < 0. 0001 were considered to be a significant difference. GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA) was used for statistical analysis and plotting the graphs.
,6tetrahydro[1,1 -biphenyl]]-2-yl)methyl] piperazin-1-yl}-N-[(3-nitro-4-{[(oxan-4-yl)methyl]amino}phenyl)sulfonyl]-2-[(1H-pyrrolo[2,3-b]pyridin-5-yl) oxy] benzamide was retrieved from the protein data bank at https://www. rcsb. org/structure/6o0k (access on 26 October 2022) using 6O0K code.
| Sample collection, measurements and photography Larval A. nudiceps and P. evansii were collected using a 2-m 2 frame Metro net (700 μm mesh) towed to approximately 120 m depth. The net tows were conducted during austral summer (January-February) as part of the Palmer Antarctica Long-Term Ecological Research (Palmer LTER) programme. Scientists on Palmer LTER cruises collect multidisciplinary data in a fixed-sampling grid (see  Smith et al. , 1995)  in the Bellingshausen Sea along the WAP  (Ducklow et al. , 2007). Specimens were preserved in a formaldehyde  (1995-2013) or 95% ethanol (2014-present).
| Amplification and sequencing The full-length mitochondrial-encoded nicotinamide adenine dinucleotide (NADH) dehydrogenase subunit 2 gene (ND2; mt-nd2) and a fragment of the cytochrome oxidase I (COI; mt-co1) mitochondrial gene were amplified in a portion of specimens that were suspected to be A. nudiceps. We selected these gene regions because they have been demonstrated to distinguish closely related notothenioid fishes  (Near & Cheng, 2008)  and because of the availability of vouchered reference sequences on GenBank. The COI region was amplified with the COI-3 primer set  (Ivanova et al. , 2007)  and the mt-nd2 region using primers GLN and ASN  (Kocher et al. , 1995). The PCR products were purified using the QIAquick PCR Purification Kit (Qiagen, Hilden, Germany). The purified PCR products were Sanger sequenced in the forward and reverse directions with an ABI BigDye Terminator v3.
1 Cycle Sequencing Kit (Thermo Fisher Scientific, Waltham, MA, USA). The resulting fragments were electrophoresed on an ABI 3500 capillary sequencer, and bases were called using the integrated Data Collection Software. The resulting sequences for each sample and locus were edited and assembled into contigs using Sequencher 5. 3. 6 (Gene Codes Corp. , Ann Arbor, MI, USA). NCBI-BLASTN searches  (Altschul et al. , 1990)  were conducted with the edited sequences to identify sequences with the highest similarity within the database using MegaBLAST. Sequences without specimen vouchers were excluded from the analysis.
To confirm the morphological identification of preflexion P. evansii larvae, we also obtained an mt-ND2 sequence (trimmed to a final length of 1048) from a preflexion specimen (VIMS 42468) which most closely aligned with P. evansii mt-nd2 sequences in GenBank (HQ170126. 1 and HQ170127. 1) with >99% identity and 91% query coverage (accessed 28 September 2022). The mt-co1 region of both specimens also most closely aligned with a mt-co1 sequence of A. nudiceps (OK493722. 1) with 96. 9% identity and 94% query coverage (accessed on 3 July 2022). We are performing a separate concurrent analysis to evaluate population connectivity of A. nudiceps based on the relatively large gap in sequence identity observed between the A. nudiceps larvae and the closest sequences available in GenBank for the mt-co1 region.
10. 8 mm L T and VIMS 43240, 19. 2 mm L T ), and obtained mt-nd2 and mt-co1 sequences, trimmed to a final length of 1048 and 693, respectively. The mt-nd2 region for both specimens most closely aligned with two mt-nd2 sequences for A. nudiceps in GenBank from the same analysis (HQ170108. 1 and HQ170109. 1) with >99% identity and 97% query coverage (accessed on 3 July 2022), supporting the initial morphologically based identification. As these results provided sufficiently clear support to the initial morphological identification of A. nudiceps,.
ACKNOWLEDGEMENTS We thank  Sarah K. Huber  for curatorial assistance,  Mario La Mesa  for discussion and  Miguel Montalvo  for photographric assistance. This work was funded by the  National Science Foundation Antarctic Organisms and Ecosystems Program  ( PLR-1440435  for specimen and environmental data collection) and  Division of Biological Infrastructure  ( DBI-1349327  for specimen preservation and analysis),  Explorers Club , and the  VIMS John Olney Fellowship.
Purpose Tumor Treating Fields (TTFields) therapy, a noninvasive, anti-mitotic treatment modality, is approved for recurrent glioblastoma (rGBM) and newly diagnosed GBM based on phase III, EF-11 (NCT00379470) and EF-14 (NCT00916409) studies, respectively. The EF-19 study aimed to evaluate efficacy and safety of TTFields monotherapy (200 kHz) vs physicians' choice standard of care (PC-SOC; EF-11 historical control group) in rGBM. Methods A prospective, post-marketing registry study of adults with supratentorial rGBM treated with TTFields therapy was conducted. Primary endpoint was overall survival (OS; intent-to-treat [ITT] population) and secondary endpoint was OS per-protocol (PP). Subgroup and toxicity analyses were conducted. Results Median OS (ITT population) was comparable with TTFields monotherapy vs PC-SOC (7.
Additionally, the National Comprehensive Cancer Network guidelines recommend TTFields therapy (category 1 recommendation) for adult patients with ndGBM  [29, 30]. Results from the EF-14 (NCT00916409) study formed the basis for approval for use in ndGBM. EF-14 demonstrated significantly improved progression-free survival (PFS) for TTFields therapy concomitant with TMZ (6. 7 months) vs TMZ alone (4. 0 months) and a significantly improved overall survival (OS) for TTFields therapy concomitant with TMZ (20. 9 months) vs TMZ alone (16. 0 months)  [31]. Additionally, 5-year OS rates were more than double that of TMZ alone (5% vs 13%; P = 0. 04)  [31]. Additionally, the EF-11 study, in which TTFields therapy was compared with best standard of care (SOC) in patients with rGBM, also showed improvements in outcomes and formed the basis for approval in rGBM  [32].
Study design The EF-19 (NCT01756729) study was a prospective, open-label, multi-site, post-marketing registry study designed to compare the efficacy and safety of TTFields monotherapy in patients with rGBM to PC-SOC (historical control group) from the EF-11 study. The recruitment period was approximately 24 months and follow-up period was 12 months from last patient recruited. Patient follow-up was conducted according to standard practices at each participating center; evaluation of AEs, vital signs, and Karnofsky Performance Status (KPS) (where available) were performed at each follow-up appointment. Patients were also seen by a Novocure-trained Device Support Specialist (DSS) once per month during follow-up.
Study population The treatment population was comprised of patients already enrolled in the PRiDe registry (which includes all patients in the US treated with TTFields therapy, commercially), therefore, patients were recruited to this study from either the outpatient clinic or inpatient hospital setting at any certified TTFields therapy center in the US. All patients provided written consent for use of their data in the registry, including protected health information. Full inclusion and exclusion criteria are listed in the supplementary material. Adult patients (≥ 22 years of age) with a histologically or radiologically confirmed diagnosis of supratentorial rGBM, per Macdonald's criteria  [25]  with KPS score of ≥ 70 enrolled in the PRiDe registry were eligible. The control arm comprised patients from the PC-SOC cohort of the EF-11 study  [32].
Statistical analyses All analyses were performed using Statistical Analysis System (SAS), version 9. 4 (SAS Institute Inc, Cary, NC, USA). A sample size of 192 patients in the TTFields arm (including estimated 10% loss to follow-up rate) was determined, based on a noninferiority log-rank test with a 2-sided alpha level of 0. 05 and a power of 80%, comparing time to event (i. e. , death) in patients treated with TTFields therapy and PC-SOC. The sample size analysis was performed using NCSS-PASS 11 16. 0 (NCSS, Kaysville, UT, USA). The null hypothesis was that the hazard-of-death would be higher for TTFields monotherapy (hazard ratio [HR] > 1. 375); and alternative hypothesis HR would be ≤ 1. 375. An HR of 0. 1083 with PC-SOC was estimated from the rate-of-death per month in the PC-SOC arm. For OS analyses, patients without a known date of death were censored at last known date documented alive.
Data on MGMT and IDH1 status were not collected in the EF-11 study. The disposition of patients treated with TTFields therapy in the EF-19 registry study is summarized in Table  1. All patients treated with PC-SOC (N = 117) received systemic therapy, either during or after the EF-11 study, and were included in the PC-SOC PP population  [32]. Of these patients, 26 (22%) withdrew consent and were lost to safety follow-up, for which only survival data was collected. Safety data are presented for the remaining 91 (78%) patients in the PC-SOC control arm. 1 3.
Subgroup analyses In additional analyses, TTFields monotherapy was associated with a statistically significant increase in OS overall, and within certain patient subgroups in the ITT population (Fig. 2 ; Cox proportional hazards, P < 0. 05 for the treatment effect within each subgroup). Clinical benefit of TTFields therapy on OS was observed regardless of KPS, age, sex, and rGBM resection status. Similar findings were obtained when patients were stratified by rGBM resection status-none/ biopsy/partial resection vs those with gross total resection (data not shown). In the TTFields therapy arm, patients whose tumors were MGMT promoter methylation status positive had a numerically extended OS (95% CI) of 10 months (6. 0-13. 9) vs 6. 5 months (4. 5-7. 4) in patients with tumor-negative status; HR (95% CI) was 0.
Adverse events Safety data for all 192 registry patients with a database cut-off date of December 31, 2018 were compared descriptively to the 91 PC-SOC patients. The number of patients with any reported AE ≥ 1 was lower with TTFields therapy vs PC-SOC (67% vs 95%, respectively) in the safety population (N = 283) (Table  2 ). Overall, PC-SOC treatment demonstrated higher AE incidence rates (≥ 5% incidence in any arm) compared to TTFields monotherapy across 12 of 15 classifications by Med-DRA body system organ class (Table  2 ). For the 12 AE classes with greater AE incidence with PC-SOC, the most commonly reported AE by body system class in the PC-SOC arm was nervous system disorders (36%), followed by gastrointestinal disorders (30%) and blood and lymphatic system disorders (19%). In contrast, AE incidence rates were greater in the TTFields monotherapy vs PC-SOC group for 3 AE classifications, which were: skin and subcutaneous tissue disorders (36%); general disorders and administration-site conditions (23%); and injury, poisoning, and procedural complications category (11%).
Acknowledgements Writing and editorial support under the direction of the authors was provided by  Huda Abdullah, PhD ,  Global Publications ,  Novocure Inc. , USA ,  Emma Butterworth, PhD ,  Excerpta Medica Inc. , and  Melissa Purves, PhD ,  CMPP TM , Prime, Knutsford, UK. Writing and editorial support provided by  Excerpta Medica Inc. and Prime was funded by  Novocure Inc. All costs related to publication were funded by  Novocure Inc. Responsibility for all opinions, conclusions, and data interpretation lies with the authors.
How it works DeepSynth is parameterised by a domain specific language (DSL), which is the programming language chosen for solving a program synthesis task. The user first specifies a DSL by giving a set of primitives together with their types and semantics, as well as semantic and syntactic constraints (such as program depth). The compilation phase constructs a Context Free Grammar (CFG) representing the set of programs. The second ingredient is the prediction model. DeepSynth leverages PyTorch  (Paszke et al. , 2019) : a neural network reads the examples and outputs predictions to guide the search towards likely programs. DeepSynth includes end to end procedures for training a neural network that makes predictions from examples. The predictions are given as distributions on the derivation rules of CFG.
State of the field Programming by example has been intensively investigated in the past years both in academia and in industry, spearheaded by the success of FlashFill in Microsoft Excel (see Gulwani (2011)), allowing users to synthesize spreadsheet programs by giving examples. FlashFill is now integrated into the larger and more ambitious project PROSE by Microsoft Research (see the website). PROSE is a general purpose program synthesis tool (see the GitHub repository). It is based on constraint programming: in addition to the DSL, the end-user needs to specify so-called witness functions, which are used for specifying a programming by example instance in a SAT or SMT solver. Other tools have emerged recently exploiting the programming by example paradigm, for instance SmartFill for Google Sheets, and the TF-Coder for TensorFlow (see  Shi et al.
(2020) ). Unlike PROSE, they are tailored to address a single domain: for SmartFill spreadsheet programs, and for TF-Coder tensorflow programs. A number of other prototype tools have been developed in the academic world: DeepCoder was the first leveraging deep learning techniques (see  Balog et al. (2017) ), PC-Coder (see  Zohar & Wolf (2018) ), and recently the general-purpose DreamCoder (see  Ellis et al. (2021) ). The recent release of the GitHub Copilot (see GitHub (2021)), powered by the Codex large language model from OpenAI, shows the wide applicability of the program synthesis: Copilot is presented as your AI pair programmer, meaning that it assists developers by autocompleting pieces of code in an interactive fashion. The key difference is that specifications in Copilot are given in natural language, which may or may not include examples.
Sequences alignment of Pho92 promoter To align the PHO92 promoter sequences, the PHO92 coding and flanking sequences from representative Saccharomycetaceae were recovered from the NCBI database using keyword and BLAST searches  (42). Sequences upstream of PHO92, often extending to the neighboring WIP1 gene, were aligned using CLUSTAL X  (43)  and alignments manually edited. Colored alignments were exported from Jalview  (44).
Low mass and high mass resolution were 2 for the first mass analyzer and 2 for the second. The transitions were, in positive mode: adenosine 268. 0 > 136. 10 and 268. 0 > 119. 10 (collision energy 15 and 42 V, respectively); N 6 -methyladenosine 281. 9 > 150. 10 and 281. 9 > 123. 10 (collision energy 17 and 41 V respectively). Data acquisition was performed with the MS Workstation 8 for the mass spectrometry and the liquid chromatography was piloted with Bruker Compass Hystar 4. 1 SR1 software and Chromeleon Xpress (Thermo). The data analysis was performed with the Bruker MS Data Review software (version 8. 2. 1). Standard solutions of adenosine (0. 05, 0. 1, 0. 5, 1, 5, 10 ng/ml; Sigma, Cat# A9251) and N 6 -methyladenosine (0. 05, 0. 1, 0. 5, 1, 5 ng/ml; Selleckchem, Cat# S3190) were used for the quantification.
6. 1 (47), using intersection-nonempty mode, and annotations coming from the Saccharomyces Genome Database (SGD). Only non-ambiguously assigned reads have been retained for further analyses. Clustering. Genes with larger average expression have on average larger observed variances across samples, that is, they vary in expression from sample to sample more than other genes with lower average expression. To avoid these genes to overly influence the variance, variance have been stabilized using the rlog (regularized log) function from DE-Seq2. Principal Component Analysis is based on their normalized and stabilized gene expression level of all samples. Statistical analysis. Comparisons of interest have been performed using the R 3. 5. 1 with DEseq2 version 1. 22. 1  (48). More precisely, read counts were normalized from the estimated size factors using the median-of-ratios method (49) and a Wald test was used to estimate the P-values.