dataesr/scientific-paragraphs-categorization huggingface dataset:
In this model, the hot-spot region is defined by its location and size. We initially attempted to recover both parameters from the data but this led to nonphysical solutions. This issue may occur because with Spitzer data only, the hot-spot size is degenerate with the thermal structure. A similar behaviour was found and explored in more detail in  Changeat et al. (2021) ;  Changeat (2022) , even when the HST data are combined with Spitzer data. We therefore fixed the hot-spot size to 40 • , but left the hot-spot offset as a free parameter. For the nightside region, we modeled clouds using an opaque grey cloud model and fitted for the cloud pressure top deck. The parameter space of this phase-curve model was explored using the MultiNest algorithm  (Feroz et al. 2009; Buchner 2016 ) with 512 live points and an evidence tolerance of 0.
JWST/NIRSpec The WASP-121 b phase curve was recently observed by JWST/NIRSpec using the G395H grating as part of program GO-1729 (P. I. Mikal-Evans, co-P. I. Kataria). This observing mode makes use of the NRS1 and NRS2 detectors, covering the 2. 70-3. 72 µm and 3. 82-5. 15 µm wavelength ranges. Mikal-  Evans et al. (2023)  presented the results of their first look analysis of the broadband light curves, integrated over each detector passband. We note that the NRS1 and NRS2 passbands largely overlap with those of Spitzer/IRAC channels 1 and 2, respectively. Hence, it makes sense to compare the results obtained from Spitzer and JWST observations. Figure  6  shows the comparison between physical parameters from our Spitzer/IRAC data analysis and those based on the JWST/NIRSpec observations reported by  Mikal-Evans et al.
Conclusions We analysed, for the first time, two thermal phase curves of WASP-121 b taken with Spitzer. Despite these datasets being affected by stronger than usual instrumental systematic effects, we obtained meaningful information on the exoplanet atmosphere. The measured brightness temperatures and transit depths are consistent within 1σ with those obtained from much more precise JWST observations in similar passbands. We estimated the Bond albedo and circulation efficiency of the WASP-121 b atmosphere, which are similar to those of other UHJs. However, we measured unusual westward hot-spot offsets, which are significantly different from the JWST measurements. These discrepancies may hint at atmospheric variability or instrumental systematic effects. We further explored the possible thermal profiles using phase-curve retrievals, which are coupled with chemistry.
Acknowledgements. This work is based on archival data obtained with the Spitzer Space Telescope, which was operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. G. M. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 895525 , and from the  Ariel Postdoctoral Fellowship  program of the  Swedish National Space Agency (SNSA). Q. C. is funded by the  European Space Agency  under the  2022 ESA Research Fellowship Program. We acknowledge the availability and support from the  High Performance Computing platforms (HPC)  from the  Simons Foundation (Flatiron) , DIRAC and  OzSTAR , which provided the computing resources necessary to perform this work.
Reagents and material Crude harvest and post protein A affinity chromatography (PPA) samples of an immunoglobulin-G4 mAb A33 were obtained from a Chinese hamster ovary DG44 cell culture, as described by Husson et al. [19]  and in the Supplementary data (Fig. S1 ). A CHO-DG44 mock cell line sample was provided by UCB Pharma S. A. (Braine l'Alleud, Belgium) to generate the spectral library further used for DIA data extraction. HCP Profiler beads (Anaquant, Villeurbanne, France), first introduced by Trauchessec et al. [23] , were spiked in all samples to derive absolute HCPs quantities. In summary, from 18 Escherichia coli (E. coli) proteins digested and analyzed by LC-MS and tryptic peptides reporting the best MS response were selected. After the confirmation of their specificity to the E. coli proteome, 54 selected peptides were adsorbed at known amounts to a water-soluble polymer bead via the READYBEADS TM technology of Anaquant.
MAb quantification The mAb titer was determined using an Agilent 1100 series high performance liquid chromatography (HPLC) system (Agilent Technologies, Santa Clara, CA, USA) and a 1 mL HiTrap protein G HP column (GE Healthcare Life Sciences, Chicago, IL, USA). The flow rate was set at 1 mL/min and 100 mL of sample were injected. A wash solution composed of 20 mM sodium phosphate at pH 7 was used to clean the column and mAb elution was performed with 20 mM glycine (pH 2. 8). The mAb concentration was determined after peaks integration using a standard curve of purified mAb (data not shown).
The system was fully controlled by XCalibur software v4. 0. 27. 19, 2013 (Thermo Fisher Scientific Inc. ) and NanoAcquity UPLC console v1. 51. 3347 (Waters). The three injection replicates of DIA and DDA were performed in a randomized injection sequence. The MS proteomics data have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD029305  [24].
DDA data treatment Raw DDA files were converted to. mgf peaklists using MsConvert and were submitted to Mascot database search on a local server (version 2. 5. 1, MatrixScience, London, UK) against a FASTA database including all Critecutulus griseus entries extracted from UniProtKB/TrEMBL (56,566 protein entries, February 15, 2021) together with their reversed sequences, as well as the iRT retention time standards, the four standard proteins of the MassPREP Digestion Standard Kit, HCP Profiler kit proteins, the mAb heavy and light chains and common contaminants. Spectra were searched with a mass tolerance of 5 ppm in MS mode and 0. 05 Da in MS/MS mode. One trypsin missed cleavage was tolerated. Carbamidomethylation of cysteine residues was set as fixed modification. Oxidation of methionine residues and acetylation of proteins N-termini were set as variable modifications.
Identification results were imported into Proline software version 1. 6 (http://proline. profiproteomics. fr) for validation  [25]. A false discovery rate (FDR) of 1% was set at the peptide level using adjusted e-value and the protein level using Mascot modified mudpit scores. Peptide abundances were extracted with Proline software using an extraction m/z tolerance and peptide spectrum matches/ peak matching m/z tolerance of 5 ppm. Alignment of the LC-MS runs was performed using loess smoothing, peptide identity method and with a time tolerance of 300 s. Cross assignment of peptide ions abundances was performed among harvest or PPA samples using a m/z tolerance of 5 ppm and a retention time tolerance of 40 s.
Cricetulus griseus, CHO spectral library generation A reference spectral library combining a series of analyses conducted on different samples in DDA mode was generated using the Spectronaut and Pulsar algorithms (v. 14. 5; Biognosys). This series of analyses comprised the 24 gel bands obtained by SDS-PAGE fractionation of the CHO DG44 mock cell line and all DDA analyses of harvest and PPA samples, including iRT retention time standards and the 18 proteins from the HCP Profiler kit. Raw DDA files were uploaded into Spectronaut and searched with the Pulsar algorithm against a FASTA database containing all Critecutulus griseus entries extracted from UniProtKB/TrEMBL (56,566 protein entries, February 15, 2021), as well as the iRT retention time standards, the 18 proteins from HCP Profiler kit, the reference sequence of the mAb and common contaminants.
DIA data treatment DIA data was analyzed with a peptide-centric approach using the Spectronaut algorithm and the upper described in house generated spectral library (v. 14. 5; Biognosys). Trypsin/P was used as digestion enzyme with one missed cleavage allowed. Carbamidomethylation of cysteine residues was set as a fixed modification. Oxidation of methionine residues and acetylation of proteins' N-termini were set as variable modifications. For quantitative data extraction, MS and MS/MS mass tolerances, extracted ion chromatogram (XIC) and retention time windows were all set as dynamic. iRT regression type was set to local (non-linear) regression. A FDR of 1% was set at precursors and proteins levels. At this extraction stage, a sparse Qvalue filter was applied. Peptide quantities corresponding to the sum of four to six fragments XIC areas (interference correction parameter was turned on) were calculated.
HCP Top3 quantification After data extraction, a list of identified peptides with their corresponding intensities was exported in Excel format for both DDA and DIA data. Prior the Top3 quantification, filters were applied to remove oxidized and acetylated peptides alongside with their non-modified counterparts. Precursors inferred to host organism proteins, standard proteins and precursors with charge states 2 and 3 were kept. For DDA data, a maximum of one precursor validated by cross-assignment was allowed. Precursors with more than one Q-value > 0. 01 or profiled were removed for DIA data. For both acquisition methods, quantity estimation was performed using precursors' intensities showing a coefficient of variation (CV) below 20% within injection triplicates. Finally, HCP peptides showing 100% sequence identity with a semi-tryptic or non-tryptic mAb peptide were removed.
On the other hand, samples were spiked with an original mixture of peptides coated on a water-soluble bead releasing controlled amounts of a 2. 5 log peptide range after solubilization (READYBEADS TM technology). Second, DIA methods were finely tuned on a fast-scanning Q-Orbitrap instrument to thoroughly compare the performances achieved with DIA methods against more classical DDA approaches on the same instrument. Finally, two DIA data extraction and interpretation strategies, a peptide-centric approach requiring the prior acquisition of a reference spectral library and a library-free spectrum-centric approach, were evaluated.
This later observation potentially highlights the presence of highly interfered MS1 signals in DDA that may result in an overestimation of the HCP amounts in highly complex harvest samples. We observed a significant benefit of DIA on the HCPs coverage, as approximately 19% more HCPs were quantified for harvest and 111% more for PPA samples. An increased intra-HCP dynamic was also noted: while DDA was able to achieve 2. 4 to 5. 5 orders of magnitude within the least and most abundant HCP, MS2-based DIA allowed to reach a dynamic between 3. 5 and 6. 1 (Table  S4 ). Furthermore, the precision of the DIA data extraction was assessed by the CVs calculated on all HCP peptides' intensities within injection replicates. A median of 9. 0% was obtained for DIA compared to 9. 8% for DDA, highlighting a slight improvement of the data extraction when taking into account that three times more peptides were quantified by DIA, i.
Evaluation of a spectral library-free DIA data interpretation strategy The generation of a spectral library requests sample preparation and instrument acquisition time. Even though this takes less time than the development of a specific ELISA kit, a new dedicated library must be generated for each drug product. In addition, HCPs not covered by the library will not be searched for, even if they are present in detectable amounts. Promising alternative approaches for DIA data extraction have been introduced to avoid the use of spectral libraries. With the aim to make our DIA method even more straightforward, we evaluated the spectrum-centric approach called directDIA in Spectronaut. This approach relies on the generation of pseudo MS/MS spectra from the DIA runs which will then be used to query a search against the organism's database as it is done in classical DDA search engines.
In addition, as for the generation of the reference spectral library, the spectral library free-based approach also strongly relies on the use of the CHO protein sequence database, which is yet poorly annotated and curated (56,565 entries in UniProtKB/TrEMBL). As a result, the high redundancy of the database likely significantly hinders the extraction of specific peptides. As a conclusion, while already showing promising results, the current limitations of the spectral library-free approach make it premature to be readily implemented in a regulated environment working with CHO cell-based bioproducts, but this may well change in a near future.
Acknowledgments This project was supported by the  "Association Nationale de la Recherche et de la Technologie " and  UCB Pharma S. A. (Belgium and France)  via the  CIFRE fellowship of Steve Hessmann. This work was supported by the  "Agence Nationale de la Recherche " via the  French Proteomic Infrastructure ProFI FR2048  ( ANR-10-INBS-08-03 ). The authors thank  Laura Herment  and  Tanguy Fortin  from Anaquant for their support in the use of the HCP Profiler standard.
One of the main challenges for any real-time OS (RTOS) is provide support for all such variations of single-and multi-core resource access protocols. Although most RTOSes provide at least one real-time resource access protocol, there is no design and implementation discussion for both single-and multi-core protocols that allow easy extensions, code reuse, and low run-time overhead. Our previous paper was the first work to design and evaluate single-and multi-core variations of real-time resource access protocols in an RTOS designed from scratch, considering both static and dynamic scheduling approaches  [1]. The proposed design is based on object-oriented techniques to maximize software reuse and minimize run-time overhead. However, in that work, we provided support only for the well-known single-core protocols -Priority Inheritance Protocol (PIP)  [6] , Priority Ceiling Protocol (PCP)  [6] , Immediate Priority Ceiling Protocol (IPCP), and SRP  [7]  -and the standard multi-core extensions (MPCP and MSRP).
Implementation in an RTOS We have implemented the described design in the Embedded Parallel Operating System (EPOS)  [24]. EPOS is a multi-platform, object-oriented, componentbased, embedded system framework implemented in C++. It is the first opensource RTOS designed from scratch that supports partitioned, global, and clustered versions of EDF, RM, LLF, and DM scheduling policies  [25]. A complete review of the real-time support on EPOS can be found in  [25]. We choose EPOS, because it supports static and dynamic scheduling, it is written in an object-oriented language, and it was used in our previous paper with the original proposed design  [1]. We believe that the proposed design can be replicated in any object-oriented RTOS written in C++ and that has EDF, RM, P-EDF, and P-RM schedulers.
Moreover, EPOS until this work did not have support for the MrsP. We have modified the implementation in  [1]  to reflect the changes in the software design described above. To exemplify how we adapt a class at compile-time, the code in Figure  4  illustrates part of the implementation of the Semaphore MSRP class. In the line 3, the class inherits from Semaphore SRP passing the queue choice (a boolean) as parameter to handle local resources. In the lines 9 and 10, the p and v operations of the Semaphore SRP are called. When Semaphore MSRP must handle GCS, then a template specialization (line 15) is used. In this case, note that after calling p or v, there is an extra call to update the ceiling (lines 20 and 21).
Memory Footprint For measuring the memory consumption of our implementation, we have executed EPOS on top of an Intel i7-2600 processor (clock of 3. 4 Ghz, 8 logical cores, 8 MB L3 cache). We used the GNU gcc compiler at version 7. 5. 0 to generate the code. For measuring the memory footprint, we used the GNU objdump tool at version 2. 30. Table  1  shows the obtained memory footprint for each class depicted in Figures  2  and 3. The table also shows the total lines of code, just to correlate the memory footprint with the implementation. Memory usage is split into code, data, and static data sections. Data represents the memory consumed by an instance of the corresponding semaphore type, not including the footprint of the base classes. The total memory consumption describes the memory consumed by the implementation of the semaphore subclass and a single corresponding object instance.
For instance, the total memory consumption of the Semaphore PIP is 876 bytes, which represents its own 580 bytes summed with Semaphore and Semaphore RT memory consumption. It is important to highlight that code from the Synchronizer Base, Synchronizer Common Simple Spin and BaseLock classes are not represented in the Table  1 , because it is inlined into the methods that use the base class. This is also true for the interfaces Static Ceiling and Dynamic Ceiling, meaning that the code of these classes are implemented in the header file to improve the run-time overhead by avoiding explicitly method calls. MPCP local has the same memory footprint as IPCP and MSRP local has the same memory footprint as SRP due to the template class parameter as discussed in Section 3 and exemplified in Figure  4.
In this application, one main task arrives first and gains access to the shared resource, after this, other tasks are launched in different cores, trying to access the same resource and end up being kept spinning without gaining access to the shared resource. Those tasks are the helpers, which borrows their processors for the task executing in the critical section. The owner of the resource is then preempted to force the helping mechanism of the MrsP to happen. In different test runs, the number of helpers was increased, varying from 1 to 8, with each possible helper task assigned to its own core. The p and v methods of every semaphore type and spinlock (for MrsP) were instrumented, as well as the system code responsible for performing queuing/dequeuing and rescheduling of tasks, and inter-processor interruptions (IPIs), which is used by the base semaphore implementation.
These sections were timed with a small helper utility, that wraps the code, setups the TSC, and uses it to count the amount of time consumed by the code section. Each task acquired and released the resource 1000 times, and the worst-case run-time overhead was extracted from the execution. Figure  5  presents the obtained worst-case run-time overhead for each protocol in ns to perform p and v operations as a function of the number of tasks waiting on the semaphore, while Table  2  presents the obtained results exclusively for the MrsP protocol. The overheads in Figure  5  depend on the number of tasks, since whenever a task blocks it is queued in a linked list. As the queue grows, the operations take more time. The queue overhead overcomes the overhead of the protocols. However, we can note a very small difference when no tasks are waiting on the semaphore (PCP, PIP, and IPCP have mostly the same behavior).
Related Work Uniprocessor resource access protocols. Suspension-based resource access protocols for uniprocessor real-time systems were first proposed by Sha et al. [6]. The authors have proposed PCP and PIP and the work has served as a basis for much other research. SRP was proposed by Baker in 1991  [7]  and it was also a seminal work, mainly for providing resource access protection for dynamic scheduling. Several general-purpose OSes and RTOSes implement some of the analyzed real-time resource access protocol. For instance, FreeRTOS employs PIP in the mutex primitive [30] and also supports SRP  [31]. LIT M U S RT supports PCP, SRP, and several multiprocessor resource access protocols  [23]. The L4 microkernel  [32]  and Linux support priority inheritance. Linux also implements IPCP, under the name PRIO PROTECT in the pthreads library.
Using average time is not fair and may hide the inherent non real-time behavior of Linux-based systems. Moreover, neither  [17]  nor  [18]  discussed the design choices and the integration of MrsP in RTOSes that provide other real-time resource access protocols, focusing on providing code reuse, easy usability, and low run-time worst-case overhead. We have shown in our evaluation lower overheads than both works  [17, 18] , even running our system in an older and slower processor (for instance, Shi et al used a 5th generation of the i7, while we used an i7 of the 2nd generation).
Conclusion In this paper we presented an object-oriented design of 7 real-time resource protocols for single and multi-core processors. The design supports both suspensionand spin-based protocols (PIP, PCP, IPCP, and SRP for single-core systems and MPCP, MSRP and MrsP for multi-core systems. MrsP is the only spinbased protocol). To the best of our knowledge, this work is the first to support such variation of resource access protocols in an RTOS. Our design recalls on a clean class hierarchy and template specialization to allow code reuse, extensibility, low memory footprint and run-time overhead. We implemented the proposed design in a modern multi-core processor and measured the memory footprint (from 876 to 1674 bytes, depending on the protocol) and run-time overhead (less than 4 µs for 20 tasks and around 6.
The transcriptomic analysis showed a higher Pazopanib efficacy predictor to be more predictive in the presence of high expression of KDR and NTRK3 genes and low expression of the IGF1R gene. This would be essential to identify patients benefiting the most from pazopanib treatment. Finally, Dr. Mughal explained her analysis of the DNA methylation data from TCGA's Sarcoma dataset to show that immune-cell infiltration differs across and within Sarcoma subtypes. 16 e key takeaway from this very informative keynote talk was that sarcoma subtypes have high heterogeneity, which calls for integrative analysis using genetic and epigenetic datasets to improve our knowledge of sarcoma genesis.
The best oral presentation prize went to Senuri De Silva from the National University of Singapore, Singapore, for the talk "A Strategized Machine Learning-based Approach for Extracting Robust Biomarkers from Proteomics Datasets: Identifying Novel Ovarian Cancer Markers". This popular presentation received 80% of the total votes for the Oral presentation category. The best flash award went to Md. Asaduzzaman from the University of Rajshahi, Bangladesh, for the presentation on "A combined approach of Machine Learning Based QSAR, Molecular Docking, and Molecular Dynamics Simulation for Screening of Taiwanese Phytochemicals Against Cancer Target GTPase KRAS. " The best poster award was bagged by Rubaiat Ahmed from the University of Dhaka, Bangladesh, for the work on "Most frequently harbored missense variants of hACE2 across different populations exhibit varying patterns of binding interaction with spike glycoproteins of emerging SARS-CoV-2 of different lineages".
The workshop was jointly offered, in The Carpentries style, by Dr. Batool Almarzouq from the University of Liverpool, Dr. Joel Nitta from the University of Tokyo, and Pradeep Eranti from Université Paris Cité. The workshop started with the importance of reproducibility, factors behind irreproducibility, explaining the conceptual difference between "reproducible, replicable, robust, and generalizable," and introducing ideas to improve reproducibility. The workshop discussed the ideas and ways to improve reproducibility using RStudio and the tools available in its ecosystem. The tutorial continued teaching good project management practices (i. e. , README, LICENSE, CITATION, file-naming, directory hierarchy, including packages and updates). This is followed by introducing how to author Quarto documents which have advanced features compared to RMarkdown documents using a live coding approach, then report generation using Quarto code chunks/ documents, citations, and references, how to set up version control in RStudio by creating a git repository (also git workflow), and saving code-generated plots.