dataesr/scientific-paragraphs-categorization huggingface dataset:
Cumulative datasets may exist in multiple locations  [576]. A crime scene in IoT involves physical things and the environment. It can be very difficult to preserve it in its entirety. IoT elements might be interacting autonomously. That can make it impossible to identify the boundaries of a crime scene and separate it from its surroundings  [152]. There is a practical question if an IoT device needs to be kept as evidence or not. By its removal from the IoT environment there is an obvious loss of functionality.
Preparation Step with Repository Most of the proposed models and frameworks define the need for a preparation phase in IoT forensics. Such a phase has been suggested for other types of digital forensics. In IoT, due to the lack of logging and local data preservation, a type of preprepared local data repository of potential evidence seems to be the most warranted. The following papers offer some ideas on how this can be achieved. The work in  [456] , from the same authors as  [455] , proposes a concept that introduces a device in between the local IoT network and the home firewall (Internet/Cloud) that provides security and forensic services. It is an end-usermanaged solution which is unusual and has its pros and cons. The device provides standard security services like IDS/IPS, network monitoring, logging and threshold establishment.
Once something happens that causes a crossing of a set threshold the forensic services are activated. They include data compression, parsing and differentiation, storage, time-line creation, alerting, preparation and presentation of results. It is an interesting idea but relies on the end user who might want to hide certain events. Authors also mentioned issues, common with all systems that monitor network traffic, when encryption, compression and steganography are used on the network data. In addition, a device that sits in the path of network traffic might become a bottleneck. The FAIoT paper  [592]  formally defined IoT forensics and listed its challenges. It proposed a forensics-aware model for the IoT infrastructures (FAIoT). The model is supposed to help researchers focus on a specific research sub-problem of the IoT forensics problem domain.
The FAIoT consists of three parts: a secure evidence preservation module, a secure provenance module, and access to evidence through an API. The authors propose a centralized trusted evidence repository as a new service available for all the IoT devices. Since the repository should handle very large datasets, the authors propose the use of the Hadoop Distributed File System (HDFS). To ensure a proper chain of custody by preserving the access history of that evidence, a provenance aware file system  [433]  for the repository is proposed. FAIoT could help with IoT forensics investigation but at this stage it is more a conceptual design than a practically usable system. One more paper that proposes a preparatory phase in order to obtain evidence is  [405]. It argues that collection of IoT devices states can enable an investigator to create a clear picture of the events that have occurred.
The authors propose a centralized controller that can be connected to devices, controllers (hubs) and the cloud to acquire IoT states. This controller can only read the state and cannot change it. It logs states with time stamps in secure storage with hashes for integrity. The authors provide a proof of concept implementation of their controller using an open source IoT device controller, OpenHAB. It is connected to one device (IP camera), one controller (Insteon Hub) and a cloud account for a device (Nest thermostat). Analysis of the logged states enables reconstruction of scenarios of events in the physical world. Wang et al. [572]  propose a system that ensures data provenance. It ensures that it is possible to establish where a piece of data came from as well as the processes and methodology by which it was produced.
In this manner the authors were able to obtain some Alexa-native artifacts. Alexa is usually managed through a mobile application or the web. The authors apply forensics of mobile applications and web browsers to retrieve additional artifacts from the client. To automate this process of data collection, visualization and evaluation, the authors created CIFT (Cloud-based IoT Forensic Toolkit). The paper emphasizes the need for a holistic approach to data collection and analysis. The DFRWS Challenge, presented previously, had a practical scenario with more popular home IoT devices, including Alexa Echo, a smart speaker that is part of the Alexa Echo system. In  [494]  the authors investigate what data can be collected in IoT attacks and what can be reconstructed from that data. They used a hub and sensors from "sen.
Conclusion and Future Research Directions IoT forensics is a new area open for research. There is already a need for practical solutions to questions that arise during investigations that include IoT. That need will help advance the research through practice. The omnipresence of IoT makes it an ideal tool for evidence collection. As part of its normal operation IoT collects data from its surroundings. That data, from different IoT devices, can be correlated to create a very detailed reconstruction of events. Suspects can easily be unaware of recordings and usually cannot destroy the evidence. Although this seems like a dream come true for surveillance agencies it can be perceived as "scary" for the common citizen. IoT forensics, with data correlation, can enable the emergence of personally identifiable information from, what seems like, irrelevant pieces of data.
The issue of privacy in IoT is a very important one and needs to be adequately addressed. With the opportunities it offers, IoT forensics has also some issues. New devices, new interfaces, new storage media, new file systems, new network protocols, dispersed cloud storage, unclear authority and jurisdiction are just some of those. The amount of data that needs to be preserved, stored and processed is huge. Even the presentation of the results can be challenging. IoT forensics research so far has three main directions: the creation of new models and interfaces, the creation of systems with a pre-prepared repository for evidence, and forensics of real-world IoT systems. It seems to be at its beginning, so there are definitely many opportunities for further research. From the author's point of view two new technologies, blockchain and SDN, can play an important role in that future research.
Acknowledgments This study was supported by the  European Community 's  Seventh Framework Programme (Animal Change project , grant agreement number  266018 ,[2007][2008][2009][2010][2011][2012][2013] and an  ANR  project grant ( VALIDATE ). It benefited from human (CNRS staff) and technical resources allocated to the  ECOTRONS Research Infrastructure , and from the state allocation ' Investissement d'Avenir' AnaEE-France   ANR-11-INBS-0001. We thank the technical staff of  Herbipole  and  INRAE UREP  groups for extracting the intact soil monoliths; the Ecotron team,  Olivier Darsonville ,  Lionel Thiery  and  Marine Zwicke  for their technical help during this experiment;  Didier Macheboeuf  for his expertise in in vitro rumen fermentation; and  Pierre-Jean Haupais  for his contribution to soluble carbohydrate analysis.
Participants This prospective descriptive cohort study was a physiological sub-study of a randomized, controlled, parallel-group, double-blind trial aiming to evaluate the effects of rectus femoris and soleus injections with botulinum toxin vs placebo on gait in hemiparesis  (GENUFLEX protocol, ClinicalTrials. gov: NCT03119948). This sub-study used the baseline evaluations of the participants of GENUFLEX study conducted from 2015 to 2019 in the Analyse et Restauration du Mouvement Laboratories of the Neurorehabilitation Department at Henri Mondor Hospital, Créteil, France. Inclusion criteria were: age ≥ 18 years, hemiparesis due to a nonevolutive central nervous system lesion, time since lesion ≥ 6 months, ability to walk 10 m barefoot without any assistance, cognitive abilities to understand the verbal instructions for a walking test according to the investigator's judgment, and absence of botulinum toxin injections within the last 3 months prior to enrolment.
Kinematic gait assessment The procedure of gait analysis was based on the recommendations of the International Society of Biomechanics, having shown good repeatability and reliability in post-stroke subjects  (Wu et al. , 2002). Gait was analyzed by the same investigator using a tridimensional motion capture system (10 cameras 4MPx, Cortex software package, Motion Analysis Corporation, Santa Rosa, CA, USA). The trajectories of 26 markers placed on anatomical landmarks using the Helen Hayes marker set were collected (sampling frequency, 100 Hz) and filtered using a 4th order zero-lag Butterworth low pass filter, with a 6 Hz cut-off frequency  (Wu et al. , 2002). At least eight gait cycles from each lower limb were used for the kinematic and EMG analysis. Five key parameters were computed: walking speed, cadence, and, for each lower limb, step length and the mean angle position in ankle dorsi-plantar flexion and in knee flexion-extension, calculated over the three thirds of the swing phase (T1, [0-33%]; T2, [34-66%]; T3, [67-100%]).
Statistics For this descriptive cohort study no formal sample size calculations were performed. After verification of normality using Kolmogorov-Smirnov tests, kinematic and EMG parameters were compared between paretic and non-paretic sides and between the three thirds of swing phase using repeated measures ANOVAs (side*period). Post-hoc comparisons were performed using Bonferroni corrections to evaluate the effects of side and period, and their interaction. For each muscle, EMGs were analyzed only in subjects for whom data were complete on both sides for each of the three periods. Statistical significance was set at P < 0. 05. Statistical analysis was conducted using Statistica software package (version 7. 0, StatSoft, Inc. , Tulsa, OK, USA).
Statistical analysis Statistical analysis was performed using SAS software version 9. 4. The Wilcoxon Mann-Whitney U test was used for data analysis due to the non-parametric nature of the data and the small sample size. Cohen's(r)was used to estimate effect sizes. Effect sizes ranging from 0. 1 to <0. 3 were interpreted as small, effect sizes ranging from 0. 3 to <0. 5 were interpreted as moderate, and effect sizes of 0. 5 and greater were interpreted as large. 9.
Limitations The current study is the first to describe and present data in support of the utilisation of an adaptive international cardiology curriculum delivered via an interactive and dynamic virtual classroom in Haiti. However, the study has specific limitations. The small sample size is a limitation. Given the grassroots nature of ICARDs-Haiti, it was initially launched at one teaching hospital as a pilot programme which limited the sample size. Additionally, only immediate preassessment and postassessment results were obtained. Thus, although knowledge acquisition was notable among the residents at HUM, the impact on long-term knowledge retention remains unclear. Additionally, language differences were associated with lower assessment performance. This is supported by the fact that topics taught by multilingual lecturers were more likely to be associated with moderate to large effect sizes.
Acknowledgements Special thank you to the internal medicine residents at  Hôpital Universitaire de Mirebalais ,  Dr. Jean-Pierre Brisma ,  Zanmi Lasante ,  Dr. Merly Robert ,  Dr. Gene Kwan , UPenn cardiology fellows,  Dr. Glenn Gaulton ,  Penn Center for Global Health ,  Dr. Garly Saint-Croix ,  Dr. Michel Ibrahim ,  Dr. Benedicte LeFevre ,  Dr. Yann Dagher-Hayck ,  Dr. Anne Christine Buteau ,  Dr. Krystal Hill ,  Dr. Paul Mather ,  Dr. Victor Ferrari ,  Dr. Mahesh Vidula ,  Dr. Sri Adusumalli ,  Dr. Thomas Cappola ,  Dr. Frank Silvestry ,  Denice Susini ,  Jennifer Cronin , and funding from the  Penn Cardiovascular Disease Fellowship Innovation Fund. Contributors NAH and VS-J conceived and planned the study. NAH, VS-J, FVV, CD, DL and MSA carried out the study. NAH and VS-J performed data collection.
g. tryptophane), and exhibit extreme properties in terms of charge distribution and hydrophobicity. It implies that no direct interaction between the PTC and the side chain could occur to orient an aminoacyl for nucleophilic attack. Instead, the PTC achieves this positioning through an induced-fit mechanism 1 that creates a compaction around the C α of the aminoacyl ester, freezing it in an appropriate orientation almost without interfering with the side chain 2. As the compact state has only recently been recognized as such, the issue of PTC decompaction after peptide bond formation, that allows the A-site peptidyl tRNA product to get free and move to the P site, has not been investigated so far. Based on a survey and examination of ribosome structures complexed with various arrest peptidyl-tRNA and peptidyl-tRNA analogs retrieved from the pdb database, and from free-energy estimates, the present work shows that some of the free-energy released by the reaction of peptide bond formation is used to decompact the PTC, which allows the peptidyl-tRNA to freely move to the P site.
's large dataset (see "Materials and methods" section). Although eGFP scores are largely spread (reflecting the stochastic nature of the underlying phenomenon), the distributions corresponding to the two highest eGFP scores (4 to 5) are clearly shifted to higher volume values compared to that of the two lowest eGFP scores (1 to 2) (Fig. 5B ). Considering average eGFP scores (Fig. 6 ), further analysis revealed that whenever a glycine is present in positions 3 to 5, the average eGFP scores have a narrower distribution that almost never reach values higher than 3. 5 (Fig. 6B ; fraction ≥ 3. 5 = 0. 4%), while triplets that include at least one alanine (Fig. 6C ; fraction ≥ 3. 5 : 4. 5%) or one serine (Fig. 6D ; fraction ≥ 3. 5 : 10. 1%) have intermediate distributions that are clearly ordered according to volume (gly < ala < ser), suggesting that an absence of side-chain generates the strongest impediment during these early elongation events.
These bacteria were subsequently sorted into 5 different bins according to the level of fluorescence (defining a variable called 'TEF': translation efficiency fraction, which is about equivalent to eGFP scores), and subsequently sequenced to determine the identity of the codons/amino acids in pos. 2-11. Although Osterman et al. already highlighted positional effects associated with each amino acid (their Suppl Fig. S4A ), no global     5 ). Only triplets for which at least 5 independent eGFP scores were reported are included (Supplementary Data 3 from Ref. 3  ). (A) Plot with all N 3 -N 5 triplets. (B) Plot for all triplets that include at least one glycine. (C) Plot for all triplets that include at least one alanine and no glycine. (D) Plot for all triplets that include at least one serine, without any glycine and alanine.
Materials and methods Analysis of ribosome structures. Crystal and cryo-EM structures of ribosomes complexed with tRNAs and/or minimal substrates analogs were retrieved from the protein databank website (https:// www. rcsb. org). The pdb database was manually screened for all ribosome structures displaying a peptidyl tRNA bound to the A site (A/A and A/P states) and the P site. Representative structures highlighted in the present analysis are pdb 5kcr, 4v5d, 1kqs, 1vy5, and 6wde. Superposition of uninduced (pdb 5kcr) and induced (pdb 4v5d) PTC structures (Fig. 1A ) was achieved with the align tool of Pymol solely based on residues C2452 and A2451 of the PTC.
Analysis of eGFP expression data. Experimental data highlighting the effect of N 3 -N 5 residues on the level of expression of eGFP reporters expressed in E. coli cells, quantified by the level of fluorescence, were retrieved from Supplementary data 3 published by Djuranovic and collaborators  3. In this study, E. coli cells were sorted based on granularity (SSC-A) and eGFP fluorescence (FITC-A) channels, and split into 5 bins according to the level of fluorescence (med. rel. fluorescence: 20, 120, 600, 3600, 12,000). A total of 215,414 eGFP scores, established for 20  3  = 8000 peptides comprizing a eGFP reported, differing only in positions 3 to 5 (N 3 N 4 N 5 triplets), were investigated in the present analysis. Average eGFP scores (Fig. 6 ) were established for each particular triplet only if at least 5 independent values were listed.
For this reason, planting in "Eden" starts earlier (February until May), to avoid extreme heat stress and yield losses. In addition, we set a small experimental field at the Volcani Center, located in a more temperate region. Overall, we tested the plants under field conditions in three different environments. Environmental data were obtained for each field from a local meteorological station, enabling recording temperature every 3 hours, hence we calculated day and night average and maximum temperatures. Considering that tomato plants experience heat stress when day temperature exceeds 32°C and night temperature exceeds 20°C, our analysis shows that heat stress conditions were indeed prevalent in all three locations, though with some differences (Figure  2a-d ). In the Eden field, due to the early planting, heat stress conditions developed around 50 days after flowering.
Tortoiseshell is a proteinaceous material derived from the scutes of marine turtles, and was shaped into an abundance of objects, especially luxurious items, at its peak in the seventeenth and eighteenth century. It has continued to be used even after the advent of plastics and remains one of the main causes of illegal poaching of marine turtles, in particular the hawksbill turtle Eretmochelys imbricata. Tortoiseshell is made of structural proteins, of which the most abundant are known as β-keratins, or 'corneous betaproteins' (CBPs), a family of short proteins containing a central structure in β-sheets. There are, however, few CBP sequences of marine turtles in protein databases. The scutes of the five main species of marine turtles (Chelonia mydas, Caretta caretta, Eretmochelys imbricata, Lepidochelys olivacea and Lepidochelys kempii) were analysed by proteomics, using nano-liquid chromatography-Orbitrap-mass spectrometry to generate peptidic markers for species identification.
The Caribbean trade of tortoiseshell In this study, archaeological samples were provided to obtain data towards the study of trade and processing of tortoiseshell originating from the Caribbean, as part of the 'Transatlantic Connections' research programme (initiative of the LandArc Laboratory). While there is an abundance of turtle remains in the Caribbean from the second millennium BCE  [21, 22]  and elsewhere in the world from at least 5000 BCE attesting of the use of marine turtles in the past, the archaeological evidence is essentially based on turtle bones found in connection with food consumption, funerary offerings, or worked into tools and other objects  [7, 21]. Tortoiseshell was used to make fishhooks in the seventeenth and eighteenth century in the Caribbean  [21, 23] , but has only been identified in one pre-Hispanic object from Mexico ( possibly Yucatan coast), a Maya mosaic mask from the thirteenth to fourteenth century AD  [24].
Due to the lack of complete genome sequences for marine turtles, proteomics identification is limited to the CBPs sequences of land turtles and a few sequences for the green turtle, making identification of unknown samples to any marine turtle impossible. Reference materials are, therefore, needed to build a database of the proteins targeted and determine the level of variation between species (robust markers need to be determined as degradation in ancient artefacts always leads to a loss of peptides that can make identification of a species more challenging). Here, proteomics analysis was performed on marine turtle scutes to identify species based on novel peptide sequencing of CBPs. Specimens were obtained from the Division of Amphibians and Reptiles of the Smithsonian's National Museum of Natural History (NMNH) for five species of sea turtles that grow scutes: green turtle, loggerhead turtle, hawksbill turtle, olive ridley turtle and Kemp's ridley turtle.
Sample preparation for proteomics analysis A review of the CBP sequences available in database indicated that the proteins have few lysine K residues but enough arginine R residues to make them suitable for trypsin digestion, especially in N-and C-termini. In these sections of the proteins, the arginine residues are also well conserved (figure  1 ; electronic supplementary material, figure  S1 ). Reference scute samples and archaeological samples were cut in a dozen pieces with a razor blade. Proteins were extracted by overnight shaking in 200 µl of a solution of 8 M urea, 50 mM Tris and 50 mM tris(2-carboxyethyl)phosphine (TCEP) at pH 8. 0. An aliquot of 100 µl supernatant was alkylated for 45 min in the dark with 11 µl of 400 mM of iodoacetamide for a final concentration of 40 mM. Samples were dialysed overnight in dialysis units at 3500 Da (Thermo Scientific™ Slide-A-Lyzer™ 3.
PEAKS 8. 5 (Bioinformatics Solutions Inc. ) was used to search the RAW data for matches against publicly available sequences. Keratin-named sequences for the order Testudine (or Chelonians) were imported on 23 January 2020 from Uniprot and NCBI, and combined into a database (Uniprot + NCBI keratin Testudines). Because of the limited number of sequences, PEAKS was used in SPIDER mode to search for amino acid substitutions and DENOVO mode to identify new sequences. For each specimen and archaeological samples, all runs were pooled into one, resulting in one output file for each sample. Searches were carried out using trypsin as enzyme with semispecific digest mode, one missed cleavage, peptide mass tolerance (PMS) of 10 ppm, fragment mass error tolerance (MS/MS) of 0. 02 Da, carbamidomethylation as a fixed modification, and deamidated (NQ) and oxidation (M) as variable modifications, and a maximum variable PTMs per peptide set at three.
Results were filtered using a peptide score of -10lgP ≥ 25 for peptide spectrum matches, a protein score of -10lgP ≥ 20, and one unique peptide, and a cut-off of 90% for de novo peptides. Identified de novo peptides were added to the in-house database (Uniprot + NCBI + Denovo keratin Testudines) and a final search conducted in PEAKs with the parameters set above, adding oxidation (PYHW), dihydroxylation (YW) and pyroglutamate (Q) as variable modifications. PEAKs PTM was run with a maximum variable PTMs per peptide set at three. Results were filtered using a peptide false discovery rate (FDR) of 1% for peptide spectrum matches, a protein score of -10lgP ≥ 20, and 0 unique peptide.
De novo sequencing The initial search on Uniprot + NCBI sequences yielded a large number of peptide matches, the majority from the PEAKs DENOVO output. De novo sequences identified with an average local confidence (ALC) greater than or equal to 90% were selected for further evaluation. For each peptide, sequences that were  royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 201857 most frequently recurring were concatenated into one fasta sequence, resulting in fasta sequences for peptides A, B, C, etc. The sequences were added to the in-house database (Uniprot + NCBI + Denovo keratin Testudines) and all files searched again. After filtering peptides at an FDR of 1% and manual validation of the peptides based on complete or near-complete y ions series, a total of 187 marker sequences were accepted (electronic supplementary material, tables S1 and SM_Peptide details) of which only 14% were obtained from known turtle CBPs; the majority of sequences were thus determined from de novo sequencing.