dataesr/scientific-paragraphs-categorization huggingface dataset:
Fecundity Assays For each line, 40 replicates of 35 mm agar plates containing 1 virgin L4 female and 3 L4 males were stored at 20 C. Plates were randomized in order to prevent bias. Every 24 h for 1 week, the worms were transferred to new 35 mm agar plates. If males died, they were replaced from the corresponding population plate to maintain three males per one female at all times. C. remanei are sperm-limited in their reproduction  (Palopoli et al. 2015)  and this ensured sufficient sperm for fecundity measures. The plates the worms were transferred from were kept at 20 C for 2 days, after which L4 progeny were counted and female deaths recorded. Any females that desiccated on the plastic wall of the petri dish before the end of 1 week were censored from the final data set. Any females that died of other causes within 3 days were also censored from the final data set.
Longevity Assays For each line, 30 replicates of 35 mm plates containing 5 virgin L4 females were stored at 20 C. Plates were randomized in order to prevent bias and examined every 1-2 days to check for dead individuals. If an individual was not moving, the plate was tapped on the counter. If no movement occurred after this stimulus, the tail of the worm was nudged using a platinum wire. If no movement occurred after this stimulus, the head of the worm was nudged using a platinum wire. If no movement occurred after applying all stimuli, the individual was recorded as dead and its position on the plate marked by a red circle. Each day, any carcasses circled the previous day would be subjected to the aforementioned stimuli in order to ensure that the death was correctly determined. Individuals that desiccated on the plastic wall of the petri dish were censored from the final data set.
Genetic Analyses DNA libraries were aligned to the PX356 reference sequence NMWX00000000. 1 using two alignment tools: BWA mem  (Li and Durbin 2009)  and GMAP-GSNAP  (Wu et al. 2016). The BWA alignment was used for all analyses, except for allele frequency estimation step where we required each site to be considered an allele by MAPGD in both alignments  (Lynch et al. 2014; Ackerman et al. 2017). Picard Tools (Broad Institute 2016) and the Genome Analysis Toolkit were used to mark duplicates, realign indels, and filter noise in alignment  (McKenna et al. 2010; DePristo et al. 2011). Allele frequencies were estimated with the pool function in the MAPGD software package  (version 0. 4. 33; Lynch et al. 2014; Ackerman et al. 2017). Sites with missing data were removed and SNPs with a log-likelihood ratio >22 and a minor allele frequency >5% were considered to be true segregating variants.
Sites were filtered for coverage (all bioinformatics scripts and workflows are available at https://github. com/ BamaComputationalBiology/Inbreeding). Inbred was sequenced at a high mean read depth of 370Â while Ancestor and Recovery were sequenced to mean depths of 25-64Â (supplementary table 2, Supplementary Material online). Ancestor and Recovery lines were filtered for a minimum sequence read coverage per site of 5Â, while the Inbred line had a minimum coverage cutoff of 10% (37Â coverage) of the mean coverage per site for the Inbred line. The maximum coverage was 3Â the mean coverage for all lines according to  Li (2014). The hard-masked genome fasta was downloaded from wormbase (https://parasite. wormbase. org/ Caenorhabditis_remanei_prjna248909/Info/Index/), and converted to a bed file format using a script from  Cook (2019).
Recombination Rates Recombination rates across the samples were calculated with the ReLERNN (version 1. 0. 0) software pool-sequencing pipeline with default settings  (Adrion et al. 2020). ReLERNN required more sites than our highly filtered data set of 150,348. Genetic Architecture of Inbreeding. doi:10. 1093/molbev/msab330 MBE.
Acknowledgments We gratefully acknowledge the helpful feedback and comments from members of the  Fierst lab. I. Ruvinsky  helpfully explained the expectation for the full-sub decrease in heterozygosity. This work was supported by  Department of Defense, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG)  Fellowship ( 32 CFR 168a  to P. E. A. ),  National Institute of General Medical Sciences at the National Institutes of Health  ( GM102511  to P. C. P. ), and  National Science Foundation  ( EF-1921562  and  DEB1941854  to J. L. F. ).
Data Availability Sequencing data associated with this article are available in the GenBank Nucleotide Database and can be accessed with BioProject PRJNA248909 (inbred line: https://www. ncbi. nlm. nih. gov/bioproject/PRJNA248909, last accessed January 22, 2021) and BioProject PRJNA562722 (ancestral and recovery lines: https://www. ncbi. nlm. nih. gov/bioproject/?term¼PRJNA562722, last accessed November 4, 2021). All bioinformatic scripts and workflows are accessible at https://github. com/Bamacomputationalbiology/ Inbreeding (last accessed November 22, 2021).
0: 10539106 2. 1 1. 8 FL81_02186 C07A12. 7; ortholog of human TOM1 Synonymous Variant and TOM1L2; human TOM1L2 exhibits clathrin and protein kinase binding activity. 0: 17433630; 2. 3; 24. 4; FL81_03749 C18B12. 6; ortholog of human ERGIC2; Intron Variants 17436635; 2. 3; 24. 4; expressed in tail neurons, the anal 17436636 2. 3 24. 4 depressor and sphincter muscles, the gon_herm_dtc_A, and the gon_herm_dtc_P; predicted to en- code an Endoplasmic reticulum ves- icle transporter, C-terminal domain. 1: 1473060 1. 6 23. 2 FL81_06934 - Intron Variant 1: 1896140 2. 3 26. 5 FL81_07024 C. nigoni Cnig_chr_II. g7686 IPR021942 Protein of unknown function DUF3557; Intron Variant 1: 10958032 2. 3 25. 4 FL81_08858 K10G6. 4; expressed in the head, the Intron Variant nervous system, and the sensillum. 3: 9671404 2.
3 25. 4 FL81_06059 - IPR019421 7TM GPCR, serpen- tine receptor class d (Srd); Intron Variant 3: 13012323 2. 3 25. 3 FL81_06442 R05A10. 2; enriched in the PLM, Intron Variant amphid sheath cell, hypodermis, and intestine; affected by several genes including daf-2, elt-2, and eat-2. 5: 19922641 2. 3 25. 4 FL81_10201 Part of a co-orthologous group with ten Stop Lost, Splice Region C. nigoni proteins Variant-High Impact 7: 499146 2. 3 3. 7 FL81_10375 - IPR001810 F-box domain; IPR002900 Domain of un- known function DUF38, Caenorhabditis species; Intron Variant 7: 1662659 2. 3 24. 8 FL81_10646 C. angaria Missense Variant Cang_2012_03_13_05027. g19294 7: 1772080 2. 3 26. 2 FL81_10653 C. brenneri CBN03163 Synonymous Variant 7: 1905299 1. 9 21. 6 FL81_10668 F45D11. 9 fbxc-42 and R07C3. 9 fbxc-31; Missense Variant both predicted to encode a Protein of unknown function DUF3557 domain.
8: 807030 1. 5 2. 5 FL81_12328 - IPR021942 Protein of unknown function DUF3557; Missense variant 8: 906126 2. 3 25. 9 FL81_12343 - IPR021109 Aspartic peptidase domain; Synonymous Variant 10: 910662 2. 3 22. 2 FL81_11390 R03D7. 4 tceb-3; ortholog of human IPR001810 F-box domain; ELOA (elongin A), ELOA2 (elongin Intron Variant A2), and ELOA3D (elongin A3 D), involved in transcription elongation from RNA polymerase II promoter; localizes to the transcription elon- gation factor complex; expressed in the alimentary, muscular, nervous and reproductive systems. (continued).
5; 2. 8; Cang_2012_03_13_00006. g545 386844 1. 3 3. 0 102: 302190 2. 3 25. 6 FL81_18400 F37C12. 1; ortholog of human CCDC94 IPR000772 Ricin B lectin do- expressed in the pharynx, tail, and main; IPR029044 Nucleotide- muscular, nervous and reproductive diphospho-sugar transfer- systems; predicted to encode a ases; Intron Variant CWC16 protein domain. 134: 246939 2. 3 25. 4 FL81_19272 C09G5. 2 dph-2; ortholog of human IPR012885 F-box associated DPH2; predicted to have transferase domain, type 2; Missense activity. Variant 222: 31392 1. 4 21. 7 FL81_20926 F58E10. 4 aip-1; ortholog of human IPR012677 Nucleotide-binding, ZFAND2A (zinc finger AN1-type alpha-beta plait; IPR000504 containing 2A) and ZFAND2B (zinc RNA recognition motif do- finger AN1-type containing 2B); main; Intron Variant predicted to have zinc ion binding activity; involved in cellular response to misfolded protein and response to arsenic-containing substance; local- izes to the cytoplasm and nucleus; expressed in the alimentary system, body wall musculature, excretory cell, head, and hypodermis.
519: 32965 2. 3 26. 2 FL81_23267 ZK550. 5; ortholog of human PHYH Missense Variant expressed in the nerve ring; human PHYH exhibits carboxylic acid bind- ing activity, cofactor binding activity, and ferrous iron binding activity. 1197: 1005 1. 9 21. 0 FL81_24477 C. brenneri CBN03810, CBN11213 IPR000719 Protein kinase do- main; IPR008271 Serine/ threonine-protein kinase, ac- tive site; IPR002290 Serine/ threonine/dual specificity protein kinase, catalytic do- main; Synonymous Variant 1342: 4099; 4102 1. 8 1. 8 FL81_24554 C34G6. 4 pgp-2; predicted to have ATP IPR008250; P-type ATPase, A binding activity and ATPase activity, domain; Intron Variants coupled to transmembrane move- ment of substances; involved in lipid storage and organelle organization; localizes to the gut granule mem- brane; expressed in the Eala, Ealp, and Eara, and the alimentary and nervous systems.
While fog resources help in providing the desired quality of service (QoS) for services, resource-limited fog devices cannot be a replacement for the the resource-rich cloud  [17]. In fact, fog computing can fill the gap between the cloud and end devices (e. g. , IoT nodes) by moving some resources (i. e. , computing, storage, networking) and data management to a closer location (i. e. , network nodes) to IoT devices. In this case, resource allocation and data management decisions are not only made in the cloud, but can also be made along the IoT-to-cloud path as data traverses to the cloud (preferred to be close to the IoT devices)  [18]. It is worth mentioning that bringing the resources closer to the edge can also increase the battery lifetime for edge devices and reduce network traffic in the core of the network  [15].
ROOF Computing The IEEE standard working group working on the project P1931. 1 TM  [22]  focuses, among other things, on two main features of semi-autonomous devices: (i) the interactions of such devices with each other and the environment, and (ii) the context-aware decisions of such devices, which are made based on a consensus in a decentralized ecosystem without any human intervention. The result of this attempt was reported as a standard called Real-time Onsite Operations Facilitation (ROOF). Next-hop connectivity for the things needing real-time context building and decision making as well as efficient backhaul connectivity to remote nodes and networks were addressed in this standard  [23]. The utilization of Cloud or Fog properties is minimized with the help of ROOF computing. This goal can be achieved by analyzing the frequently accessed data at the edge of the current user network (e.
SAFE leverages the lattice-based cryptosystem and deploys global and virtualized SDN controllers to ensure resource allocation, low latency, and services management for increasing healthcare applications in a distributed/multi-region infrastructure. In  [59] , the problem of fog node trust is exploited for the security of healthcare unit and patient records. When the cloud-fog is used for smart healthcare, health data need to be securely communicated in a parallel manner. Health data can include various unauthorized intentions, which drives the fog node for biasing behavior and violating the data security policy (e. g. , tempering attacks at Fog node, fabricated heath records). Data in the healthcare unit are processed mostly at nearby fog nodes, whereas the data heterogeneity and some real-time or delay-intensive healthcare demands can bottleneck the neighboring fog node.
Resource Provisioning in Cloud-Fog SDN can support reliable communication management for IoT delay-intensive applications. The application layer in IoT introduces continuous data packets, while the link disruption, due to various reasons, widely occurs when a resource-rich cloud serves these applications. It incurs the QoS and QoE challenges for diverse IoT edge applications. On the other hand, when IoT applications are served by near computational resources (fog nodes), any sort of failure or generic service unavailability requires abrupt flow detouring to meet the expected latency. To enable intelligent, reliable communication, in  [63, 64] , the authors employed an ML algorithm (i. e. , k-nearest neighbor regression) in the SDN control function layer for predicting the failure behavior, based on the failure event history.
Another work in  [66]  studied the same problem in a more heterogeneous environment, including software and hardware NFV platforms, such as virtual machine (VM), docker container and SmartNIC. Research work  [67]  contributed to handling dynamic IoV by enabling the hierarchical distributed controllers, where a root controller at a central fog DC manages the underlying controllers and networks. The rest of the controllers are domain and edge controllers, whereas RSUs are directly connected with edge controllers. Edge controllers are deployed at the edge layer, where servers collaborate for fog functions horizontally. However, domain controllers share the network information to the root or global controller in the vertical distributed control architecture. Majorly, the contribution of this work is efficient network management distribution over the root, domain and edge controllers to ensure the least delay for speedy vehicle support.
in  [84] , incorporating the notion of trust between SDN applications and the controller. • Efficiency of SDN: The efficency of SDN should be improved. Huge amounts of computational overhead resulting from rules enforcement, overlapping network rules and the memory-restricted capacities of the OpenFlow-enabled devices are some of the reasons for low efficiency of SDN in hybrid cloud-fog systems. Computational context aware management in the control plane can improve SDN efficiency. • ML and SDN in cloud-fog systems: The logical centralization of SDN has a crucial advantage, such as the capability to extract metrics from the SDN controller and the SDN underlying network elements. This allows to apply ML and artificial intelligence techniques to improve the efficiency and reliability of SDN through the available realtime information extracted from the data and control plane.
• Integration with other technologies for cloud-fog systems: The SDN-based cloudfog deployment model can involve other technologies, such as blockchain  [87]  and multi-agent systems  [88]. Blockchain can be used in the connection of controllers in the fog layer, which improves security issues. In addition, benefiting from the distributed nature of blockchain and multi-agent system, data integrity is ensured. The combination of these technologies with SDN needs to be studied and tested from different aspects. • Consistency of the control channel: Some of the above-mentioned challenges are given for special type of controller design. Therefore, depending on the type of the controller design that is used, the challenges are different. Having a single administrator for enterprises and data centers, a centralized controller suits them, while having a heterogeneous environment, distributed and, of course, for now, hybrid controllers/Tmodel work better for cloud-fog systems  [89].
Conclusions By increasing the number of connected devices to the internet, high communication and computation capacities will be required to meet the demands of future applications (e. g. , the massive IoT-related applications) requiring data sharing and processing. This paper discussed key enablers (i. e. , future generation cellular technologies, cloud-related technologies, and computer network with softwarization) to support these future applications. We mainly focused on the opportunities and challenges of integrating SDN in cloud-fog systems for supporting 5G and beyond-enabled future applications. To this end, we first conducted a survey on current and emerging applications that use SDN in cloud-fog systems, where we found that most of them also considered 5G/6G technologies as another key enabler.
Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N-and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions.
5 + ) charge states relative to other teams (~4. 5 + ) (Extended Data Fig. 3c ). These output data comparisons may be valuable for developers to better understand, further develop and ultimately improve their software. The team performance was assessed using orthogonal performance tests that served to comprehensively evaluate the glycopeptide identification accuracy (specificity) and glycoproteome coverage (sensitivity), two key performance characteristics in glycoproteomics (Fig. 1d ). Six (N1-N6) and five (O1-O5) performance tests were carefully designed to assess the relative performance for N-and O-glycopeptide data analysis across teams (Table  2  and Supplementary Tables  5 6 7 8 9 10 11 12 13 14 15 ). First, the ability to detect the synthetic N-glycopeptide in the datasets was assessed (N1).
High-performance informatics solutions for N-glycoproteomics. The relative team performance for N-glycoproteomics was comprehensively assessed using six independent performance tests (N1-N6) (Table  2  and Supplementary Tables  5 6 7 8 9 10 ). Among these performance tests, N1 scored the ability to accurately identify a synthetic N-glycopeptide in the sample (Extended Data Fig. 8 ). Similar to the other performance tests, N1 was used to establish the relative team performance. Founded on a 'ground truth' , the N1 data including the 12 manually annotated spectra all corresponding to the synthetic N-glycopeptide are particularly informative and may aid developers train algorithms and improve software to annotate N-glycopeptide spectral data better. The N1 data also supported observations made across the entire dataset (Extended Data Fig.
3a ). At a glance, the scorecard pointed to considerable team-to-team variations in the performance profiles suggesting that the applied software and search strategies exhibit markedly different strengths and weaknesses for N-glycoproteomics. As an example, IQ-GPA (team 1) and GlycoPAT (team 8) performed well (relative to other developers) in the N-glycan composition test (N2), while Protein Prospector (team 2) and Byonic (team 4) performed well in tests scoring the source N-glycoproteins (N3) and N-glycoproteome coverage (N4). Overall, Protein Prospector (team 2, overall score 0. 682), Byonic (team 4, 0. 676) and GlycoPAT (team 8, 0. 647) were found to be high-performance software solutions for N-glycoproteomics. Notably, our scoring did not separate these three developers by any substantial margin, but their overall performance was slightly higher than that of IQ-GPA (team 1, 0.
621) and glyXtool MS (team 3, 0. 580) and substantially higher than that of the other software (score range 0. 296-0. 366). Supporting our scoring method, an independent assessment method based on the match between reported and actual site-specific N-glycoforms of four serum glycoproteins (A1AT, CP, HP, IgG1, thus founded on a 'ground truth') recapitulated the scoring profile across teams (R 2 = 0. 82) (Extended Data Fig. 10 ). Further supporting the top ranking of Byonic and Protein Prospector, the best performing user teams employed Byonic (teams 11, 13, 15, score range 0. 687-0. 777) and Protein Prospector (team 12, 0. 709). Their overall performance scores were marginally higher than seven other Byonic users (teams 10, 16-18, 20-22, 0. 593-0. 679), but markedly higher than teams using SugarQb (team 14, 0.
172) and Mascot (team 19, 0. 413). Despite the similar overall performance among most user teams, not least the ten Byonic users, their performance profiles differed markedly across the six performance tests. We then explored the scorecard for software-independent performance-associated variables including the search settings (SS1-SS13) and search output (SO1-9) using seven different statistical methods (Fig. 3b ). Many statistically strong relationships were   2 ). See Supplementary Tables  5 6 7 8 9 10 11 12 13 14 15 16 for performance data. # The top third performing teams (white font) were placed in a high-performance band. The team scoring was later validated (extended Data Fig. 10 ). *Performance could not be determined. b, many variables (search settings, search output) showed associations (negative or positive) with N-glycopeptide performance.
See Table  1  for variables. See Supplementary Table  18 for   found revealing key performance-associated variables that either positively or negatively correlated with the glycopeptide identification efficiency. As an example, the use of decoy/contaminant databases (SS13) showed associations with performance in the synthetic N-glycopeptide test (N1) and high N-glycoproteome coverage (N4). Search strategies that allowed for a relatively high diversity and number of nonglycan variable peptide modifications (SS8, SS10) and few glycans per peptide (SS9) were also associated with high N-glycoproteome coverage (N4). As expected, allowing multiple missed peptide cleavages (SS7) and variable nonglycan modifications (SS10) in the search strategy correlated with higher glycopeptide FDRs as indicated by higher rates of NeuGc and multi-Fuc identifications (low N6 scores) (Supplementary Table  18 ).
High-performance informatics solutions for O-glycoproteomics. Protein Prospector (team 2) displayed the highest performance in tests scoring the source O-glycoproteins (O2) and consensus O-glycopeptides (O4) (Fig. 3c  and Supplementary Table  16 ). Conversely, IQ-GPA (team 1) and glyXtool MS (team 3) were the best performing software in tests scoring the O-glycan compositions (O1) and O-glycoproteome coverage (O3), respectively. Overall, Protein Prospector (team 2, overall score 0. 613) and glyXtool MS (team 3, 0. 522) were found to be high-performance software for O-glycoproteomics. Among the users, four Byonic teams (teams 13, 15, 17, 18, overall score range 0. 473-0. 701) were ranked in the high-performance band. Correlation analyses showed that accurate identification of the O-glycan compositions (O1) associated with approaches using a focused (narrow) O-glycan search space (SS2) and permitting only few missed peptide cleavages (SS7) (Fig.
The Byonic teams employed highly diverse search strategies; except for the common use of decoy/contaminant databases (SS13) and monoisotopic correction (SS14), the search settings varied considerably across these teams (Fig. 4a ). Undoubtedly, this search diversity and different output filtering methods used by the Byonic teams (e. g. , Byonic score >100, PEP-2D <0. 001, FDR <1%) contributed to the dramatic variation in reported glycopeptides (Fig. 4b  and Supplementary Table  1 ). Unsurprisingly, therefore, the relative specificity (accuracy) and sensitivity (coverage) scores (established from N1-N6/O1-O5) showed different performance profiles of the Byonic teams particularly for the O-glycopeptide analysis (Fig. 4c  and Supplementary Table  16 ). Teams achieving better than average sensitivity scores (e.