dataesr/scientific-paragraphs-categorization huggingface dataset:
This shows the good structural overlay of the query and template domains, as expected. Histidine biosynthesis is just one of the many enzymatic functions carried out by members of the TIM barrel family  (35). However, finding more distant TIM barrel homologues can be challenging because their sequence similarities are often below the detectable level  (35). To illustrate such a scenario, let us suppose that the 1GPW structure had not been solved. Part B of Figure  3  shows the template found when using the Kbdock structural neighbour list to find more remote DDI homologues. In this case, Kbdock considered the DDIs involving four neighbour Pfams of PF00117 and 16 neighbours of PF00977, and it found an interaction between the SNO (PF01174) and SOR_SNZ (PF01680) domains [a PLP synthase complex from Bacillus subtilis  (36) ] as the first instance of a candidate template (PDB code 2NV2) involving the two neighbour domains together.
DISCUSSION In recent years, many protein structure interaction databases have been described  (37). For example, SCOPPI (9) classifies DDIs using geometric overlap and face angle scores of residue contact vectors  (38). For a given SCOP family, SCOPPI outputs all PDB complexes involving the query. DDIs are grouped according to their partner domain. For each group, multiple sequence alignments annotated with the interacting residues are provided for both the query and partner family. SCOPPI also outputs its calculated interface type, area and volume, and the web interface provides a screen-shot of each interface and  external links to related publications. SCOWLP  (10)  is also a SCOP-based classification of protein domain and peptide interactions. For each SCOP family, SCOWLP performs pairwise structural alignments to identify common interacting residues with which to define and cluster 'binding regions' using a sequence-based similarity score.
The user may browse the SCOP hierarchy to view interactions or to search for interactions involving a given SCOP family. 3DID  (12)  classifies DDIs and DPIs using hierarchical complete linkage clustering of groups of interface residues or 'interface profiles'. The first version of Kbdock was built from 3DID, although this dependency has since been removed. For a given Pfam family, 3DID outputs a list of its partner domains grouped by interface profile. ProtCID (15) also uses Pfam to assign a 'chain architecture', i. e. a list of Pfam identifiers, to each protein sequence in each PDB entry, and it clusters similar chain-chain interfaces using a distance-based pairwise amino acid similarity score. The ProtCID web interface supports queries by PDB ID or by pairs of protein sequences or Pfam identifiers, and it lists the PDB IDs of structures with matching chain architectures.
IBIS (  14 ) stores experimentally determined and inferred physical interactions between proteins, peptides, DNA and RNA and other small molecules. IBIS defines DDIs using its manually curated 'CDD' domain definition and it classifies them using hierarchical complete linkage clustering of groups of interface residues. For a given query protein, IBIS outputs a list of its interaction partner proteins. The interactions are listed as DDIs, which are grouped by their partner domain and binding site. The identities of binding site residues on the query protein are also shown. GWIDD (  13 ) is a database of experimentally solved and predicted 3D structures of protein-protein complexes built from the PDB and the BIND  (39)  and DIP  (40)  databases. If GWIDD does not have a 3D structure for a given query, it builds a 3D model using Nest  (41)  to calculate structure-based superpositions.
CONCLUSIONS Kbdock provides a useful resource with a unique processing pipeline for analysing the 3D structures of DDIs within and between Pfam domain families and for finding knowledge-based docking templates to help predict the structures of unknown protein complexes. It also allows structural similarities between Pfam families to be exploited to find candidate docking templates even when no direct Pfam homologues exist. The 2013 version of Kbdock supports analyses of all available hetero and homo domain interactions and binding sites from the PDB, as well as domain-peptide interactions and binding sites. Online visualization is provided by the Jmol and Cytoscape plugins. All data and results are available for download in several formats, as well as VMD scripts for high-quality local visualization.
At the ϒð4SÞ resonance, the electron beam of 9. 0 GeV collided head-on with the positron beam of 3. 1 GeV resulting in a Lorentz boost to the ϒð4SÞ resonance of βγ ¼ 0:56. This boost made it possible to reconstruct the decay vertices of the B and B mesons, to determine their relative decay times, and to measure the time dependence of their decay rates, a feature that was critical for the observation of CP-violation in B 0 -B 0 mixing. To reach the desired sensitivity for the most interesting analyses, datasets of order 10 8 to 10 9 B mesons were needed. For the peak cross-section at the ϒð4SÞ of 1. 1 nb, this required an integrated luminosity of the order 500 fb - 1  , that is, many years of reliable and highly efficient operation of the detector, and stable operation of the PEP-II storage rings at luminosities exceeding the design of 3 Â 10 33 cm - 2  s -1.
Moreover, the instrumentation to assess, monitor, and control backgrounds and other environmental conditions, and to handle ever-increasing trigger rates had to be enhanced. These enhancements served the routine operation at higher data rates, and also provided the information needed to understand operational limitations of the detector and software and to subsequently design the necessary upgrades in a timely manner. To attain such large, high quality datasets and maximize the scientific output, the accelerator, detector, and analyses had to perform coherently in factory mode, with unprecedented operational efficiency and stability. This factory-like operation required that experimenters paid very close attention to what were often considered routine monitoring and quality assurance tasks.
1%. The new analysis benefited from overall improvements in the charged particle reconstruction efficiency, and modifications to the selection criteria to reduce sensitivity to background conditions. The central values of N BB from the earlier analysis are consistent with the new ones within the systematic error of the new analysis. The numbers of hadronic events and muon pairs were determined for each run, and analysts could then obtain BB counts and luminosity values for any subset of the full dataset. The numbers of produced ϒð3SÞ and ϒð2SÞ events in data samples collected at these resonances were determined in a similar way. The off-peak continuum scaling was performed using e þ e --γγ events, to avoid the non-negligible rate of ϒ-μ þ μ -decays. The hadronic selection criteria were modified for these analyses.
Logging Manager (LM): Responsible for receiving from OEP events selected by the L3 trigger and for writing them to disk for use as input to offline computing. The online system also included systems to control the data acquisition and the detector, to monitor data quality and to orchestrate online calibrations. The major components were: Detector Control (ODC): Responsible for the control and monitoring of environmental conditions of the detector systems and the handshake with the accelerator control system. Run Control (ORC): Responsible for tying together all the other components, and for sequencing their operation, interlocking them as appropriate, and providing a graphical user interface (GUI) for operator control. Online Databases (ODB): Comprised the databases for the storage of detector configurations, conditions, and calibration constants.
Scheduling and I/O priority issues in Solaris and in the Veritas file systems used on the logging server were found to prevent the rapid clearing of the backlog even after backpressure had been asserted, leading to occasional minute-long interruptions of the data acquisition. When performance tuning of the logging application and the logging server operating system and even major hardware upgrades of the server and its RAID disk arrays failed to resolve these problems, we concluded that the existence of this serialization point in the system was itself the core of the problem, and undertook a redesign that avoided this altogether  [43]. In the new design, introduced in 2004, each L3 node was responsible for logging its selected events locally. To facilitate this, the Linux x86 farm nodes were equipped with additional local disks, sufficient to store approximately a day's worth of data distributed across the farm.
In practice, it was able to merge the files of a data run within 10 min of its completion. This design removed the possibility of backpressure from a single point of failure and enabled extremely high peak logging rates for periods of tens of minutes or more. The latter capability was used to permit the logging of data at the full L1 trigger rate, from time to time, for calibration and diagnostic purposes. The ability to log data at Oð5 kHzÞ was used on several occasions. At the narrow ϒ resonances, data were logged continuously at rates of 900-1000 Hz. Online detector control upgrades: The detector control system was implemented using EPICS  [44] , a toolkit for building control systems. The core components of EPICS are a driver model for device abstractions, a network communications protocol and a distributed system for acquiring and processing sensor data and controlling devices (the EPICS database).
In the EPICS model, all device interaction and data processing take place on I/O Controllers (IOCs). Simple data processing can be performed in the database. More complicated calculations can be implemented using an add-on C-like state machine language. EPICS also provides tools for constructing GUIs and for managing and processing thresholds and alarms. Over the nine years of data taking, the EPICS toolkit evolved significantly. In the earlier versions, the IOC code used the proprietary vxWorks API for accessing system functions. IOCs could therefore only run on vxWorks while Unix/Linux systems were limited to running client applications. Moreover, the Unixside APIs for providing and accessing EPICS data were complicated and restrictive. Later versions of EPICS allowed native IOCs to run on other operating systems and also introduced the capability to run software IOCs on Unix/Linux.
For ambient data archiving, the initial BABAR design implemented a custom system that stored the time-series data in an Objectivity database (ambient database). This system turned out to have severe limitations in the number of channels it could handle and in the per-channel logging frequencies. In addition, there were severe reliability problems caused both by the input side interface to EPICS and by the performance and availability of the Objectivity database. Database down time or lock problems would immediately cause loss of ambient data. These issues were addressed by a complete redesign of the archiving system. The input side was changed to use a simpler and more modern EPICS APIs and the process of acquiring data was decoupled from the database by logging to an intermediate local disk buffer that would be flushed to the database in regular intervals.
The new CPUs solved the performance issues, however, RTEMS turned out to be too immature, especially in the areas of networking and network file system support, and it lacked memory protection, so that application errors would often cause system crashes. Running Linux on the MVME5500 boards seemed to be a more promising option. The Embedded Linux  [47]  distribution was finally chosen, because a board support package for the MVME5500 was available, and EPICS and the device drivers were ported to this platform. Because none of the applications had hard real-time requirements, true real-time support from the operating system was not needed. This enabled us to implement IOC functionality simply by running a software IOC process that could be restarted without the need to reboot the operating system.
For example, beam spot information calculated by the BABAR online monitoring system was transmitted to the accelerator control system. A secondary readonly path (via EPICS gateways over more general parts of the SLAC network) provided the BABAR detector control system with access to a broad variety of diagnostic information (such as temperatures or vacuum pressures in PEP-II). Some of this information was archived in the BABAR ambient database in order to be able to study correlations with ambient data generated by BABAR. Online run control upgrades: The run control system coordinated all components of the online system to accomplish the routine tasks of data taking, calibration and monitoring. It provided the primary user interface for operators to control these functions. The system relied on a model of the online components as simple state machines.
Data acquisition was cleanly separated from the subsequent analysis of the calibration events and determination of calibration constants, allowing the analysis to overlap with other operations and further improving efficiency. Database improvements: The configuration database was used to maintain hierarchical associations of system-specific configuration data, identified with a single numeric configuration key. This key was distributed to all online components and could be used to retrieve the required configuration information. At the start of BABAR operation, the originally envisioned user interface for creating and managing multiple configurations had not been developed. The management tools that were provided exposed implementation details of the database and were found to be very difficult to use.
Most configuration updates could not be performed by detector system experts, but required the intervention of the database developers. As part of the online system upgrades, the user interface was redesigned and reimplemented, including both graphical and command line tools. The ability to script configuration updates was improved. This had become mandatory to support rapid operational changes and avoid down time. Instead of exposing raw database objects, new alias tree tools allowed users to give symbolic names to sub-configurations at every level of the hierarchy. This ability to use logical names made it much easier for detector system experts to manage their own configuration data. More detailed descriptions of the configuration database and its improvements can be found in Refs. [52, 53].
When the Objectivity-based event database was replaced by a ROOT-based object event store (discussed in Section 6. 3), it was initially decided to retain the Objectivity implementations of the non-event databases (configuration, ambient, and conditions). Unfortunately, many of the same concerns that motivated the event store migration also applied to these databases, i. e. , vendor platform support, poor lock management, and ongoing maintenance cost. In 2006, the collaboration therefore decided to phase out Objectivity completely. Each of the non-event databases had an important role in the online system, so this migration had a much more profound effect on the online system than the earlier event store migration, and had to be carefully prepared to avoid interference with normal operation.
The new ambient database implementation was based entirely on ROOT files. It was introduced with minimal disruption because of the separation of data collection and archiving that had already been introduced, as described above. The new implementation offered much better performance; it also required much less storage because of data compression. Both the configuration and conditions databases were migrated from Objectivity to hybrid object-relational implementations. Because of complexity, size, and access requirements, different implementations were chosen: For the configuration database, serialized ROOT objects were directly stored as Binary Large Objects (BLOB) in a MySQL database. For the significantly larger and more complex conditions database, serialized ROOT objects were stored in ROOT files and referenced by a relational database.
To provide simple large-scale read access to the conditions database, a read-only ROOT-only implementation was developed. User interfaces and electronic logbook improvements: Over the lifetime of the experiment, major improvements and upgrades of the user interfaces for the operators were implemented. The original Sun Ultra-5 workstations with two or three CRT screens per machine were replaced with personal computer systems running Linux, each one driving two or three flat screens. XFCE  [54]  was chosen as desktop environment and window manager because of its speed and configurability. It was modified to better support multiple screens and functions to take screen shots of full screens or screen areas and automatically insert them into the electronic logbook were added. The web-based electronic logbook played a central role in data taking and detector operation.
It was implemented in Perl/ CGI and used an Oracle database backend. From the beginning, the logbook interface combined a free-form log entry tool with a front-end to the BABAR bookkeeping database. The logbook originally required operators to manually enter information about data runs, such as the run type (e. g. , colliding beams, cosmic data taking, and diagnostic data runs) and other per-run meta-data such as the reason why the data run ended and detector and global data quality assessments. Over time metadata fields were added and entry of run information was automated where possible, e. g. , if the system detected a beam loss, the end of run reason would automatically be set to beam loss. The logbook also provided separate sections for detailed information relating to each detector system.
The TDC information, which was interspersed in the waveform, was kept unchanged. The missing FADC samples were estimated by interpolation and reinserted in the ROM prior to the feature extraction, i. e. , the signal time and charge, from the waveform. Extensive tests verified that this algorithm did not noticeably affect the track efficiency or dE=dx resolution. Unfortunately, during the implementation of these changes, a minor error was introduced into the feature extraction assembly code that corrupted one half of the waveforms that contained two TDC hits, 4. 2% of the waveforms in total, which resulted in a 2% reduction of tracking efficiency. Some 30 fb -1 (5. 6% of the full dataset) were recorded before this error was identified and corrected. In the second stage, the feature extraction was implemented on Xilinx Spartan 3 FPGAs on each of the new 48 readout interface boards on the DCH endplate.
After event reconstruction, the quality of the processed data was surveyed by the Data Quality Group (DQG). The group met on a weekly basis to report on the data processed during the past few days: only runs validated by the DQG were included in the datasets prepared for physics analysis. During the data processing, a set of histograms based on 10% of the data was automatically generated and made available to the DQG. It included both raw and reconstructed quantities and allowed DQG experts to identify runs with problems and diagnose their origin. If the processing had failed, the run was processed again, whereas if the raw data were compromised, online experts would check whether parts of the data could be recovered. In addition, during the final reprocessing of the full dataset, all runs which had been declared bad were reviewed again to see if they could be repaired.
Following  In (E γ / GeV) the adoption of trickle injection, the RPC and LST chambers were constantly kept at operational voltage, except for occasional beam aborts. Chamber currents, voltages, rates, gas flow, beam conditions and temperature were recorded in the conditions database. With the addition of offline efficiency measurements, the performance of the RPCs and LSTs could be tracked and changes in efficiencies or currents could be correlated with changes in environmental or PEP-II conditions. RPC backgrounds: The central barrel and backward endcap RPCs were well-shielded from the PEP tunnel and interaction region. Noise rates with beam were similar to noise rates without beam. The only significant exceptions were associated with failures of the PEP-II beam abort system. In 2001 and 2002, there were several instances of asynchronous beam aborts in which the high energy beam was not steered onto the beam dump but was lost in the beam pipe just before the BABAR Hall.
By comparing the average muon identification of the entire BABAR dataset to this final period, one can derive a rough measure of the number of muons lost to physics analyses due to RPC hardware problems. This estimate ignores improvements from the additional absorbers installed during the IFR upgrades. The average muon efficiency for the highest purity (1. 2% pion misidentification) was 63% compared to 80%. For a higher pion misidentification rate of 5%, the muon average was 89% compared to 95% after the upgrades. This translates to about 6-17% of muons lost due to hardware problems.
Overview The offline computing system encompasses both production activities and the support of physics analyses performed by collaboration members. The system was required to provide rapid data quality feedback from full event reconstruction; keep up with the factory mode of operation and the expected large volume of data; permit the production of correspondingly large samples of simulated events; and support the simultaneous analysis efforts of hundreds of physicists. In order to maintain systematic uncertainties at a level consistent with the very high statistics of the expected dataset, the fidelity of the reconstruction and simulation had to be improved substantially over the previous generation of e þ e -B-physics experiments, and reflect advances in algorithms arising from Tevatron and LEP experience.
Building on R&D motivated by the SSC and LHC projects, BABAR initially adopted the technology of object-oriented databases for the persistence of both event and non-event data. While the early years of operation and physics analysis relied on this system, its shortcomings and scaling limitations ultimately led to the complete reimplementation of the persistence model using the object serialization and I/O libraries of the ROOT  [116]  software. The scale of computing and storage resource requirements that emerged significantly exceeded early estimates, and continued to grow rapidly with the excellent performance and significant upgrades of PEP-II. The provision of these resources at a single site proved impractical, and required the development of a system of distributed computing and network data distribution.
Processing chain The offline computing system (Fig. 92 ) supports the following key processing steps: calibration and reconstruction of raw detector data; simulation of physics processes, the detector, and its response, taking into account the recorded conditions of data-taking; and reconstruction of the resulting data; skimming -that is, the creation of a variety of overlapping subsets of the real and simulated data matching signatures for various physics processescreated to improve the efficiency of analyses; and distribution of the resulting datasets to a variety of sites, making them available to users. In this section, we describe their implementation in the releases used to prepare the final consistent version of the full dataset. In the following section we discuss some of the evolution of the system that led to that final version.