dataesr/scientific-paragraphs-categorization huggingface dataset:
The goal is not to replace the use of MD, but rather to form a useful and lightweight extension that provides a better understanding of the system under study. 29 MS systems are not limited to the use of a single type of molecular modeling code and can, for example, use approaches other than MD simulations as their underlying physics engine for driving particle motion. Alternative computational modules can be used to overcome certain limitations, such as the computational cost of all-atom MD simulations or the difficulty of setting up such simulations, extending the system to larger scales, and so on. We can cite the example of BioSpring  31  which uses a spring network model to simulate interaction between particles to facilitate interactivity and simulations at large scales. We will discuss these issues in more detail later in this paper, and now turn to a thorough discussion of the components and some of the technical details required for interactive simulations, notably visualization.
| Molecular graphics The 3D representation of molecular structures allows researchers to gain insight into the purpose of molecular shapes in cells. 37 We will now focus on the different methods used to graphically represent these structures. The preceding part F I G U R E 6 The visualization process model showing the scientific discovery cycle and integrating visualization as an interface between computer results and human perception. Reprinted with permission from Reference 33 about human intuition and understanding is closely related and complementary. The analysis of different types of biological datasets, not created by direct visualization of structures, will be specifically addressed later on in the article. Matter at the subatomic level does not behave like the macroscopic objects of daily life, hence we have no ground truth for how to represent them graphically.
Very recently, a method to ensure visual continuity of protein secondary structures for trajectories in general and interactive simulations in particular was outlined. 55 nother representation method for steered simulations is the informative visualization of atomic collisions during manipulations developed by Kreylos,  56  which allows the user to avoid conformations with steric clashes in favor of other, more plausible conformations. The overlaps between atoms are calculated in real time during the manipulation and the collisions are visually displayed. A fundamental concept is that visualization tools should not only be tools for representing raw data, but they should enable the discovery of the meaningful relationships between different structures of a complex and diverse biological datasets through the use of appropriate representations: The challenge in representing diverse data sets stems from choosing graphical elements that can properly convey the values, properties and relationships to the user.
The other device we have already talked about is the HMDs, also known as the Virtual Reality (VR) headset, which has been growing rapidly lately. The device has benefited from hardware improvement and has seen a significant price drop that has allowed for widespread adoption by the general public, especially in the video game industry, which is an emblematic example (e. g. , Occulus Rift and HTC Vive released in 2016). As early as 2010, Stone et al. , who had developed IMD with its VMD visualization software back in 2001, extended the use of their program to include immersive virtual reality displays, taking advantage of the increased capacities of multi-core CPUs and GPUs. One of the challenges cited by Stone is to make the VR technology intuitive and easily configurable so that it is accessible for scientific applications.
VMD can thus support VR hardware and software for molecular visualization, analysis, and IMD. 69 A year later, they described their new technique of out-of-core visualization (algorithms using external memory) operating on solid-state disks by taking advantage of their then technological democratization (increase in capacity/price ratio). This technique enables fast writing and reading of large-scale data to obtain trajectory animation of large MD simulations at a stereoscopic display rate high enough for effective immersive visualization and very close to the rate achieved with in-core approaches. 70 Fluid trajectory visualization for large datasets remains a topic of active research. 71 I G U R E 8 User performing an interactive molecular simulation in a CAVE environment that has an advanced haptic device with access to the entire workspace of the immersive system As for user immersion in molecular visualization, technical considerations are currently still a fundamental and limiting aspect, although computational power has improved significantly and researchers are producing cutting-edge research in this area.
A recent example is a study by Deeks et al. showing the development of their open source software framework Narupa, which aims to provide the scientific community and education with a tool that combines interactive molecular dynamics with virtual reality for a wide range of applications. The team has already demonstrated that the application of iMD-VR through their software for predicting protein-ligand interactions is an efficient and potentially useful approach for developing new drug discovery methods. 47 Recently, they described an application with fully flexible docking for generating complexes between some variants of the SARS-CoV-2 main protease (Mpro) and small ligand and oligopeptide substrates. 72 Mpro is a widely studied protein as it plays an essential role in the replication cycle of the virus.
| Exploring phase space and interacting with biomolecular trajectories Since this is a key application for interactive simulations, we will take a closer look at its origins. An important reference implementation for an interactive simulation architecture is the SMD system developed by the Leech team in 1996,  9  which combines visualization software with a molecular dynamics engine: VMD and the SIgMA molecular dynamics package, respectively. They implemented the communication protocol by modifying Sigma and VMD. Figure  10  shows the diagram of the system as presented in their original article. At each time step of the molecular dynamics simulation, SIgMA sends the calculated atomic positions to update the display in VMD by re-rendering the molecule. At the same time, the visualization interface allows the user to specify new constraints on the atoms that they have specifically selected to affect their dynamics.
The strength of interactivity lies in the general use of positions and the provision of specific force constraints for each atom. This approach has the advantage of being fairly ubiquitous, as it can be adapted to most molecular simulation programs. It was used in the development of the MDDriver library, an independent library that allows a network to be created between a molecular user interface and a molecular simulation. 141 The MDDriver library was specifically designed for easy integration into any molecular simulation program that can provide a set of particle positions for each time step. The user can then adjust the amplitude and direction of the forces acting on the selected particles according to his preferences. A haptic device and VR immersion can help enrich the user's perceptions, but the paradigm of interactivity remains the same.
For this reason, sometimes tools are developed from scratch to efficiently solve a well-designed problem, as in the BioSpring simulation engine, which uses the spring network model to connect particles according to an arbitrary distance criterion and was developed with very low inherent computational costs. As an application example, the interactive flexible molecular fitting method of Molza et al. is discussed, which: (1) is based on the elastic network model and (  2 ) is somewhat similar to the ICFF method of Flores 182 but differs in its preliminary method for defining flexible regions. Namely, it allows for full flexibility whose local amplitude depends on the corresponding particle density. 49 n the application context of creating a model of the dystrophin filament, they developed an interactive docking method based on the BioSpring software that allows the design of models that integrate qualitative experimental data that serve as constraints for simulations (or guides).
The aENM is described as a combination of the elastic network model defined by the harmonic functions and the non-bonding terms whose low-resolution force field is also used in the ATTRACT docking program  184, 185  from previous work. This force field is composed of Lennard-Jones and electrostatic potential terms. IMD in conjunction with virtual reality was explored by Deeks based on the observation that MD simulations are always particularly costly due to sampling. They developed their iMD-VR Narupa framework  44  for interactive visualization and interactive manipulation of all-atom MD simulations in a virtual environment. They emphasize the fact that humans are particularly suited to perform tasks that require the use of vision and the integration of information from the 3D world. They used the iMD-VR strategy for flexible protein-ligand docking and applied it to several systems to try to approximate protein-ligand positions obtained by crystallography.
In the final test at the CERN SPS, data were taken with beams of muons, charged hadrons and electrons with energies ranging from 20 to 300 GeV at the H2 beam line of the CERN-SPS over a period of two weeks in October 2018. The data acquisition (DAQ) system for the beam tests needed to be flexible and scalable to control and read out the increasing number of prototype silicon modules as they became available. It was designed with readily available FPGA mezzanines and low-cost Raspberry PIs, and scaled up to work with ≈12,000 channels for the final test. This paper describes the DAQ system and is structured as follows: the overall architecture of the system is described in section 2; the data format and the back-end DAQ components are described in section 3; the DAQ software is explained in section 4; in section 5 the detector systems used for system synchronisation is discussed and the operational experience is discussed in section 6.
Back-end DAQ electronics The DAQ system was designed to be easily scalable to provide a readout for different numbers of silicon modules. To minimise costs, readily available commercial components were used. Additionally, optical receiver modules (oRMs)  [6] , recovered from the CMS level-1 trigger system, when it was upgraded with faster electronics, were used. Each oRM was equipped with a Kintex-7 FPGA, 4. 8 Mbits of block RAM, two 6. 6 Gbit/s bi-directional serial ports, and a 128 Mbit FLASH • Accumulating the data received from the prototype silicon modules. • Distributing the clock, busy and trigger signals. • Distributing the low voltage power and the bias voltage for the prototype silicon modules. The readout board, shown in Figure  7 , was a custom PCB equipped with five oRMs and a Raspberry Pi.
Each board had eight HDMI ports on the front panel connected to the silicon modules through the interposers and one HDMI port on the back panel for connection to the sync board. On the front of each board there were eight RG174 connectors that were used to distribute, after filtering, the bias voltage through the interposers to each detector module. The overall readout cycle was controlled via helper processes running on the Raspberry Pi, which communicated with each oRM through the SPI bus. Each Pi was connected to the central DAQ server through its Ethernet port, from which it also received 'Start', 'Stop', and other commands. A single readout board was equipped with five oRMs: one control (CTL) oRM, and four DATA oRMs. The DATA oRMs were responsible for reading the data from up to two silicon modules, while the CTL oRM received data from the DATA oRMs and transferred it to the central server.
It also managed the communication with the sync board. The firmware installed on the CTL oRM's FPGA included the IPBus firmware  [7, 8]  for this purpose. The IPBus IP and MAC addresses of the oRM were set by the Raspberry Pi, as well as other parameters used by the CTL oRM as it combined the four streams of data. Before a run started, the helper processes on the Raspberry Pis first configured the ASICs on the prototype silicon modules, and data collection was initiated. The Trigger signal was broadcast from the sync board to the readout boards, from where it was forwarded on to the modules. On the readout boards, the helper processes running on the Raspberry Pis after receiving the trigger signal, prompted the ASICs to initiate data transmission. The data from the ASICs were then sent unprocessed, via the MAX®10 FPGA, to the DATA oRMs, where it was then merged into a single data stream by the CTL oRM.
Sync board The function of the sync board, shown in Figure  10 , was the distribution of the common signals to the readout boards and to synchronise the flow of data from the readout boards. The sync board generated the 40 MHz system clock on a small mezzanine card mounted at the rear of the board. On the same mezzanine there were four 50 Ω RG174 connectors. Two were for the Trigger and a Veto signal inputs and two for the Clock and a copy of the Trigger signal outputs. The Veto signal was not used in these tests. Processing on the sync boards was handled by a Raspberry Pi as  The EUDAQ framework was designed to be modular and portable. It was structured so that software for the readout of specific detector components was kept separate and distinct from the core processes. For this each detector component that produced data had a 'Producer' process running.
3. 'ReadoutDone' signal is sent from CTL oRM to Sync oRM. 4. Create an event block containing the raw data from each readout board, the time-stamp of the readout board and the event ID. 5. Forward the event block to the EUDAQ data collector. 6. Increment the event ID. 7. Return to step 1 and wait for the next trigger. Once the readout was complete, data from each of the event blocks from each of the Readout Producers were combined with data from the Producers connected to other detector components to form a complete event data block.
Data unpacking and first analysis steps The first step of the data analysis consisted of unpacking the CMS CE EUDAQ events. For this purpose a C++ library embedded in the EUDAQ framework has been developed. An initial data quality check was performed before unpacking the raw data by comparing the difference in time-stamp with the time-stamp of the previous event as a check of the synchronisation for all the readout boards. During the October data taking only a few runs had events with a synchronization failure. After this test the data were unpacked and the data from each ASIC were sorted into tables of 16-bit integers with the structure shown in Figure  5  and stored in a ROOT  [11]  file. As zerosuppression was not used for simplicity, these tables contained the data from every channel of the ASICs, including those not connected to a detector channel.
For proper event synchronization, the trigger for all CAEN modules stemmed from the duplicated TTL trigger signal issued by the synchronization board. After conversion to NIM, it was copied three times and fed into each module individually. After receiving a trigger, events were built, were labeled with trigger time-stamps and subsequently stored in a local buffer. Two dedicated EUDAQ producers  [9]  had been developed. They ran on a separate computer and communicated to these modules through optical link and VMEbus. These producers polled the event data from the buffers at a (configurable) frequency of 500 Hz (HGCAL DAQ rate ≈ 40 Hz), converted the raw data into the EUDAQ format and sent it to the main DAQ for storage and online data monitoring.
Summary In the upgrade of the CMS detector for when the HL-LHC is operational, the two endcap calorimeters will be replaced with high granularity sampling calorimeters equipped with silicon sensors. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors readout with a lowcost custom scalable data acquisition system. The software framework used for the run control and data collection was the portable modular EUDAQ framework. In the most recent of the tests conducted in late 2018 at the CERN SPS in 2018, the performance of a prototype calorimeter equipped with ≈12, 000 channels of silicon sensors, in conjunction with the CALICE prototype analogue hadron calorimeter, was studied with beams of high-energy electrons, pions and muons, with six million events collected over a two week period.
Acknowledgments We thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. We acknowledge the enduring support provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MSES  and  CSF (Croatia) ;  CEA  and  CNRS/IN2P3  (France);  SRNSF (Georgia) ;  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  DAE  and  DST (India) ;  MES (Latvia) ;  MOE  and  UM (Malaysia) ;  MOS (Montenegro) ;  PAEC (Pakistan) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TENMAK (Turkey) ;  STFC  ( United Kingdom );  DOE (USA).
Since the middle of the past century, the Western Antarctic Peninsula has warmed rapidly with a significant loss of sea ice but the impacts on plankton biodiversity and carbon cycling remain an open question. Here, using a 5-year dataset of eukaryotic plankton DNA metabarcoding, we assess changes in biodiversity and net community production in this region. Our results show that sea-ice extent is a dominant factor influencing eukaryotic plankton community composition, biodiversity, and net community production. Species richness and evenness decline with an increase in sea surface temperature (SST). In regions with low SST and shallow mixed layers, the community was dominated by a diverse assemblage of diatoms and dinoflagellates. Conversely, less diverse plankton assemblages were observed in waters with higher SST and/or deep mixed layers when sea ice extent was lower.
0001). At the finest taxonomic resolution, 2480 amplicon sequence variants (ASVs) were identified from the five-year amplicon dataset (119 samples). Canonical correspondence analysis (CCA) illustrates that either ice conditions or SST is the dominant driver on community structure at the ASV level (Fig. 3 ). The first axis CCA1 (17. 5% of the variance) separates samples from late (2014 and 2016) and early (2012, 2013, and 2015) ice-retreat years. The most substantial abiotic factor associated with CCA1 is SST (negatively correlated), and the most substantial biotic factors associated with CCA1 are Chl and biological O 2 (both positively correlated), consistent with ice-melt enhanced biomass and productivity. Freshwater inputs were estimated from oxygen isotope signatures. Low salinity, as well as high fractions of sea-ice melt and meteoric water, are also associated with CCA1 but to a lesser extent than SST.
Chao1, a measure for species richness, demonstrated an evident decline towards higher SST (Fig. 4 ), with a 40% decrease in the index for a 4 °C rise in SST. Pielou's evenness and Shannon, which consider both species richness and evenness, also decreased significantly with increasing SST. It indicates that communities in warmer WAP waters show lower richness and lower evenness, i. e. , that a few taxa dominate. Interestingly, in a recent global analysis on plankton biodiversity from Tara Oceans 23 , the temperature was also identified as the major explanatory factor for global-scale eukaryotic plankton biodiversity estimated by the Shannon index, but with the opposite trend, i. e. , a decreased diversity towards higher latitude or lower temperature. We note that whilst the Tara Oceans dataset represents the most comprehensive oceanic DNA sampling efforts to date, it featured only limited sampling in the Southern Ocean (three data points), and it did not include a longitudinal survey that captures the mesoscale effect of changing temperature on a given community; our five-year WAP sample collection hence complements well the Tara Oceans observations for the previously under-sampled Southern Ocean.
Chao1 Evenness Shannon    25  ), our results suggest that global warming may decrease plankton diversity in coastal Antarctica. Due to the high dimensionality of the ASV dataset, it is challenging to model NCP based on community structure. Thus, we applied a weighted gene correlation network analysis (WGCNA) approach to delineate clusters of 18S ASVs into subnetworks or modules (Fig. S5 )  26, 27. This approach allows us to reduce the total number of variables while preserving information on ASV abundances and potential interactions  28. In total, we identified 12 modules from the five-year global community structure (Fig. S5 ). Each module represents an assemblage of predicted highly interconnected plankton community members, potentially indicating a group of organisms with strong ecological overlap and/or interactions  29.
The eigenvalue of each module represents the overall abundance of the assemblage. In addition, in order to investigate the niche partitioning of the different community assemblages or modules, correlation analysis was performed in WGCNA to link them to different abiotic and biotic factors (Fig. S5 ). Next, we applied Genetic Programming (GP), a machine learning approach based on evolution computation  30, 31  , to generate and parameterize statistical models that predict NCP based on the WGCNA generated bio-assemblages (n = 12) and physical factors (n = 6) (see "Methods" for a detailed list). The relationships between carbon-based plankton biomass, their physiology (indirectly modeled as functions of environmental factors), and biogeochemical rates are often non-linear and could involve multiple layers of interactions.
Methods Environmental data and DNA sampling. Environmental data from the Palmer Long-Term Ecological Research (LTER) cruises can be accessed from the online data repository Palmer LTER DataZoo (http://pal. lternet. edu/data). The detailed sampling methods and in situ biological rates measurements were described previously in  40. In brief, each year in January, a research vessel conducted intensive oceanographic and biological surveys across the shelf-transects and a north-south gradient at the West Antarctic Peninsula (WAP). During the annual LTER cruises, underway measurements and surface water sampling were conducted from the ship's continuous flowthrough system. Discrete water samples in-depth profiles were collected using a Conductivity-Temperature-Depth (CTD) rosette. Mixed layer depth (MLD) was estimated from the ship's CTD profiles by Δσ θ = 0.
45 µm Supor filter (Pall Corporation, New York, NY, USA) for years 2012 and 2013, or a 47 mm, 0. 2 µm Supor filter for years 2014, 2015, and 2016. The filtration volumes were about 4 L or less at high biomass stations. For each filtration, the exact filtrate volume was recorded for later quantitative microbiome profiling (QMP). The filters were immediately stored at -80 °C until further analysis. Remote sensing data. January sea ice concentrations from 1979 to 2020 were downloaded from the National Snow and Ice Data Center website https://nsidc. org/. The data are in the polar stereographic projection, with each grid representing a 25 × 25 km area. January sea surface temperature (SST) data from 1982 to 2012 were acquired by the AVHRR and downloaded from NOAA website https://www. ncei. noaa.