dataesr/scientific-paragraphs-categorization huggingface dataset:
To address group collaboration requirements, Colony versions data, enables an edge group to share without relying on the cloud, and supports collaborative security features. Our design provides uniform access to data across a spectrum spanning core cloud to the far edge. Finally, we address a number of design and implementation challenges, including disconnected operation, consistency under migration, total-order consensus at the edge, and avoiding single points of failure despite the forest topology. We claim the following contributions: • A decentralised database architecture, designed for collaborative applications, that provides a continuum spanning from the core cloud to the far edge. • A hybrid consistency model, based on causal and transactional guarantees globally, strengthened to total-order consistency in edge collaboration groups and in geographical proximity.
1 Consider for instance a distributed multiplayer game; the state of the game universe is shared, persistent, and mutable, typically stored in a database. Instead of a centralised cloud, state should be decentralised, distributed and replicated across edge devices, to enable quick application response (no waiting for a network roundtrip) and availability (the application can read and write data, even when disconnected from any remote server). The local-first approach provides users with a sense of ownership and helps with confidentiality  [24]. Nonetheless, we leverage the strengths of the cloud infrastructure, i. e. , stronger consistency and well-managed, abundant resources. Edge applications must remain available despite unpredictable network connectivity, disconnection, and mobility. In this context, strong consistency is not possible globally  [16].
Local strengthening: Peer groups Another kind of SI zone is the peer group. Inconsistency is especially problematic to users who communicate directly, outside of the database. For instance, in the enhanced-reality game Pokémon Go, there is an anomaly where two users in close proximity can both become the owner of the same game character, confusing them  [28]. This and similar examples argue for groups with stronger consistency. In Colony, edge nodes that are close to each other can form an SI zone, called peer group. Their mutual strong consistency improves user experience and metadata management. To maintain availability, the system should support disconnecting and/or migrating the group, without losing the consistency guarantees.
Edge caching An edge node cannot replicate the whole database, but can only cache some small fraction of it. An edge client may declare interest in some object to add it to its node's cache. The connected DC regularly informs the client of updates to its interest set. At any point in time, the state vector of an edge node is the LUB of the state received from its connected DC (itself ≤ the DC's current state vector) and the commit vectors of local transactions. Choosing a snapshot vector ≤ the node's state vector ensures that every read could be satisfied either from the local cache or from the connected DC. It may happen that the client requires an object version that cannot be retrieved (in the cache, from the DC or from another node), in which case the transaction cannot proceed. This limitation of availability is inherent to the edge environment.
API and programming model An application node connects to a session manager (currently implemented in the core cloud), which authenticates the node. With the session opened, the node may join a collaboration or peer group, and run transactions accessing database objects. The node is notified of group change events (e. g. , a new peer joins). The database stores CRDT objects, such as counters, registers, sets, maps, or sequence datatypes. An object is stored in a namespace called a bucket. Opening a bucket caches it in the node; optional parameters can specify cache policies (e. g. , LRU, writeback, etc. ). The application can subscribe to an object's update events, in order to implement reactive programming patterns. A transaction is atomic (all-or-nothing) against multiple updates, and reads a TCC+-consistent snapshot of its opened buckets.
Experimental setup We deploy each Colony component (edge client, cloud server, peer group, etc. ) as a Docker container, on a set of dedicated servers in a cluster. Each server has two Intel Xeon Gold   CPUs, each with 16 cores per CPU, 128 GB of memory, and 2 TB of (spinning) hard disk. Nodes are all connected through 10 Gb/s network switches. A monitoring server, deployed on a separate container, captures the performance metrics. We measure an average 0. 15 ms network response time within the cluster. We use the Linux traffic shapping tool (tc) to simulate larger network response time, with a mean of 50 ms for mobile cellular data and 10 ms for carrier Ethernet. DCs are connected in a mesh using RabbitMQ sockets above TCP; peer groups are connected using WebRTC.
The code is fully available at https://github. com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPPriboswitch RNA domain. The models learned by adabmDCA are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an informationbased criterion.
BACKGROUND Protein and RNA sequence modelling In recent years, the number of available protein and RNA sequences has shown an impressive growth thanks to the development of high-throughput sequencing techniques. As a consequence, databases like Pfam  [1]  and Rfam  [2] , where such biological sequences are annotated and classified according to evolutionary relationships, play a dominant role in modeling this enormous amount of data. In spite of this constant increase of sequence data, the tertiary structure of the corresponding domains is nowadays not experimentally accessible for the majority of the known protein and RNA sequences. To compensate for this experimental gap, in silico protein and RNA domains modeling has shown an incredible predictive power in determining their structure  [3, 4].
The code implements the so-called Boltzmann machine learning algorithm  [19]  by performing a gradient ascent of the a posteriori probability of the model given the input data. At variance with other implementations, adabmDCA copes with both protein and RNA data, and encompasses the ability of pruning redundant parameters (as described in  [11] ). Finally, it provides a standard equilibrium learning and also a nonequilibrium learning, based on the contrastive divergence technique  [20] , which is more suitable for structured data.
Adaptive Monte Carlo Markov Chain The Boltzmann learning algorithm consists of a series of training epochs. At each epoch t, we estimate numerically the marginal probability distributions of the model p (t) i (a) and p (t) ij (a, b) using a MCMC strategy. More precisely, we use N s independent Markov chains, each of which samples N c configurations. The results presented in this work are obtained using a Metropolis-Hasting  [24, 25]  update scheme, but the code also allows one to opt for a Gibbs sampling strategy  [26]. At the end of each epoch t, we update the model parameters according to Eq. 9 by estimating the p p (t) i (a) = 1 N s N c NsNc µ=1 δ s µ i (t),a , (15) p (t) ij (a, b) = 1 N s N c NsNc µ=1 δ s µ i (t),a δ s µ j (t),b. (16) adabmDCA allows one to use either persistent chains, i.
RESULTS We now discuss some examples of model learning via adabmDCA on protein and RNA families. Learning at equilibrium: PF00014 and RF00059 In this section we show the results obtained for: (i) the Kunitz domain (PF00014 family from the Pfam database), (ii) the TPP riboswitch (RF00059 from the Rfam database). The PF00014 MSA is initially pre-processed to remove from the MSA all proteins with more than six consecutive gaps. This prevents a learning bias towards very gapped configurations. Eventually, the total number of considered sequences is M = 13600 for PF00014 and M = 12593 for RF00059, which correspond to a re-weighted effective number of sequences of M eff = 4364 and M eff = 4920 for PF00014 and RF00059 respectively. The Boltzmann machines are trained at equilibrium, i. e. the waiting and equilibrium times are updated at each iteration according to the test introduced in Section.
2, 3(c ) and (d) respectively. The sampled configurations in panel (d) are projected onto the first two principal components of the natural sequences in panel (c). As suggested by the spatial localization of the sequences and their distribution, our converged models are able to generate sequences that lie in the same non-trivial sub-space spanned by the natural sequences. Finally, we compare the predicted contact maps of the Kunitz domain and of the TPP riboswitch with the following state-of-the-art DCA-based algorithms: plmDCA  [33]  and Mi3-GPU  [31]  for PF00014 and bl-dca  [34]  for RF00059, one pseudo-likelihood method and two Boltzmann machine-based methods to infer Potts models for protein and RNA sequences respectively. In all cases, coupling parameters are first converted to zero-sum gauge before computing the average product corrected Frobenius norms  [33].
For PF00014, we consider as ground-truth the atomic distances retrieved by Pfam-interactions  [35] , a method which computes the minimum distance, for all possible pair of sites, among all available crystal structures in the Protein Data Bank (PDB). For RF00059 we perform an analogous analysis among the TPP riboswitch known structures downloaded from the Protein Data Bank. In Figs. 2,  3(e) we plot the positive predictive value of the prediction of the non-trivial contacts, i. e. those residue pairs (i, j) having |i-j| > 4, for the three methods, and in Figs. 2, 3 (f) we overlap our ground-truth (in gray) and the most probable contact according to the three-methods, i. e. the pairs with whom we associate a score larger than 0. 20. For Mi3-GPU we consider the model obtained applying an 2 regularization with strength parameter λ = 0.
02; the machine obtained for the 1 regularization gives a dramatically worse results in terms of contact predictions (not shown). Panel (e) suggests that the three considered methods achieve comparable performances, as it is equivalently represented in panel (f). Learning out-of-equilibrium: PF13354 In this section we show the results obtained for the Beta-lactamase2 domain. The multiple sequence alignment used within the training is constructed as follows. Using the Hidden Markov Model associated with the PF13354 family, we scanned the NCBI  [36]  database to obtain aligned sequences compatible with the model. We then keep sequences that have less than 20% of gaps and concurrently those having less than 80% redundancy (as a consequence M eff ∼ M in this case). We also removed the sequence of the TEM-1 protein, and all sequences very similar to it.
Running time We discuss in this section the computation time of adabmDCA. The running time needed by adabmDCA is often larger than the ones shown by the other methods used here for comparison: our machine spent 22, 53, and 98 hours for learning a model for PF00014, RF00059, and PF13354, respectively, against one hour and the 75 hours required by Mi3-GPU for PF00014 and PF13354 (employing two TITAN RTX GPUs) and the two hours needed by bl-dca for RF00059. We stress that the current implementation exploits a single thread and its running times are compatible with those achieved by the Boltzmann machine in  [10]. Moreover, the out-of-equilibrium learning allows for the training of an accurate machine, out-performing Mi3-GPU and spending a running time which is only slightly larger than that needed by Mi3-GPU, a highly optimized algorithm.
Quantification of the surface water offer is crucial for its management. In Peru, the low spatial density of hydrometric stations makes this task challenging. This work aims to evaluate the hydrological performance of a monthly water balance model in Peru using precipitation and evapotranspiration data from the high-resolution meteorological PISCO dataset, which has been developed by the National Service of Meteorology and Hydrology of Peru (SENAMHI). A regionalization approach based on Fourier Amplitude Sensitivity Testing (FAST) of the rainfall-runoff (RR) and runoff variability (RV) indices defined 14 calibration regions nationwide. Next, the GR2M model was used at a semi-distributed scale in 3594 sub-basins and river streams to simulate monthly discharges from January 1981 to March 2020.
In contrast, conceptual hydrological models require fewer data, making them operationally easier and decreasing the computational cost in an extensive domain  [29, 30]. For instance, the GR2M conceptual model  [31]  has been widely used in different hydroclimatic conditions around the world with satisfactory results  [32, 33]  and even performing better than other water balance models  [34]. Additionally, it is used to assess climate change effects on water resources  [35] [36] [37]. In recent years, experiments have been developed to improve hydrological modeling performance with GR2M, incorporating Bayesian calibration approaches  [38, 39]  and coupling to fuzzy models  [7]. No prior research has been reported at a national level in this region using the GR2M model. A regional hydrological simulation that incorporates observed data and provides a dataset of estimated discharges at river streams in three Peruvian slopes is still challenging.
However, in the Amazon basin, the GR2M model has been applied to assess water resources' climate change and identify annual discharge trends  [40]. A recent implementation in the Pacific slope also evaluates multidecadal runoff in a data scarcity condition, showing higher model robustness than other conceptual models  [18]. In ungauged basins, regional GR2M parameter estimation has been studied in  [23]  using a regression approach finding unsuitable model results for basins located under a semi-arid climatic regime. In  [41] , the GR2M model is applied to reconstructed monthly river streamflows in 51 gauged sub-basins. The generation of global and regional hydrological datasets such as the products Model Parameter Estimation Experiment data (MOPEX)  [42]  and the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS)  [43] , among others, are beneficial for exploring the behavior of basins  [44] , anticipating hydrological changes  [45]  and studying the impact of human activities on the hydrological cycle  [46].
In South America, local adaptations have been integrated into the CAMELS product, such as the CAMELS-BR  [47]  datasets in Brazil and CAMELS-CL  [48]  in Chile currently used to study the impact of climate change on water resources and the study of droughts, among others. In this sense, hydrological modeling at the national level is particularly useful to establish a basis for constructing a hydrological dataset in Peru. This study aims to evaluate the hydrological performance of a monthly water balance model at a national level. For this purpose, the sensitivity analysis of two hydroclimatic indices is used to define calibration regions, and the GR2M conceptual model is used to simulate monthly discharges in gauged and ungauged sub-basins from January 1981 to March 2020. Finally, a new hydrological product in Peru is introduced to provide continuous monthly streamflow information over the country and contribute to understanding the water balance in data-scarce basins.
Data and Methods The methodology used in this study is outlined in Figure  2. The details of the data and methods used are shown below. Currently, the PISCO product is only a dataset with a daily and monthly temporal resolution for the variables of precipitation (P), air temperature (TMP), and potential evapotranspiration (PET). It has a spatial coverage throughout the Peruvian territory, including transboundary basins with Ecuador, Colombia, and Brazil. In its stable version, PISCO is only available from 1981 to 2016. However, an unstable version of the precipitation sub-product used for operative purposes is available from 1981 to the present day, and it is updating daily. The gridded precipitation sub-product (0. 1 • × 0. 1 • ) is generated by applying geostatistical techniques to combine satellite estimates of precipitation data from the CHIRP project (Climate Hazards InfraRed Precipitation) and ground data of the SENAMHI pluviometric network  [11].
Similarly, the temperature sub-product (0. 1 • × 0. 1 • ) is generated by combining air temperature data from MODIS images and observations from weather stations. The evapotranspiration sub-product (0. 1 • × 0. 1 • ) is generated from the previous temperature-gridded data following the methodology proposed by  [49]. The PISCO dataset is available on the IRI Data Library: http://iridl. ldeo. columbia. edu/ SOURCES/. SENAMHI/. HSR/. PISCO (accessed on 20 March 2019). In this work, the mean areal values of P and PET are calculated for each of the 3594 subbasins from January 1981 to March 2020 and then are continuously updating for operational purposes of the continuous monthly streamflows estimation. Due to our hydrological model's operational purpose, the precipitation sub-products unstable version was used in this study.
Discharge Data In this work, monthly observed streamflows at 43 hydrometric stations were selected from January 1981 to March 2020. Most of these stations belong to the National Service of Meteorology and Hydrology of Peru (SENAMHI, https://www. gob. pe/senamhi, accessed on 13 March 2021). Specifically, in the Amazon region (Atlantic slope), most of the stations are monitored by the SENAMHI and the IRD (French Institute for Sustainable Development) in the frame of the HYBAM Project (https://hybam. obs-mip. fr/, accessed on 25 March 2021). The detail of selected stations is summarized in Table  1 , and their distribution throughout the Peruvian territory is shown in Figure  1a. The selection processes considered stations with more than 10% coverage in the study period (1981-2020), stations with high quality of data, and stations located in basins that guarantee coverage throughout the national territory.
For example, at time step t = 1, the monthly runoff (q) is first calculated for each sub-basin in the study area. Then the Weight is created by rasterizing the sub-basins based on the flow direction raster and associating the value of q to the pixel corresponding to the centroid of each sub-basin, as appropriate. Finally, the WFAC is used to accumulate the values of q and obtain a raster map with the values of discharges (Q) for each river stream. This process is repeated for each time step. The scheme of the semi-distributed GR2M adaptation for a national level modeling is shown in Figure  3. In this work, the GR2M model incorporated in the airGR R package  [52]  was used together with adaptations to calculate P and PET's areal means from gridded data, run the semi-distributed GR2M model using the WFAC algorithm, and automatically calibrate the model parameters.
Product of Simulated Monthly Discharges at a National Level The regionalization based on sensitivity analysis of GR2M parameters at a national level and using the meteorological (P and PET) PISCO dataset allows simulating continuous monthly discharges in 3594 rivers streams (including ungauged areas) from January 1981 to March 2020. This new product of simulated monthly discharges named PISCO_HyM_GR2M is available at https://doi. org/10. 6084/m9. figshare. 14382758 (accessed on 7 April 2021), and it is the new hydrological sub-product of the PISCO dataset. Additionally, it will contribute to the understanding of the water balance in data-scarce basins. For instance, the PISCO_HyM_GR2M product is currently used for drought monitoring in the National Service of Meteorology and Hydrology of Peru (available online: https://www.
Data Availability Statement: The data presented in this study are publicly available online in https://doi. org/10. 6084/m9. figshare. 14382758 (accessed on 7 April 2021), and on the IRI Data Library repository http://iridl. ldeo. columbia. edu/SOURCES/. SENAMHI/. HSR/. PISCO/ (accessed on 7 April 2021).
Around 300 nuclei were examined for each condition, and nuclear anomalies criteria were aberrant nuclear lamin A/C and/or lamin B2 staining pattern, enlarged nuclei, and aberrant nuclei shape. Confocal images were performed using an LSM 800 airyscan Axio Observer and a Z1 7 confocal microscope equipped with a 63×/1. 20 W Korr UV VIS IR C-Apochromat objective and driven by Zen 2. 3 system software (Carl Zeiss, Jena, Germany). To measure lamin A/C distribution, we compared the fluorescence intensities at the periphery and in the bulk of the nucleus. Background signal was first subtracted (FIJI software), then image processing was performed using MATLAB software (r2018b version, MathWorks). The images were segmented, and the nucleus contours were detected. For each nucleus, the contour was fitted with an ellipse to determine its centroid.
Molecular Findings No mutation in the major genes involved in severe hypertriglyceridemia (LPL, APOAV, APOCII, LMF1, GPIHBP1) was found. NGS of a panel of 14 genes involved in lipodystrophies and laminopathies was then performed (AGPAT2, BSCL2, CAV1, CAVIN1, CIDEC, FBN1, FPLD1, LIPE, LMNA, LMNB2, PLIN1, PPARG, SYNE1, ZMPSTE24). Among all variants identified, we selected rare heterozygous exonic variations corresponding to the expected dominant transmission mode of partial lipodystrophies. An allelic frequency cutoff to consider a variant as rare was established at <0. 02% in the GnomAD database (https://gnomad. broadinstitute. org/, accessed on 12 December 2021). Using VarAFT software, two non-synonymous variants in two different genes, LMNB2 and SYNE1, passed the filters (Table  1 ). Only one was predicted to be pathogenic by all the prediction software tested (Figure  2A ).
The allelic frequency of this variant is around 1% in the population database GnomAD, and the mutated amino acid is not very conserved across species (Figure  2C ). This suggests that this variant may not be the only cause of the pathological phenotype but should rather be considered as a predisposition factor. The patients carrying this mutation probably have secondary conditions triggering the occurrence of metabolic disorders. Gao et al. identified another rare heterozygous mutation p. (Tyr252His) in a woman with progressive loss of subcutaneous fat since adolescence  [7]. This mutation occurred de novo, was not reported in the GnomAD database, and modifies a highly conserved amino acid (Figure  2C ). These two mutations, p. (Arg235Gln) and p. (Tyr252His), and the one we described here, are all located in the same region of lamin B2, which corresponds to the linker-2 of the rod domain of the protein (amino acids 230-256).
We obtained TFs whose predicted target genes associated with one or more of the five different conditions (PA vs. control, PA vs. PA/OA,. PA/OA vs. control, OA vs. control, OA vs. PA/OA). Interestingly, several of the identified TFs whose activities were upregulated by PA and downregulated by OA  (Fig S2B)  have known roles in lipid metabolism (e. g. HNF4A, SREBF1) and ER stress (e. g. ATF4, ATF6). By contrast, several TFs whose activities were regulated in the opposite way function in cell cycle control (e. g. E2F4, MYC, FOXM1). Thus, the TF analysis confirmed the initial transcriptome data analysis by highlighting upregulated ER stress and lipid metabolism and downregulated cell cycle progression as the main response to PA.