dataesr/scientific-paragraphs-categorization huggingface dataset:
The Lake Ohrid record, in combination with the transient simulation time series and the NOAA reanalysis data, may provide fundamental insights into the mechanisms that are invoked by orbital forcing on Mediterranean precipitation. The monthly NOAA reanalysis data of the last 39 years show that high precipitation anomalies (defined as more than two standard deviations above the mean) occur between the months of September and December (Extended Data Fig. 8 ). The atmospheric pattern associated with these precipitation events exhibits a trough in the Gulf of Genoa region (Extended Data Fig. 8 ), pointing to either increased cyclogenesis over or advection of North Atlantic low pressure systems into the western Mediterranean region. The annual cycle of simulated Lake Ohrid precipitation in LOVECLIM is in good agreement with the reanalysis data; however, the model underestimates the annual mean precipitation (Extended Data Fig.
5-mm increments and 10-s dwell time using an ITRAX XRF core scanner (Cox Analytics) equipped with an energy dispersive silicon drift detector and a Cr tube set to 30 kV and 30 mA. Raw data were processed and element-specific photon energy peaks were integrated in Q-spec (Cox Analytics). Elemental analysis. Elemental analysis was performed on 16-cm-spaced samples (2,794 samples, around 480 years) following freeze-drying and homogenization at the University of Cologne. For total carbon and TIC measurements, an aliquot of 40 mg of the homogenized sample material was dispersed in 10 ml deionized water. Total carbon was determined at combustion of 900 °C and TIC was measured after treatment with 40% H 3 PO 4 at 160 °C using a DIMATOC 100 and a DIMATOC 200 (DIMATEC). TOC content was calculated by subtracting TIC from total carbon.
Fourier transform infrared spectroscopy. Relative concentration changes for quartz were assessed using Fourier transform infrared spectroscopy, on samples spaced at 32 cm (1,462 samples, around 1,000 years). Measurements were performed using a Bruker Vertex 70 equipped with a liquid-N 2 -cooled MCT (mercury-cadmium-telluride) detector, a KBr beam splitter and a HTS-XT accessory unit (multisampler) in an air-conditioned laboratory at the University of Bern. In brief, 11 mg of each sample and 500 mg of oven-dried spectroscopic grade KBr (Uvasol, Merck) were homogenized and scanned 64 times at a resolution of 4 cm -1 (reciprocal centimetres) for the wavenumber range from 3,750 to 520 cm -1 in diffuse reflectance mode. Data processing included a linear baseline correction to remove baseline shifts and tilts by setting two points of the recorded spectrum to zero (3,750 and 2,210-2,200 cm -1 ).
He (0. 75 l min -1 ) and Ar (around 1. 1 l min -1 ) gas flow transported the ablated material through an in-house signal-smoothing device into the plasma of the inductively coupled plasma mass spectrometer. The signal was tuned to maximum intensity as well as stability at concurrently low oxide ratios (ThO/Th of around 0. 0012) to minimize potentially interfering oxide species before analyses in low-resolution mode. Data reduction was performed using an in-house Excel spreadsheet. Trace element concentrations were calculated after calibration against NIST612 using  29  Si as the internal standard (Si concentrations were obtained by electron microprobe analysis). Accuracies of the measurements were validated using reference glasses NIST610, T1-G and ATHO-G. 40   Ar/  39  Ar dating was performed at the LSCE facility (CEA, UVSQ and University Paris-Saclay).
Reassessment of the raw Ar-isotope data of SC1-35. 30/SUL2-1, the equivalent to OH-DP-2669, by updating the value of the atmospheric Ar-composition ( 40 Ar/ 36 Ar: 298. 5 instead of 295. 5 originally) and removing xenocrysts  58  yielded a new age of 715. 02 ± 5. 4 kyr (Extended Data Table  1 ) using the previously published decay constant  48  and an age of 1. 1891 Myr for the ACs-2 flux standard  47. Our new  40  Ar/  39  Ar age of V5 (716. 2 ± 5. 4 kyr; mean square weighted deviation = 0. 8, P = 0. 7) is indistinguishable within uncertainty and thus used for our chronology. All other  40  Ar/ 39 Ar were recalculated using the software ArAR 68 with a given decay constant and age for ACs-2 (1. 1891 Myr)  47  and Fish Canyon sanidines ages 48 of 28. 294 Myr. Chronology. Following the methodological approach for the upper 247 m.
c. d. of the record  20  , the chronology of the DEEP site sediment succession to 447. 12 m. c. d. uses tephrochronological data  [44] [45] [46]  as first-order tie points. Tephrochronological data were used only in cases in which distinctive major element compositions (Extended Data Fig. 2  and Extended Data Table  2 ) or trace element datasets (Extended Data Fig. 3 ) allow unambiguous tephrostratigraphic correlations. Trace element datasets of potential tephra equivalents are extremely rare for the period greater than 200 kyr ago, thus limiting the applicability of this valuable tool. Tuning of climate-sensitive proxy data (TOC; around 480-year resolution) against orbital parameters was used as second-order tie points considering that maxima in TIC represent interglacial periods  19, 20.
The ocean-sea-ice component of LOVECLIM consists of a free-surface Ocean General Circulation Model with a 3° × 3° horizontal resolution coupled to a dynamic-thermodynamic sea-ice model  74. Atmosphere and ocean components are coupled through the exchange of freshwater and heat fluxes. The vegetation component VECODE  75  of the model computes the evolution of terrestrial vegetation cover based on annual mean surface temperature and precipitation. The transient simulations of the past 784,000 years were forced by time-dependent boundary conditions for orbital parameters, atmospheric GHG concentrations, Northern Hemisphere ice sheet orography and albedo following the previously published methodology  76. The orbital forcing was calculated as described previously  77. Atmospheric GHG concentrations were prescribed according to reconstructions from EPICA Dome C for carbon dioxide  78  as well as methane and nitrous oxide  79.
Data analysis. To assess the temporal evolution of dominant periodicities in the DEEP site TIC and deciduous oak pollen percentage data, a wavelet power spectrum was computed for the respective time series. The time series were resampled at regular intervals (linear interpolation) at 0. 3 kyr (TIC) and 1. 0 kyr (pollen), and subsequently submitted to continuous wavelet transform (Morlet window) using PAST v. 3. 21 software  81  following the previously published approach  82. Results of the continuous wavelet transform show clear 100-kyr orbital frequencies over the past 700 kyr, pronounced 41-kyr frequencies before that time and weak approximately 21-kyr orbital frequencies in the pollen record. Relative to the pollen, the continuous wavelet transform results of the TIC show a more-pronounced 100-kyr cyclicity over the entire record, clear presence of 41-kyr periodicity in the early part of the record and very weak 21-kyr signals.
To quantitatively test the observed correlation between deciduous oak and TIC maxima against precession forcing, the bandpass-filtered 18-25-kyr component of the proxy data was regressed against precession based on the La2004 orbital solution  30. Partial least squares regression (PLSR) was used to test the correlation of TIC and deciduous oaks as predictive variables with LOVECLIM temperature and precipitation output data. PLSR was performed using SIMCA 14 (Sartorius Stedim Biotech). All datasets were filtered using a frequency centred at 0. 05 and a bandwidth of 0. 02 before multivariate statistical analysis to accommodate for slight age offsets between proxy and simulation data. We quantitatively assessed the relationships between sapropel occurrence (n = 53 for the past 1. 4 Myr) and maxima in our Lake Ohrid precipitation proxies.
For this analysis, we normalized both TIC and deciduous oak data to 1 and summed the data. Using a peak threshold of 0. 5 for this dataset, we defined midpoint ages for precipitation maxima (n = 93 for the past 1. 36 Myr) and compared their timing to the closest sapropel mid-point ages. Thus, we were able to match 41 precipitation maxima to sapropel mid-point ages (mean age offset of 2. 33 kyr (s. d. , 1. 68); solid yellow lines in Fig. 2 ). The 10 sapropels for which a match of the Ohrid precipitation signal is absent (dashed yellow lines in Fig. 2 ) exclusively occur within peak glacial periods. Extended Data Fig. 1  | Map of Lake Ohrid and its surrounding area. Geology, topography and bathymetry were compiled from previous studies  19, 91.
Letter reSeArCH Extended Data Fig. 4  | Lake Ohrid LOVECLIM simulation data and sedimentary palaeoclimate and palaeoenvironment proxies. a, Simulated surface-air temperature (SAT) for the Lake Ohrid grid cell from the LOVECLIM simulation. b, Simulated precipitation amount for the Lake Ohrid grid cell from the LOVECLIM simulation. c, Lake Ohrid TOC concentrations. d, Lake Ohrid δ 13 C endogenic calcite in parts per thousand relative to VPDB. e, Lake Ohrid δ 18 O endogenic calcite in parts per thousand relative to VPDB. f, Lake Ohrid relative sedimentary quartz content. g, Lake Ohrid K intensities in kilo counts and displayed using a 11-point (pt) running mean. h, Lake Ohrid ratio of Ca/K intensities displayed using a 11-point running mean. i, Lake Ohrid Ca intensities in kilo counts and displayed using a 11-point running mean.
j, Lake Ohrid TIC concentrations. k, Percentages of deciduous oak pollen at Lake Ohrid. l, Percentages of arboreal pollen excluding Pinus pollen at Lake Ohrid. Red and white diamonds indicate the position of radiometrically dated tephra layers, blue and white diamonds the position of reversals of Earth's magnetic field in the Lake Ohrid sediment record. b, d, e, j-l, Data are the same as in Fig. 2.
Data analysis. a, b, Continuous wavelet transform on the percentage of TIC (a) and percentage of deciduous oak pollen (DOP; b) from Ohrid DEEP. Yellow, highest power; red, lowest power; grey contour, cone of influence; black contour, 5% significance level 82 against red-noise background corrected for autocorrelation 81,93. c, d, Least squares regression (red line) between band pass-filtered 18-25-kyr-old component of percentage of TIC (c) and the percentage of DOP against precession at a 1-kyr resolution (d). Blue lines indicate 95% bootstrapped (n = 1,999) confidence intervals. Significant negative responses to precession are seen in both proxies, with a stronger response in DOP. Partial datasets for the intervals <0. 78 Myr ago, <1. 2 Myr ago, <1. 36 Myr ago indicate persistence of the correlation despite changes in lake ontogeny and global scale changes in boundary conditions.
e, f, PLSR using TIC and DOP as predictive variables and LOVECLIM temperature (e) and precipitation (f) simulations as observations demonstrate significant explanatory power by the proxies on the simulation time series, particularly for precipitation. PLSR was performed using SIMCA 14 (Sartorius Stedim Biotech), using 1. 4-33-kyr bandpass-filtered data to accommodate for slight age offsets between proxy and simulation data. Letter reSeArCH Extended Data Fig. 6 | Lake Ohrid precipitation indicators and global monsoon records during MIS 5. a, Ages of sapropels and humid phases in the eastern Mediterranean based on Soreq Cave speleothem δ 18 O data and U/Th chronology 71. b, Simulated precipitation amount for the Lake Ohrid grid cell from the LOVECLIM simulation. c, Percentage of deciduous oak pollen at Lake Ohrid.
Acknowledgements The  Hydrobiological Institute in Ohrid  ( S. Trajanovski  and  G. Kostoski ) and the  Hydrometeorological Institute in Tirana  ( M. Sanxhaku  and  B. Lushaj ) provided logistic support for site surveys and the scientific drilling campaign. Drilling was carried out by  Drilling, Observation and Sampling of the Earth's Continental Crust (DOSECC). A. Skinner  provided logistic and technical advice before and during the drilling operation. The Scientific Collaboration on Past Speciation Conditions in Lake Ohrid (SCOPSCO) drilling project was funded by the  International Continental Scientific Drilling Program (ICDP) , the  German Ministry of Higher Education and Research , the  German Research Foundation , the  University of Cologne , the  British Geological Survey , the  INGV  and  CNR  (both Italy), and the governments of the republics of North Macedonia and Albania.
Data availability Data are available from the Pangaea database (https://doi. pangaea. de/10. 1594/ PANGAEA. 896848); links to the individual datasets are provided within this dataset. Data used for LOVECLIM are available at https://climatedata. ibs. re. kr/ grav/data/loveclim-784k. Additional data are available upon request made to T. F. (tobiasf@hawaii. edu).
Data Fig. 5 | 5 Data analysis. a, b, Continuous wavelet transform on the percentage of TIC (a) and percentage of deciduous oak pollen (DOP; b) from Ohrid DEEP. Yellow, highest power; red, lowest power; grey contour, cone of influence; black contour, 5% significance level 82 against red-noise background corrected for autocorrelation 81,93. c, d, Least squares regression (red line) between band pass-filtered 18-25-kyr-old component of percentage of TIC (c) and the percentage of DOP against precession at a 1-kyr resolution (d). Blue lines indicate 95% bootstrapped (n = 1,999) confidence intervals. Significant negative responses to precession are seen in both proxies, with a stronger response in DOP. Partial datasets for the intervals <0. 78 Myr ago, <1. 2 Myr ago, <1. 36 Myr ago indicate persistence of the correlation despite changes in lake ontogeny and global scale changes in boundary conditions.
As in practice an extensive computational simulation may not be viable, the authors tried to approximate these optimization Via ML, predicting the output of a series of unseen layouts without going thourgh the optimization process, thanks to a dataset built a priori. The work of  (Larsen et al. , 2018 ) is of similar nature. Where the problem of choosing the optimal load planning for containers on freight trains cannot be solved online because of time constraints and insufficient information about the instance, a set of offline cases can be collected and exploited to obtain a decription of the solution of a particular instance, at an aggregate level. Such description, the authors affirm, can provide meaningful insights to decision makers in a real-time contexts. The most prolific field so far has been that of learning to branch in BB.
They are, to the best of our knowledge, the only authors so far focusing explicitly on recurrent combinatorial optimization problems. The main methodological difference between our approach and theirs is the integration of the learned rules in the solver: while they build a greedy algorithm around their learning structure, we build the learning around the solver (BB, metaheuristics, etc. ). In the field of energy production,  (Cornelusse et al. , 2009)  has proposed a method for reacting to deviations in forecasted electricity demand, modeled as a random perturbation of some base case. After running simulations on a set of possible scenarios, the results of re-optimization of such scenarios will form a database to be exploited by the learning algorithm, mapping the current state of the system to a set of time-series describing the adjustments to be operated on each generating unit to respond to the mutated conditions.
TSP Experiments We picked a standard problem (Berlin52) from the TSPLIB 4. We selected 10% of nodes at random from the TSP instance berlin52 and perturbed the weights of the nodes within a certain radius ( 50% of the highest distance between any two nodes) by a factor decreasing the further apart the nodes are. That is, once a node is picked, the weights within a certain distance are scaled up. If contiguous nodes are selected, then the perturbations will sum up. We produced a dataset of 500 entries, each with comprising the distance matrix and the relative optimal path computed with the Concorde TSP solver. The difference matrix between the reference distance matrix and the new instance matrix form the features. For the labels, we started from the reference optimal solution x re f , taken to be the actual optimum of berlin52.
5 mM dNTPs, 0. 2 μM of each primer, 0. 4 μL FastPfu Polymerase, 0. 2 μL bovine serum albumin, and double-distilled water. PCR was started with an initial denaturation (3 min at 95°C), followed by 28 cycles of denaturation (30 s at 95°C), annealing (30 s at 55°C), and extension (45 s at 72°C), and a final extension (10 min at 72°C). Sequencing using a MiSeq platform was performed at a commercial facility (Shanghai Majorbio Bio-Pharm Technology Corporation, Shanghai, China). All the 16S raw data were trimmed and filtered using Trimmomatic software (Manual v0. 32), by trimming the average base quality region below 20 bp  (Trujillo et al. , 2014). The paired-end reads were merged using FLASH software. The sequences assigned to chloroplasts, mitochondria or eukaryotes were removed in the pretreatment of raw reads.
Bacterial operational taxonomic units (OTUs) were clustered with 97% similarity using Usearch version 7. 0 (http://drive5. com/uparse/) based on Silva Release128 (http://www. arb-silva. de). Taxonomy was assigned to OTUs using Qiime (http://qiime. org/scripts/assign_taxonomy. html) and ribosomal database project pipeline classification algorithm with a 70% confidence threshold  (Nakayama, 2010). Alpha diversity indices (ace, Shannon, Simpson evenness and Boneh) and hierarchical clustering were calculated with Qiime. Circos-0. 67-7 was used to perform the bacterial composition of each sample, and the distribution ratio of dominant bacteria in different samples. Functional prediction of bacterial communities was determined using PICRUSt, a well-documented tool to assign sequencing information based on 16S input data to reveal the functions encoded in bacterial communities  (Langille et al.
, 2013; Mchardy et al. , 2013). Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (e-value cut-off 10 -5 ) were used for functional annotation and metabolism analyses  (Mchardy et al. , 2013; Kanehisa et al. , 2014; Vrutika et al. , 2016). The weighted nearest sequenced taxon index (NSTI) was calculated to assess the accuracy of PICRUSt analysis  (Langille et al. , 2013).
Statistical analyses One-way ANOVA was applied to test the differences of geochemical factors; a level of p < 0. 05 was considered significant. The relationships between geochemical factors and alpha-diversity indexes were analyzed using Spearman correlation (SPSS v21). SIMPER analysis based on Bray-Curtis similarity measurement was used to test microbial differences in the tailings. Non-metric multidimensional scaling (NMDS) and distance-based redundancy analysis (db-RDA) analysis were conducted to test the correlation between bacterial communities and geochemical factors of tailings sites based on weighted normalized unifrac distance algorithm. The significance of geochemical factor was tested with Monte Carlo permutations (permu = 999). Correlations of each bacterial community and each geochemical factor were calculated with p-values < 0.
05 and plotted as a heatmap. Network analysis was used to reflect the relationship between tailings sites and genera. After detection for genera (Gephi software), each module was represented by network correlation shared values of abundance profile by using modularity analysis. BIOENV analysis was used to determine the combined effects of geochemical factors on the metabolic pathways of bacterial communities. All the analyses were done using R software (v 3. 4. 1) unless otherwise stated.
, 2017). It is possible that the DNA repair system participated in the sensitive targets of microbial metal(loid)s toxicity observed in our study, resulting in the adaptation of bacterial communities to the extreme tailings environments. SIMPER analysis using the KEGG database indicated that the metabolic pathways directly related to ATP, methane, nitrogen, and energy generation (such as ABC transporters) also contributed to the differences of bacterial community structures in the seven tailings sites (Table  S5  and S6 ). ABC transporters constitute large amounts of membrane proteins and could transport many diverse substrates, such as metal(loid)s and secondary metabolites  (Theodoulou and Kerr, 2015). As discussed above, metal(loid) oxidation-related genus of Acidithiobacillus could also encode the ABC transporter genes involved with zinc ion transport  (Hou et al.
Acknowledgments This work was funded by the  International Key Project from National Natural Science Foundation of China  ( 41720104007 ), and  Projects of Natural Science Foundation of China  ( 41573080 ,  U1402234 ,  41711530030 ,  41711530150 ,  41711530224 ), Public welfare project of  Chinese Ministry of Environmental Protection  ( 201509049 ),  International key project of Ministry of Science and Technology of China  ( S2016G2135 ), and  Fundamental Research Funds for the Central Universities  ( FRF-OT-16-025 ). We also acknowledge the support of the  Centre National de la Recherche Scientifique  ( CNRS PRC1416 , France), a  Royal Society Newton Mobility Grant  ( IE161198 ), and  National Natural Science Foundation International Joint collaboration China-Sweden  ( 41430106 ).
Although average sgRNA representation was overall decreased in the transduced cells treated with venetoclax compared to duplicate sets of untreated and DMSO-treated transduced cells, a clear subset of drug-treated transduced cells was enriched (Figure  2B ). By analysis with the STARS software (v1. 3, Broad Institute)  (Doench et al. , 2016) , sgRNAs corresponding to 11 genes were consistently enriched over 2 replicates after 14 days of drug treatment compared to DMSO (STARS score > 4; Figure  2C , Table  S5 ), with high consistency in the changes in representation amongst the 4 sgRNAs per gene (Figure  S2C ). Not surprisingly, four of 11 knocked-out genes encoded pro-apoptotic BCL-2-family proteins (PMAIP1, BAX, BAK1, BCL2L11). Moreover, the level of representation by sgRNAs corresponding to the other BCL-2 members matched their known functions as either pro-or anti-apoptotic family members (Figure  S2D ).
After normalizing samples to 5 ng/μL, the set was pooled and quantified using the KAPA Library Quantification Kit for Illumina Sequencing Platforms. The entire process was in a 96-well format and all pipetting is done by either Agilent Bravo or Hamilton Starlet. Illumina Sequencing: Pooled libraries were normalized to 2 nM and denatured using 0. 1 N NaOH prior to sequencing. Flowcell cluster amplification and sequencing were performed according to the manufacturer's protocols using either the HiSeq 2000 or HiSeq 2500 instrument. Each run generated a 101bp paired-end with an eight-base index barcode read. Data was analyzed using the Broad Picard Pipeline, which includes de-multiplexing and data aggregation. Data analyses: RNA-seq data were aligned to GRCh38. p5 with STAR-2. 5. 1b  (Dobin et al.
The maximum ion time utilized for MS/MS scans was 120ms; the HCD-normalized collision energy was set to 30; the dynamic exclusion time was set to 20 s, isotope exclusion function was enabled, and peptide match function was set to preferred. Charge exclusion was enabled for charge states that were unassigned, 1 and >6. Data Analysis: All data were analyzed using Spectrum Mill software package v 6. 1 prerelease (Agilent Technologies). Similar MS/MS spectra acquired on the same precursor m/z within +/-60 s were merged. MS/MS spectra were excluded from searching if they were not within the precursor MH+ range of 750-4000 Da or if they failed the quality filter by not having a sequence tag length >0. MS/MS spectra were searched against UniProt human database. All spectra were allowed +/-20 ppm mass tolerance for precursor and product ions, 30% minimum matched peak intensity, and "trypsin allow P" enzyme specificity with up to 2 missed cleavages.