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opencc-by-4.0Jul 2023View details →

Development of an Amine Transaminase-Lipase Cascade for Chiral Amide Synthesis under Flow Conditions

The use of multienzymatic systems has gained increasing attention as a method of choice for complex (asymmetric) syntheses. Incompatibilities between substrates, reagents and/or enzymes in one-pot batch conditions can hamper the applicability of a pursued cascade, so the use of flow systems provide useful synthetic solutions. The implementation of immobilised enzymes in continuous flow reactors allows the compartmentalisation and segregation of the enzymes in separate reactors, leading to otherwise disfavoured reaction cascades. Here, an amine transaminase and a lipase have been immobilised on polymer-coated controlled porosity glass carrier materials and studied for the first time together in the transamination of a prochiral ketone followed by acylation of the corresponding chiral amine in flow mode, two incompatible transformations under batch. Thus, the preparation of (R)-N-(1-phenoxypropan-2-yl)acetamide was accomplished after optimisation of the reaction conditions.

opencc-by-4.0Oct 2024View details →

Research data: Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia

Data used in the research presented in the article titled "Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia". Abstract of the article: At the end of the La Tène period and the beginning of the Roman period in the first century BC, society in Central Europe underwent a significant transformation, which included notable changes in pottery production. This transformation is often attributed to the collapse of the social structures of the La Tène period and the arrival of a new population. Pottery production, in particular, is generally considered to have undergone a complete transformation. However, previous studies on this transition have primarily focused on the stylistic analysis of shapes and decorations, as illustrated by the pottery assemblage from Slepotice (Eastern Bohemia). In order to obtain additional data on the transitional period, this study of pottery from Slepotice incorporates analyses of the materials used and the manufacturing process through macroscopic observation, X-ray fluorescence analysis, and thin-section analysis. These analyses provide new insights into the differences in pottery production and distribution during the first century BC. Our research indicates that while the transformation included the collapse of the La Tène socioeconomic network, it did not result in a complete break in the pottery production process. Link to the article: <a href="https://doi.org/10.1016/j.jasrep.2025.105073">https://doi.org/10.1016/j.jasrep.2025.105073</a>   List of the files: Supplementary Material 1 Settlement structure in the vicinity of Slepotice during the La Tène and Roman periods: 1 – Slepotice, 2 – České Lhotice, 3 – Brčekoly, 4 – Chrudim Supplementary Material 2 Values of pottery attributes (Mat, InMn, InVar, In, traces left from the shaping process, Po, Vy, and morphological groups) classified based on macroscopic observation Supplementary material 3 Schematic classification of rim attributes, illustrating different variants of rim direction (Op), thickening of the upper part of the rim (Oz), and trimming of the lip (Os) Supplementary material 4 Attributes of the 30 samples selected for XRF analysis based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary material 5 Figures of ceramic samples (with corresponding IDs) from feature 144/1998 showing preserved rims and bases Supplementary material 6 Figures of ceramic samples (with corresponding IDs) from feature 355/2001 showing preserved rims Supplementary Material 7 Chemical composition of 30 selected samples according to XRF analysis (main oxides in wt%, and elements in ppm) Supplementary Material 8 Principal Component Analysis (PCA) results: The scree plot (top left) visualises the proportion of variance explained by each principal component. The biplots (top right and bottom right) illustrate the distribution of samples, with arrows indicating the contribution of specific elements to the observed variance. The dendrogram (bottom left) shows hierarchical clustering of the samples, aiding in the selection of representative samples for thin-section petrographic analysis Supplementary Material 9 Relationships between the dating and other attributes of pottery classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 10 Relationships between the chemical groups (determined by XRF analysis) and pottery attributes classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 11 Petrography of fabric groups and subgroups, focusing on their properties. The evaluation begins with a general assessment of each fabric group as a whole, followed by a detailed examination of its subgroups Supplementary Material 12 Petrographic characterization of ceramics using a semiquantitative scale, simplified for statistical analysis (0.1 – trace, 0.5 – rare, 1 – occasional, 2 – common, 3 – frequent, 4 – abundant, 5 – dominant) Supplementary Material 13 Thin-section samples: Description of the ceramic matrix, natural inclusions, and added tempers Supplementary material 14 Variations in chemical composition among different fabric groups

opencc-by-4.0Nov 2021View details →

Impedance-based forecasting of battery performance amid uneven usage

Dataset of 88 commercial lithium-ion coin cells cycled under multistage constant current charging/discharging, with currents randomly changed between cycles to emulate realistic use patterns. raw-data.zip contains the following data: Variable Discharge: We subject 24 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V. Fixed Discharge: We subject an additional 16 Powerstream LiR2032 coin cells (of nominal capacity 1C = 35mAh) to the same cycling conditions as above, except now fixing the discharge current for all cells and cycles at 52.5mA (1.5C) instead of randomly changing the discharge current at each cycle. chemistry2-25C.zip contains the following data: Variable Discharge @ 25C: We subject 32 RS-Pro LiR2032 coin cells (of nominal capacity 1C = 40mAh) to a sequence of randomly selected charge and discharge currents at room temperature for 110-120 full charge/discharge cycles. Each cycle consists of acquisition of the galvanostatic EIS spectrum, followed by a charging and discharging stage. We collect impedance measurements at 57 frequencies uniformly distributed in the log domain in the range 0.02Hz-20kHz. Charging consists of a two stage Constant Current (CC) protocol; currents are randomly selected in the ranges 70mA-140mA (2C-4C) and 35mA-105mA (1C-3C) in stages 1 and 2 respectively. The distributions of currents are varied across different cell batches. If the safety threshold voltage of 4.3V is reached before the time limit then charging is stopped. During discharging, a single constant discharge current, randomly selected in the range 35mA-140mA (1C-4C), is applied, until the voltage drops to 3.0V. Variable Discharge @ 35C: We repeat the experiment conducted above for 16 additional RSPro cells, except that now we cycle the cells at 35C instead of 25C.

opencc-by-4.0Feb 2022View details →

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19" cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19". These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". 40-lifetables.csv Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided. 30-lt_input.csv Life table input data. <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> `death_total_prop_q1`: observed proportion of deaths in first quarter of year </li> <li> `death_total_prop_q2`: observed proportion of deaths in second quarter of year </li> <li> `death_total_prop_q3`: observed proportion of deaths in third quarter of year </li> <li> `death_total_prop_q4`: observed proportion of deaths in fourth quarter of year </li> <li> `death_expected_prop_q1`: expected proportion of deaths in first quarter of year </li> <li> `death_expected_prop_q2`: expected proportion of deaths in second quarter of year </li> <li> `death_expected_prop_q3`: expected proportion of deaths in third quarter of year </li> <li> `death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> Deaths <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> Population <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> COVID deaths <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> External life expectancy estimates <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>

opencc-by-4.0Feb 2022View details →

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19" cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19". These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19". 50-e0diffT.csv Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019. 51-arriagaT.csv Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age. 52-sexdiff.csv Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes. 53-e0diffcodT.csv Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued. 55-vaxe0.csv Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE. 54-tab_arriaga.csv Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

opencc-by-4.0Jan 2023View details →

Interpretable prediction for anticancer sensitivity of glycoside amides

<a href="https://anti-cancer.eu/" target="_blank" rel="noopener">Development Timeline: Selective Anticancer Logic of Glycoside Amides</a> / Second supplemented edition / <h2>📊 Interpretable Prediction Dataset:</h2> <h3>Transparent Modeling of Anticancer Sensitivity to Glycoside Amides</h3> The current dataset presents the exact results of our interpretable prediction model for anticancer sensitivity to glycoside amides. It is provided in *.xlsx format and includes: <ul> <li> ✅ Analytical data </li> <li> ✅ Theoretical framework </li> <li> ✅ Authorial conclusions </li> <li> ✅ Full filtered dataset </li> <li> ✅ Complete raw data </li> </ul> All information is organized in a user-friendly structure, fully compatible with standard data export formats and ready for integration into clinical modeling, pharmaceutical analysis, or transcriptomic mapping. <div> </div> <h3>🔍 Transparency and Scientific Integrity</h3> This dataset is not a closed interpretation. It reflects our original research findings, derived from a specific theoretical and biochemical framework. We fully acknowledge that the data may be interpreted differently depending on the analytical model, clinical context, or pharmacological assumptions. That is precisely why we have chosen to publish the exact numerical calculations—not just summaries or visualizations. This decision underscores our commitment to transparency, scientific reproducibility, and open dialogue with the broader research community. <div> </div> <h3>🧠 A Platform for Collaboration</h3> We invite clinicians, researchers, and data scientists to explore the dataset, challenge its assumptions, and build upon its structure. Whether used for comparative modeling, transcriptomic validation, or therapeutic design, the data is intended to serve as a foundation for further inquiry, not a final verdict.

Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.1

DFT optimised Structures of Silver NHC benzoates, and other files related to the effects of the benzoate anion in the cycloisomerisation of propargyl amides to 5-alkylidene oxazoles, including intermediates, NBO analysis and predicted NMR data, completed as a part of AMS MRes Research Module at Imperial College London, used in the MRes Thesis of Philip David Krause, under the supervision of Prof King Kuok (Mimi) Hii, and Dr Ben Deadman. Thesis: SUBSTITUENT EFFECTS OF BENZOATES IN SILVER N-HETEROCYCLIC CARBENE BENZOATE COMPLEXES AND THEIR CATALYSIS

Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.4

Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.3

Computational Data for the Cycloisomerisation of Propargyl amides by Silver NHC benzoates pt.2

opencc-by-4.0Nov 2023View details →

Primary NMR Data Supporting the Article "Synthetic approach to 2-alkyl-4-quinolones and 2-alkyl-4-quinolone-3-carboxamides based on common β-keto amide precursors"

This archive contains raw 1H/13C FIDs and associated data in Bruker-specific format that can be viewed with Bruker’s TopSpin or other appropriate NMR processing software. The subfolders are named in accordance with the compound numbering in the associated research paper (Synthetic Approach to 2-Alkyl-4-quinolones and 2-Alkyl-4-quinolone-3-carboxamides Based on Common β-Keto Amide Precursors). Correspondence: angelov@uni-plovdiv.bg

opencc-by-4.0May 2022View details →

Plant–insect interactions from the mid-Cretaceous at Puy-Puy (Aquitaine Basin, western France) indicates preferential herbivory for angiosperms amid a forest of ferns, gymnosperms, and angiosperms

The nine in-text figures and table below (Appendices S1–S10), and the additional text and excel files attached, provide the raw data, summaries of the raw data, rarefaction analyses, and nonmetric multidimensional scale analyses (NMDS) that support the discussions of the main text. The raw data and their summaries of provide for each plant species or morphotype values important for assessment of their herbivory: percentage of specimens herbivorized, damage type (DT) richness, DT frequency, DT host-plant specificity, herbivorized surface area as a proportion of total surface area, and feeding event occurrences. The rarefaction analyses furnished evaluations of whether the number of samples was sufficient, given the surface area covered by those samples. For comparison, the number of samples was rarified to the number of DTs in those samples. Lastly, two NMDS analyses produced the relationships between the plant orders present in the plant assemblage and their interactive functional feeding groups (FFGs). A separate NMDS analysis shows the association between the three most herbivorized species and their FFGs.

opencc-zeroJun 2024View details →

Dataset: Argent Mid Cap ETF (AMID) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: Argent Mid Cap ETF (AMID) Stock Performance

opencc-by-4.0Aug 2024View details →

Plasmon-Driven Chemical Transformation of a Secondary Amide Probed by Surface Enhanced Raman Scattering

This data set complements the article "Plasmon-Driven Chemical Transformation of a Secondary Amide Probed by Surface Enhanced Raman Scattering" published at https://doi.org/10.1038/s42004-024-01276-2.

opencc-by-4.0Oct 2024View details →

A human gut Faecalibacterium prausnitzii fatty acid amide hydrolase- Genome Annotation

Genome annotation for F. prausnitzii Bg7063 Science 386, eado6828 (2024) DOI: 10.1126/science.ado6828 Undernutrition in Bangladeshi children is associated with disruption of postnatal gut microbiota assembly; compared with standard therapy, a microbiota-directed complementary food (MDCF) substantially improved their ponderal and linear growth. Here, we characterize a fatty acid amide hydrolase (FAAH) from a growth-associated intestinal strain of Faecalibacterium prausnitzii cultured from these children. This enzyme, expressed and purified from Escherichia coli, hydrolyzes a variety of N-acylamides, including oleoylethanolamide (OEA), neurotransmitters, and quorum sensing N-acyl homoserine lactones; it also synthesizes a range of N-acylamides, notably N-acyl amino acids. Treating germ-free mice with N-oleoylarginine and N-oleolyhistidine, major products of FAAH OEA metabolism, markedly affected expression of intestinal immune function pathways. Administering MDCF to Bangladeshi children considerably reduced fecal OEA, a satiety factor whose levels were negatively correlated with abundance and expression of their F. prausnitzii FAAH. This enzyme, structurally and catalytically distinct from mammalian FAAH, is positioned to regulate levels of a variety of bioactive molecules.