Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Data from: Balanced mitochondrial function at low temperature is linked to cold adaptation in Drosophila species

Data from: Balanced mitochondrial function at low temperature is linked to cold adaptation in Drosophila species

opencc-by-4.0Apr 2023View details →

Data used in the paper "Optimal balancing of xylem efficiency and safety explains plant vulnerability to drought"

Data used in the paper "Optimal balancing of xylem efficiency and safety explains plant vulnerability to drought"

dryad32/100

Mindreading by body: incorporating mediolateral balance and mouse-tracking measures to examine the motor basis of adults' false-belief tracking

The role played by motor representations in tracking others' belief-based actions remains unclear. In Experiment 1, the dynamics of adults' anticipatory mediolateral motor activity (leftwards-rightwards leaning on a balance board) as well as hand trajectories were measured as they attempted to help an agent who had a true or false belief about an object's location. Participants' leaning was influenced by the agent's belief about the target's location when the agent was free to act but not when she was motorically constrained. However, the hand trajectories participants produced to provide a response were not modulated by the other person's beliefs. Therefore, we designed a simplified second experiment in which participants were instructed to click as fast as possible on the location of a target object. In Experiment 2, mouse-movements deviated from an ideal direct path to the object location, with trajectories that were influenced by the location in which the agent falsely believed the object to be located. These experiments highlight that information about an agent's false-belief can be mapped onto the motor system of a passive observer, and that there are situations in which a fully functional motor system is required for accurate belief-tracking.

opencc-zeroMay 2023View details →

dryad32/100

Exercises on a balance cushion to influence lumbar vertebral syndrome

Background: The pain in the lumbar region is common. One of the causes of low back pain is lumbar vertebral syndrome. There is a wide range of options for treating low back pain. The purpose of the study is to describe exercises on a balance cushion to influence the pain in lumbar vertebral syndrome and to track the effect of their application. Methods: We studied 20 participants with low back pain. We applied the following tests to the patients: visual analogue scale, Schober test, measurements of lateral flexion and Straight leg raise test. The methodology includes exercises on a balance cushion. We performed manual manipulation techniques on each patient by treating trigger poitns in the lower back and buttocks once a week. Results: We achieved an improvement in the studied indicators after the applied therapy, which was expressed in a reduction of pain and restoration of normal mobility in the lumbar region. Conclusions: The application of exercises on a balance cushion allows in a short time to affect the pain and mobility in the lumbar spine to fully perform their work and professional activities.

opencc-zeroMay 2023View details →

opencc-by-4.0May 2023View details →

Quantifying Interseismic Volume Strain from Chemical Mass-Balance Analysis in Tectonic Mélanges

Here, we present element concentrations in scaly fabrics, mudstone protoliths, and mineral veins of six tectonic mélanges units in the Cretaceous Shimanto belt and three mélanges units in the Kodiak accretionary complex measured with LA-ICPMS. We then employed chemical mass-balance analysis utilizing this dataset to quantify the strain related to mass transfer along the plate interface. The ensuing results, presented in this report, reveal a notable distinction in average volumetric strain between individual scaly fabrics of the Lower Mugi and Makimine mélanges, which experienced temperatures near the updip and downdip isotherms bounding the seismogenic zone, respectively. To gain further insights, we combined image analysis techniques to integrate the strain over specific areas of interest. Additionally, we compared the volume loss in scaly fabrics with the volume gain, which was represented by the proportion of mineral veins in those areas. The findings indicate a growing discrepancy between volume loss and volume gain in mélanges with higher paleotemperature records.

opencc-by-4.0Jun 2023View details →

Balancing the trade-offs between land productivity, labor productivity and labor intensity - Codes

Stata do-files that replicate the analysis in the paper titled "Balancing the trade-offs between land productivity, labor productivity and labor intensity"

opencc-by-4.0May 2023View details →

When 'good' is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis

Raw data associated with the scientific publication "When ‘good’ is not good enough: a retrospective Rasch analysis study of the Berg Balance Scale for persons with Multiple Sclerosis".

opencc-by-4.0Jun 2023View details →

Supplementary materials for the article: "Where and when the mesopelagic carbon budget balances, if at all", Oliver et al., 2023

This is the supplementary materials for the article: "Where and when the mesopelagic carbon budget balances, if at all", Oliver et al., 2023 This folder contains: MLD.mat - The annual maximum of monthly-averaged mixed layer depth (m) in ECCO: 1992-2001 mean (data retrieved from ECCO Consortium et al., 2023). The mixed layer depth is determined by the depth where waters are first 0.8◦ C colder than the surface, as per the Kara Formula (Kara et al., 2000) FBGC3mm.mat - Sinking detrital flux [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), monthly average FBGC4mm.mat - Detritus removal via remineralisation and denitrification [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), monthly average FBGC7mm.mat - Detritus creation [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), monthly average DETmm.mat - Sinking Detritus [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), monthly average NPP.mat - Primary production [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), annual average export.mat   - Export production [mmol P/m3/d], produced by MOPS (Kriest et al., 2015), annual average grid.mat - model grid information for ECCO transport matrix (see Khatiwala, S. (2018): https://zenodo.org/record/1246300 463doi: 10.5281/ZENODO.1246300) cmap5.mat - matlab colormap             bluewhitered.mat  - matlab colormap        cmap12.mat - matlab colormap PlotPaperFigures.m - matlab script for plotting journal paper figures for "Where and when the mesopelagic carbon budget balances, if at all", Oliver et al., 2023 findClosest.m - matlab script to find the closest value in a list of values to a specified value

opencc-by-4.0Jul 2023View details →

Data and Code for the Manuscript: Aquatic insects balance growth with future supply of algal food resources

Some consumers depend on the contemporaneous growth of the resources they feed on. For example, Tanytarsus gracilentus midges feed on algae, and because midge generation time is much longer than that of algae, midges benefit not just from the standing stock but also from the growth of algae. This implies that an intermediate consumption rate maximizes midge somatic growth: low consumption rates constrain midge growth, whereas high consumption rates reduce algae and future food abundance. In an experiment, we manipulated midge presence and initial algal abundance. We found that midges could suppress algal growth, as measured by changes in algal primary production (GPP). We also found a positive relationship between GPP and midge growth. We then fit a consumer-resource model to the experimental data, which showed the expected hump-shaped relationship between midge consumption rates and their somatic growth. In the model, projected midge growth rates were only positively associated with GPP when midge consumption was below the value that optimized midge growth. Therefore, the observed relationship between instantaneous primary production and midge growth seen in the experiment suggests that midges did not overexploit algae. This work highlights the challenges faced by consumers that depend on contemporaneously growing resources.

Fig. 2 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance

Fig. 2. GCxGC–MS chromatogram of silylated free sterols from S. perennans (A) and projection of the GCxGC–MS chromatogram down to two dimensions (B). Peak numbers are as used in Fig. 1. The x-axis is the ZB-5ms column retention time (min), and the y-axis is the Rxi-17 column retention time (s).

Fig. 3. Ordination diagram for discriminant analysis using the P in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance

Fig. 3. Ordination diagram for discriminant analysis using the P <0.05 chisquare subset; 21 cases were used to develop a model to discriminate among the 3 types of halophytes. Six predictor variables were entered: campesterol, β-sitosterol, stigmasterol, and the sum of Δ5 -, Δ7-and Δ0-STs. Eu – euhalophytes (n = 11), Re – recretophytes (n = 5), Ex – salt excluders (n = 5).

Fig. 1 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance

Fig. 1. Representative GC–MS chromatograms of silylated free sterols from C. crassa (A) and S. linifolia (B). Peaks: cholesterol (1), lathosterol (cholest-7-en- 3β-ol) (2), campesterol (3), campestanol (4), stigmasterol (5), lophenol (4- methylcholest-7-en-3β-ol) (6), 22-stigmastenol (stigmast-22-en-3β-ol) (7), unknown sterol X1 with m/z 484 (8), β-sitosterol (9), spinasterol (stigmasta-7,22- dien-3β-ol) (10), sitostanol (11), β-amyrin (12), Δ5-avenasterol (13), unknown sterol X2 with m/z 484 (14), α-amyrin (15), Δ7-stigmastenol (=Δ7-sitosterol) (16), Δ7-avenasterol (17). * – nonsteroid component.

Fig. 5 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance

Fig. 5. Principal component analysis of the relationship between the composition of ST and soil characteristics of Chenopodiaceae. Vector designations: M – mineralization, Н О – moisture, pH – soil acidity; Δ5, Δ7 and Δ0 are the sums of 2 Δ5 -, Δ7-and Δ0-STs, respectively; Сhol, Camp, β-sito, Stig are the contents of cholesterol, campesterol, β-sitosterol, and stigmasterol. The dots indicate the names of the species: A. aphyla – I, C. arenarius – II, C. crassa – III, H. strobilaceum – IV, B. prostrata – V, S sedoides – VI, S. perennans – VII, S. eltonica –VIII, S. linifolia – IX, S. physophora – X, S. salsa –XI, A. cana – XII, H. verucifera – XIII. The content of mineral residue, moisture and acidity of the soil in the root zone of plants of the Chenopodiaceae are shown in Table 3.

Fig. 4 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance

Fig. 4. The content of molecular types of sterols (% of the total) in the halophyte groups: Ex – excluders (n = 5), Re – recretophytes (n = 5), Eu – euhalophytes (n 11); A – sum of Δ5-STs, B – sum of Δ7-STs, C – sum of Δ0-STs; D – = stigmast-5-en-3β-ol, E stigmast-7-en-3β-ol, F – stigmastan-3β-ol, G – stigmasta-5,22-dien-3β-ol, H – stigmasta- 7,22-dien-3β-ol, I – stigmast-22-en-3β-ol. Data are presented as the mean values in each group and the error of the mean (mean ± SE).

Fig. 2 in Zinc uptake and HMA4 activity are required for micro- and macroelement balance in tobacco (Nicotiana tabacum)

Fig. 2. Classification of gene expression changes observed in both the HMA4 double-knockout mutants (cv. #3) and HMA4 RNAi plants (cv.#1) relative to their respective controls. The genes were classified manually according to the putative function of the closest Arabidopsis Blast hit (see Supplementary Table S2).

Fig. 1 in Zinc uptake and HMA4 activity are required for micro- and macroelement balance in tobacco (Nicotiana tabacum)

Fig. 1. (a) Relative element contents of HMA4-impaired plants compared with their controls. HMA4 RNAi plants in cv.#1 background were compared with their WT controls (n = 3 replicates each), and HMA4 double-mutant plants in cv. #3 background were compared with their null-segregant controls (n = 4). The elements were measured in dry weight of lower leaves of plants grown under greenhouse conditions. The error bars depict the 0.95 confidence interval (CI) corrected according to Satterthwaite approximation. (b) Pictures of representative plants of each group (sticker size = 11.9 cm).

Fig. 6 in Zinc uptake and HMA4 activity are required for micro- and macroelement balance in tobacco (Nicotiana tabacum)

Fig. 6. Relative element contents of different HMA4-double mutants versus the null-segregant WT controls in field experiments. The data were consolidated over 4 years of field experiments in Switzerland and 3 years of field experiments in Poland, as described in the Experimental section. The elements were measured in dry weight of leaves (mid-lower leaf position). Bars indicate estimates and confidence intervals (α = 0.1).

Fig. 4 in Zinc uptake and HMA4 activity are required for micro- and macroelement balance in tobacco (Nicotiana tabacum)

Fig. 4. Relative elemental content of N. tabacum cv. #1 and cv. #2 HMA4 RNAi plants compared with their WT and of HMA4 double-knockout mutants compared with their null-segregants, for four different fertilization conditions: (a) normal fertilization, (b) fertilization containing lower concentrations of phosphate, (c) fertilization containing higher concentrations of Zn, and (d) fertilization with low concentrations of phosphate and high concentrations of Zn. The high Zn condition was achieved by addition of 0.1 g additional Zn per plant. The elements were measured in dry weight of leaves (mid-lower leaf position). Absolute data are listed in Supplementary Table S3. Data represent mean (0.95 CI) of n = 5 replicate plants, corrected according to Satterthwaite approximation. (Three outliers were removed for condition (d): Al and Cu for cv.#2; Ti for cv.#3.).

Fig. 3 in Zinc uptake and HMA4 activity are required for micro- and macroelement balance in tobacco (Nicotiana tabacum)

Fig. 3. Working model of metabolic changes in HMA4-impaired plants based on the gene expression changes observed in the roots and leaves of HMA4 RNAi and HMA4-mutant N. tabacum plants relative to their controls. The lack of HMA4 function causes Zn deficiency in leaves, leading to a local Zn-deficiency response. In roots, a Fe-deficiency response is observed, caused either by a high Zn:Fe ratio in the roots or by a systemic Zn-deficiency signal. The Fe-deficiency signal leads—via the transcription factor FIT1—to upregulation of the Fe- and Mn- transporter genes IRT1 and NRAMP1 and also induces the synthesis and export of iron-binding compounds (IBC), thus additionally assisting Fe uptake. At the same time, a P-deficiency signal involving the transcription factor HHO2 is generated in the leaves. In roots, the P-deficiency signal leads to increased expression of genes encoding acid phosphatases, enabling increased phosphate uptake from the rhizosphere. Consequently, Fe, Mn, Cu, and P accumulate in the leaves. P is stored in vacuoles (via PHT5; 1, PHT5; 3) and also exported to mitochondria and chloroplasts. Cu is detoxified by transport to the chloroplasts, which are a major site of Cu use because of Cu-containing plastocyanin production. However, high levels of Cu are toxic for the photosynthetic electron transport system, especially for photosystem II, which is reflected in the upregulation of PII light harvesting complex genes (LHCB). At the same time, Cu uptake to the cells is limited by downregulation of COPT1. Ion uptake might cause a lower osmotic potential, leading to water influx via various aquaporin genes. High turgor pressure or Fe excess ultimately affects the cell wall, as observed by the increased expression of genes involved in cell wall biosynthesis, expansion, and crosslinking. bHLH115: basic helix-loop-helix transcription factor; CA: carbonic anhydrase; CESA: cellulose synthase; COPT: copper transporter; DMRL: dimethyl-8-ribityllumazine synthase; EXPA: expansin; FIT: FER-like regulator of iron uptake (transcription factor); HHO2: hypersensitivity to low phosphate-elicited primary root shortening 1 homolog 2 (myb-like transcription factor); HMA: heavy metal ATPase; IBC: iron binding compound; IRT: iron-regulated transporter; LAC: laccase; LHCB: photosystem II light harvesting complex; NA: nicotianamine; NAS: nicotianamine synthase; NRAMP: natural resistance-associated macrophage protein (metal ion transporter); PDR: pleiotropic drug resistance (ATP-binding cassette transporter); PHT: phosphate transporter; PIP: plasma membrane intrinsic protein; PME: pectin methyl esterase; PMEI: pectin methyl esterase inhibitor; PRX: peroxidase; RBCS: ribulose bisphosphate carboxylase small chain; TIP: tonoplast intrinsic protein; TBL: TRICHOME BIREFRINGENCE-LIKE (xylan acetylation); ZIP: zinc transporter.