cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23786949
title: Drosophila cSPH35 cSPH242 cleavage site identification

description: The purified pro-cSPHs (<em>ca</em>. 10 μg) were incubated with PAP3 (1.0 μg) for 1 h at 37 ℃. The mixtures and negative controls of the pro-cSPHs were denatured in urea and digested with chymotrypsin or V8 protease overnight at 37 ℃ (Zhang et al., 2014). The resulting peptides were desalted using Omix C18 affinity media, dried, and dissolved in mobile phase A (0.1% formic acid in H2O). The samples were loaded onto an Acclaim PepMap RSLC C18 column (75 μm × 50 cm, Thermo Fisher) for data-dependent LC-MS/MS analysis as described previously (Cao et al., 2020). Peptides in each sample were separated via a gradient of 0–35% mobile phase B (0.1% formic acid in 80% AcCN) developed over 120 min (Jin et al., 2022). The survey scans were followed by both HCD and CID collisional MS/MS events triggered from the hypothetical peptide ions, with scanning of collisional fragment at 15,000 resolutions in the Orbitrap sector. For identifying the PAP3 cleavage sites in cSPH242 and cSPH35, a database combining <em>D. melanogaster</em> pro-cSPH242 and pro-cSPH35 and background proteins from <em>M. sexta</em>, human, and insect cells were constructed, and peptide spectrum matches were reviewed in Byonic to detect peptides not cut by nonspecific proteases. The details of peak area calculation were described previously to quantify characteristic peptides released by PAP3 and chymotrypsin/V8 protease (Jin et al, 2022).
DOI: 10.6084/m9.figshare.23786949.v1
title: Drosophila cSPH35 cSPH242 cleavage site identification

description: The purified pro-cSPHs (<em>ca</em>. 10 μg) were incubated with PAP3 (1.0 μg) for 1 h at 37 ℃. The mixtures and negative controls of the pro-cSPHs were denatured in urea and digested with chymotrypsin or V8 protease overnight at 37 ℃ (Zhang et al., 2014). The resulting peptides were desalted using Omix C18 affinity media, dried, and dissolved in mobile phase A (0.1% formic acid in H2O). The samples were loaded onto an Acclaim PepMap RSLC C18 column (75 μm × 50 cm, Thermo Fisher) for data-dependent LC-MS/MS analysis as described previously (Cao et al., 2020). Peptides in each sample were separated via a gradient of 0–35% mobile phase B (0.1% formic acid in 80% AcCN) developed over 120 min (Jin et al., 2022). The survey scans were followed by both HCD and CID collisional MS/MS events triggered from the hypothetical peptide ions, with scanning of collisional fragment at 15,000 resolutions in the Orbitrap sector. For identifying the PAP3 cleavage sites in cSPH242 and cSPH35, a database combining <em>D. melanogaster</em> pro-cSPH242 and pro-cSPH35 and background proteins from <em>M. sexta</em>, human, and insect cells were constructed, and peptide spectrum matches were reviewed in Byonic to detect peptides not cut by nonspecific proteases. The details of peak area calculation were described previously to quantify characteristic peptides released by PAP3 and chymotrypsin/V8 protease (Jin et al, 2022).
DOI: 10.18112/openneuro.ds004647.v1.0.0
title: The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning
DOI: 10.6084/m9.figshare.23788524
title: 2023.07.27 - Data accessibility statement - JTB

description: Data accessibility statement
DOI: 10.6084/m9.figshare.23788524.v1
title: 2023.07.27 - Data accessibility statement - JTB

description: Data accessibility statement
DOI: 10.25442/hku.22267036
title: Water record of <em>Crassostrea angulata</em> transgenerational ocean acidification experiment

description: The basic observed water characteristics (pH, temperature, salinity and total alkalinity (TA)) and calculated water carbonate chemistry in the hatchery located in Zhanjiang, China (21.07°N, 110.74°E). The two-year-old Portuguese oysters <em>Crassostrea angulata</em> were collected from Wuchuan, China (21.37°N, 110.74°E) in 2020. To be specific, about 300 adult oysters were transferred into six black fibreglass tanks (500L each) randomly and each culture tank contained about 50 individuals. Six tanks were independent and completely randomized to be separated into two different pH levels: ambient pH 8.0~8.1 as control (named F0_8.1) and pH 7.4 as ocean acidification (OA) treatment (named F0_7.4) for about one month to produce F1 generation. The F1 larvae were acquired by artificial fertilization of matured F0 generation (F0_8.1 and F0_7.4). Embryos from F0_8.1 were separated into two pH levels: F1_8.1-8.1 and F1_8.1-7.4. Likewise, embryos from F0_7.4 were separated into two pH levels: F1_7.4-8.1 and F1_7.4-7.4. Thus, there were four treatments for F1 generation, and each treatment had three independent 500L tank replicates (A, B, and C). F1 generation continued to be reared to the next year (2021) till most of them reached the first year of gonad maturation to produce F2 generation. The F2 larvae were acquired by artificial fertilization of F1 generation. Embryos from F1_8.1-8.1 were separated into two pH levels: F2_8.1-8.1-8.1 and F2_8.1-8.1-7.4. The embryos from other F1 treatments were under similar processes. There were eight treatments for F2 generation and reared till 2022 to test the transgenerational OA effects on oyster biological responses. Low pH was driven by bubbling of CO<sub>2</sub>-enriched air mixed with ambient air into filter seawater in the treatment tanks. pH and temperature were monitored daily with pH meter (Mettler Toledo InLab<sup>®</sup>, Switzerland) calibrated with NBS standards. Salinity was monitored using a dual-scale refractometer (MingRui, China). The seawater was utterly changed every two days during larval stage and every week for juvenile stage. About 50 mL of seawater was preserved with HgCl<sub>2</sub> for total alkalinity (TA) measurement. The TA was measured using an alkalinity titrator (Mettler Toledo G20S Compact Titrator, Switzerland), and pCO<sub>2</sub>, CO<sub>3</sub><sup>2-</sup>, saturation states of calcite and aragonite were calculated by CO2SYS program with equilibrium constants K2 and KSO4 (Millero, F. J., Graham, T. B., Huang, F., Bustos-Serrano, H., & Pierrot, D. (2006). Dissociation constants of carbonic acid in seawater as a function of salinity and temperature. Marine Chemistry, 100(1), 80-94. https://doi.org/10.1016/j.marchem.2005.12.001
DOI: 10.25442/hku.22267036.v1
title: Water record of <em>Crassostrea angulata</em> transgenerational ocean acidification experiment

description: The basic observed water characteristics (pH, temperature, salinity and total alkalinity (TA)) and calculated water carbonate chemistry in the hatchery located in Zhanjiang, China (21.07°N, 110.74°E). The two-year-old Portuguese oysters <em>Crassostrea angulata</em> were collected from Wuchuan, China (21.37°N, 110.74°E) in 2020. To be specific, about 300 adult oysters were transferred into six black fibreglass tanks (500L each) randomly and each culture tank contained about 50 individuals. Six tanks were independent and completely randomized to be separated into two different pH levels: ambient pH 8.0~8.1 as control (named F0_8.1) and pH 7.4 as ocean acidification (OA) treatment (named F0_7.4) for about one month to produce F1 generation. The F1 larvae were acquired by artificial fertilization of matured F0 generation (F0_8.1 and F0_7.4). Embryos from F0_8.1 were separated into two pH levels: F1_8.1-8.1 and F1_8.1-7.4. Likewise, embryos from F0_7.4 were separated into two pH levels: F1_7.4-8.1 and F1_7.4-7.4. Thus, there were four treatments for F1 generation, and each treatment had three independent 500L tank replicates (A, B, and C). F1 generation continued to be reared to the next year (2021) till most of them reached the first year of gonad maturation to produce F2 generation. The F2 larvae were acquired by artificial fertilization of F1 generation. Embryos from F1_8.1-8.1 were separated into two pH levels: F2_8.1-8.1-8.1 and F2_8.1-8.1-7.4. The embryos from other F1 treatments were under similar processes. There were eight treatments for F2 generation and reared till 2022 to test the transgenerational OA effects on oyster biological responses. Low pH was driven by bubbling of CO<sub>2</sub>-enriched air mixed with ambient air into filter seawater in the treatment tanks. pH and temperature were monitored daily with pH meter (Mettler Toledo InLab<sup>®</sup>, Switzerland) calibrated with NBS standards. Salinity was monitored using a dual-scale refractometer (MingRui, China). The seawater was utterly changed every two days during larval stage and every week for juvenile stage. About 50 mL of seawater was preserved with HgCl<sub>2</sub> for total alkalinity (TA) measurement. The TA was measured using an alkalinity titrator (Mettler Toledo G20S Compact Titrator, Switzerland), and pCO<sub>2</sub>, CO<sub>3</sub><sup>2-</sup>, saturation states of calcite and aragonite were calculated by CO2SYS program with equilibrium constants K2 and KSO4 (Millero, F. J., Graham, T. B., Huang, F., Bustos-Serrano, H., & Pierrot, D. (2006). Dissociation constants of carbonic acid in seawater as a function of salinity and temperature. Marine Chemistry, 100(1), 80-94. https://doi.org/10.1016/j.marchem.2005.12.001
DOI: 10.6084/m9.figshare.23788539
title: Effects of white matter hyperintensities on isolated executive function assessed by the Trail Making Test; Extended data

description: Background: White matter hyperintensities (WMHs) on MRI are associated with cognitive dysfunction, particularly slow processing speed and executive dysfunction. However, it is not clear whether WMHs burden affects isolated executive function independent of aging when WMHs are assessed separately in periventricular hyperintensity (PVH) and deep and subcortical white matter hyperintensity (DSWMH). Purpose: To assess the relationship between the degree of WMHs and the performance on the Trail Making Test (TMT), which can evaluate isolated ability of set-shifting and working memory. Methods: 74 participants who visited our memory clinic and underwent the TMT subtests (TMT-A and TMT-B) and the Mini-Mental State Examination (MMSE). All subjects performed the TMT within the time limits and their MMSE scores were 24 or higher, and they were diagnosed as having normal cognition or mild cognitive impairment. The extent of PVH and DSWMH was graded from 0 to 3 using the Fazekas scale. We obtained testing time to complete the TMT-A and TMT-B, and calculated TMT-B minus TMT-A. We performed correlation analyses between the degree of WMHs and the time measures of the TMT subtests with adjustment of age. Results: Average scores of the MMSE were not different among the groups either by PVH grade or by DSWMH grade. In contrast, average time required for the TMT-A, TMT-B, and TMT-B minus TMT-A increased along with exacerbation of PVH and DSWMH grade. After the adjustment of age we found significant association between only DSWMH grade and the time difference of TMT-B minus TMT-A. Conclusions: Exacerbation of PVH and DSWMH differentially affected isolated executive functions assessed by the TMT subtests independent of age and general cognitive function.
DOI: 10.6084/m9.figshare.23771127.v7
title: Population genomic analysis of Ellobium chinense

description: Mapped bam file and SNPs
DOI: 10.6084/m9.figshare.23788503.v1
title: Effects of white matter hyperintensities on isolated executive function assessed by the Trail Making Test

description: Background: White matter hyperintensities (WMHs) on MRI are associated with cognitive dysfunction, particularly slow processing speed and executive dysfunction. However, it is not clear whether WMHs burden affects isolated executive function independent of aging when WMHs are assessed separately in periventricular hyperintensity (PVH) and deep and subcortical white matter hyperintensity (DSWMH). Purpose: To assess the relationship between the degree of WMHs and the performance on the Trail Making Test (TMT), which can evaluate isolated ability of set-shifting and working memory. Methods: 74 participants who visited our memory clinic and underwent the TMT subtests (TMT-A and TMT-B) and the Mini-Mental State Examination (MMSE). All subjects performed the TMT within the time limits and their MMSE scores were 24 or higher, and they were diagnosed as having normal cognition or mild cognitive impairment. The extent of PVH and DSWMH was graded from 0 to 3 using the Fazekas scale. We obtained testing time to complete the TMT-A and TMT-B, and calculated TMT-B minus TMT-A. We performed correlation analyses between the degree of WMHs and the time measures of the TMT subtests with adjustment of age. Results: Average scores of the MMSE were not different among the groups either by PVH grade or by DSWMH grade. In contrast, average time required for the TMT-A, TMT-B, and TMT-B minus TMT-A increased along with exacerbation of PVH and DSWMH grade. After the adjustment of age we found significant association between only DSWMH grade and the time difference of TMT-B minus TMT-A. Conclusions: Exacerbation of PVH and DSWMH differentially affected isolated executive functions assessed by the TMT subtests independent of age and general cognitive function.
DOI: 10.6084/m9.figshare.23788992
title: Supplementary Material for: Finerenone in Patients with Chronic Kidney Disease and Type 2 Diabetes: FIDELIO-DKD subgroup from China

description: Background: This prespecified subgroup analysis of the FIDELIO-DKD trial aimed to evaluate the efficacy and safety of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) in China. Methods: Three hundred and seventy-two participants were recruited from 67 centers in China and randomized 1:1 to oral finerenone or placebo with standard therapy for T2DM. The primary composite outcome included kidney failure, sustained decrease of estimated glomerular filtration rate (eGFR) ≥ 40% from baseline over at least 4 weeks, or renal death. The key secondary composite outcome included death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Results: After a median follow-up of 30 months, the finerenone group showed a relative risk reduction (RRR) of 41% (hazard ratio [HR]=0.59, 95% confidence interval [CI], 0.39 to 0.88; p=0.009) for the primary composite outcome compared with placebo, consistent across its components with treatment benefits with finerenone. Based on an absolute between-group difference of 12.2% after 30 months, the number of patients who needed to be treated (NNT) with finerenone to prevent one primary outcome event was eight (95%CI: 4 to 84). For the key secondary composite outcome, the finerenone group showed a RRR of 25% (HR=0.75, 95% CI, 0.38 to 1.48; p=0.408). Adverse events were similar between the two groups. The effects of finerenone on blood pressure were modest. No gynecomastia events were reported in the study. Hyperkalemia leading to discontinuation occurred in eight (4.3%) and two (1.1%) participants in the finerenone and control groups, respectively. The incidence of acute kidney injury was comparable between the two groups (1.6% vs. 1.6%). Conclusions: Finerenone resulted in lower risks of CKD progression than placebo and a balanced safety profile in Chinese patients with CKD and T2DM.
DOI: 10.6084/m9.figshare.23788995
title: Supplementary Material for: Finerenone in Patients with Chronic Kidney Disease and Type 2 Diabetes: FIDELIO-DKD subgroup from China

description: Background: This prespecified subgroup analysis of the FIDELIO-DKD trial aimed to evaluate the efficacy and safety of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) in China. Methods: Three hundred and seventy-two participants were recruited from 67 centers in China and randomized 1:1 to oral finerenone or placebo with standard therapy for T2DM. The primary composite outcome included kidney failure, sustained decrease of estimated glomerular filtration rate (eGFR) ≥ 40% from baseline over at least 4 weeks, or renal death. The key secondary composite outcome included death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Results: After a median follow-up of 30 months, the finerenone group showed a relative risk reduction (RRR) of 41% (hazard ratio [HR]=0.59, 95% confidence interval [CI], 0.39 to 0.88; p=0.009) for the primary composite outcome compared with placebo, consistent across its components with treatment benefits with finerenone. Based on an absolute between-group difference of 12.2% after 30 months, the number of patients who needed to be treated (NNT) with finerenone to prevent one primary outcome event was eight (95%CI: 4 to 84). For the key secondary composite outcome, the finerenone group showed a RRR of 25% (HR=0.75, 95% CI, 0.38 to 1.48; p=0.408). Adverse events were similar between the two groups. The effects of finerenone on blood pressure were modest. No gynecomastia events were reported in the study. Hyperkalemia leading to discontinuation occurred in eight (4.3%) and two (1.1%) participants in the finerenone and control groups, respectively. The incidence of acute kidney injury was comparable between the two groups (1.6% vs. 1.6%). Conclusions: Finerenone resulted in lower risks of CKD progression than placebo and a balanced safety profile in Chinese patients with CKD and T2DM.
DOI: 10.6084/m9.figshare.23788995.v1
title: Supplementary Material for: Finerenone in Patients with Chronic Kidney Disease and Type 2 Diabetes: FIDELIO-DKD subgroup from China

description: Background: This prespecified subgroup analysis of the FIDELIO-DKD trial aimed to evaluate the efficacy and safety of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM) in China. Methods: Three hundred and seventy-two participants were recruited from 67 centers in China and randomized 1:1 to oral finerenone or placebo with standard therapy for T2DM. The primary composite outcome included kidney failure, sustained decrease of estimated glomerular filtration rate (eGFR) ≥ 40% from baseline over at least 4 weeks, or renal death. The key secondary composite outcome included death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Results: After a median follow-up of 30 months, the finerenone group showed a relative risk reduction (RRR) of 41% (hazard ratio [HR]=0.59, 95% confidence interval [CI], 0.39 to 0.88; p=0.009) for the primary composite outcome compared with placebo, consistent across its components with treatment benefits with finerenone. Based on an absolute between-group difference of 12.2% after 30 months, the number of patients who needed to be treated (NNT) with finerenone to prevent one primary outcome event was eight (95%CI: 4 to 84). For the key secondary composite outcome, the finerenone group showed a RRR of 25% (HR=0.75, 95% CI, 0.38 to 1.48; p=0.408). Adverse events were similar between the two groups. The effects of finerenone on blood pressure were modest. No gynecomastia events were reported in the study. Hyperkalemia leading to discontinuation occurred in eight (4.3%) and two (1.1%) participants in the finerenone and control groups, respectively. The incidence of acute kidney injury was comparable between the two groups (1.6% vs. 1.6%). Conclusions: Finerenone resulted in lower risks of CKD progression than placebo and a balanced safety profile in Chinese patients with CKD and T2DM.
DOI: 10.6084/m9.figshare.22649596.v1
title: ConfigurationStabilityPredictionML

description: * Trained model parameters - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) * Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar
DOI: 10.6084/m9.figshare.23771127.v8
title: Population genomic analysis of Ellobium chinense

description: Mapped bam file and SNPs
DOI: 10.6084/m9.figshare.22649596.v2
title: ConfigurationStabilityPredictionML

description: * Trained model parameters - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) <br> * Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar WholeTrial_[2/3/4/5]/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar
DOI: 10.6084/m9.figshare.22649596
title: ConfigurationStabilityPredictionML

description: <strong>Trained model parameters [TrainedParams.zip] </strong> - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) *Note: Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar WholeTrial_[2/3/4/5]/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar <br> <strong>DSTAR-feature dataset for the OC20 dataset [DSTAR_FeatureData.zip]</strong> - TrainAds.pkl: training set data - ValAds_[id,ood_ads,ood_cat,ood_both].pkl: validation set data - TestAds_[id,ood_ads,ood_cat,ood_both].pkl: test set data <br>
DOI: 10.6084/m9.figshare.22649596.v4
title: ConfigurationStabilityPredictionML

description: <strong>Trained model parameters [TrainedParams.zip] </strong> - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) *Note: Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar WholeTrial_[2/3/4/5]/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar <br> <strong>DSTAR-feature dataset for the OC20 dataset [DSTAR_FeatureData.zip]</strong> - TrainAds.pkl: training set data - ValAds_[id,ood_ads,ood_cat,ood_both].pkl: validation set data - TestAds_[id,ood_ads,ood_cat,ood_both].pkl: test set data <br>
DOI: 10.6084/m9.figshare.22649596.v3
title: ConfigurationStabilityPredictionML

description: <strong>Trained model parameters [TrainedParams.zip] </strong> - RandomSeed_[random_seed]/[model_name]/Model_[num_dat]_ [id,ood_ads,ood_cat,ood_both].pth.tar - [random_seed]: 1111,2222,3333,4444,5555 (five independent trials for random extraction of training set) - [model_name]: CGCNN,SchNet,MPNN,MPNN_A,Matforer - [num_dat]: 10000(2%),46032(10%),115082(25%),230164(50%),345246(75%) *Note: Trained model parameters with whole training dataset (#: 460328) can be found in RandomSeed_1111/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar WholeTrial_[2/3/4/5]/[model_name]/Model_460328_[id,ood_ads,ood_cat,ood_both].pth.tar <br> <strong>DSTAR-feature dataset for the OC20 dataset [DSTAR_FeatureData.zip]</strong> - TrainAds.pkl: training set data - ValAds_[id,ood_ads,ood_cat,ood_both].pkl: validation set data - TestAds_[id,ood_ads,ood_cat,ood_both].pkl: test set data <br>
DOI: 10.6084/m9.figshare.23789447
title: Genetic Analysis Script from Cognitive functioning in anxiety and depression: results from the ALSPAC cohort

description: R Script for conducting the reported observational analyses