cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27075604.v1
title: Effects of generational low dose-rate <sup>137</sup>Cs internal exposure in descendant mice

description: To quantitatively investigate the effects of chronic low-dose internal exposure to Cesium-137 on DNA damage, carcinogenicity, and offspring over multiple generations. The potential genetic risk in humans was predicted based on next-generation murine mutation rates to confirm the reasonableness of the current Cesium-137 dose limits for food. Cesium-137 (100 Bq/mL) was provided in drinking water to A/J mice, facilitating chronic, low-dose, low-dose-rate internal exposure through sibling mating over 25 generations (G25). The A/J mice were compared with a control strain with the same origin ancestry (no Cesium-137 water) for DNA double-strand breaks (DSBs), oxidative stress, chromosome aberrations, micronucleus test results, whole genome analysis, carcinogenicity, tumor growth rate, and immune competence. Compared to the control group, DNA DSBs and oxidative stress were significantly increased in the Cesium-137 group. However, no significant differences were observed between the groups regarding chromosome aberration, micronuclei, or the whole genome sequence mutation analysis. Although the carcinogenic rate did not differ between the groups, the rate of tumor growth was significantly suppressed in the Cesium-137 group. The anti-tumor cytokine trend in the Cesium-137 group likely contributed to this effect. No pathological or genetic effects were observed in the offspring of mice drinking water containing 100 Bq/mL Cesium-137 after G25. The contribution of low dose-rate radiation to carcinogenicity was not additive but growth-inhibitory. Although the negative data are not conclusive, these findings are deemed highly reliable.
DOI: 10.7483/opendata.delphi.ymmb.sgrf
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m112.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.2qj3.pqau
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e191.6_m90.0_k99_1l_e1
DOI: 10.6084/m9.figshare.25336471
title: Modeling Out-Degree of Node in Commuter Networks: The Impact of Socioeconomic Profiles and Spatial Autocorrelation in Shaping Regional Connectivity

description: Regional commuting networks are shaped by the distribution of population and employment as well as a host of other socioeconomic and demographic factors. A commuting network type of particular interest consists of residential origins and work destinations as nodes, with the desire lines between them functioning as links. A residential origin has links not only to adjacent towns and the main city, but to work destinations in many other towns as well. Although it is expected that highly connected towns are spatially clustered around the urban core, reflected in positive spatial autocorrelation (PSA), this pattern can be interrupted by origins with seemingly low demonstrated connectivity, reflecting negative spatial autocorrelation (NSA). Principal components analysis leading to regression analysis combined with Moran eigenvector spatial filtering provide an approach for achieving deeper insight into these varied patterns of connectivity in the Pittsburgh, Pennsylvania, region, while taking the role of PSA and NSA into account. It also highlights the role of NSA, which reveals spatial patterns that can at least appear to challenge the notion of Tobler’s law that closer things are more related, and a deeper analysis suggests that PSA and NSA jointly characterize connectivity, but not in equal measure. It is argued that actors such as population age, wealth, occupation type, household car ownership, distance from the core, and so on would offer refinement to the predictive ability of density or accessibility in modeling connectivity as represented by degree of node.
DOI: 10.6084/m9.figshare.25336471.v1
title: Modeling Out-Degree of Node in Commuter Networks: The Impact of Socioeconomic Profiles and Spatial Autocorrelation in Shaping Regional Connectivity

description: Regional commuting networks are shaped by the distribution of population and employment as well as a host of other socioeconomic and demographic factors. A commuting network type of particular interest consists of residential origins and work destinations as nodes, with the desire lines between them functioning as links. A residential origin has links not only to adjacent towns and the main city, but to work destinations in many other towns as well. Although it is expected that highly connected towns are spatially clustered around the urban core, reflected in positive spatial autocorrelation (PSA), this pattern can be interrupted by origins with seemingly low demonstrated connectivity, reflecting negative spatial autocorrelation (NSA). Principal components analysis leading to regression analysis combined with Moran eigenvector spatial filtering provide an approach for achieving deeper insight into these varied patterns of connectivity in the Pittsburgh, Pennsylvania, region, while taking the role of PSA and NSA into account. It also highlights the role of NSA, which reveals spatial patterns that can at least appear to challenge the notion of Tobler’s law that closer things are more related, and a deeper analysis suggests that PSA and NSA jointly characterize connectivity, but not in equal measure. It is argued that actors such as population age, wealth, occupation type, household car ownership, distance from the core, and so on would offer refinement to the predictive ability of density or accessibility in modeling connectivity as represented by degree of node.
DOI: 10.6084/m9.figshare.25914670.v1
title: Locality, Personal Ties, and Efficiency in a Food Security Network

description: Food sharing and distribution organization systems provide critical resources for local communities and food-insecure households. In this article, we investigate a newly collected data set of the Thrive Network in southwestern Virginia, which links forty food security organizations through fifty-one connections, using a theoretical framework of organization science within geographic space. We first test whether more central and higher degree organizations are toward the geographic center of the network, near convenient points of interest. We then measure whether organizations are likely to form connections based on nearness and logistical effectiveness of moving goods, and where this occurs. Finally, we find “missed connections,” defined as sets of organizations that are nearby but highly disconnected in the network. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. As such, this system could be predicated on bottom-up personal relationships rather than a hub-and-spoke supply chain configuration, and new ties might make the system more effective. We use our findings to help the Thrive Network build a more resilient food-sharing system and better serve vulnerable clients.
DOI: 10.6084/m9.figshare.25914670
title: Locality, Personal Ties, and Efficiency in a Food Security Network

description: Food sharing and distribution organization systems provide critical resources for local communities and food-insecure households. In this article, we investigate a newly collected data set of the Thrive Network in southwestern Virginia, which links forty food security organizations through fifty-one connections, using a theoretical framework of organization science within geographic space. We first test whether more central and higher degree organizations are toward the geographic center of the network, near convenient points of interest. We then measure whether organizations are likely to form connections based on nearness and logistical effectiveness of moving goods, and where this occurs. Finally, we find “missed connections,” defined as sets of organizations that are nearby but highly disconnected in the network. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. As such, this system could be predicated on bottom-up personal relationships rather than a hub-and-spoke supply chain configuration, and new ties might make the system more effective. We use our findings to help the Thrive Network build a more resilient food-sharing system and better serve vulnerable clients.
DOI: 10.7483/opendata.delphi.on15.8qbs
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m70_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.nsp7.ty1c
title: DELPHI simulation data xs_hzha03p6156gggg_e196_m90_c99_1l_a1
DOI: 10.7483/opendata.delphi.u2gz.19uj
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e199.5_m102.5_r99_1l_e1
DOI: 10.7483/opendata.delphi.r8w1.8zh2
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e191.6_m92.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.a913.bh1j
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m90_m100_c99_1l_e1
DOI: 10.7483/opendata.delphi.3qj1.1onr
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m42.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.3h5a.dcyy
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e191.6_m87.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.jout.9a50
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m97.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.cr7s.3pf5
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e191.6_m95.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.2s5r.oqex
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m30_c99_1l_e1
DOI: 10.7483/opendata.delphi.svw9.5gle
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m45.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.p268.tpss
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m120_c99_1l_a1
DOI: 10.7483/opendata.delphi.je4x.vhqm
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m60_m90_c99_1l_e1
DOI: 10.7483/opendata.delphi.3xx1.cz12
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m100.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.iakq.1ep5
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e191.6_m97.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.dowx.5bx4
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m47.5_k99_1l_e1
DOI: 10.34943/cec78a4a-a340-42f9-8f26-eaa0df88c1b1
title: China Creek Hydrophone Deployed 2021-09-24

description: The Ocean Sonics icListen HF Hydrophone 1333 was deployed on 2021-09-24 at China Creek. China Creek is located in the Alberni Inlet, which is a long, narrow inlet on Vancouver Island. This device is a Hydrophone. Hydrophones are devices containing transducers that convert underwater sound waves into electrical signals. They are acoustic instruments that can process data while they are being collected to produce calibrated waveform data. Hydrophones are typically used to study vocalizations of marine mammals, ship traffic and ambient noise. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6084/m9.figshare.28146920
title: 2023-02-16 (Questionnaire_clean).docx

description: Questionnaire used for my thesis
DOI: 10.7483/opendata.delphi.ajmq.n780
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m110.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.jdqu.3j80
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m40_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.5qp7.nucw
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m130_c99_1l_a1
DOI: 10.7483/opendata.delphi.p5oz.f7uu
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m102.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.r45x.3rp2
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m40_c99_1l_e1
DOI: 10.7483/opendata.delphi.qbbb.xdj2
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e195.5_m62.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.9iok.o1ma
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m105.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.c2h9.df49
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m107.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.5u8m.mr4x
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m110_c99_1l_a1
DOI: 10.7483/opendata.delphi.fcox.0x9p
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m112.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.0llh.cc7d
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m102.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.4j58.k6x3
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m115.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.py7q.ilrg
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m80_c99_1l_e1
DOI: 10.7483/opendata.delphi.5r7d.z8od
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m40.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.pi5a.y5wy
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m4_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.akk2.34ux
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m140_c99_1l_a1
DOI: 10.7483/opendata.delphi.awoh.stwa
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m112.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.eaw4.m202
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m45.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.wnqk.hncv
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m85_m85_c99_1l_e1
DOI: 10.7483/opendata.delphi.qxof.69xo
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m60.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.13xo.j88k
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m47.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.ubom.0ul9
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m42.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.caq7.vpb4
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m60_c99_1l_e1
DOI: 10.34943/aed85a05-9712-4d1e-92b1-919a7d896881
title: Discovery Passage Hydrophone Deployed 2022-01-28

description: The Ocean Sonics icListen HF Hydrophone 1350 was deployed on 2022-01-28 at Discovery Passage. Discovery Passage is a channel that is part of Inside Passage. It is located between Vancouver Island and the Discovery Islands and north of the Georgia Strait. This device is a Hydrophone. Hydrophones are devices containing transducers that convert underwater sound waves into electrical signals. They are acoustic instruments that can process data while they are being collected to produce calibrated waveform data. Hydrophones are typically used to study vocalizations of marine mammals, ship traffic and ambient noise. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.7483/opendata.delphi.hk73.pkdc
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m62.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.alis.7m6q
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m30_c99_1l_a1
DOI: 10.7483/opendata.delphi.rfgo.dhn3
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m70_m100_c99_1l_e1
DOI: 10.7483/opendata.delphi.ztio.hny5
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m52.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.dl07.bkfd
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m90.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.jz2p.z00d
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m50.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.aw2s.dwlz
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m52.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.ih3q.ts5a
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m55.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.5gpx.n0fg
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m50_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.1g6d.o7hm
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m160_c99_1l_a1
DOI: 10.7483/opendata.delphi.qj3h.41ty
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m57.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.su2k.fjnx
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m70_c99_1l_e1
DOI: 10.7483/opendata.delphi.faeq.danl
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m95.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.4inf.4kl5
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m62.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.kyhd.4f5p
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m60.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.7aw7.vun2
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m150_c99_1l_a1
DOI: 10.7483/opendata.delphi.fg3o.hbx3
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m47.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.wuil.h5z3
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m65.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.bvdt.obr0
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m67.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.rspx.o346
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m100.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.h9d9.auvi
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m85_c99_1l_e1
DOI: 10.7483/opendata.delphi.lk28.vip8
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m55.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.cm5v.8rdu
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m72.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.3b51.accm
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m180_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.f742.otg3
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m40_c99_1l_a1
DOI: 10.7483/opendata.delphi.hj3h.uo22
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m70.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.ia3o.sd8l
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m85_m95_c99_1l_e1
DOI: 10.7483/opendata.delphi.fzsy.uvzb
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m110.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.8jyy.ef3n
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m75.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.q7ek.zbb7
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m80.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.urxr.289p
title: DELPHI simulation data xs_hzha03pyth6156hcqq_e201.6_m70.0_r99_1l_e1
DOI: 10.7483/opendata.delphi.sm3r.e8tk
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m95_c99_1l_e1
DOI: 10.7483/opendata.delphi.t71n.435s
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m45.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.6j9x.2evw
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m60_c99_1l_a1
DOI: 10.7483/opendata.delphi.120y.vz6q
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m70_m110_c99_1l_e1
DOI: 10.7483/opendata.delphi.s3hg.7xbu
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m82.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.s6b4.hj1d
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m50.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.kshb.lnpj
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m62.5_k99_1l_e1
DOI: 10.6084/m9.figshare.28146923
title: 2024-05-14 (Field + Tree data collection).xlsx

description: Data collection for tree diversity study. Chapter 4.
DOI: 10.7483/opendata.delphi.m2hc.ao49
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m77.5_k99_1l_e1
DOI: 10.34740/kaggle/dsv/10388337
title: Adidas Webstore Shoe Data
DOI: 10.6084/m9.figshare.28146923.v1
title: 2024-05-14 (Field + Tree data collection).xlsx

description: Data collection for tree diversity study. Chapter 4.
DOI: 10.7483/opendata.delphi.ke4d.47s6
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m90.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.hpn5.chjc
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m30_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.hfk9.0hzx
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m67.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.7vic.ny8o
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m50_c99_1l_a1
DOI: 10.7483/opendata.delphi.c8kq.n6y7
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m85.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.9ly6.hqau
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m87.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.mhvc.39ie
title: DELPHI simulation data xs_hzha03pyth6156ha50_e201.6_m90_c99_1l_e1
DOI: 10.7483/opendata.delphi.q0km.t3qn
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m85.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.p1ld.bhzj
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m40.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.6spe.mnt9
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m92.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.470z.tzi7
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m42.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.v1ar.xxko
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m70_c99_1l_a1
DOI: 10.7483/opendata.delphi.1v36.a9k0
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m97.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.adnu.b3t1
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e195.5_m95.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.wrnh.6or8
title: DELPHI simulation data xs_hzha03pyth6156ha50bbtt_e191.6_m100_c99_1l_e1
DOI: 10.7483/opendata.delphi.15cw.lznw
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m47.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.8jlo.dwot
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m97.5_k99_1l_e1
DOI: 10.7483/opendata.delphi.lxbq.21vb
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e199.5_m100.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.m5jq.me1c
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e195.5_m20_m4_c99_1l_e1
DOI: 10.7483/opendata.delphi.j49c.0bck
title: DELPHI simulation data xs_hzha03pyth6156hsnn_e199.5_m105.0_k99_1l_e1
DOI: 10.7483/opendata.delphi.1vj1.ega0
title: DELPHI simulation data xs_hzha03p6156gggg_e200_m80_c99_1l_a1
DOI: 10.7483/opendata.delphi.3thd.0u0b
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e201.6_m105.0_k99_1l_e1
DOI: 10.6084/m9.figshare.28146902
title: <b>Salivary Biomarker Panel for Detecting Periodontitis</b>

description: <b>Aim: </b>This study tested the hypothesis that a salivary biomarker panel (i.e., consisting of 2 to 6 biomarker features) could yield diagnostic accuracy of 95% for periodontitis, allowing for earlier disease detection.<b>Materials and Methods:</b> Concentrations of 11 protein biomarkers by immunoassays and oral microbiome species by 16s rRNA sequencing were evaluated in saliva samples from 28 healthy adults and 28 Type 2 diabetic patients with periodontitis. Data were analyzed for discrimination of health or periodontitis using 5-fold cross-validation logistic regression, receiver operator characteristics (ROC), and odds ratios.<b>Results:</b> Bacteria showed better predictive value than individual salivary proteins. Two bacteria (<i>Porphyromonas gingivalis</i> and <i>Mycoplasma faucium</i>) yielded sensitivities &gt;90% and <i>Treponema socranskii</i> demonstrated the top AUC (0.86). A combination of bacteria (<i>Selenomonas sputigena</i>, <i>P. gingivalis</i>, <i>Prevotella nigrescens</i>, <i>Pr. dentalis</i>) with salivary protein ratios (prostaglandin E2/tissue inhibitor of metalloproteinase-1 or macrophage inflammatory protein-1a/tissue inhibitor of metalloproteinase-1) produced robust accuracy (95%) and precision 96.5% for the detection of periodontitis.<b>Conclusions:</b> A salivary panel using bacteria and ratios of host-response biomarkers accurately discriminated oral health from periodontitis in Type 2 diabetics.
DOI: 10.7483/opendata.delphi.myqu.vaku
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e201.6_m85_m90_c99_1l_e1
DOI: 10.7483/opendata.delphi.3n8p.bn3x
title: DELPHI simulation data xs_hzha03pyth6156hcnn_e199.5_m70.0_k99_1l_e1