cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13180709.v1
title: Locomotion, Interactions and Information Transfer Vary According to Context in a Cryptic Fish Species

description: The behaviour of animals is strongly influenced by the detection of cues relating to foraging opportunity or to risk, while the social environment plays a crucial role in mediating their behavioural responses. Despite this, the role of the social environment in the behaviour of non-grouping animals has received far less attention than in social species. Here, we present the results of an experiment on a cryptic species of goby (<i>Pseudogobius</i> sp.), which does not form social groups in its natural habitat. Gobies were presented sequentially with chemical cues relating to food, conspecific alarm and a control, while in the presence of conspecifics. The intermittent locomotory behaviour of the gobies, which is typical of many cryptic animals, was influenced by the type of cues presented. Gobies decreased the duration of bouts of stasis in the presence of food cues and were generally more active. By contrast, those detecting alarm cues decreased the duration of movement bouts and were generally less active. In line with previous studies involving shoaling species, gobies in the presence of food cues adopted a more dispersed distribution, while clustering together in the presence of alarm cues. Finally, we used calculations of transfer entropy as a means of inferring information transfer among experimental subjects. In contrast to previous studies that have focussed on social species, transfer entropy between gobies was detectable only in the conspecific alarm treatment. Taken together, our results show that members of this cryptic species detect and respond to chemical cues by adjusting their movement and distancing to conspecifics. Further, they augment their own information with social cues but only when they perceive threat.
DOI: 10.5439/1666379
title: Aerosol samples overview from the AEROICESTUDY campaign

description: Mastersheet of aerosol samples collected by a Microorifice Uniform Deposit Impactor (MOUDI) during the Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY) at SGP occurring 7 – 28 October 2019. Dates and times of collection and location/storage and types of samples are described. Please read AEROICESTUDY_MOUDI_README.csv file for details.
DOI: 10.6084/m9.figshare.13138331.v3
title: Geospatial analysis of soil erosion including precipitation scenarios in a conservation area of the Amazon Region in Peru

description: The Tilacancha Private Conservation Area provides fresh water to the City of Chachapoyas, Amazonas. Therefore, the amount of soil lost in the year and under precipitation scenarios was determined. Individually, the values ​​of the factors were obtained: Rain erosivity (R) in 2019 and simulating increase and decrease of 15% of rainfall, soil erodibility (K), length and degree of slope (LS), land cover (C) and conservation practices (P); and they were integrated into USLE, obtaining A = R*K*LS*PC, (t/ha.yr). Six ranges of erosion were found, the ACP had areas where from 0.4 to 665.20 t/ha.yr of soil was lost. A 15% reduction in rainfall would represent a loss of soil from 0.20 to 301.56 t/ha.yr and an increase in rainfall by 15%, the erosion ranges would vary from 0.5 to 1028.84 t/ha.yr.
DOI: 10.6084/m9.figshare.13180994.v1
title: project pipeline and scripts

description: Here are the pipeline and scripts we used for the project of common pheasant genome sequencing.
DOI: 10.6084/m9.figshare.13180994
title: project pipeline and scripts

description: Here are the pipeline and scripts we used for the project of common pheasant genome sequencing.
DOI: 10.21227/8yh8-w517
title: model 2.4

description: test
DOI: 10.21227/rf7m-n041
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 254,079 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/8xes-f613
title: model 2.4

description: test
DOI: 10.5281/zenodo.229519
title: globalbioticinteractions/Catalogue-of-Afrotropical-Bees: prepare for publication to zenodo

description: from http://doi.org/10.15468/u9ezbh : "The purpose of this catalogue is to list the valid names, nomenclatorial history of, and references citing, the described bees of sub-saharan Africa and the western Indian Ocean islands, excluding the honey bee (Apis mellifera Linnaeus). The countries in which species occur are listed, as well as the type specimens/species of species/genera. Some ecological information is provided, namely flowers visited, plants used as nests, parasitic bees and host bees."
DOI: 10.21227/gs2x-9x23
title: model 2.5

description: test
DOI: 10.21227/nybe-yt96
title: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2, October 9, October 18, October 22)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5, October 9, October 10, October 15, October 26)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.21227/pr5z-mq41
title: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2, October 9, October 18, October 22)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5, October 9, October 10, October 15, October 26)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. Note that there are multiple instances where the average sentiment score is NULL because of technical issues (networking (at cloud service) and API).
DOI: 10.14457/cmu.res.2000.49
title: ความรู้ ทัศนคติ และการปฏิบัติของผู้ปกครองเกี่ยวกับอาหารที่ก่อให้เกิดโรคฟันผุในเด็กที่มารับบริการทันตกรรมที่ภาควิชาทันตกรรมสำหรับเด็ก คณะทันตแพทยศาสตร์ มหาวิทยาลัยเชียงใหม่
DOI: 10.14457/cmu.res.2001.72
title: ความเข้าใจของคณาจารย์ทางการแพทย์ในมหาวิทยาลัยเชียงใหม่ต่อการทำหน้าที่ของสมาชิกวุฒิสภาตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช 2540
DOI: 10.14457/cmu.the.2001.394
title: การสอนวิชาคณิตศาสตร์ชั้นมัธยมศึกษาตอนปลาย เรื่อง ฟังก์ชันตรีโกณมิติ โดยใช้คอมพิวเตอร์ช่วยสอนซ่อมเสริม
DOI: 10.14457/cmu.the.2001.395
title: ศักยภาพของโรงเรียนสารภีพิทยาคมในการเป็นโรงเรียนส่งเสริมสุขภาพ
DOI: 10.14457/cmu.the.2001.396
title: การศึกษาฤทธิ์ต้านเชื้อรา Cladosporium cladosporioides และเชื้อแบคทีเรีย Serratia marcescens ของสารสกัดจากกระชาย ฟ้าทะลายโจร มะนาวแป้น และมะนาวน้ำหอม
DOI: 10.14457/cmu.the.2001.397
title: โครงสร้างจุลภาคและสมบัติทางไดอิเล็กทริกของเลดแมกนีเซียมไนโอเบตที่เตรียมจากสารตั้งต้นเลดเมตาไนโอเบตและเลดแมกนีเซียมออกไซด์
DOI: 10.14457/cmu.the.2000.253
title: ศักยภาพในการจัดการทรัพยากรป่าไม้ของประชาชนในลุ่มน้ำแม่อิงตอนบน จังหวัดพะเยา
DOI: 10.14457/cmu.the.2001.398
title: ผลของน้ำร้อนโซเดียมคลอไรด์และไคโตแซนต่อคุณภาพและอายุการเก็บรักษาผลมะนาว
DOI: 10.14457/cmu.the.2001.399
title: การควบคุมการเน่าเสียของผลลำไยหลังการเก็บเกี่ยวด้วยสารโซเดียมเมตาไบซัลไฟต์และน้ำมันหอมระเหยจากมัสตาร์ด
DOI: 10.14457/cmu.the.2001.400
title: การศึกษาคุณภาพของสุรากลั่นพื้นบ้านที่ผลิตในเขตภาคเหนือตอนบน
DOI: 10.14457/cmu.the.2001.401
title: ความสัมพันธ์ทางเศรษฐกิจและการเมืองระหว่างประเทศไทย-ลาวที่มีผลต่อการกำหนดภาวะการค้าชายแดน
DOI: 10.14457/cmu.the.2001.402
title: การใช้การวิเคราะห์องค์ประกอบความหมายของคำศัพท์เพื่อส่งเสริมความสามารถในการแปลไทย-อังกฤษ
DOI: 10.14457/cmu.the.2001.403
title: บทบาทในการอนุรักษ์วัฒนธรรมพื้นบ้านของครูสังคมศึกษาในโรงเรียนมัธยมศึกษา สังกัดกรมสามัญศึกษา จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.404
title: การเปลี่ยนแปลงปริมาณของสารคล้ายจิบเบอเรลลินในช่วงก่อนการแตกใบอ่อนและออกดอกในยอดลำไยพันธุ์ดอ
DOI: 10.14457/cmu.the.2001.405
title: ผลของการปลูกพืชสลับต่อการแพร่ระบาดของแมลงศัตรูที่สำคัญในถั่วเหลือง
DOI: 10.14457/cmu.the.2001.406
title: การเจริญเติบโตของดอกว่านนางคุ้ม
DOI: 10.14457/cmu.the.2000.254
title: ปัจจัยเกี่ยวกับการรักษาสิ่งแวดล้อมและสิ่งจูงใจอุปถัมภ์ที่มีผลต่อการตัดสินใจเลือกห้างสรรพสินค้าของผู้บริโภคสตรีในจังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.407
title: การวิเคราะห์และการเปลี่ยนแปลงปริมาณสารคล้ายจิบเบอเรลลินในช่วงก่อนการออกดอกในยอดมะปรางพันธุ์ทูลเกล้า
DOI: 10.14457/cmu.the.2001.408
title: การสังเคราะห์และการหาลักษณะเฉพาะของไฮโดรเจลที่มีพอลิอะคริลาไมค์เป็นองค์ประกอบ สำหรับใช้ทดแทนผิวหนังแบบชั่วคราว
DOI: 10.14457/cmu.the.2001.409
title: การคัดเลือกสายพันธุ์ Aspergillus เพื่อผลิตไฮโดรเลสบางชนิดจากการหมักกากมัสตาร์ด
DOI: 10.14457/cmu.the.2001.410
title: ปฏิสัมพันธ์ทางสังคมของเครือข่ายป่าชุมชน
DOI: 10.14457/cmu.the.2001.411
title: ความตระหนักในปัญหาโรคเอดส์และพฤติกรรมการป้องกันตนเองของหญิงขายบริการทางเพศในสถานบริการบาร์เบียร์เชียงใหม่
DOI: 10.14457/cmu.the.2001.412
title: การแสดงออกของยีนของเอนไซม์ไลเปสนอกเซลล์จากเทอร์โมพิลิคแบคทีเรียสายพันธุ์ ทีพี 811
DOI: 10.14457/cmu.the.2001.413
title: การสร้างแบบวัดคุณธรรม จริยธรรมพื้นฐานสำหรับนักเรียนมัธยมศึกษาตอนต้น
DOI: 10.14457/cmu.the.2001.414
title: การเปรียบเทียบแบบจำลอง CERES-Rice และ SIMRIW และการประเมินศักยภาพผลผลิตของข้าวในที่ราบลุ่มจังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.415
title: ความสัมพันธ์ในครอบครัวเกษตรกรหลังการเปลี่ยนอาชีพ
DOI: 10.14457/cmu.the.2001.416
title: การรับรู้ของผู้ปกครองต่อการดูแลเด็กออทิสติคและการบริการของศูนย์ส่งเสริมพัฒนาการเด็กภาคเหนือ
DOI: 10.14457/cmu.the.2000.255
title: วิถีการดำเนินชีวิตที่มีผลต่อการเกิดโรคความดันโลหิตสูงของชาวบ้านโรงวัว ตำบลแม่ก๊า อำเภอสันป่าตอง จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2000.256
title: ประสบการณ์การถูกทำร้ายและความต้องการการช่วยเหลือของภรรยาที่ถูกสามีทำร้าย
DOI: 10.25919/5fa0eb224df05
title: Geophysical Interpretation of AEM data (AUS10057)

description: Grids and voxet models of interpreted AEM inversion results from the SkyTEM AUS10057 Ethel Creek - Fortescue River AEM survey north of Newman, WA.
DOI: 10.5281/zenodo.4217357
title: Effects of ion slippage in Earth's ionosphere and the plasma sheet

description: The compressed file contains text files of RCM-I simulation output. The file names specify simulation time (format: hhmmss). The stored quantities are, "I","J","COLAT","ALOCT","BNDLOC","XMIN","YMIN","FTV","BMIN","V", "BIRK_fromV(NH)","BIRK","PEDLAM", "PEDPSI", "HALL", "EFLUX", "EAVG", "eeta_k10""P(RCM),nPa", "PV_gamma","RCM_T_p","RCM_T_e","RCM_N_p","RCM_N_e","Vx_exb","Vy_exb","Vtotx","Vtoty","vel_x","Ey","f_i_005","f_i_010","f_i_020","f_i_040","f_i_060".
DOI: 10.5281/zenodo.4217356
title: Effects of ion slippage in Earth's ionosphere and the plasma sheet

description: The compressed file contains text files of RCM-I simulation output. The file names specify simulation time (format: hhmmss). The stored quantities are, "I","J","COLAT","ALOCT","BNDLOC","XMIN","YMIN","FTV","BMIN","V", "BIRK_fromV(NH)","BIRK","PEDLAM", "PEDPSI", "HALL", "EFLUX", "EAVG", "eeta_k10""P(RCM),nPa", "PV_gamma","RCM_T_p","RCM_T_e","RCM_N_p","RCM_N_e","Vx_exb","Vy_exb","Vtotx","Vtoty","vel_x","Ey","f_i_005","f_i_010","f_i_020","f_i_040","f_i_060".
DOI: 10.25919/5fa0f229bf0d3
title: Geophysical Interpretation of AEM data (AUS10057)

description: Grids and voxet models of interpreted AEM inversion results from the SkyTEM AUS10057 Ethel Creek - Fortescue River AEM survey north of Newman, WA.
DOI: 10.6084/m9.figshare.13181174
title: Rediscovery Of An Extinct Species Scleria Sumatrensis Retz. Of Taiwan Using Both Morphological And Molecular Authentication

description: Rediscovery Of An Extinct Species Scleria Sumatrensis Retz. Of Taiwan Using Both Morphological And Molecular Authentication
DOI: 10.6084/m9.figshare.13181174.v1
title: Rediscovery Of An Extinct Species Scleria Sumatrensis Retz. Of Taiwan Using Both Morphological And Molecular Authentication

description: Rediscovery Of An Extinct Species Scleria Sumatrensis Retz. Of Taiwan Using Both Morphological And Molecular Authentication
DOI: 10.6084/m9.figshare.13181213
title: Supplementary Material for: Trichostatin A Induces Autophagy in Cervical Cancer Cells by Regulating the PRMT5-STC1-TRPV6-JNK Pathway

description: <b><i>Objective:</i></b> The aim of this study was to investigate the effects of trichostatin A (TSA) on cervical cancer and the related mechanisms. <b><i>Methods:</i></b> The HeLa and Caski cervical cancer cell lines were treated with different concentrations of TSA. Cell viability was measured by MTT assays. Cell apoptosis was analysed using flow cytometry. Expression of transient receptor potential cation channel, subfamily V, member 6 (TRPV6), protein arginine methyltransferase 5 (PRMT5), and stanniocalcin 1 (STC1) was determined by qRT-PCR and Western blotting. Protein levels of LC3 II/I, beclin1, p62, JNK, and p-JNK were detected by Western blotting. <b><i>Results:</i></b> Treatment with TSA significantly decreased HeLa and Caski cell viability and enhanced the apoptosis rate in a dose-dependent manner. TSA markedly elevated beclin1 protein levels and the LC3 II/I ratio and significantly reduced p62 levels in a dose-dependent manner. In addition, TSA (1 μM) significantly suppressed PRMT5 and TRPV6 levels and enhanced STC1 and p-JNK levels. The lysosomal inhibitor bafilomycin-A1 synergistically enhanced the TSA-mediated increase in autophagic flux. Either the overexpression of TRPV6 or the inhibition of JNK signalling markedly enhanced cell viability, inhibited apoptosis, and autophagy and reduced p-JNK levels in TSA-treated cells. The inhibition of STC1 significantly increased TRPV6 protein levels and reduced p-JNK levels. Overexpression of PRMT5 dramatically decreased STC1 and p-JNK protein levels and increased TRPV6 levels. <b><i>Conclusion:</i></b> TSA suppresses cervical cancer cell proliferation and induces apoptosis and autophagy through regulation of the PRMT5/STC1/TRPV6/JNK axis.
DOI: 10.6084/m9.figshare.13181210
title: CGSS 2013

description: This data combines CGSS2013 data and environmental quality across provinces in China.
DOI: 10.21227/327g-jy25
title: Model7

description: We have followed the three-step approach to tackle this challenge.Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in consumption and temperature metadata, which we imputed using daily level linear interpolation. Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data – used as is (we have used avg. weather data only)b) Time Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the month and weekday1. Month gets mapped to Month_x = sin(2*pi*month/12) and Month_y=cos(2*pi*month/12)2. Weekday gets mapped to weekday_x = sin(2*pi*weekday/6) and weekday_y=cos(2*pi*weekday /6).Post multiple iterations, we settled with the XGBoost model since it performed the best among all. We trained the model on individual meter_ID and forecasted energy consumption at a daily level. The outcome was then rolled-up, to a monthly level to get our final submission.Update- Our model was not performing well on some of the customers, we identified those customers and used moving average to forecast for next year.
DOI: 10.6084/m9.figshare.13181231
title: Supplementary Material for: Fibrinogen Levels and Cognitive Profile Differences in Patients with Mild Cognitive Impairment

description: <b><i>Background:</i></b> Fibrinogen is considered a marker of vascular pathology, indicating a weakened blood-brain barrier, and has a causative role in neuroinflammation and neurodegeneration. Little is known about the relationship between fibrinogen levels and cognitive function in patients with mild cognitive impairment (MCI). We aimed to investigate differences in cognitive profiles according to plasma fibrinogen levels in patients with MCI and the influence of plasma fibrinogen levels on cognitive decline. <b><i>Methods:</i></b> This retrospective cohort study included 643 patients with MCI: 323 patients in the high fibrinogen (high fib) group and 320 patients in the low fibrinogen (low fib) group. A multiple linear regression model was performed to compare cognitive test performance between groups. The Cox proportional hazard model was used to analyze the hazard ratio of fibrinogen level for disease progression. <b><i>Results:</i></b> The high fib group demonstrated poorer performance in attention, executive function, and confrontation naming than the low fib group. After adjustment for <i>APOE</i> genotype, the high fib group was associated with poor attention and executive function. After adjustment for vascular risk factors including body mass index, hypertension, diabetes mellitus, dyslipidemia, and smoking history, the high fib group showed declined attention and confrontation naming ability. High fibrinogen levels did not predict disease progression to CDR 1. <b><i>Conclusion:</i></b> High plasma fibrinogen levels were associated with poor performance in attention in patients with MCI, regardless of <i>APOE</i> genotype or vascular risk factors.