Affinity purification samples performed with different sRNAs as the bait. Single lanes were cut from a gel and submitted for MS.

DRAKO is a Mobile Location Audience Targeting provider with a programmatic trading desk specialising in geolocation analytics and programmatic advertising. Through our customised approach, we offer business and consumer insights as well as addressable audiences for advertising.

Mobile Location Data can be meaningfully transformed into Audience Targeting when used in conjunction with other dataset. Our expansive POI Data allows us to segment users by visitation to major brands and retailers as well as categorizes them into syndicated segments. Beyond POI visits, our proprietary Home Location Model determines residents of geographic areas such as Designated Market Areas, Counties, or States. Relatedly, our Home Location Model also fuels our Geodemographic Census Data segments as we are able to determine residents of the smallest census units. Additionally, we also have audiences of: ticketed event and venue visitors; survey data; and retail data.

All of our Audience Targeting is 100% deterministic in that it only includes high-quality, real visits to locations as defined by a POIs satellite imagery buildings contour. We never use a radius when building an audience unless requested. We have a horizontal accuracy of 5m.

Additionally, we can always cross reference your audience targeting with our syndicated segments:

Overview of our Syndicated Audience Data Segments: - Brand/POI segments (specific named stores and locations) - Categories (behavioural segments - revealed habits) - Census demographic segments (HH income, race, religion, age, family structure, language, etc.,) - Events segments (ticketed live events, conferences, and seminars) - Resident segments (State/province, CMAs, DMAs, city, county, sub-county) - Political segments (Canadian Federal and Provincial, US Congressional Upper and Lower House, US States, City elections, etc.,) - Survey Data (Psychosocial/Demographic survey data) - Retail Data (Receipt/transaction data)

All of our syndicated segments are customizable. That means you can limit them to people within a certain geography, remove employees, include only the most frequent visitors, define your own custom lookback, or extend our audiences using our Home, Work, and Social Extensions.

In addition to our syndicated segments, we’re also able to run custom queries return to you all the Mobile Ad IDs (MAIDs) seen at in a specific location (address; latitude and longitude; or WKT84 Polygon) or in your defined geographic area of interest (political districts, DMAs, Zip Codes, etc.,)

Beyond just returning all the MAIDs seen within a geofence, we are also able to offer additional customizable advantages: - Average precision between 5 and 15 meters - CRM list activation + extension - Extend beyond Mobile Location Data (MAIDs) with our device graph - Filter by frequency of visitations - Home and Work targeting (retrieve only employees or residents of an address) - Home extensions (devices that reside in the same dwelling from your seed geofence) - Rooftop level address geofencing precision (no radius used EVER unless user specified) - Social extensions (devices in the same social circle as users in your seed geofence) - Turn analytics into addressable audiences - Work extensions (coworkers of users in your seed geofence)

Data Compliance: All of our Audience Targeting Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

Overriding aim: To develop and make available to other investigators a comprehensive immune phenotype and functional database of a cohort of at least 700 normal healthy individuals. The dataset will comprise a cross-sectional analysis of the general population between the ages of 40 and 90+ (representing equal gender and representative ethnic population, and equal distribution by decade of life). The registry will contain demographic data, race/ethnicity, prescribed medications, over the counter medications, vitamins, alternative therapies, physical function questionnaire, alternative contact person, and HIPPA release. Fasting blood will be obtained for immune phenotyping and functional analyses. The immune profile will contain the results of both conventional and novel immune profiling assays to profile immune related phenotypic and functional changes associated with aging (using PBMC subset analysis, cytokines, and activation induced signaling of PBMCs for phosphoepitope and gene expression analyses). Data from these analyses will be useful in identifying biomarkers associated with aging, gender and/or chronic infection as well as correlation with phenotypic and functional aspects of aging such as sarcopenia and disability. The immune profile (as well as normal blood chemistries and demographic data) of these subjects will be made available to serve as the basis for future longitudinal study of change in the immune profile over time in association with the development of co-morbidities associated with aging. The primary deliverable for this proposal will be a unique open access electronic data repository that has phenotypic and functional information in multiple scales (epidemiological, and clinical, and, at the cell and molecular level, of immune phenotype) and genetic and proteomic information (gene and protein expression of resting and activated PBCs) on over 700 healthy individuals at different ages from 40 to 90 years. This resource will enable a systems-based approach to the immunology of aging.

This study engaged 103 participants over a period spanning from November 14 to December 16, 2021, ensuring representation across various demographic factors: 51 females, 52 males, aged 18-70, with varied annual incomes and from 17 Spanish regions. The MobileWell100+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic details, COVID-19-related inquiries, emotional, behavioral, and well-being data. Complementing this, social welfare data from external sources offers contextual insight. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.

The following is a non-exhaustive list of collected data:

• Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, unlocked).
• Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ).
• Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) are measured.
• Data corresponding to weekly surveys prompted via the smartphone, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. is requested.

For a more detailed description of the study please refer to MobileWell100+StudyDescription.pdf.

For a more detailed description of the collected data, variables and data files please refer to MobileWell100+FilesDescription.pdf.

Results are expressed as median with range in brackets.

Demographic data of the participants.

Demographic data: age, sex and BMI.

Abbreviations: BD, bipolar disorder; HC, healthy controls; SD, standard deviation; WASI, Wechsler Abbreviated Scale of Intelligence; IDS, Inventory of Depressive Symptoms; YMRS, Young Mania Rating Scale; PANSS P score, Positive and Negative Syndrome Scale positive subscale; GAF-S, Global Assessment of Functioning–symptom score; GAF-F, Global Assessment of Functioning–function score; BD PGRS, bipolar disorder polygenic risk score; ms, milliseconds.BD PGRS values are reported as z-scores (with SD in brackets).Complete behavioral data (response times and accuracy rates per condition) were available for 80/85 BD and 119/121 HC. For the remaining individuals (5 BD, 2 HC), an accuracy rate for each session (i.e. a combined rate for negative faces and shapes, and for positive faces and shapes) was available and was used to confirm that the participants paid attention to the task (accuracy rate: 97.4% and 96.0%, respectively).a Mean age at fMRI scanning. Age range was 18 to 63.b IDS score at scanning was available for 60/85 individuals (70.6%).c YMRS score at scanning was available for 69/85 individuals (81.2%).d PANSS P score at scanning was available for 38/85 individuals (44.7%).e Last six monthsDemographic data and clinical characterization of individuals participating in a faces matching functional MRI study.

Demographic data concerns the entire patient group. Therefore, the two patients who were not presented with the block-related cue-exposure were also considered. MT: motor threshold; M: mean; SD: standard deviation.Overview of the demographic data.

Demographic Information and self-reported data of Participants: Mean (Standard Deviation).

Demographic characteristics of valid responses.

Demographic and clinical data of patients and controls groups.

Demographic data and outcome results according to MEWS protocol alarm activation.

Activating Transcription Factor 4 (ATF4) is an important regulator of gene expression in stress responses and developmental processes in many cell types. Here, we catalogued ATF4 binding sites in the human genome and identified overlaps with trait-associated genetic variants. We probed these genetic variants for allelic regulatory activity using a massively parallel reporter assay (MPRA) in HepG2 hepatoma cells exposed to tunicamycin to induce endoplasmic reticulum stress and ATF4 upregulation. The results revealed that in the majority of cases, the MPRA allelic activity of these SNPs was in agreement with the nucleotide preference seen in the ATF4 binding motif from ChIP-Seq. Luciferase and electrophoretic mobility shift assays in additional cellular models further confirmed ATF4-dependent regulatory effects for the SNPs rs532446 (GADD45A intronic; linked to hematological parameters), rs7011846 (LPL upstream; myocardial infarction), rs2718215 (diastolic blood pressure), rs281758 (psychiatric disorders) and rs6491544 (educational attainment). CRISPR-Cas9 disruption and/or deletion of the regulatory elements harboring rs532446 and rs7011846 led to the downregulation of GADD45A and LPL, respectively. Thus, these SNPs could represent examples of GWAS genetic variants that affect gene expression by altering ATF4-mediated transcriptional activation.

BackgroundIndividuals with chronic low back pain (CLBP) exhibit altered brain function and trunk muscle activation.AimThis study examined the effects of sling exercises on pain, function, and corticomuscular coherence (CMC) in healthy adults and individuals with CLBP.MethodsEight individuals with CLBP and 15 healthy adults received sling exercise training for 6 weeks. Before and after training, participants performed two motor tasks: rapid arm lifts and repeated trunk flexion–extension tasks, and electromyography of the trunk muscles and electroencephalography of the sensorimotor cortex were recorded. Chi-squared test and Mann–Whitney U tests were used for between group comparison, and Wilcoxon signed-rank tests were used for pre- and post-training comparison. Spearman’s Rank Correlation Coefficient (Rs) was used to identify for the relationship between motor performance and Corticomuscular coherence.ResultsSling exercises significantly improved pain (median from 3 to 1, p = .01) and Oswestry Disability Index scores (median from 2.5 to 2, p = .03) in the CLBP group. During rapid arm lifts, individuals with CLBP showed lower beta CMC of the transverse abdominis and internal oblique (Tra/IO) (0.8 vs. 0.49, p = .01) and lumbar erector spinae (0.70 vs. 0.38, p = .04) than the control group at baseline. During trunk flexion–extension, the CLBP group showed higher gamma CMC of the left Tra/IO than the control group at baseline (0.28 vs. 0.16 , p = .001). After training, all CMC became statistically non-significant between groups. The training induced improvement in anticipatory activation of the Tra/IO was positively correlated with the beta CMC (rs = 0.7851, p = .02).ConclusionA 6-week sling exercises diminished pain and disability in patients with CLBP and improved the anticipatory activation and CMC in some trunk muscles. These improvements were associated with training induced changes in corticomuscular connectivity in individuals with CLBP.

To determine the transcriptomic changes seen in early- to mid-stage posttraumatic osteoarthritis (PTOA) development, 72 Yucatan minipigs underwent transection of the anterior cruciate ligament. Subjects were randomized to no further intervention, ligament reconstruction, or ligament repair, followed by articular cartilage harvesting and RNA-sequencing at three different postoperative timepoints (1, 4, and 52 weeks). Six additional subjects received no ligament transection and provided cartilage tissue to serve as controls. Differential gene expression analysis between post-transection cartilage and healthy cartilage revealed an initial increase in transcriptomic differences at 1 and 4 weeks followed by a stark reduction in transcriptomic differences at 52 weeks. This analysis also showed how different treatments genetically modulate the course of PTOA following ligament disruption. Specific genes (e.g., MMP1, POSTN, IGF1, PTGFR, HK1) were identified as being upregulated in the cartilage of injured subjects across all timepoints regardless of treatment. At the 52-week timepoint, 4 genes (e.g., A4GALT, EFS, NPTXR, ABCA3) that—as far as we know—have yet to be associated with PTOA were identified as being concordantly differentially expressed across all treatment groups when compared to controls. Functional pathway analysis of injured subject cartilage compared to control cartilage revealed overarching patterns of cellular proliferation at 1 week, angiogenesis, ECM interaction, focal adhesion, and cellular migration at 4 weeks, and calcium signaling, immune system activation, GABA signaling, and HIF-1 signaling at 52 weeks.

Demographic characterization of the study group.

aAssessed by the Edinburgh handedness inventory.Abbreviations: SD, Standard Deviation; RMS, Root Mean Square; FD, Frame-wise Displacement.Demographic data.

Demographic data and clinical characteristics of participants (N = 23).

The intention of pro-environmental behavior (PEB) directly affects the sustainable development of protected areas, especially national parks, but few studies have done comparative research on tourist and hiker behaviors. This study explores the intention of tourists’ and hikers’ pro-environmental behavior based on theory of planned behavior (TPB) and norm activation theory (NAM). Researchers surveyed 454 tourists and 466 hikers in Wuyishan National Park a structural equation modeling data analysis method. The results demonstrate that the TPB and the NAM were accurate in describing for tourists’ and hikers’ pro-environmental behavior in national park. However, for specific influencing factors, hikers’ attitude, awareness of consequences, and assumption of responsibility were significantly different from those of the tourists. This study sheds light on how to better comprehend and advocate for PEB in national parks and proposes different management approaches to improve the PEB of tourists and hikers.