Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

The New York State Departments of Environmental Conservation and Health are concerned about groundwater contamination in the carbonate-bedrock aquifers with the potential to host karst features throughout New York State, especially relating to the unintended introduction of chemical or agricultural contamination into these aquifers. USGS Scientific Investigations Report, SIR 2020-5030 (Kappel and others, 2020), provides local and State regulators and the public the information needed to determine the extent of carbonate bedrock in New York, the associated environmental impacts of karst, and the means to protect New York’s karst water resources. The four geodatabases presented in this data release were compiled in support of SIR 2020-5030. Closed depression-focused recharge is one potential pathway for aquifer contamination. A closed depression is any enclosed area that has no surface drainage outlet and from which water escapes only by evaporation or subsurface drainage. On a topographic map a closed depression is typically represented by a hachured contour line forming a closed loop. The map representation applies to closed depressions of both natural and anthropogenic origin. Closed depressions formed by natural processes need not be karst in origin to represent a source of focused-recharge. Three of the four geodatabases in this data release form a comprehensive inventory of all closed depressions, natural and anthropogenic, within the State which are proximal to carbonate, evaporite, or marble units and that have the potential for developing karst features. The fourth geodatabase in this data release contains a digital representation of the study area boundary adopted for the GIS analyses. The three closed depression inventory geodatabases were compiled in the following order: 1) Digital Contour Database of Closed Depressions, 2) Digital Raster Graphic Database of Closed Depressions, and 3) LiDAR Database of Closed Depressions. There is no duplication of features among these three geodatabases. Additionally, the closed depressions inventoried for this data release, were compared with closed depressions mapped in other published geospatial data to eliminate duplication with those datasets. The datasets referenced were the New York State Department of Environmental Conservation Mining Database and the National Hydrography Dataset waterbody features. The Digital Contour Database of Closed Depressions contains features derived from data associated with U.S. Geological Survey Scientific Investigations Report 2012–5167. The source data is a statewide contour dataset that was generated from the National Elevation Dataset (NED) and the National Hydrography Dataset (NHD) in a fully automated process. Closed depressions included in the Digital Raster Graphic Database of Closed Depressions were digitized from an assemblage of approximately 650 Digital Raster Graphic (DRG) images of scanned U.S. Geological Survey 1:24,000-scale topographic maps. A DRG is a scanned image of a U.S. Geological Survey topographic map that can be added as a background layer in a GIS. The LiDAR Database of Closed Depressions contains features generated from high-resolution LiDAR-derived bare-earth DEMs obtained from the New York State Office of Information Technology Services. At the time of analysis (2017) LiDAR data existed for approximately 65 percent of the study area. The DEMs were processed to identify depressions with an area of at least 4,047 square meters (1-acre) and a depth of at least 1-meter. These threshold values are greater than what is typically used for lidar-based sinkhole identification studies. For the purpose of this study, the use of lidar was primarily intended to identify closed depressions that were not represented in the Digital Raster Graphic Database, in the same manner that the DRG images were used to identify closed depressions not represented in the Digital Contour Database. For that reason, the threshold values were based on random sampling of DRG-derived closed depressions within the study area and represent the approximate mean geometric characteristics of the closed depressions sampled. For ongoing and planned larger-scale county-based assessments in New York, the thresholds will be reduced to 10- and 30-centimeters depth and 100 square meters.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/." This table displays the proportion of adults who were ever told they had a depressive disorder in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This indicator is based on the question: "“Has a doctor, nurse or other health professional EVER told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

“Karst” landscapes are characterized by distinctive hydrology and surface features, such as closed depressions, sinkholes, and sinking streams, that result from high rock solubility and well-developed fracture porosity (Ford and Williams, 2007; Weary and Doctor, 2014). Soluble carbonate rocks underlie most of Middle Tennessee, large areas of East Tennessee, and parts of surrounding states. The U.S. Geological Survey conducted a study, in cooperation with the Tennessee Department of Transportation, to create a geospatial dataset of depressions, sinking streams, and their associated watersheds within karst areas of Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi. The purposes of the study were to determine the methods and assessment techniques used to classify depressions in digital elevation models (DEMs) as likely or unlikely to exist; to delineate depressions, sinking streams, and their associated watersheds from multiple elevation data sources and digital hydrographic data within the karst areas of Tennessee and parts of surrounding States; to characterize the spatial distribution of these features within the area; and to present geospatial datasets of these features and their watersheds. The study area encompasses the Cumberland, Tennessee, Barren, and Conasauga River watersheds and eight karst regions with boundaries and names matching those of Level III and IV ecoregions data (U.S. Environmental Protection Agency, 2013): 1) the Inner Nashville Basin, 2) the Outer Nashville Basin, 3) the Eastern Highland Rim, 4) the Western Highland Rim, 5) the Western Pennyroyal Karst Plain and Crawford-Mammoth Cave Uplands, 6) the Cumberland Plateau Escarpment and Sequatchie Valley, 7) the Ridge and Valley, and 8) the Limestone Valleys and Coves. Digital elevation data were obtained from two sources: 1) the National Elevation Dataset (NED; Gesch and others, 2002; Gesch, 2007; U.S. Geological Survey, 2011 - 2012, variously dated a), which was derived from topographic-contour source data for some areas and light detection and ranging (lidar) source data for other areas, and 2) photogrammetrically-derived data collected by the State of Tennessee as part of the Tennessee State Base Mapping Program (TNBMP; Tennessee Department of Finance and Administration, 2005, 2007). These sources of elevation data, along with flowlines from the National Hydrography Dataset (NHD; U.S. Geological Survey, variously dated b), were used to delineate depressions from each elevation data source. The NED is available for the entire study area, making it possible to delineate depressions derived from the NED across the entire study area. The TNBMP elevation data covers only parts of the study area so the depressions were delineated for parts of Tennessee. Many depressions in DEMs can be considered artificial due to errors in the source DEM (Lindsay and Creed, 2006; Zandbergen, 2010) or misrepresentation of surface water flow beneath elevated structures present in the DEM (Poppenga and others, 2010; Zandbergen, 2010; Wall and others, 2015). While many of the depressions in DEMs are artificial, some are real, such as those representing sinkholes found in a karst landscape. Initially, preliminary depressions, which include depressions classified as either likely or unlikely to exist on the landscape, were delineated from each elevation source. Assessment methods, including an estimation of elevation data accuracy, a numerical error propagation analysis of the elevation data in test areas, and a comparison of preliminary depression locations to topographic-contour source data in middle Tennessee and northern Alabama, were used to determine preliminary depression characteristics and identify proper depression-characteristic thresholds for distinguishing likely from unlikely depressions in both the NED and the TNBMP elevation data. Depressions included in this data release were those that 1) passed proximity filters, and 2) met the depression-characteristic thresholds. See the "Delineation of Depressions" processing step in the metadata document for more information about depression requirements. The DEM analysis methods used for this study provide no distinction between sinkholes, man-made depressions, or natural depressions; such distinctions are beyond the scope of the study and this data release. Depressions in the geospatial datasets represent areas of internal drainage that have the potential to store surface runoff. A given depression within the dataset may contain one, more than one, or no sinkholes depicted on a 1:24,000-scale topographic map. Flowlines from the NHD were examined and edited to produce a dataset of sinking streams within the study area, and watersheds for likely depressions and sinking streams were delineated from the NED and the TNBMP elevation data. During the NHD editing process, long flowline connectors and other NHDFlowline features (U.S. Geological Survey, variously dated b) that are not representative of natural surface-water flow paths were removed where appropriate to create a subset of sinking streams represented by individual NHDFlowline features and groups of connected NHDFlowline features that conceptually lose all surface-water flow to the subsurface at a termination point. In many cases, these sinking streams terminated within a sinkhole depicted on a 1:24,000-scale topographic map. If one or more sinking streams terminated within a likely depression identified in a DEM, the associated watershed contained the depression and all sinking streams flowing into it. If a sinking stream did not terminate within a likely depression, the associated watershed contained the sinking stream termination location and all stream lines upstream of the termination point. This collection of geospatial data includes 5 shapefiles, each organized into individual zip files: (1) depressions derived from the NED (NED_depressions.zip); (2) watersheds for depressions derived from the NED and sinking streams (NED_watersheds.zip); (3) depressions derived from the TNBMP elevation data (TNBMP_depressions.zip); (4) watersheds for depressions derived from the TNBMP elevation data and sinking streams (TNBMP_watersheds.zip); and (5) sinking streams (sinking_streams.zip).

Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a karst closed depression inventory across the conterminous U.S. using a common methodology. Automated algorithms for extraction of closed depressions from 1/3 arc-second (approximately 10 m resolution) National Elevation Dataset (NED) were run on the U.S. Geological Survey (USGS) “Yeti” high-performance computing cluster. The full NED was first conditioned to reduce the creation of artificial closed depressions by breaching digital dams at road and stream crossings, using the flowlines and transportation route vectors from the USGS National Map. The resulting depressions were selected according to location within geologic units having the potential for karst, and screened for occurrence in areas of developed land, open water and wetlands, and areas of glacial and alluvial sediment cover. The results were used as the input to create a nationwide depression density map. Our results were compared with karst depression density maps for diverse karst regions within states that have existing closed depression inventories. The individual state-scale maps compared favorably to the results obtained from the method applied universally across the nation and illustrated regional sinkhole hotspots in known areas of well-developed karst. Limitations of the automated method includes false positive depressions resulting from artifacts generated during the computer processing of the elevation models, and inclusion of depressions resulting from non-karst geomorphic processes. Although concerted efforts were made to validate the depression polygons as actual karst features, a more thorough examination of each of the resulting depressions is required on an individual basis to determine its validity as a true karst or pseudokarst landform.

Content

This report lists each failure of a commercial bank, savings association, and savings bank since the establishment of the FDIC in 1933. Each record includes the institution name and FIN number, institution and charter types, location of headquarters (city and state), effective date, insurance fund and certificate number, failure transaction type, total deposits and total assets last reported prior to failure (in thousands of dollars), and the estimated cost of resolution. Data on estimated losses are not available for FDIC insured failures prior to 1986 or for FSLIC insured failures from 1934-88.

Acknowledgements

The bank failure report was downloaded from the FDIC website.

Inspiration

What type of banking institution is the most likely to fail? How have bank failure rates changed over time? What commercial bank failure cost the federal government the most to resolve?

This dataset was created to represent the land surface elevation at 1:24,000 scale for Florida. The elevation contour lines representing the land surface elevation were digitized from United States Geological survey 1:24,000 (7.5 minute) quadrangles and were compiled by South Florida, South West Florida, St. Johns River and Suwannee River Water Management Districts and FDEP. QA and corrections to the data were supplied by the Florida Department of Environmental Protection's Florida Geological Survey and the Division of Water Resource Management. This data, representing over 1,000 USGS topographic maps, spans a variety of contour intervals including 1 and 2 meter and 5 and 10 foot. The elevation values have been normalized to feet in the final data layer. Attributes for closed topographic depressions were also captured where closed (hautchered) features were identified and the lowest elevation determined using the closest contour line minus one-half the contour interval. This data was derived from the USGS 1:24,000 topographic map series. The data is more than 20 years old and is likely out-of-date in areas of high human activity.

This dataset was used to investigate the brain mechanism underlying rumination state (Chen et al., 2020, NeuroImage). The data was shared through the R-fMRI Maps Project (RMP) and Psychological Science Data Bank.Investigators and AffiliationsXiao Chen, Ph. D. 1, 2, 3, 4, Chao-Gan Yan, Ph. D. 1, 2, 3, 41. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China;2. International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;3. Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;4. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China. AcknowledgmentsWe would like to thank the National Center for Protein Sciences at Peking University in Beijing, China, for assistance with data acquisition at PKU and Dr. Men Weiwei for his technical support during data collection. FundingNational Key R&D Program of China (2017YFC1309902);National Natural Science Foundation of China (81671774 and 81630031);13th Five-year Informatization Plan of Chinese Academy of Sciences (XXH13505);Key Research Program of the Chinese Academy of Sciences (ZDBS-SSW-JSC006);Beijing Nova Program of Science and Technology (Z191100001119104);Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y9CX422005);China Postdoctoral Science Foundation (2019M660847). Publication Related to This DatasetThe following publication include the data shared in this data collection:Chen, X., Chen, N.X., Shen, Y.Q., Li, H.X., Li, L., Lu, B., Zhu, Z.C., Fan, Z., Yan, C.G. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. Neuroimage, 221, 117185, doi:10.1016/j.neuroimage.2020.117185. Sample SizeTotal: 41 (22 females; mean age = 22.7 ± 4.1 years).Exclusion criteria: Any MRI contraindications, current psychiatric or neurological disorders, clinical diagnosis of neurologic trauma, use of psychotropic medication and any history of substance or alcohol abuse. Scan procedures and ParametersMRI scanningSeveral days prior to scanning, participants were interviewed and briefed on the purpose of the study and the mental states to be induced in the scanner. Subjects also generated key words of 4 individual negative autobiographical events as the stimuli for the sad memory phase. We measured participants’ rumination tendency with the Ruminative Response Scale (RRS) (Nolen-Hoeksema and Morrow, 1991), which can be further divided into a more unconstructive subtype, brooding and a more adaptive subtype, reflection (Treynor, 2003). All participants completed identical fMRI tasks on 3 different MRI scanners (order was counter-balanced across participants). Time elapsed between 2 sequential visits were 22.0 ± 14.6 days. The fMRI session included 4 runs: resting state, sad memory, rumination state and distraction state. An 8-minute resting state came first as a baseline. Participants were prompted to look at a fixation cross on the screen, not to think anything in particular and stay awake. Then participants would recall negative autobiographical events prompted by individualized keywords from the prior interview. Participants were asked to recall as vividly as they could and imagine they were re-experiencing those negative events. In the rumination state, questions such as “Think: Analyze your personality to understand why you feel so depressed in the events you just remembered” were presented to help participants think about themselves, while in the distraction state, prompts like “Think: The layout of a typical classroom” were presented to help participants focus on an objective and concrete scene. All mental states (sad memory, rumination and distraction) except for the resting state contained four randomly sequentially presented stimuli (keywords or prompts). Each stimulus lasted for 2 minutes, and then was switched to the next without any inter-stimuli intervals (ISI), forming an 8-minute continuous mental state. The resting state and negative autobiographical events recall were sequenced first and second while the order of rumination and distraction states was counter-balanced across participants. Before the resting state and after each mental state, we assessed participants’ subjective affect with a scale (item score ranged from 1 = very unhappy to 9 = very happy). Thinking contents and the phenomenology during each mental state were assessed with a series of items which were derived from a factor analysis (Gorgolewski et al., 2014) regarding self-generated thoughts (item scores ranged from 1 = not at all to 9 = almost all). Image AcquisitionImages were acquired on 3 Tesla GE MR750 scanners at the Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences (henceforth IPCAS) and Peking University (henceforth PKUGE) with 8-channel head-coils. Another 3 Tesla SIEMENS PRISMA scanner (henceforth PKUSIEMENS) with an 8-channel head-coil in Peking University was also used. Before functional image acquisitions, all participants underwent a 3D T1-weighted scan first (IPCAS/PKUGE: 192 sagittal slices, TR = 6.7 ms, TE = 2.90 ms, slice thickness/gap = 1/0mm, in-plane resolution = 256 × 256, inversion time (IT) = 450ms, FOV = 256 × 256 mm, flip angle = 7º, average = 1; PKUSIEMENS: 192 sagittal slices, TR = 2530 ms, TE = 2.98 ms, slice thickness/gap = 1/0 mm, in-plane resolution = 256 × 224, inversion time (TI) = 1100 ms, FOV = 256 × 224 mm, flip angle = 7º, average=1). After T1 image acquisition, functional images were obtained for the resting state and all three mental states (sad memory, rumination and distraction) (IPCAS/PKUGE: 33 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness/gap = 3.5/0.6 mm, FOV = 220 × 220 mm, matrix = 64 × 64; PKUSIEMENS: 62 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90º, thickness = 2 mm, multiband factor = 2, FOV = 224 × 224 mm). Code availabilityAnalysis codes and other behavioral data are openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Chen_2020_NeuroImage. ReferencesGorgolewski, K.J., Lurie, D., Urchs, S., Kipping, J.A., Craddock, R.C., Milham, M.P., Margulies, D.S., Smallwood, J., 2014. A correspondence between individual differences in the brain's intrinsic functional architecture and the content and form of self-generated thoughts. PLoS One 9, e97176-e97176.Nolen-Hoeksema, S., Morrow, J., 1991. A Prospective Study of Depression and Posttraumatic Stress Symptoms After a Natural Disaster: The 1989 Loma Prieta Earthquake.Treynor, W., 2003. Rumination Reconsidered: A Psychometric Analysis.(Note: Part of the content of this post was adapted from the original NeuroImage paper)

Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was measured from 60,935 participants from the National Health and Nutrition Examination Survey (NHANES) and 15,700 participants from the South Korea NHANES (K-NHANES) databases. When the proportion of calories intake by protein increased by 10%, the prevalence of depression was significantly reduced both in the United States [Odds Ratio, OR (95% CI), 0.621 (0.530–0.728)] and South Korea [0.703 (0.397–0.994)]. An association between carbohydrate intake and the prevalence of depression was seen in the United States [1.194 (1.116–1.277)], but not in South Korea. Fat intake was not significantly associated with depression in either country. Subsequent analysis showed that the low protein intake groups had significantly higher risk for depression than the normal protein intake groups in both the United States [1.648 (1.179–2.304)] and South Korea [3.169 (1.598–6.286)]. In the daily diet of macronutrients, the proportion of protein intake is significantly associated with the prevalence of depression. These associations were more prominent in adults with insufficient protein intake, and the pattern of association between macronutrients and depression in Asian American and South Korean populations were similar. Our findings suggest that the proportion of macronutrients intake in everyday life may be related to the occurrence of depression.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,

######################### Medical scope, Prompt #1 61K instruction PubMED-Unlabeled ###################################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions withoutany extra comment. Input: As a doctor that only speak in JSON, provide a "answer", "context and "keywords" about following question: Is naturopathy as effective as conventional therapy for treatment of menopausal symptoms? Output:{"answer" :Naturopathy appears to be an effective alternative for relief of specific menopausal symptoms compared to conventional therapy.,"context" :Although the use of alternative medicine in the United States is increasing, no published studies have documented the effectiveness of naturopathy for treatment of menopausal symptoms compared to women receiving conventional therapy in the clinical setting."keywords" :['Anxiety', 'Cohort Studies', 'Confidence Intervals', 'Estrogen Replacement Therapy', 'Female', 'Health Status', 'Hot Flashes', 'Humans', 'Menopause', 'Middle Aged','Naturopathy', 'Odds Ratio', 'Patient Satisfaction', 'Retrospective Studies', 'Risk Factors', 'Severity of Illness Index', 'Sleep Initiation and Maintenance Disorders','Surveys and Questionnaires', 'Time Factors', 'Urinary Incontinence', 'Vaginal Diseases', 'Washington']} ############################### Medical scope, Prompt #2 symptom-disease dataset 1K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to theprovided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a doctor that only speak in JSON. Do not output that is'nt properly formated JSON. Provide following information about Panic disorder disease. Fill information about "symptom", "reason", "TestsAndProcedures" and "commonMedications" about Panic disorder disease in JSON format. Output: {"symptom" :['Anxiety and nervousness', 'Depression', 'Shortness of breath', 'Depressive or psychotic symptoms', 'Sharp chest pain', 'Dizziness', 'Insomnia', 'Abnormal involuntary movements', 'Chest tightness', 'Palpitations', 'Irregular heartbeat', 'Breathing fast'],"reason" :Panic disorder is an anxiety disorder characterized by recurring severe panic attacks. It may also include significant behavioral changes lasting at least a month and of ongoing worry about the implications or concern about having other attacks. The latter are called anticipatory attacks (DSM-IVR). Panic disorder is not the same as agoraphobia (fear of public places), although many afflicted with panic disorder also suffer from agoraphobia. Panic attacks cannot be predicted, therefore an individual may become stressed, anxious or worried wondering when the next panic attack will occur. Panic disorder may be differentiated as a medical condition, or chemical imbalance. The DSM-IV-TR describes panic disorder and anxiety differently. Whereas anxiety is preceded by chronic stressors which build to reactions of moderate intensity that can last for days, weeks or months, panic attacks are acute events triggered by a sudden, out-of-the-blue cause: duration is short and symptoms are more intense. Panic attacks can occur in children, as well as adults. Panic in young people may be particularly distressing because children tend to have less insight about what is happening, and parents are also likely to experience distress when attacks occur. "TestsAndProcedures" : ['Psychotherapy', 'Mental health counseling', 'Electrocardiogram', 'Depression screen (Depression screening)', 'Toxicology screen', 'Psychological and psychiatric evaluation and therapy']"commonMedications" : ['Lorazepam', 'Alprazolam (Xanax)', 'Clonazepam', 'Paroxetine (Paxil)', 'Venlafaxine (Effexor)', 'Mirtazapine', 'Buspirone (Buspar)', 'Fluvoxamine (Luvox)', 'Imipramine', 'Desvenlafaxine (Pristiq)', 'Clomipramine', 'Acamprosate (Campral)']} ############################### Medical scope, Prompt #3 194K instruction MED-MCQA dataset ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a medical teacher that only speak in JSON. Do not output that is'nt properly formated JSON. Generate a multichoice question and answer inAnatomy field and Urinary tract topic with following format: { "question": "expresion" , "OPA": "expresion", "OPB" : "expresion", "OPC": "expresion","OPD": "expresion", "Answer": "3"}. Output: { "question": Chronic urethral obstruction due to benign prismatic hyperplasia can lead to the following change in kidney parenchyma, "opa": Hyperplasia, "opb": Hyperophy, "opc": Atrophy, "opd": Dyplasia, "Answer": 2. ############################### Finance scope, Prompt #4 38K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a financial news analyzer that only speaks in JSON. Do not output that isn't properly formatted JSON. Analyze this news title "Here Why Bitcoin is Likely to See Historic Volatility in the Next Few Days". Provide sentiment as a probability distribution also indicate target market and related asset to the news title in JSON format. Output:{"sentiment": {"positive": 0.4463904500007629, "negative": 0.06607405841350555,"neutral":0.4875355064868927}, "market": "cryptocurrency" , "relatedAsset": ['BTC/USDT']}.

This study fits into the field of behavioural medicine which tries to better understand the mind-body connection. That is, why does what we think and feel affect our bodies in ways which can cause us to become unwell? Quantitative research has repeatedly shown that depression is associated with the onset and progression of multiple different physical illnesses. However, we are still trying to understand why this is the case. We propose that the experience of depression in the physically ill may partly explain this, however this has yet to be addressed in previous research. Qualitative methods involve asking participants about their experiences of living with disease. In this study we propose to ask persons with 4 different diagnoses (depression only, depression comorbid with: coronary heart disease, arthritis or type 2 diabetes) in order to look for similarities and differences in individual experiences. We are particularly interested to know whether the symptoms of depression present themselves differently across the different physical illness groups and the timeline and course of depressive symptoms in relation to an individual’s physical illness symptoms. Such questions are possible to answer using quantitative methods using sophisticated statistical techniques, however such approaches strip away the context of the diagnosis which might help researchers to understand the finer details of this important issue. Three physical illness groups have been selected since they are all prevalent in the UK primary care setting and have all been associated with depression in cross-sectional and prospective analyses. These illness are: arthritis, coronary heart disease and type 2 diabetes. These three diseases have also been selected since they have all been shown to have involve inflammatory processes, which is one hypothesised mechanism linking depression to physical illness. Importantly each of the three diseases manifest themselves with different symptoms, physical limitations and treatment regimes, making cross-group comparisons possible. Using face-to-face interviews of up to 60 primary care patients we aim to better delineate the similarities and differences in the experience of depression between those patients with a psychiatric diagnosis but who are otherwise physically healthy in comparison to those with depression and a comorbid physical illness. This research will help us to better understand the experience of depression in physical illness, helping to inform studies on the early identification and treatment of depression in primary care.The purpose of this research is to understand more about biosocial pathways in health by studying depression symptoms and how they relate to physical illnesses such as diabetes, heart disease and cancer. We already know that people suffering from these diseases are more likely to experience symptoms of depression than those without them. We also know that people who experience depression symptoms are more likely to develop a physical illness later in life. However, as yet, we are not sure why depression symptoms and physical illnesses are related in these ways. I am particularly interested in the biological pathways linking depression symptoms and physical illnesses. These pathways include things like how our bodies respond to stress and how well our immune system works. For example, I am interested in a substance called cortisol which is released by the body when we feel stressed or sad. I am also interested in part of the immune system which is responsible for levels of inflammation. Research has shown that cortisol and inflammation do not work as well as they should in people who have depression symptoms or a physical illness. Therefore, I am interested in finding out whether changes in these things can explain the link between depression symptoms and physical illness morbidity in people who suffer from a variety of different physical illnesses. My research fits in well with the ESRC's priorities for this award: biosocial research and secondary analyses of longitudinal data. I am proposing to conduct biosocial research since I am planning to study the biology of a problem that society is facing. In addition, I intend to use longitudinal data that has already been collected, but has not yet been used to answer the questions I am interested in. I will use two main methods to analyse my data: quantitative analyses of existing data and qualitative analyses of a new study. I will use data from studies such as Whitehall II, the English Longitudinal Study of Ageing (ELSA), and Midlife in the United States study (MIDUS) among others. Using these datasets will allow me to partly answer my questions using statistical analyses. In addition, I will conduct a qualitative study in order to speak to individuals living with either a mental or physical illness about their experiences of depression symptoms. This will enable me to explore how people think, feel and cope with their illnesses and their mood. This research is important for a number of reasons. First of all, research has shown that people who have depression symptoms and a physical illness are likely to experience more symptoms of their physical illness than those without depression symptoms. In other words, they are more likely to feel sicker than those without depression symptoms. This links to the second important reason. If we understand why people with physical illness also get depression symptoms then we can improve our treatment of these individuals. At the moment, our current treatment options are not always very effective. So, not only do these people suffer more symptoms of their illness, their depression symptoms do not always go away with treatment. If we can improve treatment, then we can reduce suffering. Another reason that this research is important is the scale of the problem. Currently a lot of people with a physical illness also experience depression symptoms. Sadly, research has shown that a lot of these patients do not get identified by doctors as needing extra help. I plan to raise awareness of this issue during the course of my fellowship by ensuring I reach out to policymakers, health professionals and the public. Face to face qualitative interviews with primary care patients with depression.

BackgroundDepression is a common mental disorder and the diagnosis is still based on the descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis, prognosis, and treatment. In recent years, many biomarkers relevant to the mechanisms of depression have been identified. This study uses bibliometric methods and visualization tools to analyse the literature on depression biomarkers and its hot topics, and research frontiers to provide references for future research.MethodsScientific publications related to depression biomarkers published between 2009 and 2022 were obtained from the Web of Science database. The BICOMB software was used to extract high-frequency keywords and to construct binary word-document and co-word matrices. gCLUTO was used for bicluster and visual analyses of high-frequency keywords. Further graphical visualizations were generated using R, CiteSpace and VOSviewer software.ResultsA total of 14,403 articles related to depression biomarkers were identified. The United States (34.81%) and China (15.68%), which together account for more than half of all publications, can be considered the research base for the field. Among institutions, the University of California, University of London, and Harvard University are among the top in terms of publication number. Three authors (Maes M, Penninx B.W.J.H., and Berk M) emerged as eminent researchers in the field. Finally, eight research hotspots for depression biomarkers were identified using reference co-citation analysis.ConclusionThis study used bibliometric methods to characterize the body of literature and subject knowledge in the field of depression biomarker research. Among the core biomarkers of depression, functional magnetic resonance imaging (fMRI), cytokines, and oxidative stress are relatively well established; however, research on machine learning, metabolomics, and microRNAs holds potential for future development. We found “microRNAs” and “gut microbiota” to be the most recent burst terms in the study of depression biomarkers and the likely frontiers of future research.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/." This table displays the proportion of adults who were ever told they had a depressive disorder in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This indicator is based on the question: "“Has a doctor, nurse or other health professional EVER told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

This dataset contains health outcome (depressive symptoms defined by CES-D 10), neighborhood greenery (percent tree cover within 500m and 2000m from residences), historical HOLC grades, and sociodemographic factors (age, race/ethnicity, marital status, education, employment status, income, use of depression medication) for 3555 Sister Study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Please submit data request through https://sisterstudy.niehs.nih.gov/English/coll-data.htm. Format: The Sister Study data are released in SAS format. This dataset is associated with the following publication: Tsai, W., M. Nash, D. Rosenbaum, S. Prince, A. D'Aloisio, M. Mehaffey, D. Sandler, T. Buckley, and A. Neale. Association of Redlining and Natural Environment with Depressive Symptoms in Women in the Sister Study. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 131(10): 107009, (2022).

BackgroundData on Long COVID and its associations with burnout, anxiety and depression among healthcare workers (HCW) in the United States (U. S.) is limited.MethodsThis study utilized cross-sectional data from the final survey conducted in July 2023, which was part of a longitudinal cohort study assessing COVID-19-related burnout and wellbeing among healthcare workers (HCWs) in a large tertiary academic healthcare system in the Chicago area. The survey included questions on self-reported Long COVID status, as well as the Oldenburg Burnout Inventory (OLBI) to measure burnout and the Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CAT) to assess anxiety and depression. A total of 1,979 HCWs participated in the survey, yielding a response rate of 56.1%.ResultsThe analysis included 1,678 respondents with complete data, of whom 1,171 (70%) self-reported having had COVID-19. Of these, 90 (7.7%) reported Long COVID, with 53% indicating that their most bothersome symptoms persisted for more than 6 months, while 50% reported no longer experiencing those symptoms at the time of the survey. Multivariable linear regression analyses revealed that Long COVID was significantly associated with higher OLBI scores (β = 2.20, p = 0.004), PROMIS anxiety scores (β = 2.64, p = 0.001) and PROMIS depression scores (β = 1.98, p = 0.011) compared to those who had COVID-19 but not Long COVID. Similar patterns of associations were observed when comparing the Long COVID group to those who never had COVID-19. No significant differences were found between those who never had COVID-19 and those who had COVID-19 without developing Long COVID.ConclusionLong COVID was associated with higher levels of burnout, depression, and anxiety among healthcare workers compared to those who had COVID-19 alone or were never infected, despite its lower prevalence during the endemic phase. These findings underscore the need for continued prevention efforts and targeted support strategies in healthcare settings.