Application of Data Analytics for Prediction of Suicide Rates at the State and National Levels

The increasing suicide rate in the United States has amplified the need to assure that regions with high suicide risk receive adequate funding for programs and related resources for prevention methods. The way in which organizations dedicated to preventing suicides distribute funding could be improved with the development of predictive models for suicide rates. In this study, a multiple linear regression model at a national level was developed to identify relevant factors associated with suicide. The national level model was developed in two phases; the first using response variable data and explanatory variable data from the same time period, and the second with the response variable data shifted one time period to create a more accurate model for prediction. The models had k-fold R-squared values of 0.676 and 0.675. The national model identified four variables to include in a predictive state level model: Foreclosure Rates, Violent Crime Rates, Gini ratio, and Consumption Volume. In the second part of this study, the use of Twitter data in a state level model was evaluated. Tweets terms relating to suicide were identified in fifteen states over a thirty-one-day period and used to calculate three variables: Tweet rate, Favorite rate, and Retweet rate. Each of these three variables for the terms “suicide” and “suicidal” underwent an Analysis of Variance test (ANOVA) to check for differences between states. Each ANOVA test resulted in a p-value less than 0.0001 providing strong evidence that there was a difference in Tweet rate, Favorite rate, and Retweet rate for the two search phrases analyzed among the states. Next, a Pearson Product-Moment correlation coefficient and Pearson Rho correlation coefficient were evaluated for each Twitter variable and the states’ historical suicide rates. All computed correlation coefficients were between -0.15 and 0.3 suggesting that there is, at best, a weak correlation between the Twitter variables and a state’s historical suicide rate. The results from the Twitter data analysis suggest that it is too early to accurately incorporate such data into a state level multiple linear regression model. The results of this study would help in further development of a state level model that allows organizations, dedicated to reducing suicides, to allocate related resources more efficiently

Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform

Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are driven by diverse processes across differing platforms. Thus, models for one platform are not necessarily applicable to others. Here, we study the temporal dynamics of Genius, a platform primarily designed for user-contributed annotations of song lyrics. A unique aspect of Genius is that the annotations are extremely local -- an annotated lyric may just be a few lines of a song -- but also highly related, e.g., by song, album, artist, or genre. We analyze several dynamical processes associated with lyric annotations and their edits, which differ substantially from models for other platforms. For example, expertise on song annotations follows a ``U shape'' where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior. We also find several contribution traits appearing early in a user's lifespan of contributions that distinguish (eventual) experts from non-experts. Combining our findings, we develop a model for early prediction of user expertise.Comment: 9 pages. 10 figure

Picture interpretation and Jungian typology

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaf 19).This study examines a Jungian picture interpretation schema, which can be used to analyze artistic creations in a therapeutic format. This proposed schema attaches significance to specific areas of a drawing. The upper left of a drawing is associated with the father; the lower left with the unconscious; the lower right with mother; and the upper right with the future. Although this Jungian quadrant schema is intriguing, and seeing it put to practice makes it appear valid, it leads an inquisitive mind to wonder many things. Three questions will serve as the foci of this study. First I will address the question of the Jungian schema's validity. I will attempt to find whether a positive association is present between particular quadrants of a drawing and the proposed meanings suggested by this schema. The second question is whether certain personality types within Jung's typology will respond better than others to the schema. Specifically, it is hypothesized that individuals with an introverted, intuitive, feeling typology will perform better than those with other typologies. The third question I will address is whether the schema works best when it elicits a strong affective response from the person who created the picture. It is the hypothesis of this study that individuals with high levels of emotional arousal in response to a picture will tend to perform better than those with little or no emotional response in validating the proposed schema. Two hundred and seven introductory psychology students participated in the study. There were two tests a subject could take: a space domain test, or a color associative test. In both tests, subjects took the Personal Preferences Self Description Questionnaire (PPSDQ), made drawings, and filled out the Self Assessment Manikin, a measure of affective response. Means analyses and t-tests were performed. Analysis revealed only two significant effects: a positive correlation with the unconscious quadrant and a negative correlation with the mother quadrant. The remaining analyses revealed no significant effects. These results suggest that this Jungian schema used to interpret drawings may be invalid and of little practical utility

Effects of interparticle friction on the response of 3D cyclically compressed granular material

We numerically study the effect of inter-particle friction coefficient on the response to cyclical pure shear of spherical particles in three dimensions. We focus on the rotations and translations of grains and look at the spatial distribution of these displacements as well as their probability distribution functions. We find that with increasing friction, the shear band becomes thinner and more pronounced. At low friction, the amplitude of particle rotations is homogeneously distributed in the system and is therefore mostly independent from both the affine and non-affine particle translations. In contrast, at high friction, the rotations are strongly localized in the shear zone. This work shows the importance of studying the effects of inter-particle friction on the response of granular materials to cyclic forcing, both for a better understanding of how rotations correlate to translations in sheared granular systems, and due to the relevance of cyclic forcing for most real-world applications in planetary science and industry

Experimentally measuring rolling and sliding in three-dimensional dense granular packings

We experimentally measure a three-dimensional (3D) granular system's reversibility under cyclic compression. We image the grains using a refractive-index-matched fluid, then analyze the images using the artificial intelligence of variational autoencoders. These techniques allow us to track all the grains' translations and 3D rotations with accuracy sufficient to infer sliding and rolling displacements. Our observations reveal unique roles played by 3D rotational motions in granular flows. We find that rotations and contact-point motion dominate the dynamics in the bulk, far from the perturbation's source. Furthermore, we determine that 3D rotations are irreversible under cyclic compression. Consequently, contact-point sliding, which is dissipative, accumulates throughout the cycle. Using numerical simulations whose accuracy our experiment supports, we discover that much of the dissipation occurs in the bulk, where grains rotate more than they translate. Our observations suggest that the analysis of 3D rotations is needed for understanding granular materials' unique and powerful ability to absorb and dissipate energy

A 14,100 cal B. P. Rocky Mountain locust cache from Winnemucca Lake, Pershing County, Nevada

The remains of approximately 1000 (MNI) Rocky Mountain locusts (Melanoplus spretus) from an archaeological cache pit in Crypt Cave, Winnemucca (dry) Lake, Nevada, date to between 14,305–14,067 calendar years before present (95.4 % confidence; 12,238 ± 18 14C yrs. B.P.). The age of this western Great Basin occupation along the shoreline of Lake Lahontan is consistent with occupation of several other Western North American terminal Pleistocene sites dating prior to 14,000 cal. B.P., including distinctive petroglyphs on the western shore of Winnemucca Lake dating as early as 14,800–13,200 cal. B.P

Insertion sequence content reflects genome plasticity in strains of the root nodule actinobacterium Frankia

<p>Abstract</p> <p>Background</p> <p>Genome analysis of three <it>Frankia sp. </it>strains has revealed a high number of transposable elements in two of the strains. Twelve out of the 20 major families of bacterial Insertion Sequence (IS) elements are represented in the 148 annotated transposases of <it>Frankia </it>strain HFPCcI3 (CcI3) comprising 3% of its total coding sequences (CDS). EAN1pec (EAN) has 183 transposase ORFs from 13 IS families comprising 2.2% of its CDS. Strain ACN14a (ACN) differs significantly from the other strains with only 33 transposase ORFs (0.5% of the total CDS) from 9 IS families.</p> <p>Results</p> <p>Insertion sequences in the <it>Frankia </it>genomes were analyzed using BLAST searches, PHYML phylogenies and the IRF (Inverted Repeat Finder) algorithms. To identify putative or decaying IS elements, a PSI-TBLASTN search was performed on all three genomes, identifying 36%, 39% and 12% additional putative transposase ORFs than originally annotated in strains CcI3, EAN and ACN, respectively. The distribution of transposase ORFs in each strain was then analysed using a sliding window, revealing significant clustering of elements in regions of the EAN and CcI3 genomes. Lastly the three genomes were aligned with the MAUVE multiple genome alignment tool, revealing several Large Chromosome Rearrangement (LCR) events; many of which correlate to transposase clusters.</p> <p>Conclusion</p> <p>Analysis of transposase ORFs in <it>Frankia </it>sp. revealed low inter-strain diversity of transposases, suggesting that the majority of transposase proliferation occurred without recent horizontal transfer of novel mobile elements from outside the genus. Exceptions to this include representatives from the IS3 family in strain EAN and seven IS4 transposases in all three strains that have a lower G+C content, suggesting recent horizontal transfer. The clustering of transposase ORFs near LCRs revealed a tendency for IS elements to be associated with regions of chromosome instability in the three strains. The results of this study suggest that IS elements may help drive chromosome differences in different <it>Frankia </it>sp. strains as they have adapted to a variety of hosts and environments.</p

Implications of the problem orientated medical record (POMR) for research using electronic GP databases: a comparison of the Doctors Independent Network Database (DIN) and the General Practice Research Database (GPRD).

Background The General Practice Research Database (GPRD) and Doctor's Independent Network Database (DIN), are large electronic primary care databases compiled in the UK during the 1990s. They provide a valuable resource for epidemiological and health services research. GPRD (based on VAMP) presents notes as a series of discrete episodes, whereas DIN is based on a system (MEDITEL) that used a Problem Orientated Medical Record (POMR) which links prescriptions to diagnostic problems. We have examined the implications for research of these different underlying philosophies. Methods Records of 40,183 children from 141 practices in DIN and 76,310 from 464 practices in GRPD who were followed to age 5 were used to compare the volume of recording of prescribing and diagnostic codes in the two databases. To assess the importance and additional value of the POMR within DIN, the appropriateness of diagnostic linking to skin emollient prescriptions was investigated. Results Variation between practices for both the number of days on which prescriptions were issued and diagnoses were recorded was marked in both databases. Mean number of "prescription days" during the first 5 years of life was similar in DIN (19.5) and in GPRD (19.8), but the average number of "diagnostic days" was lower in DIN (15.8) than in GPRD (22.9). Adjustment for linkage increased the average "diagnostic days" to 23.1 in DIN. 32.7% of emollient prescriptions in GPRD appeared with an eczema diagnosis on the same day compared to only 19.4% in DIN; however, 86.4% of prescriptions in DIN were linked to an earlier eczema diagnosis. More specifically 83% of emollient prescriptions appeared under a problem heading of eczema in the 121 practices that were using problem headings satisfactorily. Conclusion Prescribing records in DIN and GPRD are very similar, but the usage of diagnostic codes is more parsimonious in DIN because of its POMR structure. Period prevalence rates will be underestimated in DIN unless this structure is taken into account. The advantage of the POMR is that in 121 of 141 practices using problem headings as intended, most prescriptions can be linked to a problem heading providing a specific reason for their issue

Genomics Virtual Laboratory: a practical bioinformatics workbench for the cloud

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets ; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces ; highly available, scalable computational resources ; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise