16.精子形成のホルモン支配(第669回千葉医学会例会・第38回千葉泌尿器科集談会)

Understanding the temporal and spatial variability in a crop yield is viewed as one of the key steps in the implementation of precision agriculture practices. Therefore, a study on a center pivot irrigated 23.5 ha field in Saudi Arabia was conducted to assess the variability in alfalfa yield using Landsat-8 imagery and a hay yield monitor data. In addition, the study was designed to also explore the potential of predicting the alfalfa yield using vegetation indices. A calibrated yield monitor mounted on a large rectangular hay baler was used to measure the actual alfalfa yield for four alfalfa harvests performed in the period from October 2013 to May 2014. A total of 18 Landsat-8 images, representing different crop growth stages, were used to derive different vegetation indices (VIs). Data from the yield monitor was used to generate yield maps, which illustrated a definite spatial variation in alfalfa yield across the experimental field for the four studied harvests as indicated by the high spatial correlation values (0.75 to 0.97) and the low P-values (4.7E-103 to 8.9E-27). The yield monitor-measured alfalfa actual yield was compared to the predicted yield form the Vis. Results of the study showed that there was a correlation between actual and predicted yield. The highest correlations were observed between actual yield and the predicted using NIR reflectance, SAVI and NDVI with maximum correlation coefficients of 0.69, 0.68 and 0.63, respectively

Advertising to Early Trend Propagators: Evidence from Twitter

In the digital economy, influencing and controlling the spread of information is a key concern for firms. One way firms try to achieve this is to target firm communications to consumers who embrace and propagate the spread of new information on emerging and `trending' topics on social media. However, little is known about whether early trend propagators are indeed responsive to firm-sponsored messages. To explore whether early propagators of trending topics respond to advertising messages, we use data from two field tests conducted by a charity and an emerging fashion firm on the micro-blogging service Twitter. On Twitter, 'promoted tweets' allow advertisers to target individuals based on the content of their recent postings. Twitter continuously identifies in real time which topics are newly popular among Twitter users. In the field tests, we collaborated with a charity and a fashion firm to target ads at consumers who embraced a Twitter trend early in its life-cycle by posting about it, and compared their behavior to that of consumers who posted about the same topic only later on. Throughout both field tests, we consistently find that early propagators of trends are less responsive to advertising than consumers who embrace trends later

Psychopathology in never-treated schizophrenia

Abstract The effect of drug treatment and its adverse effects confound studies on symptoms and associated factors in schizophrenia. Knowledge of psychopathology in the untreated state would identify the natural state of the illness and is relevant to understand pathology underlying the illness. We report here symptoms of schizophrenia as measured by Positive and Negative Syndrome Scale in 143 patients with schizophrenia living in the community never treated with antipsychotic drugs. Positive symptoms were more frequent than negative ones. Negative subscale scores correlated negatively with positive subscale scores and positively with general psychopathology subscale scores. Age correlated negatively with negative and general psychopathology subscale scores independent of duration of illness. Duration of illness and the proportion of life spent in psychosis did not correlate with any Positive and Negative Syndrome Scale scores. The factors (negative, positive, anxiety-depression, motor, and excitement) extracted by a forced 5-factor analysis explained 56% of variance. This factor structure resembled that of treated patients reported in most studies except for the identification of a motor symptom cluster. Psychopathology in the never-treated schizophrenia varied in some aspects from descriptions in the treated state. The differences can be said to demarcate the natural features of the illness from medication effects on the relationship of symptoms with one another and to sex, age, duration of illness, and age at onset.

Several methods applied to measuring residual stress in a known specimen

In this study, a beam with a precisely known residual stress distribution provided a unique experimental opportunity. A plastically bent beam was carefully prepared in order to provide a specimen with a known residual stress profile. 21Cr-6Ni-9Mn austenitic stainless steel was obtained as 43 mm square forged stock. Several methods were used to determine the residual stresses, and the results were compared to the known values. Some subtleties of applying the various methods were exposed

Methods of electroencephalographic signal analysis for detection of small hidden changes

The aim of this study was to select and evaluate methods sensitive to reveal small hidden changes in the electroencephalographic (EEG) signal. Two original methods were considered

Collaborative community based care for people and their families living with schizophrenia in India: protocol for a randomised controlled trial

BACKGROUND: There is a large treatment gap with few community services for people with schizophrenia in low income countries largely due to the shortage of specialist mental healthcare human resources. Community based rehabilitation (CBR), involving lay health workers, has been shown to be feasible, acceptable and more effective than routine care for people with schizophrenia in observational studies. The aim of this study is to evaluate whether a lay health worker led, Collaborative Community Based Care (CCBC) intervention, combined with usual Facility Based Care (FBC), is superior to FBC alone in improving outcomes for people with schizophrenia and their caregivers in India. METHODS/DESIGN: This trial is a multi-site, parallel group randomised controlled trial design in India.The trial will be conducted concurrently at three sites in India where persons with schizophrenia will be screened for eligibility and recruited after providing informed consent. Trial participants will be randomly allocated in a 2:1 ratio to the CCBC+FBC and FBC arms respectively using an allocation sequence pre-prepared through the use of permuted blocks, stratified within site. The structured CCBC intervention will be delivered by trained lay community health workers (CHWs) working together with the treating Psychiatrist. We aim to recruit 282 persons with schizophrenia. The primary outcomes are reduction in severity of symptoms of schizophrenia and disability at 12 months. The study will be conducted according to good ethical practice, data analysis and reporting guidelines. DISCUSSION: If the additional CCBC intervention delivered by front line CHWs is demonstrated to be effective and cost-effective in comparison to usually available care, this intervention can be scaled up to expand coverage and improve outcomes for persons with schizophrenia and their caregivers in low income countries. TRIAL REGISTRATION: The trial is registered with the International Society for the Registration of Clinical Trials and the allocated unique ID number is ISRCTN 56877013

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development