Decision Tree for Measuring the Interaction of Hyper-Saline Lake and Coastal Aquifer in Lake Urmia

© 2015 ASCE.Lake Urmia is located in the North West of Iran. The hyper saline lake is drying up very fast and more than seventy percent of the water in the lake has vanished in recent years. In this research, the West and South banks of the lake's basin which is known as the West Azerbaijan province of Iran are studied. During the period from March 2001 to August 2011, six pilot stations for ground water near the lake shore were monitored. Correlation, cross-correlation, distribution, and regression analysis were done for lake and pilot stations. Several decision trees were fitted to the model and the most proper one was selected to test the hypothesis. Results show that the North West of the basin is the most interactive part of the ground water and the fitted decision tree model with randomly selected data is performing well

The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of rule acquisition and reversal in head-fixed mice

Contains fulltext : 201816.pdf (publisher's version ) (Open Access)Behavioural flexibility is an essential survival skill, yet our understanding of its neuronal substrates is still limited. While mouse research offers unique tools to dissect the neuronal circuits involved, the measurement of flexible behaviour in mice often suffers from long training times, poor experimental control, and temporally imprecise binary (hit/miss) performance readouts. Here we present a virtual-environment task for mice that tackles these limitations. It offers fast training of vision-based rule reversals (~100 trials per reversal) with full stimulus control and continuous behavioural readouts. By generating multiple non-binary performance metrics per trial, it provides single-trial estimates not only of response accuracy and speed, but also of underlying processes like choice certainty and alertness (discussed in detail in a companion paper). Based on these metrics, we show that mice can predict new task rules long before they are able to execute them, and that this delay varies across animals. We also provide and validate single-trial estimates of whether an error was committed with or without awareness of the task rule. By tracking in unprecedented detail the cognitive dynamics underlying flexible behaviour, this task enables new investigations into the neuronal interactions that shape behavioural flexibility moment by moment.24 p

The Virtual-Environment-Foraging Task enables rapid training and single-trial metrics of attention in head-fixed mice

Contains fulltext : 198064.pdf (publisher's version ) (Open Access)Attention - the flexible allocation of processing resources based on behavioural demands - is essential to survival. Mouse research offers unique tools to dissect the underlying pathways, but is hampered by the difficulty of accurately measuring attention in mice. Current attention tasks for mice face several limitations: Binary (hit/miss), temporally imprecise metrics, behavioural confounds and overtraining. Thus, despite the increasing scope of neuronal population measurements, insights are limited without equally precise behavioural measures. Here we present a virtual-environment task for head-fixed mice based on 'foraging-like' navigation. The task requires animals to discriminate gratings at orientation differences from 90° to 5°, and can be learned in only 3-5 sessions (<550 trials). It yields single-trial, non-binary metrics of response speed and accuracy, which generate secondary metrics of choice certainty, visual acuity, and most importantly, of sustained and cued attention - two attentional components studied extensively in humans. This allows us to examine single-trial dynamics of attention in mice, independently of confounds like rule learning. With this approach, we show that C57/BL6 mice have better visual acuity than previously measured, that they rhythmically alternate between states of high and low alertness, and that they can be prompted to adopt different performance strategies using minute changes in reward contingencies.26 p

Differential microRNA expression in cultured palatal fibroblasts from infants with cleft palate and controls

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A Comprehensive Assessment of 68Ga-PSMA-11 PET in Biochemically Recurrent Prostate Cancer: Results from a Prospective Multicenter Study on 2,005 Patients.

We prospectively investigated the performance of the prostate-specific membrane antigen (PSMA) ligand 68Ga-PSMA-11 for detecting prostate adenocarcinoma in patients with elevated levels of prostate-specific antigen (PSA) after initial therapy. Methods: 68Ga-PSMA-11 hybrid PET was performed on 2,005 patients at the time of biochemically recurrent prostate cancer after radical prostatectomy (RP) (50.8%), definitive radiation therapy (RT) (19.7%), or RP with postoperative RT (PORT) (29.6%). The presence of prostate cancer was assessed qualitatively (detection rate = positivity rate) and quantitatively on a per-patient and per-region basis, creating a disease burden estimate from the presence or absence of local (prostate/prostate bed), nodal (N1: pelvis), and distant metastatic (M1: distant soft tissue and bone) disease. The primary study endpoint was the positive predictive value (PPV) of 68Ga-PSMA-11 PET/CT confirmed by histopathology. Results: After RP, the scan detection rate increased significantly with rising PSA level (44.8% at PSA &lt; 0.25%-96.2% at PSA &gt; 10 ng/mL; P &lt; 0.001). The detection rate significantly increased with rising PSA level in each individual region, overall disease burden, prior androgen deprivation, clinical T-stage, and Gleason grading from the RP specimen (P &lt; 0.001). After RT, the detection rate for in-gland prostate recurrence was 64.0%, compared with 20.6% prostate bed recurrence after RP and 13.3% after PORT. PSMA-positive pelvic nodal disease was detected in 42.7% after RP, 40.8% after PORT, and 38.8% after RT. In patients with histopathologic validation, the PPV per patient was 0.82 (146/179). The SUVmax of histologically proven true-positive lesions was significantly higher than that of false-positive lesions (median, 11.0 [interquartile range, 6.3-22.2] vs. 5.1 [interquartile range, 2.2-7.4]; P &lt; 0.001). Conclusion: We confirmed a high PPV for 68Ga-PSMA-11 PET in biochemical recurrence and the PSA level as the main predictor of scan positivity