Graduate Education in Agricultural Communication: The Need and Role

Is there a meed for graduate studies in agricultural communication

Mapping Wind Direction with HF Radar

The article of record as published may be found at https://www.jstor.org/stable/43924806Office of Naval ResearchH.C. Graber acknowledges the sup- port by the Office of Naval Research through grant N00014-94-1-1016 (DUCK94)

Retesting personality in employee selection: Implications of the context, sample, and setting

The present study sought to assess when and how actual job applicants change their responses when filling out an unproctored personality selection assessment for a second time. It was predicted feedback would be a key contextual motivator associated with how much applicants change their answers during the second administration. Mediation results showed that individuals receiving feedback that showed a low score on the personality assessment was the reason they did not get the job were more likely to employ faking response strategies in the second testing session, predicting the highest change in scores between the first and second testing sessions. Individuals receiving no feedback and those not experimentally motivated to fake (i.e., a comparison group of students) showed less change in responses across administrations. © Psychological Reports 2013

Biostratigraphy of Middle and Late Pennsylvanian (Desmoinesian-Virgilian) ammonoids

New stratigraphic ranges for genera of Desmoinesian-Virgilian ammonoids are presented, based on analysis of 40,000 specimens collected from over 70 ammonoid-bearing horizons that represent at least 40 successive stratigraphic levels in the North American midcontinent. These range revisions indicate that current generic-level ammonoid zonations are inadequate, especially for correlation of Pennsylvanian series and stage boundaries. Six high-confidence, largely generic-level first-occurrence zones are proposed for the Desmoinesian through Virgilian stages: Wellerites Zone, Eothalassoceras Zone, Pennoceras Zone, Preshumardites Zone, Pseudaktubites Zone, and Shumardites Zone. Fifteen zones of lesser confidence for correlation are also suggested. The Shumarditidae Plummer & Scott, 1937, is emended to include Preshumardites Plummer & Scott, 1937, Pseudaktubites gen. nov. (type species, Preshumardites stainbrooki Plummer & Scott, 1937), and Shumardites Smith, 1903. Early Permian (Sakmarian) species previously assigned to Preshumardites are reassigned to Andrianovia gen. nov. (type species ?Preshumardites sakmarae Ruzhencev, 1938). Aktubites Ruzhencev, 1955, Eoshumardites Popov, 1960, and Parashumardites Ruzhencev, 1939, previously included in the Shumarditidae, are assigned to the new family Parashumarditidae. Eovidrioceras inexpectans gen. nov., sp. nov. is included and is interpreted as the ancestor of the cyclobacean family Vidrioceratidae Plummer & Scott, 1937. The base of the revised Wellerites Zone, defined by the first occurrence of the nominate genus, approximates but does not coincide with the Atokan-Desmoinesian boundary. Recorrelation of the stratigraphic level of the Collinsville, Oklahoma, ammonoid locality from the "Seminole Formation" (basal Missourian) to the Holdenville Formation (upper Desmoinesian), based on lithostratigraphic evidence, effectively places the first occurrence of Eothalassoceras in the upper Desmoinesian. Because Wellerites apparently became extinct before the end of the Desmoinesian, the revised Eothalassoceras Zone is used to represent the upper Desmoinesian. The Middle-Upper Pennsylvanian boundary (Desmoinesian-Missourian boundary) can be recognized by the appearance of Pennoceras, which defines the base of the new Pennoceras Zone. The Pennoceras Zone is an excellent indicator of lower Missourian strata in the northern midcontinent, north-central Texas, the Marathon Uplift, and the Appalachian Basin. The new Preshumardites Zone occupies most of the upper part of the Missourian Stage. The appearance of the ancestral shumarditid Pseudaktubites, which defines the base of the new Pseudaktubites Zone, occurs one cycle below the Missourian-Virgilian boundary, which is currently recognized at the top of the South Bend Limestone Member in eastern Kansas. No recognizable biostratigraphic event coincides with the South Bend Member, thereby resulting in an uncorrelatable chronostratigraphic boundary. The largest changeover in ammonoid faunas takes place at the base of strata containing the upper part of the Pseudaktubites Zone (Pseudaktubites stainbrooki Subzone). The base of the Pseudaktubites stainbrooki Subzone is stratigraphically near the original Missourian-Virgilian boundary. It is recommended that the stratigraphic level containing the base of the Pseudaktubites stainbrooki Subzone be adopted as the official base of the Virgilian Stage. Recognition of the upper subzone of the Pseudaktubites Zone (Pseudaktubites stainbrooki Subzone) within the Colony Creek Shale Member in north-central Texas places the base of the Virgilian within the upper part of the Canyon Group and substantially below the current position at the Canyon-Cisco group boundary. Shumardites, a taxon previously used to mark the base of the Virgilian Stage, appears in early middle Virgilian strata; consequently, the revised Shumardites Zone represents the middle-upper Virgilian interval

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management