Grape training and pruning in Iowa

The Concord grape and grapes of this type and hardiness, which are the grapes most widely grown in Iowa, require yearly pruning if the grower wishes to obtain heavy annual yields of large-sized high-quality fruit. The illustration on the cover of this bulletin indicates the development and distribution of the fruit which may be expected on correctly pruned vines. Far too many home vineyards are left unpruned or are incorrectly pruned with the result that they are unproductive and unsightly. There are many systems used in training the American grape, but the system which seems to be best adapted to Iowa conditions is the single-stem four-cane Kniffin system. The spur method of pruning, which has been employed to a considerable extent with the European grape, is still followed in many sections of Iowa. Unfortunately, this method is unsatisfactory with the Concord grape and, except with grapes trained on arbors, should not be used. The grapevines pruned by the long cane method and trained to the Kniffin system in certain Iowa tests have repeatedly out-produced those pruned by the spur method. The Concord produces the heaviest yield of fruit from approximately the fifth to ninth bud and the lowest yield from the first four buds on the cane. Therefore, it can be seen that with the spur method of pruning, when only the first two buds are left on each cane, the heaviest producing buds are removed. The characteristic bearing habit of the American grape explains why long cane pruning is more productive than the spur cane system

Using continuous sensor data to formalize a model of in-home activity patterns

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients

Automated smart home assessment to support pain management: Multiple methods analysis

©Roschelle L Fritz, Marian Wilson, Gordana Dermody, Maureen Schmitter-Edgecombe, Diane J Cook. Objective: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.Background: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.Methods: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.Results: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P \u3c .001). The regression formulation achieved moderate correlation, with r=0.42.Conclusions: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance

A new vetulicolian from Australia and its bearing on the chordate affinities of an enigmatic Cambrian group

BACKGROUND: Vetulicolians are one of the most problematic and controversial Cambrian fossil groups, having been considered as arthropods, chordates, kinorhynchs, or their own phylum. Mounting evidence suggests that vetulicolians are deuterostomes, but affinities to crown-group phyla are unresolved. RESULTS: A new vetulicolian from the Emu Bay Shale Konservat-Lagerstätte, South Australia, Nesonektris aldridgei gen. et sp. nov., preserves an axial, rod-like structure in the posterior body region that resembles a notochord in its morphology and taphonomy, with notable similarity to early decay stages of the notochord of extant cephalochordates and vertebrates. Some of its features are also consistent with other structures, such as a gut or a coelomic cavity. CONCLUSIONS: Phylogenetic analyses resolve a monophyletic Vetulicolia as sister-group to tunicates (Urochordata) within crown Chordata, and this holds even if they are scored as unknown for all notochord characters. The hypothesis that the free-swimming vetulicolians are the nearest relatives of tunicates suggests that a perpetual free-living life cycle was primitive for tunicates. Characters of the common ancestor of Vetulicolia + Tunicata include distinct anterior and posterior body regions - the former being non-fusiform and used for filter feeding and the latter originally segmented - plus a terminal mouth, absence of pharyngeal bars, the notochord restricted to the posterior body region, and the gut extending to the end of the tail.Diego C García-Bellido, Michael S Y Lee, Gregory D Edgecombe, James B Jago, James G Gehling, and John R Paterso

Early Cambrian Arthropods from the Emu Bay Shale Lagerstatte, South Australia

© Instituto Geológico y Minero de España. The document attached has been archived with permission from the publisher.John R. Paterson, James B. Jago, James G. Gehling, Diego C. García-Bellido, Gregory D. Edgecombe and Michael S.Y. Le