Differential Modulation of Endophytic Microbiome of Ginger in the Presence of Beneficial Organisms, Pathogens and Both as Identified by DGGE Analysis

Abstract

Endophytic microorganisms play a significant role in plants response to beneficial organisms and pathogens. In the current study, endophytic microorganisms from Zingiber officinale were screened for in vitro inhibition against Pythium myriotylum. From this, Burkholderia vietnamiensis ZoB74 was selected as an organism with remarkable antifungal effect. Further, the study focussed on analysis of in vivo changes in endophytic bacterial community of Z. officinale in presence of selected organisms and the pathogen P. myriotylum by PCR-DGGE. 16S rDNA sequencing of bacterial community after DGGE has resulted in the identification of a group of uncultured bacteria as the predominant microbial community of rhizome under various conditions of treatment. High frequency dominance of these endophytic bacteria suggests their role in disease resistance to soft rot in ginger. This also revealed the variation of endophytic microbiome of Z. officinale under biotic stress. Hence the study provides molecular insight into uncultured microbiome and its stress-inducible variation in ginger rhizome.

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Comparison of Ice Hockey Goaltender Helmets for Concussion Type Impacts

Abstract

Concussions are among the most common injuries sustained by ice hockey goaltenders and can result from collisions, falls and puck impacts. However, ice hockey goaltender helmet certification standards solely involve drop tests to a rigid surface. This study examined how the design characteristics of different ice hockey goaltender helmets affect head kinematics and brain strain for the three most common impact events associated with concussion for goaltenders. A NOCSAE headform was impacted under conditions representing falls, puck impacts and shoulder collisions while wearing three different types of ice hockey goaltender helmet models. Resulting linear and rotational acceleration as well as maximum principal strain were measured for each impact condition. The results indicate that a thick liner and stiff shell material are desirable design characteristics for falls and puck impacts to reduce head kinematic and brain tissue responses. However for collisions, the shoulder being more compliant than the materials of the helmet causes insufficient compression of the helmet materials and minimizing any potential performance differences. This suggests that current ice hockey goaltender helmets can be optimized for protection against falls and puck impacts. However, given collisions are the leading cause of concussion for ice hockey goaltenders and the tested helmets provided little to no protection, a clear opportunity exists to design new goaltender helmets which can better protect ice hockey goaltenders from collisions.

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Reply to “Pain Control After Bariatric Surgery: We Still Need More Answers”

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Ethical Responsibilities of the Authors

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EEG Indices in Children with Primary Headache Disorders

Both episodic migraine and tension-type headache (TTH) are characterized by attacks of strong headache. There is growing evidence that the brain functions are abnormal even within between-attack periods. We tried to evaluate interictal brain functions in children with primary headache using quantitative EEG. Sixty patients were recruited, 25 children with migraine and 35 with TTH. Patients were classified according to the ICHD. Migraine patients had significantly higher relative spectral powers of slow-wave bands (theta and delta) and decreased alpha relative powers in most of the regions examined than those in TTH patients. Apart from the above-mentioned specificities and negative correlation of age with the powers of delta and theta bands in both groups, we could not find other clinical variables that appeared to significantly affect EEG indices. Our results support that interictal brain dysfunction is, in general, more typical of migraine patients. These results shall help to contribute to better management and brain protection in children with headache.

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Prediction and Experimental Validation of Power Consumption of Skid-Steer Mobile Robots in Manufacturing Environments

Abstract

Skid-Steer Mobile Robots (SSMRs) provide a robust and simple mechanical drive platform making them useful in many applications. Power consumption is an important consideration in the design of any mobile robot and particularly important for SSMRs because of the slipping and corresponding friction that induce large loads on the drive system. The slipping behavior is generally characterized through Instantaneous Centers of Rotation (ICR) of the contact patches and it has been established that these are functions of the system dynamics. However, the existing SSMR power models generally treat these constraints at the kinematic level by assuming constant slip rates taken from empirical data. This paper demonstrates a method to evaluate SSMR power consumption based on slip parameters that are calculated as differential equations extracted from the equations of motion. The dynamic power model is validated and then implemented on two practical manufacturing applications in which a mobile robot is climbing on steel surfaces with primary power consumption due to turning and overcoming gravity. The applications show that the dynamic ICR model plays a significant role in estimating power requirements. The results further demonstrate that the power and energy requirements for a given task depend on the payload and motion along the task in a non-linear fashion, for example showing that the minimum payload does not necessarily correspond to the minimum energy use. This outcome suggests that dynamic effects can be used to find optimal trajectories to minimize power or energy requirements.

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Information Support System for Patients with Gestational Diabetes Mellitus

We describe here the development of a telemedical system for monitoring diabetes mellitus and providing information to support patients with gestational diabetes mellitus (GDM). Recommendations for preventing postpran-dial glycemia were developed using prognostic models based on experimental data obtained from individual electronic diaries. During testing of a series of models for patients with GDM, the best results were obtained by regression modeling using rules with added instance-based corrections.

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