The effect of wind on foraging activity of the tenebrionid beetle Lepidochora discoidalis in the sand dunes of the Namib Desert

The foraging activity of the tenebrionid beetle, Lepidochora discoidalis, was studied in the sand dunes of the Namib Desert. The surface activity of this beetle species was found to be correlated both with time of day and wind speed. Higher numbers were observed on the dune surface between 17:00-19:00 h when wind speeds were consistently higher than 9 m/s. Noise and vibrations in the dune sand were found to be highly dependent on wind speed. Wind blowing at speeds higher than 5 m/s lifts the surface sand grains and generates vibrations in the sand. The peak frequency of these vibrations is in the range of 700-1000 Hz. The vibrational amplitude at the peak frequency is on average 40 dB higher at those wind speeds when the beetles are active compared to lower wind speeds. The results indicate that wind is an important cue for these beetles and can be perceived by buried beetles through substrate vibrations.S. Afr. J Zool. 1997,32(4

A Pilot Randomised Control Trial of Digitally-Mediated Social Stories for Children on the Autism Spectrum

Social stories is a widely used intervention for children on the autism spectrum, particularly within an educational context. To date, systematic reviews and meta analyses of the research evaluating social stories has produced mixed results, often due to a lack of methodological rigour and variability in the development and delivery of the social stories. To address the gap in methodological rigour, a pilot Randomised Control Trial (RCT) was conducted, incorporating a social stories intervention group (n = 9 children on the autism spectrum) and an attentional control group who received a poem (n = 6 children on the autism spectrum) using a digital platform to address variability. Digitally-mediated social stories were found to be effective in producing beneficial changes in behaviour outcomes, which were sustained at a six-week follow up.</p

Randomized Clinical Trial of the Effectiveness of a Home-Based Advanced Practice Psychiatric Nurse Intervention: Outcomes for Individuals with Serious Mental Illness and HIV

Individuals with serious mental illness have greater risk for contracting HIV, multiple morbidities, and die 25 years younger than the general population. This high need and high cost subgroup face unique barriers to accessing required health care in the current health care system. The effectiveness of an advanced practice nurse model of care management was assessed in a four-year random controlled trial. Results are reported in this paper. In a four-year random controlled trial, a total of 238 community-dwelling individuals with HIV and serious mental illness (SMI) were randomly assigned to an intervention group (n=128) or to a control group (n=110). Over 12 months, the intervention group received care management from advanced practice psychiatric nurse, and the control group received usual care. The intervention group showed significant improvement in depression (P=.012) and the physical component of health-related quality of life (P=.03) from baseline to 12 months. The advanced practice psychiatric nurse intervention is a model of care that holds promise for a higher quality of care and outcomes for this vulnerable population

Behavior Classification Using Multi-site LFP and ECoG Signals

Abstract-Deep Brain Stimulation (DBS) is an effective therapy that alleviates the motor signs of Parkinson’s disease (PD). Existing DBS is open loop, providing a time invariant stimulation pulse train that may generate cognitive, speech, and balance side effects. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators. Furthermore, by customizing DBS to a patient’s behavioral goal, side effects of stimulation may arise only when they are non-detrimental to the patient’s current goals. Therefore, classification of human behavior using physiological signals is an important step in the design of the next generation of closed-loop DBS systems. Ten subjects who were undergoing DBS implantation were recruited for the study. DBS leads were used to record bilateral STN-LFP activity and an electrocorticography (ECoG) strip was used to record field potentials over left prefrontal cortex. Subjects were cued to perform voluntary behaviors including left and right hand movement, left and right arm movement, mouth movement, and speech. Two types of algorithms were used to classify the subjects’ behavior, support vector machine (SVM) using linear, polynomial, and RBF kernels as well as lp-norm multiple kernel learning (MKL). Behavioral classification was performed using only LFP channels, only ECoG channels, and both LFP and ECoG channels. Features were extracted from the time-frequency representation of the signals. Phase locking values (PLV) between ECoG and LFP channels were calculated to determine connectivity between sites and aid in feature selection. Classification performance improved when multi-site signals were used with either SVM or MKL algorithms. Our experiments further show that the lp-norm MKL outperforms single kernel SVM-based classifiers in classifying behavioral tasks. References [1] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “A multiple kernel learning approach for human behavioral task classification using STN-LFP signal,” EMBC, 38th IEEE International Conference on., pp.1030-1033, 2016. [2] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal,” To appear in ICASSP, 42nd IEEE International Conference on., 2017

Human Behavior Recognition Ssing Brain LFP Signal in the Presence of the Stimulation Pulse

Design and Methodology This study concentrates on human behavior classification task using local field potential (LFP) signals recorded from three subjects with Parkinson’s disease (PD). Existing approaches mainly employ the LFP signals acquired under the stimulation/off condition. In practical situations, however, it is necessary to design a classification method capable of recognizing different human activities under the stimulation/on condition, where the classification task is more complicated due to the artifacts imposed by the high amplitude stimulation pulse (~1-3volts). We utilize the time-frequency representation of the acquired LFPs in the Beta frequency range (~10-30Hz) to develop a feature space based on which the classification is efficiently performed while the high frequency stimulation pulse (~130-180Hz) has no/limited impact on the classification performance. Original Data and Results All three participants had undergone DBS surgery with implanted DBS leads (Medtronic 3389, Minneapolis, MN, USA) in the subthalamic nucleus of the brain. The recording sessions required the participants to do several repetitions of designed “button press” and “reach” trials under the condition of stimulation on/off. On average, 60 recordings were performed for each trial. Our analysis on the power spectral density (PSD) of the data showed that the stimulation pulse mostly impacts the frequency components around the stimulation frequency (~140Hz). Using a linear-kernel SVM classifier for classifying the aforementioned trials based on the proposed feature space, we obtained a classification accuracy of ~88% and ~87% respectively for stimulation off and on cases. Conclusion PD incidence increases with advancing age and peaks among people in their 60s and 70s. The cost of PD in the United States is estimated to be $25 billion per year. Thus, advanced techniques to improve the performance of existing devices are highly demanded. Human behavior classification from brain signals is essential in developing the next generation of closed-loop deep brain stimulation (DBS) systems. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators

Longitudinal Evaluation of Fatty Acid Metabolism in Normal and Spontaneously Hypertensive Rat Hearts with Dynamic MicroSPECT Imaging

The goal of this project is to develop radionuclide molecular imaging technologies using a clinical pinhole SPECT/CT scanner to quantify changes in cardiac metabolism using the spontaneously hypertensive rat (SHR) as a model of hypertensive-related pathophysiology. This paper quantitatively compares fatty acid metabolism in hearts of SHR and Wistar-Kyoto normal rats as a function of age and thereby tracks physiological changes associated with the onset and progression of heart failure in the SHR model. The fatty acid analog, 123I-labeled BMIPP, was used in longitudinal metabolic pinhole SPECT imaging studies performed every seven months for 21 months. The uniqueness of this project is the development of techniques for estimating the blood input function from projection data acquired by a slowly rotating camera that is imaging fast circulation and the quantification of the kinetics of 123I-BMIPP by fitting compartmental models to the blood and tissue time-activity curves

The Solvent–Solid Interface of Acid Catalysts Studied by High Resolution MAS NMR

High-resolution magic angle spinning (HRMAS) NMR spectroscopy was used to study the effect of mixed solvent systems on the acidity at the solid−liquid interface of solid acid catalysts. A method was developed that can exploit benefits of both solution and solid-state NMR (SSNMR) by wetting porous solids with small volumes of liquids (μL/mg) to create an interfacial liquid that exhibits unique motional dynamics intermediate to an isotropic liquid and a rigid solid. Results from these experiments provide information about the influence of the solvent mixtures on the acidic properties at a solid−liquid interface. Importantly, use of MAS led to spectra with full resolution between water in an acidic environment and that of bulk water. Using mixed solvent systems, the chemical shift of water was used to compare the relative acidity as a function of the hydration level of the DMSO-d6 solvent. Nonlinear increasing acidity was observed as the DMSO-d6 became more anhydrous. 1H HR-MAS NMR experiments on a variety of supported sulfonic acid functionalized materials, suggest that the acid strength and number of acid sites correlates to the degree of broadening of the peaks in the 1H NMR spectra. When the amount of liquid added to the solid is increased (corresponding to a thicker liquid layer), fully resolved water phases were observed. This suggests that the acidic proton was localized predominantly within a 2 nm distance from the solid. EXSY 1H−1H 2D experiments of the thin layers were used to determine the rate of proton exchange for different catalytic materials. These results demonstrated the utility of using (SSNMR) on solid−liquid mixtures to selectively probe catalyst surfaces under realistic reaction conditions for condensed phase systems

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced