Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Synthesis of biodegradable polymer-mesoporous silica composite microspheres for DNA prime-protein boost vaccination

DNA vaccines or proteins are capable of inducing specific immunity; however, the translation to the clinic has generally been problematic, primarily due to the reduced magnitude of immune response and poor pharmacokinetics. Herein we demonstrate a composite microsphere formulation, composed of mesoporous silica spheres (MPS) and poly(d,l-lactide-co-glycolide) (PLGA), enables the controlled delivery of a prime-boost vaccine via the encapsulation of plasmid DNA (pDNA) and protein in different compartments. Method with modified dual-concentric-feeding needles attached to a 40 kHz ultrasonic atomizer was studied. These needles focus the flow of two different solutions, which passed through the ultrasonic atomizer. The process synthesis parameters, which are important to the scale-up of composite microspheres, were also studied. These parameters include polymer concentration, feed flowrate, and volumetric ratio of polymer and pDNA-PEI/MPS-BSA. This fabrication technique produced composite microspheres with mean D[4,3] ranging from 6 to 34 μm, depending upon the microsphere preparation. The resultant physical morphology of composite microspheres was largely influenced by the volumetric ratio of pDNA-PEI/MPS-BSA to polymer, and this was due to the precipitation of MPS at the surface of the microspheres. The encapsulation efficiencies were predominantly in the range of 93-98% for pDNA and 46-68% for MPS. In the in vitro studies, the pDNA and protein showed different release kinetics in a 40 day time frame. The dual-concentric-feeding in ultrasonic atomization was shown to have excellent reproducibility. It was concluded that this fabrication technique is an effective method to prepare formulations containing a heterologous prime-boost vaccine in a single delivery system.

The harnessing of peptide-monolith constructs for single step plasmid DNA purification

The availability of synthetic peptides has paved the way for their use in tailor-made interactions with biomolecules. In this study, a 16mer LacI-based peptide was used as an affinity ligand to examine the scale up feasibility for plasmid DNA purification. First, the peptide was designed and characterized for the affinity purification of lacO containing plasmid DNA, to be employed as a high affinity ligand for the potential capturing of plasmid DNA in a single unit operation. It was found there were no discernible interactions with a control plasmid that did not encode the lacO nucleotide sequence. The dissociation equilibrium constant of the binding between the 16mer peptide and target pUC19 was 5.0 ± 0.5 × 10-8 M as assessed by surface plasmon resonance. This selectivity and moderated affinity indicate that the 16mer is suitable for the adsorption and chromatographic purification of plasmid DNA. The suitability of this peptide was then evaluated using a chromatography system with the 16mer peptide immobilized to a customized monolith to purify plasmid DNA, obtaining preferential purification of supercoiled pUC19. The results demonstrate the applicability of peptide-monolith supports to scale up the purification process for plasmid DNA using designed ligands via a biomimetic approach.

Single step purification of plasmid DNA using peptide ligand affinity chromatography

Single step affinity chromatography was employed for the purification of plasmid DNA (pDNA), thus eliminating several steps compared with current commercial purification methods for pDNA. Significant reduction in pDNA production time and cost was obtained. This chromatographic operation employed a peptide-monolith construct to isolate pDNA from Escherichia coli (E. coli) impurities present in a clarified lysate feedstock. Mild conditions were applied to avoid any degradation of pDNA. The effect of some important parameters on pDNA yield was also evaluated with the aim of optimising the affinity purification of pDNA. The results demonstrate that 81% of pDNA was recovered and contaminating gDNA, RNA and protein were removed below detectable levels. © 2008 Elsevier B.V. All rights reserved.

The effect of using different regions of interest on local and mean skin temperature

The dynamic nature of tissue temperature and the subcutaneous properties, such as blood flow, fatness, and metabolic rate, leads to variation in local skin temperature. Therefore, we investigated the effects of using multiple regions of interest when calculating weighted mean skin temperature from four local sites. Twenty-six healthy males completed a single trial in a thermonetural laboratory (mean ± SD): 24.0 (1.2) °C; 56 (8%) relative humidity; < 0.1 m/s air speed). Mean skin temperature was calculated from four local sites (neck, scapula, hand and shin) in accordance with International Standards using digital infrared thermography. A 50 x 50 mm square, defined by strips of aluminium tape, created six unique regions of interest, top left quadrant, top right quadrant, bottom left quadrant, bottom right quadrant, centre quadrant and the entire region of interest, at each of the local sites. The largest potential error in weighted mean skin temperature was calculated using a combination of a) the coolest and b) the warmest regions of interest at each of the local sites. Significant differences between the six regions interest were observed at the neck (P < 0.01), scapula (P < 0.001) and shin (P < 0.05); but not at the hand (P = 0.482). The largest difference (± SEM) at each site was as follows: neck 0.2 (0.1) °C; scapula 0.2 (0.0) °C; shin 0.1 (0.0) °C and hand 0.1 (0.1) °C. The largest potential error (mean ± SD) in weighted mean skin temperature was 0.4 (0.1) °C (P < 0.001) and the associated 95% limits of agreement for these differences was 0.2 to 0.5 °C. Although we observed differences in local and mean skin temperature based on the region of interest employed, these differences were minimal and are not considered physiologically meaningful.

Single voxel Magnetic Resonance Spectroscopic (1H-MRS) analysis of deep brain structures after traumatic prefrontal brain injury (TBI) in rat

Chronic difficulties arising from mild brain injury (TBI) are difficult to predict because the processes underlying changes after TBI are poorly understood. In mild brain injury the extent of neuropsychiatric and cognitive symptoms correspond poorly to overt tissue loss (Barth 1983; Liu 2010). Cellular, immune and hormonal cascades occurring after injury and continuing during the healing process may impact uninjured brain regions sensitive to the effects of physiological and emotional stress, which receive projections from the injury site. Changes in these most basic properties due to injury or disease have profound implications for virtually every aspect of brain function through disruption of neurotransmitter, neuroendocrine and metabolic systems. In order to screen for changes in transmitter and metabolic activity, in this study we developed Single voxel proton Magnetic Resonance Spectroscopy (1H-MRS) for use in both injured and control animals. We first evaluated if 1H-MRS could be used to evaluate in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus in both control and injured animals after controlled cortical impact injury to the rat prefrontal cortex. We found that metabolite measurements for Myo-Inositol, Choline, creatine, Glutamate+Glutamine, and N-acetyl-acetate are attainable in deep brain structures in vivo in injured and controls rats. We next seek to evaluate longitudinally, in vivo, alterations in brain metabolism and catabolism of the prefrontal cortex, amygdala and ventral hippocampus during the first month after controlled cortical impact injury to the rat prefrontal cortex. These ongoing studies will provide data on the changes in transmitters and metabolites over time in injured and non-injured subjects. These studies address some of the fundamental questions about how mild brain injury has such diverse effects on overall brain health and function.

Hydrotherapy as a recovery strategy after exercise: A pragmatic controlled trial

Background Our aim was to evaluate the recovery effects of hydrotherapy after aerobic exercise in cardiovascular, performance and perceived fatigue. Methods A pragmatic controlled repeated measures; single-blind trial was conducted. Thirty-four recreational sportspeople visited a Sport-Centre and were assigned to a Hydrotherapy group (experimental) or rest in a bed (control) after completing a spinning session. Main outcomes measures including blood pressure, heart rate, handgrip strength, vertical jump, self-perceived fatigue, and body temperature were assessed at baseline, immediately post-exercise and post-recovery. The hypothesis of interest was the session*time interaction. Results The analysis revealed significant session*time interactions for diastolic blood pressure (P=0.031), heart rate (P=0.041), self perceived fatigue (P=0.046), and body temperature (P=0.001); but not for vertical jump (P=0.437), handgrip (P=0.845) or systolic blood pressure (P=0.266). Post-hoc analysis revealed that hydrotherapy resulted in recovered heart rate and diastolic blood pressure similar to baseline values after the spinning session. Further, hydrotherapy resulted in decreased self-perceived fatigue after the spinning session. Conclusions Our results support that hydrotherapy is an adequate strategy to facilitate cardiovascular recovers and perceived fatigue, but not strength, after spinning exercise. Trial registration ClinicalTrials.gov Identifier: NCT01765387 Keywords: Hydrotherapy; Heart rate; Fatigue; Strength; Blood pressure; Body temperature

How different are complications that affect the older adult inpatient?

Objective: The incidence and cost of complications occurring in older and younger inpatients were compared. Design: Secondary analysis of hospital-recorded diagnosis and costs for multiday-stay inpatients in 68 public hospitals in two Australian states. Main outcome measures: A complication is defined as a hospital-acquired diagnosis that required additional treatment. The Australian Classification of Hospital-Acquired Diagnoses system is used to identify these complications. Results: Inpatients aged >70 years have a 10.9% complication rate, which is not substantially different from the 10.89% complication rate found in patients aged <70 years. Examination of the probability by single years, however, showed that the peak incidence associated with the neonatal period and childbirth is balanced by rates of up to 20% in patients >80 years. Examining the adult patient population (40–70 years), we found that while some common complications are not age specific (electrolyte disorders and cardiac arrhythmias), others (urinary tract and lower respiratory tract infections) are more common in the older adult inpatient. Conclusion: For inpatients aged >70 years, the risks of complications increase. The incidence of hospital-acquired diagnoses in older adults differs significantly from incidence rates found in younger cohorts. Urinary tract infection and alteration to mental state are more common in older adult inpatients. Surprisingly, these complexities do not result in additional costs when compared with costs for the same complications in younger adults. Greater awareness of these differing patterns will allow patient safety efforts for older patients to focus on complications with the highest incidence and cost.

Fabrication of Nb2O5 nanosheets for high-rate lithium ion storage applications

Nb2O5 nanosheets are successfully synthesized through a facile hydrothermal reaction and followed heating treatment in air. The structural characterization reveals that the thickness of these sheets is around 50 nm and the length of sheets is 500~800 nm. Such a unique two dimensional structure enables the nanosheet electrode with superior performance during the charge-discharge process, such as high specific capacity (~184 mAh.g-1) and rate capability. Even at a current density of 1 A.g-1, the nanosheet electrode still exhibits a specific capacity of ~90 mAh.g-1. These results suggest the Nb2O5 nanosheet is a promising candidate for high-rate lithium ion storage applications.

Soft-switching single inductor current-fed push-pull converter for PV applications

A switching control strategy is proposed for single inductor current-fed push-pull converter with a secondary side active voltage doubler rectifier or a voltage rectifier used in photovoltaic (PV) grid interfacing. The proposed switching control strategy helps to turn-on and turn-off the primary side power switches with zero-voltage and zero-current switching. The operation of the push-pull converter is analyzed for two modes of operation. The feasibility of the proposed switching control strategy is validated using simulation and experimental results.

Learning the learning rate for prediction with expert advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow for increasingly aggressive learning. But in practice such theoretical tunings often still perform worse (as measured by their regret) than ad hoc tuning with an even higher learning rate. To close the gap between theory and practice we introduce an approach to learn the learning rate. Up to a factor that is at most (poly)logarithmic in the number of experts and the inverse of the learning rate, our method performs as well as if we would know the empirically best learning rate from a large range that includes both conservative small values and values that are much higher than those for which formal guarantees were previously available. Our method employs a grid of learning rates, yet runs in linear time regardless of the size of the grid.

Real-time assessment of GNSS observation noise with single receivers

Stochastic modelling is critical in GNSS data processing. Currently, GNSS data processing commonly relies on the empirical stochastic model which may not reflect the actual data quality or noise characteristics. This paper examines the real-time GNSS observation noise estimation methods enabling to determine the observation variance from single receiver data stream. The methods involve three steps: forming linear combination, handling the ionosphere and ambiguity bias and variance estimation. Two distinguished ways are applied to overcome the ionosphere and ambiguity biases, known as the time differenced method and polynomial prediction method respectively. The real time variance estimation methods are compared with the zero-baseline and short-baseline methods. The proposed method only requires single receiver observation, thus applicable to both differenced and un-differenced data processing modes. However, the methods may be subject to the normal ionosphere conditions and low autocorrelation GNSS receivers. Experimental results also indicate the proposed method can result on more realistic parameter precision.

Using viewer’s facial expression and heart rate for sports video highlights detection

Viewer interests, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, ‘non-neutral’ FE and ‘relatively higher and faster’ HR is able to capture 60%-80% of goal, foul, and shot-on-goal soccer video events. FE is found to be more indicative than HR of viewer’s interests, but the fusion of these two modalities outperforms each of them.

Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis

Systems-level identification and analysis of cellular circuits in the brain will require the development of whole-brain imaging with single-cell resolution. To this end, we performed comprehensive chemical screening to develop a whole-brain clearing and imaging method, termed CUBIC (clear, unobstructed brain imaging cocktails and computational analysis). CUBIC is a simple and efficient method involving the immersion of brain samples in chemical mixtures containing aminoalcohols, which enables rapid whole-brain imaging with single-photon excitation microscopy. CUBIC is applicable to multicolor imaging of fluorescent proteins or immunostained samples in adult brains and is scalable from a primate brain to subcellular structures. We also developed a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that, together with CUBIC reagents, enable the visualization and quantification of neural activities induced by environmental stimulation. CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution.

Whole-Body Imaging with Single-Cell Resolution by Tissue Decolorization

The development of whole-body imaging at single-cell resolution enables system-level approaches to studying cellular circuits in organisms. Previous clearing methods focused on homogenizing mismatched refractive indices of individual tissues, enabling reductions in opacity but falling short of achieving transparency. Here, we show that an aminoalcohol decolorizes blood by efficiently eluting the heme chromophore from hemoglobin. Direct transcardial perfusion of an aminoalcohol-containing cocktail that we previously termed CUBIC coupled with a 10 day to 2 week clearing protocol decolorized and rendered nearly transparent almost all organs of adult mice as well as the entire body of infant and adult mice. This CUBIC-perfusion protocol enables rapid whole-body and whole-organ imaging at single-cell resolution by using light-sheet fluorescent microscopy. The CUBIC protocol is also applicable to 3D pathology, anatomy, and immunohistochemistry of various organs. These results suggest that whole-body imaging of colorless tissues at high resolution will contribute to organism-level systems biology.