Effect of oxygen incorporation in semi-insulating (AlxGa1-x)yIn1-yP

Journal ArticleDiscusses a study conducted on oxygen-doped, semi-insulating layers of (aluminum-gallium) indium phosphide grown on gallium arsenide using organometallic vapor phase epitaxy. Effect of oxygen doping on semi-insulating layers of the substance; Secondary-ion mass spectrometry measurements; Measured concentration of oxygen in the layers of the substance in samples used in the study

Optimized Dipole Antennas on Photonic Band gap crystals

Cataloged from PDF version of article.Photonic band gap crystals have been used as a perfectly reflecting substrate for planar dipole antennas in the 12–15 GHz regime. The position, orientation, and driving frequency of the dipole antenna on the photonic band gap crystal surface, have been optimized for antenna performance and directionality. Virtually no radiated power is lost to the photonic crystal resulting in gains and radiation efficiencies larger than antennas on other conventional dielectric substrates. © 1995 American Institute of Physic

Laser-micromachined Millimeter-wave Photonic band gap cavity structures

Cataloged from PDF version of article.We have used laser-micromachined alumina substrates to build a three-dimensional photonic band-gap crystal. The rod-based structure has a three-dimensional full photonic band gap between 90 and 100 GHz. The high resistivity of alumina results in a typical attenuation rate of 15 dB per unit cell within the band gap. By removing material, we have built defects which can be used as millimeter-wave cavity structures. The resulting quality ~Q! factors of the millimeter-wave cavity structures were as high as 1000 with a peak transmission of 10 dB below the incident signal. © 1995 American Institute of Physics

A lightweight classification algorithm for human activity recognition in outdoor spaces

The aim of this paper is to discuss the development of a lightweight classification algorithm for human activity recognition in a defined setting. Current techniques to analyse data such as machine learning are often very resource intensive meaning they can only be implemented on machines or devices that have large amounts of storage or processing power. The lightweight algorithm uses Euclidean distance to measure the difference between two points and predict the class of new records. The results of the algorithm are largely positive achieving accuracy of 100% when classifying records taken from the same sensor position and accuracy of 80% when records are taken from different sensor positions. The outcome of this work is to foster the development of lightweight algorithms for the future development of devices that will consume less energy and will require a lower computational capacity

Clinical Characteristics and Quality of Life in Adults Initiating Medical Marijuana Treatment

Introduction: Despite the rising availability and use of medical marijuana (MM) in the USA, little is known about the demographics, clinical characteristics, or quality of life of MM patients. This study describes the demographic characteristics and health-related quality of life (HRQoL) of MM patients who are initiating treatment in Pennsylvania. Methods: Two-hundred adults naive to MM and referred for any of the 23 state-approved qualifying conditions were recruited at three MM dispensaries in Pennsylvania between September 2020 and March 2021. All participants consented to the study; completed semi-structured interviews that included demographic questionnaires, the Short Form-36 (SF-36), and Generalized Anxiety Disorder-7 (GAD-7); provided height and weight measurements; and allowed access their dispensary medical records. Results: Participants had a mean age of 48.5 ± 15.6 years, predominantly identified as female (67.5%), and were most commonly referred for chronic pain (63.5%) and/or anxiety (58.5%). Additionally, 46.0% were living with obesity as determined by BMI. Relative to a normative sample, participants reported diminished HRQoL in several domains, most notably in role limitations due to physical health (M = 46.0 ± 42.0), role limitations due to emotional problems (M = 52.5 ± 42.3), energy and fatigue (M = 39.8 ± 20.2), and pain (M = 49.4 ± 26.0). Discussion/Conclusion: Patients initiating MM treatment experienced low HRQoL in multiple domains. Future studies could evaluate the relationship between HRQoL and patients’ decisions to pursue MM treatment, as well as changes in HRQoL with MM use over time

Gap-filling carbon dioxide, water, energy, and methane fluxes in challenging ecosystems - Comparing between methods, drivers, and gap-lengths

Eddy covariance serves as one the most effective techniques for long-term monitoring of ecosystem fluxes, however long-term data integrations rely on complete timeseries, meaning that any gaps due to missing data must be reliably filled. To date, many gap-filling approaches have been proposed and extensively evaluated for mature and/or less actively managed ecosystems. Random forest regression (RFR) has been shown to be stable and perform better in these systems than alternative approaches, particularly when filling longer gaps. However, the performance of RFR gap filling remains less certain in more challenging ecosystems, e.g., actively managed agri-ecosystems and following recent land-use change due to management disturbances, ecosystems with relatively low fluxes due to low signal to noise ratios, or for trace gases other than carbon dioxide (e.g., methane). In an extension to earlier work on gap filling global carbon dioxide, water, and energy fluxes, we assess the RFR approach for gap filling methane fluxes globally. We then investigate a range of gap-filling methodologies for carbon dioxide, water, energy, and methane fluxes in challenging ecosystems, including European managed pastures, Southeast Asian converted peatlands, and North American drylands. Our findings indicate that RFR is a competent alternative to existing research standard gap-filling algorithms. The marginal distribution sampling (MDS) is still suggested for filling short ( 30 days) gaps in carbon dioxide fluxes and also for gap filling other fluxes (e.g. sensible heat, latent energy and methane). In addition, using RFR with globally available reanalysis environmental drivers is effective when measured drivers are unavailable. Crucially, RFR was able to reliably fill cumulative fluxes for gaps > 3 moths and, unlike other common approaches, key environment-flux responses were preserved in the gap-filled data

Optimized dipole antennas on photonic band gap crystals

Photonic band gap crystals have been used as a perfectly reflecting substrate for planar dipole antennas in the 12-15 GHz regime. The position, orientation, and driving frequency of the dipole antenna on the photonic band gap crystal surface, have been optimized for antenna performance and directionality. Virtually no radiated power is lost to the photonic crystal resulting in gains and radiation efficiencies larger than antennas on other conventional dielectric substrates.© 1995 American Institute of Physics

Small zinc doped iron oxide tracers for magnetic particle imaging

Magnetic particle imaging (MPI) has garnered significant attention in biomedical imaging research due to its excellent signal intensity that is generated directly from superparamagnetic iron oxide nanoparticles (SPIONs). Small nanoparticle tracers with high saturation magnetisation are crucial for MPI as they can prolong circulation, crossing the blood brain barrier and enhance cellular uptake. In this work, we demonstrate small zinc doped iron oxide nanoparticles (Zn-IONPs) are excellent MPI tracers. Our Zn-IONPs exhibited up to 37 % and 64% enhancement in saturation magnetisation (Msat) value and MPI signal intensity respectively compared to Fe3O4 of the same size. As a result, the polymer encapsulated Zn-IONPs achieved up to 2.7-fold enhancement in MPI signal intensity compared to VivoTrax. Furthermore, these polymer encapsulated NPs were also determined to be non-toxic hence making these Zn-IONPs ideal for many biomedical applications in MPI where small size is critical to prolong circulation time and crossing the blood brain barrier

STAT5 activation promotes progression and chemotherapy-resistance in early T-cell precursor acute lymphoblastic leukemia

IL-7 supports the growth and chemoresistance of T-cell acute lymphoblastic leukemia (T-ALL), particularly the early T-cell precursor subtype (ETP-ALL), which frequently has activating mutations of IL-7 signaling. STAT5 is an attractive therapeutic target because it is almost universally activated in ETP-ALL, even in the absence of mutations of upstream activators such as the IL-7R, JAK and FLT3. To examine the role of activated STAT5 in ETP-ALL, we have used a Lmo2-transgenic (Lmo2Tg) mouse model in which we can monitor chemoresistant pre-leukemia (pre-LSCs) and leukemia stem cells (LSCs) that drive T-ALL development and relapse following chemotherapy. Using IL-7R-deficient Lmo2Tg mice, we show that IL-7 signaling was not required for the formation of pre-LSCs but essential for their expansion and clonal evolution into LSCs to generate T-ALL. Activated STAT5B was sufficient for the development of T-ALL in IL-7R; Lmo2Tg mice, indicating that inhibition of STAT5 is required to block the supportive signals provided by IL-7. To further understand the role of activated STAT5 in LSCs of ETP-ALL, we developed a new transgenic mouse that enables T-cell specific and doxycycline-inducible expression of the constitutively activated STAT5B1∗6 mutant. Expression of STAT5B1∗6 in T-cells had no effect alone but promoted expansion and chemoresistance of LSCs in Lmo2Tg mice. Pharmacologic inhibition of STAT5 with Pimozide induced differentiation and loss of LSCs, whilst enhancing response to chemotherapy. Furthermore, Pimozide significantly reduced leukemia burden in vivo and overcame chemoresistance of patient-derived ETP-ALL xenografts. Overall, our results demonstrate that STAT5 is an attractive therapeutic target for eradicating LSCs in ETP-ALL

Simulation of greenhouse gases following land-use change to bioenergy crops using the ECOSSE model. A comparison between site measurements and model predictions

This article evaluates the suitability of the ECOSSE model to estimate soil greenhouse gas (GHG) fluxes from short rotation coppice willow (SRC-Willow), short rotation forestry (SRF-Scots Pine) and Miscanthus after landuse change from conventional systems (grassland and arable). We simulate heterotrophic respiration (Rh), nitrous oxide (N2O) and methane (CH4) fluxes at four paired sites in the UK and compare them to estimates of Rh derived from the ecosystem respiration estimated from eddy covariance (EC) and Rh estimated from chamber (IRGA) measurements, as well as direct measurements of N2O and CH4 fluxes. Significant association between modelled and EC-derived Rh was found under Miscanthus, with correlation coefficient (r) ranging between 0.54 and 0.70. Association between IRGA-derived Rh and modelled outputs was statistically significant at the Aberystwyth site (r = 0.64), but not significant at the Lincolnshire site (r = 0.29). At all SRC-Willow sites, significant association was found between modelled and measurement-derived Rh (0.44 ≤ r ≤ 0.77); significant error was found only for the EC-derived Rh at the Lincolnshire site. Significant association and no significant error were also found for SRF-Scots Pine and perennial grass. For the arable fields, the modelled CO2 correlated well just with the IRGA-derived Rh at one site (r = 0.75). No bias in the model was found at any site, regardless of the measurement type used for the model evaluation. Across all land uses, fluxes of CH4 and N2O were shown to represent a small proportion of the total GHG balance; these fluxes have been modelled adequately on a monthly time-step. This study provides confidence in using ECOSSE for predicting the impacts of future land use on GHG balance, at site level as well as at national level