Texas Forestry Paper No. 5

Experimental planting of eucalyptus in the rio grand valley, texashttps://scholarworks.sfasu.edu/texas_forestry_papers/1006/thumbnail.jp

Forestry Bulletin No. 24: Silviculture and Management of Teak

Teak is the principal timber tree of Peninsular India, Burma, Indonesia and Thailand and one of the most important timbers of the world. It has been planted extensively in warm climates throughout the world.https://scholarworks.sfasu.edu/forestrybulletins/1022/thumbnail.jp

A Quality of Service Aware Source Routing Based Protocol for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) handle many underwater applications such as environment monitoring, surveillance and navigation. These applications generate varied types of traffic such as continuous bit rate, sporadic and different packet sizes, leading to additional QoS requirements that are traffic and application dependent. This paper presents the development of a Quality of Service Aware Source Routing (QASR) protocol. QASR discovers multiple paths from the sources to the sinks and selects the most QoS compatible route among them. QASR is distinctive because it incorporates multiple QoS parameters such as Signal to Noise Ratio (SNR), latency and residual energy. Depending on which of these parameters are chosen, QASR has three variants, namely, QASR-Latency (QASR-L), QASR-Residual Energy (QASR-RE) and QASR-Signal to Noise Ratio (QASR-SNR). The performance of QASR protocol is compared against traditional source routing protocols, with simulations showing a reduction of about 10% to 20% in latency and about 5% to 10% lesser energy consumption than source routing. QASR protocol exhibits comparable performance to classic source routing protocols while simultaneously adhering to the QoS requirements of the application. It is also worth noting that the performance profile of all the three variants of QASR do not have sudden and drastic variations, with the performance profiles showing consistent trend-lines

A Collision Avoidance Based Energy Efficient Medium Access Control Protocol for Clustered Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are typically deployed in energy constrained environments where recharging energy sources and replacing batteries are not viable. This makes energy efficiency in UWSNs a crucial directive to be followed during Medium Access Control (MAC) design. Multiplexing and scheduling based protocols are not ideal for UWSNs because of their strict synchronization requirements, longer latencies and constrained bandwidth.This paper presents the development and simulation analysis of a novel cross-layer communication based MAC protocol called Energy Efficient Collision Avoidance (EECA) MAC protocol. EECA-MAC protocol works on the principle of adaptive power control, controlling the transmission power based on the signal strength at the receiver. EECA-MAC enhances the conventional 4-way handshake to reduce carrier sensing by implementing an enhanced Request to Send (RTS) and Clear to Send (CTS) handshake and an improved back-off algorithm.Simulation analysis shows that the measures taken to achieve energy efficiency have a direct effect on the number of packet retransmissions. Compared to the Medium Access with Collision Avoidance (MACA) protocol, EECA-MAC shows a 40% reduction in the number of packets that are delivered after retransmissions. This reduction, coupled with the reduced signal interference, results in a 16% drop in the energy utilized by the nodes for data transmission

Accurate detection of spontaneous seizures using a generalized linear model with external validation

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures

Development of a real-time quantitative PCR assay for detection of a stable genomic region of BK virus

<p>Abstract</p> <p>Background</p> <p>BK virus infections can have clinically significant consequences in immunocompromised individuals. Detection and monitoring of active BK virus infections in certain situations is recommended and therefore PCR assays for detection of BK virus have been developed. The performance of current BK PCR detection assays is limited by the existence of viral polymorphisms, unknown at the time of assay development, resulting in inconsistent detection of BK virus. The objective of this study was to identify a stable region of the BK viral genome for detection by PCR that would be minimally affected by polymorphisms as more sequence data for BK virus becomes available.</p> <p>Results</p> <p>Employing a combination of techniques, including amino acid and DNA sequence alignment and interspecies analysis, a conserved, stable PCR target region of the BK viral genomic region was identified within the VP2 gene. A real-time quantitative PCR assay was then developed that is specific for BK virus, has an analytical sensitivity of 15 copies/reaction (450 copies/ml) and is highly reproducible (CV ≤ 5.0%).</p> <p>Conclusion</p> <p>Identifying stable PCR target regions when limited DNA sequence data is available may be possible by combining multiple analysis techniques to elucidate potential functional constraints on genomic regions. Applying this approach to the development of a real-time quantitative PCR assay for BK virus resulted in an accurate method with potential clinical applications and advantages over existing BK assays.</p

Abnormalities of calcium metabolism and myocardial contractility depression in the failing heart

Heart failure (HF) is characterized by molecular and cellular defects which jointly contribute to decreased cardiac pump function. During the development of the initial cardiac damage which leads to HF, adaptive responses activate physiological countermeasures to overcome depressed cardiac function and to maintain blood supply to vital organs in demand of nutrients. However, during the chronic course of most HF syndromes, these compensatory mechanisms are sustained beyond months and contribute to progressive maladaptive remodeling of the heart which is associated with a worse outcome. Of pathophysiological significance are mechanisms which directly control cardiac contractile function including ion- and receptor-mediated intracellular signaling pathways. Importantly, signaling cascades of stress adaptation such as intracellular calcium (Ca2+) and 3′-5′-cyclic adenosine monophosphate (cAMP) become dysregulated in HF directly contributing to adverse cardiac remodeling and depression of systolic and diastolic function. Here, we provide an update about Ca2+ and cAMP dependent signaling changes in HF, how these changes affect cardiac function, and novel therapeutic strategies which directly address the signaling defects