Abundance and distribution of the green alga Botryococcus braunii Kützing (1849) in Paoay Lake, Ilocos Norte

Botryococcus braunii, a green colonial freshwater microalga that is currently found in Paoay Lake, Ilocos Norte, is recognized as a renewable source of energy. The monthly abundance and distribution of B. braunii in the lake in relation to the different physico-chemical parameters and other phytoplankton species were done from April 2009 to March 2010. Throughout the period of study, analysis of samples showed that B. braunii populations were present in all samples collected in the four sampling sites. Changes in cell density were noticeable, especially during the months of April and August. The highest recorded mean density was in August (2515 cells /mL) while the lowest mean density was in May (83 cells/mL). There were 38 genera of phytoplankton that co-existed with B. braunii in the lake. Physico-chemical parameters and nutrients were still within the range for growth of the alga. Fluctuations of these nutrients may be caused by seasonal variation and the occurrence of typhoons in the region. Growth of B. braunii population is positively correlated with temperature, conductivity and phytoplankton count while it is negatively correlated with DO, pH, transparency depth, NO3-, and PO43. Although B. braunii populations were present within the water column throughout the sampling period, the present environmental conditions did not allow the formation of blooms of this algal species

Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

Background and purposeQuantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.Materials and methodsMachine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.ResultsWe established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (P < .05) but not with age or sex.ConclusionsOur machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future

Low voltage and high ON/OFF ratio field-effect transistors based on CVD MoS 2 and ultra high-k gate dielectric PZT

Title on author’s file: Low voltage and high ON/OFF ratio field-effect transistors based on CVD MoS2 and ultra high-k PZT gate dielectric202211 bcwwAccepted ManuscriptRGCOthersThe Hong Kong Polytechnic University; AOEPublishe