New records of ichneumon wasps (Hymenoptera, Ichneumonidae) from Malta

Recently some Maltese Hymenoptera were donated to the Hungarian Natural History Museum (HNHM) and some other material was sent to the Natural History Museum in London (BMNH) for identification by the second author. Amongst these specimens were six ichneumon wasp species new to the fauna of Malta. Ichneumonidae taxonomy and nomenclature follow Yu et al. (2012), and host records were traced through this resource. Identifications were based on keys provided by Szépligeti (1905), Schmiedeknecht (1909), Bajári (1960), Townes et al. (1965), Bajári & Móczár (1969), Townes (1969; 1970a; 1970b; 1971), Horstmann (1976), Gauld & Mitchell (1977), Fitton et al. (1988), Wahl (1993), and Tolkanitz (2007). The voucher specimens are deposited in the Hymenoptera Collection of HNHM, Budapest, Hungary (those indicated by a HNHM id. number below), and some duplicate specimens in D. Mifsud’s private insect collection (CDM) in Malta.peer-reviewe

Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing?

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a highly efficient implementation for embedded systems like wearables. While fast implementations have been presented, other constraints have not been considered for edge computing. In this work, we aim at answering how thermal-friendly HDC for edge computing is. Devices like smartwatches, smart glasses, or even mobile systems have a restrictive cooling budget due to their limited volume. Although HDC operations are simple, the vectors are large, resulting in a high number of CPU operations and thus a heavy load on the entire system potentially causing temperature violations. In this work, the impact of HDC on the chip's temperature is investigated for the first time. We measure the temperature and power consumption of a commercial embedded system and compare HDC with conventional CNN. We reveal that HDC causes up to 6.8{\deg}C higher temperatures and leads to up to 47% more CPU throttling. Even when both HDC and CNN aim for the same throughput (i.e., perform a similar number of classifications per second), HDC still causes higher on-chip temperatures due to the larger power consumption.Comment: 4 pages, 3 figure

Biomimetic strategies for fracture repair: engineering the cell microenvironment for directed tissue formation

Complications resulting from impaired fracture healing have major clinical implications on fracture management strategies. Novel concepts taken from developmental biology have driven research strategies towards the elaboration of regenerative approaches that can truly harness the complex cellular events involved in tissue formation and repair. Advances in polymer technology and a better understanding of naturally derived scaffolds have given rise to novel biomaterials with an increasing ability to recapitulate native tissue environments. This coupled with advances in the understanding of stem cell biology and technology has opened new avenues for regenerative strategies with true clinical translatability. These advances have provided the impetus to develop alternative approaches to enhance the fracture repair process. We provide an update on these advances, with a focus on the development of novel biomimetic approaches for bone regeneration and their translational potential

Youth of West Cameroon are at high risk of developing IDD due to low dietary iodine and high dietary thiocyanate.

Objectives: Hypothyroidism in utero leading to mental retardation is highly prevalent and recurrent in developing countries where iodine deficiency and thiocyanate overload are combined. So, to explore and identify human population's risks for developing iodine deficiency disorders and their endemicity in Western Cameroon, with the aim to prevent this deficiency and to fight again it, urinary iodine and thiocyanate levels were determined. Methods: The district of Bamougoum in Western Cameroon was selected for closer study due to its geographic location predisposing for iodine deficiency disorders (IDD). A comprehensive sampling strategy included 24-h urine samples collected over three days from 120 school-aged children. Urinary iodine and thiocyanate levels were measured by colorimetric methods. Results: Twenty one percent of boys between the ages 3 and 19 were classified as iodine deficient. The prevalence of thiocyanate overload in the same population was found to be 20%. Conclusion: Presence of endemic iodine deficiency and excessive thiocyanate in the population indicates that the region is at risk of iodine deficiency disorder. A multifactorial approach that includes improvement of diet, increasing iodine and minimizing goitrogen substances intake, soil and crop improvement and an iodine supplementation program may help alleviate IDD in the affected area studied. African Health Sciences Vol. 8 (4) 2008: pp. 227-23

The efficacy of two bupivacaine hydrochloride injection products

No Abstract

Investigation of the correlations between the microstructure and the tensile properties multi-scale composites with a polylactic acid matrix, reinforced with carbon nanotubes and carbon fibers, with the use of the fiber bundle cell theory

In this paper, we established a relationship between the microstructure and the tensile properties of nano- and hybrid composites with a thermoplastic, semi- crystalline poly(lactic acid) (PLA) matrix, reinforced with carbon fibers and carbon nanotubes, using the fiber bundle cell model. The microstructure of the ma- trix and the volume ratio of the mobile amorphous, rigid amorphous and crystalline fractions were determined by differential scanning calorimetry and the elastic modulus of these fractions was measured by atomic force microscopy. These data served as input parameters for the fiber bundle cell model, in which each structural unit corresponded to a single bundle of fibers. For the parameters for which no experimental method was available, we determined structure-independent constants. With these model parameters, we fitted a fiber bundle cell model curve to the averaged tensile curves of each material. By analyzing the fitted model, we concluded that the mobile and the rigid amorphous fraction of the matrix has a key role in the properties of the initial, load-carrying section of the tensile curve. The crystalline fraction and the carbon nanotubes elongated the failure section. By examining the damage maps, we found that overlapping interphases in the vicinity of carbon fibers and carbon nanotubes improved the load transfer between the carbon fiber and the matrix, thus allowing better utilization of the reinforcing effect of the carbon fibers

Prognostic Implications of Lymph Node Yield and Lymph Node Ratio in Papillary Thyroid Carcinoma

<p>Background: The lymph node yield (LNY) and the lymph node ratio (LNR) have been shown to be important prognostic factors in oral, colon, and gastric cancers. The role of the LNY and LNR in papillary thyroid cancer (PTC) is unclear. The aims of this study were to determine if a high LNR and a low LNY decrease disease-free survival rates. This study further aimed to determine an optimum nodal yield.</p><p>Methods: A retrospective analysis was conducted of 198 patients with PTC undergoing total thyroidectomy with neck dissection between 1987 and 2011. The LNY and LNR were adjusted by relevant covariates in a multivariate Cox regression analysis with Andersen-Gill extension.</p><p>Results: The LNR was associated with a decrease in disease-free survival (hazard ratio 3.2 [95% confidence interval 1.4-7.3], p = 0.005). Patients with an LNR of 0.30 or higher had a 3.4 times higher risk of persistent or recurrent disease compared with patients with an LNR of 0.00 ([95% confidence interval 1.1-10.5], p = 0.031). Conversely, patients with an LNR of 0.11 or lower had an 80% chance of remaining disease free during 5 years of follow-up. The LNY showed no significant independent effect and an optimum nodal yield was not determined.</p><p>Conclusions: The LNR is an important independent prognostic factor in PTC and can be used in conjunction with existing staging systems. A clinical relevant cut-off point of 0.3 (one positive lymph node out of three total) is proposed. No prognostic implications for LNY were identified.</p>

Hydrological, Sedimentological, and Meteorological Observations and Analysis on the Sagavanirktok River

The Dalton Highway near Deadhorse was closed twice during late March and early April 2015 because of extensive overflow from the Sagavanirktok River that flowed over the highway. That spring, researchers from the Water and Environmental Research Center at the University of Alaska Fairbanks (UAF) monitored the river conditions during breakup, which was characterized by unprecedented flooding that overtopped and consequently destroyed several sections of the Dalton Highway near Deadhorse. The UAF research team has monitored breakup conditions at the Sagavanirktok River since that time. Given the magnitude of the 2015 flooding, the Alyeska Pipeline Service Company started a long-term monitoring program within the river basin. In addition, the Alaska Department of Transportation and Public Facilities (ADOT&PF) funded a multiyear project related to sediment transport conditions along the Sagavanirktok River. The general objectives of these projects include determining ice elevations, identifying possible water sources, establishing surface hydro-meteorological conditions prior to breakup, measuring hydro-sedimentological conditions during breakup and summer, and reviewing historical imagery of the aufeis extent. In the present report, we focus on new data and analyze it in the context of previous data. We calculated and compared ice thickness near Franklin Bluffs for 2015, 2016, and 2017, and found that, in general, ice thickness during both 2015 and 2016 was greater than in 2017 across most of the study area. Results from a stable isotope analysis indicate that winter overflow, which forms the aufeis in the river area near Franklin Bluffs, has similar isotopic characteristics to water flowing from mountain springs. End-of-winter snow surveys (in 2016/2017) within the watershed indicate that the average snow water equivalent was similar to what we observed in winter 2015/2016. Air temperatures in May 2017 were low on the Alaska North Slope, which caused a long and gradual breakup, with peak flows occurring in early June, compared with mid-May in both 2015 and 2016. Maximum discharge measured at the East Bank station, near Franklin Bluffs was 750 m3/s (26,485 ft3/s) on May 30, 2017, while the maximum measured flow was 1560 m3/s (55,090 ft3/s) at the same station on May 20, 2015. Available cumulative rainfall data indicate that 2016 was wetter than 2017. ii In September 2015, seven dry and wet pits were dug near the hydro-sedimentological monitoring stations along the Sagavanirktok River study reach. The average grain-size of the sediment of exposed gravel bars at sites located upstream of the Ivishak-Sagavanirktok confluence show relatively constant values. Grain size becomes finer downstream of the confluence. We conducted monthly topo-bathymetric surveys during the summer months of 2016 and 2017 in each pit. Sediment deposition and erosion was observed in each of the pits. Calculated sedimentation volumes in each pit show the influence of the Ivishak River in the bed sedimenttransport capacity of the Sagavanirktok River. In addition, comparison between dry and wet pit sedimentation volumes in some of the stations proves the complexity of a braided river, which is characterized by frequent channel shifting A two-dimensional hydraulic model is being implemented for a material site. The model will be used to estimate the required sediment refill time based on different river conditions.ABSTRACT ..................................................................................................................................... i LIST OF FIGURES ......................................................................................................................... i LIST OF TABLES ....................................................................................................................... xiv ACKNOWLEDGMENTS AND DISCLAIMER ........................................................................ xvi CONVERSION FACTORS, UNITS, WATER QUALITY UNITS, VERTICAL AND HORIZONTAL DATUM, ABBREVIATIONS, AND SYMBOLS .......................................... xvii ABBREVIATIONS, ACRONYMS, AND SYMBOLS .............................................................. xix 1 INTRODUCTION ................................................................................................................... 1 2 STUDY AREA ........................................................................................................................ 2 2.1 Sagavanirktok River near MP318 Site 066 (DSS4) ......................................................... 7 2.2 Sagavanirktok River at Happy Valley Site 005 (DSS3) .................................................. 7 2.3 Sagavanirktok River below the Confluence with the Ivishak River (DSS2) ................... 9 2.4 Sagavanirktok River near MP405 Site 042 (DSS1) ....................................................... 10 3 METHODOLOGY AND EQUIPMENT .............................................................................. 13 3.1 Pits .................................................................................................................................. 13 3.1.1 Excavation............................................................................................................... 13 3.1.2 Surveying ................................................................................................................ 14 3.2 Surface Meteorology ...................................................................................................... 15 3.3 Aufeis Extent .................................................................................................................. 17 3.3.1 Field Methods ......................................................................................................... 18 3.3.2 Imagery ................................................................................................................... 18 3.4 Water Level Measurements ............................................................................................ 19 3.5 Runoff............................................................................................................................. 20 3.6 Suspended Sediment ...................................................................................................... 21 3.7 Turbidity ......................................................................................................................... 22 3.8 Stable Isotopes................................................................................................................ 22 4 RESULTS .............................................................................................................................. 23 4.1 Meteorology ................................................................................................................... 23 4.1.1 Air Temperature ...................................................................................................... 23 4.1.2 Precipitation ............................................................................................................ 31 4.1.2.1 Cold Season Precipitation ................................................................................ 31 4.1.2.2 Warm Season Precipitation ............................................................................. 36 4.1.3 Wind Speed and Direction ...................................................................................... 39 iv 4.2 Aufeis Extent .................................................................................................................. 40 4.2.1 Historical Aufeis at Franklin Bluffs ........................................................................ 41 4.2.2 Delineating Ice Surface Elevation with GPS and Aerial Imagery .......................... 45 4.3 Surface Water Hydrology ............................................................................................... 52 4.3.1 Sagavanirktok River at MP318 (DSS4) .................................................................. 58 4.3.2 Sagavanirktok River at Happy Valley (DSS3) ....................................................... 61 4.3.3 Sagavanirktok River near MP347 (ASS1) .............................................................. 65 4.3.4 Sagavanirktok River below the Ivishak River (DSS2) ........................................... 66 4.3.5 Sagavanirktok River at East Bank (DSS5) near Franklin Bluffs ............................ 70 4.3.6 Sagavanirktok River at MP405 (DSS1) West Channel .......................................... 78 4.3.7 Additional Field Observations ................................................................................ 82 4.3.8 Preliminary Rating Curves and Estimated Discharge ............................................. 85 4.4 Stable Isotopes................................................................................................................ 86 4.5 Sediment Grain Size Distribution .................................................................................. 90 4.5.1 Streambed Sediment Grain Size Distribution ......................................................... 90 4.5.2 Suspended Sediment Grain Size Distribution ......................................................... 94 4.6 Suspended Sediment Concentration ............................................................................... 95 4.6.1 Sagavanirktok River near MP318 (DSS4) .............................................................. 95 4.6.2 Sagavanirktok River at Happy Valley (DSS3) ..................................................... 100 4.6.3 Sagavanirktok River below the Ivishak River (DSS2) ......................................... 105 4.6.4 Sagavanirktok River near MP405 (DSS1) ............................................................ 111 4.6.5 Discussion ............................................................................................................. 114 4.7 Turbidity ....................................................................................................................... 116 4.7.1 Sagavanirktok River near MP318 (DSS4) ............................................................ 116 4.7.2 Sagavanirktok River at Happy Valley (DSS3) ..................................................... 119 4.7.3 Sagavanirktok River below the Ivishak (DSS2) ................................................... 124 4.7.4 Sagavanirktok River near MP405 (DSS1) ............................................................ 126 4.7.5 Discussion ............................................................................................................. 130 4.8 Analysis of Pits............................................................................................................. 130 4.8.1 Photographs of Pits ............................................................................................... 130 4.8.2 GIS Analysis of Pit Bathymetry ........................................................................... 141 4.8.3 Pit Sedimentation .................................................................................................. 142 4.8.4 Erosion Surveys .................................................................................................... 149 4.8.5 Patterns of Sediment Transport Along the River .................................................. 156 v 4.9 Hydraulic Modeling ..................................................................................................... 158 4.9.1 Model Development .............................................................................................. 160 4.9.2 Results of Simulation ............................................................................................ 165 5 CONCLUSIONS ................................................................................................................. 171 6 REFERENCES .................................................................................................................... 174 7 APPENDICES ..................................................................................................................... 18

Chronic wounds in Sierra Leone: searching for Buruli ulcer, a NTD caused by Mycobacterium ulcerans, at Masanga Hospital

BACKGROUND: Chronic wounds pose a significant healthcare burden in low- and middle-income countries. Buruli ulcer (BU), caused by Mycobacterium ulcerans infection, causes wounds with high morbidity and financial burden. Although highly endemic in West and Central Africa, the presence of BU in Sierra Leone is not well described. This study aimed to confirm or exclude BU in suspected cases of chronic wounds presenting to Masanga Hospital, Sierra Leone. METHODOLOGY: Demographics, baseline clinical data, and quality of life scores were collected from patients with wounds suspected to be BU. Wound tissue samples were acquired and transported to the Swiss Tropical and Public Health Institute, Switzerland, for analysis to detect Mycobacterium ulcerans using qPCR, microscopic smear examination, and histopathology, as per World Health Organization (WHO) recommendations. FINDINGS: Twenty-one participants with wounds suspected to be BU were enrolled over 4-weeks (Feb-March 2019). Participants were predominantly young working males (62% male, 38% female, mean 35yrs, 90% employed in an occupation or as a student) with large, single, ulcerating wounds (mean diameter 9.4cm, 86% single wound) exclusively of the lower limbs (60% foot, 40% lower leg) present for a mean 15 months. The majority reported frequent exposure to water outdoors (76%). Self-reports of over-the-counter antibiotic use prior to presentation was high (81%), as was history of trauma (38%) and surgical interventions prior to enrolment (48%). Regarding laboratory investigation, all samples were negative for BU by microscopy, histopathology, and qPCR. Histopathology analysis revealed heavy bacterial load in many of the samples. The study had excellent participant recruitment, however follow-up proved difficult. CONCLUSIONS: BU was not confirmed as a cause of chronic ulceration in our cohort of suspected cases, as judged by laboratory analysis according to WHO standards. This does not exclude the presence of BU in the region, and the definitive cause of these treatment-resistance chronic wounds is uncertain