Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings

The aim of this paper is to develop a method to extract relevant activities from surface electromyography (SEMG) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics. The CEM algorithm proved to be useful for segmentation of SEMG signals into high amplitude artifacts (HAA), phasic activity (PA) and background postural activity (BA) classes. This segmentation was performed on signal energy, with classes belonging to a χ² distribution. Ninety-five percent of HAA events and 96.25% of BA events were detected, and the remaining noise was then identified using AR modeling, a classification based upon the position of the coordinates of the pole of highest module. This method eliminated 91.5% of noise and misclassified only 3.3% of EMG events when applied to SEMG recorded on passengers subjected to lateral accelerations.     

Time Slot Modeling of Life Habits in the Elderly for Decision-Making Support

This article investigates new steps for the implementation and the utilization of presence indicators to identify displacement activities of a person and to model their life habits. This modeling is based on preliminary measures of displacement rate variations over a period of time. A possible time slot division of activities is highlighted by the analysis of these measures, slight variations of slot boundaries could appear from day to day, from person to person. On this basis, we show how to build three new indicators by working from time slot to time slot, in order to detect alerts or drifts from “normal” behavior as soon as possible and in a more reliable way. These indicators include start and end times of time slots, displacement rate and duration of each time slot. The algorithm we propose has been tested in real situations to show its use and relevance. Results are finally integrated in a more ambitious process of detection and automatic decision-making support through the conception of a web interface.     

Relationships among rodent taxa as indicated by reproductive biology1

Demonstrated that in rodents the duration of ontogenetic development (from conception to the age of eye openin) is allometrically related to the maternal body weight. The differences between actual developmental time and the expected time (counted for animals of that systematic position and that body weight) can be used as a tool to test phylogenetic relationships. As far as the relationships between sister-taxa are concerned, the results of the present analysis corroborate the conclusions of modern classifications. A long developmental time (which means by implication also a long pregnancy and precocity) is couled with hystricognathy and hystricomorhy. These traits must be considered plesiomorphic and their occurrence suggests conservatism of articular taxa. Parallell (coupled) with adopting sciurognathy and, particularly, protrogomorpiy, sciuromorphy, and eventually, myomorphy, rodents were able to shorten their developmental times. In this way they could increase the reproductive rate and made their reproduction more efficient. Consequently the respective taxa radiated more rapidly and successfully.     

Deep learning-based segmentation for disease identification

Plant diseases have recently increased and exacerbated due to several factors such as climate change, chemicals’ misuse and pollution. They represent a severe threat for both economy and global food security. Recently, several researches have been proposed for plant disease identification through modern image-based recognition systems based on deep learning. However, several challenges still require further investigation. One is related to the high variety of leaf diseases/ species along with constraints related to the collection and annotation of real-world datasets. Other challenges are related to the study of leaf disease in uncontrolled environment. Compared to major existing researches, we propose in this article a new perspective to handle the problem with two main differences: First, while most approach aims to identify simultaneously a pair of species-disease, we propose to identify diseases independently of leaf species. This helps to recognize new species holding diseases that were previously learnt. Moreover, instead of using the global leaf image, we directly predict disease on the basis of the local disease symptom features. We believe that this may decrease the bias related to common context and/or background and enables to build a more generalised model for disease classification. In particular, we propose an hybrid system that combines strengths of deep learning-based semantic segmentation with classification capabilities to respectively extract infected regions and determine their identity. For that, an extensive experimentation including a comparison of different semantic segmentation and classification CNNs has been conducted on PlantVillage dataset (leaves within homogeneous background) in order to study the extent of use of local disease symptoms features to identify diseases. Specifically, a particular enhancement of disease identification accuracy has been demonstrated in IPM and BING datasets (leaves within uncontrolled background).     

Validation of Diagnostic Coding for Diabetes Mellitus in Hospitalized Patients

ObjectiveSome studies have shown that there is an undercoding of diabetes mellitus among hospitalized patients, which can have adverse clinical and financial implications for health systems. We aimed to validate the discharge diagnostic coding of diabetes mellitus in hospitalized patients using clinical and laboratory-based diagnostic indicators as the reference.MethodsThis was a retrospective cohort study of 83 690 discharges of 48 615 unique adult patients who were hospitalized in an academic medical center over 4.5 years and had at least 4 blood glucose measurements during admission. A missing diabetes code (MDC) was defined using 2 criteria. MDC₁ was defined as the presence of any of the following: blood glucose ≥200 (x2), A1C ≥6.5%, home antihyperglycemic medication, or preadmission code for diabetes, whereas MDC₂ was defined as preadmission diabetes or at least 2 other criteria in MDC₁. Multivariable logistic regression was used to identify factors associated with MDC compared to the present diabetes code.ResultsMDC₁ and MDC₂ were present in 12 186 (14.6%) and 3542 (4.7%) discharges, respectively. Factors associated with both MDC₁ and MDC₂ were medium-dose steroid use [adjusted odds ratio (aOR) 2.11, 2.01], high-dose steroid use (aOR 4.70, 2.50), intermediate medical care service (aOR 1.65, 1.55), infection (aOR 1.21, 1.34), and hepatic disease (aOR 1.93, 1.92).ConclusionIn this retrospective study, MDC ranged from 5% to 15% and was associated with various clinical factors. Further prospective studies are needed to validate these findings, explore the mechanisms behind these associations, and understand the clinical and financial implications.     

George Gaylord Simpson as mentor and apologist for paleoanthropology

The influence of George Gaylord Simpson (1902–1984) on paleoanthropology provides a well-documented, historical example of how one scientific discipline can impact upon another, bringing the latter quickly “up to speed” without having to retrace ground covered by the former. Paleoanthropologists were bystanders during the formulation of the evolutionary synthesis (1936–1947). After World War II, the younger paleoanthropologists looked to George Gaylord Simpson as one of several mentors regarding the implications of the synthesis for their own discipline. But why Simpson? Having earlier defined the superfamily Hominoidea (1931) as holding the Pongidae and Hominidae and monographed lower primate fossils (e.g., “Studies on the earliest primates,” 1940), Simpson's “Principles of classification and a classification of mammals” (1945) further solidified his reputation as a mammalian systematist. Simpson's Meaning of Evolution (1949) was widely read as an introduction to the synthesis, and his Tempo and Mode in Evolution (1944) made accessible the more complex aspects. Consequently, in the 1950s and 1960s paleoanthropologists invited Simpson to participate in their symposia (e.g., “Some principles of historical biology bearing on human origins,” 1951; “The meaning of taxonomic statements,” 1964), used his books as classroom texts, and cited his publications to support claims for their own work. Later in the 1960s, Simpson moved from mentor to apologist, as the paleoanthropologists were by then familiar with the synthesis and incorporated its theoretical concepts in their interpretations of the many newly discovered hominoid fossils. Simpson now took special care to celebrate these results in his more general, less technical writings, acting as a forceful apologist for the materialistic view of human origins (e.g., “The biological nature of man,” 1966; “The evolutionary concept of man,” 1972). During the 1970s, Simpson's influence waned, and he became just another practitioner at the margin of the discipline. However, anthropologists acknowledged Simpson's earlier impact, for example, when he was invited, yet again, to address them at the 50th anniversary celebration of the American Association of Physical Anthropologists in 1981.     

Distribution and extent of unimproved lowland grassland National Vegetation Classification (NVC) types in Scotland

Summary

National Vegetation Classification (NVC) data from surveys of Scottish unimproved lowland grasslands were reviewed. The surveys dated from 1980 to 2000. The mapped area of each lowland grassland NVC type was measured or, where vegetation maps were not of sufficient quality, estimated. These measurements and estimates were summed to give the total recorded area of each lowland grassland NVC type. An estimate of the total area likely to occur throughout Scotland was made for the more thoroughly recorded lowland grassland NVC types.

The data from this review has been used to create Scottish Natural Heritage's Lowland Grassland Database, containing details of all lowland grassland sites recorded by the grassland surveys, their grid reference, conservation status, site area, surveyor, date of survey, NVC lowland grassland types present and their areas. Council Area, old local authority District and SNH Area are also included.

A total of 8700 ha of unimproved lowland grassland NVC types (excluding coastal grasslands) has been recorded in Scotland. This habitat is semi-natural and of high conservation value.

The total area of unimproved lowland grassland in Scotland is estimated to be in the order of 30,000 ha.