The behavior of postreproductive females in a wild population of toque macaques (Macaca sinica) in Sri Lanka

I compared the behavior of three old postreproductive females in a wild population of toque macaques (Macaca sinica)in Polonnaruwa with those of reproductive females via focal-animal sampling techniques. Postreproductives foraged less, slept more, and were less active overall than reproductive females were. They also had significantly lower rates of agonistic behavior, were more peripheral, and had lower frequencies of overall affiliative contact. Although postreproductives initiated contact with others as frequently as reproductives did, group members initiated contact with them significantly less than they did with reproductive females. Postreproductives associated more with adult females than reproductives did and less with adult and subadult males than high-ranking reproductives did. Juvenile and infant females associated more frequently with reproductive females of high or low rank than with postreproductives. Postreproductives resembled low-ranking reproductive females in giving less grooming to others than they received. This contrasts with high-ranking females, which gave more grooming to others than they received. The results suggest that old age and cessation of reproduction are evident through the manifestation of distinct behavioral characteristics in toque macaque females.     

Screening species and cultivars for their tolerance to acidic soil conditions.

The major phytotoxins in acid soils are aluminium and manganese. Tolerances to Al and to excessive Mn are independently inherited and Al and Mn solubilities in soils vary. In this work, the response of pasture grasses and legumes to soil acidity was studied on three soils with different Al and Mn concentrations. One provides moderate concentrations of Al with little Mn; one provides high concentrations of both Al and Mn and another provides a very high concentration of Mn at relatively low concentrations of Al. The response of a plant cultivar to changes in the soil acidity induced by lime or acid additions reflects the degree of Al and/or Mn stress provided by a particular soil, and the ability of the cultivar to tolerate those stresses. Examples are given of the way cultivars with different tolerances to Al and Mn toxicity respond to changes in acidity on the soils with different Al and Mn solubility characteristics. The utility of this screening technique to define the tolerance of cultivars to acidity on classically different soils is highlighted.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

The Exact Distribution of the Anderson-Kannemann Statistic

The Anderson-Kannemann test is a rank test for treatment effects in a randomized block design with K treatments and N blocks. In this paper, an algorithm for computing the exact distribution of the Anderson-Kannemann test statistic under the null hypothesis is deduced. Then, the exact distribution is compared with the asymptotic χ²-distribution, and it is shown that the exact distribution is approximated fairly well by the asymptotic distribution. Tables of the exact distribution are given for K = 3, N = 3(1)15; K = 4, N = 3(1)11; K = 5, N = 3(1)7; and K = 6, N = 3(1)5.     

Dispersal traits as indicators of vegetation dynamics in long-term old-field succession

Succession is a key ecological process that supports our understanding of community assembly and biotic interactions. Dispersal potential and dispersal strategies, such as wind- or animal-dispersal, have been assumed to be highly relevant for the success of plant species during succession. However, research yielded varying results on changes in dispersal modes between successional stages. Here, we test the hypotheses that (a) vascular plant species that use a number of dispersal modes dominate in early stages of succession while species specialized on one/few dispersal modes increase in abundance towards later stages of succession; (b) species well adapted to wind-dispersal (anemochory) will peak in abundance in early successional stages and (c) species well adapted to adhesive dispersal (epizoochory) will increase with proceeding succession. We test these hypotheses in four sites within agriculturally dominated landscapes in Germany. Agricultural use in these sites was abandoned 20–28 years ago, leaving them to secondary succession. Sites have been monitored for plant biodiversity ever since. We analyze changes in plant species richness and abundance, number of dispersal modes and two ranking indices for wind- and adhesive dispersal by applying generalized linear mixed-effect models. We used both abundance-weighted and unweighted dispersal traits in order to gain a comprehensive picture of successional developments. Hypothesis (a) was supported by unweighted but not abundance-weighted data. Anemochory showed no consistent changes across sites. In contrast, epizoochory (especially when not weighted by abundance) turned out to be an indicator of the transition from early to mid-successional stages. It increased for the first 9–16 years of succession but declined afterwards. Species richness showed an opposing pattern, while species abundance increased asymptotically. We suggest that plant-animal interactions play a key role in mediating these processes: By importing seeds of highly competitive plant species, animals are likely to promote the increasing abundance of a few dominant, highly epizoochorous species. These species outcompete weak competitors and species richness decreases. However, animals should as well promote the subsequent increase of species richness by disturbing the sites and creating small open patches. These patches are colonized by weaker competitors that are not necessarily dispersed by animals. The changes in the presence of epizoochorous species indicate the importance of plant traits and related plant–animal interactions in the succession of plant communities.     

Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach

ObjectivesObjective of this paper is to present a reliable and accurate technique for Myocardial Infarction (MI) detection and localization.Material and methodsStationary wavelet transform has been used to decompose the ECG signal. Energy, entropy and slope based features were extracted at specific wavelet bands from selected lead of ECG. k-Nearest Neighbors (kNN) with Mahalanobis distance function has been used for classification. Sensitivity (Se), specificity (Sp), positive predictivity (+P), accuracy (Acc), and area under the receiver operating characteristics curve (AUC) analyzed over 200 subjects (52 health control, 148 with MI) from Physikalisch-Technische Bundesanstalt (PTB) database has been used for performance analysis. To handle the imbalanced data adaptive synthetic (ADASYN) sampling approach has been adopted.ResultsFor detection of MI, the proposed technique has shown an AUC = 0.99, Se = 98.62%, Sp = 99.40%, PPR = 99.41% and Acc = 99.00% using 12 top ranked features, extracted from multiple leads of ECG and AUC = 0.99, Se = 98.34%, Sp = 99.77%, PPR = 99.77% and Acc = 99.05% using 12 features extracted from a single ECG lead (i.e. lead V5). For localization of MI, the proposed technique has an AUC = 0.99, Se = 98.78%, Sp = 99.86%, PPR = 98.80%, and Acc = 99.76% using 5 top ranked features from multiple leads of ECG and AUC = 0.98, Se = 96.47%, Sp = 99.60%, PPR = 96.49% and Acc = 99.28% using 8 features extracted from a single ECG lead (i.e. lead V3).ConclusionThus for MI detection and localization, the proposed technique is independent of time-domain ECG fiducial markers and can work using specific leads of ECG.     

Design a system for measuring individual cow feed intake in commercial dairies

Monitoring individual cow feed intake is necessary for calculating the cow individual feed efficiency. The cost and maintenance time necessary for research systems make them impractical for most of the commercial producers. We developed a measurement system with producer convenience and low investment as key design criteria. The goal of this study was to design the system and validate its ability to rank cows by their feed conversion efficiency in commercial farms. The new system consisted of three principal parts: (a) a hanging weighing system, (b) a visual cow identification system and (c) an automatic cleaning system. The weighing system consisted of hanging a single load cell to provide feed mass measurements. The image-based cow identification system (replacing Radio-Frequency Identification) entailed cameras installed above the feeding area and an image processing algorithm that recognized cows by their collar numbers. The new system worked within normal farm routines: the feed supplying truck distributed the animal feed, and a tractor cleaned feed residual. To validate the accuracy and convenience of the system and to rank the cows by their efficiency, an experiment with six scales and 12 cows was conducted in a research barn, succeeded by eight-scale system in a commercial farm with 16 cows. The feed intake of each cow participating in the experiments was monitored for one month. The validation experiment showed that the system had the following specification: scales were accurate within 120 g; the visual cow identification rate was greater than 96%; feeding duration was accurate to 52 s; and routine farm practices (feed distribution, pushing, and residual removal) continued as usual. The cost for a feeding station (utilized consequently for a number of cows) was about 1 500 USD. An example of application of the system to rank cows by their efficiency under commercial conditions was shown. The system can potentially be used for ranking cows by their efficiency in commercial facilities.     

Automatic methods for characterization of sexual dimorphism of adult femora: distal femur

Quantifying sex differences in femoral size and shape has extensive applications in forensics and prosthesis design. By applying strong statistical techniques such as principal component analysis (PCA), certain three-dimensional (3D) morphological variations of adult femora can be quantified over various femoral sizes. Coupling this statistical approach with a novel feature generation and extraction technique, localization of statistically significant (p < 0.05) features are automatically defined and measured. Also, predefined anatomical landmarks and surgical axes have been calculated automatically. In all methods, femoral scale is controlled as a possible parameter of shape. By extensively comparing measurements across 92 male and 74 female femora, the dimorphic characteristics of the distal femur are shown. These differences have not been accounted for in many prosthetic systems and consequently these systems have limited sizing accuracy.     

Pattern classification of kinematic and kinetic running data to distinguish gender,shod/barefoot and injury groups with feature ranking

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified.

The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task.

Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.