Structure from motion photogrammetry in ecology: Does the choice of software matter?

Image‐based modeling, and more precisely, Structure from Motion (SfM) and Multi‐View Stereo (MVS), is emerging as a flexible, self‐service, remote sensing tool for generating fine‐grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone‐based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer‐grade camera was used to capture replicate, centimeter‐resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM‐MVS software—Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset (n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets (n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for “High,” “Medium,” and “Low” quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using “High” and “Medium” quality settings are of small overall magnitude.     

Clinical mutants of human glucose 6-phosphate dehydrogenase: Impairment of NADP+ binding affects both folding and stability

Human glucose 6-phosphate dehydrogenase (G6PD) has both the “catalytic” NADP⁺ site and a “structural” NADP⁺ site where a number of severe G6PD deficiency mutations are located. Two pairs of G6PD clinical mutants, G6PD_Wisconsin (R393G) and G6PD_Nashville (R393H), and G6PD_Fukaya (G488S) and G6PD_Campinas (G488V), in which the mutations are in the vicinity of the “structural” NADP⁺ site, showed elevated K_d values of the “structural” NADP⁺, ranging from 53 nM to 500 nM compared with 37 nM for the wild-type enzyme. These recombinant enzymes were denatured by Gdn-HCl and refolded by rapid dilution in the presence of l-Arg, NADP⁺ and DTT at 25 °C. The refolding yields of the mutants exhibited strong NADP⁺-dependence and ranged from 1.5% to 59.4% with 1000 μM NADP⁺, in all cases lower than the figure of 72% for the wild-type enzyme. These mutant enzymes also displayed decreased thermostability and high susceptibility to chymotrypsin digestion, in good agreement with their corresponding melting temperatures in CD experiments. Taken together, the results support the view that impaired binding of “structural” NADP⁺ can hinder folding as well as cause instability of these clinical mutant enzymes in the fully folded state.     

The effect of chemical and biological treatment on root and crown rot disease caused by Phytophthora cryptogea Pethybr. & Lafferty in Gerbera

Phytophthora root and crown rot (Phytophthora cryptogea) on gerbera is difficult to manage because most gerbera cultivars are susceptible to P. cryptogea. This study was conducted in order to determine the in vivo (pot experiment) efficacy of some fungicides and biofungicides. In pot experiments, fungicides were applied 7 days after inoculation with P. cryptogea, while biofungicide was applied 7 days before inoculation. In this study, soil drenches of five fungicides were tested. “Ametoctradin+dimethomorph (100 ml/day),” “mandipropamid+difenoconazole (60 ml/day),” “propamocarb+fosetyl‐Al (200 ml/day),” “mancozeb+metalaxyl‐M (250 g/day)” and “azoxystrobin+difenoconazole (100 ml/day)” active substances were used. Similarly, one biofungicide Bacillus amyloliquefaciens syn. MBI 600 (50 g/100 L) was applied by soil drenching. Efficacy of treatments was assessed according to the percentage of the root system which was visibly rotten at the end of the experiment. Root and crown rot severity was rated on a scale of 0 = 0% root system necrotic, 1 = 1%‐25% necrotic, 2 = 26%‐50% necrotic, 3 = 51%‐75% necrotic and 4 = 76%‐100% necrotic from 12 to 21 days. In this experiment, “azoxystrobin 200 g/L + difenoconazole 125 g/L” exhibited the highest efficacy against P. cryptogea with a ratio of 43.75%. The other fungicides and biofungicides ametoctradin 300 g/L + dimethomorph 225 g/L, mandipropamid 250 g/L + difenoconazole 250 g/L, propamocarb 530 g/L + fosety‐Al 310 g/L, mancozeb 64%+metalaxyl‐M 4% and Bacillus amyloliquefaciens syn. MBI 600 11% were ineffective. Importance should be given to management strategies of P. cryptogea of and more experiments should be carried out for a better understanding of the use of registered fungicides and biofungicides.     

SegNet and Salp Water Optimization-driven Deep Belief Network for Segmentation and Classification of Brain Tumor

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.     

Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

Marketability of agricultural products depends heavily on appearance attributes such as color, size, and ripeness. Sorting plays an important role in increasing marketability by separating crop classes according to appearance attributes, thus reducing waste. As an expert technique, image processing and artificial intelligence (AI) techniques have been applied to classify hawthorns based on maturity levels (unripe, ripe, and overripe). A total of 600 hawthorns were categorized by an expert and the images were taken by an imaging box. The geometric properties, color and, texture features were extracted from segmented hawthorns using the Gray Level Co-occurrence Matrix (GLCM) and evaluation of various color spaces. The efficient feature vector was created by QDA feature reduction method and then classified using two classical machine learning algorithms: Artificial Neural Network (ANN) and Support Vector Machine (SVM). The obtained results indicated that the efficient feature-based ANN model with the configuration of 14–10-3 resulted in the accuracy of 99.57, 99.16, and 98.16% and the least means square error (MSE) of 1 × 10⁻³, 8 × 10⁻³, and 3 × 10⁻³ for training, validation and test phases, respectively. The machine vision system combined with the machine learning algorithms can successfully classify hawthorns according to their maturity levels.     

Citrus orchard mapping in Juybar,Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries

Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and remote sensing researchers' interests. This study proposed an automatic method to map citrus orchards in Juybar, Iran, where planting citrus trees is booming there. In this regard, 148 Sentinel-1, Sentinel-2, and ALOS Digital Surface Model (DSM) tiles are processed in Google Earth Engine to provide a hybrid feature set including initial satellite images, Gray Level Co-occurrence Matrix (GLCM) textural features, and spectral features such as vegetation, built-up, bare-soil indices, and the proposed Vegetation Dynamic Index (VDI). A semi-automatic sample selection paradigm is also developed based on a time-series analysis of 12 monthly Normalized Difference Vegetation Indices (NDVIs), Otsu thresholding, multi-level thresholding (MLT), and using two proposed indices called Evergreenness Index (EGI) and Water-covered or No-vegetation (WCNV) index, and finally human post-revision. The output of the Support Vector Machine (SVM) classification using 60,000 samples and the post-classification operation showed that the classified map has an average overall accuracy (OA) and an average kappa coefficient (KC) equal to 99.7% and 0.992, respectively. The results show that multispectral bands lonely extracted orchards with high accuracy (OA: 99.55%, KC: 0.986), and the rest of the features only made a slight improvement to that. For the year 2019, an area of about 4351 ha is estimated as citrus orchards, which is in accordance with the agriculture department's reports (~4700 ha). The results indicate that from 2016 to 2019, despite a “citrus to non-citrus” land-use conversion of about 754 ha, the citrus orchards area was totally expanded by about 17%. Comparing the results with the Google Earth images indicates the precise extraction of orchards with a 10 m spatial resolution. To use the proposed method for extensive cases or areas with other types of evergreen trees, it is recommended to use high-resolution normalized DSMs (nDSMs) and textural features.     

Supramolecular structure of the OXPHOS system in highly thermogenic tissue of Arum maculatum

The protein complexes of the mitochondrial respiratory chain associate in defined ways forming supramolecular structures called respiratory supercomplexes or respirasomes. In plants, additional oxidoreductases participate in respiratory electron transport, e.g. the so-called “alternative NAD(P)H dehydrogenases” or an extra terminal oxidase called “alternative oxidase” (AOX). These additional enzymes were previously reported not to form part of respiratory supercomplexes. However, formation of respiratory supercomplexes might indirectly affect “alternative respiration” because electrons can be channeled within the supercomplexes which reduces access of the alternative enzymes towards their electron donating substrates. Here we report an investigation on the supramolecular organization of the respiratory chain in thermogenic Arum maculatum appendix mitochondria, which are known to have a highly active AOX for heat production. Investigations based on mild membrane solubilization by digitonin and protein separation by blue native PAGE revealed a very special organization of the respiratory chain in A. maculatum, which strikingly differs to the one described for the model plant Arabidopsis thaliana: (i) complex I is not present in monomeric form but exclusively forms part of a I + III₂ supercomplex, (ii) the III₂ + IV and I + III₂ + IV supercomplexes are detectable but of low abundance, (iii) complex II has fewer subunits than in A. thaliana, and (iv) complex IV is mainly present as a monomer in a larger form termed “complex IVa”. Since thermogenic tissue of A. maculatum at the same time has high AOX and I + III₂ supercomplex abundance and activity, negative regulation of the alternative oxidase by supercomplex formation seems not to occur. Functional implications are discussed.     

The harmful effect of removing the extracellular vitrification medium during embryo cryopreservation using a nylon mesh device in rabbit

During the last decades, many techniques have been developed to reduce sample volume and improve cooling and warming rates during embryo vitrification. The vast majority are based on the “minimum drop size” concept, in which the vitrification solution around embryos is reduced by aspiration, leaving a tiny part of volume surrounding embryos. However, novel cryodevices were aimed to remove the entire vitrification solution. This study was designed to compare the “minimum drop size” technique using Cryotop® with the nylon mesh as cryodevice on rabbit morula embryos. The outcomes assessed were the in vitro development rates (experiment 1) and the offspring rates at birth (experiment 2). Embryos were vitrified in a two-step procedure; equilibrium (10% EG + 10% Me2SO) for 2 min and vitrification (20% EG + 20% Me2SO) for 1 min. In experiment 1, embryos (n = 323) were warmed and subsequently in vitro cultured for 48 h to assess the embryo developmental capability to reach the hatching-hatched blastocyst stage. In experiment 2, embryos were transferred using the laparoscopic technique (n = 369) to assess the offspring rate at birth. In this context, rates of in vitro embryo development were similar between vitrified groups (0.73 ± 0.042% and 0.66 ± 0.047% for Cryotop® and nylon mesh device, respectively), but lower than in the fresh group (0.97 ± 0.016%, p < 0.05). In experiment 2, there were no significant differences in survival rates (offspring born/total embryos transferred) among the Cryotop® device group and fresh group (0.41 ± 0.049% and 0.49 ± 0.050%, respectively). But significantly lower value was obtained in the nylon mesh device group (0.18 ± 0.030%). These results indicate that nylon mesh is not suitable as cryodevice for rabbit morula vitrification, remaining those using the “minimum drop size” methodology as the best option.     

Feature selection and nearest centroid classification for protein mass spectrometry

Background  

The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry.     

An unsupervised hyperspheric multi-layer feedforward neural network model

This paper introduces a novel Neural Network model intended for classification of patterns into distinct categories. Arbitrary accurate category formation in a predefined feature space is asymptotically achieved by means of an unsupervised learning algorithm. Learning takes place by assignment of labeled neurons to unrecognized input exemplars and subsequent labels merging subject to similarity constraints. Two model variants, one under category separability assumption, and a second under a more general category probability density unimodality (and non-separability) assumption are suggested. The hyperspheric nature (as opposed to hyperplanar — typical of some current classifiers) of this model and its multilayer feedforward architecture are explained. Simulation results demonstrating asymptotic convergence and excellent classification accuracy are provided.     

White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis

White Blood Cells play an important role in observing the health condition of an individual. The opinion related to blood disease involves the identification and characterization of a patient's blood sample. Recent approaches employ Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and merging of CNN and RNN models to enrich the understanding of image content. From beginning to end, training of big data in medical image analysis has encouraged us to discover prominent features from sample images. A single cell patch extraction from blood sample techniques for blood cell classification has resulted in the good performance rate. However, these approaches are unable to address the issues of multiple cells overlap. To address this problem, the Canonical Correlation Analysis (CCA) method is used in this paper. CCA method views the effects of overlapping nuclei where multiple nuclei patches are extracted, learned and trained at a time. Due to overlapping of blood cell images, the classification time is reduced, the dimension of input images gets compressed and the network converges faster with more accurate weight parameters. Experimental results evaluated using publicly available database show that the proposed CNN and RNN merging model with canonical correlation analysis determines higher accuracy compared to other state-of-the-art blood cell classification techniques.     

Priming and filtering of antiherbivore defences among Nicotiana attenuata plants connected by mycorrhizal networks

Arbuscular mycorrhizal fungi (AMF) establish symbiotic associations with a majority of terrestrial plants to form underground common mycorrhizal networks (CMNs) that connect neighbouring plants. Because Nicotiana attenuata plants do not respond to herbivory‐elicited volatiles from neighbours, we used this ecological model system to evaluate if CMNs function in interplant transmission of herbivory‐elicited responses. A mesocosm system was designed to establish and remove CMNs linking N. attenuata plants to examine the herbivory‐elicited metabolic and hormone responses in CMNs‐connected “receiver” plants after the elicitation of “donor” plants by wounding (W) treated with Manduca sexta larval oral secretions (OS). AMF colonization increased constitutive jasmonate (JA and JA‐Ile) levels in N. attenuata roots but did not affect well‐characterized JAs‐regulated defensive metabolites in systemic leaves. Interestingly, larger JAs bursts, and higher levels of several amino acids and particular sectors of hydroxygeranyllinalool diterpene glycoside metabolism were elevated in the leaves of W + OS‐elicited “receivers” with CMN connections with “donors” that had been W + OS‐elicited 6 hr previously. Our results demonstrate that AMF colonization alone does not enhance systemic defence responses but that sectors of systemic responses in leaves can be primed by CMNs, suggesting that CMNs can transmit and even filter defence signalling among connected plants.     

A computational algorithm for classifying step and spin turns using pelvic center of mass trajectory and foot position

Transient changes in direction during ambulation are typically performed using a step (outside) or spin (inside) turning strategy, often identified through subjective and time-consuming visual rating. Here, we present a computational, marker-based classification method utilizing pelvic center of mass (pCOM) trajectory and time-distance parameters to quantitatively identify turning strategy. Relative to visual evaluation by three independent raters, sensitivity, specificity, and overall accuracy of the pCOM-based classification method were evaluated for 90-degree turns performed by 3 separate populations (5 uninjured controls, 5 persons with transtibial amputation, and 5 persons with transfemoral amputation); each completed turns using two distinct cueing paradigms (i.e., laser-guided “freeform” and verbally-guided “forced” turns). Secondarily, we compared the pCOM-based turn classification method to adapted versions of two existing computational turn classifiers which utilize trunk and shank angular velocities (AV). Among 366 (of 486 total) turns with unanimous intra- and inter-rater agreement, the pCOM-based classification algorithm was 94.5% accurate, with 96.6% sensitivity (accuracy of spin turn classification), and 93.5% specificity (accuracy of step turn classification). The pCOM-based algorithm (vs. both AV-based methods) was more accurate (94.5% vs. 81.1–80.6%; P < 0.001) overall, as well as specifically in freeform (92.9 vs. 80.4–76.8%; P < 0.003) and forced (96.0 vs. 83.8–81.8%; P < 0.001) cueing, and among individuals with (92.4 vs. 80.2–78.8%; P < 0.001) and without (99.1 vs. 86.2–80.8%; P < 0.001) amputation. The pCOM-based algorithm provides an efficient and objective method to accurately classify 90-degree turning strategies using optical motion capture in a laboratory setting, and may be extended to various cueing paradigms and/or populations with altered gait.     

Improving the “chain and tape” method: A combined topography index for marine fish ecology studies

The “chain and tape” method is used to quantify topographic complexity in reef ecology studies, consisting of the ratio of the linear distance between the start and end points of a chain moulded to the surface of the substrate to its stretched length, a measure known as the substrate rugosity (SR) index. This measure has several advantages in the field when compared to other methods, but some weaknesses have been pointed out. However, it is still one of the most frequently used topography measures in reef fish ecology. The present study proposes a combined topography index (CTI) that uses the “chain and tape” method in the field, with results that can match more complex methods, outperforming the traditional SR index. The CTI is structured as a weighted sum of 3 topographic features: SR index, number of corrugations (NC) and maximum vertical relief (MVR), where NC and MVR are given weight coefficients ranging from 0 to 1. In order to establish weight coefficients, fish assemblages were sampled at 6 training sites, representing a topographic complexity gradient. A series of candidate weight combinations were then selected so that CTI was optimally correlated with each one of several fish assemblage parameters. The list of possible solutions was then applied to nine previously published schematic profiles and compared with other methods. The final index was established as: CTI = (1 − SR) + NC/25 + MVR/25. Ultimately, the predictive performance of CTI and SR was tested by applying them to 11 new sites as predictors of species abundances in distance-based linear models. The CTI outperformed SR when added to 3 previously fitted variables (depth, percent cover of sand, percent cover of cobble), explaining 5.6% additional variation when using all species and 8.1% when using only cryptobenthic species, whereas SR showed no significant additional effects.     

Injury due to extravasation of thiopental and propofol: Risks/effects of local cooling/warming in rats

Inadvertent leakage of medications with vesicant properties can cause severe necrosis in tissue, which can have devastating long-term consequences. The aim of this study was to evaluate the extent of extravasation injury induced by thiopental and propofol, and the effects of cooling or warming of local tissue on extravasation injury at macroscopic and histopathologic levels. Rats were administered intradermally thiopental (2.5 mg/100 µL) or propofol (1.0 mg/100 µL). Rats were assigned randomly to three groups: control (no treatment), cooling and warming. Local cooling (18–20 °C) or warming (40–42 °C) was applied for 3 h immediately after agent injection. Lesion sizes (erythema, induration, ulceration, necrosis) were monitored after agent injection. Histopathology was evaluated in skin biopsies taken 24 h after agent injection. Thiopental injection induced severe skin injury with necrosis. Peak lesions developed within 24 h and healed gradually 18–27 days after extravasation. Propofol induced inflammation but no ulceration, and lesions healed within 1–2 days. Local cooling reduced thiopental- and propofol-induced extravasation injuries but warming strongly exacerbated the skin lesions (e.g., degeneration, necrosis) induced by extravasation of thiopental and propofol. Thiopental can be classified as a “vesicant” that causes tissue necrosis and propofol can be classified as an “irritant”. Local cooling protects (at least in part) against skin disorders induced by thiopental and propofol, whereas warming is harmful.     

Transcriptional analysis for carbon metabolism and kinetic modeling for heterologous proteins productions by Pichia pastoris in induction process with methanol/sorbitol co-feeding

It is difficult to control concentrations of methanol/dissolved oxygen at high levels simultaneously in heterologous proteins productions by Pichia pastoris during induction phase. Two strains, a methanol utilization slow (Mut^S) type and a plus (Mut⁺) type were used with methanol/sorbitol co-feeding strategy to induce porcine interferon-α and human serum albumin-human granulocyte colony stimulating factor respectively, under the conditions of “methanol sufficient-oxygen limited (MS-OL)” and “methanol limited-oxygen sufficient (ML-OS)”. For the Mut^S/Mut⁺ strains, the target proteins titers under “MS-OL” were 6-fold/19.2% of those under “ML-OS”. The key genes in methanol metabolism of the Mut^S strain were up-regulated under “MS-OL”, but they were not differently expressed in the Mut⁺ strain. Methanol utilization rate (r_MeOH) of the Mut^S strain reduced when decreasing methanol concentration, but r_MeOH of the Mut⁺ strain unchanged unless methanol concentration decreased to a low-limit of 0.6 g/L. Finally, kinetic models were designed to describe the methanol/sorbitol co-feeding process.     

Biological treatment of n-hexane and methanol in trickle bed air biofilters under acidic conditions

This study focuses on evaluating the degradation of n-hexane/methanol mixture in trickle-bed-air-biofilters (TBABs). Two different concentration ratios of methanol:n-hexane were evaluated (3:1) for TBAB “A” and (5:1) for TBAB “B”. Both TBABs were run and fed with nutrients buffered at pH 4 for encouraging the growth of fungi. The TBABs were loaded with pelletized diatomaceous earth support media and were run at an empty bed residence time of 120 s. n-Hexane loading rates (LRs) ranged from 0.9 to 13.2 g/m³ h for both TBABs. The corresponding methanol LRs varied from 2.3 to 37.7 g/m³ h and from 4.6 to 64.5 g/m³ h for TBABs “A” and “B”, respectively. Experimental results have shown that the degradation of n-hexane in presence of methanol is enhanced for n-hexane LRs less than 10.6 g/m³ h as compared to previous study for sole-fed n-hexane, but for n-hexane LRs of 13.2 g/m³ h, the performance of TBABs in eliminating n-hexane depended on the methanol to n-hexane ratios. The impact was less severe for TBAB “A” (RE 85%) as compared to TBAB “B” (RE 72%). This is attributed to the high LRs of methanol in TBAB “B”. n-Hexane performance stability was another advantage attained.     

A new data of worker polymorphism in the ant genus Dorylus (Hymenoptera: Formicidae: Dorylinae)

Recently, in southern and central Vietnam, foraging columns of D. orientalis which contained not only “typical” workers but also a few “atypical” workers were collected. The atypical worker mentioned above is characterized by a set of the following features: (1) head narrowed anteriorly, (2) median portion of clypeus strongly projecting anteriad, and (3) antenna 8-segmented. Sequences of the 658-base standard mitochondrial DNA barcoding region were completely identical between typical and atypical workers. Therefore, the condition observed was a case of worker polymorphism within a colony. The mode of polymorphism observed has the following interesting aspects: (1) workers are clearly subdivided into two series by a set of qualitative characters; (2) the “typical series” is numerically much more dominant than the “atypical series” (the latter occupied less than 1% of the whole of the workers collected); (3) a wider size variation was observed in the former (HW, 0.48–1.41 mm; ML, 0.42–1.12 mm) than in the latter (HW, 0.44–1.13 mm; ML, 0.35–0.79 mm); and (4) within the atypical series, smaller workers are numerically much dominant. Rareness of the workers belonging to the atypical series in foraging column as well as morphological differences between the two series suggests a certain possibility that the atypical series does not adapt to foraging but to other tasks in the colony's life history.