Explore the vast range of titles available.

The current paper provides a detailed review of the historical outbreaks of each of the four plague locust species found in South Africa, namely the brown locust, the African migratory locust, the red locust, and the southern African desert locust. The history and dynamics of the plague infestations and the major local outbreaks are summarized. The typical patterns of the outbreaks of the different species are described, and the threat of these locusts to agriculture in South Africa is defined. The brown locust produces regular outbreaks in the semi-arid Karoo, with large-scale eruptions of plague proportions occurring about once per decade. Patterns of outbreaks often repeat themselves, but the sheer size of the plague outbreaks is almost impossible to stop, and the brown locust has the potential to threaten food security throughout southern Africa. The African migratory locust produces outbreaks in some of the main maize and wheat cropping areas where it is difficult to control. This locust has taken advantage of the man-made crop environment to produce an extra generation per year that was not previously possible in the original grasslands. The coastal area of KwaZulu Natal Province in South Africa was a prime reception and breeding area for plague invasions of the red locust in the past, and the country, therefore, relies on the successful control of outbreaks in east and central Africa to prevent the recurrence of the plague invasions. The southern African desert locust occurs in the Kalahari Desert area, and outbreaks requiring chemical control are rare.

In order to understand the connectivity of neuronal networks, their constituent neurons should ideally be studied in a common framework. Since morphological data from physiologically characterized and stained neurons usually arise from different individual brains, this can only be performed in a virtual standardized brain that compensates for interindividual variability. The desert locust, Schistocerca gregaria , is an insect species used widely for the analysis of olfactory and visual signal processing, endocrine functions, and neural networks controlling motor output. To provide a common multi-user platform for neural circuit analysis in the brain of this species, we have generated a standardized three-dimensional brain of this locust. Serial confocal images from whole-mount locust brains were used to reconstruct 34 neuropil areas in ten brains. For standardization, we compared two different methods: an iterative shape-averaging (ISA) procedure by using affine transformations followed by iterative nonrigid registrations, and the Virtual Insect Brain (VIB) protocol by using global and local rigid transformations followed by local nonrigid transformations. Both methods generated a standard brain, but for different applications. Whereas the VIB technique was designed to visualize anatomical variability between the input brains, the purpose of the ISA method was the opposite, i.e., to remove this variability. A novel individually labeled neuron, connecting the lobula to the midbrain and deutocerebrum, has been registered into the ISA atlas and demonstrates its usefulness and accuracy for future analysis of neural networks. The locust standard brain is accessible at http://www.3d-insectbrain.com .

Time series of abundance indices for Desert Locusts Schistocerca gregaria (Forskål 1775) and Oriental Migratory Locusts Locusta migratoriamanilensis (Meyen 1835) were analysed independently and in relation to measures of solar activity and ocean oscillation systems. Data were compiled on the numbers of territories infested with swarms of the Desert Locust from 1860–2015 and an inferred series that compensated for poor reporting in the 1860 to 1925 period. In addition, data for 1930 to 2014, when reports are considered to have been consistently reliable were converted to numbers of 1° grid squares infested with swarms and separated according to four different geographical regions. Spectral analysis to test the hypothesis that there are cycles in the locust dynamics revealed periodicities of 7.5 and 13.5 years for the inferred series that were significant according to the Ornstein-Uhlenbeck state-space (OUSS) test. Similar periodicities were evident in the 1° grid square data and in each of the regions but even though these were significantly different from white noise, they were not significant according to the OUSS criterion. There were no significant peaks in the Oriental Migratory Locust results with the OUSS test, but the data were significantly different from white noise. To test hypotheses that long term trends in the locust dynamics are driven by solar activity and/or oceanic oscillation systems (the Southern Oscillation Index (SOI), the North Atlantic Oscillation Index (NAO) and the Indian Ocean Dipole (IOD)), the original locust data series and their Kalman-filtered low frequency (LF) components were tested for causality using both spectral coherence tests and convergent cross mapping. Statistically significant evidence was found that solar activity measured by numbers of sunspot groups drive the dynamics, especially the LF components, of both species. In addition, causal links were inferred between both the SOI and NAO data and Desert Locust dynamics. Spectral coherence was also found between sunspot groups and the NAO, the IOD and LF SOI data. The data were also analysed showing that the LF SOI had causal links with the LF inferred Desert Locust series. In addition, the LF NAO was causally linked to the LF 1° grid square data, with the NAO for December-March being most influential. The results suggest that solar activity plays a role in driving locust abundance, but that the mechanisms by which this happens, and whether they are mediated by fluctuations in oceanic systems, is unclear. Furthermore, they offer hope that information on these phenomena might enable a better early warning forecasting of Desert Locust upsurges.

Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences. Studying animal behavior can reveal how animals make decisions based on what they sense in their environment, but measuring behavior can be difficult and time-consuming. Computer programs that measure and analyze animal movement have made these studies faster and easier to complete. These tools have also made more advanced behavioral experiments possible, which have yielded new insights about how the brain organizes behavior. Recently, scientists have started using new machine learning tools called deep neural networks to measure animal behavior. These tools learn to measure animal posture – the positions of an animal’s body parts in space – directly from real data, such as images or videos, without being explicitly programmed with instructions to perform the task. This allows deep learning algorithms to automatically track the locations of specific animal body parts in videos faster and more accurately than previous techniques. This ability to learn from images also removes the need to attach physical markers to animals, which may alter their natural behavior. Now, Graving et al. have created a new deep learning toolkit for measuring animal behavior that combines components from previous tools with the latest advances in computer science. Simple modifications to how the algorithms are trained can greatly improve their performance. For example, adding connections between layers, or ‘neurons’, in the deep neural network and training the algorithm to learn the full geometry of the body – by drawing lines between body parts – both enhance its accuracy. As a result of adding these changes, the new toolkit can measure an animal's pose from previously unseen images with high speed and accuracy, after being trained on just 100 examples. Graving et al. tested their model on videos of fruit flies, zebras and locusts, and found that, after training, it was able to accurately track the animals’ movements. The new toolkit has an easy-to-use software interface and is freely available for other scientists to use and build on. The new toolkit may help scientists in many fields including neuroscience and psychology, as well as other computer scientists. For example, companies like Google and Apple use similar algorithms to recognize gestures, so making those algorithms faster and more efficient may make them more suitable for mobile devices like smartphones or virtual-reality headsets. Other possible applications include diagnosing and tracking injuries, or movement-related diseases in humans and livestock.

The central complex of the grasshopper Schistocerca gregaria develops to completion during embryogenesis. A major cellular contribution to the central complex is from the w, x, y, z lineages of the pars intercerebralis, each of which comprises over 100 cells, making them by far the largest in the embryonic protocerebrum. Our focus has been to find a cellular mechanism that allows such a large number of cell progeny to be generated within a restricted period of time. Immunohistochemical visualization of the chromosomes of mitotically active cells has revealed an almost identical linear array of proliferative cells present simultaneously in each w, x, y, z lineage at 50% of embryogenesis. This array is maintained relatively unchanged until almost 70% of embryogenesis, after which mitotic activity declines and then ceases. The array is absent from smaller lineages of the protocerebrum not associated with the central complex. The proliferative cells are located apically to the zone of ganglion mother cells and amongst the progeny of the neuroblast. Comparisons of cell morphology, immunoreactivity (horseradish peroxidase, repo, Prospero), location in lineages and spindle orientation have allowed us to distinguish the proliferative cells in an array from neuroblasts, ganglion mother cells, neuronal progeny and glia. Our data are consistent with the proliferative cells being secondary (amplifying) progenitors and originating from a specific subtype of ganglion mother cell. We propose a model of the way that neuroblasts, ganglion mother cells and secondary progenitors together produce the large cell numbers found in central complex lineages.

The objective of this study was to examine the inhibition of the activity of enzymes associated with development of the metabolic syndrome by peptide fractions received from simulated gastrointestinal digestion and absorption of heat-treated edible insects. The inhibitory activities of insect-derived peptides were determined against key enzymes relevant to the metabolic syndrome such as the angiotensin-converting enzyme (ACE), pancreatic lipase, and α-glucosidase. After the in vitro absorption process, all hydrolysates showed high inhibitory activity; however, the most effective metabolic syndrome-inhibitory peptides were received after separation on Sephadex G10. The best results were found for peptide fractions obtained from Schistocerca gregaria. The highest enzymes inhibitory activities were obtained for peptide fractions from S. gregaria: boiled for ACE (IC50 3.95 µg mL−1), baked for lipase (IC50 9.84 µg mL−1), and raw for α-glucosiadase (IC50 1.89 µg mL−1) S. gregaria, respectively. Twelve sequences of peptides from the edible insects were identified and their chemical synthesis was carried out as well. Among the synthesized peptides, the KVEGDLK, YETGNGIK, AIGVGAIR, IIAPPER, and FDPFPK sequences of peptides exhibited the highest inhibitory activity. Generally, the heat treatment process applied to edible insects has a positive effect on the properties of the peptide fractions studied.

Nitric oxide (NO) is a gaseous messenger molecule formed during conversion of L-arginine into L-citrulline by the enzyme NO synthase (NOS), which belongs to a group of NADPH diaphorases. Because of its gaseous diffusion properties, NO differs from classical neurotransmitters in that it is not restricted to synaptic terminals. In target cells, NO activates soluble guanylyl cyclase leading to an increase in cGMP levels. In insects, this NO/cGMP-signalling pathway is involved in development, memory formation and processing of visual, olfactory and mechanosensory information. We have analysed the distribution of putative NO donor and target cells in the central complex, a brain area involved in sky-compass orientation, of the locust Schistocerca gregaria by immunostaining for L-citrulline and cGMP. Six types of citrulline-immunostained neurons have been identified including a bilateral pair of hitherto undescribed neurons that connect the lateral accessory lobes with areas anterior to the medial lobes of the mushroom bodies. Three-dimensional reconstructions have revealed the connectivity pattern of a set of 18 immunostained pontine neurons of the central body. All these neurons appear to be a subset of previously mapped NADPH-diaphorase-positive neurons of the central complex. At least three types of central-complex neurons show cGMP immunostaining including a system of novel columnar neurons connecting the upper division of the central body and the lateral triangle of the lateral accessory lobe. Our results provide the morphological basis for further studies of the function of the labelled neurons and new insights into NO/cGMP signalling.

This study investigated the effect of heat treatment of edible insects on antioxidant and anti-inflammatory activities of peptides obtained by in vitro gastrointestinal digestion and absorption process thereof. The antioxidant potential of edible insect hydrolysates was determined as free radical-scavenging activity, ion chelating activity, and reducing power, whereas the anti-inflammatory activity was expressed as lipoxygenase and cyclooxygenase-2 inhibitory activity. The highest antiradical activity against DPPH• (2,2-diphenyl-1-picrylhydrazyl radical) was noted for a peptide fraction from baked cricket Gryllodes sigillatus hydrolysate (IC50 value 10.9 µg/mL) and that against ABTS•+ (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical) was the highest for raw mealworm Tenebrio molitor hydrolysate (inhibitory concentration (IC50 value) 5.3 µg/mL). The peptides obtained from boiled locust Schistocerca gregaria hydrolysate showed the highest Fe2+ chelation ability (IC50 value 2.57 µg/mL); furthermore, the highest reducing power was observed for raw G. sigillatus hydrolysate (0.771). The peptide fraction from a protein preparation from the locust S. gregaria exhibited the most significant lipoxygenase and cyclooxygenase-2 inhibitory activity (IC50 value 3.13 µg/mL and 5.05 µg/mL, respectively).

Recent studies of the Central Complex in the brain of the fruit fly have identified neurons with activity that tracks the animal’s heading direction. These neurons are part of a neuronal circuit with dynamics resembling those of a ring attractor. The homologous circuit in other insects has similar topographic structure but with significant structural and connectivity differences. We model the connectivity patterns of two insect species to investigate the effect of these differences on the dynamics of the circuit. We illustrate that the circuit found in locusts can also operate as a ring attractor but differences in the inhibition pattern enable the fruit fly circuit to respond faster to heading changes while additional recurrent connections render the locust circuit more tolerant to noise. Our findings demonstrate that subtle differences in neuronal projection patterns can have a significant effect on circuit performance and illustrate the need for a comparative approach in neuroscience.

We have examined the developmental expression of the neuromodulators locustatachykinin, leucokinin-1, allatostatin and serotonin in a subset of lineages (Y, Z) of the central complex in the brain of the grasshopper Schistocerca gregaria. First, we show that all these neuromodulators are expressed in the same lineages during embryogenesis. The neuroblasts generating these lineages are therefore biochemically multipotent. Second, the neurons expressing the different neuromodulators are found clustered at stereotypic locations in their respective lineages. Locustatachykinin and leucokinin-1 map to the apical region of the lineage, allatostatin medially and serotonin to the base of the lineage. Since the location in these lineages translates into their birth order, we have been able ontogenetically to analyse their biochemical expression patterns. The age-profile within a lineage reveals that locustatachykinin- and leucokinin-1-expressing neurons are born first, then allatostatin neurons and finally serotoninergic neurons. Co-expression has been tested for serotonin with locustatachykin, leucokinin-1 or allatostatin and is negative but is positive for locustatachykinin and leucokinin-1, consistent with the stereotypic location of cells in the lineages. The delay between the birth of a neuron and the expression of its neuromodulator is stereotypic for each substance. Combined with a known birth date, this delay translates into a developmental expression pattern for the central complex itself.