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Locusts

Locusts are a type of grasshopper that can form large swarming groups, posing a significant threat to agriculture and food security.
These insects are known for their ability to rapidly multiply and migrate, consuming vast quantities of crops and vegetation.
PubCompare.ai's AI-powered platform can help researchers optimize their locust studies by easily identifying the best protocols, products, and methods from the scientific literature, preprints, and patents.
With advanced comparison features, users can pinpoint the most reproducible and accurate techniques for their locust research, streamlining the process and enhancing the quality of their work.
Discover how PubCompare.ai can support your locust-related studies today.

Most cited protocols related to «Locusts»

Transcriptional Adaptation of B. bassiana

Cited 19 times

Conidia of B. bassiana ARSEF 2860 strain were harvested from 14-day old potato dextrose agar and used for different assays. To examine gene induction on insect cuticle, locust (Locusta migratoria) hind wings were collected, air-dried and surface sterilized in 10% H₂O₂ (10 min). The wings were washed in sterile water (twice) and immersed in a Bb conidial suspension (2 × 10⁷ spores per ml) for 20 seconds17 (link). The inoculated wings were placed on 1% water agar and incubated at 25°C for 24 hrs for total RNA extraction. For analysis of transcriptional adaptation to insect hemocoel, the 5^th instar cotton bollworm (Helicoverpa armigera) larvae were each injected with 10 μl of a spore suspension (10⁸ spores/ml). Hemolymph from infected insects 48 hours post inoculation was collected on ice and immediately applied on top of a step gradient of 25 and 50% Centricoll (Sigma). The fungal cells were purified for RNA extraction by centrifugation at 10,000 g for 10 min at 4°C. For analysis of transcriptional adaptation to plant root exudates, mycelia harvested from 36 hour Sabouraud dextrose broth were incubated in corn root exudates for another 24 hours before being used for RNA extraction. Root exudates were prepared as described before81 (link). RNA was extracted with a Qiagen RNeasy kit plus on-column treatment with RNase-free DNase I. Messenger RNA was purified, and after reverse transcription into cDNA libraries were constructed for tag preparation according to the massively parallel signature sequencing protocol82 (link). The tags were sequenced with an Illumina technique. We omitted tags from further analysis if only one copy was detected or it could be mapped to a different transcript. Other tags were mapped to the genome or annotated genes if they possessed no more than one nucleotide mismatch17 (link)18 (link). The level of gene transcription was converted to transcripts per million tags (TPM) for each mapped gene for expressional comparison between samples. The RNA_seq expression dataset is available at the Gene Expression Omnibus under the accession GSE32699.

Xiao G., Ying S.H., Zheng P., Wang Z.L., Zhang S., Xie X.Q., Shang Y., St. Leger R.J., Zhao G.P., Wang C, & Feng M.G. (2012). Genomic perspectives on the evolution of fungal entomopathogenicity in Beauveria bassiana. Scientific Reports, 2, 483.

Publication 2012

Acclimatization Agar Biological Assay cDNA Library Cells Centrifugation Conidia Deoxyribonuclease I Endoribonucleases Exudate Gene Expression Genes Glucose Gossypium Hemolymph Induction, Genetic Insecta Larva Locusta migratoria Locusts Maize Mycelium Nucleotides Peroxide, Hydrogen Plant Roots Plants Reverse Transcription RNA, Messenger Solanum tuberosum Spores Sterility, Reproductive Strains Transcription, Genetic Vaccination

Behavioral Assays for Locust Gregariousness

Cited 13 times

The arena assay experiment was performed in a rectangular perspex arena (40 cm length×30 cm width×10 cm height) with opaque walls and a clear top (Figure S14A) according to previous study [17] . One of the separated chambers (7.5 cm length×30 cm width×10 cm height) contained 15 fourth-instar gregarious locusts as a stimulus group, and the other chamber was left empty. Before measurement, the locusts were restricted in a Perspex cylinder for 2 min. Then, locusts were released into the arena and monitored for 300 s. EthoVision video tracking system (Netherlands, Noldus Information Technology) was used to automatically record individual behavior. Eleven behavioral variables were acquired: entry frequency in stimulus area (stimulus area was defined as 25% of the arena closest to the stimulus group, 14A in red), latency of first occurrence in stimulus area, total duration in area close to the wall, entry frequency in area close to the wall, entry frequency in the region opposite to stimulus area (opposite of stimulus area was defined as the 25% of the arena at the opposite end to the stimulus group, Figure S14A in blue), latency of first occurrence in the opposite of stimulus area, mean distance to the stimulus group, attraction index (total duration in stimulus area, total duration in the opposite of stimulus area and total duration in middle area were weighted by 1, −1 and 0, respectively. Attraction index = 1×total duration in stimulus area+(−1)×total duration in the opposite of stimulus area+0×total duration in middle area), total distance moved, total duration of movement, and frequency of movement. Based on the data from 86 gregarious and 69 solitarious fourth-instar nymphs, a forward stepwise approach was performed to construct the binary logistic regression model: P_greg = e^η/ (1+e^η), where η = β₀+β₁·X₁+β₂·X₂+…+β_k·X_k. P_greg indicates the probability of a locust being considered as the gregarious. P_greg = 1 means fully gregarious behavior and P_greg = 0 means fully solitarious behavior. This model shared similar features with previous logistic model [11] (link), [17] as the retained variables indicated: total distance moved and total duration of movement represent the activity levels, and attraction index represent the attraction or repulsion to the stimulus group.
A Y-tube olfactometer was used to analyze the behavioral responses of individual locust to volatiles (including volatiles from body and feces) from 30 fourth-instar gregarious locusts in the absence of any visual cues (Figure S14B). Individual locust was recorded as “first choice” for volatile or air (whenever the locust moved more than 5 cm into either arm) or “no choice” (N.C) in 5 min.

Guo W., Wang X., Ma Z., Xue L., Han J., Yu D, & Kang L. (2011). CSP and Takeout Genes Modulate the Switch between Attraction and Repulsion during Behavioral Phase Change in the Migratory Locust. PLoS Genetics, 7(2), e1001291.

Publication 2011

Biological Assay Disgust Feces Human Body Locusts Movement Nymph Perspex

Genome Sequencing of Solitarious Locusts

Cited 12 times

The strain for genome sequencing originated from inbred laboratory strains of solitarious locusts, which were produced from eight generations of sib mating at the Institute of Zoology, CAS, China. DNA for genome sequencing was extracted from the whole body of one female adult, with the exception of its guts. Genomic DNA was isolated using standard molecular biology techniques. Gradient increased insert-size libraries, 170, 200, 500 and 800 bp and 2, 5, 10, 20 and 40 kb were constructed. All libraries were sequenced on Illumina HiSeq 2000. In total, 42 paired-end sequencing libraries were constructed and 82 lanes were sequenced, producing 1,135 Gb of raw data. Further low-quality and duplicated reads filtering resulted in 720 Gb (114 × coverage) data for genome assembly (Supplementary Table S1).

Wang X., Fang X., Yang P., Jiang X., Jiang F., Zhao D., Li B., Cui F., Wei J., Ma C., Wang Y., He J., Luo Y., Wang Z., Guo X., Guo W., Wang X., Zhang Y., Yang M., Hao S., Chen B., Ma Z., Yu D., Xiong Z., Zhu Y., Fan D., Han L., Wang B., Chen Y., Wang J., Yang L., Zhao W., Feng Y., Chen G., Lian J., Li Q., Huang Z., Yao X., Lv N., Zhang G., Li Y., Wang J., Wang J., Zhu B, & Kang L. (2014). The locust genome provides insight into swarm formation and long-distance flight. Nature Communications, 5, 2957.

Publication 2014

Genome Human Body Intestines Locusts Woman

Differential DNA Methylation in Locust Brains

Cited 8 times

Solitarious and gregarious locusts were reared as described in a previous publication11 (link). Brain tissues of 3-day-old fourth-instar gregarious and solitarious females were dissected and placed in liquid nitrogen. Each sample contained 100 brain tissues. DNA was extracted using the Gentra puregene Kit (Qiagen, USA). Next, 5 μg of genomic DNA was digested with 300 U of the MspI enzyme (New England Biolabs, USA) in 100 μl reactions at 37 °C for 16–19 h. After purification, the digested products were subjected to blunt ending, dA addition and methylated-adapter ligation. To obtain DNA fractions of 40–120 bp and 120–220 bp ranges of MspI-digested products, two ranges of 160–240 bp and 240–340 bp adapter-ligated fractions were excised and purified from a 2% agarose gel. Bisulphite conversion was conducted using the ZYMO EZ DNA Methylation-Gold Kit (ZYMO) following the manufacturer’s instructions. The same bisulphite conversion was also conducted for one sample that was not digested by MspI. The final libraries were generated by PCR amplification of 11–13 cycles using JumpStart Taq DNA Polymerase (Sigma). After testing using an Agilent 2100 Bioanalyzer (Agilent Technologies) and real-time PCR, the libraries were analysed on the HiSeq 2000 system. In total, 13.5 Gb and 12.8 Gb of data were produced for gregarious and solitarious samples, respectively. To validate the methylation level, 63 TA clones (26 in 120–220 bp and 37 in 40–120 bp) were sequenced by bisulphite PCR using the Sanger method. A methylation ratio of 3.42% was detected for the CG sites.

Publication 2014

Brain Clone Cells DNA Methylation Enzymes Females Genome Gold hydrogen sulfite Ligation Locusts Methylation Nitrogen Real-Time Polymerase Chain Reaction Sepharose Taq Polymerase Tissues

Insect Tissue RNA Extraction and Purification

Cited 8 times

Locust tissues were micro-dissected under a binocular microscope and immediately collected in liquid nitrogen-cooled MagNA Lyser Green Beads (Roche, Indianapolis, IN, USA) tubes to prevent degradation. Until further processing, these pooled tissue samples were stored at -80°C. For the preparation of each total RNA sample, the pooled tissue material (≤ 20 mg) was homogenized using the MagNA Lyser instrument (Roche) according to the manufacturer's instructions. Subsequently, total RNA was extracted from the tissue homogenate utilizing the RNeasy Lipid Tissue Mini Kit (Qiagen, Valencia, CA, USA). In combination with this extraction procedure, a DNase treatment (RNase-free DNase set, Qiagen) was performed to eliminate potential genomic DNA contamination.
After spectrophotometric quantification and verification of the RNA quality via the Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA, USA), the resulting total RNA was reverse transcribed (Superscript III, Invitrogen Life Technologies, Carlsbad, CA, USA) utilizing random hexamers as described in the provided protocol. To minimize variations during the cDNA synthesis step, all RNA samples were reverse transcribed simultaneously in triplicate. After cDNA synthesis the three cDNA samples from one RNA sample were mixed and 10 times diluted. Furthermore, negative control reactions, i.e. without the reverse transcriptase, were prepared and analyzed prior to the quantitative PCR assay to ascertain that no DNA contamination was present.

Van Hiel M.B., Van Wielendaele P., Temmerman L., Van Soest S., Vuerinckx K., Huybrechts R., Broeck J.V, & Simonet G. (2009). Identification and validation of housekeeping genes in brains of the desert locust Schistocerca gregaria under different developmental conditions. BMC Molecular Biology, 10, 56.

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Publication 2009

Anabolism Biological Assay Deoxyribonucleases DNA, Complementary DNA Contamination Endoribonucleases Genome Lipids Locusts Microscopy Nitrogen RNA-Directed DNA Polymerase Spectrophotometry Tissues

Most recents protocols related to «Locusts»

Locust Jump Biomechanics Characterization

Linear Velocity (v) (m / s) v = \sqrt{{\begin{matrix} (\frac{dx}{dt}) \end{matrix}}^{2} + {\begin{matrix} (\frac{dy}{dt}) \end{matrix}}^{2}}

Translational Kinetic Energy (E_{T}) (mJ) K E = \frac{1}{2} m v^{2}

Angular Velocity (\dot{θ}) (rads / s, ^{}) \dot{θ} = (\frac{d θ}{dt})

Rotational Kinetic Energy (E_{R}) (mJ) R E = \frac{1}{2} I {\dot{θ}}^{2}

Gravitational Potential Energy (GPE) (mJ) G P E = m . g . h

Inertia of a Rod about its centre (I) (Kg . m^{2}) I = \frac{1}{12} {(M a s s . L e n g t h}^{2})

Excel was used for statistical analysis. On each graph, every point represents the mean value from multiple jumps from an individual locust. Data for animal 44 is presented as the mean + / − standard deviation. For all regression tests, p = < 0.05 was used as the threshold value to signify statistical significance.
After take-off insects visibly decelerate. Therefore, in this study we only measured rotation rates immediately (10 ms) after take-off and did not measure subsequent deceleration.
In total, 44 locusts were used in this experiment. Each locust was filmed jumping a minimum of 1 and a maximum of 11 times, with the exception of animal 44 which was jumped 61 times. A total of 263 videos were used in the final analysis for this study. Summaries of the entire data set are presented as means + / − standard error. Parameters for the individual 44 locusts are, unless otherwise stated, the mean for that locust from its individual jumps. To analyse inter-individual variation, we filmed 61 consecutive jumps, over a three-hour period, from animal number 44. The mean from this individual is included in our data set (for a ‘N’ of 44 locusts), but its data point is shown on each graph in yellow with black standard error bars. Over the 61 jumps, there was no significant difference in take-off velocity or take-off angular velocity showing that the animal did not experience fatigue (data not shown).

Goode C.K, & Sutton G.P. (2023). Control of high-speed jumps: the rotation and energetics of the locust (Schistocerca gregaria). Journal of Comparative Physiology. B, Biochemical, Systemic, and Environmental Physiology, 193(2), 145-153.

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Publication 2023

Animals Deceleration Fatigue Insecta Locusts RRAD protein, human

Modeling Locust Jumping Mechanics

The centre of mass of the locust is located above the coxa joint on the abdomen (Bennet-Clark 1975 (link)). The abdomen of the locust is flexible and can be moved through contraction of the dorso-longitudinal muscles (Baader 1990 (link)). After take-off and during flight, flexion of the abdomen has been shown to create a counter torque to reduce angular rotation (Cofer et al. 2010 (link)). However, in this study we only focus on the first 10 ms of the jump after take-off, during which, angular rotation was not seen to be corrected through movements of the abdomen (see Fig. 1 for an example). Therefore, the locusts in this study were modelled as a rigid uniform rod (Fig. 1c). This model allows us to calculate the inertia (Eq. 6) of the locust using the standard equation of a uniform rod rotating about its centre (Idema 2018 ).

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Publication 2023

Abdomen Coxa Joints Locusts Movement Muscle Contraction Muscle Rigidity Torque

Modeling Food Insecurity Indicators

Having uncovered 1949 text features semantically related to food insecurity, we then discard the irrelevant ones using a Granger causality test (29 ). We define the following:
We estimate a panel autoregressive distributed lag model of the IPC phase y_{d, t} in district d during the quarter ending on month t

y_{d, t} = a_{o} + a_{1} y_{d, t - 3} + a_{2} y_{d, t - 6} + \dots + a_{p} y_{d, t - 3 p} + b_{1} x_{w, d, t - 3} + \dots + b_{q} x_{w, d, t - q - 2}

where p > 0 and q > 0 are chosen on the basis of the Akaike information criterion. Each observation in Eq. 1 is at the district-quarter level. While news articles are time-stamped by the minute, they get aggregated into a district-month indicator by counting the number of articles mentioning a district and a key phrase in a month and dividing by the number of articles mentioning the district during the same month. Equation 1 is estimated using 4648 quarterly observations across 1162 districts over the period from July 2009 to April 2010 and evaluated using 1162 observations from July 2010. We reject the null hypothesis that x_w does not Granger-cause y if the news factor and its lagged values whose coefficients are statistically different from zero add explanatory power to the regression according to an F-test at the 1% level. As explained in more detail below, news factors are selected over the period from July 2009 to July 2010 to ensure that no observation used for feature selection contributes to evaluating the predictive models’ performances.
Because the Granger causality test assumes stationarity, we take the first difference of each nonstationary news time series until it passes an augmented Dickey-Fuller test, a common statistical test used to determine whether a time series is stationary. We end up differencing 15 news factors—“slashed export,” “price rise,” “oppressive regimes,” “failed rains,” “migration,” “climate change,” “price of food,” “rising food prices,” “locusts,” “coup,” “severe rains,” “harvest decline,” “call for donations,” “cholera outbreak,” and “d’etat”—and we keep the differenced news indicators that Granger-cause the IPC phase for the rest of the analysis.
Of the 1949 text features previously selected, we retain the 167 that Granger-cause the IPC phase (fig. S3C). We find that expanding to candidate features up to a distance of 10 of an original feature does not lead to any additional relevant text features: the proportion of expansions passing the Granger causality test decreases up to a distance equal to 6 and becomes negligible thereafter (fig. S4).

Balashankar A., Subramanian L, & Fraiberger S.P. (2023). Predicting food crises using news streams. Science Advances, 9(9), eabm3449.

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Publication 2023

Cholera Climate Change Food Locusts Rain

Whole-mount FIHC of Locust Antennae

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Publication 2023

Antibodies DAPI Goat Head Locusts Microscopy Nymph Rabbits Triton X-100

Rearing Desert Locusts for Research

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Publication 2023

Females Insecta Locusts Metals Oats Oviposition Seedlings Triticum aestivum

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What are locusts and how do they differ from regular grasshoppers?

What are some of the key challenges in studying locusts?

Studying locusts can be challenging due to their rapid multiplication and migratory nature. Researchers often struggle to identify the most effective protocols and methods for their locust-related research, as the scientific literature can be vast and scattered. Ensuring the reproducibility and accuracy of locust research findings is also critical, as these insects can have a significant impact on food security and agricultural productivity.

How can PubCompare.ai help with locust research?

PubCompare.ai's AI-powered platform can help researchers optimize their locust studies by easily identifying the best protocols, products, and methods from the scientific literature, preprints, and patents. With advanced comparison features, users can pinpoinf the most reproducible and accurate techniques for their locust research, streamlining the process and enhancing the quality of their work. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for their specific research goals.

What are some of the different types or variations of locusts?

There are several species of locusts found around the world, each with their own unique characteristics and behaviors. Some of the most well-known locust species include the Desert Locust, the Migratory Locust, and the Italian Locust. These locusts can differ in their geographic distribution, swarming patterns, and the crops they target, which can impact the specific protocols and methods needed for their study and management.

More about "Locusts"

Grasshoppers, Swarming Insects, Agricultural Pests, Crop Damage, Migratory Behavior, Rapid Reproduction, Insect Research, Protocols, Scientific Literature, Preprints, Patents, Optimization, Reproducibility, Accuracy, Research Methods, T7 RiboMAX Express RNAi System, TRIzol Reagent, TRIzol, LightCycler 480, Agilent 2100 Bioanalyzer, MagNA Lyser Green Beads Tubes, Prism 6, Bicinchoninic Acid Protein Assay Kit, Polyvinylidene Difluoride Membranes, NanoDrop ND-1000, Food Security, Agricultural Threat, Insect Swarming, Pest Control, Locust Researh, Experimental Techniques.