Short Life Cycle Research Articles

Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing

Generally, ground-coupled floor heat transfer is supposed as annual periodic, which is reasonable for conventional buildings. However, for emergency housing with a short life cycle, the influence of initial soil temperature needs to be considered. In a previous study, the Wiener–Hopf technique was introduced to solve the two-dimensional transient heat transfer equation with mixed Dirichlet and Robin boundary conditions. Based on that, an analytical solution of the dynamic heat transfer equation with initial soil temperature conditions was obtained. Since the solution was in the form of a double integral, its numerical evaluation method was also analyzed to improve computational efficiency. The accuracy and efficiency of the solution were validated by the finite volume method. Then, the effects of initial soil temperatures in different seasons, soil heat conductivities, and floor insulation on ground-coupled heat transfer were discussed. Results showed significant temperature differences between the current solution and the annual periodic solutions (long-time solutions), especially in hot and cold climates. Moreover, the larger the thermal capacity of the soil, the bigger temperature differences occurred. Therefore, this study is expected to provide a theoretical foundation for the indoor environment prediction and optimization design of emergency temporary housing.

Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic

Accurate demand forecasting has always been essential for retailers in order to be able to survive in the highly competitive, volatile modern market. However, anticipating product demand is an extremely difficult task in the context of short product life cycles in which consumer demand is influenced by many heterogeneous variables. During the COVID-19 pandemic in particular, with all its related new constraints, the fashion industry has seen a huge decline in sales, which makes it difficult for existing sales forecasting methods to accurately predict new product sales. This paper proposes an original sales forecasting framework capable of considering the effect of the COVID-19 related crisis on sales. The proposed framework combines clustering, classification, and regression. The main goals of this framework are (1) to predict a sales pattern for each item based on its attributes and (2) to correct it by modelling the impact of the crisis on sales. We evaluate our proposed framework using a real-world dataset of a French fashion retailer with Omnichannel sales. Despite the fact that during the lockdown period online sales were still possible, consumer purchases were significantly impacted by this crisis. Experimental analysis show that our methodology learns the impact of the crisis on consumer behavior from online sales, and then, adapts the sales forecasts already obtained.

Assessing the effects of irrigation and hydropower dams on river communities using taxonomic and multiple trait-based approaches

Rivers and streams have suffered multiple transformations to attend the increasing water demands worldwide. Among these, dams and reservoirs cause some of the most severe ecological impacts on rivers, altering the river flow and thermal regimens, nutrient and sediment fluxes, and network connectivity. However, in the context of the different dam purposes and operational schemes, knowledge of the ecological impacts on the riverine biota is still limited. In this study, our main goal was to assess dam-related effects (e.g. hydrological and thermal alteration, water quality changes) on river biological communities and identify key ecological responses associated to flow regulation. To achieve this, diatom, macroinvertebrate, and fish communities were surveyed in control (n = 8) and impacted (n = 11) streams (i.e. downstream of irrigation or hydropower dams) along three consecutive years. The study design aimed at minimising the environmental variability among control and impacts using previously established hydrological classifications. This allowed focusing primarily on the effects of dam operation schemes. In addition to traditional biotic indices based on the composition and structure of these communities, we assessed community-level responses using trait-based analyses with multivariate and fourth-corner analyses. The ecological changes varied with dam purpose and, in general, favoured disturbance-tolerant traits. Common biotic indices did not consistently respond to dam uses; trait-based analyses, in contrast, provided a more detailed picture of the dam-related effects on the studied river communities, with macroinvertebrate traits showing the strongest correlations to dam-related hydrological and physico-chemical variables, followed by diatoms and fish. Changes in the biological communities downstream of irrigation dams were mostly related to the inversion of the seasonal flow regimes (e.g. increases in the summer flows and magnitude of low flow extremes; decreases in the winter flows and in the frequency and magnitude of extreme high flow events) and the reduction of nutrient concentration. These changes favoured planktonic diatoms, macroinvertebrates with short life cycles and small body sizes, and fish feeding on the water column. Hydropower dams elevated significantly the rate of flow change and water temperature, favouring low profile adnate diatoms, multivoltine and passively dispersed macroinvertebrates, and scrapers. The key relationships identified in our study are useful to underpin river biodiversity conservation strategies and to set future research directions aiming at reducing the negative effects of dam operation schemes.

A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales of newly launched, short-lived products that are similar to previous products when there is limited availability of historical sales data for the new product. In addition to exploiting historical data using time-series clustering, we perform data augmentation to generate sufficient sales data and consider two quantitative cluster assignment methods. We apply one traditional statistical (ARIMAX) and three machine learning methods based on deep neural networks (DNNs) – long short-term memory, gated recurrent units, and convolutional neural networks. Using two large data sets, we investigate the forecasting methods’ comparative performance and, for the larger data set, show that clustering generally results in substantially lower forecast errors. Our key empirical finding is that simple ARIMAX considerably outperforms the more advanced DNNs, with mean absolute errors up to 21%–24% lower. However, when adding Gaussian white noise in our robustness analysis, we find that ARIMAX’s performance deteriorates dramatically, whereas the considered DNNs display robust performance. Our results provide insights for practitioners on when to use advanced deep learning methods and when to use traditional methods.

Resistance of Panonychus citri (McGregor) (Acari: Tetranychidae) to pyridaben in China: monitoring and fitness costs.

Panonychus citri is a major citrus pest worldwide. The short life cycle and high reproductive potential of P. citri, combined with heavy acaricide use, have led to high levels of resistance to acaricides, posing a threat to global resistance management programs. Here, resistance monitoring was established to determine the pyridaben resistance status of ten P. citri populations in China from 2014 to 2021 using a leaf-dipping assay. Four characterized strains-the susceptible strain (Lab_S), the resistant strain (Pyr_R), as well as the segregated resistant strain (Pyr_Rs) and the segregated susceptible strain (Pyr_Control) derived from the crossing of the Lab_S and Pyr_R strains, were used to evaluate the life-history characteristics using age-stage, two-sex life tables. Most P. citri populations developed high resistance to pyridaben. Resistance levels exceeded 1000-fold in Yuxi, Anyue, Nanning, and Ganzhou populations compared with the Lab_S strain. Compared with Pyr_Control, two key fitness cost criteria, developmental period and fecundity, showed significant differences in Pyr_Rs under consistent conditions. The intrinsic rate of increase, net reproductive rate and gross reproductive rate were lower in the resistant strain compared with the Pyr_Control strain. The Pyr_Rs strain had a lower relative fitness of 0.934 compared with the Pyr_Control. Moreover, the life-history traits and population parameters of the Pyr_R strain also showed significant differences compared with the Lab_S strain. The resistance levels to pyridaben varied greatly among the different P. citri populations and showed regional differences. Substantial fitness costs are associated with pyridaben resistance. This study provides potential implications for developing strategies for resistance management in P. citri. © 2022 Society of Chemical Industry.

Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs’ network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs’ nodes.

Insect Models in Nutrition Research.

Insects are the most diverse organisms on earth, accounting for ~80% of all animals. They are valuable as model organisms, particularly in the context of genetics, development, behavior, neurobiology and evolutionary biology. Compared to other laboratory animals, insects are advantageous because they are inexpensive to house and breed in large numbers, making them suitable for high-throughput testing. They also have a short life cycle, facilitating the analysis of generational effects, and they fulfil the 3R principle (replacement, reduction and refinement). Many insect genomes have now been sequenced, highlighting their genetic and physiological similarities with humans. These factors also make insects favorable as whole-animal high-throughput models in nutritional research. In this review, we discuss the impact of insect models in nutritional science, focusing on studies investigating the role of nutrition in metabolic diseases and aging/longevity. We also consider food toxicology and the use of insects to study the gut microbiome. The benefits of insects as models to study the relationship between nutrition and biological markers of fitness and longevity can be exploited to improve human health.

COVID-19 Lockdowns Promoted Recovery of the Yangtze River’s Aquatic Ecosystem

The impacts of COVID-19 lockdowns on human life, air quality, and river water quality around the world have received significant attention. In comparison, assessments of the implications for freshwater ecosystems are relatively rare. This study explored the impact of COVID-19 lockdowns on aquatic ecosystems in the Yangtze River by comparing river water quality, phytoplankton, zooplankton, and fish data collected at the site in the middle reach of the Yangtze River in 2018 and 2020. The results show that during COVID-19 lockdowns, the reduction in industrial and domestic effluent discharge led to a reduction in organic pollution and industrial plant nutrient pollution in rivers. Among them, PO43−−P, CODMn, and TP were significantly decreased (p < 0.05). During lockdowns, nutrient supplies such as TN and TP were reduced, which led to inhibition of algae growth and decreased phytoplankton abundance. Phytoplankton affects the abundance of zooplankton through a bottom-up effect, and a decrease in phytoplankton density leads to a decrease in zooplankton density. The decrease in plankton density led to lower primary productivity in rivers, reduced fish feed supplies, intensified competition among fish populations, with increases in population dominated by high trophic level carnivorous fish. In addition, the decrease in fishing intensity has contributed to an increase in the number of rivers–sea migratory fish; the fish community was earlier mainly dominated by small-sized species with a short life cycle, and the number of supplementary populations has now increased. As a consequence, the fish community structure shows a tendency toward high complexity and high fish diversity. Overall, these observations demonstrate that the rapid revival of the retrogressive Yangtze River ecosystem is possible through limitation of anthropic interferences.

A Two-level clustering chain energy heterogeneous routing protocol for WSN

In view of the disadvantages of high energy consumption, short life cycle, low throughput and no application correlation in heterogeneous WSN, a two-level clustering chain routing protocol is proposed under the heterogeneous WSN of intelligent water-saving irrigation system of ecological agriculture in Hexi Gobi. The irrigation subnets are divides into clusters according to the actual layout in the wireless network and the nodes in the cluster form the chain on the basis of the minimum distance. The two-level cluster headers in reserve are selected by rate of the remaining energy of terminal nodes in cluster vs. average energy in local region. Eventually the level clusters heads and the two-level cluster headers are chosen according to the largest ratio of the remaining energy to the distance between the terminal nodes and the base station. Results in simulation show that the proposed algorithm is superior to PEGASIS and SEP in energy consumption, life cycle and throughput.

Mating system of a Mammillaria magnimamma (Cactaceae) population of the semi-arid central Mexican region

Knowledge on the reproductive biology of globose cacti in the genus Mammillaria is incipient, despite being one of the most diverse and threatened groups in Cactaceae. We studied a clonal population of Mammillaria magnimamma (Hawort) located in the Valle del Mezquital, Mexico in which sexual reproduction occurs, although neither seedlings nor juveniles were observed. Based on their short life cycle, globose life form, and melittophilous pollination syndrome, this species was expected to exhibit a mixed mating system and null seed recruitment. We examined the population structure, reproductive phenology, and the mating system by means of controlled-pollination experiments (autonomous self-pollination, manual self-pollination, manual outcross pollination and open pollination). We tested for differences between treatments in the number of seeds per fruit and estimated outcrossing rates. Most (88%) of the M. magnimamma population consisted of clones (10.6 stems/ind) and no juvenile plants or seedlings were recorded. Seed were produced in all the experimental treatments. The average number of seeds per fruit was significantly higher (χ2 = 29.52; d.f. = 3; P < 0.0001) in the outcross than the self-pollination treatments. Outcrossing rates estimated from fruits, te, fruits = 0.829, and seeds, te, seeds = 0.8774, showed that M. magnimamma exhibits a mixed mating system. Despite the large number of seed produced, the persistence of these populations likely depends on clonal reproduction. The null recruitment and elevated anthropogenic pressure that this species undergoes across its distribution range threaten its populations.

Arundina graminifolia (D.Don) Hochr. (Orchidaceae): A review of its medicinal importance, phytochemistry and pharmacology activities

• Recent medicinal importance of Arundina graminifolia are described. • This review brings the current status of phytochemicals status of Arundina graminifolia. • Pharmacological studies and in vitro conservation strategies of Arundina graminifolia are also discussed. Traditional medicines play an important role in protecting the health of all ethnic groups around the world, and their importance in the development of medical and health services cannot be overstated. The Orchidaceae family includes some of the most beautiful flowers in the world, each with its own unique shape, color, and form. Potted and cut flower orchids are big business because the bioactive components in them are great for your health. Arundima graminifolia (Orchidaceae) has been extensively used for wide range of ethnopharmacological activities, including rheumatism, food poisoning, snake bites, traumatic injuries, detoxification and heat clearance. It is also utilized to treat pneumonia, tuberculosis and bronchitis by the ethnic people. The recent two decades of fruitful achievements in its chemical constituent investigations have laid a foundation for the study of its therapeutically material basis and action mechanism, which will help clarify its broad-spectrum detoxification mechanism and develop into a modern pharmaceutical preparation. This suggests that the whole plant of A. graminifolia is more important than other orchid species for extracting bioactive constituents such as flavonoids, phenylpropanoid, phenanthrene, glycosides, bibenzyl and phenolic compounds for medicinal purposes. The bamboo orchid is a promising species for studying the efficacy to obtain pharmaceutical products due to its short life cycle and accumulation of bioactive ingredients in multiple plant parts. This review summarizes the current medicinal usage, phytochemistry and pharmacological activities of this high value species.

Current trends and future perspectives towards sustainable and economically viable peach training systems

• Intensification of peach planting systems aims at facilitating mechanization and reduce labor cost. • High-density 2-D walls are recommended for intensive and highly mechanized systems with limited accessible labor. • Medium-density 3-D small open vases are recommended for low frost-risk areas and farms with fully available labor. • The employment of multifunctional net protection on high-density orchards can counteract the adverse effects of climate change. • Soil conservative management is suggested towards environmentally sustainable peach orchards through augmentation of carbon sequestration. Considering the limited areas suitable for peach cultivation, the short life cycle of the orchards, as well as aspects regarding appropriate rootstock availability and soil properties due to replant conditions, the sustainable intensification became increasingly necessary on peach production systems. Based on the local environment and labor availability, two new training systems have been introduced and are being widely adopted towards efficient small canopies for high- and medium-density orchards, respectively. The so-called two-dimensional (2-D) fruiting wall is an intensive, highly-mechanized/high-density training system and is commonly accompanied by multifunctional net protection in order to counteract the adverse effects of climate change. On the other hand, the medium-density three dimensional (3-D) small open vase is suitable for low frost-risk areas and farms with fully available manpower. In both cases, the decisions taken during the orchard establishment and the first years regarding soil fertility and orchard mechanization have strategic importance to guarantee a sustainable peach production system on both quantitative and qualitative terms. The employment of both spring and summer pruning increases work efficiency and improves fruit quality, as well as fruit thinning, management efficiency and mechanization. Sustainable intensification not only reshapes the use of chemicals and irrigation, but also weed control and soil amendment with organic matter to support the complexity and heterogeneity of the agroecosystem with the circular economy.

Black Soldier Fly Larvae (Hermetia illucens) for Biodiesel and/or Animal Feed as a Solution for Waste-Food-Energy Nexus: Bibliometric Analysis

In this research, an emerging study of the utilization of black soldier fly (BSF, Hermetia illucens) larvae for the preparation of biodiesel (and organic waste treatment) and the generation of alternative feed for improved food production was mapped bibliometrically from the Scopus database. BSF is a promising biological agent for tackling the waste-food-energy (WFE) nexus, which is a problematic vicious cycle that may threaten Earth’s sustainability, hence its emergence. With its short life cycle, ability to consume organic waste equal to its own weight on a daily basis, and ability for conversion to larvae with a high protein and lipid content, BSF larvae is the perfect choice as a one-step solution of the WFE nexus. To further perfect the research of BSF for the WFE nexus, this bibliometric analysis, and the citation evolution profile, were carried out with the objectives of characterizing the progress of publications in the last 10 years (2011–2022) in order to determine future research directions in this field, identify the top publications for wider reach to the public, and identify productive authors and leading countries to visualize opportunities for future collaborations.

EXPERT SYSTEM FOR DIAGNOSING TOMATO PESTS AND DISEASES USING FORWARD CHAINING METHOD

Tomato (solanum lyco persicum) is a plant from the solanceae family, native to Central and South America, from Mexico and Peru. Tomatoes have a short life cycle and a height ranging from 1 to 3 meters. Currently, farmers often experience disturbances from pests and diseases of tomatoes due to a lack of knowledge. They do not know how to control it and to eliminate pests that reduce productivity. In identifying diseases in tomato plants we use the Forward Chaining method which is a description that starts from knowing the facts and then matching them with IF and IF-THEN rules. Diseases with a high prevalence in tomatoes are: fruit worms, earthworms, green aphids, white flies, fruit flies (bractrotera) and the diseases are: fusarium wilt, leaf spot disease, bacterial spot, fruit rot. This expert system application provides tomato disease information and disease diagnosis, including control solutions that can be used to reduce the risk of tomato crop damage&#x0D; &#x0D;

V‐model with fuzzy quality function deployments for mobile application development

AbstractMobile application (app) development is a subset of software development distinguished by a short development life cycle, rapidly changing business requirements, mobile device technical specifications, and operating system compatibility. The study presents a V‐model mobile app development technique that combines two fuzzy quality function deployment (FQFD) phases. In mobile app development, user requirements are often misunderstood, resulting in poor mobile app design. The research motivation for employing FQFD is to obtain more accurate deductions of human (user or design team) inputs affected by intrinsic vagueness and ambiguity in linguistics. The combination hence delivers synergistic benefits, where FQFDs more effectively solicits and breaks down the voice of the customer (VOC) and system requirements, and the V‐model aligns the development to the broken‐down requirements. The development of an online statistical process control app was demonstrated. FQFD structurally related user requirements, system requirements, and design strategies in the V‐model's verification phases. The validation phases used test plans created during the verification phases to detect and correct programming errors. In the final validation stage, the focus group agreed that the mobile app met all of the user requirements. This study adds to the methodology's novelty by proposing and demonstrating the integration of the FQFD and V‐model into the development of mobile apps. As a result of the limitation, other areas of software engineering, such as time and resource coordination and release management, are not considered.

Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii).

Pacific herring (Clupea pallasii) is an essential target of commercial fishing in the North Pacific Ocean. Previous studies have suggested the existence of marine and lake ecological forms of this species within its range. The lake ecological form of herring has a shortened life cycle, spending the winter and spawning in brackish waters near the shoreline without long migrations for feeding; it also has a relatively smaller body size than the marine form. Genetic-based studies have shown that brackish water Pacific herring not only can be distinguished as a separate lake ecological form but possibly has its genetic legacy. Here, as part of an ongoing study, using ddRAD-sequencing data for marine and lake ecological forms from a total of 54 individuals and methods of comparative bioinformatics, we describe genomic signatures of freshwater adaptivity in Pacific herring. In total, 253 genes containing discriminating SNPs were found, and part of those genes was organized into genome clusters, also known as “genomic islands of divergence”. Moreover, the Tajima’s D test showed that these loci are under directional selection in the lake populations of the Pacific herring. Yet, most discriminating loci between the lake and marine ecological forms of Pacific herring do not intersect (by gene name) with those in other known marine fish species with known freshwater/brackish populations. However, some are associated with the same physiological trait—osmoregulation.

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing.

In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users' perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett's sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

Cluster head selection method of multiple UAVs under COVID-19 situation

As COVID-19 continues to spread, people are unable to move freely when their residence region is temporarily lockdown, supplies cannot normally enter into such zones, leading to the shortage of supplies in these areas. Thus to ensure the delivery of supplies while reducing contact, the unmanned aerial vehicle (UAV) deliveries have become a common way. In order to efficiently use UAV resources and reduce energy loss in data transmission while performing the tasks, clustering is often used for achieving the above objectives, where the selected cluster heads centrally plan tasks so that reduce the communication times. However, problems such as unreasonable clustering, high energy consumption of cluster heads, and high mortality of cluster heads, directly lead the low cooperation efficiency and short life cycle of UAVs. Considering the nodes often died earlier through the k-means algorithm and ant colony algorithm, and highly dependent on the base station, these factors affect the working cycle and coordination efficiency of the UAVs. Facing the issues above, the cluster head selection algorithm of UAV based on game (CHSA) is proposed, where the mixed game model is adopted to select cluster heads for each region after regional division, and selecting the representative node to perform the cluster head selection algorithm, which help to reduce the energy consumption of each round of communication between nodes. Moreover, the key properties of the CHSA algorithm are proved, and the comparison experiment are conducted to prove the CHSA algorithm can effectively reduce energy consumption and prolong the network life cycle.

Evaluation of Castor (Ricinus communis L.) Genotypes and Their Feeding Values on Rearing Performance of Eri Silkworm (Samia cynthia ricini Boisduval) (Lepidoptera: Saturniidae) in Southwest Ethiopia

The quality of feed plays an important role in the growth and development of silkworms and eventually in the economic traits of cocoons. This study was conducted to evaluate ten castors (Ricinus communis L.) genotypes and their feeding values on the rearing performance of Eri Silkworm (Samia cynthia ricini Boisduval) (Lepidoptera: Saturniidae) at Tepi, southwest Ethiopia. A total of ten castor genotypes were evaluated in a randomized complete block design (RCBD), and the suitability of castor genotypes as feed for a mixed strain of Eri-silkworm was also evaluated in a completely randomized design (CRD) under laboratory conditions. A hundred worms were used in each replication. Castor genotypes showed significant differences in fresh leaf yield. Among the castor genotypes tested, genotype 219645 recorded 439 g of ten fresh leaf yields. Results of Eri-silkworm rearing performance depict that a shorter larval period (22 days), a higher effective rate of rearing (94.54%), and a shorter life cycle (58 days) were observed in Eri-silkworm fed on leaves of the 200390 genotype, while a higher larval weight (6.16 g) was recorded in the Abaro genotype. However, higher cocoon weight (3.26 g), pupal weight (2.46 g), shell weight (0.45 g), and silk ratio (13.80%) were found in Eri-silkworms fed on leaves of genotype 219645. Hence, based on silkworm rearing performance, genotype 219645 showed relatively superior results and is recommended for future development work. Further studies should continue giving more emphasis to the multilocation study of genotype 219645 to understand its performance in the diverse growing environment.

C. elegans as a test system to study relevant compounds that contribute to the specific health-related effects of different cannabis varieties

BackgroundThe medicinal effects of cannabis varieties on the market cannot be explained solely by the presence of the major cannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Evidence for putative entourage effects caused by other compounds present in cannabis is hard to obtain due to the subjective nature of patient experience data. Caenorhabditis elegans (C. elegans) is an objective test system to identify cannabis compounds involved in claimed health and entourage effects.MethodsFrom a medicinal cannabis breeding program by MariPharm BV, the Netherlands a set of 12 varieties were selected both THC rich varieties as well as CBD rich varieties. A consecutive extraction process was applied resulting in a non-polar (cannabinoid-rich) and polar (cannabinoid-poor) extract of each variety. The test model C. elegans was exposed to these extracts in a broad set of bioassays for appetite control, body oscillation, motility, and nervous system function.ResultsExposing C. elegans to extracts with a high concentration of cannabinoids (> 1 μg/mL) reduces the life span of C. elegans dramatically. Exposing the nematodes to the low-cannabinoid (< 0.005 μg/mL) polar extracts, however, resulted in significant effects with respect to appetite control, body oscillation, motility, and nervous system-related functions in a dose-dependent and variety-dependent manner.DiscussionC. elegans is a small, transparent organism with a complete nervous system, behavior and is due to its genetic robustness and short life cycle highly suitable to unravel entourage effects of Cannabis compounds. Although C. elegans lacks an obvious CB1 and CB2 receptor it has orthologs of Serotonin and Vanilloid receptor which are also involved in (endo)cannabinoid signaling.ConclusionBy using C. elegans, we were able to objectively distinguish different effects of different varieties despite the cannabinoid content. C. elegans seems a useful test system for studying entourage effects, for targeted medicinal cannabis breeding programs and product development.