Current rice planting operations are mainly transplanting and direct seeding. However, many operational procedures involving transplanting use poor quality of machinery resulting in high labour costs ...and low working efficiency. Here a no-tube seeding (NTS) method is proposed and the effects of technical parameters (travel speed, seeding wheel rotational speed and ground clearance) on the seeding quality (accuracy, dispersion and uniformity) are investigated using a single factor bench test. A multifactor bench test was carried out to optimise the technical parameters based on the requirements of different seeding rate. The optimal value of the travel speed was constant at 0.80 m s−1 at different seeding rates, the optimised range of seeding wheel rotational speed and ground clearance is 17.5–23.53 r min−1 and 81.2–85.6 mm, respectively. A field experimental comparison between NTS, traditional tube seeding (TTS) showed that the seeding quality of NTS was significantly better than that of TTS. Compared with transplanting, rice growth status at booting stage for NTS and TTS was not significantly different with rows closed. However, the number of panicles per unit area was greater which was beneficial to the formation of high yield. The maximum and average yield of NTS increased by 10.1% and 6.7% compared with TTS and compared with transplanting it maximum and average yield increased by 11.6% and 9.3% respectively. These results indicate that NTS is feasible and efficient method.
•Lower ground clearance promotes higher seeding accuracy.•Higher ground clearance promotes greater seeding dispersion.•Higher seed wheel rotational speed promotes higher seeding uniformity.•Travel speed has limited effect on seeding quality.•The comprehensive benefit of NTS is better than that of TTS and transplanting.
Mesenchymal stem cells (MSCs) have become one of the candidates for regenerative medicine. Such cells have a multi-lineage differentiation ability, can modulate immune responses, and can also promote ...tissue regeneration through the secretome.
As the number of MSCs isolated from the tissues is much less than the number of cells needed for treatment, an in vitro expansion process is necessary. Microcarriers (MCs), which are a class of small beads is used as a substrate to prepare suspension cultures and have been recognized as a potential tool for cost-effective large-scale cell expansion process due to providing a larger surface area per unit volume compared to planar cultures.
Seeding is the first step of the expansion process to perform cell attachment on MCs. Quantity, quality and stability are required for a considerable seeding process. By increasing microcarrier concentration, CMC (cm2/mL), the cell attachment efficiency of bioreactor showed an increasing trend and finally plateaued in a static seeding condition. While at the same time, we observed a decreasing trend of apparent specific growth rate. Hence, we began to consider the heterogeneity of cell attachment on single MC as a crucial factor of stability.
MSCs were seeding on MC with lower CMC condition (3.84 cm2/mL) or higher CMC condition (23.04 cm2/mL) and static or dynamic condition. After 24 hours, 3-dimensional images of MCs and MSCs labelled by fluorescence with nuclei were analyzed to calculate the cell attachment efficiency on MC, αMC (-). Three batches of each seeding condition were performed for the reproducibility. Increasing of the CMC improve the average cell attachment efficiency of MC. (Figure 1) The distribution of αMC in the lower CMC condition had less fluctuation than higher CMC condition indicated by the box size. In lower CMC condition, the dynamic condition was preferred as the static condition had a peak skewed to the 0.
Our research provided a method to analysis the stability of seeding process by heterogeneity. We found that the heterogeneity of seeding would influence by the CMC and mixing operation which are suggested to consider during development of seeding process.
Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) has been widely used for diagnosis of both inflammatory and tumor lesions located in and adjacent to the gastrointestinal tract. EUS-FNA ...has been considered to be a safe technique with few complications, as shown in recent review articles in which EUS-FNA-related morbidity and mortality rates were reported to be <1%. It should be noted, however, that needle tract seeding, although uncommon, can occur after diagnostic EUS-FNA and that this complication affects the prognosis of patients. Although an accurate value for the frequency of needle tract seeding caused by EUS-FNA has not been reported, the numbers of case reports on needle tract seeding have been rapidly increasing, especially in Japan. These case reports regarding EUS-FNA-related needle tract seeding prompted us to reevaluate the safety of EUS-FNA because this complication may have a significant influence on patients' prognoses. In this review, we summarize the clinical features and outcomes of needle tract seeding after EUS on the basis of the previously reported cases and provide useful information to prevent and reduce this serious complication.
•Provided a method that could quickly evaluate rowing performance of rice seeding.•Obtained the position coordinates of each seed through image recognition.•Designed a shot seeding device that can ...realize rice seeding in rows by UAV.•Optimized the existing UAV-based shot seeding device through the evaluation method.
In recent years, unmanned aerial vehicle (UAV) has been widely used in the agricultural production. Combined with the promotion of rice direct seeding technology, UAV rice seeding has become an important planting method. In order to quickly obtain the rowing performance on rice sowing, this study proposes an evaluation method based on image recognition technology. In the process of image recognition, a binary method of dynamic threshold based on mean filter difference (BD-MFD) was adopted, and obtained the position coordinates of the seeds. According to the evaluation method, the key structural parameters and operational parameters of the UAV-based shot seeding device are optimized. In the bench test, the size parameters of guide tube were optimized, and the best seeding effect was obtained when the length and diameter of the guide tube were 100 mm and 17 mm, respectively. Experiments were carried out on the seeding effect under different seed discharge rate levels, and the reasonable range of the seed metering wheel rotational speed was 16–24 rpm. In the field seeding test, the appropriate friction wheel rotation speed at different working heights was obtained. In the two-factor comprehensive test of flying speed and seed metering wheel rotational speed, when the flying speed was 1.0–2.0 m/s and the seed metering rotational wheel speed was 22–26 rpm, it has better seeding uniformity. In the five-row seeding experiment, the average width of seed row (Bd) ranged from 66.17 mm to 75.34 mm, and the C.V. (coefficient of variation) of seeding uniformity was between 18.44 % and 27.04 %. The study provided a new idea for UAV rice seeding, and optimized the structural parameters and operation parameters through experiments.
Seed delivery to site is a critical step in seed‐based restoration programs. Months or years of seed collection, conditioning, storage, and cultivation can be wasted if seeding operations are not ...carefully planned, well executed, and draw upon best available knowledge and experience. Although diverse restoration scenarios present different challenges and require different approaches, there are common elements that apply to most ecosystems and regions. A seeding plan sets the timeline and details all operations from site treatments through seed delivery and subsequent monitoring. The plan draws on site evaluation data (e.g. topography, hydrology, climate, soil types, weed pressure, reference site characteristics), the ecology and biology of the seed mix components (e.g. germination requirements, seed morphology) and seed quality information (e.g. seed purity, viability, and dormancy). Plan elements include: (1) Site treatments and seedbed preparation to remove undesirable vegetation, including sources in the soil seed bank; change hydrology and soil properties (e.g. stability, water holding capacity, nutrient status); and create favorable conditions for seed germination and establishment. (2) Seeding requirements to prepare seeds for sowing and determine appropriate seeding dates and rates. (3) Seed delivery techniques and equipment for precision seed delivery, including placement of seeds in germination‐promotive microsites at the optimal season for germination and establishment. (4) A monitoring program and adaptive management to document initial emergence, seedling establishment, and plant community development and conduct additional sowing or adaptive management interventions, if warranted. (5) Communication of results to inform future seeding decisions and share knowledge for seed‐based ecological restoration.
Observation-validated cloud seeding simulation is valuable in assisting in evaluating seeding effect, but its sensitivity to microphysics schemes and cloud seeding parameterizations is rarely ...investigated. In this research three cloud seeding parameterizations (the Hsie, Demott and Xue parameterizations) are coupled with two microphysics schemes (the Thompson and Milbrandt schemes), to perform simulations of the rainfall suppression cloud seeding operation on a convective rainfall event occurred in North China on 1 July 2021, aiming to evaluate the seeding effect and investigate its sensitivity to microphysics schemes and seeding parameterizations. The differences of rainfall suppression effect between three seeding parameterizations are smaller than those between two microphysics schemes. The rainfall suppression ratios produced by the simulations configured with the Milbrandt scheme range from 16.3% to 24.7%, while those with the Thompson scheme range from 0.47% to 1.38%. The difference of the seeding effect between these two microphysics schemes arises from their different method in parameterizing the snow deposition growth process. In addition, in the seeding simulations with the Milbrandt scheme, the surface rainfall decrease region is followed by a rainfall increase region. This rainfall decrease-increase pattern is caused by the fact that the reduced graupel particles caused by cloud seeding falls to the ground earlier with its larger fall velocity, while the seeding-increased snow particle falls to the ground later due to its smaller fall velocity. This result suggests there is an ephemeral cloud seeding window for rainfall suppression operation.
•Three cloud seeding approaches are coupled with two microphysics schemes.•Seeding effect is more sensitive to microphysics schemes than seeding approaches.•Diversity in seeding effect arises from different snow deposition parametrizations.•Ensemble cloud seeding simulation is valuable for evaluating cloud seeding effect.
Concurrent to yield, maize (Zea Mays L.) plant density has significantly increased over the years. Unlike yield, however, the rate of change in plant density and its contribution to maize yield gain ...are rarely reported. The main objectives of this study were to examine the trend in the agronomic optimum plant density (AOPD) and quantify the contribution of plant density to yield gain. Maize hybrid by seeding rate trials were conducted from 1987-2016 across North America (187,662 data points). Mixed model, response surface, and simple linear regression analyses were applied on the meta-data. New outcomes from this analysis are: (i) an increase in the AOPD at rate of 700 plant ha
yr
, (ii) increase in the AOPD of 1386, 580 and 404 plants ha
yr
for very high yielding (VHY, > 13 Mg ha
), high yielding (HY, 10-13 Mg ha
) and medium yielding (MY, 7-10 Mg ha
), respectively, with a lack of change for the low yielding (LY, < 7 Mg ha
) environment; (iii) plant density contribution to maize yield gain ranged from 8.5% to 17%, and (iv) yield improvement was partially explained by changes in the AOPD but we also identified positive impacts on yield components as other sources for yield gain.
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