Genetic variation within and between three Vietnamese pine populations (Pinus merkusii) using random amplified polymorphic DNA (RAPD) markers

Pinus merkusii is an important species in Vietnam with many economic and biological contributions. The information on diversity within and between populations of a species is necessary for plantation programs, breeding and conservation strategies. Genetic diversity of three Vietnamese populations (NA, QB and QN) was analyzed using the random amplified polymorphic DNA (RAPD) markers. Nine RAPD primers produced 82 markers, 77 of which were polymorphic with 93.9% of polymorphism. The results showed higher genetic variation within populations (72%) than between populations (28%) and low Nei’s genetic differentiation index among populations (0.1867). The populations also clustered based on PCoA analysis where cluster I included NA and QB populations and Cluster II, the QN population. These results suggest that P. merkusii populations in Vietnam is necessary to develop the genetic resources.Keywords: DNA markers, genetic diversity, Pinus merkusii, random amplified polymorphic DNA (RAPD), Vietna

Impact of GnRH agonist triggering and intensive luteal steroid support on live-birth rates and ovarian hyperstimulation syndrome:a retrospective cohort study

BACKGROUND: Conventional luteal support packages are inadequate to facilitate a fresh transfer after GnRH agonist (GnRHa) trigger in patients at high risk of developing ovarian hyperstimulation syndrome (OHSS). By providing intensive luteal-phase support with oestradiol and progesterone satisfactory implantation rates can be sustained. The objective of this study was to assess the live-birth rate and incidence of OHSS after GnRHa trigger and intensive luteal steroid support compared to traditional hCG trigger and conventional luteal support in OHSS high risk Asian patients. METHODS: We conducted a retrospective cohort study of 363 women exposed to GnRHa triggering with intensive luteal support compared with 257 women exposed to conventional hCG triggering. Women at risk of OHSS were defined by ovarian response ≥15 follicles ≥12 mm on the day of the trigger. RESULTS: Live-birth rates were similar in both groups GnRHa vs hCG; 29.8% vs 29.2% (p = 0.69). One late onset severe OHSS case was observed in the GnRHa trigger group (0.3%) compared to 18 cases (7%) after hCG trigger. CONCLUSIONS: GnRHa trigger combined with intensive luteal steroid support in this group of OHSS high risk Asian patients can facilitate fresh embryo transfer, however, in contrast to previous reports the occurrence of late onset OHSS was not completely eliminated

Computing on Wheels: A Deep Reinforcement Learning-Based Approach

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption

Modeling and simulation of the effects of social relation and emotion on decision making in emergency evacuation

International audienceApplying agent-based modeling to simulate the evacuation in case of emergency situations is recognized by many research works as an efficient tool for understanding the behavior and decision making of occupants in these situations.In this paper, we present our work aiming to modeling the influence of the emotion and social relationship of occupants on their behaviors and decision making in emergency as in case of fire disaster. Firstly, we proposed a formalization of occupants' behavior at group level in emergency situations based on the social theory. This formalization details possible behaviors and actions of people in emergency evacuations, taking into account occupant's social relationship. The formalization will facilitate the construction of simulation for emergency evacuation. Secondly, we modeled the influence of emotion and group behavior on the decision making of occupants in crisis situations. Thirdly, we developed an agent-based simulation that took into account the effect of group and emotion on the decision making of occupants in emergency situations. We conducted a set of experiments allowing to observe and analyze the behavior of people in emergency evacuation

BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY A N I N T E R N A T I O N A L J O U R N A L Cloning and Overexpression of GmDREB2 Gene from a Vietnamese Drought-resistant Soybean Variety

ABSTRACT This work studied the amplification, cloning and determination of the GmDREB2 gene from the soybean cultivar DT2008 and five Vietnamese local soybean cultivars (DT26, DT51, DVN5, CB, CBD) and designed the vector carrying the structure containing GmDREB2 gene from cultivar DT2008 (best drought tolerant). The coding region of GmDREB2 gene isolated from six soybean cultivars was 480 nucleotides in length, encoding 159 amino acids. The recombinant structure was designed as 35S-GmDREB2-c-myc and its expression was analysed in transgenic tobacco plants. Recombinant DREB2 protein was expressed in five transgenic tobacco lines with molecular weights close to 20 kDa. During the drought conditions, the proline accumulation of the transgenic tobacco lines was higher than on wild-type (WT) plant

Morphological characteristics of Talinum paniculatum, and nucleotide sequences of ITS region, rpoC1 and rpoB genes

Jewels of Opar (T. paniculatum) belongs to Talinum genus, Portulacaceae family which contains secondary metabolites such as phytosterols, saponins, flavonoids, tannins, steroids. These organic compounds have anti-viral effects and are very effective on Herpes’ disease and skin infections. Besides, Jewels of Opar can also be used as a supporting medicine for Parkinson’s disease, heart disease and for lowering blood cholesterols. Currently, the identification of T. paniculatum has been mainly based on morphological analysis. However, this method often encounters obstacles when T. paniculatum has been completely or partially processed. In this work, we present the results of morphological characteristics, taxonomy and sequences characterisation of ITS region and rpoC1, rpoB genes of T. paniculatum in Northern provinces of Vietnam. Tuberous roots of T. paniculatum are cylindrical with many small roots. The stems are upright and divided into several branches. The stems are upright and divided into several branches. The leaves are staggered, generally oval, ovate-oblong, or egg back shaped; thick, glossy with wavy veins, without hairs. The flowers of the plants have five reddish purple wings, two sepals, more than ten stamens, and a spherical ovary. The fruits are small, and the ripe fruit is ash gray in color. The seeds are very small, slightly flat, and black. Internal transcribed spacer (ITS) region and two partial sequences of rpoC1 and rpoB genes isolated from T. paniculatum plants are 643 bp, 595 bp and 518 bp in length, respectively. Based on the combination of the characteristics of morphology and nucleotide sequences of ITS region, rpoC1 and rpoB genes, the Jewels of Opar samples collected in some northern provinces of Vietnam were determined to belong to T. paniculatum species, Talinum genus, Portulacaceae family. Characteristics of sequences of ITS region and rpoC1, rpoB genes are valuable for exploiting DNA barcodes to identify T. paniculatum in Vietnam

Cloning and Overexpression of GmDREB2 Gene from a Vietnamese Drought-resistant Soybean Variety

ABSTRACTThis work studied the amplification, cloning and determination of the GmDREB2 gene from the soybean cultivar DT2008 and five Vietnamese local soybean cultivars (DT26, DT51, DVN5, CB, CBD) and designed the vector carrying the structure containing GmDREB2 gene from cultivar DT2008 (best drought tolerant). The coding region of GmDREB2 gene isolated from six soybean cultivars was 480 nucleotides in length, encoding 159 amino acids. The recombinant structure was designed as 35S- GmDREB2- c-myc and its expression was analysed in transgenic tobacco plants. Recombinant DREB2 protein was expressed in five transgenic tobacco lines with molecular weights close to 20 kDa. During the drought conditions, the proline accumulation of the transgenic tobacco lines was higher than on wild-type (WT) plants, with the rate from 211.17 to 332.44% after five days of drought stress, and from 262.79 to 466.04% after nine days of drought stress. The two lines, TG2 and TG12 had the highest increase rate. These results provided the basis to generate drought-tolerant soybean plants by GmDREB2 overexpression

The prevalence of job stressors among nurses in private in vitro fertilization (IVF) centres

Aim: The primary aim of this study was to identify the level of stress and the stressors having an impact on nurses compared with other medical workers in private IVF centres. Background: Stressful working conditions can an adversely affect not only the health and well-being of health professionals but also subsequently to patient outcomes if care is given to infertile couples. This is of relevance particularly in view of Vietnam's recent economic growth and the increase in the number of private IVF centres. This is the first study looking at the levels of stress experienced by health workers (especially nurses) providing IVF services. Design: A cross-sectional survey. Methods: All health workers in seven IVF Clinics in HCMC were invited to complete an Occupational Stress Index (OSI) questionnaire. Results: Of the invited 131 medical professionals, 105 (80%) completed the confidential self-administered questionnaire. Thirty-five participants (33.3%) were nurses, 19 (18.1%) were doctors and 51 (48.6%) were lab technicians. Approximately two-thirds reported not having children (67.6%), half (50.48%) married and three-quarters (76.2%) were women, with a significant difference by medical worker group (p\ua

Computing on Wheels: A Deep Reinforcement Learning-Based Approach

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption