Innovation in the Breeding of Common Bean Through a Combined Approach of in vitro Regeneration and Machine Learning Algorithms

Common bean is considered a recalcitrant crop for in vitro regeneration and needs a repeatable and efficient in vitro regeneration protocol for its improvement through biotechnological approaches. In this study, the establishment of efficient and reproducible in vitro regeneration followed by predicting and optimizing through machine learning (ML) models, such as artificial neural network algorithms, was performed. Mature embryos of common bean were pretreated with 5, 10, and 20 mg/L benzylaminopurine (BAP) for 20 days followed by isolation of plumular apice for in vitro regeneration and cultured on a post-treatment medium containing 0.25, 0.50, 1.0, and 1.50 mg/L BAP for 8 weeks. Plumular apice explants pretreated with 20 mg/L BAP exerted a negative impact and resulted in minimum shoot regeneration frequency and shoot count, but produced longer shoots. All output variables (shoot regeneration frequency, shoot counts, and shoot length) increased significantly with the enhancement of BAP concentration in the post-treatment medium. Interaction of the pretreatment × post-treatment medium revealed the need for a specific combination for inducing a high shoot regeneration frequency. Higher shoot count and shoot length were achieved from the interaction of 5 mg/L BAP × 1.00 mg/L BAP followed by 10 mg/L BAP × 1.50 mg/L BAP and 20 mg/L BAP × 1.50 mg/L BAP. The evaluation of data through ML models revealed that R2 values ranged from 0.32 to 0.58 (regeneration), 0.01 to 0.22 (shoot counts), and 0.18 to 0.48 (shoot length). On the other hand, the mean squared error values ranged from 0.0596 to 0.0965 for shoot regeneration, 0.0327 to 0.0412 for shoot count, and 0.0258 to 0.0404 for shoot length from all ML models. Among the utilized models, the multilayer perceptron model provided a better prediction and optimization for all output variables, compared to other models. The achieved results can be employed for the prediction and optimization of plant tissue culture protocols used for biotechnological approaches in a breeding program of common beans

Constraint on compactification scale via recently observed baryonic Λb→Λℓ+ℓ−\Lambda_b\rightarrow \Lambda \ell^+ \ell^- channel and analysis of the Σb→Σℓ+ℓ−\Sigma_b \rightarrow \Sigma \ell^+ \ell^- transition in SM and UED scenario

We obtain a lower limit on the compactification scale of extra dimension via comparison of the branching ratio in the baryonic $\Lambda_b\rightarrow \Lambda \mu^+ \mu^-$ decay channel recently measured by CDF collaboration and our previous theoretical study. We also use the newly available form factors calculated via light cone QCD sum rules in full theory to analyze the flavour changing neutral current process of the $\Sigma_b \rightarrow \Sigma \ell^+ \ell^-$ in universal extra dimension scenario in the presence of a single extra compact dimension. We calculate various physical quantities like branching ratio, forward-backward asymmetry, baryon polarizations and double lepton polarization asymmetries defining the decay channel under consideration. We also compare the obtained predictions with those of the standard model.Comment: 32 Pages, 27 Figures and one Tabl

The prediction of the ZnNi thickness and Ni % of ZnNi alloy electroplating using a machine learning method

ZnNi alloy coating is commonly used to enhance the corrosion resistance of steel. The percentage of Ni should be maintained between 12% and 14% in the coating for best corrosion performance. The response surface design (RSD), polynomial regression (PR), support vector regression (SVR), XGBoost regression (XGB), K-nearest neighbours regression and Gaussian process regression (GP) algorithms have been used to predict the ZnNi alloy coating thickness and Ni % amount in the coating. As statistical indices mean square error (MSE) and correlation coefficient (R 2) were used to compare the models. The results of the analysis show that the XGB algorithm gives the best estimation for both ZnNi thickness and Ni%. A high correlation was observed between the predicted values and experimental results. R 2 values of 0.87 and 0.81 were acquired for ZnNi thickness and Ni %, respectively, using the XGB algorithm. This study has proved that the machine learning algorithm is a promising method to predict the ZnNi coating thickness and Ni % in the alloy based on the composition of the ZnNi electroplating bath. © 2021 Institute of Materials Finishing Published by Taylor & Francis on behalf of the Institute

Seasonal variations of patients presenting dyspnea to emergency departments in europe: Results from the eurodem study

Background/aim: To describe seasonal variations in epidemiology, management, and short-term outcomes of patients in Europe presenting to an emergency department (ED) with a main complaint of dyspnea. Materials and methods: An observational prospective cohort study was performed in 66 European EDs which included consecutive patients presenting to EDs with dyspnea as the main complaint during 3 72-h study periods. Data were collected on demographics, comorbidities, chronic treatment, prehospital treatment, mode of arrival of patient to ED, clinical signs at admission, treatment in the ED, ED diagnosis, discharge from El), and in-hospital outcome. Results: The study included 2524 patients with a median age of 69 (53-80) years old. Of the patients presented, 991 (39.3\%) were in autumn, 849 (33.6\%) were in spring, and 48 (27.1\%) were in winter. The winter population was significantly older (P < 0.001) and had a lower rate of ambulance arrival to ED (P < 0.001). In the winter period, there was a higher rate for lower respiratory tract infection (35.1\%), and patients were more hypertensive, more hypoxic, and more hyper/hypothermic compared to other seasons. The ED mortality was about 1\% and, in hospital, mortality for admitted patients was 7.4\%. Conclusion: The analytic method and the outcome of this study may help to guide the allocation of ED resources more efficiently and to recommend seasonal ED management protocols based on the seasonal trend of dyspneic patients

Impact of family-non-universal Z′Z^\prime Z ′ boson on pure annihilation Bs→π+π−B_s \rightarrow \pi ^+ \pi ^- B s → π + π - and Bd→K+K−B_d \rightarrow K^+ K^- B d → K + K - decays

We study the $$B_s \rightarrow \pi ^+ \pi ^-$$ and $$B_d \rightarrow K^+ K^-$$ decays in the standard model and the family-non-universal $$Z^\prime$$ model. Since none of the quarks in the final states is the same as the initial quark, these decay modes can occur only via power-suppressed annihilation diagrams. Despite the consistency of the standard model prediction with the available data, room remains for a light $$Z^\prime$$ boson. Taking into account the $$Z^\prime$$ contribution, we find that theoretical results for the branching fractions can better accommodate the data. With the relevant data, we also derive a constraint on the parameter space for the $$Z^\prime$$. Moreover, for $$B_d \rightarrow K^+ K^-$$, both the direct and the mixing-induced CP asymmetry are sensitive to the couplings between $$Z^\prime$$ and fermions in the parameter spaces constrained by the data. The measurements at future experimental facilities, including the LHCb, Belle-II, and the proposed high energy $$e^+e^-$$ collider, will provide us useful hints for direct searching for the light $$Z^\prime$$ boson