Activation-induced deoxycytidine deaminase (AID) co-transcriptional scanning at single-molecule resolution

Activation-induced deoxycytidine deaminase (AID) generates antibody diversity in B cells by initiating somatic hypermutation (SHM) and class-switch recombination (CSR) during transcription of immunoglobulin variable (IgV) and switch region (IgS) DNA. Using single-molecule FRET, we show that AID binds to transcribed dsDNA and translocates unidirectionally in concert with RNA polymerase (RNAP) on moving transcription bubbles, while increasing the fraction of stalled bubbles. AID scans randomly when constrained in an 8 nt model bubble. When unconstrained on single-stranded (ss) DNA, AID moves in random bidirectional short slides/hops over the entire molecule while remaining bound for ~5 min. Our analysis distinguishes dynamic scanning from static ssDNA creasing. That AID alone can track along with RNAP during transcription and scan within stalled transcription bubbles suggests a mechanism by which AID can initiate SHM and CSR when properly regulated, yet when unregulated can access non-Ig genes and cause cancer

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf

Fast Ensemble Smoothing

Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an ensemble-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that ensemble methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical ensemble application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue

One-loop flavor changing electromagnetic transitions

We discuss the effect of the external fermion masses in the flavor-changing radiative transitions of a heavy fermion (quark or lepton) to a lighter fermion at the one-loop level, and point out an often overlooked crucial difference in the sign of a charge factor between transitions of the down type $s\to d\gamma$ and the up type $c\to u\gamma$. We give formulas for the $F\to f\gamma$ effective vertex in various approximations and the exact formula for $t\to c\gamma$ and $\tau \to \mu \gamma$.Comment: LaTeX 16 pages + 4 postscript figures. Misprints corrected, some Comments adde

An Eight-Term Novel Four-Scroll Chaotic System with Cubic Nonlinearity and its Circuit Simulation

This research work proposes an eight-term novel four-scroll chaotic system with cubic nonlinearity and analyses its fundamental properties such as dissipativity, equilibria, symmetry and invariance, Lyapunov exponents and KaplanYorke dimension. The phase portraits of the novel chaotic system, which are obtained in this work by using MATLAB, depict the four-scroll attractor of the system. For the parameter values and initial conditions chosen in this work, the Lyapunov exponents of the novel four-scroll chaotic system are obtained as L1 = 0.75335, L2 = 0 and L3 = −22.43304. Also, the Kaplan-Yorke dimension of the novel four-scroll chaotic system is obtained as DKY = 2.0336. Finally, an electronic circuit realization of the novel four-scroll chaotic system is presented by using SPICE to confirm the feasibility of the theoretical model

Microwave-assisted noncatalytic esterification of fatty acid for biodiesel production: A kinetic study

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This study developed a microwave-mediated noncatalytic esterification of oleic acid for producing ethyl biodiesel. The microwave irradiation process outperformed conventional heating methods for the reaction. A highest reaction conversion, 97.62%, was achieved by performing esterification with microwave irradiation at a microwave power of 150 W, 2:1 ethanol:oleic acid molar ratio, reaction time of 6 h, and temperature of 473 K. A second-order reaction model (R2 of up to 0.997) was established to describe esterification. The reaction rate constants were promoted with increasing microwave power and temperature. A strong linear relation of microwave power to pre-exponential factors was also established, and microwave power greatly influenced the reaction due to nonthermal effects. This study suggested that microwave-assisted noncatalytic esterification is an efficient approach for biodiesel synthesis

Shear Behaviour of Cold-Formed Stainless Steel Lipped Channels with Reduced Support Restraints

Lipped channel beams are commonly used in buildings for load-bearing components such as floor joists and roof purlins. The typical practice is to use one-sided web side plates (WSPs) to attach beams from their webs to the supports at the connections, through bolts. In such realistic conditions, it is not practical to use WSPs over the full height of the webs, thus only a part of the web height is restrained at the supports. This controls the mobilising of diagonal tension field in the web and also provides less restraint to the lateral movement of the web. Therefore, realistic support conditions affect the shear capacity due to the lack of restraint of the web at the supports. On the other hand, the current shear design rules are based on ideal support conditions which do not represent the true scenario. Therefore, it is critical to investigate the effect of reduced support restraints on the shear capacity since it has been given less attention in the literature. This paper presents the effect of reduced support restraints on the shear capacity of stainless steel lipped channel beams. Finite element models were developed to study the effect with regard to various influential parameters. From the finite element results, it was found that the shorter the WSP—the higher the shear capacity reduction, where about 50% shear capacity reduction was observed for 60% reduction in WSP height. Furthermore, it was concluded that compact sections exhibit more significant capacity reduction than slender sections when reducing the WSP height. Therefore, a reduction factor was introduced to the current direct strength method shear design rules considering the effect of reduced support restraints on the shear capacity

Diabetes Is the Main Factor Accounting for Hypomagnesemia in Obese Subjects

OBJECTIVE: Type 2 diabetes (T2DM) and obesity are associated with magnesium deficiency. We aimed to determine whether the presence of type 2 diabetes and the degree of metabolic control are related to low serum magnesium levels in obese individuals. METHODS: A) Case-control study: 200 obese subjects [50 with T2DM (cases) and 150 without diabetes (controls)] prospectively recruited. B) Interventional study: the effect of bariatric surgery on serum magnesium levels was examined in a subset of 120 obese subjects (40 with type 2 diabetes and 80 without diabetes). RESULTS: Type 2 diabetic patients showed lower serum magnesium levels [0.75±0.07 vs. 0.81±0.06 mmol/L; mean difference -0.06 (95% CI -0.09 to -0.04); p<0.001] than non-diabetic patients. Forty-eight percent of diabetic subjects, but only 15% of non-diabetic subjects showed a serum magnesium concentration lower than 0.75 mmol/L. Significant negative correlations between magnesium and fasting plasma glucose, HbA1c, HOMA-IR, and BMI were detected. Multiple linear regression analysis showed that fasting plasma glucose and HbA1c independently predicted serum magnesium. After bariatric surgery serum magnesium increased only in those patients in whom diabetes was resolved, but remain unchanged in those who not, without difference in loss weight between groups. Changes in serum magnesium negatively correlated with changes in fasting plasma glucose and HbA1c. Absolute changes in HbA1c independently predicted magnesium changes in the multiple linear regression analysis. CONCLUSIONS: Our results provide evidence that the presence of diabetes and the degree of metabolic control are essential in accounting for the lower levels of magnesium that exist in obese subjects