Method to extract multiple states in F₁-ATPase rotation experiments from jump distributions

A method is proposed for analyzing fast (10 μs) single-molecule rotation trajectories in F₁ adenosinetriphosphatase (F₁-ATPase). This method is based on the distribution of jumps in the rotation angle that occur in the transitions during the steps between subsequent catalytic dwells. The method is complementary to the “stalling” technique devised by H. Noji et al. [Biophys. Rev. 9, 103–118, 2017], and can reveal multiple states not directly detectable as steps. A bimodal distribution of jumps is observed at certain angles, due to the system being in either of 2 states at the same rotation angle. In this method, a multistate theory is used that takes into account a viscoelastic fluctuation of the imaging probe. Using an established sequence of 3 specific states, a theoretical profile of angular jumps is predicted, without adjustable parameters, that agrees with experiment for most of the angular range. Agreement can be achieved at all angles by assuming a fourth state with an ∼10 μs lifetime and a dwell angle about 40° after the adenosine 5′-triphosphate (ATP) binding dwell. The latter result suggests that the ATP binding in one β subunit and the adenosine 5′-diphosphate (ADP) release from another β subunit occur via a transient whose lifetime is ∼10 μs and is about 6 orders of magnitude smaller than the lifetime for ADP release from a singly occupied F₁-ATPase. An internal consistency test is given by comparing 2 independent ways of obtaining the relaxation time of the probe. They agree and are ∼15 μs

Characterization of Lenticulostriate Arteries and Its Associations With Vascular Risk Factors in Community-Dwelling Elderly

Lenticulostriate arteries (LSAs) supply blood to important subcortical areas and are, therefore, essential for maintaining the optimal functioning of the brain’s most metabolically active nuclei. Past studies have demonstrated the potential for quantifying the morphology of LSAs as biomarkers of vascular fragility or underlying arteriopathies. Thus, the current study aims to evaluate the morphological features of LSAs, their potential value in cerebrovascular risk stratification, and their concordance with other vascular risk factors in community-dwelling elderly people. A total of 125 community-dwelling elderly subjects who underwent a brain MRI scan were selected from our prospectively collected imaging database. The morphological measures of LSAs were calculated on the vascular skeletons obtained by manual tracing, and the number of LSAs was counted. Additionally, imaging biomarkers of small vessel disease were evaluated, and the diameters of major cerebral arteries were measured. The effects of vascular risk factors on LSA morphometry, as well as the relationship between LSA measures and other imaging biomarkers, were investigated. We found that smokers had shorter (p = 0.04) and straighter LSAs (p < 0.01) compared to nonsmokers, and the presence of hypertension is associated with less tortuous LSAs (p = 0.03) in community-dwelling elderly. Moreover, the middle cerebral artery diameter was positively correlated with LSA count (r = 0.278, p = 0.025) and vessel tortuosity (r = 0.257, p = 0.04). The posterior cerebral artery diameter was positively correlated with vessel tortuosity and vessel length. Considering the scarcity of noninvasive methods for measuring small artery abnormalities in the brain, the LSA morphological measures may provide valuable information to better understand cerebral small vessel degeneration during aging

Total synthesis of (+)-SCH 351448 and rhodium catalyzed stereoselective nitrene/alkyne cycloaddition cascade

Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.SCH-351448 is a 28-membered C2-symmetric macrocyclic metabolite isolated from the fermentation broth of a Micromonospora microorganism. This macrodiolide selectively activates transcription of the low-density lipoprotein receptor (LDL-R) promoter, which is important in the treatment of people with high cholesterol levels. The biological potential as well as the intriguing structure of this natural product prompted us to initiate synthetic studies towards its preparation. A convergent, enantioselective total synthesis of (+)-SCH 351448 was achieved. The tetrahydropyran ring systems in both fragments were constructed through our organosilane-based [4+2]-annulation methodology. Olefin cross metathesis was utilized in the union of two advanced fragments to generate the monomeric subunit. A metal-template directed macrodimerization strategy was examined but proved unsuccessful. Thus, the macrodiolide was assembled through a two-step sequence involving dioxinone ring-opening with concomitant esterification followed by DMC/DMAP mediated macrolactonization. Due to the prevalence of nitrogen-containing heterocycles in many natural products and pharmaceutical agents, the development of efficient methods for N-incorporation has remained at the forefront of synthetic research. Transition metal-catalyzed nitrene chemistry, an effective method to incorporate N-containing functionality into simple organic substrates, has become an attractive field for direct carbon-nitrogen bond formation. Of particular interest in this area is the metallonitrene/alkyne cycloaddition cascade reaction, a process in which nitrenes formed from sulfamate esters undergo addition to alkynes. In light of this, homopropargylic ethers, derived from chiral allenylsilanes in high yields and levels of diastereoselectivity , were converted into sulfamate esters bearing an internal alkyne. The generated sulfamate esters then underwent a metallonitrene cycloaddition and a subsequent highly stereoselective dearomative cyclopropanation, which resulted in unique tetracyclic norcaradiene-like products with two contiguous quaternary stereocenters. After subsequent opening of the sulfamate ester ring, the norcaradiene scaffold rearranged via a 6π electrocyclic ring opening process to form a fused tricyclic cycloheptatriene

Modelling signaling pathways on diverse scales

The dynamic behavior of cell is a broad topic covering wide scope of subjects of varying scales and species. According to the great complexity and diversity involved in understanding cellular behavior, it is necessary to combine the experimental observations from different methods and scales to build up a thorough picture. Modeling techniques originating from physics and mathematics have helped to solve puzzles for complex systems over diverse space and time scales, including numerous biological related systems. In this thesis, we aim to apply the theoretical modeling methods to several biological systems scaling from molecular to ecological levels to develop a quantitative understanding of the interaction and dynamics involved in these subjects. On molecular level, we model the tubulin protein dimer as a feedback control system to show the rich dynamics ranging from picoseconds to hundreds of nanoseconds, as well as the sensitivity of such dimer structure on surrounding biophysical environment. Based on the experimental results of bacteria related study, we mainly focus on the quorum sensing pathway analysis to identify the key components and the robust topological motifs of the interaction network. We also analyze the bacterial ecology system of Ace lake of large scale and high complexity by reaction-diffusion theory and figure out the reason of spatial stratification of different bacteria species. With these modeling works, we are able to further both qualitative and quantitative understanding of molecular interactions and large scale observation of cellular behaviors.Doctor of Philosophy (IGS

Engagement Recognition in an E-learning Environment Using Convolutional Neural Network

Background. Under the current situation, distance education has rapidly become popular among students and teachers. This educational situation has changed the traditional way of teaching in the classroom. Under this kind of circumstance, students will be required to learn independently. But at the same time, it also brings some drawbacks, and teachers cannot obtain the feedback of students’ engagement in real-time. This thesis explores the feasibility of applying a lightweight model to recognize student engagement and the practicality of the model in a distance education environment. Objectives. This thesis aims to develop and apply a lightweight model based on Convolutional Neural Network(CNN) with acceptable performance to recognize the engagement of students in the environment of distance learning. Evaluate and compare the optimized model with selected original and other models in different performance metrics. Methods. This thesis uses experiments and literature review as research methods. The literature review is conducted to select effective CNN-based models for engagement recognition and feasible strategies for optimizing chosen models. These selected and optimized models are trained, tested, evaluated and compared as independent variables in the experiments. The performance of different models is used as the dependent variable. Results. Based on the literature review results, ShuffleNet v2 is selected as the most suitable CNN architecture for solving the task of engagement recognition. Inception v3 and ResNet are used as the classic CNN architecture for comparison. The attention mechanism and replace activation function are used as optimization methods for ShuffleNet v2. The pre-experiment results show that ShuffleNet v2 using the Leaky ReLU function has the highest accuracy compared with other activation functions. The experimental results show that the optimized model performs better in engagement recognition tasks than the baseline ShuffleNet v2 model, ResNet v2 and Inception v3 models. Conclusions. Through the analysis of the experiment results, the optimized ShuffleNet v2 has the best performance and is the most suitable model for real-world applications and deployments on mobile platforms

Engagement Recognition in an E-learning Environment Using Convolutional Neural Network

Background. Under the current situation, distance education has rapidly become popular among students and teachers. This educational situation has changed the traditional way of teaching in the classroom. Under this kind of circumstance, students will be required to learn independently. But at the same time, it also brings some drawbacks, and teachers cannot obtain the feedback of students’ engagement in real-time. This thesis explores the feasibility of applying a lightweight model to recognize student engagement and the practicality of the model in a distance education environment. Objectives. This thesis aims to develop and apply a lightweight model based on Convolutional Neural Network(CNN) with acceptable performance to recognize the engagement of students in the environment of distance learning. Evaluate and compare the optimized model with selected original and other models in different performance metrics. Methods. This thesis uses experiments and literature review as research methods. The literature review is conducted to select effective CNN-based models for engagement recognition and feasible strategies for optimizing chosen models. These selected and optimized models are trained, tested, evaluated and compared as independent variables in the experiments. The performance of different models is used as the dependent variable. Results. Based on the literature review results, ShuffleNet v2 is selected as the most suitable CNN architecture for solving the task of engagement recognition. Inception v3 and ResNet are used as the classic CNN architecture for comparison. The attention mechanism and replace activation function are used as optimization methods for ShuffleNet v2. The pre-experiment results show that ShuffleNet v2 using the Leaky ReLU function has the highest accuracy compared with other activation functions. The experimental results show that the optimized model performs better in engagement recognition tasks than the baseline ShuffleNet v2 model, ResNet v2 and Inception v3 models. Conclusions. Through the analysis of the experiment results, the optimized ShuffleNet v2 has the best performance and is the most suitable model for real-world applications and deployments on mobile platforms

Cascade self-splitting of a Hermite-cos-Gaussian correlated Schell-model beam

We propose a new kind of partially coherent beam with a nonconventional correlation function termed as Hermite-cos-Gaussian correlated Schell-model (HcGCSM) beam. The propagation properties of this novel model beam are investigated. It is found that the HcGCSM beam exhibits cascade self-splitting properties on propagation in free space, i.e., the initial single beam spot is proved to be successively split for two times during the whole propagation. Furthermore, we demonstrate that the cascade self-splitting phenomenon can be closely controlled through modulating the spectral degree of coherence of a HcGCSM beam in the source plane

An Optimized CNN Model for Engagement Recognition in an E-Learning Environment

In the wake of the restrictions imposed on social interactions due to the COVID-19 pandemic, traditional classroom education was replaced by distance education in many universities. Under the changed circumstances, students are required to learn more independently. The challenge for teachers has been to duly ascertain students’ learning efficiency and engagement during online lectures. This paper proposes an optimized lightweight convolutional neural network (CNN) model for engagement recognition within a distance-learning setup through facial expressions. The ShuffleNet v2 architecture was selected, as this model can easily adapt to mobile platforms and deliver outstanding performance compared to other lightweight models. The proposed model was trained, tested, evaluated and compared with other CNN models. The results of our experiment showed that an optimized model based on the ShuffleNet v2 architecture with a change of activation function and the introduction of an attention mechanism provides the best performance concerning engagement recognition. Further, our proposed model outperforms many existing works in engagement recognition on the same database. Finally, this model is suitable for student engagement recognition for distance learning on mobile platforms. © 2022 by the authors.open access</p

Propagation Property of an Astigmatic sin–Gaussian Beam in a Strongly Nonlocal Nonlinear Media

Based on the Snyder and Mitchell model, a closed-form propagation expression of astigmatic sin-Gaussian beams through strongly nonlocal nonlinear media (SNNM) is derived. The evolutions of the intensity distributions and the corresponding wave front dislocations are discussed analytically and numerically. It is generally proved that the light field distribution varies periodically with the propagation distance. Furthermore, it is demonstrated that the astigmatism and edge dislocation nested in the initial sin-Gaussian beams greatly influence the pattern configurations and phase singularities during propagation. In particular, it is found that, when the beam parameters are properly selected, a vortex beam with perfect doughnut-shaped profile can be obtained for astigmatic sin-Gaussian beams with two-lobe pattern propagating in SNNM