The existence of two non-contractible closed geodesics on every bumpy Finsler compact space form

Let $M=S^n/ \Gamma$ and $h$ be a nontrivial element of finite order $p$ in $\pi_1(M)$, where the integer $n\geq2$, $\Gamma$ is a finite group which acts freely and isometrically on the $n$-sphere and therefore $M$ is diffeomorphic to a compact space form. In this paper, we establish first the resonance identity for non-contractible homologically visible minimal closed geodesics of the class $[h]$ on every Finsler compact space form $(M, F)$ when there exist only finitely many distinct non-contractible closed geodesics of the class $[h]$ on $(M, F)$. Then as an application of this resonance identity, we prove the existence of at least two distinct non-contractible closed geodesics of the class $[h]$ on $(M, F)$ with a bumpy Finsler metric, which improves a result of Taimanov in [Taimanov 2016] by removing some additional conditions. Also our results extend the resonance identity and multiplicity results on $\mathcal{R}P^n$ in [arXiv:1607.02746] to general compact space forms.Comment: 33 pages, All comments are welcome. arXiv admin note: substantial text overlap with arXiv:1607.0274

Hydrogen-Deuterium Exchange Mass Spectrometry and Molecular Dynamics Simulations for Studying Protein Structure and Dynamics

Deciphering properties of proteins are essential for human health and aiding in the development of new pharmaceuticals. This dissertation uses hydrogen-deuterium exchange (HDX) mass spectrometry (MS) and molecular dynamics (MD) simulations to study protein dynamics, for improving the understanding of protein folding/unfolding mechanisms, and ligand binding and allosteric regulation. Chapter 2 uses HDX-MS for probing the conformational dynamics of myoglobin in the presence of N2 bubbles. We propose a dynamic model that reproduces the observed data: “semi-unfolded” “native” “globally unfolded” -\u3e “aggregated”. Chapter 3 focuses on osteoprotegerin (OPG), which hinders bone resorption by inhibiting RANK/RANKL interactions. The dimerization of OPG is regulated by heparan sulfate (HS). Basing on HDX data, a mechanism is proposed for the formation of the RANKL/OPG/HS ternary complex, according to which HS-mediated C-terminal contacts on OPG lower the entropic penalty for RANKL binding. Chapter 4 represents the centerpiece of this thesis. It explores the allosteric regulation of S100A11, a dimeric EF-hand protein with two hydrophobic target binding sites. Both HDX/MS and MD data showed the metalation sites become more dynamic after Ca2+ loss. However, these enhanced dynamics do not represent the trigger of the allosteric cascade. Instead, a labile salt bridge acts as an active “agitator” that destabilizes the packing of adjacent residues, causing a domino chain of events that culminates in target binding site closure. Overall, this thesis highlights how the combination of HDX/MS and computational techniques can provide detailed insights into protein conformational fluctuations and their implications for protein function

Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

Intracranial hemorrhage (ICH) is a life-threatening medical emergency caused by various factors. Timely and precise diagnosis of ICH is crucial for administering effective treatment and improving patient survival rates. While deep learning techniques have emerged as the leading approach for medical image analysis and processing, the most commonly employed supervised learning often requires large, high-quality annotated datasets that can be costly to obtain, particularly for pixel/voxel-wise image segmentation. To address this challenge and facilitate ICH treatment decisions, we proposed a novel weakly supervised ICH segmentation method that leverages a hierarchical combination of head-wise gradient-infused self-attention maps obtained from a Swin transformer. The transformer is trained using an ICH classification task with categorical labels. To build and validate the proposed technique, we used two publicly available clinical CT datasets, namely RSNA 2019 Brain CT hemorrhage and PhysioNet. Additionally, we conducted an exploratory study comparing two learning strategies - binary classification and full ICH subtyping - to assess their impact on self-attention and our weakly supervised ICH segmentation framework. The proposed algorithm was compared against the popular U-Net with full supervision, as well as a similar weakly supervised approach using Grad-CAM for ICH segmentation. With a mean Dice score of 0.47, our technique achieved similar ICH segmentation performance as the U-Net and outperformed the Grad-CAM based approach, demonstrating the excellent potential of the proposed framework in challenging medical image segmentation tasks

SONIA: an immersive customizable virtual reality system for the education and exploration of brain networks

While mastery of neuroanatomy is important for the investigation of the brain, there is an increasing interest in exploring the neural pathways to better understand the roles of neural circuitry in brain functions. To tackle the limitations of traditional 2D-display-based neuronavigation software in intuitively visualizing complex 3D anatomies, several virtual reality (VR) and augmented reality (AR) solutions have been proposed to facilitate neuroanatomical education. However, with the increasing knowledge on brain connectivity and the functioning of the sub-systems, there is still a lack of similar software solutions for the education and exploration of these topics, which demand more elaborate visualization and interaction strategies. To address this gap, we designed the immerSive custOmizable Neuro learnIng plAform (SONIA), a novel user-friendly VR software system with a multi-scale interaction paradigm that allows flexible customization of learning materials. With both quantitative and qualitative evaluations through user studies, the proposed system is shown to have high usability, attractive visual design, and good educational value. As the first immersive system that integrates customizable design and detailed narratives of the brain sub-systems for the education of neuroanatomy and brain connectivity, SONIA showcases new potential directions and provides valuable insights regarding medical learning and exploration in VR

Calcium-Mediated Control of S100 Proteins: Allosteric Communication via an Agitator/Signal Blocking Mechanism.

Allosteric proteins possess dynamically coupled residues for the propagation of input signals to distant target binding sites. The input signals usually correspond to effector is present or effector is not present . Many aspects of allosteric regulation remain incompletely understood. This work focused on S100A11, a dimeric EF-hand protein with two hydrophobic target binding sites. An annexin peptide (Ax) served as the target. Target binding is allosterically controlled by Ca2+ over a distance of ∼26 Å. Ca2+ promotes formation of a [Ca4 S100 Ax2] complex, where the Ax peptides are accommodated between helices III/IV and III\u27/IV\u27. Without Ca2+ these binding sites are closed, precluding interactions with Ax. The allosteric mechanism was probed by microsecond MD simulations in explicit water, complemented by hydrogen exchange mass spectrometry (HDX/MS). Consistent with experimental data, MD runs in the absence of Ca2+ and Ax culminated in target binding site closure. In simulations on [Ca4 S100] the target binding sites remained open. These results capture the essence of allosteric control, revealing how Ca2+ prevents binding site closure. Both HDX/MS and MD data showed that the metalation sites become more dynamic after Ca2+ loss. However, these enhanced dynamics do not represent the primary trigger of the allosteric cascade. Instead, a labile salt bridge acts as an incessantly active agitator that destabilizes the packing of adjacent residues, causing a domino chain of events that culminates in target binding site closure. This agitator represents the starting point of the allosteric signal propagation pathway. Ca2+ binding rigidifies elements along this pathway, thereby blocking signal transmission. This blocking mechanism does not conform to the commonly held view that allosteric communication pathways generally originate at the sites where effectors interact with the protein

A Span-Extraction Dataset for Chinese Machine Reading Comprehension

Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short paper