The Trends of De Novo Molecular Designs in the Twenty-First Century: A Mini-Review

The inception of advanced bioactive agents has driven the growth for sustained drug delivery and the boom of new medicines. The future of the medical and chemical biology relies on the amalgamation of the advanced systematic and analytical techniques, which shall be tethered together with a robust theoretical framework. The de novo drug design is one of such exciting strategies that use computational theories to generate novel molecules with a good affinity to the desired biological target. This mini-review provides a basic overview of the current trends and algorithms, which aids in the advancement of the de novo molecular framework

Thermodynamic geometry of holographic superconductors

We obtain the thermodynamic geometry of a (2+1) dimensional strongly coupled quantum field theory at a finite temperature in a holographic set up, through the gauge/gravity correspondence. The bulk dual gravitational theory is described by a (3+1) dimensional charged AdS black hole in the presence of a massive charged scalar field. The holographic free energy of the (2+1) dimensional strongly coupled boundary field theory is computed analytically through the bulk boundary correspondence. The thermodynamic metric and the corresponding scalar curvature is then obtained from the holographic free energy. The thermodynamic scalar curvature characterizes the superconducting phase transition of the boundary field theory.Comment: 7 Pages and 3 Figure

Sparse bounds for pseudo-multipliers associated to Grushin operators, II

In this article, we establish pointwise sparse domination results for Grushin pseudo-multipliers corresponding to various symbol classes, as a continuation of our investigation initiated in [BBGG21]. As a consequence, we deduce quantitative weighted estimates for these pseudo-multipliers.Comment: 39 page

Sparse bounds for pseudo-multipliers associated to Grushin operators, I

In this article, we prove sharp quantitative weighted $L^p$-estimates for Grushin pseudo-multipliers satisfying H\"ormander's condition as an application of pointwise domination of Grushin pseudo-multipliers by appropriate sparse operators.Comment: We have removed the analysis pertaining to the family of operator-valued Fourier pseudo-multipliers from the original version, and we plan to submit those results elsewhere. Effectively, the introductory section is majorly revised, and as long as the mathematical results are concerned, this version is a proper subset of the first version, consisting of main results on Grushin pseudo-multiplier

IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation

Phenotypic and Genotypic Analysis of Hereditary Ataxia Patients in Sakarya City, Turkey

Conclusion: Hereditary ataxias are rare neurodegenerative disorders. Large genetic pool, ethnic and local differences complicate diagnosing even further. Our study contributes to the literature by reflecting phenotypic and genotypic characteristics of hereditary SCA patients in our region and reporting rare hereditary ataxia genotypes

Hysteresis and Pattern Formation in Electronic Phase Transitions in Quantum Materials

We propose an order parameter theory of the quantum Hall nematic in high fractional Landau levels in terms of an Ising description. This new model solves a couple of extant problems in the literature: (1) The low-temperature behavior of the measured resistivity anisotropy is captured better by our model than previous theoretical treatments based on the electron nematic having XY symmetry. (2) Our model allows for the development of true long-range order at low temperature, consistent with the observation of anisotropic low-temperature transport. We furthermore propose new experimental tests based on hysteresis that can distinguish whether any twodimensional electron nematic is in the XY universality class (as previously proposed in high fractional Landau levels), or in the Ising universality class (as we propose). Given the growing interest in electron nematics in many materials, we expect our proposed test of universality class to be of broad interest. Whereas the XY model in two dimensions does not have a long-range ordered phase, the addition of uniaxial random field disorder induces a long-range ordered phase in which the spontaneous magnetization points perpendicular to the random field direction, via an order-by-disorder transition. We have shown that this spontaneous magnetization is robust against a rotating driving field, up to a critical driving field amplitude. Thus we have found evidence for a new non-equilibrium phase transition that was unknown before in this model. Moreover, we have discovered an incredible anomaly at this nonequilibrium phase transition: the critical region is accompanied by a cascade of period multiplication events. This physics is reminiscent of the period bifurcation cascade signaling the transition to chaos in nonlinear systems, and of the approach to the irreversibility transition in models of yield in amorphous solids [1,2]. This period multiplication cascade is surprising to be present in a statistical mechanics model, and suggests that the non-equilibrium transition as a function of driving field amplitude is part of a larger class of transitions in dynamical systems. Moreover, we show that this multi-period behavior represents a new emergent classical discrete time-crystal, since the new period is robust against changes to initial conditions and low-temperature fluctuations over hundreds of driving period cycles. We expect this work to be of broad interest, further encouraging cross-fertilization between the rapidly growing field of time-crystals with the well-established fields of nonequilibrium phase transitions and dynamical systems. Geometrical configurations gave us a better understanding of the multi-period behavior of the limit-cycles. Moreover, surface probes are continually evolving and generating vast amounts of spatially resolved data of quantum materials, which reveal a lot of detail about the microscopic and macroscopic properties of the system. Materials undergoing a transition between two distinct states, phase separate. These phase-separated regions form intricate patterns on the observable surface, which can encode model-specific information, including interaction, dimensionality, and disorder. While there are rigorous methods for understanding these patterns, they turn out to be time-consuming as well as requiring expertise. We show that a well-tuned machine learning framework can decipher this information with minimal effort from the user. We expect this to be widely used by the scientific community to fast-track comprehension of the underlying physics in these materials