Probing Dynamic Excitations in Novel Quantum Magnets Using Raman Spectroscopy

Quantum materials are a class of materials where charge, orbit, spin, and lattice degrees of freedoms (DOFs) entangle to give rise to novel phases. A major subset of quantum materials, quantum magnets, span a broad spectrum including quantum dimer magnets hosting singlet ground states, pseudospin magnets with strong spin-orbit coupling (SOC), spin liquid and spin ice magnets where magnetic frustrations lead to absence of magnetic long-range order (LRO) and fractionalized excitations, 2-dimensional (2D) magnets with strong quantum fluctuations, as well as topological magnets whose magnetic order is an essential ingredient in their topological phases. Using a combination of polarized Raman spectroscopy, an inelastic optical scattering technique, and spin-wave calculations, we study magnetic excitations in two classes of quantum magnets, namely, the bilayer perovskite iridate Sr3Ir2O7 with strong SOC and the 2D Ising ferromagnet (FM) CrI3 which is one of the first 2D magnets discovered. In Sr3Ir2O7, we discovered two sets of two-magnon modes, one of which arises from a pair of Brillouin zone-center optical magnons, and the other one from zone-boundary magnons. In particular, the former type is unconventional as it preserves the full symmetries of the underlying crystal lattice (i.e., A1g). Our findings not only reveal such A1g magnetic excitation, but also show the magnetic ground state (GS) of Sr3Ir2O7 is a conventional antiferromagnet (AFM), which offers insight into the heated debate on the nature of Sr3Ir2O7 magnetism. In CrI3, which has been thought to be an interlayer AFM in its few-layer form and an interlayer FM in its bulk form, we found that bulk CrI3 in fact hosts a mixed state with interlayer AFM at its surface and interlayer FM in its deep bulk. By applying an out-of-plane magnetic field, we induced an interlayer AFM to FM phase transition at a critical field of BC = 2 T, and observed a concurrent structural phase transition. Our results unambiguously address the puzzle of how the interlayer magnetism evolves upon decreasing thickness in CrI3. In conclusion, we used polarized Raman measurements and spin-wave calculations to study two types of quantum magnets. In the SOC magnet, Sr3Ir2O7, we discovered a unique A1g zone-center optical two-magnon excitation and confirmed its conventional AFM GS. In the bulk form of the 2D magnet, CrI3, we uncovered a coexistence of interlayer AFM and FM, and induced concurrent magnetic and structural phase transitions with external magnetic field.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169774/1/lisiwen_1.pd

The emerging role of cellular senescence in renal diseases

Cellular senescence represents the state of irreversible cell cycle arrest during cell division. Cellular senescence not only plays a role in diverse biological events such as embryogenesis, tissue regeneration and repair, ageing and tumour occurrence prevention, but it is also involved in many cardiovascular, renal and liver diseases through the senescence-associated secretory phenotype (SASP). This review summarizes the molecular mechanisms underlying cellular senescence and its possible effects on a variety of renal diseases. We will also discuss the therapeutic approaches based on the regulation of senescent and SASP blockade, which is considered as a promising strategy for the management of renal diseases

Deep Contrastive Multi-view Clustering under Semantic Feature Guidance

Contrastive learning has achieved promising performance in the field of multi-view clustering recently. However, the positive and negative sample construction mechanisms ignoring semantic consistency lead to false negative pairs, limiting the performance of existing algorithms from further improvement. To solve this problem, we propose a multi-view clustering framework named Deep Contrastive Multi-view Clustering under Semantic feature guidance (DCMCS) to alleviate the influence of false negative pairs. Specifically, view-specific features are firstly extracted from raw features and fused to obtain fusion view features according to view importance. To mitigate the interference of view-private information, specific view and fusion view semantic features are learned by cluster-level contrastive learning and concatenated to measure the semantic similarity of instances. By minimizing instance-level contrastive loss weighted by semantic similarity, DCMCS adaptively weakens contrastive leaning between false negative pairs. Experimental results on several public datasets demonstrate the proposed framework outperforms the state-of-the-art methods

Recent Advance in Tumor Microenvironment-Based Stimuli-Responsive Nanoscale Drug Delivery and Imaging Platform

The tumor microenvironment (TME) plays an important role in the development, progression, and metastasis of cancer, and the extremely crucial feature is hypoxic and acidic. Cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), mesenchymal cells, blood vessels, and interstitial fluid are widely recognized as fundamentally crucial hallmarks for TME. As nanotechnology briskly boomed, the nanoscale drug delivery and imaging platform (NDDIP) emerged and has attracted intensive attention. Based on main characteristics of TME, NDDIP can be classified into pH-sensitive delivery and imaging platforms, enzyme-sensitive delivery and imaging platforms, thermo-sensitive delivery and imaging platforms, redox-sensitive delivery and imaging platforms, and light-sensitive delivery and imaging platforms. Furthermore, imageology is one of the significant procedures for disease detection, image-guided drug delivery, and efficacy assessment, including magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), and fluorescence imaging. Therefore, the stimuli-responsive NDDIP will be a versatile and practicable tumor disease diagnostic procedure and efficacy evaluation tool. In this review article, we mainly introduce the characteristics of TME and summarize the progress of multitudinous NDDIP as well as their applications

Expression pattern and polymorphism of three microsatellite markers in the porcine CA3 gene

Carbonic anhydrase III (CA3) is an abundant muscle protein characteristic of adult type-1, slow-twitch, muscle fibres. In order to further understand the functions of the porcine CA3 protein in muscle, the temporal and spatial distributions of its gene product were analysed and the association between the presence of specific polymorphisms and carcass traits in the pig was also examined. Real-time PCR revealed that the CA3 mRNA expression showed no differences with age in skeletal muscles from Yorkshire pigs at postnatal day-1, month-2, and month-4. We provide the first evidence that CA3 is differentially expressed in the skeletal muscle of Yorkshire and Meishan pig breeds. In addition, the whole pig genomic DNA sequence of CA3 was investigated and shown to contain seven exons and six introns. Comparative sequencing of the gene from three pig breeds revealed the existence of microsatellite SJ160 in intron 5 and microsatellite SJ158 and a novel microsatellite marker that includes a tandem repeat of (TC)n in intron 4. We also determined the allele number and frequencies of the three loci in seven pig breeds and found that they are low polymorphic microsatellite markers. Statistical analysis showed that the CA3 microsatellite polymorphism was associated with dressing percentage, internal fat rate, carcass length, rib number and backfat thickness in the pig

Absorption-based algorithm for satellite estimating the particulate organic carbon concentration in the global surface ocean

Particulate organic carbon (POC) in the surface ocean contributes to understanding the global ocean carbon cycle system. The surface POC concentration can be effectively detected using satellites. In open oceans, the blue-to-green band ratio (BG) algorithm is often used to obtain global surface ocean POC concentrations. However, POC concentrations are underestimated in waters with complex optical environments. To generate a more accurate global POC mapping in the surface ocean, we developed a new ocean color algorithm using a mixed global-scale in situ POC dataset with the concentration ranging from 11.10 to 4389.28 mg/m3. The new algorithm (a-POC) was established to retrieve the POC concentration using the strong relationship between the absorption coefficient at 490 nm (a(490)) and POC, in which a(490) was from the Ocean Color Climate Change Initiative (OC-CCI) v5.0 suite. Afterward, the a-POC algorithm was applied to OC-CCI v5.0 data for special regions and the global ocean. The performances of the a-POC algorithm and the BG algorithm were compared by combining the match-ups of satellite data and in situ dataset. The results showed that the statistical parameters of the a-POC algorithm were similar to those of the BG algorithm in the Atlantic oligotrophic gyre regions, with a median absolute percentage deviation (MAPD) value of 22.04%. In the eastern coastal waters of the United States and the Chesapeake Bay, the POC concentration retrieved by the a-POC algorithm was highly consistent with the match-ups, and MAPD values were 33.06% and 26.11%. The a-POC algorithm was also applied to the Ocean and Land Color Instrument (OLCI) data pre-processed with different atmospheric correction algorithms to evaluate the universality. The result showed that the a-POC algorithm was robust and less sensitive to atmospheric correction than the BG algorithm