Spin-Orbit Excitons in a Correlated Metal: Raman Scattering Study of Sr_{2}RhO_{4}.

Using Raman spectroscopy to study the correlated 4d-electron metal Sr_{2}RhO_{4}, we observe pronounced excitations at 220 meV and 240 meV with A_{1g} and B_{1g} symmetries, respectively. We identify them as transitions between the spin-orbit multiplets of the Rh ions, in close analogy to the spin-orbit excitons in the Mott insulators Sr_{2}IrO_{4} and α-RuCl_{3}. This observation provides direct evidence for the unquenched spin-orbit coupling in Sr_{2}RhO_{4}. A quantitative analysis of the data reveals that the tetragonal crystal field Δ in Sr_{2}RhO_{4} has a sign opposite to that in insulating Sr_{2}IrO_{4}, which enhances the planar xy orbital character of the effective J=1/2 wave function. This supports a metallic ground state, and suggests that c-axis compression of Sr_{2}RhO_{4} may transform it into a quasi-two-dimensional antiferromagnetic insulator.

Genome-wide identification and expression analysis of PYL family genes and functional characterization of GhPYL8D2 under drought stress in Gossypium hirsutum.

Cotton is a crucial economic crop, serving as a natural fiber source for the textile industry. However, drought stress poses a significant threat to cotton fiber quality and productivity worldwide. Pyrabactin Resistance 1-Like (PYL) proteins, as abscisic acid (ABA) receptors, play a crucial role in adverse stress responses, but knowledge about the PYLs in cotton remains limited. In our study, we identified 40 GhPYL genes in Gossypium hirsutum through a genome-wide analysis of the cotton genome database. Our analysis revealed that the PYL family formed three distinct subfamilies with typical family characteristics in G. hirsutum. Additionally, through quantitative expression analysis, including transcriptome dataset and qRT-PCR, we found that all GhPYLs were expressed in all tissues of G. hirsutum, and all GhPYLs were differentially expressed under drought stress. Among them, GhPYL4A1, GhPY5D1, GhPY8D2, and a member of the type 2C protein phosphatases clade A family in Gossypium hirsutum (GhPP2CA), GhHAI2D, showed significant differences in expression levels within 12 h after stress treatment. Our protein interaction analysis and BiFC demonstrated the complex regulatory network between GhPYL family proteins and GhPP2CA proteins. We also found that there is an interaction between GhPYL8D2 and GhHAI2D, and through drought treatment of transgenic cotton, we found that GhPYL8D2 played a vital role in the response of G. hirsutum to drought through stomatal control via co-regulation with GhHAI2D. Our findings provide useful insights into the regulation of GhPYL family genes that occur in response to abiotic stresses in cotton.

Large Anomalous Nernst Effects at Room Temperature in Fe3 Pt Thin Films.

Heat current in ferromagnets can generate a transverse electric voltage perpendicular to magnetization, known as anomalous Nernst effect (ANE). ANE originates intrinsically from the combination of large Berry curvature and density of states near the Fermi energy. It shows technical advantages over the conventional longitudinal Seebeck effect in converting waste heat to electricity due to its unique transverse geometry. However, materials showing giant ANE remain to be explored. Herein,  a large ANE thermopower of Syx ≈ 2 µV K-1 at room temperature in ferromagnetic Fe3 Pt epitaxial films is reported, which also show a giant transverse thermoelectric conductivity of αyx ≈ 4 A K-1  m-1 and a remarkable coercive field of 1300 Oe. The theoretical analysis reveals that the strong spin-orbit interaction in addition to the hybridization between Pt 5d and Fe 3d electrons leads to a series of distinct energy gaps and large Berry curvature in the Brillouin zone, which is the key for the large ANE. These results highlight the important roles of both Berry curvature and spin-orbit coupling in achieving large ANE at zero magnetic field, providing pathways to explore materials with giant transverse thermoelectric effect without an external magnetic field.

Experimental and Numerical Investigation of the Mechanical Properties of a CFRP Tendon-Wedge Assembly Loaded under Transverse Compressive Loading.

Carbon-fiber-reinforced polymer (CFRP) tendons have become a viable alternative to steel cables in cable roof structures owing to their high tensile strength, low weight, and resistance to corrosion. However, the effective anchoring of CFRP tendons is a challenge because of their poor transverse mechanical properties. Therefore, the mechanical properties of CFRP tendons and a tendon-wedge assembly under transverse compression were investigated by simulating the force environment of the CFRP tendon inside an integrated-wedge anchorage. The deformation of and local damage to CFRP tendons under transverse compression were explored using load-strain curves and full-field strain measured using digital image correlation. The experimental and numerical results show that large-diameter CFRP tendons with a length in the range of 90-110 mm had better cross-sectional deformation resistance and more stable transverse mechanical properties. Longer CFRP tendons with larger diameters have lower contact compressive stress and local maximal shear stress under the same transverse compressive load. Based on the analysis of the experimental and numerical results, we propose design suggestions for tendon size selection and integrated-wedge design details, such as the manufacturing materials of the wedge, the radius through the gap of the wedge, and the radial difference of the groove, to improve the anchoring properties and efficiency of the integrated-wedge anchorage.

Classification and Prediction of Skyrmion Material Based on Machine Learning.

The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology. The database of 196 materials, including 64 skyrmions, was established and predicted based on machine learning. A variety of intrinsic features are classified to optimize the model, and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials, such as support vector machines, k-nearest neighbor, and ensembles of trees. It is found that magnetic materials can be more accurately divided into skyrmion and non-skyrmion classes by using the classification of electronic layer. Note that the rare earths are the key elements affecting the production of skyrmion. The accuracy and reliability of random undersampling bagged trees were 87.5% and 0.89, respectively, which have the potential to build a reliable machine learning model from small data. The existence of skyrmions in LaBaMnO is predicted by the trained model and verified by micromagnetic theory and experiments.

Improving the Function of Meningeal Lymphatic Vessels to Promote Brain Edema Absorption after Traumatic Brain Injury.

Brain edema is the most common and fatal complication after traumatic brain injury (TBI). Meningeal lymphatic vessels (MLVs) are the conduits that transport cerebrospinal fluid (CSF) and macromolecules to deep extracranial cervical lymph nodes (dCLNs). After TBI, the drainage function of MLVs can become impaired. However, the scenario in which the improvement of the function of MLVs can promote brain edema absorption after TBI has not been reported. Therefore, the purpose of this study was to investigate the effects of ketoprofen, 9-cis retinoic acid (RA) and vascular endothelial cell growth factor-C (VEGF-C), which promote the proliferation of peripheral lymphatic vessels, on the cerebellar medullary cistern injection of TBI rats, as well as their mechanism of action on brain edema after TBI. In the experiment, we found that ketoprofen, 9-cisRA, and VEGF-C can improve the function of MLVs, promote the extracranial drainage of CSF and the absorption of brain edema, weaken the neuroinflammatory response, reduce reactive oxygen species (ROS) production, maintain the structural integrity of MLVs, and improve neurological function. In addition, ketoprofen, 9-cisRA, and VEGF-C upregulated the lymphatic-specific proteins VEGF receptor (VEGFR)3, PROX1, forkhead box protein C2 (FOXC2), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1). These results indicate that ketoprofen, 9-cisRA, and VEGF-C may maintain the integrity of the meningeal lymphatic wall and promote lymphatic proliferation by upregulating the expression of lymphatic vessel-specific proteins, improve meningeal lymphatic function after TBI, promote CSF drainage and brain edema absorption, reduce the immune response of the nervous system, and reduce ROS formation, thereby improving prognoses. These findings may provide new ideas for the treatment of brain edema after TBI.

Semi-Supervised Domain Adaptive Structure Learning.

Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. Unfortunately, a simple combination of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail to address such two objects because of training data bias towards labeled samples. In this paper, we introduce an adaptive structure learning method to regularize the cooperation of SSL and DA. Inspired by the multi-views learning, our proposed framework is composed of a shared feature encoder network and two classifier networks, trained for contradictory purposes. Among them, one of the classifiers is applied to group target features to improve intra-class density, enlarging the gap of categorical clusters for robust representation learning. Meanwhile, the other classifier, serviced as a regularizer, attempts to scatter the source features to enhance the smoothness of the decision boundary. The iterations of target clustering and source expansion make the target features being well-enclosed inside the dilated boundary of the corresponding source points. For the joint address of cross-domain features alignment and partially labeled data learning, we apply the maximum mean discrepancy (MMD) distance minimization and self-training (ST) to project the contradictory structures into a shared view to make the reliable final decision. The experimental results over the standard SSDA benchmarks, including DomainNet and Office-home, demonstrate both the accuracy and robustness of our method over the state-of-the-art approaches.

Genetic basis and principal component analysis in cotton (Gossypium hirsutum L.) grown under water deficit condition.

Cotton is considered as the main crop in the agricultural sector of Pakistan. Water deficiency in this region in recent years has reduced the chances of high yields of cotton. Selection and creation of high-yielding varieties of cotton, even in water deficit conditions, is one of urgent tasks of today. For this purpose, 40 diverse genotypes of upland cotton were screened in normal and water deficit conditions in triplicate arrangement under split plot in a randomized complete block design. All the genotypes showed significant difference under both water regimes. Ten upland cotton accessions were screened out as water deficit tolerant (VH-144, IUB-212, MNH-886, VH-295, IR-3701, AA-802, NIAB-111, NS-121, FH-113, and FH-142) and five as water deficit sensitive (IR-3, CIM-443, FH-1000, MNH-147, and S-12) based on seed cotton yield and stress susceptibility index. These tolerant and sensitive genotypes were crossed in line × tester mating design. For further evaluation of genetic material, the seed of 50 F1 crosses and their 15 parents were field planted under normal and water deficit conditions during next cotton growing season. Traits related to yield under the study showed significant variations among the accessions and their half sibs. The results of the principal component analysis (PCA) exhibited that total variation exhibited by factors 1 and factor 2 were 55.55 and 41.95%, respectively. PCA transformed the variables into three factors, and only two factors (F1 and F2) had eigenvalue > 1. The degree of dominance revealed that all parameters were highly influenced by non-additive gene action under both water regimes. Furthermore, the line VH-295 and tester CIM-443 had better yield performance under water deficit stress. The cross-combinations, viz., VH-144 × S-12, NIAB-111 × IR-3, and VH-295 × MNH-147, were the best for yield contributing traits. These combinations may be helpful for germplasm enhancement on large scale under water scarcity. All the studied traits have non-additive types of gene action suggesting the usage of these genotypes in cotton hybrid development program against water deficit tolerance.

Mechanism of cotton resistance to abiotic stress, and recent research advances in the osmoregulation related genes.

Abiotic stress is an important factor affecting the normal growth and development of plants and crop yield. To reduce the impact of abiotic adversity on cotton growth and development, the material basis of cotton resistance and its physiological functions are analyzed at the molecular level. At the same time, the use of genetic engineering methods to recombine resistance genes has become a hot spot in cotton resistance research. This paper provides an overviews of the resistance mechanism of cotton against the threat of non-biological adversity, as well as the research progress of osmoregulation-related genes, protein-acting genes, and transcription regulatory factor genes in recent years, and outlines the explored gene resources in cotton resistance genetic engineering, with the aim to provide ideas and reference bases for future research on cotton resistance.

Paramagnons and high-temperature superconductivity in a model family of cuprates.

Cuprate superconductors have the highest critical temperatures (Tc) at ambient pressure, yet a consensus on the superconducting mechanism remains to be established. Finding an empirical parameter that limits the highest reachable Tc can provide crucial insight into this outstanding problem. Here, in the first two Ruddlesden-Popper members of the model Hg-family of cuprates, which are chemically nearly identical and have the highest Tc among all cuprate families, we use inelastic photon scattering to reveal that the energy of magnetic fluctuations may play such a role. In particular, we observe the single-paramagnon spectra to be nearly identical between the two compounds, apart from an energy scale difference of ~30% which matches their difference in Tc. The empirical correlation between paramagnon energy and maximal Tc is further found to extend to other cuprate families with relatively high Tc's, hinting at a fundamental connection between them.

Ultrathin ferroic HfO2-ZrO2 superlattice gate stack for advanced transistors.

With the scaling of lateral dimensions in advanced transistors, an increased gate capacitance is desirable both to retain the control of the gate electrode over the channel and to reduce the operating voltage1. This led to a fundamental change in the gate stack in 2008, the incorporation of high-dielectric-constant HfO2 (ref. 2), which remains the material of choice to date. Here we report HfO2-ZrO2 superlattice heterostructures as a gate stack, stabilized with mixed ferroelectric-antiferroelectric order, directly integrated onto Si transistors, and scaled down to approximately 20 ångströms, the same gate oxide thickness required for high-performance transistors. The overall equivalent oxide thickness in metal-oxide-semiconductor capacitors is equivalent to an effective SiO2 thickness of approximately 6.5 ångströms. Such a low effective oxide thickness and the resulting large capacitance cannot be achieved in conventional HfO2-based high-dielectric-constant gate stacks without scavenging the interfacial SiO2, which has adverse effects on the electron transport and gate leakage current3. Accordingly, our gate stacks, which do not require such scavenging, provide substantially lower leakage current and no mobility degradation. This work demonstrates that ultrathin ferroic HfO2-ZrO2 multilayers, stabilized with competing ferroelectric-antiferroelectric order in the two-nanometre-thickness regime, provide a path towards advanced gate oxide stacks in electronic devices beyond conventional HfO2-based high-dielectric-constant materials.

Magnetically tightened form-stable phase change materials with modular assembly and geometric conformality features.

Phase change materials have attracted significant attention due to their promising applications in many fields like solar energy and chip cooling. However, they suffer leakage during the phase transition process and have relatively low thermal conductivity. Here, through introducing hard magnetic particles, we synthesize a kind of magnetically tightened form-stable phase change materials. They achieve multifunctions such as leakage-proof, dynamic assembly, and morphological reconfiguration, presenting superior high thermal (increasing of 1400-1600%) and electrical (>104 S/m) conductivity, and prominent compressive strength, respectively. Furthermore, free-standing temperature control and high-performance thermal and electric conversion systems based on these materials are developed. This work suggests an efficient way toward exploiting a smart phase change material for thermal management of electronics and low-grade waste heat utilization.

Bone Tumor Suppression in Rabbits by Hyperthermia below the Clinical Safety Limit Using Aligned Magnetic Bone Cement.

Demonstrating highly efficient alternating current (AC) magnetic field heating of nanoparticles in physiological environments under clinically safe field parameters has remained a great challenge, hindering clinical applications of magnetic hyperthermia. In this work, exceptionally high loss power of magnetic bone cement under the clinical safety limit of AC field parameters, incorporating direct current field-aligned soft magnetic Zn0.3 Fe2.7 O4 nanoparticles with low concentration, is reported. Under an AC field of 4 kA m-1 at 430 kHz, the aligned bone cement with 0.2 wt% nanoparticles achieves a temperature increase of 30 °C in 180 s. This amounts to a specific loss power value of 327 W gmetal-1 and an intrinsic loss power of 47 nHm2 kg-1 , which is enhanced by 50-fold compared to randomly oriented samples. The high-performance magnetic bone cement allows for the demonstration of effective hyperthermia suppression of tumor growth in the bone marrow cavity of New Zealand White Rabbits subjected to rapid cooling due to blood circulation, and significant enhancement of survival rate.

Human Motion Segmentation via Velocity-Sensitive Dual-Side Auto-Encoder.

Human motion segmentation (HMS) aims to segment a long human action video into a bunch of short and meaningful action clips. Existing supervised learning approaches need a large amount of training data which may be costly in real-world scenario, while most unsupervised clustering methods cannot fully explore the temporal correlations among human motions and hard to achieve promising performances. In our paper, we design a novel unsupervised framework, called Velocity-Sensitive Dual-Side Auto-Encoder (VSDA), for HMS task. Specifically, a multi-neighbor auto-encoder (MNA) is proposed to extract informative temporal features, which fully explores the local temporal patterns of human motions. In addition, a long-short distance encoding (LSE) strategy is designed. It constrains the encoded representations of close (short-distance) frames becoming similar while the representations of far-away (long-distance) frames becoming distinctive. Similarly, this strategy is also deployed on the decoded outputs as the long-short distance decoding (LSD) module. The LSE/LSD guides the learning process explicitly and implicitly to achieve the dual-side structure. Moreover, we consider the energy variations during the human motion to propose the velocity-sensitive (VS) guidance mechanism for further model improvement. VSDA leverages the temporal characteristics of human motion and derives promising HMS performance. Comprehensive experiments on six real-world human motion datasets illustrate the effectiveness of our proposed model.

Semi-Supervised Dual Relation Learning for Multi-Label Classification.

In a real-world scenario, an object could contain multiple tags instead of a single categorical label. To this end, multi-label learning (MLL) emerged. In MLL, the feature distributions are long-tailed and the complex semantic label relation and the long-tailed training samples are the main challenges. Semi-supervised learning is a potential solution. While, existing methods are mainly designed for single class scenario while ignoring the latent label relations. In addition, they cannot well handle the distribution shift commonly existing across source and target domains. To this end, a Semi-supervised Dual Relation Learning (SDRL) framework for multi-label classification is proposed. SDRL utilizes a few labeled samples as well as large scale unlabeled samples in the training stage. It jointly explores the inter-instance feature-level relation and the intra-instance label-level relation even from the unlabeled samples. In our model, a dual-classifier structure is deployed to obtain domain invariant representations. The prediction results from the classifiers are further compared and the most confident predictions are extracted as pseudo labels. A trainable label relation tensor is designed to explicitly explore the pairwise latent label relations and refine the predicted labels. SDRL is able to effectively and efficiently explore the feature-label relation as well as the label-label relation knowledge without any extra semantic knowledge. We evaluated SDRL in general and zero-shot multi-label classification tasks and we concluded that SDRL is superior to other SOTA baselines. Furthermore, extensive ablation studies have been done which reveal the effectiveness of each component in our framework.

Comparative transcriptome analysis uncovers cell wall reorganization and repressed cell division during cotton fiber initiation.

BACKGROUND: Tetraploid cotton plants serve as prime natural fiber source for the textile industry. Although various omics studies have revealed molecular basis for fiber development, a better understanding of transcriptional regulation mechanism regulating lint fiber initiation is necessary to meet global natural fiber demand. RESULTS: Here, we aimed to perform transcriptome sequencing to identify DEGs (differentially expressed genes) in ovules of the cotton variety Xu142 and its fibreless mutant Xu142fl during early lint fiber initiation period. Totally, 5516 DEGs including 1840 upregulated and 3676 downregulated were identified. GO enrichment analysis revealed that the downregulated DEGs were mainly associated with biological processes such as transcription related biosynthesis and metabolism, organic cyclic compound biosynthesis and metabolism, photosynthesis, and plant cell wall organization, with molecular functions involving transcription related binding, organic cyclic compound binding, and dioxygenase activity, while the upregulated DEGs were associated with DNA replication and phospholipid biosynthetic related processes. Among the 490 DEGs annotated as transcription factor genes, 86.5% were downregulated in the mutant including the Malvaceae-specific MMLs, expression patterns of which were confirmed during the central period of lint fiber initiation. Investigation of the 16 genes enriched in the cell wall organization revealed that 15 were EXPA coding genes. CONCLUSIONS: Overall, our data indicate that lint fiber initiation is a complicated process involving cooperation of multiple transcription factor families, which might ultimately lead to the reorganization of the cell wall and terminated cell division of the differentiating fiber initials.

Highly interconnected inverse opal extracellular matrix scaffolds enhance stem cell therapy in limb ischemia.

The therapeutic effectiveness of cell transplantation in treatment of diseases and injuries is often limited by low cell retention, survivability, and engraftment. Extracellular matrix (ECM)-derived scaffolds are capable of controlling cell responses, thereby offering potential solutions to current challenges associated with cell therapy. However, it remains a technical challenge to produce ECM scaffolds with highly interconnected porous structure specifically required for cell transplantation. Here, we developed inverse opal porous extracellular matrix (ioECM) scaffolds through subcutaneous implantation of sacrificial templates assembled from polymer microspheres, followed by removal of the microsphere template and cellular content. Such highly interconnected porous ioECM scaffolds supported the anchorage, survival, viability, anti-apoptotic and paracrine activities of rat bone marrow mesenchymal stem cells (BMSCs), which further promoted endothelial cell migration and tube formation and viability. Upon transplantation into nude mouse critical limb ischemic model, ioECM promoted the engraftment of laden BMSCs, facilitated interconnected vascular network formation with accelerated recovery of blood perfusion and inhibited muscle atrophy and fibrosis. Our study demonstrates a unique strategy to engineer highly porous yet well-interconnected ECM scaffolds specifically for cell transplantation with marked improvement of survivability and vascularization, which offers an essential step toward the success of cell therapy and regenerative medicine. STATEMENT OF SIGNIFICANCE: Cell-based therapy has a good developing foreground applied in a variety of tissue regeneration. Extracellular matrix (ECM) scaffolds is an optimal choice for cell delivery duo to its superior biocompatibility and favorable immune responses. However, the current ECM scaffolds lacking of the controllable pore structure restrict the cell delivery efficiency and therapeutic outcome. Here, we fabricated highly interconnected inverse opal extracellular matrix (ioECM) scaffolds, which can enhance the effect of stem cell therapy in limb ischemic model by improving the survival, viability, and paracrine activities of stem cells. Our study provides reference value for the design and fabrication of ECM based biomaterials for cell transplantation.

Characteristics of Cortical Atrophy and White Matter Lesions Between Dementia With Lewy Bodies and Alzheimer's Disease: A Case-Control Study.

Introduction: Currently, there is still clinical overlap between dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) patients, which may affect the accuracy of the early diagnosis of DLB. For better diagnosis and prognosis, further exploration of local cortical atrophy patterns and white matter lesions is needed. Methods: We reviewed the outpatient medical records of 97 DLB patients and 173 AD patients from January 2018 to September 2020 along with 30 matched outpatient clinic normal elderly people. MRI visual rating scales, including medial temporal lobe atrophy (MTA), global cortical atrophy-frontal subscale (GCA-F), posterior atrophy (PA), Fazekas scale, Evans Index and cerebral microbleeds were evaluated and analyzed in DLB and AD patients with different severities and normal controls. Results: Overall, patients with DLB had higher scores on all visual rating scales than the normal controls. Meanwhile, compared with AD, DLB had lower MTA scores in the mild to moderate groups (both p ≤ 0.001), but the GCA-F and PA scores were similar (all p > 0.05). The Fazekas scores in the moderate to severe DLB group were lower than those in the AD group (p = 0.024 and p = 0.027, respectively). In addition, the diagnostic performance and sensitivity of multiple imaging indicators for DLB were better than that of MTA alone (the combination of MTA, GCA-F, PA, Fazekas visual rating scales, AUC = 0.756, 95%CI: 0.700-0.813, sensitivity = 0.647, specificity = 0.804 and MTA visual rating scale, AUC = 0.726, 95%CI: 0.667-0.785, sensitivity = 0.497, specificity = 0.876, respectively). Conclusion: The medial temporal lobe of DLB patients was relatively preserved, the frontal and parietal lobes were similarly atrophied to AD patients, and the white matter hyperintensity was lighter than that in AD patients. Combined multiple visual rating scales may provide a novel idea for the diagnosis of early DLB.

Understanding the role of phytohormones in cotton fiber development through omic approaches; recent advances and future directions.

Cotton is among the most important fiber crops for the textile-based industry, thanks to its cellulose-rich mature fibers. The fiber initiation and elongation are one of the best models for deciphering mechanisms of single-cell differentiation and growth, that also target of fiber development programs. During the last couple of decades, high yielding omics approaches (genomics, transcriptomics, and proteomics), have helped in the identification of several genes and gene products involved in fiber development along with functional relationship to phytohormones. For example, MYB transcription factor family and Sus gene family have been evidenced by controlling cotton fiber initiation. Most importantly, the biosynthesis, responses, and transporting of phytohormones is documented to participate in the initiation of cotton fibers. Herein, in this review, the reliable genetic evidence by manipulating the above genes in cotton have been summarized to describe the relationships among key phytohormones, transcription factors, proteins, and downstream fiber growth-related genes such as Sus. The effect of other important factors such as ROS, fatty acid metabolism, and actin (globular multi-functional proteins) over fiber development has also been discussed. The challenges and deficiencies in the research of cotton fiber development have been mentioned along with a future perspective to discover new crucial genes using multiple omics analysis.