Carrier transport engineering in a polarization-interface-free ferroelectric PN junction for photovoltaic effect.

The carrier transport performances play key roles in the photoelectric conversion efficiency for photovoltaic effect. Hence, the low carrier mobility and high photogenerated carrier recombination in ferroelectric materials depress the separation of carriers. This work designs a ferroelectric polarization-interface-free PN junction composed with P-type semiconductor BiFeO3 (BFO) derived from the variable valence of Fe and N-type semiconductor BiFe0.98Ti0.02O3 (BFTO) through Ti donor doping. The integration of the ferroelectricity decides the PN junction without polarization coupling like the traditional heterojunctions but only existing carrier distribution differential at the interface. The carrier recombination in PN junction is significantly reduced due to the driving force of the built-in electric field and the existence of depletion layer, thereby enhancing the switching current 3 times higher than that of the single ferroelectric films. Meanwhile, the carrier separation at the interface is significantly engineered by the polarization, with open circuit voltage and short circuit current of photovoltaic effect increased obviously. This work provides an alternative strategy to regulate bulk ferroelectric photovoltaic effects by carrier transport engineering in the polarization-interface-free ferroelectric PN junction.

Simultaneous improvement of polarization and bandgap by finite solid solution engineering.

A narrow-bandgap-induced potential field always results in decreased photovoltaic performance. Here, a finite solid solution was designed to explore the simultaneous improvement of the polarization property and bandgap obtained from the critical effect in which BiMnO3 (BM) enters the Na0.5Bi0.5TiO3 (NBT) crystal lattice, resulting in a strong lattice expansion; by contrast, the incorporation of Mn without a d-orbital weakened the orbital hybridization accompanied by Jahn-Teller (J-T) distortion to reduce the optical bandgap. A narrow bandgap of 2.90 eV and polarization of 65.9 µC cm-2 were achieved by finite solid solution engineering. The open-circuit voltage and the short-circuit current with a BM doping component of 0.04 reached as high as 1.1 V and 0.0132 mA cm-2, respectively. This work provides an optimized strategy for the mutual benefit of the polarization and bandgap by finite solid solution engineering.

Haspin balances the ratio of asymmetric cell division through Wnt5a and regulates cell fate decisions in mouse embryonic stem cells.

Many different types of stem cells utilize asymmetric cell division (ACD) to produce two daughter cells with distinct fates. Haspin-catalyzed phosphorylation of histone H3 at Thr3 (H3T3ph) plays important roles during mitosis, including ACD in stem cells. However, whether and how Haspin functions in ACD regulation remains unclear. Here, we report that Haspin knockout (Haspin-KO) mouse embryonic stem cells (mESCs) had increased ratio of ACD, which cumulatively regulates cell fate decisions. Furthermore, Wnt5a is significantly downregulated due to decreased Pax2 in Haspin-KO mESCs. Wnt5a knockdown mESCs phenocopied Haspin-KO cells while overexpression of Wnt5a in Haspin-KO cells rescued disproportionated ACD. Collectively, Haspin is indispensable for mESCs to maintain a balanced ratio of ACD, which is essential for normal development and homeostasis.

A global-frequency-domain network for medical image segmentation.

The UNet series networks have been a leader in the field of medical image segmentation since their introduction. However, encoder and decoder structures of the traditional UNet series network are complex, with a large number of parameters and floating-point operations. This requires a large amount of data as support for model training, but most medical datasets only contain limited numbers of samples. To address this issue, we propose a global frequency domain UNet (GFUNet), a novel architecture for fast medical image segmentation. Inspired by recent modified Multi-Layer Perceptron(MLP)-like models, we combine Fourier Transform with UNet structure to achieve more efficient and effective encoding and decoding processes. Meanwhile, A dual-domain encoding module is designed to improve the performance of the encoder and decoder by fully used frequency domain feature. Furthermore, due to the excellent property of the Fourier Transform and its optimization, our network greatly reduces the number of parameters compared to other UNets. We evaluate GFUNet on several medical segmentation tasks, achieving improved segmentation performance compared to state-of-the-art network architectures for medical image segmentation. Compared to the original UNet, the results show that we reduce the number of parameters by 46 times, reduce computational complexity by 114 times, and improved the considerable dice score.

Evolution between ferroelectric photovoltaic effect and resistance switching behavior engineered by the polarization field and barrier characteristics.

The bandgap and polarization field play a key role in the ferroelectric photovoltaic effect. However, narrow bandgap induced electrical conductivity always brings out a depression of the photovoltaic performances. Based on the mechanisms of the photovoltaic effect and resistance switching behaviors in ferroelectric materials, this work realizes an evolution between the two effects by engineering the polarization field and barrier characteristics, which addresses the trade-off issues between the bandgap and polarization for ferroelectric photovoltaic effect. SrCoOx (SC, 2.5≤x≤3) with multivalent transition is introduced into Na0.5Bi0.5TiO3 (NBT) matrix material to engineered the polarization field and barrier characteristics. (1-x)NBT-xSC (x=0.03, 0.05, 0.07) solid solution films present an evolution of ferroelectric photovoltaic effect to grow out of nothing again to the disappearance of the photovoltaic effect and the appearance of resistance switching behavior. The 0.95NBT-0.05SC film achieve the open-circuit voltage of 0.81 V and the short-circuit current of 23.52 µA/cm2, and the 0.93NBT-0.07SC film obtains the resistive switching behavior with switch ratio of 100. This work provides a practicable strategy to achieve the fascinating evolution between photovoltaic effect and resistive switching.

Ferroelectric photovoltaic response engineered by lattice strain derived from local metal-ion dipoles.

An unfavorable inverse relationship between polarization, bandgap, and leakage always limits the ferroelectric photovoltaic performances. This work proposes a strategy of lattice strain engineering different from traditional lattice distortion by introducing a (Mg2/3Nb1/3)3+ ion group into the B site of BiFeO3 films to construct local metal-ion dipoles. A giant remanent polarization of 98 µC/cm2, narrower bandgap of 2.56 eV, and the decreased leakage current by nearly two orders of magnitude are synchronously obtained in the BiFe0.94(Mg2/3Nb1/3)0.06O3 film by engineering the lattice strain, breaking through the inverse relationship among these three. Thereby, the open-circuit voltage and the short-circuit current of the photovoltaic effect reach as high as 1.05 V and 2.17 µA /cm2, respectively, showing an excellent photovoltaic response. This work provides an alternative strategy to enhance ferroelectric photovoltaic performances by lattice strain derived from local metal-ion dipoles.

Gradient Strain-Induced Room-Temperature Ferroelectricity in Magnetic Double-Perovskite Superlattices.

Single-phase multiferroics suffer from a fundamental contradiction between polarity and magnetism in d0 electronic configuration, motivating studies of unconventional ferroelectricity in magnetic oxides. However, low critical temperature and polarization still need to be overcome. Here, it is reported that the switchable polarization behavior at room temperature in [(La2 NiMnO6 )/(La2 CoMnO6 )]n double-perovskite magnetic superlattice films is achieved by engineering a microstructure with gradient strains, and the ferromagnetic Curie temperature did not show a rapid decrease. The synergy of gradient strains and superlattice components plays a decisive role in inducing ferroelectricity via the tilting or rotation of various oxygen octahedra. Such distortion responses to gradient strains are accompanied by slight magnetic fluctuations, maximizing the preservation of the initial magnetic exchange interactions, which alleviates the contradiction of multiferroic coexistence to a certain extent. This work confirms the room-temperature ferroelectricity in double-perovskite superlattices and provides a preferred strategy for confronting the difficulty of multiferroic coexistence in single-phase materials.

Phytotoxicity and the molecular response in yttrium oxide nanoparticle-treated Arabidopsis thaliana seedlings.

Due to the widespread application of rare earth oxide nanoparticles in various fields, their release into the environment is inevitable, and their potential toxicity and ecological impact have become a concern. Yttrium oxide nanoparticles are important rare earth oxide nanoparticles; however, their impact on plants and the molecular mechanism underlying their influence on plant growth and development are unclear. In this study, we found that yttrium oxide nanoparticles at concentrations exceeding 2 mM significantly inhibited the growth of Arabidopsis seedlings. Using Arabidopsis marker lines for auxin signaling, we found that the application of yttrium oxide nanoparticles resulted in disordered auxin signaling in root cells. Auxin signaling in the cells of the quiescent center and columella stem cells decreased, while auxin signaling in the cells of the stele was enhanced. In addition, trypan blue staining showed that yttrium oxide nanoparticles induced root cell death. Transcriptome analysis showed that the nanoparticles specifically inhibited the expression of lignin synthesis-related genes, activated the MAPK signaling pathway, and enhanced the ethylene and abscisic acid signaling pathways in plants. This study demonstrates the phytotoxicity of yttrium oxide nanoparticles at the molecular level in Arabidopsis, and it provides a new perspective on how plants respond to rare earth oxide stress.

Brachypodium BdCHS is a homolog of Arabidopsis AtCHS involved in the synthesis of flavonoids and lateral root development.

Flavonoids are a kind of plant-specific secondary metabolites, which play an important role in regulating plant growth and development, stress response, and also have medicinal value. Chalcone synthase is the key enzyme in the synthesis of flavonoids. The function of chalcone synthase in Arabidopsis thaliana has been well studied, but its homologous protein in Brachypodium distachyon has not been reported. In this study, we identified a homolog of AtCHS in B. distachyon, named BdCHS, and described its function. Phylogenetic tree analysis showed that BdCHS was most closely related to CHS in Triticum aestivum. Transgene analysis revealed that BdCHS protein was localized in the cytoplasm of Arabidopsis root cells. BdCHS protein can complement the phenotype of AtCHS mutants with lighter seed coat color and increased lateral root density. The content of superoxide anion in the cortical cells above the lateral root primordium in AtCHS mutants was higher than that in the wild-type, and BdCHS protein could restore the content of superoxide anion in AtCHS mutant to the level of that in the wild-type. The results showed that BdCHS was a functional homolog of AtCHS, which laid a foundation for the subsequent application of BdCHS in genetic breeding and crop improvement.

Cardiac magnetic resonance radiomics for disease classification.

OBJECTIVES: This study investigated the discriminability of quantitative radiomics features extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and healthy (NOR) patients. METHODS: The data of two hundred and eighty-three patients with HCM (n = 48) or DCM (n = 52) and NOR (n = 123) were extracted from two publicly available datasets. Ten feature selection methods were first performed on twenty-one different sets of radiomics features extracted from the left ventricle, right ventricle, and myocardium segmented from CMR images in the end-diastolic frame, end-systolic frame, and a combination of both; then, nine classical machine learning methods were trained with the selected radiomics features to distinguish HCM, DCM, and NOR. Ninety classification models were constructed based on combinations of the ten feature selection methods and nine classifiers. The classification models were evaluated, and the optimal model was selected. The diagnostic performance of the selected model was also compared to that of state-of-the-art methods. RESULTS: The random forest minimum redundancy maximum relevance model with features based on LeastAxisLength, Maximum2DDiameterSlice, Median, MinorAxisLength, Sphericity, VoxelVolume, Kurtosis, Flatness, and Skewness was the highest performing model, achieving 91.2% classification accuracy. The cross-validated areas under the curve on the test dataset were 0.938, 0.966, and 0.936 for NOR, DCM, and HCM, respectively. Furthermore, compared with those of the state-of-the-art methods, the sensitivity and accuracy of this model were greatly improved. CONCLUSIONS: A predictive model was proposed based on CMR radiomics features for classifying HCM, DCM, and NOR patients. The model had good discriminability. KEY POINTS: • The first-order features and the features extracted from the LOG-filtered images have potential in distinguishing HCM patients from DCM patients. • The features extracted from the RV play little role in distinguishing DCM from HCM. • The VoxelVolume of the myocardium in the ED frame is important in the recognition of DCM.

Iron carbide nanoparticles supported on an N-doped carbon porous framework as a bifunctional material for electrocatalytic oxygen reduction and supercapacitors.

Highly active and durable bifunctional materials are of pivotal importance for energy conversion and storage devices, yet a comprehensive understanding of their geometric and electronic influence on electrochemical activity is urgently needed. Fe-N-C materials with physical and chemical structural merits are considered as one of the promising candidates for efficient oxygen reduction reaction electrocatalysts and supercapacitor electrodes. Herein, Fe3C nanoparticles supported on a porous N-doped carbon framework (denoted as Fe3C/PNCF) were readily prepared by one-step chemical vapor deposition under the assistance of a NaCl salt template. The experiment results revealed that the as-synthesized Fe3C/PNCF nanocomposites successfully displayed attractive electrocatalytic oxygen reduction reaction (ORR) activity comparable to that of the Pt/C catalyst (E1/2 of 0.84 V and 0.83 V, respectively), and a superior capacitance of 385.3 F g-1 under 1 A g-1 for a supercapacitor. It's proposed that the increased pyridinic and graphitic N coordination on the hydrophilic porous framework provides more electrochemical active surface area for the storage and transport of electrolyte ions. Additionally, an appropriate d-band center created by the optimized adsorption function endows Fe3C/PNCF with excellent electrochemical properties. The results confirmed that the integration strategy of porous heterogeneous structure and accessible active sites balanced the complex relationship between geometry, electronic structure, and electrochemical activity. Our research provides a facile approach for fabricating multi-functional nanomaterials applicable in both ORR and supercapacitors in the future.

Giant Negative Electrocaloric Effect over an Ultra-Wide Temperature Region in Relaxation Frozen State Ferroelectrics.

A giant negative electrocaloric effect with the electrocaloric temperature change range from -27.3 to -9.8 K over an ultra-wide temperature region of 260 K (-180 to 80 °C) is obtained in a relaxation frozen state ferroelectric film of Bi5Ti3AlO15. A strategy is proposed to extend the temperature region of the electrocaloric effect by gradually slowing and locally freezing the relaxation dynamics. The slow and frozen nanodomains below freezing temperature Tf of ∼80 °C do not yield to the applied electric field but are noncolinear to the field, which results in an inverse configuration entropy change and negative electrocaloric effect over an ultra-wide temperature region in frozen relaxor state ferroelectric films. A semiphenomenological model based on modified Ginzburg-Landau-Devonshire theory is presented to reveal the evolution of the slowing and freezing of the nanodomains. The breakthroughs in the operating temperature region and cooling temperature change make relaxation frozen state ferroelectrics excellent electrocaloric refrigeration materials.

Introns in the Naa50 gene act as strong enhancers of tissue-specific expression in Arabidopsis.

Naa50 is the catalytic subunit of N-terminal acetyltransferase complex E, which plays an important role in regulating plant development, endoplasmic reticulum stress and immune responses in Arabidopsis. In this study, the complete genomic sequence (but not the coding sequence) of Naa50 rescued the phenotype of Naa50 deletion mutants. Naa50 expression was noted in whole roots except for central root cap cells. The deletion of intron 1 resulted in a loss of Naa50 expression in the root meristem zone and in the epidermis, cortex and endodermis of the elongation zone and mature zone, while the deletion of intron 2 decreased Naa50 expression in the epidermis, cortex and endodermis of the root elongation zone and mature zone. The native Naa50 promoter together with introns 1 and 2 promotes the expression of Naa50 in sepal vascular bundles, filaments, pollen and stigmas; however, neither intron has positive effect on Naa50 expression in mature rosette leaves. The results of this study show that introns 1 and 2 in the Naa50 gene function as enhancers to promote the tissue-specific expression of Naa50.

3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion.

BACKGROUND: Cervical cancer cell detection is an essential means of cervical cancer screening. However, for thin-prep cytology test (TCT)-based images, the detection accuracies of traditional computer-aided detection algorithms are typically low due to the overlapping of cells with blurred cytoplasmic boundaries. Some typical deep learning-based detection methods, e.g., ResNets and Inception-V3, are not always efficient for cervical images due to the differences between cervical cancer cell images and natural images. As a result, these traditional networks are difficult to directly apply to the clinical practice of cervical cancer screening. METHOD: We propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion; the proposed network consists of the backbone network and a detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and expression ability of the model. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure the accurate capture of small cells; then, based on the Faster region-based convolutional neural network (R-CNN), adaptive cervical cancer cell anchors are generated via unsupervised clustering. Furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss. RESULT: Baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network are used on two different datasets (the Data-T and Herlev datasets), and the final quantitative results show the effectiveness of the proposed dilated convolution ResNet (DC-ResNet) backbone network. Furthermore, experiments conducted on both datasets show that the proposed 3cDe-Net, based on the optimal anchors, the defined new loss function, and DC-ResNet, outperforms existing methods and achieves a mean average precision (mAP) of 50.4%. By performing a horizontal comparison of the cells on an image, the category and location information of cancer cells can be obtained concurrently. CONCLUSION: The proposed 3cDe-Net can detect cancer cells and their locations on multicell pictures. The model directly processes and analyses samples at the picture level rather than at the cellular level, which is more efficient. In clinical settings, the mechanical workloads of doctors can be reduced, and their focus can be placed on higher-level review work.

Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition.

The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis. The clinical manifestations of patients with tooth-marked tongue include loss of appetite, borborygmus, gastric distention, and loose stool. Traditional clinical tooth-marked tongue recognition is conducted subjectively based on the doctor's visual observation, and its performance is affected by the doctor's subjectivity, experience, and environmental lighting changes. In addition, the tooth marks typically have various shapes and colors on the tongue, which make it very challenging for doctors to identify tooth marks. The existing methods based on deep learning have made great progress for tooth-marked tongue recognition, but there are still shortcomings such as requiring a large amount of manual labeling of tooth marks, inability to detect and locate the tooth marks, and not conducive to clinical diagnosis and interpretation. In this study, we propose an end-to-end deep neural network for tooth-marked tongue recognition based on weakly supervised learning. Note that the deep neural network only requires image-level annotations of tooth-marked or non-tooth marked tongues. In this method, a deep neural network is trained to classify tooth-marked tongues with the image-level annotations. Then, a weakly supervised tooth-mark detection network (WSTDN) as an architecture variant of the pre-trained deep neural network is proposed for the tooth-marked region detection. Finally, the WSTDN is re-trained and fine-tuned using only the image-level annotations to simultaneously realize the classification of the tooth-marked tongue and the positioning of the tooth-marked region. Experimental results of clinical tongue images demonstrate the superiority of the proposed method compared with previously reported deep learning methods for tooth-marked tongue recognition. The proposed tooth-marked tongue recognition model may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms.

Sensitive angle-dependent magnetoelectric coupling in cluster-assembled flexible composites.

Flexible magnetoelectric (ME) device is one of the indispensable elements. However, the complicated fabrication process and low sensitivity hinder the practical applications. Here, flexible NiFe anisotropic magnetoelastic composites were prepared by cluster-supersonic expansion method assistant with polyvinylidene fluoride (PVDF) substrates. The NiFe/PVDF composites possess sensitive angle-resolution ME coupling coefficient at room temperature, and the value can reach 0.66µV deg-1. The strong anisotropic magnetoelasticity phenomenon is reminiscent of the short-range ordered cluster structure. The anisotropic magnetoelastic coefficient can be deduced by temperature- and magnetic field strength-dependent anisotropic magnetoresistance. The magnetic torque results also prove the strong anisotropic magnetoelastic trait. The coupling between piezoelectricity and anisotropic magnetostrictive effect endows great possibilities toward flexible electronic compass. These results shed light on future in non-invasive tracking of vital biological health via wearable electronic devices.

Capturing Carriers and Driving Depolarization by Defect Engineering for Dielectric Energy Storage.

The inevitable defect carriers in dielectric capacitors are generally considered to depress the polarization and breakdown strength, which decreases energy storage performances. Distinctive from the traditional aims of reducing defects as much as possible, this work designs (FeTi' - Vo••)• and (FeTi″ - Vo••) defect dipoles by oxygen vacancy defect engineering in acceptor doped Sr2Bi4Ti(5-x)FexO18 layered perovskite films with n-type leakage conductance. It is shown that oxygen vacancies effectively capture electrons (carriers) in n-type dielectrics to enhance the breakdown strength. Meanwhile, defect dipoles provide a driving field for depolarization to engineer the generation energy of domains and the domain wall energy, which effectively lowers the residual polarization Pr but not at the expense of the maximum polarization Pmax as relaxor ferroelectric regulations. Such defect engineering effectively breaks through the limitation, in which the energy storage density suffers from the trade-off relationship between polarization and breakdown strength. The Sr2Bi4Ti4.92Fe0.08O18 film with the proper oxygen vacancy content achieves a high energy density of 110.5 J/cm3 and efficiency of 70.0% at a high breakdown strength of 3915 kV/cm. This work explores an alternative way for breakthroughs possible in the intrinsic trade-off relationship to regulate dielectric energy storage by defect engineering.

Discovery of N-(2-benzyl-4-oxochroman-7-yl)-2-(5-(ethylsulfonyl) pyridin-2-yl) acetamide (b12) as a potent, selective, and orally available novel retinoic acid receptor-related orphan receptor γt inverse agonist.

The nuclear receptor retinoic acid receptor-related orphan receptor γ (RORγ, NR1F3, or RORc) exists in two isoforms, with one isoform (RORγ or RORc1) widely expressed in a variety of tissues, and the expression of the second isoform (RORγt or RORc2) restricted to the thymus and cells of the immune system. RORγt is a key regulator of the development and functions of T-helper 17 (Th17) cells. Clinical proof-of-concept (PoC) with small molecule inverse agonists of RORγt has been achieved with VTP-43742 (Phase II) for the treatment of psoriasis, and pre-clinical PoC for this mechanism has also been established for the treatment of autoimmune diseases. A series of aryl sulfonyl derivatives as novel RORγt inverse agonists were designed and synthesized based on VTP-43742. We conducted structural modifications that improved the activity profile. In pharmacodynamic (PD) studies, oral administration of compound b12 showed robust and dose-dependent inhibition of IL-6 and IL-17A cytokine expression. The ability of compound b12 to reduce the levels of IL-6 and IL-17A in vivo after oral dosing in mice, and a corresponding reduction in skin inflammation further supports the potential of small molecule RORγt modulation as a therapeutic target for the treatment of inflammatory diseases.

Discovery of 2H-chromone-4-one based sulfonamide derivatives as potent retinoic acid receptor-related orphan receptor γt inverse agonists.

Retinoic acid receptor related orphan receptor γt (RORγt), identified as the essential functional regulator of IL-17 producing Th17 cells, is an attractive drug target for treating autoimmune diseases. Starting from the reported GSK2981278 (Phase II), we structurally modified and synthesized a series of 2H-chromone-4-one based sulfonamide derivatives as novel RORγt inverse agonists, which significantly improved their human metabolic stabilities while maintaining a potent RORγt inverse agonist profile. Efforts in reducing the lipophilicity and improving the LLE values led to the discovery of c9, which demonstrated potent RORγt inverse agonistic activity and consistent metabolic stability. During in vivo studies, oral administration of compound c9 exhibited a robust and dose-dependent inhibition of IL-17A cytokine expression and significantly lessened the skin inflammatory symptoms in the mouse imiquimod-induced skin inflammation model. Docking analysis of the binding mode revealed that c9 can suitably occupy the active pocket, and the introduction of the morpholine pyridine group can interact with Leu396, His479, and Cys393. Thus, compound c9 was selected as a preclinical compound for treating Th17-driven autoimmune diseases.