The genus Sialis Latreille, 1802 (Megaloptera: Sialidae) in Palaearctic China, with description of a new species

The species of the genus Sialis from Palaearctic China are revised. Three species are described, including Sialis henanensis sp. n. A key to the males of the species of Sialis from Palaearctic China is presented

Review time of oncology drugs and its underlying factors: an exploration in China

Introduction: How the launch delay of drugs and other factors of interest can influence the length of the review period by drug agencies is still unknown, and understanding this can help better strike the trade-off related to review speed.Methods: We included all new oncology drug applications submitted to China’s National Medical Product Administration (NMPA) between 1 January 2018 and 31 December 2021, and ultimately succeeded in achieving marketing approval. For each drug, the length of the NMPA review process and other major characteristics were collected, including the registration class, approval class, priority review designation, and launch delay relative to the United States, as well as the number of patients enrolled, comparator, and primary endpoint of the pivotal trials supporting the approval. Linear regression model was employed to analyze the effects of factors of interest on the NMPA review time.Results: From 2018 to 2021, NMPA received 137 oncology applications that were ultimately approved. Half of the approvals [76 (55.5%)] were first licensed in the US, leaving a median launch delay of 2.71 years (IQR, 1.03–5.59) in China. In the pivotal studies, the median enrollment was 361 participants (IQR, 131–682), and the use of control groups [90 (65.7%)] and surrogate endpoints [101 (73.7%)] was prevalent. The median review time was 304 days (IQR, 253–376). Multivariate analysis for log-transformed review time showed that larger enrollment (> 92) was associated with a drop of 20.55% in review time (coefficient = −0.230; 95% CI, −0.404 to −0.055; p = 0.010); and a short delay (0 < delay ≤ 1.95 years) was associated with a drop of 17.63% in review time (coefficient = −0.194; 95% CI, −0.325 to −0.062; p = 0.004).Discussion: The short launch delay relative to the US was one important driver to the review speed of NMPA, which might suggest its latent regulatory reliance on the other global regulator during the post-marketing period when new information on the drug’s clinical benefit was still lacking

Negative electrocaloric effect in nonpolar phases of perovskite over wide range of temperature

The electrocaloric effect (ECE) offers a promising alternative to the traditional gas compressing refrigeration due to its high efficiency and environmental friendliness. The unusual negative electrocaloric effect refers to the adiabatic temperature drops due to application of electric field, in contrast with the normal (positive) ECE, and provides ways to improve the electrocaloric efficiency in refrigeration cycles. However, negative ECE is unusual and requires a clear understanding of microscopic mechanisms. Here, we found unexpected and extensive negative ECE in nonpolar orthorhombic, tetragonal, and cubic phases of halide and oxide perovskite at wide range of temperature by means of first-principle-based large scale Monte Carlo methods. Such unexpected negative ECE originates from the octahedral tilting related entropy change rather than the polarization entropy change under the application of electric field. Furthermore, a giant negative ECE with temperature change of 8.6 K is found at room temperature. This giant and extensive negative ECE in perovskite opens up new horizon in the research of caloric effects and broadens the electrocaloric refrigeration ways with high efficiency.Comment: 11 pages, 7 figure

Flattening Singular Values of Factorized Convolution for Medical Images

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabilities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the burden of limited computational resources at the expense of expressiveness. To this end, given weak medical image-driven CNN model optimization, a Singular value equalization generalizer-induced Factorized Convolution (SFConv) is proposed to improve the expressive power of factorized convolutions in MIP models. We first decompose the weight matrix of convolutional filters into two low-rank matrices to achieve model reduction. Then minimize the KL divergence between the two low-rank weight matrices and the uniform distribution, thereby reducing the number of singular value directions with significant variance. Extensive experiments on fundus and OCTA datasets demonstrate that our SFConv yields competitive expressiveness over vanilla convolutions while reducing complexity

Prognostic value of HMGN family expression in acute myeloid leukemia

Aim: The objective of this work was to investigate the prognostic role of the HMGN family in acute myeloid leukemia (AML). Methods: A total of 155 AML patients with HMGN1-5 expression data from the Cancer Genome Atlas database were enrolled in this study. Results: In the chemotherapy-only group, patients with high HMGN2 expression had significantly longer event-free survival (EFS) and overall survival (OS) than those with low expression (all p < 0.05), whereas high HMGN5 expressers had shorter EFS and OS than the low expressers (all p < 0.05). Multivariate analysis identified that high HMGN2 expression was an independent favorable prognostic factor for patients who only received chemotherapy (all p < 0.05). HMGN family expression had no impact on EFS and OS in AML patients receiving allogeneic hematopoietic stem cell transplantation. Conclusion: High HMGN2/5 expression is a potential prognostic indicator for AML

Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.Comment: 14 pages, 9 figure

ZnO/Cu₂O heterojunction integrated fiber-optic biosensor for remote detection of cysteine

Indium tin oxide, semiconductor nanomaterial ZnO, and Cu2O were first loaded on the surface of the optical fiber to form an optical fiber probe. Large-volume macroscopic spatial light is replaced by an optical fiber path, and remote light injection is implemented. Based on the optical fiber probe, a photoelectrochemical biosensor was constructed and remote detection of cysteine was realized. In this tiny device, the optical fiber probe not only acts as a working electrode to react with the analyte but also directs the light exactly where it is needed. Simultaneously, the electrochemical behavior of cysteine on the surface of the working electrode is dominated by diffusion-control, which provides strong support for quantitative detection. Then, under the bias potential of 0 V, the linear range of the fiber-optic-based cysteine biosensor was 0.01∼1 μM, the regression coefficient (R2) value was 0.9943. In spiked synthetic urine, the detection of cysteine was also realized by the integrated biosensor. Moreover, benefiting from the low optical fiber loss, the new structure also possesses a unique remote detection function. This work confirms that photoelectrochemical biosensors can be integrated via optical fibers and retain comparable sensing performance. Based on this property, different materials can also be loaded on the surface of the optical fiber for remote detection of other analytes. It is expected to facilitate the research on fiber-optic-based integrated biosensors and show application prospects in diverse fields such as biochemical analysis and disease diagnosis.</p

Ultrafast switching of sliding ferroelectricity and dynamical magnetic field in van der Waals bilayer induced by light

Sliding ferroelectricity is a unique type of polarity recently observed in a properly stacked van der Waals bilayer. However, electric-field control of sliding ferroelectricity is hard and could induce large coercive electric fields and serious leakage currents which corrode the ferroelectricity and electronic properties, which are essential for modern two-dimensional electronics and optoelectronics. Here, we proposed laser-pulse deterministic control of sliding ferroelectricity in bilayer h-BN by first principles and molecular dynamics simulation with machine-learned force fields. The laser pulses excite shear modes which exhibit certain directional movements of lateral sliding between bilayers. The vibration of excited modes under laser pulses is predicted to overcome the energy barrier and achieve the switching of sliding ferroelectricity. Furthermore, it is found that three possible sliding transitions - between AB (BA) and BA (AB) stacking - can lead to the occurrence of dynamical magnetic fields along three different directions. Remarkably, the magnetic fields are generated by the simple linear motion of nonmagnetic species, without any need for more exotic (circular, spiral) pathways. Such predictions of deterministic control of sliding ferroelectricity and multi-states of dynamical magnetic field thus expand the potential applications of sliding ferroelectricity in memory and electronic devices