Influenza A virus (IAV) has been raising public health and safety concerns worldwide. Cyanovirin-N (CVN) is a prominent anti-IAV candidate, but both cytotoxicity and immunogenicity have hindered the development of this protein as a viable therapy. In this article, linker-CVN (LCVN) with a flexible and hydrophilic polypeptide at the N-terminus was efficiently produced from the cytoplasm of Escherichia coli at a >15-l scale. PEGylation at the N-terminal α-amine of LCVN was also reformed as 20 kDa PEGylated linkered Cyanovirin-N (PEG_20k–LCVN). The 50% effective concentrations of PEG_20k–LCVN were 0.43 ± 0.11 µM for influenza A/HK/8/68 (H3N2) and 0.04 ± 0.02 µM for A/Swan/Hokkaido/51/96 (H5N3), dramatically lower than that of the positive control, Ribavirin (2.88 ± 0.66 × 10³ µM and 1.79 ± 0.62 × 10³ µM, respectively). A total of 12.5 µM PEG_20k–LCVN effectively inactivate the propagation of H3N2 in chicken embryos. About 2.0 mg/kg/day PEG_20k–LCVN increased double the survival rate (66.67%, P = 0.0378) of H3N2 infected mice, prolonged the median survival period, downregulated the mRNA level of viral nuclear protein and decreased (attenuated) the pathology lesion in mice lung. A novel PEGylated CVN derivative, PEG_20k–LCVN, exhibited potent and strain-dependent anti-IAV activity in nanomolar concentrations in vitro, as well as in micromolar concentration in vivo.

In 1964 and 1965, Shen & Tai described two species of calanoids, which they classified in the South American genus Argyrodiaptomus. We examined new material from the terra typica, South China, and show that both species belong in the Asian genus Sinodiaptomus. A biogeographic anomaly is thus corrected. Moreover, the two appear to be sister species and males are morphologically well separable. No intermediate morphotypes have so far been detected. S. cavernicolax (Shen & Tai, 1965), known only from the type locality, was claimed to be cavernicolous, but shows no stygobitic adaptations, and has eyes. It may have been pushed back into the cave environment by its congener S. ferus (Shen & Tai, 1964) with which it coexists in the same lake system. So far, four species of Sinodiaptomus have been found in China: the two mentioned above, i.e., S. cavernicolax only in Longyan Cave of Zhaoqing City, and S. ferus at about five locations, but its range is limited to a small part of Guangdong Province. All five other species of Sinodiaptomus, among which the other two that occur (also) in China, also occupy small to very small ranges, with the type species (S. chaffanjoni Richard, 1897) only living in North China, and S. indicus Kiefer, 1936 and S. mahanandiensis Reddy & Radhakrishna, 1980 confined to South India (Reddy & Radhakrishna, 1980). S. valkanovi Kiefer, 1938 from Japan is invasive (Ueda & Ohtsuka, 1998; Makino et al., 2010). S. sarsi Rylov, 1923, distributed in Japan, China, and most of Mongolia, is separated by a wide disjunction from a group of populations in Iran, the Caucasus, and East Anatolia. This western group of populations might be a separate species and deserves more study.

Cranial microsurgery is a prerequisite for neuroscience research, which provides an accessible brain for neural recording and optogenetic stimulation. As the scale of electrode implantation increases, massive delicate craniotomies are required to perform on the sub-millimeter-thick skull of small animals (e.g. mice). Automated craniotomy (AMC) is ideal for improving surgery efficiency and ensuring consistent results. However, the current AMC platforms are prone to over-drilling and injuring brain tissues due to the lack of effective feedback. Fragile and ultrathin skulls of these small objects also make it hard to integrate suitable sensors into the end-effector for accurate perception. Thus, this paper proposes an integrated electrical bioimpedance measurement (EBIM) solution for the closed-loop AMC. A sensing drill and a custom EBIM device are implemented to form a reliable electrical contact with the object and accurately measure the impedance. After evaluating the integrated EBIM device’s accuracy(<2% relative error) and stability, we provide a modified judgment signal for breakthrough detection and further simulate closed-loop craniotomies on an equivalent model. The experimental results demonstrate that the proposed system can achieve sensitive breakthrough detection (<0.1s delay at 100Hz excitation) and consistent drilling depth (10.3μm deviation with 10mm/min feed rate) in contrast to the gold standard (LCR meter).

The implantation of ultra-flexible neural probes is one of the key technical challenges in the field of brain-computer interfaces. Existing implantation techniques are constrained in both the overall surgical procedure and operational reliability. This study introduces a novel microneedle-forceps mechanism designed to implant ultra-flexible probes individually into brain tissue. A multimode motion control framework based on multiple linear actuators and a hybrid PID controller is developed for delicate manipulation of the probes. A needle-probe interaction model is established for force analysis in the peeling and insertion process. Detailed experiments are conducted to demonstrate the clamping ability of forceps to promote probe peeling and reduce needle deflection. Moreover, cooperative pull-up, insertion, release and retraction by the microneedle-forceps are validated to achieve multiple probe implantations within a single insertion duration of 10 s and a density of 0.2 mm.

Multi-thread flexible microelectrodes hold the promise of high-quality and long-term neural signal recording. To implant fragile microelectrodes into brain, a microneedle is usually used as a shuttle to increase stiffness. However, hooking microelectrodes with a microneedle is error-prone and time-consuming, due to the difficulty in accurately obtaining micro-object coordinates through low-quality microscope images. To solve this problem, this paper proposes an intelligent microscope vision system based on few-shot segmentation method. Firstly, a stereo vision system is designed to enable cameras to focus on all microelectrode threads simultaneously. Secondly, a few-shot instance segmentation method is proposed to obtain keypoint coordinates for vision guidance, which consists of data augmentation, improved Mask-RCNN and anchor-free clustering loss. The data augmentation method synthesizes low-quality images to strengthen model’s generalization ability. To improve the proposal quality, a residual region proposal module is introduced into Mask-RCNN. By clustering instance features in metric space, the anchor-free clustering loss enhances the model capability of predicting hard instances and avoids the intra-class bias of fixed anchor. Experimental result shows that the proposed method achieves 99.72% and 90% correct rate for microelectrode and microneedle segmentation with 5-shot training. Code is available at https://github.com/NamingIsEasy/FSS_implantation.