Propagation properties of elegant modified Bessel Gaussian beams.

A kind of optical beam with a radially parabolic propagating manner and intensity decay inversely proportional to propagating distance in the far field is investigated. The initial complex amplitudes of this kind of beam have the form of a Gaussian function multiplied by a m/2-order modified Bessel function and a helical phase factor with topological charge m. The arguments for Bessel and Gauss parts in the propagating solutions of these beams are complex and symmetric as elegant Laguerre and Hermite Gaussian beams. As a result, the beams can be referred to as elegant modified Bessel Gauss (EMBG) beams. Similar to non-diffractive beams such as Bessel and Airy beams, the EMBG beams also carry infinite power due to a transversely slowly decaying tail of complex amplitude. The EMBG beams demonstrate intermediate propagating properties between non-diffractive and finite-power beams. Unlike non-diffractive beams that never spread their power and finite-power beams that always diverge in a linear manner and spread their power by inversely square law in the far field, the EMBG beams demonstrate a far-field parabolic propagating manner and decay their power by inversely linear law. In addition, the EMBG beams have total Gouy phase, which is only half of that of elegant Laguerre Gauss beams with the same topological charge, and have far-field intensity distributions regardless of the beam waist radius in the initial plane. The propagating and focusing properties of EMBG beams represent an intermediate status between the non-diffractive and finite-power beams.

Superhydrophilic Modification of Polycarbonate Substrate Surface by Organic Plasma Polymerization Film.

Superhydrophilicity performs well in anti-fog and self-cleaning applications. In this study, polycarbonate substrate was used as the modification object because of the low surface energy characteristics of plastics. Procedures that employ plasma bombardment, such as etching and high surface free energy coating, are applied to improve the hydrophilicity. An organic amino silane that contains terminal amine group is introduced as the monomer to perform plasma polymerization to ensure that hydrophilic radicals can be efficiently deposited on substrates. Different levels of hydrophilicity can be reached by modulating the parameters of plasma bombardment and polymerization, such as plasma current, voltage of the ion source, and bombardment time. The surface of a substrate that is subjected to plasma bombarding at 150 V, 4 A for 5 min remained superhydrophilic for 17 days. After 40 min of Ar/O2 plasma bombardment, which resulted in a substrate surface roughness of 51.6 nm, the plasma polymerization of organic amino silane was performed by tuning the anode voltage and operating time of the ion source, and a water contact angle < 10° and durability up to 34 days can be obtained.

Unsupervised Person Re-Identification With Stochastic Training Strategy.

Unsupervised person re-identification (re-ID) has attracted increasing research interests because of its scalability and possibility for real-world applications. State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning. This approach suffers two problems. First, the centroid generated by unsupervised learning may not be a perfect prototype. Forcing images to get closer to the centroid emphasizes the result of clustering, which could accumulate clustering errors during iterations. Second, previous instance memory based methods utilize features updated at different training iterations to represent one centroid, these features are inconsistent due to the change of encoder. To this end, we propose an unsupervised re-ID approach with a stochastic learning strategy. Specifically, we adopt a stochastic updated memory, where a random instance from a cluster is used to update the cluster-level memory for contrastive learning. In this way, the relationship between randomly selected pair of images are learned to avoid the training bias caused by unreliable pseudo labels. By picking a sole last seen sample to directly update each cluster center, the stochastic memory is also always up-to-date for classifying to keep the consistency. Besides, to relieve the issue of camera variance, a unified distance matrix is proposed during clustering, where the distance bias from different camera domains is reduced and the variances of identities are emphasized. Our proposed method outperforms the state-of-the-arts in all the common unsupervised and UDA re-ID tasks. The code will be available at https://github.com/lithium770/Unsupervised-Person-re-ID-with-Stochastic-Training-Strategy.

Predicting the Risk of Postoperative Complications of Schwannoma Surgery: Development and Assessment of a New Predictive Nomogram.

BACKGROUND: The aim of this study was to develop and internally validate a risk nomogram for postoperative complications of schwannoma surgery. METHODS: From 2016 to 2020, we reviewed 83 patients who underwent schwannoma resection with a total number of 85 schwannomas. A predictive model was developed based on the dataset of this group. During model construction, univariate and multivariate logistic regression analysis were used to determine the independent predictors of postoperative complications. Assessment of the discriminative function, calibrating proficiency, and clinical usefulness of the predicting model was performed using C-index, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis. Internal validation was assessed using bootstrapping validation. RESULTS: Predictors contained in the prediction nomogram included age, tumor location, symptoms, and surgical approach. The model displayed satisfying abilities of discrimination and calibration, with a C-index of 0.901 (95% confidence [CI]: 0.837-0.965). A high C-index value of 0.853 was achieved in the interval verification. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the complication possibility threshold of 2%. CONCLUSION: This new risk nomogram for postoperative complications of schwannoma surgery has taken age, tumor location, symptoms, and surgical approach into account. It has reasonable predictive accuracy and can be conveniently used. It shall help patients understand the risk of postoperative complications before surgery, and offer guidance to surgeons in deciding on the surgical approach.

Disulfiram Chelated with Copper Promotes Apoptosis in Osteosarcoma via ROS/Mitochondria Pathway.

Disulfiram (DSF) chelated with copper has been confirmed to have a strong anti-tumor ability. In this study, we determined that DSF-Cu induced mitochondria-dependent apoptosis in osteosarcoma (OS), reflecting in DSF-Cu induces mitochondrial membrane potential decline, the production of reactive oxygen species (ROS), and inhibiting cells migration and invasion along with decreasing the concentration of intracellular glutathione (GSH) and facilitating the opening of mitochondrial permeability transition pore (PT) in osteosarcoma cells. These anti-tumor activities can be reversed by Cyclosporine A (CsA, PT inhibitors) and N-acetyl-L-cysteine (NAC, antioxidants). Our results suggested that DSF-Cu exerts its anti-tumor effects in OS via regulation of the ROS/Mitochondria pathway. Our findings provide the basis for DSF-Cu to treat osteosarcoma, even might develop as a potential therapy for other tumors.

Delivery of pOXR1 through an injectable liposomal nanoparticle enhances spinal cord injury regeneration by alleviating oxidative stress.

Oxidation resistance 1 (OXR1) is regarded as a critical regulator of cellular homeostasis in response to oxidative stress. However, the role of OXR1 in the neuronal response to spinal cord injury (SCI) remains undefined. On the other hand, gene therapy for SCI has shown limited success to date due in part to the poor utility of conventional gene vectors. In this study, we evaluated the function of OXR1 in SCI and developed an available carrier for delivering the OXR1 plasmid (pOXR1). We found that OXR1 expression is remarkably increased after SCI and that this regulation is protective after SCI. Meanwhile, we assembled cationic nanoparticles with vitamin E succinate-grafted ε-polylysine (VES-g-PLL) (Nps). The pOXR1 was precompressed with Nps and then encapsulated into cationic liposomes. The particle size of pOXR1 was compressed to 58 nm, which suggests that pOXR1 can be encapsulated inside liposomes with high encapsulation efficiency and stability to enhance the transfection efficiency. The agarose gel results indicated that Nps-pOXR1-Lip eliminated the degradation of DNA by DNase I and maintained its activity, and the cytotoxicity results indicated that pOXR1 was successfully transported into cells and exhibited lower cytotoxicity. Finally, Nps-pOXR1-Lip promoted functional recovery by alleviating neuronal apoptosis, attenuating oxidative stress and inhibiting inflammation. Therefore, our study provides considerable evidence that OXR1 is a beneficial factor in resistance to SCI and that Nps-Lip-pOXR1 exerts therapeutic effects in acute traumatic SCI.

Olfactory ensheathing cells seeded decellularized scaffold promotes axonal regeneration in spinal cord injury rats.

Spinal cord decellularized (DC) scaffolds can promote axonal regeneration and restore hindlimb motor function of spinal cord defect rats. However, scarring caused by damage to the astrocytes at the margin of injury can hinder axon regeneration. Olfactory ensheathing cells (OECs) integrate and migrate with astrocytes at the site of spinal cord injury, providing a bridge for axons to penetrate the scars and grow into lesion cores. The purpose of this study was to evaluate whether DC scaffolds carrying OECs could better promote axon growth. For these studies, DC scaffolds were cocultured with primary extracted and purified OECs. Immunofluorescence and electron microscopy were used for verification of cells adhere and growth on the scaffold. Scaffolds with OECs were transplanted into rat spinal cord defects to evaluate axon regeneration and functional recovery of hind limbs. Basso, Beattie, and Bresnahan (BBB) scoring was used to assess motor function recovery, and glial fibrillary acidic protein (GFAP) and NF200-stained tissue sections were used to evaluate axonal regeneration and astrological scar distribution. Our results indicated that spinal cord DC scaffolds have good histocompatibility and spatial structure, and can promote the proliferation of adherent OECs. In animal experiments, scaffolds carrying OECs have better axon regeneration promoting protein expression than the SCI model, and improve the proliferation and distribution of astrocytes at the site of injury. These results proved that the spinal cord DC scaffold with OECs can promote axon regeneration at the site of injury, providing a new basis for clinical application.

Unsupervised Person Re-identification via Cross-camera Similarity Exploration.

Most person re-identification (re-ID) approaches are based on supervised learning, which requires manually annotated data. However, it is not only resource-intensive to acquire identity annotation but also impractical for large-scale data. To relieve this problem, we propose a cross-camera unsupervised approach that makes use of unsupervised style-transferred images to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples for person re-ID. Our algorithm considers two fundamental facts in the re- ID task, i.e., variance across diverse cameras and similarity within the same identity. In this paper, we propose an iterative framework which overcomes the camera variance and achieves across-camera similarity exploration. Specifically, we apply an unsupervised style transfer model to generate style-transferred training images with different camera styles. Then we iteratively exploit the similarity within the same identity from both the original and the style-transferred data. We start with considering each training image as a different class to initialize the Convolutional Neural Network (CNN) model. Then we measure the similarity and gradually group similar samples into one class, which increases similarity within each identity. We also introduce a diversity regularization term in the clustering to balance the cluster distribution. The experimental results demonstrate that our algorithm is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably compared with other competing unsupervised domain adaptation methods (UDA) and semi-supervised learning methods.

Antibacterial harziane diterpenoids from a fungal symbiont Trichoderma atroviride isolated from Colquhounia coccinea var. mollis.

Fungal endophytes from plants are an important source for discovery of novel bioactive natural products. In this study, five undescribed harziane diterpenoids with a 4/7/5/6 tetracyclic scaffold, harzianols F‒J and three known derivatives, were obtained from the liquid fermentation of an endophytic fungus Trichoderma atroviride B7, which was isolated from the healthy flower of a Lamiaceae plant Colquhounia coccinea var. mollis. Their structures were elucidated by comprehensive spectroscopic analyses and X-ray crystallographic diffraction in the case of harzianol F. Harzianol I exhibited significant antibacterial effect against the growth of Staphylococcus aureus (EC50 = 7.7 ± 0.8 µg/mL), Bacillus subtilis (EC50 = 7.7 ± 1.0 µg/mL), and Micrococcus luteus (EC50 = 9.9 ± 1.5 µg/mL). Meanwhile, cytotoxic activity of harzianol I against three cancer cell lines was also observed. A plausible biosynthetic pathway for harziane diterpenoids was proposed.

Progressive Learning for Person Re-Identification with One Example.

In this paper, we focus on the one-example person re-identification (re-ID) task, where each identity has only one labeled example along with many unlabeled examples. We propose a progressive framework which gradually exploits the unlabeled data for person re-ID. In this framework, we iteratively (1) update the Convolutional Neural Network (CNN) model and (2) estimate pseudo labels for the unlabeled data. We split the training data into three parts, i.e., labeled data, pseudo-labeled data, and indexlabeled data. Initially, the re-ID model is trained using the labeled data. For the subsequent model training, we update the CNN model by the joint training on the three data parts. The proposed joint training method can optimize the model by both the data with labels (or pseudo labels) and the data without any reliable labels. For the label estimation step, instead of using a static sampling strategy, we propose a progressive sampling strategy to increase the number of the selected pseudo-labeled candidates step by step. We select a few candidates with most reliable pseudo labels from unlabeled examples as the pseudo-labeled data, and keep the rest as index-labeled data by assigning them with the data indexes. During iterations, the index-labeled data are dynamically transferred to pseudo-labeled data. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 21.6 points (absolute, i.e., 62.8% vs. 41.2%) on MARS, and 16.6 points on DukeMTMC-VideoReID. Extended to the few-example setting, our approach with only 20% labeled data surprisingly achieves comparable performance to the supervised state-of-the-art method with 100% labeled data.

Analysis of batch and repeated fedbatch productions of Candida utilis cell mass using mathematical modeling method

Background: Candida utilis is widely used in bioindustry, and its cell mass needs to be produced in a cost effective way. Process optimization based on the experimental results is the major way to reduce the production cost. However, this process is expensive, time consuming and labor intensive. Mathematical modeling is a useful tool for process analysis and optimization. Furthermore, sufficient information can be obtained with fewer experiments by using the mathematical modeling, and some results can be predicted even without doing experiments. Results: In the present study, we performed the mathematical modeling and simulation for the cell mass production of Candida utilis based on limited batch and repeated fedbatch experiments. The model parameters were optimized using genetic algorithm (GA), and the processes were analyzed. Conclusions: Taken together, this newly developed method is efficient, labor saving and cost effective.