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Cumulus cells (CCs) originating from undifferentiated granulosa cells (GCs) differentiate in mural granulosa cells (MGCs) and CCs during antrum formation in the follicle by the distribution of location. CCs are supporting cells of the oocyte that protect the oocyte from the microenvironment, which helps oocyte growth and maturation in the follicles. Bi-directional communications between an oocyte and CCs are necessary for the oocyte for the acquisition of maturation and early embryonic developmental competence following fertilization. Follicle-stimulation hormone (FSH) and luteinizing hormone (LH) surges lead to the synthesis of an extracellular matrix in CCs, and CCs undergo expansion to assist meiotic resumption of the oocyte. The function of CCs is involved in the completion of oocyte meiotic maturation and ovulation, fertilization, and subsequent early embryo development. Therefore, understanding the function of CCs during follicular development may be helpful for predicting oocyte quality and subsequent embryonic development competence, as well as pregnancy outcomes in the field of reproductive medicine and assisted reproductive technology (ART) for infertility treatment.

Compelling evidence supports a tight link between oxidative stress and protein aggregation processes, which are noticeably involved in the development of proteinopathies, such as Alzheimer's disease, Parkinson's disease, and prion disease. The literature is tremendously rich in studies that establish a functional link between both processes, revealing that oxidative stress can be either causative, or consecutive, to protein aggregation. Because oxidative stress monitoring is highly challenging and may often lead to artefactual results, cutting-edge technical tools have been developed recently in the redox field, improving the ability to measure oxidative perturbations in biological systems. This review aims at providing an update of the previously known functional links between oxidative stress and protein aggregation, thereby revisiting the long-established relationship between both processes.

Underwater target detection techniques have been extensively applied to underwater vehicles for marine surveillance, aquaculture, and rescue applications. However, due to complex underwater environments and insufficient training samples, the existing underwater target recognition algorithm accuracy is still unsatisfactory. A long-term effort is essential to improving underwater target detection accuracy. To achieve this goal, in this work, we propose a modified YOLOv5s network, called YOLOv5s-CA network, by embedding a Coordinate Attention (CA) module and a Squeeze-and-Excitation (SE) module, aiming to concentrate more computing power on the target to improve detection accuracy. Based on the existing YOLOv5s network, the number of bottlenecks in the first C3 module was increased from one to three to improve the performance of shallow feature extraction. The CA module was embedded into the C3 modules to improve the attention power focused on the target. The SE layer was added to the output of the C3 modules to strengthen model attention. Experiments on the data of the 2019 China Underwater Robot Competition were conducted, and the results demonstrate that the mean Average Precision (mAP) of the modified YOLOv5s network was increased by 2.4%.

The metazoan 70-kDa heat shock protein (HSP70) family contains several members localized in different subcellular compartments. The cytosolic members have been classified into inducible HSP70s and constitutive heat shock cognates (HSC70s), but their distinction and evolutionary relationship remain unclear because of occasional reports of “constitutive HSP70s” and the lack of cross-phylum comparisons. Here we provide novel insights into the evolution of these important molecular chaperones. Phylogenetic analyses of 125 full-length HSP70s from a broad range of phyla revealed an ancient duplication that gave rise to two lineages from which all metazoan cytosolic HSP70s descend. One lineage (A) contains a relatively small number of genes from many invertebrate phyla, none of which have been shown to be constitutively expressed (i.e., either inducible or unknown). The other lineage (B) included both inducible and constitutive genes from diverse phyla. Species-specific duplications are present in both lineages, and Lineage B contains well-supported phylum-specific clades for Platyhelminthes, Rotifera, Nematoda, Porifera/Cnidaria, and Chordata. Some genes in Lineage B have likely independently acquired inducibility, which may explain the sporadic distribution of “HSP70” or “HSC70” in previous phylogenetic analyses. Consistent with the diversification history within each group, inducible members show lower purifying selection pressure compared to constitutive members. These results illustrate the evolutionary history of the HSP70 family, encouraging us to propose a new nomenclature: “HSP70 + subcellular localization + linage + copy number in the organism + inducible or constitutive, if known.” e.g., HSP70cA1i for cytosolic Lineage A, copy 1, inducible.

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the radius and the density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its -th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

In the early stage of acute pancreatitis (AP), abundant cytokines induced by local pancreatic inflammation enter the bloodstream, further cause systemic inflammatory response syndrome (SIRS) by “trigger effect”, which eventually leads to multiple organ dysfunction syndrome (MODS). During SIRS and MODS, the intestinal barrier function was seriously damaged accompanied by the occurrence of gut-derived infection which forms a “second hit summit” by inflammatory overabundance. Gastrointestinal microecology, namely the biologic barrier, could be transformed into a pathogenic state, which is called microflora dysbiosis when interfered by the inflammatory stress during AP. More and more evidences indicate that gastrointestinal microflora dysbiosis plays a key role in “the second hit” induced by AP gut-derived infection. Therefore, the maintenance of gastrointestinal microecology balance is likely to provide an effective method in modulating systemic infection of AP. This article reviewed the progress of gastrointestinal microecology in AP to provide a reference for deeply understanding the pathogenic mechanisms of AP and identifying new therapeutic targets.

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.

Recently, traditional Chinese medicine and medicinal herbs have attracted more attentions worldwide for its anti-tumor efficacy. Celastrol and Triptolide, two active components extracted from the Chinese herb Tripterygium wilfordii Hook F (known as Lei Gong Teng or Thunder of God Vine), have shown anti-tumor effects. Celastrol was identified as a natural 26 s proteasome inhibitor which promotes cell apoptosis and inhibits tumor growth. The effect and mechanism of Triptolide on prostate cancer (PCa) is not well studied. Here we demonstrated that Triptolide, more potent than Celastrol, inhibited cell growth and induced cell death in LNCaP and PC-3 cell lines. Triptolide also significantly inhibited the xenografted PC-3 tumor growth in nude mice. Moreover, Triptolide induced PCa cell apoptosis through caspases activation and PARP cleavage. Unbalance between SUMOylation and deSUMOylation was reported to play an important role in PCa progression. SUMO-specific protease 1 (SENP1) was thought to be a potential marker and therapeutical target of PCa. Importantly, we observed that Triptolide down-regulated SENP1 expression in both mRNA and protein levels in dose-dependent and time-dependent manners, resulting in an enhanced cellular SUMOylation in PCa cells. Meanwhile, Triptolide decreased AR and c-Jun expression at similar manners, and suppressed AR and c-Jun transcription activity. Furthermore, knockdown or ectopic SENP1, c-Jun and AR expression in PCa cells inhibited the Triptolide anti-PCa effects. Taken together, our data suggest that Triptolide is a natural compound with potential therapeutic value for PCa. Its anti-tumor activity may be attributed to mechanisms involving down-regulation of SENP1 that restores SUMOylation and deSUMOyaltion balance and negative regulation of AR and c-Jun expression that inhibits the AR and c-Jun mediated transcription in PCa.

Memristors are frequently utilized as essential blocks in characterizing new nonlinear circuits consisting of fewer elements. In this paper, mathematical modeling of a fractional-order memristor circuit is developed by replacing classical circuit elements by fractional elements. The main contributions of this paper are: (1) A fractional order non-linear memristive hyper-chaotic system (MHCS) is remodeled into linear memristive hyperchaotic sub-models by using T-S fuzzy rules for the first time. (2) Synchronization of a sender and receiver system with applications in video cryptosystem is addressed. (3) A fuzzy feedback controller based on T-S fuzzy control laws which guarantee synchronization of the MHCS is designed. (4) Video encryption and decryption algorithms based on converting video samples into image frame are developed and a sequence of random masks is securely transacted with the help of a sequence of chaotic self-invertible masks for encryption and decryption process. (5) High-level security of the proposed work is entrusted by analyzing various metrics including entropy, Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), and edge difference ratio (EDR) along with numerical simulations. (6) The proposed encryption algorithms have several advantages such as high-level randomness, key sensitivity, and motion control of encrypted frames. Numerical simulation and experimental results demonstrate the effectiveness, efficiency and feasibility of real-world applications of the proposed algorithms.

Redox homeostasis is an equilibrium between reducing and oxidizing reactions within cells. It is an essential, dynamic process, which allows proper cellular reactions and regulates biological responses. Unbalanced redox homeostasis is the hallmark of many diseases, including cancer or inflammatory responses, and can eventually lead to cell death. Specifically, disrupting redox balance, essentially by increasing pro-oxidative molecules and favouring hyperoxidation, is a smart strategy to eliminate cells and has been used for cancer treatment, for example. Selectivity between cancer and normal cells thus appears crucial to avoid toxicity as much as possible. Redox-based approaches are also employed in the case of infectious diseases to tackle the pathogens specifically, with limited impacts on host cells. In this review, we focus on recent advances in redox-based strategies to fight eukaryotic pathogens, especially fungi and eukaryotic parasites. We report molecules recently described for causing or being associated with compromising redox homeostasis in pathogens and discuss therapeutic possibilities.