Signature functions of knots

The signature function of a knot is an integer-valued step function on the unit circle in the complex plane. Necessary and sufficient conditions for a function to be the signature function of a knot are presented.Comment: 4 page

Coniferyl ferulate alleviate xylene-caused hematopoietic stem and progenitor cell toxicity by Mgst2

Xylene exposure is known to induce toxicity in hematopoietic stem and progenitor cells (HSPCs), leading to bone marrow suppression and potential leukemogenesis. However, research on the gene expression profiles associated with xylene-induced toxicity in HSPCs, and effective therapeutic interventions, remains scarce. In our study, we employed single-cell RNA sequencing to capture the transcriptomic shifts within bone marrow HSPCs both prior to and following treatment with coniferyl ferulate (CF) in a mouse model of xylene-induced hematotoxicity. Subsequently, we pinpointed CF as a targeted agent using SPR-LC/MS analysis. This enabled us to confirm the link between the gene Mgst2 and specific cellular subtypes. Our data revealed that CF significantly countered the reduction of both monocyte and neutrophil progenitor cells, which are commonly affected by xylene toxicity. Through targeted analysis, we identified Mgst2 as a direct molecular target of CF. Notably, Mgst2 is preferentially expressed in neutrophil progenitor cells and is implicated in mitochondrial metabolic processes. By selectively inhibiting Mgst2 in bone marrow, we observed amelioration of xylene-induced hematotoxic effects. In summary, our findings suggest that coniferyl ferulate can mitigate the detrimental impact of xylene on hematopoietic stem and progenitor cells by targeting Mgst2, particularly within subpopulations of neutrophil progenitors. This discovery not only advances our comprehension of the cellular response of HSPCs to xenobiotic stressors like xylene but also identifies CF and Mgst2 as potential therapeutic targets for alleviating xylene-induced hematotoxicity

MiR-29a Knockout Aggravates Neurological Damage by Pre-polarizing M1 Microglia in Experimental Rat Models of Acute Stroke

ObjectiveBy exploring the effects of miR-29a-5p knockout on neurological damage after acute ischemic stroke, we aim to deepen understanding of the molecular mechanisms of post-ischemic injury and thus provide new ideas for the treatment of ischemic brain injury.MethodsmiR-29a-5p knockout rats and wild-type SD rats were subjected to transient middle cerebral artery occlusion (MCAO). miR-29a levels in plasma, cortex, and basal ganglia of ischemic rats, and in plasma and neutrophils of ischemic stroke patients, as well as hypoxic glial cells were detected by real-time PCR. The infarct volume was detected by TTC staining and the activation of astrocytes and microglia was detected by western blotting.ResultsThe expression of miR-29a-5p was decreased in parallel in blood and brain tissue of rat MCAO models. Besides, miR-29a-5p levels were reduced in the peripheral blood of acute stroke patients. Knockout of miR-29a enhanced infarct volume of the MCAO rat model, and miR-29a knockout showed M1 polarization of microglia in the MCAO rat brain. miR-29a knockout in rats after MCAO promoted astrocyte proliferation and increased glutamate release.ConclusionKnockout of miR-29a in rats promoted M1 microglial polarization and increased glutamate release, thereby aggravating neurological damage in experimental stroke rat models

Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

Although the original particle swarm optimizer (PSO) method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO). In case that the interacting of the elements in D-dimensional function vector X=[x1,x2,…,xd,…,xD] is independent, cooperative particle swarm optimizer (CPSO) is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED) learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms

Iterative Learning-Based Path and Speed Profile Optimization for an Unmanned Surface Vehicle

Most path-planning algorithms can generate a reasonable path by considering the kinematic characteristics of the vehicles and the obstacles in hydrographic survey activities. However, few studies consider the influence of vehicle dynamics, although excluding system dynamics may considerably damage the measurement accuracy especially when turning at high speed. In this study, an adaptive iterative learning algorithm is proposed to optimize the turning parameters, which accounts for the dynamic characteristics of unmanned surface vehicles (USVs). The resulting optimal turning radius and speed are used to generate the path and speed profiles. The simulation results show that the proposed path-smoothing and speed profile design algorithms can largely increase the path-following performance, which potentially can help to improve the measurement accuracy of various activities

Neurobehavioral Effects of Cephalosporins: Assessment of Locomotors Activity, Motor and Sensory Development in Zebrafish

Most third- and fourth-generation cephalosporins, such as cefotaxime, cefmenoxime, cefepime, and cefpirome, contain an aminothiazoyl ring at the C-7 position. Drug impurity, which may be produced either during synthesis or upon degradation, can induce adverse effects. Various reports have indicated that neurotoxicity is a side effect of cephalosporin. In this study, we developed methods for assessing the free-swimming activities and behaviors in zebrafish larvae in response to continuous darkness and stimulation of light-to-dark photoperiod transition by chemical treatments. We also performed transcriptome analysis to identify differentially expressed genes (DEGs). Gene ontology analysis revealed that various processes related to nervous system development were significantly enriched by DEGs. We integrated 16 DEGs with protein–protein interaction networks and identified that neuroactive ligand–receptor interaction [e.g., λ-aminobutyric acid and glutamate receptor, metabotropic 1a (GRM1A)] pathway was regulated by the compounds. Our findings suggested that neurobehavioral effects mainly depend on the mother nucleus structure 7-aminocephalosporanic acid and the substitution at the C-3 position. In addition, gad2, or111-4, or126-3, grm1a, opn8c, or111-5, or113-2, and or118-3 may potentially be utilized as novel biomarkers for this class of cephalosporins, which causes neurotoxicity. This study provides neurological behavior, transcriptome, and docking information that could be used in further investigations of the structures and developmental neurotoxicity relationship of chemicals

Univariate Gaussian model for multimodal inseparable problems

It has been widely perceived that a univariate Gaussian model for evolutionary search can be used to solve separable problems only. This paper explores whether and how the univariate Gaussian model may also be used to solve inseparable problems. The analysis is followed up with experimental tests. The results show that the univariate Gaussian model stipulates no inclination towards separable problems. Further, it is revealed that the model is not only an efficient but also an effective method for solving multimodal inseparable problems. To verify its relative convergence speed, a restart strategy is applied to a univariate Gaussian model (the univariate marginal distribution algorithm) on inseparable problems. The results confirm that the univariate Gaussian model outperforms the five peer algorithms studied in this paper