Effects of Honeysuckle Varieties on Protective and Detoxifying Enzyme Activities in Heterolocha Jinyinhuaphaga Chu (Lepidoptera: Geometridae) Larvae.

Investigating the effects of various host plants on protective and detoxifying enzyme activities in insects could provide insights into the adaptation mechanisms of insects to host plants. In the present study, we measured superoxide dismutase (SOD), peroxidase (POD), catalase(CAT), carboxylesterase(CarE), acetylcholinesterase (AchE), and glutathione S-transferase (GST) activity levels in Heterolocha jinyinhuaphaga Chu (Lepidoptera: Geometridae) larvae fed on four honeysuckle varieties (wild variety, Jiufeng 1, Xiangshui 1, and Xiangshui 2). The results showed that levels of SOD, POD, CAT, CarE, AchE, and GST activities in H. jinyinhuaphaga larvae fed on the four honeysuckle varieties differed. The enzyme activity levels were the highest when larvae were fed on the wild variety, followed by Jiufeng 1 and Xiangshui 2, and the lowest when fed on Xiangshui 1. Furthermore, the enzyme activity levels increased with an increase in larval age. According to the results of two - way analysis of variance, the interaction between host plants and larval age had no significant effect on SOD, POD, CAT, CarE, AchE, and GST activities in H. jinyinhuaphaga larvae (p ˃0.05).

Long non-coding RNA MALAT1 regulates cell proliferation and apoptosis via miR-135b-5p/GPNMB axis in Parkinson's disease cell model.

BACKGROUNDS: Parkinson's disease (PD) is a common age-related neurodegenerative disorder worldwide. This research aimed to investigate the effects and mechanism underlying long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in PD. METHODS: SK-N-SH and SK-N-BE cells were treated with MPP+ to establish the MPP+-stimulated cell model of PD, and MALAT1 expression was determined. Then, the effects of MALAT1 depletion on cell proliferation and apoptosis were determined in the MPP+-stimulated cell model of PD. Besides, the correlations between microRNA-135b-5p (miR-135b-5p) and MALAT1 or glycoprotein nonmetastatic melanoma protein B (GPNMB) in MPP+-stimulated cell model of PD were explored. RESULTS: MALAT1 was increasingly expressed and downregulation of MALAT1 promoted cell proliferation while inhibited apoptosis in MPP+-stimulated cells. Besides, miR-135b-5p was a target of MALAT1 and directly targeted to GPNMB. Further investigation indicated that suppression of MALAT1 regulated cell proliferation and apoptosis by miR-135b-5p/GPNMB axis. CONCLUSION: Our findings reveal that MALAT1/miR-135b-5p/GPNMB axis regulated cell proliferation and apoptosis in MPP+-stimulated cell model of PD, providing a potential biomarker and therapeutic target for PD.

Long non-coding RNA MALAT1 regulates cell proliferation and apoptosis via miR-135b-5p/GPNMB axis in Parkinson's disease cell model

BACKGROUNDS: Parkinson's disease (PD) is a common age-related neurodegenerative disorder worldwide. This research aimed to investigate the effects and mechanism underlying long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in PD. METHODS: SK-N-SH and SK-N-BE cells were treated with MPP+ to establish the MPP+-stimulated cell model of PD, and MALAT1 expression was determined. Then, the effects of MALAT1 depletion on cell proliferation and apoptosis were determined in the MPP+-stimulated cell model of PD. Besides, the correlations between microRNA-135b-5p (miR-135b-5p) and MALAT1 or glycoprotein nonmetastatic melanoma protein B (GPNMB) in MPP+-stimulated cell model of PD were explored. RESULTS: MALAT1 was increasingly expressed and downregulation of MALAT1 promoted cell proliferation while inhibited apoptosis in MPP+-stimulated cells. Besides, miR-135b-5p was a target of MALAT1 and directly targeted to GPNMB. Further investigation indicated that suppression of MALAT1 regulated cell proliferation and apoptosis by miR-135b-5p/GPNMB axis. CONCLUSION: Our findings reveal that MALAT1/miR-135b-5p/GPNMB axis regulated cell proliferation and apoptosis in MPP+-stimulated cell model of PD, providing a potential biomarker and therapeutic target for PD.

Harsh parenting, child behavior problems, and the dynamic coupling of parents' and children's positive behaviors.

We examined self-reported maternal and paternal harsh parenting (HP) and its effect on the moment-to-moment dynamic coupling of maternal autonomy support and children's positive, autonomous behavior. This positive behavior coupling was measured via hidden Markov models as the likelihood of transitions into specific positive dyadic states in real time. We also examined whether positive behavior coupling, in turn, predicted later HP and child behavior problems. Children (N = 96; age = 3.5 years at Time 1) and mothers completed structured clean-up and puzzle tasks in the laboratory. Mothers' and fathers' HP was associated with children's being less likely to respond positively to maternal autonomy support; mothers' HP was also associated with mothers' being less likely to respond positively to children's autonomous behavior. When mothers responded to children's autonomous behavior with greater autonomy support, children showed fewer externalizing and internalizing problems over time and mothers showed less HP over time. These results were unique to the dynamic coupling of maternal autonomy support and children's autonomous behavior: The overall amount of these positive behaviors did not similarly predict reduced problems. Findings suggest that HP in the family system compromises the coregulation of positive behavior between mother and child and that improving mothers' and children's abilities to respond optimally to one another's autonomy-supportive behaviors may reduce HP and child behavior problems over time. (PsycINFO Database Record

Series-nonuniform rational B-spline signal feedback: From chaos to any embedded periodic orbit or target point.

The self-controlling feedback control method requires an external periodic oscillator with special design, which is technically challenging. This paper proposes a chaos control method based on time series non-uniform rational B-splines (SNURBS for short) signal feedback. It first builds the chaos phase diagram or chaotic attractor with the sampled chaotic time series and any target orbit can then be explicitly chosen according to the actual demand. Second, we use the discrete timing sequence selected from the specific target orbit to build the corresponding external SNURBS chaos periodic signal, whose difference from the system current output is used as the feedback control signal. Finally, by properly adjusting the feedback weight, we can quickly lead the system to an expected status. We demonstrate both the effectiveness and efficiency of our method by applying it to two classic chaotic systems, i.e., the Van der Pol oscillator and the Lorenz chaotic system. Further, our experimental results show that compared with delayed feedback control, our method takes less time to obtain the target point or periodic orbit (from the starting point) and that its parameters can be fine-tuned more easily.

Recovering chaotic properties from small data.

Physical properties are obviously essential to study a chaotic system that generates discrete-time signals, but recovering chaotic properties of a signal source from small data is a very troublesome work. Existing chaotic models are weak in dealing with such case in that most of them need big data to exploit those properties. In this paper, geometric theory is considered to solve this problem. We build a smooth trajectory from series to implicitly exhibit the chaotic properties with series-nonuniform rational B-spline (S-NURBS) modeling method, which is presented by our team to model slow-changing chaotic time series. As for the part of validation, we reveal how well our model recovers the properties from both the statistical and the chaotic aspects to confirm the effectiveness of the model. Finally a practical chaotic model is built up to recover the chaotic properties contained in the Musa standard dataset, which is used in analyzing software reliability, thereby further proves the high credibility of this model in practical time series. The effectiveness of the S-NURBS modeling leads us to believe that it is really a feasible and worthy research area to study chaotic systems from geometric perspective. For this reason, we reckon that we have opened up a new horizon for chaotic system research.

A geovisual analytic approach to understanding geo-social relationships in the international trade network.

The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

Series-NonUniform Rational B-Spline (S-NURBS) model: a geometrical interpolation framework for chaotic data.

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.