Different Types of Love in Polyamory: Between Primary and Secondary

Polyamory is a relationship model where every partner involved in the relationship practices or consents to the practice of multiple simultaneous relationships. Polyamory typically consists of at least two partners, and the most common model is the primary-secondary relationship. Previous research found higher intimacy, commitment, and investment in primary relationships, while greater sexual frequency and satisfaction in secondary relationships (Mogilski, Memering, Welling, & Shackelford, 2015; Mitchell, Bartholomew, & Cobb, 2014; Balzarini, Campbell, Holmes, Lehmiller, Harman, Kohut, & Atkins, 2017). As these relationship outcomes are related to romantic attraction, passionate love, companionate love, and jealousy, the purpose of the study was to investigate the differences in feelings of love and jealousy towards primary partners compared to secondary partners. Two hundred and twenty-six self-identified polyamorists, who were above the age of majority and had at least two partners (one as primary and another as secondary) were included in the study. Participants completed a survey, which included a Romantic Attraction Scale, a Passionate Love Scale, a Companionate Love Scale, and a modified Jealousy Scale testing for emotional and sexual jealousy. Participants were recruited through online polyamorous groups and social media. Consistent with the hypotheses, results showed higher companionate love and emotional jealousy for primary partners than secondary partners. However, results for passionate love and romantic attraction were contrary to predictions, both resulting higher for primary partners than secondary partners

Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Journal version of arXiv:2010.05300 (NeurIPS 2020). The first two authors contributed equall

Research on low frequency ripple suppression technology of inverter based on model prediction

The low frequency ripple of the input side current of the single-phase inverter will reduce the efficiency of the power generation system and affect the overall performance of the system. Aiming at this problem, this paper proposes a two-modal modulation method and its MPC multi-loop composite control strategy on the circuit topology of a single-stage boost inverter with a buffer unit. The control strategy achieves the balance of active power on both sides of AC and DC by controlling the stable average value of the buffer capacitor voltage, and provides a current reference for inductance current of the DC input side. At the same time, the MPC controller uses the minimum inductor current error as the cost function to control inductor current to track its reference to achieve low frequency ripple suppression of the input current. In principle, it is expounded that the inverter using the proposed control strategy has better low frequency ripple suppression effect than the multi-loop PI control strategy, and the conclusion is proved by the simulation data. Finally, an experimental device of a single-stage boost inverter using MPC multi-loop composite control strategy is designed and fabricated, and the experimental results show that the proposed research scheme has good low frequency ripple suppression effect and strong adaptability to different types of loads

Effective noninvasive zygosity determination by maternal plasma target region sequencing

Background: Currently very few noninvasive molecular genetic approaches are available to determine zygosity for twin pregnancies in clinical laboratories. This study aimed to develop a novel method to determine zygosity by using maternal plasma target region sequencing. Methods: We constructed a statistic model to calculate the possibility of each zygosity type using likelihood ratios (Li) and empirical dynamic thresholds targeting at 4,524 single nucleotide polymorphisms (SNPs) loci on 22 autosomes. Then two dizygotic (DZ) twin pregnancies, two monozygotic (MZ) twin pregnancies and two singletons were recruited to evaluate the performance of our novel method. Finally we estimated the sensitivity and specificity of the model in silico under different cell-free fetal DNA (cff-DNA) concentration and sequence depth. Results/Conclusions: We obtained 8.90 Gbp sequencing data on average for six clinical samples. Two samples were classified as DZ with L values of 1.891 and 1.554, higher than the dynamic DZ cut-off values of 1.162 and 1.172, respectively. Another two samples were judged as MZ with 0.763 and 0.784 of L values, lower than the MZ cut-off values of 0.903 and 0.918. And the rest two singleton samples were regarded as MZ twins, with L values of 0.639 and 0.757, lower than the MZ cut-off values of 0.921 and 0.799. In silico, the estimated sensitivity of our noninvasive zygosity determination was 99.90% under 10% total cff-DNA concentration with 2 Gbp sequence data. As the cff-DNA concentration increased to 15%, the specificity was as high as 97% with 3.50 Gbp sequence data, much higher than 80% with 10% cff-DNA concentration. Significance: This study presents the feasibility to noninvasively determine zygosity of twin pregnancy using target region sequencing, and illustrates the sensitivity and specificity under various detecting condition. Our method can act as an alternative approach for zygosity determination of twin pregnancies in clinical practice.Multidisciplinary SciencesSCI(E)2ARTICLE6null

Nanodelivery of nucleic acids

Funding: This work was supported by the European Research Council (ERC) Starting Grant (ERC-StG-2019-848325 to J. Conde) and the Fundação para a Ciência e a Tecnologia FCT Grant (PTDC/BTM-MAT/4738/2020 to J. Conde). J.S. acknowledges US National Institute of Health (NIH) grants (R01CA200900, R01HL156362 and R01HL159012), the US DoD PRCRP Idea Award with Special Focus (W81XWH1910482), the Lung Cancer Discovery Award from the American Lung Association and the Innovation Discovery Grants award from the Mass General Brigham. H.L., D.Y. and X.Z. were supported by the National Key R&D Program of China (no. 2020YFA0710700), the National Natural Science Foundation of China (nos 21991132, 52003264, 52021002 and 52033010) and the Fundamental Research Funds for the Central Universities (no. WK2060000027).There is growing need for a safe, efficient, specific and non-pathogenic means for delivery of gene therapy materials. Nanomaterials for nucleic acid delivery offer an unprecedented opportunity to overcome these drawbacks; owing to their tunability with diverse physico-chemical properties, they can readily be functionalized with any type of biomolecules/moieties for selective targeting. Nucleic acid therapeutics such as antisense DNA, mRNA, small interfering RNA (siRNA) or microRNA (miRNA) have been widely explored to modulate DNA or RNA expression Strikingly, gene therapies combined with nanoscale delivery systems have broadened the therapeutic and biomedical applications of these molecules, such as bioanalysis, gene silencing, protein replacement and vaccines. Here, we overview how to design smart nucleic acid delivery methods, which provide functionality and efficacy in the layout of molecular diagnostics and therapeutic systems. It is crucial to outline some of the general design considerations of nucleic acid delivery nanoparticles, their extraordinary properties and the structure–function relationships of these nanomaterials with biological systems and diseased cells and tissues.publishersversionpublishe

Conjugation of Functionalized SPIONs with Transferrin for Targeting and Imaging Brain Glial Tumors in Rat Model

Currently, effective and specific diagnostic imaging of brain glioma is a major challenge. Nanomedicine plays an essential role by delivering the contrast agent in a targeted manner to specific tumor cells, leading to improvement in accurate diagnosis by good visualization and specific demonstration of tumor cells. This study investigated the preparation and characterization of a targeted MR contrast agent, transferrin-conjugated superparamagnetic iron oxide nanoparticles (Tf-SPIONs), for brain glioma detection. MR imaging showed the obvious contrast change of brain glioma before and after administration of Tf-SPIONs in C6 glioma rat model in vivo on T2 weighted imaging. Significant contrast enhancement of brain glioma could still be clearly seen even 48 h post injection, due to the retention of Tf-SPIONs in cytoplasm of tumor cells which was proved by Prussian blue staining. Thus, these results suggest that Tf-SPIONs could be a potential targeting MR contrast agent for the brain glioma

Video delivery networks : challenges, solutions and future directions

Internet video ecosystems are faced with the increasing requirements in versatile applications, ubiquitous consumption and freedom of creation and sharing, in which the user experience for high-quality services has become more and more important. Internet is also under tremendous pressure due to the exponential growth in video consumption. Video providers have been using content delivery networks (CDNs) to deliver high-quality video services. However, the new features in video generation and consumption require CDN to address the scalability, quality of service and flexibility challenges. As a result, we need to rethink future CDN for sustainable video delivery. To this end, we give an overview for the Internet video ecosystem evolution. We survey the existing video delivery solutions from the perspective of economic relationships, algorithms, mechanisms and architectures. At the end of the article, we propose a data-driven information plane for video delivery network as the future direction and discuss two case studies to demonstrate its necessity