YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

AQP3 Increases Intercellular Cohesion in NSCLC A549 Cell Spheroids through Exploratory Cell Protrusions

Tumor cell aggregation is critical for cell survival following the loss of extracellular matrix attachment and dissemination. However, the underlying mechanotransduction of clustering solitary tumor cells is poorly understood, especially in non-small cell lung cancers (NSCLC). Here, we examined whether cell surface protrusions played an important role in facilitating the physical contact between floating cells detached from a substrate. We employed poly-2-hydroxyethyl methacrylate-based 3D culture methods to mimic in vivo tumor cell cluster formation. The suprastructural analysis of human NSCLC A549 cell spheroids showed that finger-like protrusions clung together via the actin cytoskeleton. Time-lapse holotomography demonstrated that the finger-like protrusions of free-floating cells in 3D culture displayed exploratory coalescence. Global gene expression analysis demonstrated that the genes in the organic hydroxyl transport were particularly enriched in the A549 cell spheroids. Particularly, the knockdown of the water channel aquaporin 3 gene (AQP3) impaired multicellular aggregate formation in 3D culture through the rearrangement of the actomyosin cytoskeleton. Moreover, the cells with reduced levels of AQP3 decreased their transmigration. Overall, these data indicate that cell detachment-upregulated AQP3 contributes to cell surface protrusions through actomyosin cytoskeleton remodeling, causing the aggressive aggregation of free-floating cells dependent on the property of the substratum and collective metastasis.     

Diphenyl-Methane Based Thyromimetic Inhibitors for Transthyretin Amyloidosis

Thyromimetics, whose physicochemical characteristics are analog to thyroid hormones (THs) and their derivatives, are promising candidates as novel therapeutics for neurodegenerative and metabolic pathologies. In particular, sobetirome (GC-1), one of the initial halogen-free thyromimetics, and newly synthesized IS25 and TG68, with optimized ADME-Tox profile, have recently attracted attention owing to their superior therapeutic benefits, selectivity, and enhanced permeability. Here, we further explored the functional capabilities of these thyromimetics to inhibit transthyretin (TTR) amyloidosis. TTR is a homotetrameric transporter protein for THs, yet it is also responsible for severe amyloid fibril formation, which is facilitated by tetramer dissociation into non-native monomers. By combining nuclear magnetic resonance (NMR) spectroscopy, computational simulation, and biochemical assays, we found that GC-1 and newly designed diphenyl-methane-based thyromimetics, namely IS25 and TG68, are TTR stabilizers and efficient suppressors of TTR aggregation. Based on these observations, we propose the novel potential of thyromimetics as a multi-functional therapeutic molecule for TTR-related pathologies, including neurodegenerative diseases.     

Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

Role of Post-translational Modifications in Alzheimer's Disease

The global burden of Alzheimer's disease (AD) is growing. Valiant efforts to develop clinical candidates for treatment have continuously met with failure. Currently available palliative treatments are temporary and there is a constant need to search for reliable disease pathways, biomarkers and drug targets for developing diagnostic and therapeutic tools to address the unmet medical needs of AD. Challenges in drug-discovery efforts raise further questions about the strategies of current conventional diagnosis; drug design; and understanding of disease pathways, biomarkers and targets. In this context, post-translational modifications (PTMs) regulate protein trafficking, function and degradation, and their in-depth study plays a significant role in the identification of novel biomarkers and drug targets. Aberrant PTMs of disease-relevant proteins could trigger pathological pathways, leading to disease progression. Advancements in proteomics enable the generation of patterns or signatures of such modifications, and thus, provide a versatile platform to develop biomarkers based on PTMs. In addition, understanding and targeting the aberrant PTMs of various proteins provide viable avenues for addressing AD drug-discovery challenges. This review highlights numerous PTMs of proteins relevant to AD and provides an overview of their adverse effects on the protein structure, function and aggregation propensity that contribute to the disease pathology. A critical discussion offers suggestions of methods to develop PTM signatures and interfere with aberrant PTMs to develop viable diagnostic and therapeutic interventions in AD.     

Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

Complexity classification in qualitative temporal constraint reasoning

We study the computational complexity of the qualitative algebra which is a temporal constraint formalism that combines the point algebra, the point-interval algebra and Allen's interval algebra. We identify all tractable fragments and show that every other fragment is NP-complete.     

Understanding and Modeling the Effect of Surfactants in Enhanced Particle Removal from Surfaces in Aqueous Media

The removal of particulate contamination is a critical issue for many manufacturing processes. It is particularly critical to the electronics industry in which small pieces of microscopic debris remaining after chemical mechanical planarization (cmp) using submicron polishing particles can cause device failure. One way to enhance particle removal following the cmp process is to utilize surfactants. Recent research has shown ways to model the effect of surfactants on enhanced particle removal. However, previous research has not demonstrated the effect of ionic strength on enhanced particle removal associated with surfactant use. Past research has also not shown the combined effects of ionic strength and surfactant concentration on enhanced particle removal using surfactants. This article summarizes the parameters affecting particle removal, and it provides data and analysis on the effect of ionic strength as well as the combined effects of ionic strength and surfactant concentration on particle removal following cmp processing.