Set Of Pairs Research Articles

Prefrontal Cortex Activity in Siblings With and Without Developmental Coordination Disorder: An Exploratory Study

Background: Previous research suggests that children with or at risk of probable developmental coordination disorder (pDCD) activate different areas of the brain when performing certain motor skills compared with typically developing (TD) children. This study used functional near-infrared spectroscopy to compare prefrontal cortex (PFC) activity in TD and pDCD during the completion of manual (three-dimensional [3D]) and computerized versions (two-dimensional) of the Tower of Hanoi (ToH) puzzle. Method: A total of 12 children (TD and pDCD; one female/11 male, , SD ± 1.52 years) performed the two-dimensional and 3D conditions of the ToH disk tasks, with equivalent executive function but different motor requirements, with functional near-infrared spectroscopy to compare PFC activity. Results: Interestingly, brain oxygenation levels were more apparent in the 3D versus two-dimensional ToH. In the 3D, there were large differences between pairs of discordant sibling sets and this was located to the right medial PFC, with pDCD exhibiting less activation in this region. Conclusion: While only exploratory, we have identified potential differences in the right medial PFC region, which differs within sibling sets with different pDCD status. These results concur with previous studies and are an area that needs to be explored further with a larger cohort of TD and pDCD.

Dynamic Set Similarity Join: An Update Log Based Approach

The set similarity join finds all pairs of similar sets from two collections of sets. It has many real world applications, such as personalized recommendation and community mining. In this paper, we study the problem of computing the similarity join in a dynamic context, where the sets are updated dynamically. This, however, is inefficient with the state-of-the-art join methods, because they usually assume that data collections are static and have to compute the join result from scratch whenever a set is updated. To address this issue, we propose <inline-formula><tex-math notation="LaTeX">${{\sf ALJoin}}$</tex-math></inline-formula> , an adaptive filtering approach that computes the join result incrementally based on the update logs. We first investigate the effect of set updates on the similarity values, and on this basis we propose to build a neighborhood index for each set. The neighborhood index of a specific set consists of any other sets that can be transformed into its similar sets within a threshold number of update operations. <inline-formula><tex-math notation="LaTeX">${{\sf ALJoin}}$</tex-math></inline-formula> then uses this index to effectively identify both similar and dissimilar set pairs based on their update logs. To efficiently build the neighborhood index, we devise several filtering techniques and propose a “lazy-forward” method to reduce the computational cost. In addition, to improve the efficiency on varying workloads, we propose an analytical cost model, and design an online algorithm with performance guarantees to dynamically consolidate the update logs and adapt the neighborhood indexes. We evaluated our method using four real-world datasets. Experimental results show that our approach outperforms existing methods by up to <inline-formula><tex-math notation="LaTeX">$3.7\times$</tex-math></inline-formula> .

Design and Analysis of the Mechanical Structure of Land-Air Logistics UAV

In the post-epidemic era, drone distribution has attracted increasing public attention because of its excellent contact-free characteristics and capacity to greatly improve the efficiency and reliability of material distribution. Combining land-air drones with logistics distribution, this design breaks through the limitations of the single-movement ability of traditional drones and land robots to fly in the air or move on the ground. Based on the collection and statistics of technical indicators related to logistics distribution, this paper focuses on the design of land-air conversion mode, and proposes an innovative land-air conversion mechanism composed of ball screw nut pair and multiple sets of gears to complete the design of the related mechanical structure of land-air logistics UAV. In addition, the mechanical system simulation of drones is carried out by ADAMS software. Moreover, the static and aerodynamic simulation analysis of UAVs is conducted by ANSYS software to optimize the overall structure.

Removal of Artifacts in Borehole Images Using Machine Learning

In this paper, a supervised machine-learning (ML) method to remove artifacts and noise from borehole images is described. Borehole images may exhibit a variety of issues and artifacts due to reasons such as environmental and thermal noise, imperfect calibration, and current leakage through the tool body. Methods that are currently employed to improve these images are based on traditional signal-processing techniques. Although these methods are capable of removing the artifacts in images and significantly improving image quality, they have some drawbacks as well. These drawbacks include not being entirely suitable for real-time implementation and issues with reproducibility. The alternative method presented here is based on an ML algorithm that is trained using a data set pairing raw data with data processed using a traditional signal-processing-based approach. The resulting ML model is capable of being implemented in near-real time. Furthermore, the application of the algorithm does not require user supervision, increasing the reproducibility of the results.

Impact of environmental heat exposure on the health status in farmworkers, Nakhon Ratchasima, Thailand.

Thailand is a tropical developing country which has a serious increase in health risk due to hot weather exposure among outdoor workers. The aims of this study were to compare the factors related to environmental heat exposure in three different seasons, and to assess the relationship between environmental heat and dehydration status in each season among farmworkers in Nakhon Ratchasima, Thailand. A semi-longitudinal study was carried out in 22 male farmworkers throughout a year of farming. The primary data were collected in farmworkers for socio-demographic information, clinical assessments, and heat-related illnesses. Average of environmental heat index (Median, SD) were severe in summer (WBGT=38.1, 2.8°C), rainy season (WBGT=36.1, 2.1°C), and winter (WBGT=31.5, 2.7°C). Average urine Sp. Gr. in summer, rainy season, and winter were 1.022, 1.020, and 1.018 respectively. The third sentence should be corrected as follows: The Friedman analysis revealed a statistically significant difference between the three different seasons in WBGT (wet bulb globe temperature), body temperature, heart rate (P<0.01), and respiratory rate (P<0.05). There was a statistically significant difference between the three different seasons for skin rash/itching, dizziness, muscle cramp dyspnea (P<0.05), and weakness (P<0.01). Wilcoxon signed-ranks analysis found a significant difference in the medians of the paired sets of urine Sp. Gr. values between baseline and summer (P<0.05). Spearman's rank correlation coefficient did not find a relationship between WBGT and urine Sp. Gr. in the three different seasons. This study demonstrated that farmworkers had exposure to environmental heat stress which was expressed through physical changes. Therefore, there is a need for either interventions or guidelines to prevent dehydration for outdoor workers in this region.

External Electric Field Effects on AFM-like Magnetism in Nitroxide-Functionalized C+:C Base Pair.

In this work, we computationally designed a set of nitroxide diradical base pairs (rC+:rC) to propose promising magnetic building blocks for spintronic or magnetic molecular materials. C+:C12 is found to possess a considerably large antiferromagnetic-like (AFM-like) spin-coupling magnitude (J = -3286.681 cm-1) and sensitive magnetic responses to the external electric field. Especially, the presence of the Y direction field that is oriented perpendicular to intermolecular hydrogen bonds has the greatest influence on the magnetic exchange interaction (J being from -2549.578 to -4231.286 cm-1, ΔJY = 1681.708 cm-1), which could be understood by two simultaneously occurring effects. On the one hand, the external electric field in the -Y direction can regulate the charge polarization of negative and positive electrostatic potentials on C12 moiety and further facilitate the spin transport property. On the other hand, with increasing electric field strength on the -Y axis, the spin density on diradical sources diminishes and that on the coupler increases, which can lead to a homogenous spin-density distribution. The achieved understanding provides a new strategy for designing self-assembly magnetic nanomaterials or nanodevices and enhancing the AFM coupling through the assistance of external electric field.

Multiple-Attribute Decision Making Based on Intuitionistic Hesitant Fuzzy Connection Set Environment

The intuitionistic hesitant fuzzy set (IHFS) is an enriched version of hesitant fuzzy sets (HFSs) that can cover both fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). By assigning membership and non-membership grades as subsets of [0, 1], the IHFS can model and handle situations more proficiently. Another related theory is the theory of set pair analysis (SPA), which considers both certainties and uncertainties as a cohesive system and represents them from three aspects: identity, discrepancy, and contrary. In this article, we explore the suitability of combining the IHFS and SPA theories in multi-attribute decision making (MADM) and present the hybrid model named intuitionistic hesitant fuzzy connection number set (IHCS). To facilitate the design of a novel MADM algorithm, we first develop several averaging and geometric aggregation operators on IHCS. Finally, we highlight the benefits of our proposed work, including a comparative examination of the recommended models with a few current models to demonstrate the practicality of an ideal decision in practice. Additionally, we provide a graphical interpretation of the devised attempt to exhibit the consistency and efficiency of our approach.

Current data are consistent with flat spatial hypersurfaces in the ΛCDM cosmological model but favor more lensing than the model predicts

We study the performance of three pairs of tilted $\Lambda$CDM cosmological models, two pairs allowing for non-flat spatial hypersurfaces with CMB temperature and polarization power spectrum data (P18) from Planck, P18 lensing (lensing), and non-CMB data (non-CMB). For the six models, we measure cosmological parameters and study whether or not pairs of the data sets are mutually consistent in these models. Half of these models allow the lensing consistency parameter $A_L$ to be an additional free parameter, while the other three have $A_L = 1$. The tilted spatially-flat models assume the usual primordial spatial inhomogeneity power spectrum. The tilted non-flat models assume either the primordial power spectrum used in the Planck group analyses [Planck $P(q)$] or a recently computed power spectrum [new $P(q)$]. In the tilted non-flat models with $A_L=1$ we find differences between P18 data and non-CMB data cosmological parameter constraints, which are large enough to rule out the Planck $P(q)$ model at 3$\sigma$ but not the new $P(q)$ model. While both P18 data and non-CMB data separately favor a closed geometry when P18+non-CMB data are jointly analyzed the evidence in favor of non-flat hypersurfaces subsides. Differences between P18 data and non-CMB data cosmological constraints subside when $A_L$ is allowed to vary. From the most restrictive P18+lensing+non-CMB data combination we get almost model-independent constraints and find that the $A_L>1$ option is preferred over the $\Omega_k<0$ one, with the $A_L$ parameter, for all models, being larger than unity by $\sim 2.5\sigma$. According to the deviance information criterion, in the P18+lensing+non-CMB analysis, the varying $A_L$ option is on the verge of being strongly favored over the $A_L=1$ one, which could indicate a problem for the standard tilted flat $\Lambda$CDM model (Abridged abstract).

Plasmonic Metasurfaces for Superposition of Profile-Tunable Tightly Focused Vector Beams and Generation of the Structured Light

Vector beams (VBs) and their superposition have found important applications in versatile fields such as optical communications, super-resolution microscopy and quantum information, and metasurfaces have enabled the miniaturization and integration of the optical systems manipulating the vector beams, providing potential applications to subwavelength regimes. In this work, we propose a metasurface to realize the superposition of profile-tunable tightly focused VBs, with the novel structured light fields generated. The metasurface is composed of two sets of orthogonal-nanoslit pairs arranged on the inner and outer rings. By realizing the chiral conversion of circularly polarized light with the slit-pairs which act as half-wave plates, and by creating helical phase profiles of optical vortices with the geometrical phase of rotational nano-slit pairs, two focused Bessel VBs are formed. By finely varying the diameters of two sets of rings, the doughnuts of the two Bessel VBs of different orders are tuned to be of the same size, and the superposition of the two VBs is realized. The theoretical analyses of the superimposed fields were presented, the FDTD simulations were performed to optimize the designed metasurfaces, and the experimental measurements were carried out to validate feasibility of the metasurface. The novel and interesting characteristics of the superposed fields different from those of the conventional VBs were demonstrated. This work will be of significance for classical and quantum applications of VBs in various fields.

Image Super-resolution Methods for FY-3E X-EUVI 195 Å Solar Images

Solar eruptions and the solar wind are sources of space weather disturbances, and extreme-ultraviolet (EUV) observations are widely used to research solar activity and space weather forecasts. Fengyun-3E is equipped with the Solar X-ray and Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by the lower resolution, however, we research super-resolution techniques to improve the data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning methods can learn to reconstruct high-quality images through training on paired data sets. There is a wide variety of super-resolution models. We try these three representative models: Real-ESRGAN combined with generative adversarial networks, residual channel-attention networks (RCAN) based on channel attention, and SwinIR, based on self-attention. Instruments on different satellites differ in observation time, angle, and resolution, so we selected Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 193 Å images with similar wavelengths as a reference and used a feature-based method for image registration to eliminate slight deformations to build training data sets. Finally, we compare the above methods in their evaluation metrics and visual quality. RCAN has the highest peak signal-to-noise ratio and structural similarity evaluation. Real-ESRGAN model is the best in the Learned Perceptual Image Patch Similarity index, and its results visually show that it has more highly detailed textures. The corrected super-resolution results can complement the SDO/AIA data to provide solar EUV images with a higher temporal resolution for space weather forecasting and solar physics research.

Break and Splice: A Statistical Method for Non‐Rigid Point Cloud Registration

Abstract3D object matching and registration on point clouds are widely used in computer vision. However, most existing point cloud registration methods have limitations in handling non‐rigid point sets or topology changes (e.g. connections and separations). As a result, critical characteristics such as large inter‐frame motions of the point clouds may not be accurately captured. This paper proposes a statistical algorithm for non‐rigid point sets registration, addressing the challenge of handling topology changes without the need to estimate correspondence. The algorithm uses a novel Break and Splice framework to treat the non‐rigid registration challenges as a reproduction process and a Dirichlet Process Gaussian Mixture Model (DPGMM) to cluster a pair of point sets. Labels are assigned to the source point set with an iterative classification procedure, and the source is registered to the target with the same labels using the Bayesian Coherent Point Drift (BCPD) method. The results demonstrate that the proposed approach achieves lower registration errors and efficiently registers point sets undergoing topology changes and large inter‐frame motions. The proposed approach is evaluated on several data sets using various qualitative and quantitative metrics. The results demonstrate that the Break and Splice framework outperforms state‐of‐the‐art methods, achieving an average error reduction of about 60% and a registration time reduction of about 57.8%.

Risk assessment of methanol storage tank fire accident using hybrid FTA-SPA.

Fire accidents in storage tanks are of great importance due to the difficulty in extinguishing and ease of spread to nearby products. This study aimed to introduce a framework based on FTA-based Set Pair Analysis (SPA) established via experts' elicitation to identify and assess the risk of storage tank fire. In the quantitative FTA of a system, sufficient data are only sometimes available to calculate the failure probability of the system appertains to study. Thus, the obtained result of the SPA added new value to the Basic Events (BEs) and estimated top event. To illustrate the applicability of the proposed approach, a fault tree of the methanol storage tank fire is performed and analyzed BEs. According to the obtained results, the fire accident was computed by 48 BEs, and the occurrence probability value of the top event was estimated 2.58E-1/year. In addition, the most crucial paths that led to the fire accident are listed in this study. The proposed approach established in the present study can assist decision-makers in determining where to take preventative or appropriate action on the storage tank system. Moreover, it can be adjusted for various systems with limited manipulation.

Sigmoid dose accumulation and reporting for multifractionated brachytherapy for cervical cancer: Methodological development of sigmoid points through virtual endoscopic method

PURPOSEThe sigmoid is an important organ at risk for gynecological brachytherapy (BT). However, the reliability of localization of high-dose regions during multi-fractionated treatment is limited. This work reports the methodological development of sigmoid points to summate multi-fractionated doses. METHODS AND MATERIALTen paired MRI data sets of ring-based intracavitary brachytherapy were obtained. Simulating a virtual endoscope, a reference line was created along the central axis of the anorectosigmoid for each implant. A trendline was generated, and linear dose was determined. Three-dimensional (3D) coordinates of high-dose regions were identified, and overlap was determined. In the next step, 3D coordinates of high-dose sigmoid points were localized in reference to cervical os and re-verified for location in reference to sigmoid lumen and corroboration with 2cc doses. With minor modifications, sigmoid points were proposed. RESULTSIn 6 of 10 patients, high-dose regions co-localized in subsequent fractions of BT. Three high-dose regions were identified along the sigmoid length and proposed as sigmoid points in reference to cervical os. (S1’= 0.5 cm right, 1.5 cm posterior, and 2.4 cm cranial; S2’ = 0.3 cm anterior and 4.5 cm cranial; S3’ = 2.7 cm left, 3 cm anterior, and 3.6 cm cranial to the cervical os). S1’ and S2’ were located in the sigmoid in 70% and 60% of data sets. The mean difference between D2cc and S1’/S2’ was 0.30 Gy and 1.06 Gy respectively. S3’ had limited corroboration to sigmoid lumen or 2 cc doses. The points S1’ and S2’ were further modified (minor) for applicability and proposed as sigmoid points 1 and 2 (SP1 0.5 right,1.5 posterior and 2.5 cm cranial to cervical os and SP2 (0.5 cm anterior and 4.5 cm cranial to cervical os)). CONCLUSIONSP1 and SP 2 are proposed as a surrogate for 2 cc sigmoid doses and may provide a method of reliable inter-fraction dose summation. This pilot work requires further validation.

Computational design and experimental verification of pseudoknotted ribozymes.

The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.

Research on Safety Evaluation and System Development of Tunnel Lining Based on Set Pair Analysis

Considering the importance of lining thickness in lining safety, and realizing the synchronization of safety evaluation and information development, this paper combines the methods of literature research and field investigation, focuses on six influencing factors, such as the deterioration of lining materials, the deformation of lining and the lack of tunnel clearance, et al. An evaluation index system of structural safety classification is established, and then substantiates each index and defines its quantitative or qualitative evaluation criteria. Through the normalization processing, the state of the evaluation index can be characterized by the normalized value. Analysing the characteristics, structure, relationship, trend and the mode of their mutual connection between the indexes by set pair analysis method, the calculation formula of the connection degree is obtained, and then the set pair potential vector is obtained. According to the principle of maximum set pair potential, the safety evaluation of lining structure is divided into five levels: safe, safer, less safe, unsafe and extremely unsafe. With the help of App Designer tool in MATLAB software, a program suitable for lining safety evaluation is designed and developed independently. Combined with a tunnel project in Chongqing, the above method is used to judge the lining safety of the tunnel project in order to provide a quick way for tunnel safety evaluation.

Label-Guided Generative Adversarial Network for Realistic Image Synthesis.

Generating photo-realistic images from labels (e.g., semantic labels or sketch labels) is much more challenging than the general image-to-image translation task, mainly due to the large differences between extremely sparse labels and detail rich images. We propose a general framework Lab2Pix to tackle this issue from two aspects: 1) how to extract useful information from the input; and 2) how to efficiently bridge the gap between the labels and images. Specifically, we propose a Double-Guided Normalization (DG-Norm) to use the input label for semantically guiding activations in normalization layers, and use global features with large receptive fields for differentiating the activations within the same semantic region. To efficiently generate the images, we further propose Label Guided Spatial Co-Attention (LSCA) to encourage the learning of incremental visual information using limited model parameters while storing the well-synthesized part in lower-level features. Accordingly, Hierarchical Perceptual Discriminators with Foreground Enhancement Masks are proposed to toughly work against the generator thus encouraging realistic image generation and a sharp enhancement loss is further introduced for high-quality sharp image generation. We instantiate our Lab2Pix for the task of label-to-image in both unpaired (Lab2Pix-V1) and paired settings (Lab2Pix-V2). Extensive experiments conducted on various datasets demonstrate that our method significantly outperforms state-of-the-art methods quantitatively and qualitatively in both settings.

Transitive inference and the testing effect: Retrieval practice impairs transitive inference.

There is substantial interest in the extent to which the testing effect (the finding that retrieval practice enhances memory) extends to more complex forms of learning, especially those entailing greater element interactivity. Transitive inference (TI) requires just such interactivity, in which information must be combined across multiple learning elements or premises to extract an underlying structure. Picklesimer et al. provided preliminary evidence that retrieval practice fails to enhance, and actually disrupts, TI. This study assessed the generality of that result. The current experiments employed a seven- or eight-element TI paradigm in which participants initially learned a set of premise pairs (e.g., A > B, B > C, and C > D) and then engaged in either restudy or retrieval practice of the premise pairs before taking a final test that assessed memory for the original premise pairs and one's ability to make TIs (e.g., to infer that B > D). Experiments 1 and 2 used pictorial materials and simultaneous presentation of premises during learning, a form of presentation that has induced testing effects on other forms of inference. For TI, the results were unchanged from Picklesimer et al.-TI was worse for retrieval practice than restudy. Experiment 3 used verbal materials and likewise found worse TI for retrieval practice. A small-scale meta-analysis combining the current experiments with those of Picklesimer et al. revealed a significant negative testing effect on TI (d = -0.37). Although retrieval practice enhances many aspects of memory, this fundamental aspect of human reasoning may be impaired by retrieval practice.

Mirar a los mirones. Adaptation of Los mirones (1617) attributed to Miguel de Cervantes. Adaptation, Carlota Gaviño

<i>Mirar a los mirones</i>. Adaptation of <i>Los mirones</i> (1617) attributed to Miguel de Cervantes. Adaptation, Carlota Gaviño

Abstract PD5-08: PD5-08 Drug target activation mapping of matched triple negative primary and axillary lymph node metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation

Abstract Title: Drug target activation mapping of matched triple negative primary and axillary LN metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation Background: Triple negative breast cancer (TNBC) is comprised of heterogeneous subtypes that arise from various molecular alterations that result in TNBC having the poorest overall prognosis. There is growing interest in more sensitive ways to measure estrogen receptor (ER) and HER2 levels in the ER “negative” and HER2 “low/negative” settings, as there are patients with TNBC whose tumors are actually ER and/or HER2 “expressing/active”, and who may benefit from ER- and/or HER2-targeted therapies. Given the limitations of IHC in reliably quantitating ER and HER2 in low (0-1+) expressing tumors, we utilized highly sensitive reverse phase protein array (RPPA) to quantitate expression of these therapeutic targets along with downstream signaling activation mapping in a unique pilot set of patient-matched primary (P) and axillary lymph node (LN) metastases obtained synchronously. The quantitative expression of steroid hormone receptor expression/activation (ER, glucocorticoid receptor (GR), androgen receptor (AR)) along with their signaling architecture and dynamics were analyzed. Methods: A 12 P-LN paired tumor set was chosen for analysis (N=24). Manual macrodissection was used to enrich for tumor epithelium prior to RPPA analysis that quantitatively measured 155 proteins/phosphoprotein drug targets in key signaling pathways known to be involved in steroid hormone receptor biology and tumorigenesis. Statistical analysis using paired sample t-testing or Wilcoxon rank testing was performed (p&amp;lt; 0.05 uncorrected). Results: Protein pathway activation mapping and unsupervised clustering analysis revealed a subset of TNBCs from 3 of the 12 (25%) patients (N=4: 1 LN and 1 P, and one patient-matched LN and P) that was characterized by high relative levels of total ER. Interestingly, these same tumors also had amongst the highest relative levels of both total AR and activated (phosphorylated) GR compared to the rest of the 20 tumor samples. Downstream signaling analysis found significant (p&amp;lt; 0.05) activation/phosphorylation of AKT (S473 and/or T308) associated with the ER-AR-GR/steroid hormone activated/”high” TNBC signature compared to the 20 tumors that were steroid hormone “low”. Conclusions: Functional protein pathway activation mapping of a unique study set of synchronously obtained TNBC primary and LN metastasis revealed a steroid hormone receptor expression/activation signature of co-incidentally activated/expressed ER, GR and AR, that was also characterized by increased AKT activation (p&amp;lt; 0.05). This steroid hormone receptor “activated” signature was not observed to be significantly differentially expressed in LN compared to P tissues in cohort or matched pair analysis, suggesting that this signature may be present as an intrinsic event in the P, and not acquired in LN metastases. Recent data from stage I-III TNBC patients treated in the neoadjuvant setting with an AKT inhibitor1 revealed that responding TNBC patients had pre-treatment tumor biopsies that were enriched for both high AR expression and high phosphoAKT (S473 and/or T308) levels. If these observations that a subset of TNBCs are steroid hormone receptor “activated” despite being ER “negative” are confirmed in larger study sets, we hypothesize that this subgroup, identified by RPPA analysis of both P and LN metastases, could be potentially sensitive to AKT inhibition in combination with agents that target steroid hormone receptors. 1Shi Z, et al. Functional Mapping of AKT Signaling and Biomarkers of Response from the FAIRLANE Trial of Neoadjuvant Ipatasertib plus Paclitaxel for Triple-Negative Breast Cancer. Clin Cancer Res. 2022 Mar 1;28(5):993-1003 Citation Format: Julia Wulfkuhle, Page Blas, Heather Williams, Claudius Mueller, Rosa I. Gallagher, Mariaelena Pierobon, Joyce O’Shaughnessy, Emanuel F. Petricoin. PD5-08 Drug target activation mapping of matched triple negative primary and axillary lymph node metastases identifies and links the ER-AR-GR steroid hormone receptor axis with downstream AKT activation [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-08.

Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research is still in its infancy. In order to facilitate the research into uncertainty theory in complex network modeling, this paper summarizes and analyzes the research hotspots of set pair analysis, rough set theory and fuzzy set theory in complex network modeling. This paper firstly introduces three kinds of uncertainty theories: the basic definition of set pair analysis, rough sets and fuzzy sets, as well as their basic theory of modeling in complex networks. Secondly, we aim at the three uncertainty theories and the establishment of specific models. The latest research progress in complex networks is reviewed, and the main application fields of the three uncertainty theories are discussed, respectively: community discovery, link prediction, influence maximization and decision-making problems. Finally, the prospect of the modeling and development of uncertain complex networks is put forward.