Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-free DNA ultra-low-pass whole genome sequencing to distinguish malignant peripheral nerve sheath tumor (MPNST) from its benign precursor lesion: A cross-sectional study

Background: The leading cause of mortality for patients with the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome is development of Malignant Peripheral Nerve Sheath Tumor (MPNST), an aggressive soft tissue sarcoma. In the setting of NF1, this cancer type frequently arises from within its common and benign precursor, plexiform neurofibroma (PN). Transformation from the benign PN to MPNST is challenging to diagnose due to difficulties in distinguishing cross-sectional imaging results and intralesional heterogeneity resulting in biopsy sampling errors. Methods and Findings: This multi-institutional study used ultra-low-pass whole genome sequencing (ULP-WGS) of plasma cell-free DNA (cfDNA) to distinguish between MPNST and PN in patients with NF1. Using copy number to estimate the fraction of plasma cfDNA originating from tumor (tumor fraction), we developed a noninvasive classifier which differentiates MPNST from PN with 91% specificity. Healthy controls without NF1 (subjects = 14, plasma samples= 14), PN (subjects = 45, plasma samples = 45), and MPNST (subjects = 14, plasma samples = 48) cohorts showed significant differences in tumor fraction in plasma (P = 0.006) as well as cfDNA fragment length (P < 0.001) with MPNST samples harboring shorter fragments and being enriched for tumor-derived cfDNA relative to PN and healthy controls. Mutational analysis demonstrated focal NF1 copy number loss in PN and MPNST patient plasma but not in healthy controls. Genomic alterations associated with malignant transformation (focal copy number gains in chromosome 8 and copy number losses in SUZ12, SMARCA2,CDKN2A/B, and chromosome arm 9p) were more prominently observed in MPNST plasma. Furthermore, the sum of longest tumor diameters (SLD) visualized by crosssectional imaging correlated significantly with paired tumor fractions in plasma from MPNST patients ({rho} = 0.50, P = 0.0007). On case series analysis, tumor fraction levels in plasma correlated with treatment response to therapy and minimal residual disease (MRD) detection before relapse. Conclusions: Tumor fraction levels derived from copy number alteration analysis of plasma cfDNA using ULP-WGS significantly correlated with MPNST tumor burden, distinguished MPNST from its benign PN precursor, and correlated with treatment response. In the future, our findings could form the basis for improved early cancer detection and monitoring in high-risk cancer-predisposed populations.
Research Progress on PATJ and Underlying Mechanisms Associated with Functional Outcomes After Stroke

Abstract Cell polarity is an intrinsic property of epithelial cells regulated by scaffold proteins. The CRB (crumbs) complex is known to play a predominant role in the dynamic cooperative network of polarity scaffold proteins. PATJ (PALS1-associated tight junction) is the core component in the CRB complex and has been highly conserved throughout evolution. PATJ is crucial to several important events in organisms’ survival, including embryonic development, cell polarity, and barrier establishment. A recent study shows that PATJ plays an important role in functional outcomes of stroke. In this article, we elaborate on the biological structure and physiological functions of PATJ and explore the underlying mechanisms of PATJ genetic polymorphism that are associated with poor functional outcomes in ischemic stroke.
Deep Dual Support Vector Data description for anomaly detection on attributed networks

Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end‐to‐end model of Deep Dual Support Vector Data description based Autoencoder (Dual‐SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual‐SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively. Then, a dual‐hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be detected by measuring the distance of nodes to the learned center of each hypersphere in the latent structure space and attribute space, respectively. Extensive experiments on the real‐world attributed networks show that Dual‐SVDAE consistently outperforms the state‐of‐the‐arts, which demonstrates the effectiveness of the proposed method.
Knowledge Based Keyword-Aware Representative Travel Route Recommendations .

: Online Social Networks (OSNs) are widely used by people to share their activities related to travelling, check-in and others. People often have travel plans with preferences. Maintaining travel histories of people with personalization can help in making future decisions with ease. However, it is non-trivial to have queries processed with the help of previous history of knowledge and to produce more useful personalized representative travel recommendations. The notion of point of interest apart for giving search keywords can help users express more skyline options in order to help in effective processing. The concept of finding representative travel recommendations that consider POIs and other user parameters besides historical skylines is an important research to be carried out. In the proposed system, the keyword-aware representative travel route recommendation system is further improved to support the use of previously repeated queries and to learn the approximate parameters automatically. Thus the system becomes a knowledge based keyword-aware representative travel route recommendation system to reduce computational cost and improve response time. An algorithm by name Knowledge based Query Processing (KQP) is used to achieve this. We built a prototype application with web interface to demonstrate proof of the concept.
Flipped Classroom as an Alternative Strategy for Teaching Stoichiometry

This study aimed to compare the effectiveness of flipped classroom and traditional classroom instruction in measuring conceptual change and to determine if flipped classroom instruction would be an alternative method of teaching to traditional lecture method. This study covered the level of conceptual understanding of students on stoichiometry and the type of conceptual change before and after exposure to flipped and traditional classroom environment. Qualitative and quantitative research methods were used in the study. Respondents were two sections of third year Bachelor of Secondary Education, Biological Science. Frequency, percentage, ranking, mean, standard deviation, Hake factor test, and t-test were the statistical tools applied to answer specific questions. Results showed profound increase towards conceptual change representing a shift from intuitive understanding to correct incomplete understanding level. Thus, change for the better, in theoretical type was determined from pretest to posttest of students exposed to flipped and traditional classroom. Results also indicated that there is no significant difference on students’ conceptual change on stoichiometry exposed to flipped and traditional classroom environment thus, flipped classroom instruction can be used as an alternative teaching method to traditional lecture method in teaching stoichiometry.
Stitch Fix for Mapper and Topological Gains

The mapper construction is a powerful tool from topological data analysis that is designed for the analysis and visualization of multivariate data. In this paper, we investigate a method for stitching a pair of univariate mappers together into a bivariate mapper, and study topological notions of information gains, referred to as topological gains, during such a process. We further provide implementations that visualize such topological gains for mapper graphs.
Handling of beam spectra in training and application of proton RBE models

Published data from cell survival experiments are frequently used as training data for models of proton relative biological effectiveness (RBE). The publications rarely provide full information about the primary particle spectrum of the used beam, or its content of heavy secondary particles. The purpose of this paper is to assess to what extent heavy secondary particles may have been present in published cell survival experiments, and to investigate the impact of non-primary protons for RBE calculations in treatment planning. We used the Monte Carlo code Geant4 to calculate the occurrence of non-primary protons and heavier secondary particles for clinical protons beams in water for four incident energies in the [100, 250] MeV interval. We used the resulting spectra together with a conservative RBE parameterization and an RBE model to map both the rise of RBE at the beam entry surface due to heavy secondary particle buildup, and the difference in estimated RBE if non-primary protons are included or not in the beam quality metric. If included, non-primary protons cause a difference of 2% of the RBE in the plateau region of an spread out Bragg peak and 1% in the Bragg peak. Including non-primary protons specifically for RBE calculations will consequently have a negligible impact and can be ignored. A buildup distance in water of one millimeter was sufficient to reach an equilibrium state of RBE for the four incident energies selected. For the investigated experimental data, 83 out of the 86 data points were found to have been determined with at least that amount of buildup material. Hence, RBE model training data should be interpreted to include the contribution of heavy secondaries.
Deformed Kac–Moody algebra and noncommutative Fermi theory in two dimensions

Starting from noncommutative Fermi theory in two dimensions, we construct a deformed Kac–Moody algebra between its vector and chiral currents. The higher-order corrections to the deformed Kac–Moody algebra are explicitly calculated. We observe that the ordinary Schwinger terms are not affected by noncommutativity. Finally we conclude that the deformed Kac–Moody algebra can be given in term of ordinary Kac–Moody algebra plus infinitely many Lie algebraic structures at each nonzero power of the antisymmetric coefficient [Formula: see text].
The Popularity-Homophily Index: A new way to measure Homophily in Directed Graphs

In networks, the well-documented tendency for people with similar characteristics to form connections is known as the principle of homophily. Being able to quantify homophily into a number has a significant real-world impact, ranging from government fund-allocation to finetuning the parameters in a sociological model. This paper introduces the Popularity-Homophily Index (PH Index) as a new metric to measure homophily in directed graphs. The PH Index takes into account the popularity of each actor in the interaction network, with popularity precalculated with an importance algorithm like Google PageRank. The PH Index improves upon other existing measures by weighting a homophilic edge leading to an important actor over a homophilic edge leading to a less important actor. The PH Index can be calculated not only for a single node in a directed graph but also for the entire graph. This paper calculates the PH Index of two sample graphs and concludes with an overview of the strengths and weaknesses of the PH Index, and its potential applications in the real world.
It’s not Rocket Science: Interpreting Figurative Language in Narratives

Figurative language is ubiquitous in English. Yet, the vast majority of NLP research focuses on literal language. Existing text representations by design rely on compositionality, while figurative language is often non- compositional. In this paper, we study the interpretation of two non-compositional figurative languages (idioms and similes). We collected datasets of fictional narratives containing a figurative expression along with crowd-sourced plausible and implausible continuations relying on the correct interpretation of the expression. We then trained models to choose or generate the plausible continuation. Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language types: inferring meaning from the context and relying on the constituent words’ literal meanings. The knowledge-enhanced models improve the performance on both the discriminative and generative tasks, further bridging the gap from human performance.
DPA: Learning Robust Physical Adversarial Camouflages for Object Detectors

Adversarial attacks are feasible in the real world for object detection. However, most of the previous works have tried to learn “patches” applied to an object to fool detectors, which become less effective or even ineffective in squint view angles. To address this issue, we propose the Dense Proposals Attack (DPA) to learn robust, physical and targeted adversarial camouﬂages for detectors. The camouﬂages are robust because they remain adversarial when ﬁlmed under arbitrary viewpoint and different illumination conditions, physical because they function well both in the 3D virtual scene and the real world, and targeted because they can cause detectors to misidentify an object as a spe-ciﬁc target class. In order to make the generated camouﬂages robust in the physical world, we introduce a combination of viewpoint shifts, lighting and other natural transformations to model the physical phenomena. In addition, to improve the attacks, DPA substantially attacks all the classiﬁ-cations in the ﬁxed region proposals. Moreover, we build a virtual 3D scene using the Unity simulation engine to fairly and reproducibly evaluate different physical attacks. Extensive experiments demonstrate that DPA outperforms the state-of-the-art methods signiﬁcantly, and generalizes well to the real world, posing a potential threat to the security-critical computer vision systems.
Students' Information Privacy Concerns in Learning Analytics: Towards a Model Development

The widespread interest in learning analytics (LA) is associated with increased availability of and access to student data where students’ actions are monitored, recorded, stored and analyzed. The availability and analysis of such data is argued to be crucial for improved learning and teaching. Yet, these data can be exposed to misuse, for example, to be used for commercial purposes, consequently, resulting in information privacy concerns (IPC) of students who are the key stakeholders and data subjects in the LA context. The main objective of this study is to propose a theoretical model to understand the IPC of students in relation to LA. We explore the concept of IPC as a central construct between its two antecedents: perceived privacy vulnerability and perceived privacy control, and its consequences, trusting beliefs and self-disclosure behavior. Although these relationships have been investigated in other contexts, this study aims to offer mainly theoretical insights on how these relationships may be shaped in the context of LA in higher education. Understanding students’ IPC, the related root causes and consequences in LA is the key step to a more comprehensive understanding of privacy issues and the development of effective privacy practices that would protect students’ privacy in the evolving setting of data-driven higher education.
Differentiable Rendering and Identiﬁcation of Impact Sounds

: Rigid objects make distinctive sounds during manipulation. These 1 sounds are a function of object features, such as shape and material, and of contact 2 forces during manipulation. Being able to infer from sound an object’s acoustic 3 properties, how it is being manipulated, and what events it is participating in could 4 augment and complement what robots can perceive from vision, especially in case 5 of occlusion, low visual resolution, poor lighting, or blurred focus. Annotations 6 on sound data are rare. Therefore, existing inference systems mostly include a 7 sound renderer in the loop, and use analysis-by-synthesis to optimize for object 8 acoustic properties. Optimizing parameters with respect to a non-differentiable 9 renderer is slow and hard to scale to complex scenes. We present DiffImpact, a 10 fully differentiable model for sounds rigid objects make during impacts, based 11 on physical principles of impact forces, rigid object vibration, and other acoustic 12 effects. Its differentiability enables gradient-based, efﬁcient joint inference of 13 acoustic properties of the objects and characteristics and timings of each individual 14 impact. DiffImpact can also be plugged in as the decoder of an autoencoder, 15 and trained end-to-end on real audio data, so that the encoder can learn to solve 16 the inverse problem in a self-supervised way. Experiments demonstrate that 17 our model’s physics-based inductive biases make it more resource efﬁcient and 18 expressive than state-of-the-art pure learning-based alternatives, on both forward 19 rendering of impact sounds and inverse tasks such as acoustic property inference 20 and blind source separation of impact sounds. 21
Nectin-1 Is an Entry Mediator for Varicella-Zoster Virus Infection of Human Neurons

Varicella-zoster virus (VZV) causes chickenpox, gains access to neurons during primary infection where it resides lifelong, and can later be reactivated. Reactivation is associated with shingles and postherpetic neuralgia, as well as with severe neurologic complications, including vasculitis and encephalitis. ABSTRACT Varicella-zoster virus (VZV) maintains lifelong latency in neurons following initial infection and can subsequently be reactivated to result in herpes zoster or severe neurological manifestations such as encephalitis. Mechanisms of VZV neuropathogenesis have been challenging to study due to the strict human tropism of the virus. Although neuronal entry mediators of other herpesviruses, including herpes simplex virus, have been identified, little is known regarding how VZV enters neurons. Here, we utilize a human stem cell-based neuronal model to characterize cellular factors that mediate entry. Through transcriptional profiling of infected cells, we identify the cell adhesion molecule nectin-1 as a candidate mediator of VZV entry. Nectin-1 is highly expressed in the cell bodies and axons of neurons. Either knockdown of endogenous nectin-1 or incubation with soluble forms of nectin-1 produced in mammalian cells results in a marked decrease in infectivity of neurons. Notably, while addition of soluble nectin-1 during viral infection inhibits infectivity, addition after infection has no effect on infectivity. Ectopic expression of human nectin-1 in a cell line resistant to productive VZV infection confers susceptibility to infection. In summary, we have identified nectin-1 as a neuronal entry mediator of VZV. IMPORTANCE Varicella-zoster virus (VZV) causes chickenpox, gains access to neurons during primary infection where it resides lifelong, and can later be reactivated. Reactivation is associated with shingles and postherpetic neuralgia, as well as with severe neurologic complications, including vasculitis and encephalitis. Although the varicella vaccine substantially decreases morbidity and mortality associated with primary infection, the vaccine cannot prevent the development of neuronal latency, and vaccinated populations are still at risk for reactivation. Furthermore, immunocompromised individuals are at higher risk for VZV reactivation and associated complications. Little is known regarding how VZV enters neurons. Here, we identify nectin-1 as an entry mediator of VZV in human neurons. Identification of nectin-1 as a neuronal VZV entry mediator could lead to improved treatments and preventative measures to reduce VZV related morbidity and mortality.
What does modularity mean?

Modularity is now generally recognized as a fundamental feature of organisms, one that may have profound consequences for evolution. Modularity has recently become a major focus of research in organismal biology across multiple disciplines including genetics, developmental biology, functional morphology, population and evolutionary biology. While the wealth of new data, and also new theory, has provided exciting and novel insights, the concept of modularity has become increasingly ambiguous. That ambiguity is underlain by diverse intuitions about what modularity means, and the ambiguity is not merely about the meaning of the word—the metrics of modularity are measuring different properties and the methods for delimiting modules delimit them by different, sometimes conflicting criteria. The many definitions, metrics and methods can lead to substantial confusion not just about what modularity means as a word but also about what it means for evolution. Here we review various concepts, using graphical depictions of modules. We then review some of the metrics and methods for analyzing modularity at different levels. To place these in theoretical context, we briefly review theories about the origins and evolutionary consequences of modularity. Finally, we show how mismatches between concepts, metrics and methods can produce theoretical confusion, and how potentially illogical interpretations can be made sensible by a better match between definitions, metrics, and methods.
Detection of Circulating Tumor DNA in Patients with Thyroid Nodules

Objective Detection of circulating tumor DNA (ctDNA) in cancer patients can potentially serve as a noninvasive, sensitive test of disease status. The purpose of this study was to determine the ability to detect BRAF (V600E) mutations in the plasma of patients with thyroid nodules, with the goal of distinguishing between benign and malignant nodules. Methods Consecutive patients with thyroid nodules who consented for surgery were recruited. Plasma samples were obtained preoperatively and one month postoperatively. Quantitative PCR was used to determine the levels of the BRAF (V600E) mutation preoperatively and postoperatively. Results A total of 109 patients were recruited. On final pathology, 38 (32.8%) patients had benign thyroid nodules, 45 (38.8%) had classical papillary thyroid cancer (PTC), 23 (19.8%) had nonclassical PTC, and 3 (2.6%) had follicular thyroid cancer. 15/109 patients had detectable BRAF (V600E) ctDNA in their preoperative samples—all of them having classical PTC. Higher T-stage and extrathyroidal extension in PTC were associated with positive BRAF (V600E) ctDNA (p < 0.05). Eighty-eight pairs of preoperative and postoperative plasma samples were collected and analyzed. Of these eighty-eight paired samples, a total of 13/88 (14.8%) patients had detectable BRAF (V600E) ctDNA in their preoperative samples—all of them having classical PTC. 12 of these 13 patients had no detectable BRAF (V600E) postoperatively, while one remaining patient had a significant decline in his levels (p < 0.05). Conclusion BRAF (V600E) circulating thyroid tumor DNA can be detected in plasma and is correlated with a final diagnosis of the classical variant of PTC. Given that a postoperative drop in BRAF (V600E) ctDNA levels was observed in all cases suggests its utility as a tumor marker.