Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved cell metabolism prolongs photoreceptor survival upon retinal-pigmented epithelium loss in the sodium iodate induced model of geographic atrophy

Age-related macular degeneration (AMD) is characterized by malfunction and loss of retinal-pigmented epithelium (RPE) cells. Because the RPE transfers nutrients from the choriocapillaris to photoreceptor (PR), PRs are affected as well. Geographic atrophy (GA) is an advanced form of AMD characterized by severe vision impairment due to RPE loss over large areas. Currently there is no treatment to delay the degeneration of nutrient deprived PRs once RPE cells die. Here we show that cell-autonomous activation of the key regulator of cell metabolism, the kinase mammalian target of rapamycin complex 1 (mTORC1), delays PR death in the sodium iodate induced model of RPE atrophy. Consistent with this finding loss of mTORC1 in cones accelerates cone death as cones fail to balance demand with supply. Interestingly, promoting rod survival does not promote cone survival in this model of RPE atrophy as both, rods and cones suffer from a sick and dying RPE. The findings suggest that activation of metabolic genes downstream of mTORC1 can serve as a strategy to prolong PR survival when RPE cells malfunction or die.
An Eavesdropping Model for Securing Communications over Wireless Broadcast Networks

Wireless broadcast networks constitute one class of basic and important wireless networks, in which a source node simultaneously transmits a number of information messages to different destinations. However, broadcast communications make use of the open nature of the wireless medium, which presents a great challenge to achieve the secure communication for individual users. This is because information for all users is contained in one transmitted signal, and hence information destined for one user may be obtained by non- intended users unless special coding is used. In this paper, we study a broadcast network, in which a source node transmits confidential message flows to the user nodes, and each message flow is intended to be decoded accurately by one node while being kept secret from all other nodes. Nodes are thus considered to be eavesdroppers with regard to all other messages but their own. Here we consider two eavesdropping models. The first model is referred to as a collaborative eavesdropping model, in which the eavesdroppers can exchange their outputs to interpret the message. The second model is referred to as a non- collaborative eavesdropping model, in which eavesdroppers do not exchange their outputs.
Mep72, a Metzincin Protease That Is Preferentially Secreted by Biofilms of Pseudomonas aeruginosa

ABSTRACT In this work, we compared the profile of proteins secreted by planktonic and biofilm cultures of Pseudomonas aeruginosa using two-dimensional difference gel electrophoresis (2D-DiGE). This revealed that a novel metzincin protease, Mep72, was secreted during biofilm growth. Subsequent Western blotting and reverse transcription-PCR (RT-PCR) analyses demonstrated that Mep72 was expressed only during biofilm growth. Mep72 has a tridomain structure comprised of a metzincin protease-like domain and two tandem carbohydrate-binding domains. Unlike the only other metzincin (alkaline protease; AprA) in P. aeruginosa, Mep72 is secreted through the type II pathway and undergoes processing during export. During this processing, the metzincin domain is liberated from the carbohydrate-binding domains. This processing may be self-catalyzed, since purified Mep72 autodegraded in vitro. This autodegradation was retarded in the presence of alginate (an extracellular matrix component of many P. aeruginosa biofilms). The expression of full-length mep72 in Escherichia coli was toxic. However, this toxicity could be alleviated by coexpression of mep72 with the adjacent gene, bamI. Mep72 and BamI were found to form a protein-protein complex in vitro. 2D-DiGE revealed that the electrophoretic mobility of several discrete protein spots was altered in the biofilm secretome of an mep72 mutant, including type III secretion proteins (PopD, PcrV, and ExoS) and a flagellum-associated protein (FliD). Mep72 was found to bind directly to ExoS and PcrV and to affect the processing of these proteins in the biofilm secretome. We conclude that Mep72 is a secreted biofilm-specific regulator that affects the processing of a very specific subset of virulence factors.
Working Paper Series Congressional Budget Office Does Government Debt Crowd out Investment? a Bayesian Dsge Approach Does Government Debt Crowd out Investment? a Bayesian Dsge Approach

CBO's Working and Technical Papers are preliminary and are circulated to stimulate discussion and critical comments. The papers are not subject to CBO's formal review and editing process. The analysis and conclusions expressed are those of the authors and should not be interested as those of the Congressional Budget Office. References in publications should be cleared with authors. Papers in this series can be obtained from www.cbo. gov/pubications. Abstract. We estimate the crowding-out effects of government debt for the U.S. economy using a New Keynesian model with a detailed fiscal specification. The estimation accounts for the interaction between monetary and fiscal policies. Whether private investment is crowded in or out in the short term depends on the fiscal or monetary shock that triggers debt expansion. Contrary to the conventional view of crowding out, no systematic relationship among debt, the real interest rate, and investment exists. At longer horizons, distortionary financing is important for the negative investment response to a debt expansion. We thank Eric Leeper for his advice and support throughout the project. Also, we thank views expressed in this paper are those of authors and should not be interpreted as those of the Congressional Budget Office.
Furin-, ADAM 10-, and γ-Secretase-Mediated Cleavage of a Receptor Tyrosine Phosphatase and Regulation of β-Catenin's Transcriptional Activity

ABSTRACT Several receptor protein tyrosine phosphatases (RPTPs) are cell adhesion molecules involved in homophilic interactions, suggesting that RPTP outside-in signaling is coupled to cell contact formation. However, little is known about the mechanisms by which cell density regulates RPTP function. We show that the MAM family prototype RPTPκ is cleaved by three proteases: furin, ADAM 10, and γ-secretase. Cell density promotes ADAM 10-mediated cleavage and shedding of RPTPκ. This is followed by γ-secretase-dependent intramembrane proteolysis of the remaining transmembrane part to release the phosphatase intracellular portion (PIC) from the membrane, thereby allowing its translocation to the nucleus. When cells were treated with leptomycin B, a nuclear export inhibitor, PIC accumulated in nuclear bodies. PIC is an active protein tyrosine phosphatase that binds to and dephosphorylates β-catenin, an RPTPκ substrate. The expression of RPTPκ suppresses β-catenin's transcriptional activity, whereas the expression of PIC increases it. Notably, this increase required the phosphatase activity of PIC. Thus, both isoforms have acquired opposing roles in the regulation of β-catenin signaling. We also found that RPTPμ, another MAM family member, undergoes γ-secretase-dependent processing. Our results identify intramembrane proteolysis as a regulatory switch in RPTPκ signaling and implicate PIC in the activation of β-catenin-mediated transcription.
Professional Development for a Middle School Mathematics-Infused Science Curriculum

ABSTRACT Contemporary research has suggested that for students to compete globally, pedagogy must shift to emphasize the infusion of mathematics into science instruction to strengthen understanding. Innovative professional development programs are necessary to achieve this goal, particularly with respect to teaching graphing skills in the context of science. Although there is literature to substantiate that students respond positively in terms of interest, there has been little evidence to support improved science performance when both subjects are integrated. This large-scale quasi-experimental study examined impacts of a mathematics-infused science curriculum on students’ science attitudes and achievement (N =1695). Data were collected from science assessments and attitudes surveys in mathematics-infused 8th grade science classrooms and compared to those from students in non-integrated settings. Data revealed positive student outcomes, including the ability to understand mathematics-infused scientific concepts (F(1, 1693) = 17.02, p < . 001), higher confidence in graphing, and greater agreement with the notion that mathematics is a valuable tool in learning science (F(1, 1693) = 33.76, p < . 001). Females outperformed males in science but there were no significant differences in achievement between students from high and low needs schools. Data from a single infused unit taught within one school district (n = 136) revealed similar science achievement findings with a large effect (F(1, 134) = 37.18, p < . 001), though gender was not a significant predictor. These research findings will benefit educators seeking innovative, integrated curricula that improve science and mathematics understanding, as well as researchers who inform policy reform efforts.
The design of dynamically reconfigurable datapath coprocessors

Increasing nonrecurring engineering and mask costs are making it harder to turn to hardwired application specific integrated circuit (ASIC) solutions for high-performance applications. The volume required to amortize these high costs has been increasing, making it increasingly expensive to afford ASIC solutions for medium-volume products. This has led to designers seeking programmable solutions of varying sorts using these so-called programmable platforms. These programmable platforms span a large range from bit-level programmable field programmable gate arrays to word-level programmable application-specific, and in some cases even general-purpose processors. The programmability comes with a power and performance overhead. Attempts to reduce this overhead typically involve making some core hardwired ASIC like logic blocks accessible to the programmable elements. This paper presents one such hybrid solution in this space---a relatively simple processor with a dynamically reconfigurable datapath acting as an accelerating coprocessor. This datapath consists of hardwired function units and reconfigurable interconnect. We present a methodology for the design of these solutions and illustrate it with two complete case studies: an MPEG2 coder, and a GSM coder, to show how significant speedups can be obtained using relatively little hardware. This work is part of the MESCAL project, which is geared towards developing design environments for the development of application-specific platforms.
RDoC, DSM, and the reflex physiology of fear: A biodimensional analysis of the anxiety disorders spectrum.

Evidence is presented supporting a dimension of defensive reactivity that varies across the anxiety disorder spectrum and is defined by physiological responses during threat-imagery challenges that covary with objective measures of psychopathology. Previous imagery studies of anxiety disorders are reviewed, highlighting that, regardless of contemporary diagnostic convention, reliable psychophysiological patterns emerge for patients diagnosed with circumscribed fear compared to those diagnosed with pervasive anxious-misery disorders. Based on the heuristic outlined by the Research Domain Criteria (RDoC) initiative, an exploratory transdiagnostic analysis is presented, based on a sample of 425 treatment-seeking patients from across the spectrum of DSM-IV anxiety diagnoses. Using a composite index of startle reflex and heart rate reactivity during idiographic fear imagery for each patient, a defensive dimension was defined by ranking patients from most defensively reactive to least reactive and then creating five groups of equivalent size (quintile; N = 85). Subsequent analyses showed significant parallel trends of diminishing reactivity in both electrodermal and facial electromyographic reactions across this defensive dimension. Negative affectivity, defined by questionnaire and extent of functional interference, however, showed consistent, inverse trends with defensive reactivity-as reports of distress increased, defensive reactivity was increasingly attenuated. Notably, representatives of each principal diagnosis appeared in each quintile, underscoring the reality of pronounced within-diagnosis heterogeneity in defensive reactivity. In concluding, we describe our new RDoC research project, focusing on the assessment of brain circuit function as it determines hypo/hyperreactivity to challenge-somatic and autonomic-and may relate to patients' stress history and genetic inheritance.
Detection of Ascitic Feline Coronavirus RNA from Cats with Clinically Suspected Feline Infectious Peritonitis

ABSTRACT Ascitic feline coronavirus (FCoV) RNA was examined in 854 cats with suspected feline infectious peritonitis (FIP) by RT-PCR. The positivity was significantly higher in purebreds (62.2%) than in crossbreds (34.8%) (P<0.0001). Among purebreds, the positivities in the Norwegian forest cat (92.3%) and Scottish fold (77.6%) were significantly higher than the average of purebreds (P=0.0274 and 0.0251, respectively). The positivity was significantly higher in males (51.5%) than in females (35.7%) (P<0.0001), whereas no gender difference has generally been noted in FCoV antibody prevalence, indicating that FIP more frequently develops in males among FCoV-infected cats. Genotyping was performed for 377 gene-positive specimens. Type I (83.3%) was far more predominantly detected than type II (10.6%) (P<0.0001), similar to previous serological and genetic surveys.
An analytical derivation of the relation between idiosyncratic volatility and expected stock return

Modelling stock return generating process as a single factor model, we show analytically that the relation between idiosyncratic volatility measured as variance of the residuals and expected stock return in the cross-section may be represented by a parabola that opens to the left and has horizontal axis. This relation is uncovered for stocks of similar volatility and no abnormal return. The sensitivity of the derived relation when these restrictions are relaxed is discussed. Our findings, to a great extent, help uncover the shape of the non-linear inverse relation between idiosyncratic volatility and expected stock return observed in the cross-section in previous empirical studies.
Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data

Real-world sensors suffer from noise, blur, and other imperfections that make high-level computer vision tasks like scene segmentation, tracking, and scene understanding difficult. Making high-level computer vision networks robust is imperative for real-world applications like autonomous driving, robotics, and surveillance. We propose a novel end-to-end differentiable architecture for joint denoising, deblurring, and classification that makes classification robust to realistic noise and blur. The proposed architecture dramatically improves the accuracy of a classification network in low light and other challenging conditions, outperforming alternative approaches such as retraining the network on noisy and blurry images and preprocessing raw sensor inputs with conventional denoising and deblurring algorithms. The architecture learns denoising and deblurring pipelines optimized for classification whose outputs differ markedly from those of state-of-the-art denoising and deblurring methods, preserving fine detail at the cost of more noise and artifacts. Our results suggest that the best low-level image processing for computer vision is different from existing algorithms designed to produce visually pleasing images. The principles used to design the proposed architecture easily extend to other high-level computer vision tasks and image formation models, providing a general framework for integrating low-level and high-level image processing.
Improving pediatricians’ knowledge and skills in suicide prevention: Opportunities for social work

Primary care physicians are key gatekeepers for detecting suicidal intent. However, research indicates training gaps for these providers. Standardized screening for suicide risk in primary care can detect youth with suicidal ideation and prompt a referral to behavioral health care before a suicide attempt. What is needed in adolescent primary care is further training in utilizing standardized mental health screening and employing best practices for suicide prevention. In this study, qualitative research methods were used in surfacing community and medical perspectives regarding skills, knowledge, and values that are needed in pediatric medicine to adequately assess for depression and anxiety. Five focus groups were conducted with pediatric residents, adolescents, parents of adolescents who died by suicide, parents with adolescents in the mental health system, and community mental health professionals. Four themes were identified that illustrate what is needed in pediatric training to lower the risks for adolescent suicide: broken mental health system of care; improving doctor/patient/family communication; alleviating stigma; and early detection and treatment that addresses medications, substance abuse, and recovery resources. The goals of this grant funded study were to analyze the data from these focus groups and compare findings across groups to create modules for Saint Louis University pediatric resident training in suicide prevention. This research project can serve as a springboard for social workers to partner with medical educators in their communities to train primary care physicians for early detection of depression, anxiety, and substance abuse to lower adolescent suicide risks.
Hunting for Entailing Pairs in the Penn Discourse Treebank

Given the growing amount of resources developed in the NLP community, it is crucial to exploit as much as possible annotated data and tools across different research domains. Past works on discourse analysis have been conducted in parallel with research on semantic inference and, although the two fields of study are intertwined, there have been only few initiatives to put them into relation. Our work addresses the issue of interoperability by investigating the connection between implicit Restatement relations in the Penn Discourse Treebank (PDTB) and Textual Entailment. We compare the performance of two TE systems on the Restatement pairs and we argue that TE is a subclass of Restatement through a manual validation of the pairs. Furthermore, we observe that entailing pairs extracted from the PDTB add interesting and additional levels of complexity to TE, since inference relation relies less on lexical-syntactic variations, and more on reasoning.
The Meaning of Working for Young People: The Case of the Millennials

The meaning people attribute to working is a multidimensional construct, accounting for personal values, expectations, beliefs and attitudes towards work, which originates and develops because of life cycle experiences. The main aim of this paper is to contribute to the investigation of the ‘meaning of working’ by focusing on the causal relationships between the core components of the construct in a sample of young people – namely the Millennials – who have not yet entered the labour market. Participants, 466 high school and 357 university students, selected from different educational domains, were invited to fill in a questionnaire encompassing the central variables of the ‘meaning of working’ protocol (that is, work centrality, work goals, valued working outcomes and societal norms about work). Results showed a direct relationship between work centrality and valued working outcomes, and a partial mediation effect of work goals and societal norms about work, in the relationship with work centrality. The results raise several research questions which need to be answered by further investigation, both as regards the current evolution of the construct of the meaning of working and as regards its measurement. The results can also be useful in helping to plan tailor‐made vocational guidance programmes as well as organizational training and development interventions targeted on the specific features of this new workforce.
Impact of Vulnerability Disclosure and Patch Availability - An Empirical Analysis

Vulnerability disclosure is an area of public policy that has been subject to considerable debate, particularly between proponents of full and instant disclosure, and those of limited or no disclosure. This paper is an attempt to empirically test the impact of vulnerability information disclosure and availability of patches on attackers’ tendency to exploit vulnerabilities on one hand and on the vendors’ tendency to release patches on the other. Our results suggest that while vendors are quick to respond to instant disclosure, vulnerability disclosure also increases the frequency of attacks. However, the frequency of attacks decreases over time. We also find that open source vendors patch more quickly than closed source vendors and that large vendors are more responsive.
SIMPLE-OWL : a Generative Lexicon Ontology for NLP and the Semantic Web

This research deals with the modelling of a Generative Lexicon based ontology to be used in the Semantic Web and Natural Language Processing semantic tasks. This ontology is imported from a existing computational Lexical Resource and is converted to the W3C standard Web Ontology Language. This presents some challenges, as for example the multidimensionality of the original ontology, which are covered in the current paper. The result of this research is an OWL compliant semantically rich and linguistically-based ontology, thus useful to the automatic processing of text within the Semantic Web paradigm.
A time-frequency distribution based on the empirical mode decomposition

This paper proposes a new time-frequency distribution (TFD) based on the empirical mode decomposition (EMD). The TFD, referred to as the EMD-TFD, is developed using a multiple view approach formulated in the ambiguity domain. The individual intrinsic mode functions (IMFs) of the EMD are used to construct each view in the ambiguity domain and an adaptive filter is centered around the signal energy. This filter utilises the fact that the IMFs correspond closely to the definition of signal components in the time-frequency domain and a multiple view approach will remove the intercomponent cross-terms. The comparison of the EMD-TFD to several other TFDs using two correlation measures shows improved performance.
Self-Expressive Decompositions for Matrix Approximation and Clustering

Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vectors to represent the full collection, has been developed as an alternative to learning. Here, we introduce a scalable method for computing sparse SElf-Expressive Decompositions (SEED). SEED is a greedy method that constructs a basis by sequentially selecting incoherent vectors from the dataset. After forming a basis from a subset of vectors in the dataset, SEED then computes a sparse representation of the dataset with respect to this basis. We develop sufficient conditions under which SEED exactly represents low rank matrices and vectors sampled from a unions of independent subspaces. We show how SEED can be used in applications ranging from matrix approximation and denoising to clustering, and apply it to numerous real-world datasets. Our results demonstrate that SEED is an attractive low-complexity alternative to other sparse matrix factorization approaches such as sparse PCA and self-expressive methods for clustering.