Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure to Acute Stress Induces Brain Interleukin-1β Protein in the Rat

Peripheral immune stimulation such as that provided by lipopolysaccharide (LPS) has been reported to increase brain levels of IL-1β mRNA, immunoreactivity, and bioactivity. Stressors produce many of the same neural and endocrine responses as those that follow LPS, but the impact of stressors on brain interleukin-1β (IL-1β) has not been systematically explored. An ELISA designed to detect IL-1β was used to measure levels of IL-1β protein in rat brain. Brain IL-1β was explored after exposure to inescapable shock (IS; 100 1.6 mA tail shocks for 5 sec each) and LPS (1 mg/kg) as a positive control. Rats were killed either immediately or 2, 7, 24, or 48 hr after IS. Brains were dissected into hypothalamus, hippocampus, cerebellum, posterior cortex, and nucleus tractus solitarius regions. LPS produced widespread increases in brain IL-1β, but IS did not. Adrenal glucocorticoids are known to suppress IL-1β production in both the periphery and brain. Thus, it was possible that the stressor did provide stimulus input to the brain IL-1β system(s), but that the production of IL-1β protein was suppressed by the rapid and prolonged high levels of glucocorticoids produced by IS. To test this possibility rats were adrenalectomized or given sham surgery, with half of the adrenalectomized rats receiving corticosterone replacement to maintain basal corticosterone levels. IS produced large increases in brain IL-1β protein in the adrenalectomized subjects 2 hr after stress, whether basal corticosterone levels had been maintained. Thus elimination of the stress-induced rise in corticosterone unmasked a robust and widespread increase in brain IL-1β.
The Importance of Awareness for Team Cognition in Distributed Collaboration

Although the phrase team cognition suggests something that happens inside people’s heads, teams are very much situated in the real world, and there are a number of things that have to happen out in that world for teams to be able to think and work together. This is not just spoken communication. Depending on the circumstances, effective team cognition includes things like using environmental cues to establish a common ground of understanding, seeing who is around and what they are doing, monitoring the state of artefacts in a shared work setting, noticing other people’s gestures and what they are referring to, and so on (Clark, 1996; Hutchins, 1996).
On lattice reduction for polynomial matrices

A simple algorithm for lattice reduction of polynomial matrices is described and analysed. The algorithm is adapted and applied to various tasks, including rank profile and determinant computation, transformation to Hermite and Popov canonical form, polynomial linear system solving and short vector computation. © 2003 Elsevier Science Ltd. All rights reserved.
Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer.

PURPOSE Panitumumab, a fully human antibody against the epidermal growth factor receptor (EGFR), has activity in a subset of patients with metastatic colorectal cancer (mCRC). Although activating mutations in KRAS, a small G-protein downstream of EGFR, correlate with poor response to anti-EGFR antibodies in mCRC, their role as a selection marker has not been established in randomized trials. PATIENTS AND METHODS KRAS mutations were detected using polymerase chain reaction on DNA from tumor sections collected in a phase III mCRC trial comparing panitumumab monotherapy to best supportive care (BSC). We tested whether the effect of panitumumab on progression-free survival (PFS) differed by KRAS status. RESULTS KRAS status was ascertained in 427 (92%) of 463 patients (208 panitumumab, 219 BSC). KRAS mutations were found in 43% of patients. The treatment effect on PFS in the wild-type (WT) KRAS group (hazard ratio [HR], 0.45; 95% CI: 0.34 to 0.59) was significantly greater (P < .0001) than in the mutant group (HR, 0.99; 95% CI, 0.73 to 1.36). Median PFS in the WT KRAS group was 12.3 weeks for panitumumab and 7.3 weeks for BSC. Response rates to panitumumab were 17% and 0%, for the WT and mutant groups, respectively. WT KRAS patients had longer overall survival (HR, 0.67; 95% CI, 0.55 to 0.82; treatment arms combined). Consistent with longer exposure, more grade III treatment-related toxicities occurred in the WT KRAS group. No significant differences in toxicity were observed between the WT KRAS group and the overall population. CONCLUSION Panitumumab monotherapy efficacy in mCRC is confined to patients with WT KRAS tumors. KRAS status should be considered in selecting patients with mCRC as candidates for panitumumab monotherapy.
Laplacian of Gaussian Md - Morphological Dilation Svm - Support Vector Machine Cc - Connected Component Ccl - Connected Component Labeling Bb - Bounding Boxes

The main purpose of this paper is to extract objects from Images and recognizing text characters. Our approach uses Morphological operation for text localization, and, Support Vector Machine for character recognition, along with some preprocessing and post processing steps. This algorithm is tested with natural Images as well as Images taken from ICDAR 2003 Robust Reading Competition dataset. The proposed work is evaluated using ICDAR 2003 competition metrics specification and the performance is compared with the existing methods developed in the previous three years. I. Introduction Extracting and recognizing text in Images has become a potential application in many fields like Robotics, Intelligent transport systems etc. In the field of Multimedia, information extraction and recognition has become a necessity now [for example capturing number plate information through a video camera and extracting license number in traffic signals]. The three main modules of such applications are [a]. Object Localization [b]. Object extraction and [c]. Text recognition. In this paper, we have presented a robust approach for text extraction and recognition in images. First, the input image is filtered by the Median filter to remove any noises. Then edges are detected using LOG edge detector. Then the morphological dilation operation is applied for object localization. All the connected Components are then extracted and all non-text character components are discarded by a two step process. Features are then extracted from the extracted Components. These features form the feature vector for SVM. These features are tested with SVM for recognizing individual characters. Then, all recognized characters are merged to form text lines.
Gait Analysis in Cerebral Palsy

A recent advance in the study of walking patterns has been the development of the gait laboratory, where the use of a videotape, EMG and force plates allows the study of the origins of a child's abnormal gait. This book describes the use of gait analysis in the treatment of cerebral palsy.
Detect and Notify Abnormal SMTP Traffic and Email Spam over Aggregate Network

As all the traffic between the public Internet and the customer’s desktop must be interconnected through ISP’s access network, this work thus makes use of the transportation traffic log gathered from backbone router to develop SMTP flooding detection system (SFDS), so that the most spam could be detected and stopped at the original fan-out network. The system has been deployed over a TANet (Taiwan Academic Network) backbone node for assisting network users grasping the abnormal SMTP sources with suddenly increase email requests. The result indicates that there is a high proportion of the notified spam could be detected in advance.
DNA: An SDN framework for distributed network analytics

Analytics of network telemetry data helps address many important operational problems. Traditional Big Data approaches run into limitations even as they push scale boundaries for processing data further. One reason for this is the fact that in many cases, the bottleneck for analytics is not analytics processing itself but the generation and export of the data on which analytics depends. The amount of data that can be reasonably collected from the network runs into inherent limitations due to bandwidth and processing constraints in the network itself. In addition, management tasks related to determining and configuring which data to generate lead to significant deployment challenges. In order to address these issues, we propose a novel distributed solution to network analytics. Analytics processing is performed at the source of the data by specialized agents embedded within network devices, which also dynamically set up and reconfigure telemetry data sources as required by an analytics task. An SDN controller application orchestrates network analytics tasks across the network to allow users to interact with the network as a whole instead of individual devices one at a time. The solution has been implemented as a proof-of-concept, called DNA (Distributed Network Analytics)1.
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks

In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.
Predictable Processors for Mixed-Criticality Systems and Precision-Timed I/O

In cyber-physical systems, where embedded computation interacts with physical processes, correctness depends on timing behavior, not just functionality. For high confidence in the timing behavior of real-time embedded software, the underlying hardware must support predictable and isolated task execution. These properties are sacrificed in many conventional processors that use frequent interrupts and hardware prediction mechanisms to improve average-case performance. Mixed-criticality systems—where tasks with different levels of safety criticality are integrated on a single hardware platform to share resources and reduce costs—facilitate complex functionality but further complicate design and verification. The challenge is designing processor architectures that provide high confidence in software functionality and timing behavior without sacrificing processor throughput.This dissertation presents architectural techniques for task-level trade-offs between predictability, hardware-based isolation, and overall instruction throughput—facilitating the verification of safety-critical tasks and allowing software to meet precise timing constraints. Our processor design, named FlexPRET, uses fine-grained multithreading with flexible hard- ware thread scheduling and integrated timers to evaluate these techniques. With no restrictions on thread interleaving, the hardware thread scheduler executes hard real-time threads (HRTTs) at specific cycles for isolated and predictable behavior and allows soft real-time threads (SRTTs) to use both specific and spare cycles for efficient operation.A configurable version of FlexPRET is implemented in simulation and on FPGA using Chisel, a hardware construction language that generates both C++ and Verilog code. For a given program path at a constant thread scheduling frequency, the latency of every instruction is constant and known, and the precision of input/output (I/O) instruction timing is bounded. The comparison of FlexPRET with two baseline processors provides the FPGA resource costs of FlexPRET’s microarchitectural features. Using two example applications, we demonstrate a mixed-criticality deployment methodology that provides hardware-based isolation to critical tasks and improves overall instruction throughput by using spare cycles and software scheduling for less critical tasks. FlexPRET can also use software to perform multiple independent I/O operations instead of requiring hardware peripherals.
Automated Design of Complex Dynamic Systems

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems.
Long noncoding RNA PVT1 inhibits renal cancer cell apoptosis by up-regulating Mcl-1

Long non-coding RNA plasmacytoma variant translocation 1 (PVT1) is up-regulated in various human cancers, and our results indicated that PVT1 was up-regulated in clear cell renal cell carcinoma tissues. The Cancer Genome Atlas cohort analysis revealed that in clear cell renal cell carcinoma, higher PVT1 expression correlated with advanced TNM stage, histological grade, and poor survival. PVT1 knockdown promoted apoptosis, inhibited renal cancer cell proliferation, decreased Mcl-1, and increased cleaved caspase-3 and cleaved PARP. PVT1 increased Mcl-1 mRNA levels in renal cancer cells by promoting mRNA stability without influencing its transcription. in vitro, the enhanced apoptosis arising from PVT1 suppression was attenuated by overexpressing Mcl-1. In addition, in vivo experiments showed that PVT1 knockdown repressed xenograft tumor growth, while Mcl-1 overexpression partially rescued xenograft tumor growth. These results indicate the PVT1/Mcl-1 pathway inhibits renal cancer cell apoptosis in vitro and in vivo. PVT1 may thus serve as a novel biomarker, and the PVT1/Mcl-1 pathway may be a useful therapeutic target for clear cell renal cell carcinoma.
‘They wasn't makin’ my kinda music’: a hip-hop musician's perspective on school, schooling, and school music

ABSTRACT This article focuses on a hip-hop perspective of school, schooling, and school music. The study involves applications of ethnographic (including autoethnographic) techniques within the framework of a holistic multiple case study. One case is an adult amateur hip-hop musician named Terrence (pseudonym), and the other is myself (a traditionally trained American music educator and scholar). My history as a professional educator and scholar juxtaposed with Terrence's perspective as a high school dropout offers a valuable contrast of beliefs, values, and assumptions about school and education. Exploring Terrence's experiences and perceptions helps us to understand him as a musician and learner and also allows for a critical investigation of my own perspectives. Recognising my assumption-laden perspectives offers meaningful layers of nuance towards complicating the relationships between schools, those who school, and those who are schooled. Implications for teachers and researchers include expanding considerations of vernacular music in school settings and encouraging critical self-reflection.
The experience of perimenopausal distress: examining the role of anxiety and anxiety sensitivity

Abstract Objective: The hormonal changes that occur in perimenopause can result in distress for a significant proportion of women years before reaching menopause. Previous studies have suggested that various biopsychosocial characteristics and personality traits contribute to more intense reactions to these hormonal changes. In the present study, we examined the contribution of trait anxiety and anxiety sensitivity in predicting the experience of perimenopausal distress in pre- and early perimenopausal women while controlling for some menstrual experiences. Method: A sample of 660 women aged 35–52 years was selected from a broader online survey of biopsychosocial changes in middle-aged women that considered age, menstrual cycle pattern, physical and mental health, and childbearing experience. Three hierarchical multiple regression analyses were conducted for different age subgroups: women aged 35–40, women aged 41–45 and women aged 46–52. Results: Results indicated that anxiety and anxiety sensitivity, along with a number of features of the menstrual experience, explained 56–66% of the perimenopausal distress variance. Different personality trait predictors were found to be important in different age subgroups. In the youngest and middle subgroups (45 years and younger), trait anxiety was found to be more significant, whereas anxiety sensitivity was found to be more important in explaining perimenopausal distress experienced by women older than 40. Conclusion: Anxiety sensitivity (dimension of psychological concerns) might be an important vulnerability factor in the experience of perimenopausal distress among women closer to menopause, whereas the predisposition to an anxiety response might contribute to perimenopausal distress near the beginning of reproductive hormonal changes. Implications for better understanding the development of perimenopausal distress and psychological intervention are discussed.
The human translation initiation multi-factor complex promotes methionyl-tRNAi binding to the 40S ribosomal subunit

The delivery of Met-tRNAi to the 40S ribosomal subunit is thought to occur by way of a ternary complex (TC) comprising eIF2, GTP and Met-tRNAi. We have generated from purified human proteins a stable multifactor complex (MFC) comprising eIF1, eIF2, eIF3 and eIF5, similar to the MFC reported in yeast and plants. A human MFC free of the ribosome also is detected in HeLa cells and rabbit reticulocytes, indicating that it exists in vivo. In vitro, the MFC-GTP binds Met-tRNAi and delivers the tRNA to the ribosome at the same rate as the TC. However, MFC-GDP shows a greatly reduced affinity to Met-tRNAi compared to that for eIF2-GDP, suggesting that MFC components may play a role in the release of eIF2-GDP from the ribosome following AUG recognition. Since an MFC–Met-tRNAi complex is detected in cell lysates, it may be responsible for Met-tRNAi–40S ribosome binding in vivo, possibly together with the TC. However, the MFC protein components also bind individually to 40S ribosomes, creating the possibility that Met-tRNAi might bind directly to such 40S-factor complexes. Thus, three distinct pathways for Met-tRNAi delivery to the 40S ribosomal subunit are identified, but which one predominates in vivo remains to be elucidated.
Resistive Threshold Logic

We report a resistance-based threshold logic family useful for mimicking brain-like large variable logic functions in VLSI. A universal boolean logic cell based on an analog resistive divider and threshold logic circuit is presented. The resistive divider is implemented using memristors, and provides output voltage as a summation of weighted product of input voltages. The output of the resistive divider is converted into a binary value by a threshold operation implemented by CMOS inverter and/or Opamp. A universal cell structure is presented to decrease the overall implementation complexity and number of components. When the number of input variables becomes very high, the proposed cell offers advantages of smaller area and design simplicity in comparison with CMOS-based logic circuits.
Mapping Gay L.A.: The Intersection of Place and Politics

List of Maps Foreword Robert Dawidoff Acknowledgments 1. Locating the Politics of Difference 2. Inclusion and Exclusion in West Hollywood 3. Beyond Gentrification: Social Services and the Redevelopment of Hollywood Boulevard 4. Separate Space and Separatism: Lesbian Culture and Community 5. Out of the Bars and into the Streets: Direct Action from Liberation to Transformation 6. The Remapped City Notes Index
Security sandbox model for modern web environment

We require a very good technical knowledge to create automated tests to exploit the browser vulnerabilities. It is usually a combination of technical abilities and set of specific tools. Security concerns is of prime importance when it comes to web browsers. Attacks during surfing, executing any downloaded file and while transmission are very frequent these days and hence all browsers need to be hardened to ensure security. Sandbox is one of the feature where we can prevent malicious applications to run directly on hardware. It is an environment where new or non-trusted applications are executed. Many leading web browsers are trying their level best to implement sandbox. In this paper, we have mentioned the basic necessity of sandbox, current implementations in different web browsers and also present a self-proposed approach.
Use of Gene Ontology semantic information in protein interaction data visualization

The Gene Ontology project is an effort to structure knowledge on biological products and processes by adding semantic information to them. This is done in a systematic way so that this additional information can be automatically processed. In this contribution a protein-protein interaction visualization algorithm is proposed, which combines protein interaction data with Gene Ontology semantic information. The information is integrated using a semantic distance measure defined in ontologies or taxonomies. Multidimensional scaling is applied to this measure and the output complements protein interaction data in building an interaction visualization map.