Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibiting CD146 by its Monoclonal Antibody AA98 Improves Radiosensitivity of Cervical Cancer Cells

Background Cervical cancer is one of the major causes of cancer death of females worldwide. Radiotherapy is considered effective for cervical cancer treatment, but the low radiosensitivity found in some cases severely affects therapeutic outcomes. This study aimed to reveal the role of CD146, an important adhesion molecule facilitating tumor angiogenesis, in regulating radiosensitivity of cervical cancer cells. Material/Methods CD146 protein expression was compared in normal cells, cervical cancer cells with lower radiosensitivity, and cervical cancer cells with higher sensitivity from cervical squamous cell carcinoma patients. Anti-CD146 monoclonal antibody AA98 was used to inhibit CD146 in human cervical cancer SiHa cells with relatively low radiosensitivity, and then the cell survival and apoptosis changes after radiation were detected by colony formation assay and flow cytometry. Results CD146 protein was significantly up-regulated in cervical cancer cells (P<0.001), especially in cancer cells with lower radiosensitivity. The SiHa cells treated with AA98 showed more obvious inhibition in cell survival (P<0.05) and promotion in cell apoptosis (P<0.01) after radiation, compared to the untreated cells. More dramatic changes in apoptotic factors Caspase 3 and Bcl-XL were also detected in AA98-treated cells. Conclusions These results indicate that inhibiting CD146 improves the effect of radiation in suppressing SiHa cells. This study shows the potential of CD146 as a target for increasing radiosensitivity of cervical cancer cells, which might allow improvement in treatment outcome in cervical cancer. Further studies are necessary for understanding the detailed mechanism of CD146 in regulating radiosensitivity.
‘Looking back, i wouldn’t join up again’: the lived experiences of police officers as victims of bias and prejudice perpetrated by fellow staff within an english police force

ABSTRACT Women, ethnic minority and LGB police officers often experience prejudice, disadvantage and exclusion within police forces because of their perceived ‘otherness’ in a predominantly white, heterosexual, male organisation. In the context of an increasingly diverse service, the paper argues that the concept of intersectionality is important in order to understand the experiences of police officers who encounter bias and prejudice because of their multiple, intersecting identities. Drawing on data from qualitative interviews with 20 individuals based in an English police force, the paper examines their occupational experiences of bias, discrimination and exclusion perpetrated by their colleagues and supervisors. Utilising the ‘exit, voice and loyalty’ model, the paper analyses how police officers are affected by, and respond to these experiences. Taken together, these arguments lay the foundation for future work to further understand the experiences of police officers as victims of bias and prejudice due to their multiple, intersecting identities.
Discovering Knowledge from Conference Web Pages

This paper proposes an approach to discover knowledge from conference web pages and organize such knowledge as ontologies. Firstly, conference web pages are segmented into blocks by analyzing visual features and the DOM structure. Then we use Bayes Network to classify the blocks into predefined categories. Finally, ontologies are generated from the classified text blocks. The experimental results on real-world datasets show that the proposed method is effective and efficient for discovering ontology concepts and hierarchies from conference web pages.
Arginine-Vasopressin Receptor Blocker Conivaptan Reduces Brain Edema and Blood-Brain Barrier Disruption after Experimental Stroke in Mice

Background Stroke is a major cause of morbidity and mortality. Stroke is complicated by brain edema and blood-brain barrier (BBB) disruption, and is often accompanied by increased release of arginine-vasopressin (AVP). AVP acts through V1a and V2 receptors to trigger hyponatremia, vasospasm, and platelet aggregation which can exacerbate brain edema. The AVP receptor blockers conivaptan (V1a and V2) and tolvaptan (V2) are used to correct hyponatremia, but their effect on post-ischemic brain edema and BBB disruption remains to be elucidated. Therefore, we conducted this study to investigate if these drugs can prevent brain edema and BBB disruption in mice after stroke. Methods Experimental mice underwent the filament model of middle cerebral artery occlusion (MCAO) with reperfusion. Mice were treated with conivaptan, tolvaptan, or vehicle. Treatments were initiated immediately at reperfusion and administered IV (conivaptan) or orally (tolvaptan) for 48 hours. Physiological variables, neurological deficit scores (NDS), plasma and urine sodium and osmolality were recorded. Brain water content (BWC) and Evans Blue (EB) extravasation index were evaluated at the end point. Results Both conivaptan and tolvaptan produced aquaresis as indicated by changes in plasma and urine sodium levels. However plasma and urine osmolality was changed only by conivaptan. Unlike tolvaptan, conivaptan improved NDS and reduced BWC in the ipsilateral hemisphere: from 81.66 ± 0.43% (vehicle) to 78.28 ± 0.48% (conivaptan, 0.2 mg, p < 0.05 vs vehicle). Conivaptan also attenuated the EB extravasation from 1.22 ± 0.08 (vehicle) to 1.01 ± 0.02 (conivaptan, 0.2 mg, p < 0.05). Conclusion Continuous IV infusion with conivaptan for 48 hours after experimental stroke reduces brain edema, and BBB disruption. Conivaptan but not tolvaptan may potentially be used in patients to prevent brain edema after stroke.
Development of an interprofessional program for cardiovascular prevention in primary care: A participatory research approach

Background: The chronic care model provides a framework for improving the management of chronic diseases. Participatory research could be useful in developing a chronic care model–based program of interventions, but no one has as yet offered a description of precisely how to apply the approach. Objectives: An innovative, structured, multi-step participatory process was applied to select and develop (1) chronic care model–based interventions program to improve cardiovascular disease prevention that can be adapted to a particular regional context and (2) a set of indicators to monitor its implementation. Methods: Primary care clinicians (n = 16), administrative staff (n = 2), patients and family members (n = 4), decision makers (n = 5), researchers, and a research coordinator (n = 7) took part in the process. Additional primary care actors (n = 26) validated the program. Results: The program targets multimorbid patients at high or moderate risk of cardiovascular disease with uncontrolled hypertension, dyslipidemia or diabetes. It comprises interprofessional follow-up coordinated by case-management nurses, in which motivated patients are referred in a timely fashion to appropriate clinical and community resources. The program is supported by clinical tools and includes training in motivational interviewing. A set of 89 process and clinical indicators were defined. Conclusion: Through a participatory process, a contextualized interventions program to optimize cardiovascular disease prevention and a set of quality indicators to monitor its implementation were developed. Similar approach might be used to develop other health programs in primary care if program developers are open to building on community strengths and priorities.
Measurement and 3D simulations of substrate noise isolation and resistance for mixed signal applications

In this work an enhanced approach to 3D process modeling using mixed element meshes followed by tetrahedralization and small signal device simulation by Fielday is presented. This methodology is then used to analyze and predict the effects of various substrate isolation schemes on substrate noise coupling. Experimental results are used to calibrate and verify the simulation results. Our proposed various substrate isolation test structure results reveal that layout guidelines and isolation parameter analysis are useful in signal isolation optimization.
Position estimation from range measurements using adaptive networks

In this paper we introduce a new multilateration-based method for source localization. Sensors with known positions collect noisy signals whose strength depends on the relative position between the sensors and the source. A nonlinear system of equations is obtained which is then recast into a linearized least squares (LS) problem, which resembles a multilateration scenario. From the LS problem a low-complexity Adaptive Network (AN) solution is proposed via a distributed Diffusion Least Mean Square (LMS) implementation. Two well known signal strenght models are reformulated to fit the AN framework. The resulting distributed solution inherits the robustness, low complexity and intrinsic tracking ability of ANs. Simulations show the effectiveness of the new method in noisy scenarios when localizing stationary sources and when tracking moving targets, even for a small number of sensors.
A Hardware/Software Co-design vs. Hardware Implementation of the Modular Exponentiation Using the Sliding-Window Method with Constant-Length Partitioning

Modular exponentiation is a basic operation in cryptosystems. Generally, the performance of this operation has a tremendous impact on the efficiency of the whole application. The efficiency of the modular exponentiation, in turn, depends mainly on that of modular multiplications as the former is somehow a repetition of the latter. One of the methods that computes the modular power is the sliding-window method, which pre-processes the exponent into zero and non-zero partitions. Zero partitions allow for a reduction of the number of modular multiplications required in the exponentiation process. In this paper, we devise a novel system-on-chip (SoC) implementation for computing modular exponentiation using the sliding-window method. We also propose a hardware-only implementation for that operation. The partitioning strategy used in both approaches allows constant-length non-zero partitions, which increases the average number of zero partitions and so decreases that of non-zero partitions. The partitioning strategy allows variable-length zero partitions. The hardware/software co-design implements the modular multiplication on hardware and the rest of the system in software. We provide a useful comparison of the SoC-based implementation against hardware-only implementation. Both of the proposed implementations can be used in any industrial embedded system that needs to secure the handled information.
Information on ancestry from genetic markers

It is possible to estimate the proportionate contributions of ancestral populations to admixed individuals or populations using genetic markers, but different loci and alleles vary considerably in the amount of information that they provide. Conventionally, the allele frequency difference between parental populations (δ) has been used as the criterion to select informative markers. However, it is unclear how to use δ for multiallelic loci, or populations formed by the mixture of more than two groups. Moreover, several other factors, including the actual ancestral proportions and the relative genetic diversities of the parental populations, affect the information provided by genetic markers. We demonstrate here that using δ as the sole criterion for marker selection is inadequate, and we propose, instead, to use Fisher's information, which is the inverse of the variance of the estimated ancestral contributions. This measure is superior because it is directly related to the precision of ancestry estimates. Although δ is related to Fisher's information, the relationship is neither linear nor simple, and the information can vary widely for markers with identical δs. Fortunately, Fisher's information is easily computed and formally extends to the situation of multiple alleles and/or parental populations. We examined the distribution of information for SNP and microsatellite loci available in the public domain for a variety of model admixed populations. The information, on average, is higher for microsatellite loci, but exceptional SNPs exceed the best microsatellites. Despite the large number of genetic markers that have been identified for admixture analysis, it appears that information for estimating admixture proportions is limited, and estimates will typically have wide confidence intervals. © 2004 Wiley‐Liss,Inc.
Intrusion Detection System Based on KNN-MARS

The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. In this paper, we propose a hybrid of KNN and MARS which deals naturally with the multi-class setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local MARS that preserves the distance function on the collection of neighbors. A wide variety of distance functions are used and our experiments show performance on a number of benchmark data sets for IDS classification and object recognition. It draws a conclusion that KNN-MARS is a good method for multi-class setting.
Relational graph clustering based on spectral coefficient angle

This paper introduces a relational graph representation method using the angle between spectral coefficient vectors. A relational graph clustering system builds on this presentation method. The system adopts fuzzy C-mean (FCM) as clustering algorithm. FCM exerts on the pattern space which embedded by locality preserving projections (LPP). The pattern space obtains from Laplacian matrix constructed by the corner points oriented graph from image sequence. After matrix decomposition at hand, the angle between spectral coefficients vectors as spectral features are computed through eigen value and eigen vectors of it. These features can describe the distribution and relationship of all graph nodes. Experiment shows that the spectral features of the angle between spectral coefficient vectors of Laplacian graph represent image sequence correctly and graph clustering in the feature pattern space is valid.
Audio quality assessment in packet networks: an "inter-subjective" neural network model

Transmitting digital audio signals in real time over packet switched networks (e.g. the Internet) has set forth the need for developing signal processing algorithms that objectively evaluate audio quality. So far, the best way to assess audio quality are subjective listening tests, the most commonly used being the mean opinion score (MOS) recommended by the International Telecommunication Union (ITU). The goal of this paper is to show how artificial neural networks (ANNs) can be used to mimic the way human subjects estimate the quality of audio signals when distorted by changes in several parameters that affect the transmitted audio quality. To validate the approach, we carried out an MOS experiment for speech signals distorted by different values of IP-network parameters (e.g. loss rate, loss distribution, packetization interval, etc.), and changes in the encoding algorithm used to compress the original signal. Our results allow us to show that ANNs can capture the nonlinear mapping, between certain characteristics of audio signals and a subjective five points quality scale, "built" by a group of human subjects when participating in an MOS experiment, creating, in this way, an "inter-subjective" neural network (INN) model that might effectively "evaluate", in real time, the audio quality in packet switched networks.
The distribution of lightning strokes to towers and along the span of shielded and unshielded power lines

A knowledge of the distribution of lightning strokes to towers and along the span of shielded power lines is needed to refine the outage rate calculation method. Such information is also needed for unshielded line because the outage mechanism differs depending on whether the stroke terminates on a tower or on a live wire. The authors provide the needed information based on a 3-D representation of the line and on a validated revised electrogeometric model. The effects of height, span and width of tower are studied and the span is found to be the most important governing parameter. Unshielded lines are found to have similar characteristics except that the tower collects a larger percentage of the total strokes and its shielding influence on adjacent wire segments is more pronounced. The effects of the form of striking distance equation and the coefficient of striking distance to the tower are also investigated.
Twitter Data Mining for Events Classification and Analysis

The increasing popularity of the micro-blogging sites like Twitter, which facilitates users to exchange short messages (aka tweets) is an impetus for data analytics tasks for varied purposes, ranging from business intelligence to nation security. Twitter is being used by a large number of users for events update and sentiment expression. Since tweets are generally unstructured in nature and do not follow grammatical structures, parsing techniques generally do not work well due to incorrect parts-of-speech assignment to individual words. In this paper, we have proposed an n-gram based statistical approach to identify significant terms and using them for vector-space modelling of the tweets. Thereafter, a social graph generation method is proposed, considering tweets as nodes and the degree of similarity between a pair of tweets as a weighted edge between them. The social graph is decomposed into various clusters using Markov Clustering technique, wherein each cluster corresponds to a particular event. The experiment is carried out using a corpus of 3100 tweets related to Israel-Gaza conflicts, Delhi assembly election, and union budget 2015. The experimental results are encouraging, showing the efficacy of the proposed social graph generation and event classification methods.
On Hamilton-Jacobi theory: its geometry and relation to pilot-wave theory

This paper gives a technically elementary treatment of some aspects of Hamilton-Jacobi theory, especially in relation to the calculus of variations. The second half of the paper describes the application to geometric optics, the optico-mechanical analogy and the transition to quantum mechanics. Finally, I report recent work of Holland providing a Hamiltonian formulation of the pilot-wave theory.
A Modular Approach to Facial Expression Recognition

We study the use of multi-layer perceptrons in applying artifical learning to the recognition of emotional expressions from frontal images of human faces. The perceptrons are trained using per-pixel luma data from the images’ mouth and eye areas, and map the inputs to one of 6 emotions. We compare 3 different methods for processing input information: 1) one network module for all inputs; 2) one network module for both eyes, and one for the mouth; 3) one network module for the mouth, one for the left eye, and one for the right eye. Our results show that involving multiple modules leads to better results, resulting in an overall performance of 84% images classified correctly.
Coordinated Activation of Toll-Like Receptor8 (TLR8) and NLRP3 by the TLR8 Agonist, VTX-2337, Ignites Tumoricidal Natural Killer Cell Activity

VTX-2337 (USAN: motolimod) is a selective toll-like receptor 8 (TLR8) agonist, which is in clinical development as an immunotherapy for multiple oncology indications, including squamous cell carcinoma of the head and neck (SCCHN). Activation of TLR8 enhances natural killer cell activation, increases antibody-dependent cell-mediated cytotoxicity, and induces Th1 polarizing cytokines. Here, we show that VTX-2337 stimulates the release of mature IL-1β and IL-18 from monocytic cells through coordinated actions on both TLR8 and the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome complex. In vitro, VTX-2337 primed monocytic cells to produce pro-IL-1β, pro-IL-18, and caspase-1, and also activated the NLRP3 inflammasome, thereby mediating the release of mature IL-1β family cytokines. Inhibition of caspase-1 blocked VTX-2337-mediated NLRP3 inflammasome activation, but had little impact on production of other TLR8-induced mediators such as TNFα. IL-18 activated natural killer cells and complemented other stimulatory pathways, including FcγRIII and NKG2D, resulting in IFNγ production and expression of CD107a. NLRP3 activation in vivo was confirmed by a dose-related increase in plasma IL-1β and IL-18 levels in cynomolgus monkeys administered VTX-2337. These results are highly relevant to clinical studies of combination VTX-2337/cetuximab treatment. Cetuximab, a clinically approved, epidermal growth factor receptor-specific monoclonal antibody, activates NK cells through interactions with FcγRIII and facilitates ADCC of tumor cells. Our preliminary findings from a Phase I open-label, dose-escalation, trial that enrolled 13 patients with recurrent or metastatic SCCHN show that patient NK cells become more responsive to stimulation by NKG2D or FcγRIII following VTX-2337 treatment. Together, these results indicate that TLR8 stimulation and inflammasome activation by VTX-2337 can complement FcγRIII engagement and may augment clinical responses in SCCHN patients treated with cetuximab. Trial Registration: ClinicalTrials.gov NCT01334177
Dynamic-Bias Induction

A tradeoff exists between the range of learning tasks solved by an induction system, and its performance on those tasks. We propose dynamic-bias induction, an approach in which bias is dynamically constructed as a function of the learning task. This admits the possibility of a high performance inductive learner that applies to a wide range of learning tasks. We assess the benefits and limitations of dynamic-bias induction by comparing an implementation of the approach to two existing inductive learning systems.
An Indoor Wayfinding System Based on Geo-coded QR Codes for Individuals with Cognitive Impairments

A novel wayfinding system is presented with an aim to increase workplace and life independence for cognitive-impaired patients such as people with traumatic brain injury, cerebral palsy, mental retardation, schizophrenia, and Alzheimer’s disease. It is based on geo-coded QR codes which embed the coordinate (x, y, floor) and social computing that helps shorten the learning curve for the people who use it. According to psychological model of spatial navigation and the requirements of rehabilitation professionals, PDAs prompting with spatial photos at the right time and place can assist cognitively-impaired persons with navigating indoors or on the road. To do so, geo-coded QR codes which can be imagined as a new traffic sign system, are posted to selected positions on routes. The navigational photos are served on demand to the user who uses the built-in PDA camera to shoot the QR code when it is in eyesight range. A tracking function is integrated to timestamp the visited positions and issue alerts in case of anomalies. The tracking system increases the sense of security and also lowers the entry threshold to accepting the assistive technology. A prototype which consists of wayfinding devices, a training blog, and a tracking system, is designed and tested on a university campus. Compared to the sensor network approach, QR codes are easier and faster to deploy and the cost is lower. The experimental results show the computer-human interface is friendly and the capabilities of wayfinding are reliable.