Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase I Study of Recombinant Human Interleukin-7 Administration in Subjects with Refractory Malignancy

Purpose: Interleukin-7 (IL-7) has critical and nonredundant roles in T-cell development, hematopoiesis, and postdevelopmental immune functions as a prototypic homeostatic cytokine. Based on a large body of preclinical evidence, it may have multiple therapeutic applications in immunodeficiency states, either physiologic (immunosenescence), pathologic (HIV), or iatrogenic (postchemotherapy and posthematopoietic stem cell transplant), and may have roles in immune reconstitution or enhancement of immunotherapy. We report here on the toxicity and biological activity of recombinant human IL-7 (rhIL-7) in humans. Design: Subjects with incurable malignancy received rhIL-7 subcutaneously every other day for 2 weeks in a phase I interpatient dose escalation study (3, 10, 30, and 60 μg/kg/dose). The objectives were safety and dose-limiting toxicity determination, identification of a range of biologically active doses, and characterization of biological and, possibly, antitumor effects. Results: Mild to moderate constitutional symptoms, reversible spleen and lymph node enlargement, and marked increase in peripheral CD3+, CD4+, and CD8+ lymphocytes were seen in a dose-dependent and age-independent manner in all subjects receiving ≥10 μg/kg/dose, resulting in a rejuvenated circulating T-cell profile, resembling that seen earlier in life. In some subjects, rhIL-7 induced in the bone marrow a marked, transient polyclonal proliferation of pre-B cells showing a spectrum of maturation as well as an increase in circulating transitional B cells. Conclusion: This study shows the potent biological activity of rhIL-7 in humans over a well-tolerated dose range and allows further exploration of its possible therapeutic applications. Clin Cancer Res; 16(2); 727–35
A Super Energy Mitigation Nanostructure at High Impact Speed Based on Buckyball System

The energy mitigation properties of buckyballs are investigated using molecular dynamics (MD) simulations. A one dimensional buckyball long chain is employed as a unit cell of granular fullerene particles. Two types of buckyballs i.e. C60 and C720 with recoverable and non-recoverable behaviors are chosen respectively. For C60 whose deformation is relatively small, a dissipative contact model is proposed. Over 90% of the total impact energy is proven to be mitigated through interfacial reflection of wave propagation, the van der Waals interaction, covalent potential energy and atomistic kinetic energy evidenced by the decent force attenuation and elongation of transmitted impact. Further, the C720 system is found to outperform its C60 counterpart and is able to mitigate over 99% of the total kinetic energy by using a much shorter chain thanks to its non-recoverable deformation which enhances the four energy dissipation terms. Systematic studies are carried out to elucidate the effects of impactor speed and mass, as well as buckyball size and number on the system energy mitigation performance. This one dimensional buckyball system is especially helpful to deal with the impactor of high impact speed but small mass. The results may shed some lights on the research of high-efficiency energy mitigation material selections and structure designs.
On-line SLAM Using Clustered Landmarks with Omnidirectional

The problem of SLAM (simultaneous localization and mapping) is a fundamental problem in autonomous robotics. It arises when a robot must create a map of the regions it has navigated while localizing itself on it, using results from one step to increase precision in another by eliminating errors inherent to the sensors. One common solution consists of establishing landmarks in the environment which are used as reference points for absolute localization estimates and form a sparse map that is iteratively refined as more information is obtained. This paper introduces a method of landmark selection and clustering in omnidirectional images for on-line SLAM, using the SIFT algorithm for initial feature extraction and assuming no prior knowledge of the environment. Visual sensors are an attractive way of collecting information from the environment, but tend to create an excessive amount of landmarks that are individually prone to false matches due to image noise and object similarities. By clustering several features in single objects, our approach eliminates landmarks that do not consistently represent the environment, decreasing computational cost and increasing the reliability of information incorporated. Tests conducted in real navigational situations show a significant improvement in performance without loss of quality.
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'

Agent-based models are increasingly used to address questions regarding real-world phenomena and mechanisms; therefore, the calibration of model parameters to certain data sets and patterns is often needed. Furthermore, sensitivity analysis is an important part of the development and analysis of any simulation model. By exploring the sensitivity of model output to changes in parameters, we learn about the relative importance of the various mechanisms represented in the model and how robust the model output is to parameter uncertainty. These insights foster the understanding of models and their use for theory development and applications. Both steps of the model development cycle require massive repetitions of simulation runs with varying parameter values. To facilitate parameter estimation and sensitivity analysis for agent-based modellers, we show how to use a suite of important established methods. Because NetLogo and R are widely used in agent-based modelling and for statistical analyses, we use a simple model implemented in NetLogo as an example, packages in R that implement the respective methods, and the RNetLogo package, which links R and NetLogo. We briefly introduce each method and provide references for further reading. We then list the packages in R that may be used for implementing the methods, provide short code examples demonstrating how the methods can be applied in R, and present and discuss the corresponding outputs. The Supplementary Material includes full, adaptable code samples for using the presented methods with R and NetLogo. Our overall aim is to make agent-based modellers aware of existing methods and tools for parameter estimation and sensitivity analysis and to provide accessible tools for using these methods. In this way, we hope to contribute to establishing an advanced culture of relating agent-based models to data and patterns observed in real systems and to foster rigorous and structured analyses of agent-based models.
Transcriptional Activation of the Pirb Gene in B Cells by PU.1 and Runx3

Cells in the immune system are regulated positively or negatively by sets of receptor pairs that conduct balanced, activating, or inhibitory intracellular signaling. One such receptor pair termed paired Ig-like receptor (PIR) is composed of the inhibitory PIR-B and its activating isoform, PIR-A. Upon binding to their shared ligand, MHC class I molecules, these receptors control the threshold for immune cell activation. Gene-targeting studies on PIR-B in mice revealed the importance of the inhibition mediated by the PIR-B–MHC interaction in the immune system. Recent studies also revealed the significance of the interaction of PIR-B with neurite outgrowth inhibitors, including Nogo in the CNS. The coordinated regulation by PIR-B and PIR-A is considered to be primarily dependent on their expression balance in cells. However, the mechanism underlying transcriptional control of the genes for PIR-B and PIR-A (Pirb and Pira, respectively) remains to be clarified. In this study, we identified the major cis-acting promoter segment for Pirb and Pira in B cells as the −212 to −117 region upstream from the translation initiation codon. PU.1 and Runx3 were found to bind to this Pirb promoter. Truncation of the PU.1-binding motif significantly reduced the promoter activity, whereas the influence of elimination of the Runx3 site was marginal in B lymphoma BCL1-B20 cells. Unexpectedly, PU.1, but not Runx3, knockdown reduced the levels of both the Pirb and Pira transcripts. We conclude that the major promoter of Pirb, and probably Pira as well, is activated dominantly by PU.1 and marginally by Runx3 in B cells.
- 1-Support Vector Machines versus Boosting

Support Vector Machines (SVMs) and Adaptive Boosting (AdaBoost) are two successful classification methods. They are essentially the same as they both try to maximize the minimal margin on a training set. In this work, we present an even platform to compare these two learning algorithms in terms of their test error, margin distribution and generalization power. Two basic models of polynomials and decision stumps are used to evaluate eight real-world binary datasets. We concluded that the generalization power of AdaBoost with linear SVMs as base learners and that of SVMs with decision stumps as kernels outperform other scenarios. Although the training error of AdaBoost approaches to zero with the increase number of weak learners, its test error starts to rise at a certain step. For both SVMs and AdaBoost, the cumulative margin distribution is indicative of the test error.
Cognitive radio unified Spectral efficiency and Energy Efficiency trade-off analysis

Spectral efficiency and energy efficiency are fundamental trade-off in wireless communications. Spectral efficiency (SE), defined as the average data rate per unit bandwidth, quantifies how efficiently the available spectrum is utilized. Energy efficiency (EE), defined as the successful transmitted information bits per unit energy from transmitter to receiver, quantifies how efficiently the energy is utilized. Basically, with higher average energy per bit to noise power spectral density ratio at the receiver, the packet can be more successfully detected, thus utilizing the spectrum more efficiently, giving higher SE; however, in this case, it requires more energy, lowering EE, and vice versa. In this paper, we study the trade-off between SE and EE, specifically for the cognitive radio considering its configurability. We propose a general metric SEE (Spectral/Energy Efficiency) to facilitate the analysis which quantifies the preference of SE or EE. Closed-form solutions for symbol transmission energy and the length of information bits per frame are obtained for various combined modulation and channel coding schemes. The closed-form solutions further facilitate the adaptivity of cognitive radio considering both SE and EE in various scenarios. Using the proposed metric shows that our scheme is capable to perform balanced trade-off between SE and EE. Considering only maximizing SE, our scheme gains much larger EE while only sacrificing little SE; and comparing with maximizing EE, larger SE can be obtained while sacrificing a small amount EE.
Unraveling the architecture of the dorsal raphe synaptic neuropil using high-resolution neuroanatomy

The dorsal raphe nucleus (DRN), representing the main source of brain’s serotonin, is implicated in the pathophysiology and therapeutics of several mental disorders that can be debilitating and life-long including depression, anxiety and autism. The activity of DRN neurons is precisely regulated, both phasically and tonically, by excitatory glutamate and inhibitory GABAergic axons arising from extra-raphe areas as well as from local sources within the nucleus. Changes in serotonin neurotransmission associated with pathophysiology may be encoded by alterations within this network of regulatory afferents. However, the complex organization of the DRN circuitry remains still poorly understood. Using a recently developed high-resolution immunofluorescence technique called array tomography (AT) we quantitatively analyzed the relative contribution of different populations of glutamate axons originating from different brain regions to the excitatory drive of the DRN. Additionally, we examined the presence of GABA axons within the DRN and their possible association with glutamate axons. In this review, we summarize our findings on the architecture of the rodent DRN synaptic neuropil using high-resolution neuroanatomy, and discuss possible functional implications for the nucleus. Understanding of the synaptic architecture of neural circuits at high resolution will pave the way to understand how neural structure and function may be perturbed in pathological states.
Temporal dynamics of 2 D and 3 D shape representation in macaque visual area V 4

We studied the temporal dynamics of shape representation in area V4 of the alert macaque monkey. Analyses were based on two large stimulus sets, one equivalent to the 2D shape stimuli used in a previous study of V2, and the other a set of stereoscopic 3D shape stimuli. As in V2, we found that information conveyed by individual V4 neurons about the stimuli tended to be maximal during the initial transient response and generally lower, albeit statistically significant, afterwards. The population response was substantially correlated from one stimulus to the next during the transients, and decorrelated as responses decayed. V4 responses showed significantly longer latencies than in V2, especially for the 3D stimulus set. Recordings from area V1 in a single animal revealed temporal dynamic patterns in response to the 2D shape stimuli that were largely similar to those in V2 and V4. Together with earlier results, these findings provide evidence for a distributed process of coarse-to-fine representation of shape stimuli in the visual cortex.
Conformal Prediction with Neural Networks

Conformal prediction (CP) is a method that can be used for complementing the bare predictions produced by any traditional machine learning algorithm with measures of confidence. CP gives good accuracy and confidence values, but unfortunately it is quite computationally inefficient. This computational inefficiency problem becomes huge when CP is coupled with a method that requires long training times, such as neural networks. In this paper we use a modification of the original CP method, called inductive conformal prediction (ICP), which allows us to a neural network confidence predictor without the massive computational overhead of CP The method we propose accompanies its predictions with confidence measures that are useful in practice, while still preserving the computational efficiency of its underlying neural network.
AIDS‐Related Stigmatization: Instrumental and Symbolic Attitudes

We investigate the role that instrumental and symbolic functions of attitudes play in the rejection of people infected with HIV. In a series of studies (total N = 431), we manipulate the symbolic component of AIDS, by comparing it to a fictitious disease that is identical in every way to AIDS, excepting its symbolic association with homosexuality and IV drug use. Two studies indicated that the symbolic component had no effect on either social distance or perceptions of disease severity. A third study indicated that instrumental aspects of severity, contagiousness, and treatability were significant determinants of social distance. A fourth study found that the stigma of association with homosexuality increased a mild disease's stigma. A fifth study showed that previous research showing the importance of a symbolic component in AIDS-related attitudes may have relied on a confounded measure of the symbolic component of attitudes. These 5 studies suggest the importance of both instrumental and symbolic attitudes in illness. Instrumental functions seem to outweigh the impact of symbolic functions of AIDS-related attitudes.
Online multiple object tracking by hierarchical association of detection responses

We present a framework addressing the problem of multi-pedestrian tracking in a crowded scene from a single view camera. The key factor to improve the performance of tracking results in data association is providing an effective appearance model for each target. There are many efforts in developing such models in generative and discriminative ways to either describe the object well or discriminate it from others. Here we propose an efficient algorithm to learn both discriminative and generative appearance models online for different targets in a hierarchical framework. Thus, we are able to emphasize the similarity between a current tracklet (short track segment) and its new association target while differentiate itself from others. Within a time sliding window, detection responses are gathered to form short track fragments based on spatial-temporal information. These tracklets provide us affinity and discriminative information, which can be used to collect samples for learning models. To learn appearance model, we use AdaBoost to maximize discriminative capability. Kalman filter is also used to model the motion as an important factor to associate tracklets together. In the experiment, we evaluate different features setup of our framework on CAVIAR public dataset. It shows that our method can successfully track in heavy occluded scenes. It also helps improve tracking accuracy by overcoming missed detections, false alarms and ID switching issue. We compare our method with several state-of-the-art algorithms for evaluation.
Recurrence of mediastinal node cancer after lobe-specific systematic nodal dissection for non-small-cell lung cancer.

OBJECTIVES The standard surgical treatment for patients with non-small-cell lung cancer (NSCLC) is lobectomy with systematic nodal dissection (SND). Lobe-specific patterns of nodal metastases have been recognized, and lobe-specific SND (L-SND) has been reported. We performed L-SND depending on patient-related factors, such as age or the presence of diabetes or respiratory dysfunction, or in the context of specific tumour-related factors, such as the presence of a tumour with a wide area of ground-glass opacity. METHODS Between September 2002 and December 2008, 335 consecutive patients with clinical and intraoperative N0 NSCLC underwent curative lobectomies at Shizuoka Cancer Center Hospital. Among these 335 patients, 206 underwent SND (Group A) and 129 underwent L-SND. Of the 129 patients undergoing L-SND, 98 underwent L-SND due to patient-related factors (Group B) and 31 underwent L-SND due to tumour-related factors (Group C). RESULTS There were no significant differences in morbidity or blood loss between patients undergoing SND or L-SND, but there was a significant difference in the mean operative times. The 5-year disease-free survival (5-DFS) and 5-year overall survival (5-OS) of patients in Group C were 100%. Although the patients in Group B showed no significant difference in 5-DFS and 5-OS compared with Group A, patients in Group B had significantly more initial recurrence of mediastinal node cancer than did the Group A patients (P = 0.0050). CONCLUSIONS The recurrence of mediastinal node cancer in patients undergoing L-SND was significantly greater than that in those undergoing SND.
Tensions and Risks in the Blanket Use of Locked Door Policies in Acute Mental Health Inpatient Facilities: Balancing Human Rights, Clinical Utility and Public and Patient Protection

Although there has been a consistent trend away from institutionalised mental health care over the past decades, this has occurred at the same time as increased coercive care measures are employed in mental health inpatient facilities, as these facilities become weighted to more serious cases. Locked wards, which regulate the ability of both voluntary and involuntary patients to leave psychiatric units, are increasingly common in acute mental health inpatient settings. This article focuses on the controversy surrounding a recent initiative to extend locked door policies across all public acute inpatient facilities in Queensland, explores the legal, ethical and clinical issues around the implementation of locked door policies in acute mental health inpatient facilities, and examines the tensions in balancing individual rights and public and personal protections in the treatment of acute mental health disorders.
A Delay Circuit for Build-Out-Self-Test in 0.18-μm CMOS

This paper describes a digital controlled delay circuit for BOST(build-out-self-test) with picosecond resolution. The proposed circuit operates at 250MHz under 1.8V supply according to Hspice simulation on the extracted layout. The complete design was fabricated in a standard TSMC 0. 18μm CMOS process technology. The proposed circuit draws 7mw of static power and occupies an area of 0.09mm2.
Effective character segmentation for license plate recognition under illumination changing environment

In this paper, we propose a novel image segmentation algorithm for license plate recognition (LPR) in video based traffic surveillance system. The license plate character segmentation is most important procedure in LPR system. However, in real situation, the character segmentation algorithms are challenged by drastic performance decrease due to sudden local illumination changes, especially when the color of characters is similar to that of background in LP. To mitigate this problem, we introduce a novel LP character segmentation algorithm by employing an adaptive binarization method using super-pixel based degeneracy factor. The proposed method demonstrates a significant improvement over conventional methods.
Fuzzy Inference Model for Type 2 Diabetes Management: a Tool for Regimen Alterations

This paper aims to demonstrate the utility of fuzzy set theory in the design process of a diabetes management system that enables patients to make short term alterations (particularly lifestyle) to their overall regimen as required. The model is a Mamdani Fuzzy Inference System (FIS) configured through domain specific information from experts and recognised diabetes management algorithms. The FIS takes a multi-input multi-output (MIMO) design approach with seven inputs variables (age, gender, weight, height, blood glucose (BG), exercise and diet) and three outputs (glycatedhaemoglobin (A1c), exercise and diet level assessments). Goodness of fit test was conducted based on Mean Square Error (MSE), Normalised Mean Square Error (NMSE) and Normalised Root Mean Square Error (NRMSE) between observed/advised and predicted output values. Overall MSE of 0.0899 shows good fit. For each of the output pairs (A1c, exercise and diet), NRMSE (0.7387, 0.7881 and 0.3716) and NMSE (0.9317, 0.9551 and 0.6051) shows good fit for A1c and exercise, but poor fit for diet. Intelligent models of this sort can help simplify management information for diabetes patients, reduce routine workload for clinicians and allow them to focus more on critical issues. Fully developed, this system can be used to build a database of diabetes management cases that includes daily life event information, ultimately leading to automated care for patients through technology.
Direct identification of nonlinear structure using Gaussian process prior models

When inferring nonlinear dependence from measured data, the nonlinear nature of the relationship may be characterised in terms of all the explanatory variables. However, this is rarely the most parsimonious, or insightful, approach. Instead, it is usually much more useful to be able to exploit the inherent nonlinear structure to characterise the nonlinear dependence in terms of the least possible number of variables. In this paper a new way of inferring nonlinear structure from measured data is investigated. The measured data is interpreted as providing information on a nonlinear map. The space containing the domain of the map is sub-divided into unique linear and nonlinear sub-spaces that are structural invariants. The most parsimonious representation of the map is obtained by the restriction of the map to the nonlinear sub-space. A direct constructive algorithm based on Gaussian process prior models, defined using a novel covariance function, is proposed. The algorithm infers the linear and nonlinear sub-spaces from noisy data and provides a non-parametric model of the parsimonious map. Use of the algorithm is illustrated by application to a Wiener-Hammerstein system.