Miracle-dz/UniFAR-FTUs huggingface dataset:
Reorganization of multivesicular bodies regulates MHC class II antigen presentation by dendritic cells

Immature dendritic cells (DCs) sample their environment for antigens and after stimulation present peptide associated with major histocompatibility complex class II (MHC II) to naive T cells. We have studied the intracellular trafficking of MHC II in cultured DCs. In immature cells, the majority of MHC II was stored intracellularly at the internal vesicles of multivesicular bodies (MVBs). In contrast, DM, an accessory molecule required for peptide loading, was located predominantly at the limiting membrane of MVBs. After stimulation, the internal vesicles carrying MHC II were transferred to the limiting membrane of the MVB, bringing MHC II and DM to the same membrane domain. Concomitantly, the MVBs transformed into long tubular organelles that extended into the periphery of the cells. Vesicles that were formed at the tips of these tubules nonselectively incorporated MHC II and DM and presumably mediated transport to the plasma membrane. We propose that in maturing DCs, the reorganization of MVBs is fundamental for the timing of MHC II antigen loading and transport to the plasma membrane.
State-Frequency Memory Recurrent Neural Networks

Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community. Among those efforts on improving the capability to represent temporal data, the Long Short-Term Memory (LSTM) has achieved great success in many areas. Although the LSTM can capture long-range dependency in the time domain, it does not explicitly model the pattern occurrences in the frequency domain that plays an important role in tracking and predicting data points over various time cycles. We propose the State-Frequency Memory (SFM), a novel recurrent architecture that allows to separate dynamic patterns across different frequency components and their impacts on modeling the temporal contexts of input sequences. By jointly decomposing memorized dynamics into state-frequency components, the SFM is able to offer a fine-grained analysis of temporal sequences by capturing the dependency of uncovered patterns in both time and frequency domains. Evaluations on several temporal modeling tasks demonstrate the SFM can yield competitive performances, in particular as compared with the state-of-the-art LSTM models.
Lexicon adaptation with reduced character error (LARCE) - a new direction in Chinese language modeling

Good language modeling relies on good predeﬁned lexicons. For Chinese, since there are no text word boundaries and the concept of “word” is not very well deﬁned, constructing good lexicons is difﬁcult. In this paper, we propose lexicon adaptation with reduced character error (LARCE), which learns new word tokens based on the criterion of reduced adaptation corpus error rate. In this approach, a multi-character string is taken as a new “word” as long as it is helpful in reducing the error rate, and minimum number of new, high-quality words can be obtained. This algorithm is based on character-based consensus networks. In initial experiments on Chinese broadcast news, it is shown that LARCE not only signiﬁcantly outperforms PAT-tree-based word extraction algorithms, but even out-performs manually augmented lexicons. It is believed the concept is equally useful for other character-based languages.
Object detection and tracking using multi-layer laser for autonomous urban driving

A real-time approach to detecting and tracking multiple objects for an urban driving environment in multi-layer laser data is proposed in this paper. Since situational awareness is crucial for autonomous driving in complicate urban environments, object detection and tracking with cameras or laser has become a popular research topic. With 3D range data, we take the advantage of geometry to cluster point cloud into objects in a fast way. Model-based object tracking framework used in this paper relies on Kalman filter. We set up geometry model for each object and evaluate the observation condition before model updating. We also provide the solution to complicate situations, like splitting, merging and degradation. Our approach has been applied to the multi-layer laser set up on our autonomous driving vehicle under different circumstances, and experiment results are presented.
Natural Variations in SLG7 Regulate Grain Shape in Rice

Rice (Oryza sativa) grain shape, which is controlled by quantitative trait loci (QTL), has a strong effect on yield production and quality. However, the molecular basis for grain development remains largely unknown. In this study, we identified a novel QTL, Slender grain on chromosome 7 (SLG7), that is responsible for grain shape, using backcross introgression lines derived from 9311 and Azucena. The SLG7 allele from Azucena produces longer and thinner grains, although it has no influence on grain weight and yield production. SLG7 encodes a protein homologous to LONGIFOLIA 1 and LONGIFOLIA 2, both of which increase organ length in Arabidopsis. SLG7 is constitutively expressed in various tissues in rice, and the SLG7 protein is located in plasma membrane. Morphological and cellular analyses suggested that SLG7 produces slender grains by longitudinally increasing cell length, while transversely decreasing cell width, which is independent from cell division. Our findings show that the functions of SLG7 family members are conserved across monocots and dicots and that the SLG7 allele could be applied in breeding to modify rice grain appearance.
Lipocalin-2 as an Infection-Related Biomarker to Predict Clinical Outcome in Ischemic Stroke

Objectives From previous data in animal models of cerebral ischemia, lipocalin-2 (LCN2), a protein related to neutrophil function and cellular iron homeostasis, is supposed to have a value as a biomarker in ischemic stroke patients. Therefore, we examined LCN2 expression in the ischemic brain in an animal model and measured plasma levels of LCN2 in ischemic stroke patients. Methods In the mouse model of transient middle cerebral artery occlusion (tMCAO), LCN2 expression in the brain was analyzed by immunohistochemistry and correlated to cellular nonheme iron deposition up to 42 days after tMCAO. In human stroke patients, plasma levels of LCN2 were determined one week after ischemic stroke. In addition to established predictive parameters such as age, National Institutes of Health Stroke Scale and thrombolytic therapy, LCN2 was included into linear logistic regression modeling to predict clinical outcome at 90 days after stroke. Results Immunohistochemistry revealed expression of LCN2 in the mouse brain already at one day following tMCAO, and the amount of LCN2 subsequently increased with a maximum at 2 weeks after tMCAO. Accumulation of cellular nonheme iron was detectable one week post tMCAO and continued to increase. In ischemic stroke patients, higher plasma levels of LCN2 were associated with a worse clinical outcome at 90 days and with the occurrence of post-stroke infections. Conclusions LCN2 is expressed in the ischemic brain after temporary experimental ischemia and paralleled by the accumulation of cellular nonheme iron. Plasma levels of LCN2 measured in patients one week after ischemic stroke contribute to the prediction of clinical outcome at 90 days and reflect the systemic response to post-stroke infections.
Hybrid model to improve time complexity of words search in POS Tagging

POS Tagging is the tagging of each word with the most appropriate category listed in the lexicon which indicates its syntactic role in the sentence. POS Tagging enables tracking of user reviews and can even be used for Human-Robot-Interaction in future. In this paper, we are presenting a model which deals with the limitations of previously existing POS Tagging algorithm, namely, Memory Based Learning Algorithm and Multi-Domain Web Based Algorithm. In Multi-Domain Web Based Algorithm, the unknown word is searched over the web for its possible tags which creates a runtime overhead (increases execution time), the tag with highest probability of occurrence is assigned to the word. The process repeats itself every time the algorithm runs, even for the words previously been searched. Whereas, Memory Based Learning Algorithm is a lazy learning algorithm. In this algorithm, the word is first searched in the lexicon, if the word is found, its lexical representation is retrieved, but, if it is not found, its lexical representation is computed with the help of similarity metrics. The computed tag may not be accurate. Therefore, Adding results of Multi-domain Web Based POS tagging to the lexicon, will improve efficiency of the lexicon as well as the overall time complexity of the algorithm whenever the same word appears next. Thus, we can calculate the time complexity of the model and devise a generalized formula for efficiency and performance of the model.
Computational Complexity of Iterated Maps on the Interval (Extended Abstract)

The exact computation of orbits of discrete dynamical systems on the interval is considered. Therefore, a multiple-precision floating point approach based on error analysis is chosen and a general algorithm is presented. The correctness of the algorithm is shown and the computational complexity is analyzed. As a main result, the computational complexity measure considered here is related to the Ljapunow exponent of the dynamical system under consideration.
Symbolic reachability computation of a class of second-order systems

Reachability is an important property of safety which widely analyzed in designing physical control systems. By contrast to issues related to stability and controllability of physical systems are well-studied in control theory, there are many results on reachability of those systems in computer science, but mainly for some trivial linear systems developed by formal methods and tools. In this paper, we present the first known family of second-order systems with the decidable symbolic computation problem of its reachable state space at the best of our knowledge. We extend the approach that reducing reachability computation into semi-algebraic system solving and analyzing the special type of second-order systems carefully. We also illustrate the application of our method by performing the Maple package DISCOVERER successfully.
Policy search via the signed derivative

We consider policy search for reinforcement learning: learning policy parameters, for some fixed policy class, that optimize performance of a system. In this paper, we propose a novel policy gradient method based on an approximation we call the Signed Derivative; the approximation is based on the intuition that it is often very easy to guess the direction in which control inputs affect future state variables, even if we do not have an accurate model of the system. The resulting algorithm is very simple, requires no model of the environment, and we show that it can outperform standard stochastic estimators of the gradient; indeed we show that Signed Derivative algorithm can in fact perform as well as the true (model-based) policy gradient, but without knowledge of the model. We evaluate the algorithm’s performance on both a simulated task and two realworld tasks — driving an RC car along a specified trajectory, and jumping onto obstacles with an quadruped robot — and in all cases achieve good performance after very little training.
An Uncertain Continuous Collaborative Users Finding Algorithm for Location Privacy Protection

The centralized and distributed models are the main system architecture of privacy protection in location-based services (LBS). In the model of centralized, the trusted third party (TTP) is used to provide location privacy, but it is usually thought as the point of attack and the bottleneck of service, which makes the distributed model attract more attention. However, although a large number of algorithms were proposed under this model, they were mainly focussed on the protection of the snapshot query and difficult to provide privacy in the continuous query. Especially that, with the difference of anonymous users, the adversary can get rid of the anonymous users and identify the issuer, and then the privacy of the issuer will be revealed. In order to deal with this problem, we propose an uncertain continuous collaborative users finding algorithm (UCCUFA), which utilizes the uncertainty cells of region grid and the collaborative users to provide query results to make the issuer hiding behind the collaborative users. In this algorithm, all query results are provided by collaborative users, and no information interacted between the issuer and the LBS server during the procedure of continuous query. At last, we utilize the security analysis and experimental results further verify the effectiveness of location privacy protection and the performance efficiency of our proposed algorithm.
Isolation and Culture of Single Cell Types from Rat Liver

There have been few reports on the simultaneous isolation of multiple liver cell populations thus far. As such, this study was aimed at establishing a protocol for the simultaneous separation of hepatocytes (HCs), hepatic stellate cells (HSCs), liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs) from the rat liver and assessing the in vitro culture of these cells. Single-cell suspensions from the liver were obtained by ethylene glycol tetraacetic acid/collagenase perfusion. After low-speed centrifugal separation of HCs, pronase was added to the nonparenchymal cell fraction to eliminate the remaining HCs. Subsequently, HSCs, LSECs and KCs were purified by two steps of density gradient centrifugation using Nycodenz and Percoll in addition to selective attachment. Pronase treatment increased the HSC yield (1.5 ± 0.2 vs. 0.7 ± 0.3 cells/g liver, p < 0.05) and improved LSEC purity (93.6 ± 3.6 vs. 82.5 ± 5.6%, p < 0.01). The isolated cells could also be cultured in vitro. LSEC apoptosis began on day 3 and reached a maximum on day 7. A few surviving LSECs began proliferating and split to form a cobblestone, sheet-like appearance on day 14. The LSECs on day 14 lost fenestrations but retained scavenger function. Thus, viable and purified liver cells were obtained with a high yield from the rat liver using the developed method, which may be useful for studying the physiology and pathology of the liver in the future.
The Brain Connectivity Basis of Semantic Dementia: A Selective Review

Semantic dementia (SD) is a neurodegenerative disorder characterized by the progressive loss of semantic memory and conceptual knowledge, coupled with asymmetric local brain atrophy concentrated in the anterior temporal lobe. Recent developments in neuroimaging techniques, especially the emergence of the “human connectomics,” have made possible the study of the brain's functional and structural connections and the topological properties of the brain networks. Recent studies applying these techniques have shown that SD manifests extensive structural and functional connectivity alterations, providing important insights into the pathogenesis of SD and the neural basis of semantic memory in general. In this review, we present and discuss the existing findings about the brain connectivity changes in SD and how they might be related to the various behavioral deficits associated with this disorder and propose important unanswered questions that warrant further investigation.
Next generation sequencing for clinical diagnostics and personalised medicine: implications for the next generation cardiologist

The fast moving field of genomic medicine is already impacting on clinical care and cardiologists are fortunate to be in a position to benefit early from the transformative advances in genomics. However, the challenges associated with genomics in the clinic in general, and with next generation sequencing technologies in particular, are significant and cardiologists need to be prepared if they wish to surf the wave of genomic opportunity. This paper presents an overview of the implications of next generation sequencing for clinical diagnostics and personalised medicine in the cardiology clinic.
Improved Sparsest Random Scheduling for Compressive Data Gathering under Unreliable Link in Wireless Sensor Networks

Compressive sensing (CS) is a promising data acquisition and compression technology that can be used to reduce and balance the transmission cost in wireless sensor networks (WSNs). However, the transmission links are unreliable frequently and data loss is very common in wireless sensor networks. What's more, the reconstruction accuracy is reduced greatly due to unreliable links and data loss. In order to address this problem, a data gathering and reconstruction algorithm is proposed, which is based on improved sparsest random measurement matrix and collection tree protocol (CTP). It needs less necessary number of measurements than state-of-the-art methods to reach high reconstruction accuracy, and has the ability to resist the negative impact of data loss on the recovery accuracy. The real sensory data from Intel Indoor project are used in simulation and the results show that the proposed algorithm is effective.
The role of hospital managers in quality and patient safety: a systematic review

Objectives To review the empirical literature to identify the activities, time spent and engagement of hospital managers in quality of care. Design A systematic review of the literature. Methods A search was carried out on the databases MEDLINE, PSYCHINFO, EMBASE, HMIC. The search strategy covered three facets: management, quality of care and the hospital setting comprising medical subject headings and key terms. Reviewers screened 15 447 titles/abstracts and 423 full texts were checked against inclusion criteria. Data extraction and quality assessment were performed on 19 included articles. Results The majority of studies were set in the USA and investigated Board/senior level management. The most common research designs were interviews and surveys on the perceptions of managerial quality and safety practices. Managerial activities comprised strategy, culture and data-centred activities, such as driving improvement culture and promotion of quality, strategy/goal setting and providing feedback. Significant positive associations with quality included compensation attached to quality, using quality improvement measures and having a Board quality committee. However, there is an inconsistency and inadequate employment of these conditions and actions across the sample hospitals. Conclusions There is some evidence that managers’ time spent and work can influence quality and safety clinical outcomes, processes and performance. However, there is a dearth of empirical studies, further weakened by a lack of objective outcome measures and little examination of actual actions undertaken. We present a model to summarise the conditions and activities that affect quality performance.
Lateral Inhibition Overcomes Limits of Temporal Difference Learning

There is growing support for Temporal Difference (TD) Learning as a formal account of the role of the midbrain dopamine system and the basal ganglia in learning from reinforcement. This account is challenged, however, by the fact that realistic implementations of TD Learning have been shown to fail on some fairly simple learning tasks — tasks well within the capabilities of humans and non-human animals. We hypothesize that such failures do not arise from natural learning systems because of the ubiquitous appearance of lateral inhibition in the cortex, producing sparse conjunctive internal representations that support the learning of predictions of future reward. We provide support for this conjecture through computational simulations that compare TD Learning systems with and without lateral inhibition, demonstrating the benefits of sparse conjunctive codes for reinforcement learning.
Understanding Muslim Women's Self-Identity and Resistance to Feminism and Participatory Action Research

Copyright 2004, Praeger Publishing. This is a pre-copyedited version of an article accepted for publication in the edited book Traveling Companions: Feminism, Teaching, and Action Research, following peer review. The definitive publisher-authenticated version is available through Praeger Publishing: http://www.greenwood. com/catalog/C8027.aspx. See also: http://www.eself-learning-arabic.cornell. edu/publications.htm#2
A Mobile Mechatronic Platform Architecture for Flexible Materials Handling

Mass production of customised products requires materials handling platform architectures to be flexible enough to accommodate varying geometries, masses and volumes of materials. Multi-agent control and coordination structures are under development to provide flexible and scalable transportation systems. In this paper the concept of Mechatronics has been used to develop a Flexible Materials Handling mobile platform architecture, suited to operation in both singular mobile platform and multiple cooperating mobile platform contexts. Materials handling was provided by the cooperation of multiple differential drive platforms fitted with servo operated and articulated conveyors.
Optical Induction of 2D and 3D Photonic Lattices in Photorefractive Materials based on Talbot effect

In this paper we report the technique of optical induction of 2 and 3-dimensional (2D and 3D) photonic lattices in photorefractive materials based on diffraction grating self replication -Talbot effect. 1D and 2D different rotational symmery diffraction masks with the periods of few tens micrometers and 532 nm cw laser beam were used in the experiments to form an intensity modulated light beam profile. A few hundred micrometric scale replications of mask generated intensity structures along the beam propagation axis were observed. Up to 20 high contrast replications were detected for 1D annular mask with 30 m radial period. The spatial modulated intensity pattern from this mask was imparted into the lithium niobate crystal via electro-optic effect, thus creating refractive index lattice. The recorded lattices were tested by phase microscope. The spatial frequency doubling of the recorded lattice due to fractional Talbot effect was observed in the depth scanned phase microscope images. Keywords—Diffraction gratings, laser, photonic lattice, Talbot
Spatial Precoder Design for Differential Space-Time Coded Systems: Based on Fixed Parameters of MIMO Channels

In this paper, we introduce the novel idea of linear spatial precoding based on fixed and known parameters of MIMO channels to improve the performance of differential space-time coding schemes. Antenna spacing and antenna placements (geometry) are considered as fixed parameters of MIMO channels, which are known at the transmitter. This new linear spatial precoding scheme exploits the antenna placement information at both ends of the MIMO channel to ameliorate the effect of non-ideal antenna placement on the performance of differential space-time coded systems. With this design, the precoder is fixed for fixed antenna placement and the transmitter does not require any feedback of channel state information (partial or full) from the receiver. A second precoding scheme is proposed by exploiting the non-isotropic scattering distribution parameters of the scattering channel. Unlike in the first scheme, the second scheme requires the receiver to estimate the non-isotropic distribution parameters and feed them back to the transmitter. Simulation results show that first scheme provides significant performance improvements at low SNRs and the second scheme provides significant performance improvements both at low and high SNRs