Miracle-dz/UniFAR-FTUs huggingface dataset:
The exocrine protein trypsinogen is targeted into the secretory granules of an endocrine cell line: studies by gene transfer

The exocrine protein rat anionic trypsinogen has been expressed and is secreted from the murine anterior pituitary tumor cell line AtT-20. We examined which secretory pathway trypsinogen takes to the surface of this endocrine-derived cell line. The "constitutive" pathway externalizes proteins rapidly and in the absence of an external stimulus. In the alternate, "regulated" pathway, proteins are stored in secretory granules until the cells are stimulated to secrete with 8-Br- cAMP. On the basis of indirect immunofluorescence localization, stimulation of release, and subcellular fractionation, we find that trypsinogen is targeted into the regulated secretory pathway in AtT-20 cells. In contrast, laminin, an endogenous secretory glycoprotein, is shown to be secreted constitutively. Thus it appears that the transport apparatus for the regulated secretory pathway in endocrine cells can recognize not only endocrine prohormones, but also the exocrine protein trypsinogen, which suggests that a similar sorting mechanism is used by endocrine and exocrine cells.
On Defect Oriented Testing for Hybrid CMOS/Memristor Memory

Hybrid CMOS/memristor memory (hybrid memory)technology is one of the emerging memory technologies potentially to replace conventional non-volatile flash memory. Existing research on such novel circuits focuses mainly on the integration between CMOS and non-CMOS, fabrication techniques and reliability improvement. However, research on defect analysis for yield and quality improvement is still in its infancy stage. This paper presents a framework of defect oriented testing in hybrid memory based on electrical simulation. First, a classification and definition of defects is introduced. Second, a simulation model for defect injection and circuit simulation is proposed. Third, a case study to illustrate how the proposed approach can be used to analyze the defects and translate their electrical faulty behavior into fault models - in order to develop the appropriate tests and design for testability schemes - is provided. The simulation results show that in addition to the occurrence of conventional semiconductor memories faults, new unique faults take place, e.g., faults that cause the cell to hold an undefined state. These new unique faults require new test approaches (e.g., DfT) in order to be able to detect them.
Wireless Rate Adaptation via Smart Pilot

Rate adaptation is an essential component in today's wireless standards, for its ability to adaptively approach the channel capacity, and maximize the system throughput. The difficulty in rate adaptation stems from estimating the optimal data rate in a fluctuated channel. Previous wisdoms leverage PHY layer information for rate estimation, such as Soft PHY hints or Channel State Information. These information solely comes from one same layer, which are insufficient to track the optimal data rate. We observe that by investigating the information in both PHY layer decoder and upper layer protocol headers, more pilots can be exploited to estimate the optimal data rate across both time and frequency domain. These smart pilots help remove the residual channel effect and calibrate the CSI with minimum overhead. Based on the calibrated CSI, we propose a novel greedy rate selection algorithm to harness frequency diversity, which obtains the optimal data rate over all the subcarriers. Our experiments on GNU radio test bed show that Smart Pilot quickly tracks the link variance, and reduces the residual channel effect by 87%. Further, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.
Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.
Asymptomatic bacteriuria treatment is associated with a higher prevalence of antibiotic resistant strains in women with urinary tract infections.

BACKGROUND Women suffering from recurrent urinary tract infections (rUTIs) are routinely treated for asymptomatic bacteriuria (AB), but the consequences of this procedure on antibiotic resistance are not fully known. The aim of this study was to evaluate the impact of AB treatment on antibiotic resistance among women with rUTIs. METHODS The study population consisted of 2 groups of women who had previously been enrolled in a randomized clinical trial: group A was not treated, and group B was treated. All women were scheduled for follow-up visits every 6 months, or more frequently if symptoms arose. Microbiological evaluation was performed only in symptomatic women. All women were followed up for a mean of 38.8 months to analyze data from urine cultures and antibiograms. RESULTS The previous study population consisted of 673 women, but 123 did not attend the entire follow-up period. For the final analysis, 257 of the remaining 550 patients were assigned to group A, and 293 to group B. At the end of follow-up, the difference in recurrence rates was statistically significant (P < .001): 97 (37.7%) in group A versus 204 (69.6%) in group B. Isolated Escherichia coli from group B showed higher resistance to amoxicillin-clavulanic acid (P = .03), trimethoprim-sulfamethoxazole (P = .01), and ciprofloxacin (P = .03) than that from group A. CONCLUSIONS This study shows that AB treatment is associated with a higher occurrence of antibiotic-resistant bacteria, indicating that AB treatment in women with rUTIs is potentially dangerous.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differences between these two types of images. Specifically, fine details are necessary for detection in medical images, unlike in natural images where coarse structures matter most. This difference makes it inadequate to use the existing network architectures developed for natural images, because they work on heavily downscaled images to reduce the memory requirements. This hides details necessary to make accurate predictions. Additionally, a single exam in medical imaging often comes with a set of views which must be fused in order to reach a correct conclusion. In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images. We evaluate it on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 886,000 images. We focus on investigating the impact of the training set size and image size on the prediction accuracy. Our results highlight that performance increases with the size of training set, and that the best performance can only be achieved using the original resolution. In the reader study, performed on a random subset of the test set, we confirmed the efficacy of our model, which achieved performance comparable to a committee of radiologists when presented with the same data.
Consensus Statement on Concussion in Sport: the 3rd International Conference on Concussion in Sport held in Zurich, November 2008

This paper is a revision and update of the recommendations developed following the 1st (Vienna) and 2nd (Prague) International Symposia on Concussion in Sport. (1, 2) The Zurich Consensus statement is designed to build on the principles outlined in the original Vienna and Prague documents and to develop further conceptual understanding of this problem using a formal consensus-based approach. A detailed description of the consensus process is outlined at the end of this document under the "background" section (See Section 11). This document is developed for use by physicians, therapists, certified athletic trainers, health professionals, coaches and other people involved in the care of injured athletes, whether at the recreational, elite or professional level. While agreement exists pertaining to principal messages conveyed within this document, the authors acknowledge that the science of concussion is evolving and therefore management and return to play decisions remain in the realm of clinical judgment on an individualized basis. Readers are encouraged to copy and distribute freely the Zurich Consensus document and / or the Sports Concussion Assessment Tool (SCAT2) card and neither is subject to any copyright restriction. The authors request, however that the document and / or the SCAT2 card be distributed in their full and complete format.
Future options for disease intervention: important advances in phosphodiesterase 4 inhibitors

Current drug treatments for chronic obstructive pulmonary disease (COPD) focus on managing symptoms of the disease and include short- and long-acting β2-agonists, anticholinergic agents (ipratroprium, tiotropium), methylxanthines (theophylline) and inhaled corticosteroids (ICS). Cyclic nucleotide phosphodiesterases (PDEs) play a key role in cell signalling by degrading cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate. PDE4 is expressed in inflammatory cells and inhibition of this enzyme enhances the anti-inflammatory effects of cAMP in all key cells involved in COPD. Two PDE4 inhibitors, roflumilast and cilomilast, have been extensively evaluated in patients with COPD. Results from patients with moderate-to-severe and severe-to-very severe COPD have shown that roflumilast significantly improves forced expiratory volume in one second (FEV1) and significantly decreases exacerbations, particularly in patients with severe disease. Roflumilast is well tolerated with a low incidence of gastrointestinal adverse events that declines with continued treatment. Clinical trials with cilomilast have produced more varied results. Significant improvements in FEV1 and reductions in exacerbation rates versus placebo were observed in two of four trials. Cilomilast also has a high risk for gastrointestinal adverse events that does not appear to dissipate over 24 weeks of treatment. While further research is needed to fully determine the place in chronic obstructive pulmonary disease therapy for phosphodiesterase 4 inhibitors, they have several important potential benefits in the treatment of this disease, including convenient once-daily oral administration and freedom from adverse effects associated with corticosteroids. The fact that phosphodiesterase 4 inhibitors have potent anti-inflammatory effects and are administered orally, thereby reaching the systemic circulation, may decrease the severity of systemic comorbidities in chronic obstructive pulmonary disease patients.
Cross-Community Sensing and Mining ( CSM )

With the developments in ICT techniques, people are involving in and connecting via various forms of communities in the cyber-physical space, such as online communities, opportunistic (offline) social networks, and location-based social networks. Different communities have distinct features and strengths. With humans playing the bridge role, these communities are implicitly interlinked. In contrast to the existing studies that mostly consider a single community, this paper addresses the interaction among distinct communities. In particular, we present an emerging research area – cross-community sensing and mining (CSM), which aims to connect heterogeneous, cross-space communities by revealing the complex linkage and interplay among their properties and identifying human behavior patterns by analyzing the data sensed/collected from multicommunity environments. The paper describes and discusses the research background, characters, general framework, research challenges, as well as our practice of CSM.
Implementation of Algorithms Used in Distributed Optical Fiber Sensor System

Sensor technology has been applied widely. Optical fiber sensor has lots of advantages: high sensitivity, long measurement length, high geometric plasticity, has become a hot research topic in sensor technology. Since modern industrial depends on temperature measurement at a large extent, it is significant for the research of optical fiber temperature sensor system. In this paper, the implementation of algorithm used in distributed optical fiber sensor system was discussed.
An adaptive cross-layer routing protocol for delay-sensitive applications over MANETs

In this paper, we present an adaptive delay-aware multipath routing (ADAMR) framework for reliable transmission of delay-sensitive applications over mobile ad-hoc networks (MANETs). The proposed method uses cross-layer design to abstract the Quality-of-Service (QoS) constraints of requesting applications in terms of end-to-end delay. To ensure that each admitted data session packet gets the required delay, we use an adaptive flow admission control technique. The proposed admission control procedure admits only those data sessions for which our multipath routing protocol is able to find a delay-aware route. Proposed multipath routing protocol not only discovers routes that satisfy the given delay constraints, but also makes sure that the discovered routes are node-disjoint. When a link break is detected on an active route, we use backup route to bypass another overhead generating route discovery process. Extensive simulations prove the effectiveness of the proposed approach under various network scenarios. We use H.264/SVC encoded video traces to model the video source nodes in the network. The proposed method provides required QoS guarantees and perform congestion control based on the current network load levels. It is also observed from simulation results that ADAMR performs accurate admission control and discovers stable multiple node-disjoint routes with minimal routing overhead.
A Projection-Based Extension of the Phase Correlation Method

Phase-correlation (PC) is a computationally efcient method that estimates two and three dimensional translations. We present a projection operator which signicantly improves the accuracy and robustness of the PC scheme. The operator projects the estimated correlation function on the space of correlation functions that result from a certain range of translations, while rejecting components, which are unrelated to the estimated motion. Thus, the registration accuracy is improved by an order of magnitude, especially when we register noisy images and volumes. In addition, this approach is shown to be complementary to other subpixel PC based techniques.
E-FallD: A fall detection system using android-based smartphone

With the rapid growth of the population of the elderly in the world, many countries in the world have been in the aging society. Falls are a major public health concern and one of the greatest obstacles to independent living for elder adults in terms of social services. In this paper, we present a fall detection system derived from some motion sensor via an android-based smart phone in which we utilize adaptive threshold algorithm for fall detection. Furthermore, we implement this system called E-FallD on the HTC G8 mobile phone. As demonstrated by the experimental results, E-FallD has a good detection performance and it is an efficient and smart application. In the mean time, using adaptive threshold algorithm could increase sensitivity dramatically. In a word ,our system provides a realizable, effective and portal solution to fall detection for users.
Velocity Structure of the Tibetan Lithosphere : Constraints from P-Wave Travel Times of Regional Earthquakes by

Using data from regional earthquakes recorded by the Hi-CLIMB array in Tibet, we model P-wave arrival times to constrain the velocity structure in the crust and the upper mantle in central and western Tibet. Of more than 30 high-quality, regional seismic profiles that have been assembled, we have selected 10 that show excellent crustal and Pn arrivals for further analysis. Travel times from four events along the Hi-CLIMB array provide details on crustal velocities, and six events at regional distances to the array provide further constraints on Moho structure and upper-mantle–lid velocities. We use three-dimensional ray tracing to model the travel times, and the results indicate that the Moho beneath the Lhasa terrane of southern Tibet is over 73 km deep, with a Pn speed of about 8:2 km=s. The Qiangtang terrane north of the Bangong–Nujiang suture (BNS) shows a thinner crust, by up to 10 km, and a lower Pn speed of 7:8–7:9 km=s. Travel times from events to the west and east of the array indicate that both Moho structure and mantle–lid velocities in the region are three-dimensional in nature but approximately follow the trend of the BNS. Although only a limited number of events were used for the travel-time modeling, the results are consistent with earlier results from teleseismic imaging using the Hi-CLIMB array. Online Material: Regional earthquake locations.
Testing Dietary Hypotheses of East African Hominines Using Buccal Dental Microwear Data

There is much debate on the dietary adaptations of the robust hominin lineages during the Pliocene-Pleistocene transition. It has been argued that the shift from C3 to C4 ecosystems in Africa was the main factor responsible for the robust dental and facial anatomical adaptations of Paranthropus taxa, which might be indicative of the consumption of fibrous, abrasive plant foods in open environments. However, occlusal dental microwear data fail to provide evidence of such dietary adaptations and are not consistent with isotopic evidence that supports greater C4 food intake for the robust clades than for the gracile australopithecines. We provide evidence from buccal dental microwear data that supports softer dietary habits than expected for P. aethiopicus and P. boisei based both on masticatory apomorphies and isotopic analyses. On one hand, striation densities on the buccal enamel surfaces of paranthropines teeth are low, resembling those of H. habilis and clearly differing from those observed on H. ergaster, which display higher scratch densities indicative of the consumption of a wide assortment of highly abrasive foodstuffs. Buccal dental microwear patterns are consistent with those previously described for occlusal enamel surfaces, suggesting that Paranthropus consumed much softer diets than previously presumed and thus calling into question a strict interpretation of isotopic evidence. On the other hand, the significantly high buccal scratch densities observed in the H. ergaster specimens are not consistent with a highly specialized, mostly carnivorous diet; instead, they support the consumption of a wide range of highly abrasive food items.
Bounded decentralised coordination over multiple objectives

We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with 100 agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents.
Loss of hydraulic conductivity due to water stress in intact juveniles of Quercus rubra and Populus deltoides.

Despite many studies of the percent loss of hydraulic conductivity in excised branches, there is doubt as to whether cutting stems in air introduces unnatural embolism into the xylem at the cut surface. To address this question, hydraulic conductivity was measured in seedlings of northern red oak (Quercus rubra L.) and rooted scions of eastern cottonwood (Populus deltoides Bartr. ex Marsh.) that had been droughted in pots. Results indicate that in situ dehydration produced a very similar vulnerability curve (% loss of conductivity versus water potential) to those previously obtained by bench-top dehydration of excised branches of eastern cottonwood and red oak. In eastern cottonwood cuttings, conductivity loss increased sharply below water potentials of -1.0 MPa, with 100% loss of conductivity occurring by -2.0 MPa, whereas conductivity loss in red oak seedlings was more gradual, i.e., increasing below -1.5 MPa and sustaining 100% loss of conductivity by about -4.0 MPa.