Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and scale-up of a commercial fed batch refolding process for an anti-CD22 two chain immunotoxin

We describe the development and scale‐up of a novel two chain immunotoxin refolding process. This work provides a case study comparing a clinical manufacturing process and the commercial process developed to replace it. While the clinical process produced high quality material, it suffered from low yield and high yield variability. A systematic approach to process development and understanding led to a number of improvements that were implemented in the commercial process. These include a shorter inclusion body recovery process, limiting the formation of an undesired deamidated species and the implementation of fed batch dilution refolding for increased refold titers. The use of a combination of urea, arginine and DTT for capture column cleaning restored the binding capacity of the capture step column and resulted in consistent capture step yields compared to the clinical process. Scalability is shown with data from 250 L and 950 L scale refolding processes. Compared to the clinical process it replaces, the commercial process demonstrated a greater than fivefold improvement in volumetric productivity at the 950 L refolding scale. © 2014 American Institute of Chemical Engineers Biotechnol. Prog. , 30:1380–1389, 2014
Can We Predict Foraging Success in a Marine Predator from Dive Patterns Only? Validation with Prey Capture Attempt Data

Predicting how climatic variations will affect marine predator populations relies on our ability to assess foraging success, but evaluating foraging success in a marine predator at sea is particularly difficult. Dive metrics are commonly available for marine mammals, diving birds and some species of fish. Bottom duration or dive duration are usually used as proxies for foraging success. However, few studies have tried to validate these assumptions and identify the set of behavioral variables that best predict foraging success at a given time scale. The objective of this study was to assess if foraging success in Antarctic fur seals could be accurately predicted from dive parameters only, at different temporal scales. For this study, 11 individuals were equipped with either Hall sensors or accelerometers to record dive profiles and detect mouth-opening events, which were considered prey capture attempts. The number of prey capture attempts was best predicted by descent and ascent rates at the dive scale; bottom duration and descent rates at 30-min, 1-h, and 2-h scales; and ascent rates and maximum dive depths at the all-night scale. Model performances increased with temporal scales, but rank and sign of the factors varied according to the time scale considered, suggesting that behavioral adjustment in response to prey distribution could occur at certain scales only. The models predicted the foraging intensity of new individuals with good accuracy despite high inter-individual differences. Dive metrics that predict foraging success depend on the species and the scale considered, as verified by the literature and this study. The methodology used in our study is easy to implement, enables an assessment of model performance, and could be applied to any other marine predator.
GEMs: shared-memory parallel programming for Node.js

JavaScript is the most popular programming language for client-side Web applications, and Node.js has popularized the language for server-side computing, too. In this domain, the minimal support for parallel programming remains however a major limitation. In this paper we introduce a novel parallel programming abstraction called Generic Messages (GEMs). GEMs allow one to combine message passing and shared-memory parallelism, extending the classes of parallel applications that can be built with Node.js. GEMs have customizable semantics and enable several forms of thread safety, isolation, and concurrency control. GEMs are designed as convenient JavaScript abstractions that expose high-level and safe parallelism models to the developer. Experiments show that GEMs outperform equivalent Node.js applications thanks to their usage of shared memory.
A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface

Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (μ=0.74) was significantly lower than finger tapping by 8 patients (μ=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (μ=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (μ=0.76) were not significantly different from the first session (μ=0.72, p=0.16), or from the on-line accuracies of the third independent test session (μ=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.
Understanding Cognition Through Large-Scale Cortical Networks

An emerging body of evidence from a number of fields is beginning to reveal general neural principles underlying cognition. The characteristic adaptability of cognitive function is seen to derive from large-scale networks in the cerebral cortex that are able to repeatedly change the state of coordination among their constituent areas on a subsecond time scale. Experimental and theoretical studies suggest that large-scale network dynamics operate in a metastable regime in which the interdependence of cortical areas is balanced between integrating and segregating activities. Cortical areas, through their coordination dynamics, are thought to rapidly resolve a large number of mutually imposed constraints, leading to consistent local states and a globally coherent state of cognition.
Robust Bandwidth Extension of Narrowband Speech

Telephone speech often sounds muffled and thin due to its narrowband characteristics. With the increased availability of terminals capable of receiving wideband signals, extending the bandwidth of narrowband telephone speech at the receiver has drawn much research interest. Currently, there exist many methods that can provide good reconstructions of the wideband spectra from narrowband speech; however, they often lack robustness to different channel conditions, and their performances degrade when they operate in unknown environments. This thesis presents a bandwidth extension algorithm that mitigates the effects of adverse conditions. The proposed system is designed to work with noisy input speech and unknown channel frequency response. To maximize the naturalness of the reconstructed speech, the algorithm estimates the channel and applies equalization to recover the attenuated bands. Artifacts are reduced by employing an adaptive and a fixed postfilters. Subjective test results suggest that the proposed scheme is not affected by channel conditions and is able to produce speech with enhanced quality in adverse environments.
Robust and Efficient Wave Simulations on Deforming Meshes

The goal of this paper is to enable the interactive simulation of phenomena such as animated fluid characters. While full 3D fluid solvers achieve this with control algorithms, these 3D simulations are usually too costly for real‐time environments. In order to achieve our goal, we reduce the problem from a three‐ to a two‐dimensional one, and make use of the shallow water equations to simulate surface waves that can be solved very efficiently. In addition to a low runtime cost, stability is likewise crucial for interactive applications. Hence, we make use of an implicit time integration scheme to obtain a robust solver. To ensure a low energy dissipation, we apply an Implicit Newmark time integration scheme. We propose a general formulation of the underlying equations that is tailored towards the use with an Implicit Newmark integrator. Furthermore, we gain efficiency by making use of a direct solver. Due to the generality of our formulation, the fluid simulation can be coupled interactively with arbitrary external forces, such as forces caused by inertia or collisions. We will discuss the properties of our algorithm, and demonstrate its robustness with simulations on strongly deforming meshes.
High Proficiency in a Second Language is Characterized by Greater Involvement of the First Language Network: Evidence from Chinese Learners of English

The assimilation hypothesis argues that second language learning recruits the brain network for processing the native language, whereas the accommodation hypothesis argues that learning a second language recruits brain structures not involved in native language processing. This study tested these hypotheses by examining brain activation of a group of native Chinese speakers, who were late bilinguals with varying levels of proficiency in English, when they performed a rhyming judgment to visually presented English word pairs (CE group) during fMRI. Assimilation was examined by comparing the CE group to native Chinese speakers performing the rhyming task in Chinese (CC group), and accommodation was examined by comparing the CE group to native English speakers performing the rhyming task in English (EE group). The CE group was very similar in activation to the CC group, supporting the assimilation hypothesis. Additional support for the assimilation hypothesis was the finding that higher proficiency in the CE group was related to increased activation in the Chinese network (as defined by the CC > EE), including the left middle frontal gyrus, the right inferior parietal lobule, and the right precuneus, and decreased activation in the English network (as defined by the EE > CC), including the left inferior frontal gyrus and the left inferior temporal gyrus. Although most of the results support assimilation, there was some evidence for accommodation as the CE group showed less activation in the Chinese network including the right middle occipital gyrus, which has been argued to be involved in holistic visuospatial processing of Chinese characters.
THE EXCITE RESEARCH PROJECT : A STUDY OF SEARCHING BY WEB USERS

This article details the methodology and analysis used in the Excite Research Project, a currently three year and still on-going research project to study the nature of searching on a major Web search engine. The article begins with general information concerning the Web, the unique and fascinating insights into searchers on the Web, and the Web's impact. The major thrust of the article concerns the structure and approached utilized to analyze the data sets so far. We end with conclusions and expectation for future Web studies.
Partitioning of oxygen uptake between the gills and skin in fish larvae: a novel method for estimating cutaneous oxygen uptake.

The goal of this study was to develop an alternative to the traditional rubber dam method for measuring cutaneous oxygen uptake in bimodally respiring (skin + gills) fish larvae. The method tested involved using microelectrodes to measure the PO2 gradient in the diffusive boundary layer adjacent to seven positions on the skin surface (one on the head, two on the yolk sac, two on the trunk, one at the base of the dorsal fin-fold and one on the proximal portion of the caudal fin-fold) of rainbow trout (Oncorhynchus mykiss) larvae in still water. The PO2 gradient (deltaPO2/delta x, where x is the distance from the skin surface) was then used to calculate area-specific rate of O2 uptake (.MO2/A) according to the Fick equation, . MO2/A=Dbeta(deltaPO2/deltax), where A is the cross-sectional area of the boundary layer, D is the diffusion coefficient and beta is the capacitance coefficient for O2 in water. The accuracy of the method was assessed by comparing it with the rubber dam method. After correcting for differences in body mass, the two methods gave essentially identical results. According to the boundary layer method, the mean (+/-95 % CI) rate of O2 uptake across the skin of newly hatched rainbow trout at 10 degrees C is 3.13+/-0.18 microg O2 cm-2h-1 (N=265). The corresponding value obtained using the rubber dam method was 3. 36+/-0.35 microg O2 cm-2 h-1 (N=27). The advantages of the boundary layer method are that it can be used with smaller, more delicate larvae and that variables, such as flow rate, that can affect the efficiency of gas exchange can be regulated more precisely. The boundary layer method also permits examination of regional differences in exchange efficiency, although in still water such differences do not appear to be significant in trout larvae. The mean steepness of the PO2 gradient in the boundary layer and, hence, the mean rate of area-specific O2 uptake were essentially the same (P>0.05) at all seven locations tested in this study. The boundary layer method can potentially be used to study the transcutaneous flux, not only of O2 but of virtually any diffusible substance that can be measured with microelectrodes and that is consumed (e.g. Na+, Ca2+) or excreted (e.g. CO2, NH3) by fish larvae or other small organisms.
Gaussian Process Model Predictive Control of Unknown Nonlinear Systems

Model Predictive Control (MPC) of an unknown system that is modelled by Gaussian Process (GP) techniques is studied in this paper. Using GP, the variances computed during the modelling and inference processes allow us to take model uncertainty into account. The main issue in using MPC to control systems modelled by GP is the propagation of such uncertainties within the control horizon. In this paper, two approaches to solve this problem, called GPMPC1 and GPMPC2, are proposed. With GPMPC1, the original Stochastic Model Predictive Control (SMPC) problem is relaxed to a deterministic nonlinear MPC based on a basic linearized GP local model. The resulting optimization problem, though non-convex, can be solved by the Sequential Quadratic Programming (SQP). By incorporating the model variance into the state vector, an extended local model is derived. This model allows us to relax the non-convex MPC problem to a convex one which can be solved by an active-set method efficiently. The performance of both approaches is demonstrated by applying them to two trajectory tracking problems. Results show that both GPMPC1 and GPMPC2 produce effective controls but GPMPC2 is much more efficient computationally.
An Extensive Input Voltage and Fixed-Frequency Single Stage Series-Parallel LLC Resonant Converter for Dc Drive

In this paper design of a single-stage LLC resonant converter are presented. A single-stage converter uses only one control signal to drive two power converters, a power factor corrector (PFC) converter and a dc/dc converter, for reducing the cost of the system. However, this simplicity induces power imbalance between two converters, and then, the bus voltage between two converters drifts and becomes unpredictable. To ensure that the bus capacitor voltage can be kept in a tolerable region, the characteristics of a PFC converter and an LLC tank are investigated, and then, a design procedure is proposed correspondingly. Finally, a single-stage LLC resonant converter is implemented to verify the analysis.
Analysis of Ventricular Wall Motion Based on an Electromechanical Biventricular Model

A biventricular model, which coupled the electrical property and mechanical property of the heart, was constructed and the ventricular wall motion and deformation have been simulated based on this model. The biventricular mechanical model was constructed based on composite material theory and finite element method. The excitation propagation was simulated by electrical heart model, and the resulted active forces were used to calculate the ventricular wall motion. Regional deformation and Lagrangian strain tensors were calculated during systole phase. Displacements, minimum principal strains and torsion angle were used to describe the motion of two ventricles. The results are in good accordance with solutions obtained from MR tagging images that reported in the literatures. This simulation suggests that such electromechanical biventricular model can be used to assess the mechanical function of two ventricles.
Adaptive speed identification for vector control of induction motors without rotational transducers

A model-reference adaptive system (MRAS) for the estimation of induction motor speed from measured terminal voltages and currents is described. The estimated speed is used as feedback in a vector control system, thus achieving moderate bandwidth speed control without the use of shaft-mounted transducers. This technique is less complex and more stable than previous MRAS tacholess drives. It was implemented on a 30 hp laboratory drive, verifying its effectiveness. <<ETX>>
COMPARATIVE ANALYSIS OF DIFFERENT ROUTING PROTOCOLS IN DELAY TOLERANT NETWORKS

The modern wireless networks have provided a wide range of applications making it possible to successfully interconnect devices and systems which have the wireless connection facility, such as mobile phones, tablets, laptops etc. all around the world. Modern Internet protocols exhibits inefficient performance in those networks where the connectivity between end nodes has intermittent property due to dynamic topology such as Mobile Ad-hoc Networks (MANET) or Vehicular Ad-hoc Networks (VANET). The network environments where the nodes are characterized by opportunistic connectivity are referred to as Delay Tolerant Networks (DTNs). DTNs have been one of the growing areas of interest characterized by the significant amount of research efforts invested in this area over the past decade. Routing is one of the major issues affecting the overall performance of DTN networks in terms of resource consumption, data delivery and latency. Over the past few years a number of routing protocols have been proposed for DTN networks. In this paper mainly three DTN routing protocols: Direct Delivery, Epidemic Routing and Spray and Wait Routing, are advocated and compared in terms of Average Packet Delivery Probability and Average Latency. To better judge the performance of these routing protocols, the series of simulations are carried out in The ONE simulator with program version of 1.4. 1 KeywordsDTN, Epidemic Routing, Spray and Wait Routing, Packet Delivery Probability, Average Latency
Groundwater and Surface Water Availability via a Joint Simulation with a Double Control of Water Quantity and Ecologically Ideal Shallow Groundwater Depth: A Case Study on the Sanjiang Plain, Northeast China

Joint assessment of groundwater-surface water resources can help develop sustainable regional water management plans for intensive agriculture. In this study, we estimated allowable groundwater and surface water quantities using a water balance model, WetSpass-GMS, for the Sanjiang Plain (10.9 × 104 km2), one of the most important grain production bases in China. We then applied a double control based on the groundwater availability and the concept of an ecologically ideal shallow groundwater depth (EISGD) to three different water use scenarios: (A) continuation of the current water use management; (B) maximal use of water resources under a double control; and (C) irrigation of 266.7 × 104 hectares that are suitable for rice cultivation. We found an annual allowable surface water quantity of 4.71 billion cubic meters for the region and an annual exploitable groundwater quantity of 4.65 billion cubic meters under full consideration of water requirements, i.e., sustaining river base flow, necessary riverine sediment transport, and ecological water supplies for wetlands and reservoirs. Our simulation results showed that for Scenario A, groundwater level in the region would continue falling, and that the groundwater levels in wet, normal and dry years would drop below the EISGD level in 2028, 2023 and 2019, respectively. For Scenario B, groundwater and surface water would be able to support rice paddies of 219.7 × 104 hectares, 212.7 × 104 hectares, and 209.3 × 104 hectares during wet, normal and dry years, respectively. For Scenario C, future demands on groundwater and surface water under wet, dry and normal years would all exceed their allowable supplies. Overall, this study indicates that integrated management plans promoting an increase of surface water use and a reduction in irrigation with groundwater should be developed for sustainable agriculture and ecological preservation on the Sanjiang Plain.
Study of Feature Selection Techniques using Microarray Data.

Using unique feature selection technique for achieving most accurate classifier, for all domains, is next to impossible. Suitable domain specific features can yield better performance for that particular domain. So, we are going to implement Occam’s Razor, Sample Compression, and PAC-Bayes learning algorithms using Decision Stump. We are going to apply these feature selection techniques on microarray data and on the basis of specific efficiency measures we can choose a best suitable technique particularly for microarray data. This paper presents the comparison in between proposed techniques along with traditional feature selection techniques. We apply the projected approaches for gene identification from deoxyribonucleic acid microarray data and compare our results to those of the well-known triple-crown approaches projected for the task. We tend to show that our algorithmic rule not solely finds hypotheses with a far smaller variety of genes whereas giving competitive classification accuracy, in contrast to different approaches. The projected approaches are general and protrusile in terms of each coming up with novel algorithms and application to different domains.
The role of drinking norms and social networking sites on alcohol consumption in university students

ABSTRACT Background: Descriptive alcohol norms refer to the perception of how frequently and how much peers consume alcohol. University students frequently overestimate descriptive norms, which is positively associated with their own alcohol consumption. Exposure to alcohol-related content on social networking sites (SNS) is also predictive of alcohol-related problems. Objectives: The purpose of this study is to investigate the role of descriptive norms as a potential mediator of the relationship between alcohol exposure via SNS and problematic alcohol consumption. Methods: A total of 175 students in a Canadian university completed the Alcohol and Social Networking Practice Questionnaire to assess activity on SNS, the degree of exposure to alcohol-related content on SNS via peers, and perceptions of the estimated frequency and quantity of alcohol consumed by other students. The Alcohol Use Disorders Identification Test was administered to assess a participant’s alcohol consumption patterns. Results: Both frequency and quantity norms partially mediated the relationship between alcohol exposure on SNS and drinking behavior. Conclusion: These results suggest that SNS may be useful to identify at-risk students and may serve as an effective platform for personalized normative feedback interventions.