Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive enzyme changes in liver and muscle of rats during protein depletion and refeeding

1. Young rats were kept on a 6% casein diet for 4–7 weeks and then either killed or returned to a stock diet. Control rats were given a stock diet. 2. Argininosuccinate lyase (EC 4.3.2.1, L-argininosuccinate arginine-lyase) was assayed in liver, and amino acid activating enzymes were assayed in liver and muscle of rats after depletion and while they were refed on the stock diet. Levels of enzymes were compared with those in the control rats. 3. Amino acid activating enzymes were markedly increased in liver at the end of the depletion period, but returned almost to normal levels after 4–6 days of refeeding. In muscle the enzyme levels were not decreased after depletion, but began to rise after 4 days of refeeding. 4. Argininosuccinate lyase activity in depleted female rats was about half that in control rats and rose again slowly on refeeding. Male rats were hardly affected by depletion. 5. It is suggested that these enzyme changes play a part in the adaptation of the rat to a reduced protein intake.
Variation in Milk Protein Composition and its Importance for the Quality of Cheese Milk

Variations within the bovine milk protein profile can have both positive and negative impacts on the quality of cheese milk. As main contributors to the cheese structure, the caseins are important for the cheese yield. During the last decades, the proportion of casein in relation to the total protein amount (the casein number) has decreased in Swedish bulk milk. This decline is unexplained and the reason for this deteriorating trend is unknown. Therefore, more knowledge in how the protein composition varies is needed. The main objective of this thesis was to increase the understanding of how the cheese yield is affected by variation in the detailed milk protein profile. Milk was sampled from a total of 134 individual cows of Swedish red and white (SRB), Swedish Holstein (SLB) and Danish Holstein (SDM). The milk was analysed for major milk components, urea, calcium, somatic cell count, pH, level of protein degradation, the detailed milk protein composition and low-molecular-weight milk peptides. Low-fat model cheeses were made from four litres of pasteurised skimmed milk and cheese yield was recorded. Variations in the milk protein profile between breeds or herds, before and after pasteurisation and the effect of protein composition on cheese yield were studied. Finally, low-molecular-weight peptides identified in the acid whey fraction from milk samples with different levels of somatic cell count were related to earlier publications on identified cleavage specificities of milk proteases. Concentrations of total protein, total casein, β-casein and κ-casein were higher in SRB milk compared to SLB milk. As expected, pasteurisation did not influence the milk protein markedly and only small changes in concentration of αS1-casein and proteose peptone component 3 were seen. The milk proteins αS1-, β-, and κ-casein and β-lactoglobulin B had significantly positive effects on the cheese yield whereas the casein number was significant for the transfer of protein from milk to cheese. Moreover, several forms or proteolytic products of αS1-, αS2-, and β-casein positively affected this trait. About 30% of the milk samples were poorly coagulating or non-coagulating with a significantly lower concentration of κ-casein. Finally, peptide cleavage sites corresponding to earlier identified specificities of elastase and cathepsing B were identified in milk with high somatic cell count.
Key Frame Extraction Based on Chaos Theory and Color Information for Video Summarization

Key frame based video summarization has emerged as an important task for efficient video data management. This paper proposes a novel technique for key frame extraction based on chaos theory and color information. By applying chaos theory, a large content change between frames becomes more chaos-like and results in a more complex fractal trajectory in phase space. By exploiting the fractality measured in the phase space between frames, it is possible to evaluate inter-frame content changes invariant to effects of fades and illumination change. In addition to this measure, the color histogram-based measure is also used to complement the chaos-based measure which is sensitive to changes of camera /object motion. By comparing the last key frame with the current frame based on the proposed frame difference measure combining these two complementary measures, the key frames are robustly selected even under presence of video fades, changes of illumination, and camera/object motion. The experimental results demonstrate its effectiveness with significant improvement over the conventional method. key words: key frame extraction, video summarization, chaos theory, content change
AP24163 Inhibits the Gatekeeper Mutant of BCR‐ABL and Suppresses In vitro Resistance

Mutation in the ABL kinase domain is the principal mechanism of imatinib resistance in patients with chronic myelogenous leukaemia. The second generation BCR/ABL inhibitors nilotinib and dasatinib effectively inhibit most imatinib resistance variants, but are ineffective against the gatekeeper mutant, T315I. Gatekeeper mutation activates the kinase by stabilizing the hydrophobic spine. Here, we describe that the rationally designed compound AP24163 can inhibit native and gatekeeper mutants of the BCR/ABL kinase. Structural modelling suggests that AP24163 affects the flexibility of the P‐loop and destabilizes the active conformation by disrupting the hydrophobic spine. In vitro screening for drug resistance identified clones with compound mutations involving both the P‐loop and T315I. Our studies provide structural insights for the design of inhibitors against the gatekeeper mutant and suggest that up‐front combination therapy may be required to prevent the emergence of compound‐resistant mutations.
A Bullseye Display of Regional Perfusion of the Brain

The authors submit a method of viewing Tc-99m HMPAO SPECT images of the brain based on the principles of the myocardial bullseye or polar mapping. Commercially available software for the production of a myocardial bullseye display is suitable for the preparation of a bullseye display of regional perfusion image of the brain. This display facilitates the detection of cerebral cortical perfusion defects by displaying all SPECT oblique slices concentrically from base to vertex in a single image.
Successful leadership practices of head teachers for school improvement: Some evidence from Pakistan

Purpose – The main purpose of this study is to identify the successful leadership practices of head teachers for school improvement at secondary level in Pakistan.Design/methodology/approach – The study was descriptive (survey type) in nature. It was conducted on a sample of 351 secondary school head teachers, 702 elementary and secondary school teachers working in the government secondary schools of Punjab province. Data were collected using a mixed‐methods research design that included: review of related literature, documents indicating school achievements and student attainment, questionnaires and in‐depth semi‐structured interviews with different stakeholders including the head teachers, teachers, parents and students. The validity and reliability of the instruments was ensured through experts' opinions and pilot testing in mid‐2007; the overall reliability was established at 0.923 alpha level.Findings – The findings of the study revealed that the majority of the head teachers of successful schools de.
A Class-G Switched-Capacitor RF Power Amplifier

A switched-capacitor power amplifier (SCPA) that realizes an envelope elimination and restoration/polar class-G topology is introduced. A novel voltage-tolerant switch enables the use of two power supply voltages which increases efficiency and output power simultaneously. Envelope digital-to-analog conversion in the polar transmitter is achieved using an SC RF DAC that exhibits high efficiency at typical output power backoff levels. In addition, high linearity is achieved and no digital predistortion is required. Implemented in 65 nm CMOS, the measured peak output power and power-added efficiency (PAE) are 24.3 dBm and 43.5%, respectively, whereas when amplifying 802.11g 64-QAM OFDM signals, the average output power and PAE are 16.8 dBm and 33%, respectively. The measured EVM is 2.9%.
Noise delays bifurcation in a positively coupled neural circuit

We report a noise induced delay of bifurcation in a simple pulse-coupled neural circuit. We study the behavior of two neural oscillators, each individually governed by saddle-node dynamics, with reciprocal excitatory synaptic connections. In the deterministic circuit, the synaptic current amplitude acts as a control parameter to move the circuit from a mono-stable regime through a bifurcation into a bistable regime. In this regime stable sustained anti-phase oscillations in both neurons coexist with a stable rest state. We introduce a small amount of random current into both neurons to model possible randomly arriving synaptic inputs. We find that such random noise delays the onset of bistability, even though in decoupled neurons noise tends to advance bifurcations and the circuit has only excitatory coupling. We show that the delay is dependent on the level of noise and suggest that a curious stochastic ``anti-resonance'' is present.
MixedNotes: a digital tool to prepare physical notes for affinity diagramming

Affinity Diagramming is a technique to organize and make sense of qualitative data. It is commonly used in Contextual Design and HCI research. However, preparing notes for and building an Affinity Diagram remains a laborious process, with a wide variety of different approaches and practices. In this paper, we present MixedNotes, a novel technique to prepare physical paper notes for Affinity Diagramming, and a software tool to support this technique. The technique has been tested with large real-life Affinity Diagrams with overall positive results.
What Would You Do? Acting by Learning to Predict

We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perform a human demonstrated task without knowledge of the thought processes or actions of the human, only their visually observable state transitions. We evaluate our approach on two table-top, object manipulation tasks and demonstrate generalisation to previously unseen states. Our approach reduces the priors required to implement a robot task learning system compared with the existing approaches of Learning from Demonstration, Reinforcement Learning and Inverse Reinforcement Learning.
Measuring the reputation of web sites: a preliminary exploration

We describe the preliminary results from a pilot study, which assessed the perceived reputation - authority and trustworthiness - of the output from five WWW indexing/ranking tools. The tools are based on three techniques: external link structures, internal content, or human selection/indexing. Twenty-two participants reviewed the output from each tool and assessed the reputation of the retrieved sites.
FLIPPER: Fault-tolerant distributed network management and control

The current developments of software defined networking (SDN) paradigm provide a flexible architecture for network control and management, in the cost of deploying new hardwares by replacing the existing routing infrastructure. Further, the centralized controller architecture of SDN makes the network prone to single point failure and creates performance bottleneck. To avoid these issues and to support network manageability over the existing network infrastructure, we develop Flipper in this paper, that uses only software augmentation to convert existing off-the-shelf routers to network policy design and enforcement points (PDEP). We develop a distributed self-stabilized architecture for dynamic role change of network devices from routers to PDEPs, and make the architecture fault-tolerant. The performance of Flipper has been analyzed from both simulation over synthetic networks, and emulation over real network protocol stacks, and we observe that Flipper is scalable, flexible and fail-safe that can significantly boost up the manageability of existing network infrastructure.
Guarding the Gate

Background Failed treatment trials are common in major depressive disorder and treatment-resistant depression, and remotely performed multifaceted, centralized structured interviews can potentially enhance signal detection by ensuring that enrolled patients meet eligibility criteria. Methods We assessed the use of a specific remote structured interview that validated the diagnosis, level of treatment resistance, and depression severity. The objectives were to (1) assess the rate at which patients who were deemed eligible for participation in trials by site investigators were ineligible, (2) assess the reasons for ineligibility, (3) compare rates of ineligibility between academic and nonacademic sites, (4) compare eligibility between US and non-US sites, and (5) report the placebo response rates in trials utilizing this quality assurance approach, comparing its placebo response rates with those reported in the literature. Methods included a pooled analysis of 9 studies that utilized this methodology (SAFER interviews). Results Overall, 15.33% of patients who had been deemed eligible at research sites were not eligible after the structured interviews. The most common reason was that patients did not meet the study requirements for level of treatment resistance. Pass rates were significantly higher at non-US compared with US sites (94.6% vs 83.3%, respectively; P < 0.001). There was not a significant difference between academic and nonacademic sites (87.8% vs 82.4%; P = 0.08). Placebo response rates were 13.0% to 27.3%, below the 30% to 40% average in antidepressant clinical trials, suggesting a benefit of the quality assurance provided by these interviews. Conclusions The use of a remotely structured interview by experienced clinical researchers was feasible and possibly contributed to lower-than-average placebo response rates. The difference between US and non-US sites should be the subject of further research.
The Effect of Chinese Learners’ Modality Converting Competence on Their EFL Output System

This research introduces modality converting competence into foreign language aptitude composition and makes a diachronic study on the relationship between the internal variables for Chinese learners’ EFL written output system and their modality converting competence. Through multiple correlation analysis and multiple regression modeling, it could be concluded that compared with the variables of the same modal, the variables of modality converting competence were more correlated to the variables of EFL written system and the latter could respectively account for 72.2%, 57.8%, 65.9% and 67.0% of the variation of the written system variables W/T, DC/ T. S/T and LC/T. As the advantage of aptitude, modal converting competence might produce influence upon learners’ syntactic and lexical system complexity in their written output via information processing process. The significance of this research lies in that it might provide more practical approaches for the realization of aptitude treatment interaction (ATI).
Ambivalent Dutch lifestyle migrants in rural Sweden

This paper focuses on Dutch families who moved to Hallefors (rural Sweden) in the early 21st century. It discusses contradictory and ambivalent discourses between life before and life after migration. As studied in this text, the direction of the move (north), the destination (a problematic municipality) and the structure for the decision process (provided by a municipality and an agency actively attracting incomers) are novel aspects to existing studies of lifestyle migration. The paper aims to examine the migration process of Dutch lifestyle migrants in Hallefors and their ambivalent attitudes towards returning. The main empirical question is ‘after migrating to Hallefors, what influences the Dutch families’ attitude towards returning? ’ This question is addressed through an interview study, conducted in 2011. One of the findings is that long-term planning migrants show less ambivalence than spontaneous movers.
Face recognition by Zero-Ratio based LGBP features

LGBP has been successfully applied in face recognition, which combines Gabor decomposition and LBP descriptor. However, in this method LBP histograms from multi-scale and multi-orientation Gabor energy images are concatenated to form the feature vector for input image, so the dimension of generated features is very high. In this paper we propose a novel face recognition method called Zero-Ratio based LGBP, which uses a feature selection method called Zero-Ratio to reduce the dimension of LGBP features effectively. We evaluate our method on AR face database, the experimental results show that our method can greatly reduce dimension of LGBP features without significantly affecting recognition accuracy.
MULTIMODAL EMOTION RECOGNITION

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing–emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This chapter explores new ways of human-computer interaction that enable the computer to be more aware of the user’s emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and pshysiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.
Bit-flipping post-processing for forced convergence decoding of LDPC codes

The recently proposed forced convergence technique allows for reducing the decoding complexity of Low-Density Parity-Check Codes (LDPCC) at only slight deterioration in performance. The basic idea is to restrict the message passing during LDPC decoding to the nodes that still significantly contribute to the decoding result. In this paper, we propose to add a bit-flipping post-processor to the forced convergence decoder in order to alleviate some problems of this novel technique, namely the error floors observed when aiming for high reduction in decoding complexity. Our results show that combining a hard decision bit-flipping with the forced convergence approach enables to almost retain original error correction performance while further reducing decoding complexity.
Truthfulness Analysis of Fact Statements Using the Web

Web users increasingly treat the Web as a very large knowledge base and use it to acquire all kinds of knowledge. In this article, we introduce a method that leverages the information on the Web to determine the truthfulness of any statement that attempts to state a fact. This method accomplishes the goal by checking whether there is an alternative statement that is more likely to be truthful. As a result, this method can find an alternative truthful statement when the given statement is not truthful. Our experimental results show that the proposed method is effective. In this article, we also provide some insights on how to extend this work and how to leverage this technique to estimate the trustworthiness of web pages and websites.
Journal of Experimental Psychology : Learning , Memory , and Cognition Gradient Phonological Inconsistency Affects Vocabulary Learning

Learners frequently experience phonologically inconsistent input, such as exposure to multiple accents. Yet, little is known about the consequences of phonological inconsistency for language learning. The current study examines vocabulary acquisition with different degrees of phonological inconsistency, ranging from no inconsistency (e.g., both talkers call a picture /vig/) to mild but detectable inconsistency (e.g., one talker calls a picture a /vig/, and the other calls it a /vIg/), up to extreme inconsistency (e.g., the same picture is both a /vig/ and a /dId/). Previous studies suggest that learners readily extract consistent phonological patterns, given variable input. However, in Experiment 1, adults acquired phonologically inconsistent vocabularies more slowly than phonologically consistent ones. Experiment 2 examined whether word-form inconsistency alone, without phonological competition, was a source of learning difficulty. Even without phonological competition, listeners learned faster in 1 accent than in 2 accents, but they also learned faster in 2 accents (/vig/ ϭ /vIg/) than with completely different labels (/vig/ ϭ /dId/). Overall, results suggest that learners exposed to multiple accents may experience difficulty learning when 2 forms mismatch by more than 1 phonological feature, plus increased phonological competition due to a greater number of word forms. Implications for learning from variable input are discussed. A major challenge in language acquisition is identifying the relevant sources of variability in a complex signal. A listener hearing a novel language must rapidly determine which of numerous differences from sentence to sentence, or from word to word, indicate a change in meaning and which changes are irrelevant to meaning. Further, recent research indicates that high levels of variability can lead to language change (e. regularize their input, making morphosyntactic forms more consistent than their input would dictate (see also Kirby, Cornish, & Smith, 2008). However, most research on the role of variability in language acquisition has focused on morphosyntax and semantics, leaving open what happens in situations of phonological input variability. This is an important consideration, because most listeners experience some degree of phonological inconsistency within single word forms due to accent variability: Children in South Carolina must learn that their parents mentioning a pen (/pIn/) and a newscaster mentioning a pen (/pεn/) refer to the same object. Children whose parents are native Spanish speakers of English may hear their parents call a pen something closer to " pain " /peIn/ due to the different distributions of vowel sounds in English and Spanish (e.g., Fox, …
Evaluating the Practical Usability of a Defect Prediction Model for a Large Evolving Software System

A plethora of defect prediction models has been proposed and empirically evaluated, often using standard classification performance measures. Recently, flaws of these measures in this context have been highlighted, and more specific performance metrics were proposed. In this paper, we describe our experiences creating a defect prediction model for a large software system from the telecommunications domain. The system have been evolved since 2001, resulting in more than 3 million lines of C/C++ code. A history of roughly 3 years is analyzed to extract process and static code metrics that are used to build a prediction model with Random Forests. The performance of the resulting model is comparable to previously published work. Additionally, an evaluation using the performance criteria that were recently proposed is carried out. Furthermore, we develop a new evaluation measure based on the comparison to the optimal model.