Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell motility of neural stem cells is reduced after SPIO‐labeling, which is mitigated after exocytosis

MRI is used for tracking of superparamagnetic iron oxide (SPIO)‐labeled neural stem cells. Studies have shown that long‐term MR tracking of rapidly dividing cells underestimates their migration distance. Time‐lapse microscopy of random cellular motility and cell division was performed to evaluate the effects of SPIO‐labeling on neural stem cell migration. Labeled cells divided symmetrically and exhibited no changes in cell viability, proliferation, or apoptosis. However, SPIO‐labeling resulted in decreased motility of neural stem cells as compared with unlabeled controls. When SPIO‐labeled neural stem cells and human induced pluripotent stem cells were transplanted into mouse brain, rapid exocytosis of SPIO by live cells was observed as early as 48 h postengraftment, with SPIO‐depleted cells showing the farthest migration distance. As label dilution is negligible at this early time point, we conclude that MRI underestimation of cell migration can also occur as a result of reduced cell motility, which appears to be mitigated following SPIO exocytosis. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.
A novel Fuzzy-Sliding Mode Controller for Antilock Braking System

In this paper a hybrid controller for wheel slip ratio control has been designed base on a quarter vehicle model. The proposed controller is a combination of a sliding mode controller (SMC) with a fuzzy controller to improve sliding mode controller efficiency. Three different sliding mode controllers are compared and the best one of them combined with a fuzzy controller to increase controller performance by eliminating the time responses oscillation. Three different road conditions have been chosen base on a look-up table. Simulation results show good performance of the proposed controller for different road conditions.
Estrogen Receptor (cid:1) Inhibits IL-1 (cid:2) Induction of Gene Expression in the Mouse Liver

Estrogens have been suggested to modulate several inflam- matory processes. Here, we show that IL-1 (cid:2) treatment induced the expression of approximately 75 genes in the liver of ovariectomizedmice.17 (cid:1) -Ethinylestradiol(EE)pretreatment reduced the IL-1 (cid:2) induction of approximately one third of these genes. Estrogen receptor (cid:1) (ER (cid:1) ) was required for this inhibitory activity, because EE inhibition of IL-1 (cid:2) -stimulated gene expression occurred in ER (cid:2) knockout mice, but not in ER (cid:1) knockout mice. EE treatment induced expression of 40 genes, including the transcriptional repressor short heterodimer partner and prostaglandin D synthase, known mod- ulators of nuclear factor- (cid:3) B signaling. However, the ER agonists genistein and raloxifene both inhibited IL-1 (cid:2) gene induction without stimulating the expression of prostaglan- din D synthase, short heterodimer partner, or other ER-inducible genes, indicating that induction of gene expression was not required for ER inhibition of IL-1 (cid:2) signaling. Finally, the ability of EE to repress IL-1 (cid:2) gene induction varied among tissues. For example, EE inhibited IL-1 (cid:2) induction of lipopo-lysaccharide-induced c-x-c chemokine (LIX) in the liver, but not in the spleen or lung. The degree of EE repression did not correlate with ER expression. cAMP response element binding protein-binding protein (CBP)/p300 levels also varied between tissues. Together, these results are consistent with a model of in vivo ERinterferencewithIL-1 (cid:2) signalingthroughacoactivator-based , CCAAT/enhancer binding protein- ; EE, 17 (cid:1) estradiol; ELF3, E74-like factor-3; ER, estrogen receptor; GAPDH, glyceraldehyde-3-phosphate dehydroge-nase;JAB,JAK-bindingprotein;LIX,LPS-inducedc-x-cchemokine;LPS,lipopolysaccharide;MCP,monocytechemoattractantprotein;MIP,mac-rophageinflammatoryprotein;NRF2,NF-E2-relatedfactor2;NF-
Application of Visible-Near Infrared Spectra Technique Based on to the Non-Destructive Measurement of the Salmon Quality

Because the reflectivity of astaxanthin vary in different bands (mainly 400nm-600nm), so we use the visible-near infrared spectra technique to irradiate the salmon. Because in daily life, people grade the salmon flesh with a color card. In this paper, we first use principal component analysis to reduce the dimensionality of the spectral data of salmon, then use linear discriminant analysis method, least squares support vector machine classification method to distinguish the flesh quality. The correct classification rates are 60%and73.3%. The results show that we can use visible – near infrared spectra to distinguish the quality of the salmon which doesn’t be dissected.
Understanding Hollywood through Dialogues

Movies are a huge part of most of our lives. They reflect, distort and influence how our society works. The systematic bias against female and other minority characters and actors in Hollywood has been a hot topic for a while now. However, there has been very little quantitative analysis for this debate. Embodying Silicon Valley’s zeal for data-driven problem solving, we explored a corpus of movie conversations and memorable quotes to computationally learn about movies and analyze the differences in movie dialogues uttered by female vs male characters using deep learning and natural language processing.
Negative Effect of Glucose on ompA mRNA Stability: a Potential Role of Cyclic AMP in the Repression of hfq in Escherichia coli

ABSTRACT Glucose is a carbon source that is capable of modulating the level of cyclic AMP (cAMP)-regulated genes. In the present study, we found that the stability of ompA mRNA was reduced in Escherichia coli when glucose (40 mM) was present in Luria-Bertani (LB) medium. This effect was associated with a low level of cAMP induced by the glucose. The results were confirmed with an adenylyl cyclase mutant with low levels of cAMP that are not modulated by glucose. Northern blot and Western blot analyses revealed that the host factor I (Hfq) (both mRNA and protein) levels were downregulated in the presence of cAMP. Furthermore, we showed that a complex of cAMP receptor protein (CRP) and cAMP binds to a specific P3hfq promoter region of hfq and regulates hfq expression. The regulation of the hfq gene was confirmed in vivo using an hfq-deficient mutant transformed with an exogenous hfq gene containing the promoter. These results demonstrated that expression of hfq was repressed by the CRP-cAMP complex. The presence of glucose resulted in increased Hfq protein levels, which decreased ompA mRNA stability. An additional experiment showed that cAMP also increased the stability of fur mRNA. Taken together, these results suggested that the repression of Hfq by cAMP may contribute to the stability of other mRNA in E. coli.
Normal Diet within Two Postoperative Days—Realistic or Too Ambitious?

Enhanced Recovery After Surgery (ERAS) protocols advocate early postoperative resumption of normal diet to decrease surgical stress and prevent excessive catabolism. The aim of the present study was to identify reasons for delayed tolerance of normal postoperative diet. This was a retrospective analysis including all consecutive colorectal surgical procedures since May 2011 until May 2017. Data was prospectively recorded by an institutional data manager in a dedicated database. Uni- and multivariate risk factors associated with delayed diet (beyond POD 2) were identified by multiple logistic regression among demographic, surgery- and modifiable pre- and intraoperative ERAS-related items. In a second step, univariate analysis was performed to compare surgical outcomes for patients with early vs. delayed oral intake. The study cohort consisted of 1301 consecutive colorectal ERAS patients. Herein, 691 patients (53%) were able to resume normal diet within two days of surgery according to ERAS protocol, while in 610 patients (47%), a delay in tolerance of normal diet was observed. Male gender was independently correlated to early tolerance (Odds Ratio (OR) 0.66; 95% Confidence Interval (CI) 0.46–0.84, p = 0.002), while ASA score ≥ 3 (OR 1.60; 95% CI 1.12–2.28, p = 0.010), abdominal drains (OR 1.80; 95% CI 1.10–2.49, p = 0.020), right colectomy (OR 1.64; 95% CI 1.08–2.49, p = 0.020) and Hartmann reversal (OR 2.61; 95% CI 1.32–5.18, p = 0.006) constituted risk factors for delayed tolerance of normal diet. Patients with delayed resumption of normal diet experienced more overall (Clavien grade I–V) (47% vs. 21%, p < 0.001) and major (Clavien grade IIIb–V) (11% vs. 4%, p < 0.001) complications and had a longer length of stay (9 ± 5 vs. 5 ± 4 days, p < 0.001). Over half of patients could not tolerate early enteral realimentation and were at higher risk for postoperative complications. Prophylactic drain placement was the only independent modifiable risk factor for delayed oral intake.
Micromechanical modeling of polycrystalline shape-memory alloys including thermo-mechanical coupling

The main objective of this paper is to derive a simple, engineering model for a NiTi-based shape memory alloy (SMA) that can be used as a first-step, inexpensive computational tool in designing components including SMAs. The model is based on a Reuss approximation in which the stress in every grain is considered the same. A random and a simple texture distribution for the grain orientations as well as a normal distribution for the transformation force are used in the calculations so that a round shape of stress–strain curve and transformation start and finish temperatures can be considered. A new algorithm based on minimizing the energy at each transformation step is provided that is simple, fast and accurate. Thermo-mechanical coupling is taken into account, therefore various strain-rate regimes can be modeled. Both superelastic and shape memory effect (SME) are analyzed. The model can also replicate complex behavior encountered in real materials such as small strain–amplitude hysteresis cycles, ratio of lateral to longitudinal strain during transformation and asymmetric behavior in tension compared with compression, while keeping the number of modeling parameters small. Numerical simulations show excellent agreement with available experimental results by applying the adequate grain orientation and the transformation force distribution.
Environment adaptive control of noise reduction parameters for improved robustness of ASR

This paper describes an extension to an automatic speech recognition system that improves the robustness concerning varying environments. A dedicated control unit tries to derive an optimal set of parameters for a Wiener Filter based noise reduction unit aiming at maximum recognition performances in different environments. The input measure for the control unit is derived from the speech signal. Apart from the SNR level, several other measures are investigated. The controlled parameters are closely related to the strength of the noise reduction. Several non-linear methods such as the Tabulated References and Neural Networks serve as the core of the control unit. Experiments on realistic handsfree as well as non-handsfree speech data show that the word error rate can be reduced by as much as 31% through the proposed methods. An already optimized static conﬁguration of the applied noise reduction hereby serves as the baseline level.
Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes. DOI: http://dx.doi. org/10.7554/eLife.09457. 001
Mice fed a lipogenic methionine-choline-deficient diet develop hypermetabolism coincident with hepatic suppression of SCD-1 s⃞ Published, JLR Papers in Press, July 8, 2006.

Lipogenic diets that are completely devoid of methionine and choline (MCD) induce hepatic steatosis. MCD feeding also provokes systemic weight loss, for unclear reasons. In this study, we found that MCD feeding causes profound hepatic suppression of the gene encoding stearoyl-coenzyme A desaturase-1 (SCD-1), an enzyme whose regulation has significant effects on metabolic rate. Within 7 days of MCD exposure, hepatic SCD-1 mRNA decreased to nearly undetectable levels. By day 21, SCD-1 protein was absent from hepatic microsomes and fatty acids showed a decrease in monounsaturated species. These changes in hepatic SCD-1 were accompanied by signs of hypermetabolism. Calorimetry revealed that MCD-fed mice consumed 37% more energy than control mice (P = 0.0003). MCD feeding also stimulated fatty acid oxidation, although fatty oxidation genes were not significantly upregulated. Interestingly, despite their increased metabolic rate, MCD-fed mice did not increase their food consumption, and as a result, they lost 26% of their body weight in 21 days. In summary, MCD feeding suppresses SCD-1 in the liver, which likely contributes to hypermetabolism and weight loss. MCD feeding also induces hepatic steatosis, by an independent mechanism. Viewed together, these two disparate consequences of MCD feeding (weight loss and hepatic steatosis) give the appearance of an unusual form of lipodystrophy.
Random Projections for Anchor-based Topic Inference

Recent spectral topic discovery methods are extremely fast at processing large document corpora, but scale poorly with the size of the input vocabulary. Random projections are vital to ensure speed and limit memory usage. We empirically evaluate several methods for generating random projections and measure the effect of parameters such as sparsity and dimensionality. We find that methods with structured sparsity are faster than Gaussian random projections and more accurate than standard sparse random projections.
Hydrological Evaluation of TRMM Rainfall over the Upper Senegal River Basin

The availability of climatic data, especially on a daily time step, has become very rare in West Africa over the last few years due to the high costs of climate data monitoring. This scarcity of climatic data is a huge obstacle to conduct hydrological studies over some watersheds. In this context, our study aimed to evaluate the capacity of Tropical Rainfall Measuring Mission (TRMM) satellite data to simulate the observed runoffs over the Bafing (the main important tributary of the Senegal River) before their potential integration in hydrological studies. The conceptual hydrological model GR4J (modele du Genie Rural (Agricultural Engineering Model) a 4 parametres Journalier (4 parameters Daily)) has been used, calibrated and validated over the 1961–1997 period with rainfall and Potential Evapotranspiration (PET) as inputs. Then, the parameters that best reflect the rainfall-runoff relation, obtained during the cross-calibration-validation phase, were used to simulate runoff over the 1998–2004 period using observed and TRMM rainfalls. The findings of this study show that there is a high consistency between satellite-based estimates and ground-based observations of rainfall. Over the 1998–2004 simulation period, the two rainfall data series show quite satisfactorily results. The output hydrographs from satellite-based estimates and ground-based observations of rainfall coincide quite well with the shape of observed hydrographs with Nash-Sutcliffe Efficiency coefficient (NSE) of 0.88 and 0.80 for observed rainfalls and TRMM rainfalls, respectively.
Systolic algorithms for the CMU warp processor

CMU is building a 32-bit floating-point systolic array that can efficiently perform many essential computations in signal processing like the FFT and convolution. This is a one-dimensional systolic array that in general takes inputs from one end cell and produces outputs at the other end, with data and control all flowing in one direction. We call this particular systolic array the Warp processor, suggesting that it can perform various transformations at a very high speed We expect to have wide applications for the Warp processor, especially for the CMU prototype which has high degrees of flexibility at the expense of a relatively high chip count for each cell. The prototype has 10 cells, each of which is capable of performing 10 million floating-point operations per second (10 MFLOPS) and is build on a single board using only off-the-shelf components. This 10-ccll processor for example can process 1024-point complex FFft at a rate of one FFT every 600 /is. Under program control, the same processor can perform many other primitive computations in signal, image and vision processing, including two-dimensional convolution and complex matrix multiplication, at a rate of 100 MFLOPS. Together with another processor capable of performing divisions and square roots, the processor can also efficiently carry out a number of difficult matrix operations such as solving covariant linear systems, a crucial computation in real-time adaptive signal processing. This paper outlines the architecture of the Warp processor and describes how the signal processing tasks are implemented on the processor.
Crystal Structure of Arabidopsis Deg2 Protein Reveals an Internal PDZ Ligand Locking the Hexameric Resting State

Background: The PDZ protease Deg2 is involved in chloroplast protein quality control through a yet unknown molecular mechanism. Results: A novel PDZ domain with an internal ligand mediates hexamer formation and locks Deg2 into the resting state. Conclusion: Formation of the resting hexamer may be a common strategy in a Deg protease subfamily. Significance: We provide structural insights into the PDZ domain-mediated regulation of Deg proteases. Eukaryotic organelles have developed elaborate protein quality control systems to ensure their normal activity, among which Deg/HtrA proteases play an essential role. Plant Deg2 protease is a homologue of prokaryotic DegQ/DegP proteases and is located in the chloroplast stroma, where its proteolytic activity is required to maintain the efficiency of photosynthetic machinery during stress. Here, we demonstrate that Deg2 exhibits dual protease-chaperone activities, and we present the hexameric structure of Deg2 complexed with co-purified peptides. The structure shows that Deg2 contains a unique second PDZ domain (PDZ2) following a conventional PDZ domain (PDZ1), with PDZ2 orchestrating the cage assembly of Deg2. We discovered a conserved internal ligand for PDZ2 that mediates hexamer formation and thus locks the protease in the resting state. These findings provide insight into the diverse modes of PDZ domain-mediated regulation of Deg proteases.
Mixing Implicit and Explicit Probes: Finding a Ground Truth for Engagement in Social Human-Robot Interactions

In our work we explore the development of a computational model capable of automatically detecting engagement in social human-robot interactions from real-time sensory and contextual input. However, to train the model we need to establish ground truths of engagement from a large corpus of data collected from a study involving task and social-task engagement. Here, we intend to advance the current state-ofthe-art by reducing the need for unreliable post-experiment questionnaires and costly time-consuming annotation with the novel introduction of implicit probes. A non-intrusive, pervasive and embedded method of collecting informative data at different stages of an interaction.
Using adaptation to improve speech transcription alignment in noisy and reverberant environments

When using data retrieved from the internet to create new speech databases, the recording conditions can often be highly variable within and between sessions. This variance influences the overall performance of any automatic speech and text alignment techniques used to process this data. In this paper we discuss the use of speaker adaptation methods to address this issue. Starting from a baseline system for automatic sentence-level segmentation and speech and text alignment based on GMMs and grapheme HMMs, respectively, we employ Maximum A Posteriori (MAP) and Constrained Maximum Likelihood Linear Regression (CMLLR) techniques to model the variation in the data in order to increase the amount of confidently aligned speech. We tested 29 different scenarios, which include reverberation, 8 talker babble noise and white noise, each in various combinations and SNRs. Results show that the MAP-based segmentation’s performance is very much influenced by the noise type, as well as the presence or absence of reverberation. On the other hand, the CMLLR adaptation of the acoustic models gives an average 20% increase in the aligned data percentage for the majority of the studied scenarios. Index Terms: speech alignment, speech segmentation, adaptive training, CMLLR, MAP, VAD
Follow‐up results on monitoring and discussing health‐related quality of life in adolescent diabetes care: benefits do not sustain in routine practice

de Wit M, Delemarre‐Van de Waal HA, Bokma JA, Haasnoot K, Houdijk MC, Gemke RJ, Snoek FJ. Follow‐up results on monitoring and discussing health‐related quality of life in adolescent diabetes care: benefits do not sustain in routine practice.
Process Mining, Discovery, and Integration using Distance Measures

Business processes continue to play an important role in today's service-oriented enterprise computing systems. Mining, discovering, and integrating process-oriented services has attracted growing attention in the recent year. In this paper we present a quantitative approach to modeling and capturing the similarity and dissimilarity between different process designs. We derive the similarity measures by analyzing the process dependency graphs of the participating workflow processes. We first convert each process dependency graph into a normalized process matrix. Then we calculate the metric space distance between the normalized matrices. This distance measure can be used as a quantitative and qualitative tool in process mining, process merging, and process clustering, and ultimately it can reduce or minimize the costs involved in design, analysis, and evolution of workflow systems