Miracle-dz/UniFAR-FTUs huggingface dataset:
A genetic approach to the biogenesis of peroxisomes in the yeast Saccharomyces cerevisiae

The budding yeast Succhurom,vces cerevisiue offers the advantages of combined genetic, molecular genetic and biochemical approaches to study cell biological processes. S. cerevisiae mutants have been powerful tools to dissect the biogenesis of subcellular structures, such as mitochondria,'. 2 endoplasmic r e t i ~ u l u m , ~ . ~ g ~ l g i , ~ vacuoles,6 and most recently peroxi~omes. ~'~ The presence of peroxisomes in S. cerevisiue was first demonstrated by Avers and Federman,' ' but in contrast to other yeasts and filamentous fungi,' 2 . ' ' peroxisomes of S. cerevisiue have not been characterized in great detail. This lack of information is explained by the observation that under the growth conditions originally chosen, peroxisomes in S. cerevisiue are very rare and ~rna1l . l~ In several yeasts, proliferation of this organelle can be induced by growth on distinct carbon and nitrogen sources1' but attempts to induce the proliferation of peroxisomes in S. cerevisiue failed for a long time. I t was not until Veenhuis et d. '" and Kunau et al.' demonstrated the inducibility of peroxisomal P-oxidation as well as peroxisome proliferation in yeasts by growth on oleic acid as the single carbon source that remarkable amounts of peroxisomes were also found in S. ccw~ci.siur~. '4.1 This finding opened the way for the development of a genetic screen to isolate peroxisoma1 mutants of S. ~erevis iue. ~ In this report we review the first attempts to characterize peroxisome biogenesis in S. cerevisiue by a combined genetic and biochemical approach. We describe the different types of peroxisomal mutants and our current knowledge of the primary
IR-UWB RFID Circuits and Systems

Radio-frequency identification (RFID) technology has been applied extensively in logistic and supply chain applications. Long-range passive RFID systems offer higher reliability and flexibility compared to conventional bar-code systems. However, limitations such as sensitivity to multipath fading and diffraction grating exist due to the narrowband technologies used. Also, industries show interest in embedding precise localization ability in such systems which is difficult to achieve using narrowband communications. After the Federal Communications Commission released an ultra-wide bandwidth (3.1–10.6 GHz) for unlicensed use in 2002, impulse-radio (IR) ultra-wideband (UWB) communications have drawn lots of attention from both academia and industry. The large bandwidth (in other words short pulse duration) not only improves the communication quality compared to the narrowband counterparts, but also provides an excellent localization ability. This makes IR-UWB technology a potential candidate for next generation long-range passive RFID systems. The main goal of this work is to apply UWB technology to advance the current longrange passive RFID system performance. A novel passive IR-UWB RFID system has been proposed. The potential advantages are long tag reading range with localization ability, insensitivity to surrounding environments and high security. A wireless-powered IR-UWB transmitter for the proposed system has been successfully demonstrated in a TSMC 90 nm CMOS process. Together with the coherent IR-UWB receiver implemented by the author’s research group, implementations of the proposed RFID system become possible. Moreover, the power consumption and other specifications of individual components have to be considered also in order to maximize the system performance. Research has been conducted on several functional blocks including IR-UWB pulse generator, clock generator and reference circuit. Novel topologies and solutions have been proposed and verified. Experimental results have proven competitive performance compared to other published stateof-the-art counterparts.
Interpreting standing stones in Africa: a case study in north-west Cameroon

Combining history and ethnography with a survey on the ground, the author shows how the megalithic monuments of Cameroon were the remains of many different kinds of site. Some were house platforms, others places for washing dishes. Others are certainly ceremonial, for family and kin-group meetings. The memory and opinion of current residents adds a fascinating aside to the function of these monuments, probably introduced four centuries ago, and their subsequent rôles in society.
Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device

Efforts to develop scalable learning algorithms for implementation of networks of spiking neurons in silicon have been hindered by the considerable footprints of learning circuits, which grow as the number of synapses increases. Recent developments in nanotechnologies provide an extremely compact device with low-power consumption. In particular, nanoscale resistive switching devices (resistive random-access memory (RRAM)) are regarded as a promising solution for implementation of biological synapses due to their nanoscale dimensions, capacity to store multiple bits and the low energy required to operate distinct states. In this paper, we report the fabrication, modeling and implementation of nanoscale RRAM with multi-level storage capability for an electronic synapse device. In addition, we first experimentally demonstrate the learning capabilities and predictable performance by a neuromorphic circuit composed of a nanoscale 1 kbit RRAM cross-point array of synapses and complementary metal–oxide–semiconductor neuron circuits. These developments open up possibilities for the development of ubiquitous ultra-dense, ultra-low-power cognitive computers.
Calories Prediction from Food Images

Calculating the amount of calories in a given food item is now a common task. We propose a machine-learning-based approach to predict the amount of calories from food images. Our system does not require any input from the user, except from an image of the food item. We take a pragmatic approach to accurately predict the amount of calories in a food item and solve the problem in three phases. First, we identify the type of the food item in the image. Second, we estimate the size of the food item in grams. Finally, by taking into consideration the output of the first two phases, we predict the amount of calories in the photographed food item. All these three phases are based purely on supervised machinelearning. We show that this pipelined approach is very effective in predicting the amount of calories in a food item as compared to baseline approaches which directly predicts the amount of calories from the image.
Modélisation numérique tridimensionnelle de l'hydrodynamique et des transports sédimentaires en Manche orientale et dans le sud de la Mer du Nord

Ce travail est consacre a la mise au point d'un modele numerique tridimensionnel aux differences finies en zone cotiere a forte maree soumise a des apports fluviatiles. D'un point de vue fondamental, nos travaux ont consiste dans un premier temps a ameliorer le schema de fermeture de la turbulence en introduisant une equation de transport de l'energie cinetique turbulente. Dans un second temps, nous avons integre le transport des particules sedimentaires de tailles diverses en resolvant un jeu d'equations d'advection-dispersion et en introduisant une para metrisation des processus hydro-sedimentaires a l'interface eau-sediment. Sur un plan pratique, le code de calcul elabore a ete applique a la zone cotiere de la manche orientale et du sud de la mer du nord. Les resultats ont ete compares a diverses mesures disponibles. Sur le plan numerique, le filtrage des parasites numeriques a ete ameliore. D'autre part, le code actuel etant affecte par les effets de diffusion numerique lies au schema upwind, divers schemas localement d'ordre deux de type limiteur de pente ont ete testes sur des cas schematiques bidimensionnels.
Integrative Transcript and Metabolite Analysis of Nutritionally Enhanced DE-ETIOLATED1 Downregulated Tomato Fruit[W]

The downregulation of DE-ETIOLATED1 (DET1) results in tomato fruits with the simultaneous elevation of multiple nutritional antioxidants. Characterization through the analysis and integration of large-scale metabolomic and transcriptomic data sets reveals a coordinated activation of core metabolic processes underlying DET1 loss of function in developing tomato fruit. Fruit-specific downregulation of the DE-ETIOLATED1 (DET1) gene product results in tomato fruits (Solanum lycopersicum) containing enhanced nutritional antioxidants, with no detrimental effects on yield. In an attempt to further our understanding of how modulation of this gene leads to improved quality traits, detailed targeted and multilevel omic characterization has been performed. Metabolite profiling revealed quantitative increases in carotenoid, tocopherol, phenylpropanoids, flavonoids, and anthocyanidins. Qualitative differences could also be identified within the phenolics, including unique formation in fruit pericarp tissues. These changes resulted in increased total antioxidant content both in the polar and nonpolar fractions. Increased transcription of key biosynthetic genes is a likely mechanism producing elevated phenolic-based metabolites. By contrast, high levels of isoprenoids do not appear to result from transcriptional regulation but are more likely related to plastid-based parameters, such as increased plastid volume per cell. Parallel metabolomic and transcriptomic analyses reveal the widespread effects of DET1 downregulation on diverse sectors of metabolism and sites of synthesis. Correlation analysis of transcripts and metabolites independently indicated strong coresponses within and between related pathways/processes. Interestingly, despite the fact that secondary metabolites were the most severely affected in ripe tomato fruit, our integrative analyses suggest that the coordinated activation of core metabolic processes in cell types amenable to plastid biogenesis is the main effect of DET1 loss of function.
Particle swarm optimization algorithms with novel learning strategies

This paper proposes three versions of particle swarm optimizers (PSO) with novel learning strategies where each dimension of a particle learns from just one particle's historical best information, while each particle learns from different particles' historical best information for different dimensions for a few generations. These strategies ensure that the diversity of the swarm is preserved to discourage premature convergence. In addition, these novel PSO variants do not introduce any complex computations to the original PSO algorithm. We obtain outstanding performance on solving multimodal problems in comparison to several other variants of PSO.
[Validation of a Korean translated version of the Critical Care Pain Observation Tool (CPOT) for ICU patients].

PURPOSE The purpose of this methodological study was to examine the reliability and validity of a translated Korean version of the Critical Care Pain Observation Tool (CPOT) developed for assessment of pain in critically ill nonverbal patients. METHODS A cross-sectional study design was used. Data were collected from a convenience sample of 202 critically ill patients admitted to a university hospital. Upon establishment of content and translation equivalence between the English and Korean version of CPOT, psychometric properties were evaluated. RESULTS The interrater reliability was found to be acceptable with the weighted kappa coefficients of . 81-. 88. Significant high correlations between the CPOT and the Checklist of Nonverbal Pain Indicators were found indicating good concurrent validity (r=.72-.83, p<.001). Data showed the area under the ROC curve of 0.86 with a cut-off point of 1, which resulted in 76.9% sensitivity and 88.6% specificity. The mean score of CPOT during suctioning was significantly different from that of before (t=-14.16, p<.001) or 20 minutes after suctioning (t=16.31, p<.001). CONCLUSION Results of this study suggest that the CPOT can be used as a reliable and valid measure to assess pain in critically ill nonverbal patients.
Changing the Home Nutrition Environment: Effects of a Nutrition and Media Literacy Pilot Intervention

The specific aim for this pilot study was to evaluate the effectiveness of a nutrition and media literacy intervention targeting elementary students and their parents. The purpose of the intervention was to increase child fruit and vegetables (FV) consumption and change the home nutrition environment (measured with FV availability and accessibility and parental social support). During the intervention, students learned about nutrition, the role media plays in shaping values concerning nutrition, and developed a media campaign for their parents. A quasi-experimental research design was used to evaluate the effectiveness of the intervention. The media intervention was effective in changing the home environment.
High-Dimensional and Multiple-Failure-Region Importance Sampling for SRAM Yield Analysis

The failure rate of static RAM (SRAM) cells is restricted to be extremely low to ensure sufficient high yield for the entire chip. In addition, multiple performances of interest and influences from peripherals make SRAM failure rate estimation a high-dimensional multiple-failure-region problem. This paper proposes a new method featuring a multistart-point sequential quadratic programming (SQP) framework to extend minimized norm importance sampling (IS) to address this problem. Failure regions in the variation space are first found by the low-discrepancy sampling sequence. Afterward, start points are generated in all identified failure regions and local optimizations based on SQP are invoked from these start points searching for the optimal shift vectors (OSVs). Based on the OSVs, a Gaussian mixture distorted distribution is constructed for IS. To further reduce the computational cost of IS while fully considering the influence of increasing dimensionality, an adaptive model training framework is proposed to keep high efficiency for both low- and high-dimensional problems. The experimental results show that the proposed method can not only approximate failure rate with high accuracy and efficiency in low-dimensional cases but also keep these features in high-dimensional ones.
Association of hydroxyethyl starch administration with mortality and acute kidney injury in critically ill patients requiring volume resuscitation: a systematic review and meta-analysis.

IMPORTANCE Hydroxyethyl starch is commonly used for volume resuscitation yet has been associated with serious adverse events, including acute kidney injury and death. Clinical trials of hydroxyethyl starch are conflicting. Moreover, multiple trials from one investigator have been retracted because of scientific misconduct. OBJECTIVES To evaluate the association of hydroxyethyl starch use with mortality and acute kidney injury. DATA SOURCES Randomized controlled trials from MEDLINE, EMBASE, CENTRAL, Global Health, HealthStar, Scopus, Web of Science, the International Clinical Trials Registry Platform (inception to October 2012), reference lists of relevant articles, and gray literature. STUDY SELECTION Two reviewers independently identified randomized controlled trials comparing hydroxyethyl starch with other resuscitation fluids in critically ill patients receiving acute volume resuscitation. DATA EXTRACTION Two reviewers independently extracted trial-level data including population characteristics, interventions, outcomes, and funding sources. Risk of bias was assessed using the risk of bias tool; the strength of evidence was adjudicated using the GRADE methodology. RESULTS We included 38 eligible trials comparing hydroxyethyl starch to crystalloids, albumin, or gelatin. The majority of trials were categorized as having an unclear risk or high risk of bias. For the 10,880 patients in studies contributing mortality data, the risk ratio (RR) for death among patients randomized to receive hydroxyethyl starch was 1.07 (95% CI, 1.00 to 1.14; I2, 0%; absolute risk [AR], 1.20%; 95% CI, -0.26% to 2.66%). This summary effect measure included results from 7 trials performed by an investigator whose subsequent research had been retracted because of scientific misconduct. When we excluded these 7 trials that involved 590 patients, hydroxyethyl starch was found to be associated with increased mortality among 10,290 patients (RR, 1.09; 95% CI, 1.02 to 1.17; I2, 0%; AR, 1.51%; 95% CI, 0.02% to 3.00%), increased renal failure among 8725 patients (RR, 1.27; 95% CI, 1.09 to 1.47; I2, 26%; AR, 5.45%; 95% CI, 0.44% to 10.47%), and increased use of renal replacement therapy among 9258 patients (RR, 1.32; 95% CI, 1.15 to 1.50; I2, 0%; AR, 3.12%; 95% CI, 0.47% to 5.78%). CONCLUSION AND RELEVANCE In critically ill patients requiring acute volume resuscitation, use of hydroxyethyl starch compared with other resuscitation solutions was not associated with a decrease in mortality. Moreover, after exclusion of 7 trials performed by an investigator whose research has been retracted because of scientific misconduct, hydroxyethyl starch was associated with a significant increased risk of mortality and acute kidney injury. Clinical use of hydroxyethyl starch for acute volume resuscitation is not warranted due to serious safety concerns.
A two-staged residual for resilient external torque estimation with series elastic actuators

Collaborative robots driven by Series Elastic Actuators (SEA) exploit physical compliance, which enables joint torque sensing to accurately estimate external contact torques based on residual momenta. The deflection measurement precision for the compliant element determines the torque accuracy. It is affected by support clearances, assembly tolerances, stiffness calibration errors as well as unmodelled non-linearity. Moreover, the stiffness value amplifies deflection errors leading to larger torque estimation errors, which limits the benefits of integrated joint torque sensing for enhanced external torque estimation with higher stiffness SEAs. Accordingly, this paper newly proposes a two-staged approach of the residual based external torque estimation for SEA driven robots. The proposed method augments the residual calculation by the motor-side dynamics, uses both, motor and link side angular measurements, but entirely waives the need to model the spring dynamics for the external torque estimation. Consequently, this improves accuracy and sensitivity in wide-ranging stiffness applications. The performance of the two-staged residual is verified by experiments with the SEA of the WALK-MAN humanoid robot.
ECG data compression with the Karhunen-Loeve transform

The authors analyze a Karhunen-Loeve transform technique for ECG data compression. This transform has been, applied in two different ways: to the entire beat signal and to independent windows (P wave, QRS complex and ST-T complex). The optimum number of coefficients and bits for coding the signal is analyzed for the MIT-BIH Arrythmia database. The data compression performance of both choices are: for the entire beat a mean compression ratio of 12.1 with a mean MSE of 0.3% and for shorter windows a mean compression ratio of 17.21 with a mean value of MSE of 0.44%.
A Simple PTAS for the Dual Bin Packing Problem and Advice Complexity of Its Online Version

Recently, Renault (2016) studied the dual bin packing problem in the per-request advice model of online algorithms. He showed that given $O(1/\epsilon)$ advice bits for each input item allows approximating the dual bin packing problem online to within a factor of $1+\epsilon$. Renault asked about the advice complexity of dual bin packing in the tape-advice model of online algorithms. We make progress on this question. Let $s$ be the maximum bit size of an input item weight. We present a conceptually simple online algorithm that with total advice $O\left(\frac{s + \log n}{\epsilon^2}\right)$ approximates the dual bin packing to within a $1+\epsilon$ factor. To this end, we describe and analyze a simple offline PTAS for the dual bin packing problem. Although a PTAS for a more general problem was known prior to our work (Kellerer 1999, Chekuri and Khanna 2006), our PTAS is arguably simpler to state and analyze. As a result, we could easily adapt our PTAS to obtain the advice-complexity result. We also consider whether the dependence on $s$ is necessary in our algorithm. We show that if $s$ is unrestricted then for small enough $\epsilon > 0$ obtaining a $1+\epsilon$ approximation to the dual bin packing requires $\Omega_\epsilon(n)$ bits of advice. To establish this lower bound we analyze an online reduction that preserves the advice complexity and approximation ratio from the binary separation problem due to Boyar et al. (2016). We define two natural advice complexity classes that capture the distinction similar to the Turing machine world distinction between pseudo polynomial time algorithms and polynomial time algorithms. Our results on the dual bin packing problem imply the separation of the two classes in the advice complexity world.
Structural basis of HP 1 / PXVXL motif peptide interactions and HP 1 localisation to heterochromatin

HP1 family proteins are adaptor molecules, containing two related chromo domains that are required for chromatin packaging and gene silencing. Here we present the structure of the chromo shadow domain from mouse HP1b bound to a peptide containing a consensus PXVXL motif found in many HP1 binding partners. The shadow domain exhibits a novel mode of peptide recognition, where the peptide binds across the dimer interface, sandwiched in a b-sheet between strands from each monomer. The structure allows us to predict which other shadow domains bind similar PXVXL motif-containing peptides and provides a framework for predicting the sequence specificity of the others. We show that targeting of HP1b to heterochromatin requires shadow domain interactions with PXVXL-containing proteins in addition to chromo domain recognition of Lys-9-methylated histone H3. Interestingly, it also appears to require the simultaneous recognition of two Lys-9-methylated histone H3molecules. This finding implies a further complexity to the histone code for regulation of chromatin structure and suggests how binding of HP1 family proteins may lead to its condensation. The EMBO Journal (2004) 23, 489–499. doi:10.1038/ sj.emboj. 7600088; Published online 5 February 2004 Subject Categories: structural biology; chromatin & transcription
Modeling Human-Robot Interactions as Systems of Distributed Cognition

Robots that are integrated into day-to-day settings as assistants, collaborators, and companions will engage in dynamic, physically-situated social interactions with their users. Enabling such interactions will require appropriate models and representations for interaction. In this paper, we argue that the dynamic, physically-situated interactions between humans and robots can be characterized as a system of distributed cognition, that this system can be represented using probabilistic graphical models (PGMs), and that the parameters of these models can be learned from human interactions. We illustrate the application of this perspective in our ongoing research on modeling dyadic referential communication.