Miracle-dz/UniFAR-FTUs huggingface dataset:
REGROWTH OF CHLORELLA SOROKINIANA ON RECYCLED MEDIA WITH REPLENISHED NUTRIENTS

Growth media recycling during algae cultivation is necessary to increase the efficiency and reduce the cost of biofuel production from algae feedstocks. Without recycling media, the cost of algae based biofuel production would be prohibitively high and large scale algae based biofuel production would not be economically viable. The ratio of media recycled to media wasted assumed for algae farms is generally calculated to maintain salt concentrations below growth inhibitory levels, ignoring the influence of secondary metabolites which might decrease productivity. Secondary metabolites, which include allelopathic or auto-inhibitory biological contaminants, might lead to the accumulation of growth-inhibiting compounds in recycled media used in algae production. Chlorella sorokiniana (strain DOE1412) was a leading algae biofuel feedstock candidate and has not previously been evaluated for inhibitor production. To test the effects of water recycling on the growth of DOE1412, media was recycled through multiple rounds of algae cultivation. DOE1412 was grown in modified BG11 culture media until reaching the end of linear growth phase, at which point the biomass was removed, nutrients replenished to their initial concentrations, and the recycled culture media used for a subsequent round of growth. The culture media was recycled through five rounds of growth with cultures grown on recycled media compared to controls grown on freshly prepared growth media. Biomass density was monitored via optical density and the specific and productivity growth rates were used to quantify the extent of inhibition. Exploratory work was performed with the goal of identifying potential inhibitory substances produced by DOE1412 during cultivation. Samples of recycled media were analyzed for polyunsaturated fatty acids which have been demonstrated to be inhibitory. The carbohydrates content of used media was analyzed to assess the amount of organic materials shed by DOE1412 into recycled media during growth. The log phase growth rate (day-1) of DOE1412 was inhibited by 3±2%, 8±1%, 10±2%, and 18.6±0.9% when grown in media recycled 1-4 times, respectively, with a 99% level of confidence that inhibition was observed in each round of regrowth. The productivity growth rate (OD 750 /day) of DOE1412 was not inhibited in media recycled 1-3 times. The productivity growth rate of DOE1412 was inhibited by 13±3% when grown in media recycled 4 times with a 99% level of confidence that inhibition was observed. Zinc was found to accumulate in the recycled media to potentially toxic levels (>0.09 mg/L), therefore it is uncertain if the observed inhibition was due to …
An EMG-based Human-Machine Interface to control multimedia player

The electromyogram signals generated by muscles are used in numerous fields such as augmented reality, biomedical, kinematics, gaming, 3D animations and Human-Machine Interfaces. The latter is specifically used to help persons with reduced mobility or amputee with specific constraints to remote control machines. In this paper, we present a novel EMG-based system that aims to control multimedia player in simple, efficient and flexible manner. The implementation of our proposed approach was realized in order to achieve experiments and to conduct performance analysis. Our approach uses pattern recognition and contraction duration to derive four predefined actions. Our experimental results show the capacity of our system to achieve good detection accuracy of user EMG-based commands and to translate these commands into actions in media player system.
Quality improvement strategies to optimise transition of patients with heart failure to independent living: protocol for a scoping review

Introduction Heart failure (HF) is a leading reason for hospitalisation and readmissions to hospital, particularly among individuals older than 65 years of age. The prognosis of patients with HF is grim, with high rates of mortality risk and hospital readmissions. The transition period early after hospital discharge represents a window of opportunity to positively influence patient outcomes using quality improvement (QI) strategies. However, little is known about which QI interventions exist for early events of HF after discharge, so the main objective of our study is to conduct a scoping review of the literature to determine which QI strategies are effective for reducing hospital readmissions and mortality for patients with HF who transition from the hospital back into independent living. We will also investigate which elements contribute to effective QI strategies. Methods and analysis We will search the literature in MEDLINE, EMBASE, CINAHL and the Cochrane library for randomised controlled trials and systematic reviews evaluating QI interventions aimed at improving outcomes for patients with HF transitioning from the hospital back into the community. Two reviewers will independently apply our eligibility criteria at level 1 (abstract/title) and level 2 (full-text) screening; disagreements will be resolved through consensus. We will extract data in duplicate on study characteristics, population, setting, QI intervention and outcomes. We will synthesise results descriptively and explore QI elements to determine which aspect contributes to its impact. We will also consider synthesis of our data according to several conceptual frameworks such as Wagner's Chronic Care Model. Discussion and dissemination The findings of this scoping review will be used to determine which elements should comprise a QI intervention aimed at facilitating the transition of newly admitted patients with HF back into the community. We will disseminate our findings through publications, presentations as well as through a stakeholder meeting to generate key messages most relevant to each.
Augmentation of spelling therapy with transcranial direct current stimulation in primary progressive aphasia: Preliminary results and challenges

Background: Primary progressive aphasia (PPA) is a neurodegenerative disease that primarily affects language functions and often begins in the fifth or sixth decade of life. The devastating effects on work and family life call for the investigation of treatment alternatives. In this article, we present new data indicating that neuromodulatory treatment, using transcranial direct current stimulation (tDCS) combined with a spelling intervention, shows some promise for maintaining or even improving language, at least temporarily, in PPA. Aims: The main aim of the present article is to determine whether tDCS plus spelling intervention is more effective than spelling intervention alone in treating written language in PPA. We also asked whether the effects of tDCS are sustained longer than the effects of spelling intervention alone. Methods & Procedures: We present data from six PPA participants who underwent anodal tDCS or sham plus spelling intervention in a within-subject crossover design. Each stimulation condition lasted 3 weeks or a total of 15 sessions with a 2-month interval in between. Participants were evaluated on treatment tasks as well as on other language and cognitive tasks at 2-week and 2-month follow-up intervals after each stimulation condition. Outcomes & Results: All participants showed improvement in spelling (with sham or tDCS). There was no difference in the treated items between the two conditions. There was, however, consistent and significant improvement for untrained items only in the tDCS plus spelling intervention condition. Furthermore, the improvement lasted longer in the tDCS plus spelling intervention condition compared to sham plus spelling intervention condition. Conclusions: Neuromodulation with tDCS offers promise as a means of augmenting language therapy to improve written language function at least temporarily in PPA. The consistent finding of generalisation of treatment benefits to untreated items and the superior sustainability of treatment effects with tDCS justifies further investigations. However, the small sample size still requires caution in interpretation. Present interventions need to be optimised, and particular challenges, such as ways to account for the variable effect of degeneration in each individual, are discussed.
Secretion of Polypeptide Crystals from Tetrahymena thermophila Secretory Organelles (Mucocysts) Depends on Processing by a Cysteine Cathepsin, Cth4p

ABSTRACT In many organisms, sophisticated mechanisms facilitate release of peptides in response to extracellular stimuli. In the ciliate Tetrahymena thermophila, efficient peptide secretion depends on specialized vesicles called mucocysts that contain dense crystalline cores that expand rapidly during exocytosis. Core assembly depends of endoproteolytic cleavage of mucocyst proproteins by an aspartyl protease, cathepsin 3 (CTH3). Here, we show that a second enzyme identified by expression profiling, Cth4p, is also required for processing of proGrl proteins and for assembly of functional mucocysts. Cth4p is a cysteine cathepsin that localizes partially to endolysosomal structures and appears to act downstream of, and may be activated by, Cth3p. Disruption of CTH4 results in cells (Δcth4) that show aberrant trimming of Grl proproteins, as well as grossly aberrant mucocyst exocytosis. Surprisingly, Δcth4 cells succeed in assembling crystalline mucocyst cores. However, those cores do not undergo normal directional expansion during exocytosis, and they thus fail to efficiently extrude from the cells. We could phenocopy the Δcth4 defects by mutating conserved catalytic residues, indicating that the in vivo function of Cth4p is enzymatic. Our results indicate that as for canonical proteins packaged in animal secretory granules, the maturation of mucocyst proproteins involves sequential processing steps. The Δcth4 defects uncouple, in an unanticipated way, the assembly of mucocyst cores and their subsequent expansion and thereby reveal a previously unsuspected aspect of polypeptide secretion in ciliates.
Fitted Q Iteration with CMACs

A major issue in model-free reinforcement learning is how to efficiently exploit the data collected by an exploration strategy. This is especially important in case of continuous, high dimensional state spaces, since it is impossible to explore such spaces exhaustively. A simple but promising approach is to fix the number of state transitions which are sampled from the underlying Markov decision process. For several kernel-based learning algorithms there exist convergence proofs and notable empirical results, if a fixed set of transition instances is used. In this article, we will analyze how function approximators similar to the CMAC-architecture can be combined with this idea. We will show both analytically and empirically the potential power of the CMAC architecture combined with an offline version of Q-learning
Mécanismes cellulaires de l'induction du facteur de transcription Nur77 après un traitement aux antipsychotiques

Antipsychotic drugs have been used to alleviate schizophrenia symptoms for more than 50 years. Despite extensive research, little is known about the molecular and cellular mechanism responsible for their clinical outcome. These drugs are usually recognized as dopamine D2 antagonists and are known to modulate gene expression in the striatum. In the present thesis, we used the expression of Nur77, a transcription factor of the orphan nuclear receptor family, to explore the role of dopamine, serotonin, glutamate and adenosine receptors in the effect of a dopamine D2 antagonist in the striatum. First, we examined the abilities of serotoninergic and adrenergic receptor drugs to modify the pattern of Nur77 mRNA expression induced by haloperidol, a first generation antipsychotic drug. We observed that 8-OH-DPAT and MDL11939 partially prevent haloperidol-induced Nur77 upregulation. On the contrary, idazoxan consistently potentiated haloperidol-induced Nur77 mRNA levels in the striatum whereas prazosin remained without effect. Taken together, these results show the ability of a 5-HT1A agonist or a 5-HT2A antagonist to reduce haloperidol-induced Nur77 striatal expression. Subsequently, we evaluated in different experimental designs the effect of eticlopride, a specific D2 antagonist, to provide additional information on the mechanism by which D2 antagonist controls transcriptional activity in the striatum. Surprisingly, deletion of the D2L receptor isoform did not reduce eticlopride-induced upregulation of Nur77 mRNA levels in the striatum. However, cortical lesions with ibotenic acid strongly reduced eticlopride-induced upregulation of Nur77 mRNA, suggesting a role for glutamate neurotransmission. A combination of a metabotropic glutamate type 5 (mGluR5) antagonist with an antagonist of its synergistic partner adenosine A2A receptor abolished eticlopride-induced upregulation of Nur77 mRNA levels in the striatum. Direct modulation of striatal Nur77 expression by glutamate and adenosine receptors was confirmed using corticostriatal organotypic cultures. Taken together, these results indicate that modulation of gene expression in the striatum, following a D2 antagonist, might not involve a direct interaction of the drug at postsynaptic D2 receptors, but rather relies on its interaction with corticostriatal presynaptic D2 receptors
Labeling schemes for small distances in trees

We consider labeling schemes for trees, supporting various relationships between nodes at small distance. For instance, we show that given a tree <i>T</i> and an integer <i>k</i> we can assign labels to each node of <i>T</i> such that given the label of two nodes we can decide, from these two labels alone, if the distance between <i>v</i> and <i>w</i> is at most <i>k</i> and if so compute it. For trees with <i>n</i> nodes and <i>k</i> ≥ 2, we give a lower bound on the maximum label length of log <i>n</i> + Ω(log log <i>n</i>) bits, and for constant <i>k</i>, we give an upper bound of log <i>n</i> + <i>O</i>(log log <i>n</i>). Bounds for ancestor, sibling, connectivity and bi- and triconnectivity labeling schemes are also presented.
Multi-space Projection Based Search Engine: Theoretical Model Instantiation and Prototype

Information Retrieval (IR) is still one of the most important and active research areas in computer science. The most popular applications of IR are Search Engines that help users locate information in the Web. It has to be noted that they have some limitations such as results' relevance and exploration. We propose a Multi-Space Search Engine model that is based on multiple interpretation spaces. To evaluate the effectiveness of the proposed model, we developed a prototype and conducted a comparative study that includes content interpretation, search features and usability. The experiments showed that our prototype performs well when compared to popular search engines with regard to the visibility of interpretations and the navigability of the results.
A multi-phase approach for classifying multi-dimensional sequence data

We present a new methodology for sequential classification, which employs sequential pattern generation and classification, in a two-stage process. In the first phase, a set of sequential patterns are generated from multi-dimensional sequence data. We proposes a novel method for inducing multi-dimensional sequential patterns with the use of Hellinger measure. The importance of each sequential pattern is also calculated. In the second phase, the generated sequential patterns are used for classifying multi-dimensional sequence data. A number of theorems are proposed to reduce the computational complexity of generating sequential patterns. The proposed method is tested on some synthesized sequence databases.
The Extent of Linkage Disequilibrium Caused by Selection on G6PD in Humans

The gene coding for glucose-6-phosphate dehydrogenase (G6PD) is subject to positive selection by malaria in some human populations. The G6PD A− allele, which is common in sub-Saharan Africa, is associated with deficient enzyme activity and protection from severe malaria. To delimit the impact of selection on patterns of linkage disequilibrium (LD) and nucleotide diversity, we resequenced 5.1 kb at G6PD and ∼2–3 kb at each of eight loci in a 2.5-Mb region roughly centered on G6PD in a diverse sub-Saharan African panel of 51 unrelated men (including 20 G6PD A−, 11 G6PD A+, and 20 G6PD B chromosomes). The signature of selection is evident in the absence of genetic variation at G6PD and at three neighboring loci within 0.9 Mb from G6PD among all individuals bearing G6PD A− alleles. A genomic region of ∼1.6 Mb around G6PD was characterized by long-range LD associated with the A− alleles. These patterns of nucleotide variability and LD suggest that G6PD A− is younger than previous age estimates and has increased in frequency in sub-Saharan Africa due to strong selection (0.1 < s < 0.2). These results also show that selection can lead to nonrandom associations among SNPs over great physical and genetic distances, even in African populations.
A Lyapunov function approach to longitudinal control of vehicles in a platoon

The Lyapunov stability theorem is used to derive a control law which can be used to control the spacing between vehicles in a platoon. A third order system is adopted to model the vehicle and powertrain dynamics. In addition, the concept of "expected spacing error" is introduced and used to form a Lyapunov function. Then a control law which always decreases the Lyapunov function is selected. A platoon of four vehicles and various scenarios are used to demonstrate the performance of the proposed control law.
A Peer-to-Peer Resource Sharing System with Data Grid Technology for Mobile Devices

Increases in mobile device processing power, storage capacity, and connectivity in recent years have led to a rapid rise in mobile device use. But mobile users face challenges in making connections with other users in order to share resources. To address this issue, we propose an architecture that uses peer-to-peer protocol to establish a resource sharing platform for mobile devices, combined with a data grid to help analyze and classify resources.
Biped locomotion methodologies applied to humanoid robotics

Controlling a biped robot with several degrees of freedom is a challenging task that takes the attention of several researchers in the fields of biology, physics, electronics, computer science and mechanics. For a humanoid robot to perform in complex environments, fast, stable and adaptable behaviors are required. Developing robust behaviors requires the development of methods for joint trajectory planning and lowlevel control. Several methods are part of the state of the art, including trajectory-based methods, virtual model control, passive-dynamic walking and central pattern generators. This paper proposes a solution for automatic generation of a walking gait using genetic algorithms. The experimental results are shown in terms of the evolution of the ground projection of the center of mass, walking velocity and average oscillation of torso. Genetic algorithms proved to be a powerful method for automatic generation of humanoid behaviors. The robot was able to reach a walk forward velocity of 0.51m/s which is a good result considering the results of the three best teams of RoboCup 3D simulation league for the same movement.
The Crisis in Doctoral Education: A sociological diagnosis

Sociological thinkers help us understand the crisis in doctoral training. Weber shows us that the crisis is the result of a clash of rationalities. Foucault shows us the highly-developed rationality of policymakers/administrators, which emerged in "disciplinary societies". I discuss the new doctoral training that reshapes subjectivity and retunes the relationship between self and education in the name of social progress. Borrowing from Rose, I stress two themes that are crucial for modern doctoral training: expertise and audit. Latour shows how this rationalisation of doctoral education occurred when it did. It had to await a series of translations--beliefs, knowledges, practices, documents, technologies and so forth, which originally arose in another language (business), and had to be packaged up in simplified forms.
Deep learning based nonlinear principal component analysis for industrial process fault detection

Principal component analysis (PCA) and kernel PCA (KPCA) are the state-of-art machine learning methods widely used in industrial process monitoring and fault detection field. However, these methods build shallow statistical models based on single layer of features and may not achieve the best monitoring performance. In order to sufficiently mine the intrinsic data features, a deep learning based nonlinear PCA method, referred to as deep PCA (DePCA), is proposed in this paper. Motivated by the idea of deep learning, a layer-wise statistical model structure is designed to extract multilayer data features, including both linear and nonlinear principal components. At each layer, two monitoring statistics are constructed to monitor the feature changes. For integrating the monitoring statistics of all feature layers, a Bayesian inference strategy is applied to convert the monitoring statistics into fault probabilities, which are weighted to form two probability-based comprehensive monitoring statistics for process fault detection. A case study using the benchmark Tennessee Eastman process demonstrates the superior performance of the proposed DePCA method over the traditional PCA and KPCA methods.
Complex Biphasic Changes of Neuropeptide Concentrations in the Rat Limbic System During Pregnancy and Parturition

Sex hormones including estrogens affect brain areas involved in mood and cognition in addition to directly controlling reproduction and reproductive behavior. We studied the effect of pregnancy and puerperium on the concentra- tions of cholecystokinin (CCK), neuropeptide Y (NPY), substance P (SP) and galanin in tissue extracts from the rat stria- tum, frontal cortex and the hippocampal formation by means of radioimmunoassay. The most profound effects were found in the frontal cortex. Thus, cholecystokinin-like immunoreactivity (CCK-LI) was increased by 40 % during late pregnancy (p<0.01) compared to estrous whereas SP-LI and galanin-LI decreased 25 % and 10 %, respectively. Postpartum, CCK- LI decreased by 26% compared to pregnancy (p<0.05) whereas SP-LI and galanin-LI were increased to a level above es- trous (SP, P<0.01; galanin, P<0.05). No significant effect was observed in NPY-LI in this area. In the striatum during late pregnancy the concentrations of cholecystokinin-LI increased by 29 % (p<0.05), NPY-LI by 22% (p<0.05) whereas SP-LI slightly increased (not significant). Postpartum, cholecystokinin-LI decreased by 25 % (p<0.01) compared to pregnancy and NPY by 16 % (p<0.01). SP continued to increase postpartum by 33 % (p<0.05) whereas no effect was observed on galanin-LI concentration. Surprisingly, we did not observe any changes in any peptide or groups measured in the hippo- campal formation. The complex hormonal adjustments occurring during pregnancy and in the puerperium induce pro- found changes in the concentrations of several neuropeptides in regions of the rat brain involved in the control of mood and motor control.
Automated Registration of 3D Faces using Dense Surface Models

Dense surface models can be used to register unseen surfaces, using an algorithm which is a hybrid of iterative closest-point (ICP) and active shape model (ASM) fitting. In this paper we give details of this procedure and show how it can be improved by sequentially extending the transform group over which it operates. We also evaluate it for robustness to the position of the target and to shape variation across a set of unseen examples. The fit was successful on all 21 examples in our test set, with an average RMS error of 3.0mm. An initial comparison of 3 people landmarking the same scans suggests that this is within the normal landmark reproducibility range for 3D face scans.