Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of Specific Clones from Nonarrayed BAC Libraries through Homologous Recombination

We have developed a new approach to screen bacterial artificial chromosome (BAC) libraries by recombination selection. To test this method, we constructed an orangutan BAC library using an E. coli strain (DY380) with temperature inducible homologous recombination (HR) capability. We amplified one library segment, induced HR at 42°C to make it recombination proficient, and prepared electrocompetent cells for transformation with a kanamycin cassette to target sequences in the orangutan genome through terminal recombineering homologies. Kanamycin-resistant colonies were tested for the presence of BACs containing the targeted genes by the use of a PCR-assay to confirm the presence of the kanamycin insertion. The results indicate that this is an effective approach for screening clones. The advantage of recombination screening is that it avoids the high costs associated with the preparation, screening, and archival storage of arrayed BAC libraries. In addition, the screening can be conceivably combined with genetic engineering to create knockout and reporter constructs for functional studies.
AP180 binds to the C-terminal SH2 domain of phospholipase C-gamma1 and inhibits its enzymatic activity.

The role of phospholipase Cgamma1 (PLCgamma1) in signal transduction was investigated by characterizing its SH domain-binding proteins that may represent components of a novel signaling pathway. A 180-kDa protein that binds to the SH2 domain of PLCgamma1 was purified from rat brain. The amino acid sequence of peptide derived from the purified protein is now identified as AP180, a clathrin assembly protein that has been implicated in clathrin-mediated synaptic vesicle recycling in synapses. In this report, we demonstrate the stable association of PLCgamma1 with AP180 in a clathrin-coated vesicle complex, which not only binds to the carboxyl-terminal SH2 domain of PLCgamma1, but also inhibits its enzymatic activity in a dose-dependent manner.
Q-learning and enhanced policy iteration in discounted dynamic programming

We consider the classical finite-state discounted Markovian decision problem, and we introduce a new policy iteration-like algorithm for finding the optimal Q-factors. Instead of policy evaluation by solving a linear system of equations, our algorithm involves (possibly inexact) solution of an optimal stopping problem. This problem can be solved with simple Q-learning iterations, in the case where a lookup table representation is used; it can also be solved with the Q-learning algorithm of Tsitsiklis and Van Roy [TsV99], in the case where feature-based Q-factor approximations are used. In exact/lookup table representation form, our algorithm admits asynchronous and stochastic iterative implementations, in the spirit of asynchronous/modified policy iteration, with lower overhead advantages over existing Q-learning schemes. Furthermore, for large-scale problems, where linear basis function approximations and simulation-based temporal difference implementations are used, our algorithm resolves effectively the inherent difficulties of existing schemes due to inadequate exploration.
Moving shadow detection based on normalized eigenvalue of Wishart matrix

Moving objects detection is one basic problem in computer vision and various methods have been proposed. However, the performance of these methods may be deteriorated by the moving shadows. In this paper we present a novel method for the moving shadow detection. Based on the analysis of the illumination model, we prove that the normalized eigenvalue of image block is illumination invariant. The distribution of the normalized eigenvalue is discussed and a significance test is performed to classify each image pixel into foreground object or moving shadow. Experimental results on typical scenes show that the proposed method can detect moving shadows correctly. Quantitative comparison with the state-of-the-art demonstrates that the proposed method provides much improved results.
Genetic Susceptibility Factors on Genes Involved in the Steroid Hormone Biosynthesis Pathway and Progesterone Receptor for Gastric Cancer Risk

Background The objective of the study was to investigate the role of genes (HSD3B1, CYP17A1, CYP19A1, HSD17B2, HSD17B1) involved in the steroid hormone biosynthesis pathway and progesterone receptor (PGR) in the etiology of gastric cancer in a population-based two-phase genetic association study. Methods In the discovery phase, 108 candidate SNPs in the steroid hormone biosynthesis pathway related genes and PGR were analyzed in 76 gastric cancer cases and 322 controls in the Korean Multi-Center Cancer Cohort. Statistically significant SNPs identified in the discovery phase were re-evaluated in an extended set of 386 cases and 348 controls. Pooled- and meta-analyses were conducted to summarize the results. Results Of the 108 SNPs in steroid hormone biosynthesis pathway related genes and PGR analyzed in the discovery phase, 23 SNPs in PGR in the recessive model and 10 SNPs in CYP19A1 in the recessive or additive models were significantly associated with increased gastric cancer risk (p<0.05). The minor allele frequencies of the SNPs in both the discovery and extension phases were not statistically different. Pooled- and meta-analyses showed CYP19A1 rs1004982, rs16964228, and rs1902580 had an increased risk for gastric cancer (pooled OR [95% CI] = 1.22 [1.01–1.48], 1.31 [1.03–1.66], 3.03 [1.12–8.18], respectively). In contrast, all PGR SNPs were not statistically significantly associated with gastric cancer risk. Conclusions Our findings suggest CYP19A1 that codes aromatase may play an important role in the association of gastric cancer risk and be a genetic marker for gastric cancer susceptibility.
Mining constraint violations

In this article, we introduce pesudoconstraints, a novel data mining pattern aimed at identifying rare events in databases. At first, we formally define pesudoconstraints using a probabilistic model and provide a statistical test to identify pesudoconstraints in a database. Then, we focus on a specific class of pesudoconstraints, named cycle pesudoconstraints, which often occur in databases. We define cycle pesudoconstraints in the context of the ER model and present an automatic method for detecting cycle pesudoconstraints from a relational database. Finally, we present an experiment to show cycle pesudoconstraints “at work” on real data.
Modulation of retinal capillary endothelial cells by Müller glial cell-derived factors

Purpose The inner blood-retinal barrier (BRB) is a gliovascular unit in which macroglial cells surround capillary endothelial cells and regulate retinal capillaries by paracrine interactions. The purpose of the present study was to identify genes of retinal capillary endothelial cells whose expression is modulated by Müller glial cell-derived factors. Methods Conditionally immortalized rat retinal capillary endothelial (TR-iBRB2) and Müller (TR-MUL5) cell lines were chosen as an in vitro model. TR-iBRB2 cells were incubated with conditioned medium of TR-MUL5 (MUL-CM) for 24 h and subjected to microarray and quantitative real-time PCR analysis. Results TR-MUL5 cell-derived factors increased alkaline phosphatase activity in TR-iBRB2 cells, indicating that paracrine interactions occurred between TR-iBRB2 and TR-MUL5 cells. Microarray analysis demonstrated that MUL-CM treatment leads to a modulation of several genes including an induction of plasminogen activator inhibitor 1 (PAI-1) and a suppression of an inhibitor of DNA binding 2 (Id2) in TR-iBRB2 cells. Treatment with TGF-β1, which is incorporated in MUL-CM, also resulted in an induction of PAI-1 and a suppression of Id2 in TR-iBRB2 cells. Conclusions In vitro inner BRB model study revealed that Müller glial cell-derived factors modulate endothelial cell functions including the induction of anti-angiogenic PAI-1 and the suppression of pro-angiogenic Id2. Therefore, Müller cells appear to be one of the modulators of retinal angiogenesis.
Imitation dynamics of vaccination behaviour on social networks

The problem of achieving widespread immunity to infectious diseases by voluntary vaccination is often presented as a public-goods dilemma, as an individual's vaccination contributes to herd immunity, protecting those who forgo vaccination. The temptation to free-ride brings the equilibrium vaccination level below the social optimum. Here, we present an evolutionary game-theoretic approach to this problem, exploring the roles of individual imitation behaviour and population structure in vaccination. To this end, we integrate an epidemiological process into a simple agent-based model of adaptive learning, where individuals use anecdotal evidence to estimate costs and benefits of vaccination. In our simulations, we focus on parameter values that are realistic for a flu-like infection. Paradoxically, as agents become more adept at imitating successful strategies, the equilibrium level of vaccination falls below the rational individual optimum. In structured populations, the picture is only somewhat more optimistic: vaccination is widespread over a range of low vaccination costs, but coverage plummets after cost exceeds a critical threshold. This result suggests parallels to historical scenarios in which vaccination coverage provided herd immunity for some time, but then rapidly dropped. Our work sheds light on how imitation of peers shapes individual vaccination choices in social networks.
Nanofluid Heat Transfer Enhancement in Engineering Applications

In this study, the effect of nanofluid heat transfer enhancement in water based systems was investigated from an engineering system perspective. One such system considered was a " Class 8 " truck, diesel engine cooling circuit using two different nanofluids to replace the conventional engine coolant. Results include quantifications of increase in engine heat rejection, reduction in truck frontal area and reduction in pumping power for the coolant circuit by the use of nanofluids instead of the conventional coolant. " Nanofluid " refers to a fluid in which nanometer sized particles are suspended in a fluid, usually a liquid. Water based nanofluids have been considered as potential coolants for nuclear applications. In pool boiling situation, a critical heat flux enhancement of up to 200% have been reported. Potential benefits include increased safety margins in existing reactors and very high power density in PWRs. Nanofluids can also be used in Emergency Core Cooling Systems (ECCS) incorporated in both PWRs and BWRs. Experiments were conducted at an experimental facility that has been developed at the Argonne National Lab to test heat transfer and flow characteristics of nanofluids have been used in this study. The experimental test section represented a second engineering iii system considered in this study, and it was modeled using FLUENT ® with the numerical results benchmarked to experimental observations for water as the working fluid. Subsequently, a nanofluid was simulated in FLUENT ® and the performance compared to the base fluid of water. Based on investigations pertaining to the " Class 8 " truck diesel engine cooling circuit, modeled using FLOWMASTER ® , it is observed that the coolant pump power can be reduced by a factor of eight, the engine heat rejection can be increased by up to 5%, and the frontal area of the truck can be decreased by up to 5%. Also, the engine temperature drops by up to 20% from the base case simulation. The results obtained using the k-ω model in FLUENT ® for water as the working fluid have been benchmarked using the experimental data from the nanofluid test facility at ANL. A nanofluid was simulated using FLUENT ® to analyze potential enhancement. The results obtained using simulations show that though there is a decrease in the Nusselt number (~ 10%) when the base fluid is replaced with the nanofluid, the overall heat transfer coefficient increases (~ 30%) along with the increase in …
Allergies and Schizophrenia : Immune System Starvation ?

A possible genetic link between certain Type I atopic allergies and some forms of thought/behavior disturbance and/or schizophrenia was explored through a 19-month trial of high dosage nicotinic acid in an allergic subject with a familial history of atopic allergies and schizophrenia in different individuals. The asthma and allergic rhinitis of this subject were controllable by niacin in the presence of known allergens. Niacin dosage needs, after an initial rise, displayed a downward inclination. After a period of violent exacerbation, the eczema healed and did not recur. Allergic rhinitis almost totally disappeared, and asthma was moderately frequent but of minimal severity at the conclusion of the trial. Other suggestions of internal healing processes were indicated. Results focus on a variation of the biogenic amine hypothesis for schizophrenia, and incorporating atopic allergies, through a combination of genetic abnormality in amino acid metabolism and the environmental
Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities

MicroRNAs (miRNAs) are a type of non-coding RNAs with about ∼22nt nucleotides. Increasing evidences have shown that miRNAs play critical roles in many human diseases. The identification of human disease-related miRNAs is helpful to explore the underlying pathogenesis of diseases. More and more experimental validated associations between miRNAs and diseases have been reported in the recent studies, which provide useful information for new miRNA-disease association discovery. In this study, we propose a computational framework, KBMF-MDI, to predict the associations between miRNAs and diseases based on their similarities. The sequence and function information of miRNAs are used to measure similarity among miRNAs while the semantic and function information of disease are used to measure similarity among diseases, respectively. In addition, the kernelized Bayesian matrix factorization method is employed to infer potential miRNA-disease associations by integrating these data sources. We applied this method to 6,084 known miRNA-disease associations and utilized 5-fold cross validation to evaluate the performance. The experimental results demonstrate that our method can effectively predict unknown miRNA-disease associations.
Cardiac Autonomic Nervous System in Heart Failure: Imaging Technique and Clinical Implications

The autonomic nervous system interacts in the pathophysiology of heart failure. Dysfunction of the sympathetic nervous system has been identified as an important prognostic marker in patients with chronic heart failure. At present, cardiac sympathetic nerve imaging with 123-iodine metaiodobenzylguanidine [123-I MIBG] has been employed most frequently for the assessment of cardiac sympathetic innervation and activation pattern. The majority of studies have shown that cardiac sympathetic dysfunction as assessed with 123-I MIBG imaging is a powerful predictor for heart failure mortality and morbidity. Additionally, 123-I MIBG imaging can be used for prediction of potentially lethal ventricular tachyarrhythmias in heart failure patients. At present however, the lack of standardization of 123-I MIBG imaging procedures represents an evident issue. Standardized criteria on the use of 123-I MIBG imaging will further strengthen the clinical use of 123-I MIBG imaging in heart failure patients.
Transmission Electron Microscopy of Interfaces in Joints between Pb-Free Solders and Electroless Ni-P

Microstructures of joints between a Ni-8 mass%P UBM (under bump metallization) and three different Pb-free solders, i.e., Sn-Ag, Sn-Ag-Cu and Sn-Ag-Cu-Bi were studied by transmission electron microscopy. The phases formed near the joint interfaces as well as inside the solders during soldering were identified on the basis of electron diffraction and EDX analysis without ambiguity. In the Ni-P under bump metallization near the interface with the solders, P-denuded Ni-20 mass%P layer was observed, where a high density of columnar voids were formed. Also, in Ni(Cu) SnP intermetallic, which is adjacent to the P-denuded Ni-20 mass%P, a high density of spherical voids were observed. These voids degrade the joint strength. Change of the above microstructures during thermal cycling between 253 K and 453 K was also observed.
A polyaromatic nanocapsule as a sucrose receptor in water

A supramolecular capsule with a polyaromatic shell binds d-sucrose from natural saccharide mixtures with perfect selectivity. Selective recognition of saccharides by artificial receptors in water is a challenging goal due to their strong hydrophilicities and complex molecular structures with subtle regio- and stereochemical differences. We report the selective and efficient encapsulation of d-sucrose within a coordination-driven molecular capsule from natural saccharide mixtures in water (~100% selectivity, >85% yield, and ~103 M−1 binding constant). Unlike previous artificial receptors and natural receptors that rely on multiple hydrogen-bonding interactions, theoretical calculations and control experiments indicate that the observed unique selectivity arises from multiple CH-π interactions between the sucrose hydrocarbon backbone and the shape-complementary polyaromatic cavity (~1 nm in diameter) of the capsule.
Left and right basal ganglia and frontal activity during language generation: Contributions to lexical, semantic, and phonological processes

fMRI was used to determine the frontal, basal ganglia, and thalamic structures engaged by three facets of language generation: lexical status of generated items, the use of semantic vs. phonological information during language generation, and rate of generation. During fMRI, 21 neurologically normal subjects performed four tasks: generation of nonsense syllables given beginning and ending consonant blends, generation of words given a rhyming word, generation of words given a semantic category at a fast rate (matched to the rate of nonsense syllable generation), and generation of words given a semantic category at a slow rate (matched to the rate of generating of rhyming words). Components of a left pre-SMA–dorsal caudate nucleus–ventral anterior thalamic loop were active during word generation from rhyming or category cues but not during nonsense syllable generation. Findings indicate that this loop is involved in retrieving words from pre-existing lexical stores. Relatively diffuse activity in the right basal ganglia (caudate nucleus and putamen) also was found during word-generation tasks but not during nonsense syllable generation. Given the relative absence of right frontal activity during the word generation tasks, we suggest that the right basal ganglia activity serves to suppress right frontal activity, preventing right frontal structures from interfering with language production. Current findings establish roles for the left and the right basal ganglia in word generation. Hypotheses are discussed for future research to help refine our understanding of basal ganglia functions in language generation. (JINS, 2003, 9, 1061–1077.)
Goals , Desires , Utilities and PreferencesLeendert

In this paper we study the logic of goals, which are formalized as desires with an utilitarian semantics. In our framework goals have a dual character, because they are constraints on utility functions as well as constructors of these utility functions. The non-monotonic reasoning related to the constructors reeects that goals are used as heuristic approximations of preferences in decision making and planning. Moreover , our framework is based on bipolar additive preferences, where bipolarity means that goals can either result in a gain of utility if achieved, or a loss of utility if not achieved. The framework is used to illustrate diierent types of context-dependence and connicts of goals.
Low-Rank Hidden State Embeddings for Viterbi Sequence Labeling

In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-term input co-occurrence patterns. Representation of output co-occurrence patterns is typically limited to a hand-designed graphical model, such as a linear-chain CRF representing short-term Markov dependencies among successive labels. This paper presents a method that learns embedded representations of latent output structure in sequence data. Our model takes the form of a finite-state machine with a large number of latent states per label (a latent variable CRF), where the state-transition matrix is factorized---effectively forming an embedded representation of state-transitions capable of enforcing long-term label dependencies, while supporting exact Viterbi inference over output labels. We demonstrate accuracy improvements and interpretable latent structure in a synthetic but complex task based on CoNLL named entity recognition.
Spectral Methods for Fractional ARIMA Processes Metodi Spettrali per Processi ARIMA Frazionali Mauro Coli

Riassunto: Nel presente lavoro, nella finalità di stimare il parametro di lunga memoria di un processo ARIMA frazionale, ci si avvale di metodologie basate sulla decomposizione ortogonale del processo temporale. In particolare si propone un’integrazione dell’analisi di Fourier con l’espansione di Karhunen-Loève e con la trasformata discreta wavelet al fine di migliorare le proprietà campionarie degli stimatori basati sull’approssimazione della densità spettrale alle basse frequenze.
Lateralization of face processing in the human brain

Are visual face processing mechanisms the same in the left and right cerebral hemispheres? The possibility of such ‘duplicated processing’ seems puzzling in terms of neural resource usage, and we currently lack a precise characterization of the lateral differences in face processing. To address this need, we have undertaken a three-pronged approach. Using functional magnetic resonance imaging, we assessed cortical sensitivity to facial semblance, the modulatory effects of context and temporal response dynamics. Results on all three fronts revealed systematic hemispheric differences. We found that: (i) activation patterns in the left fusiform gyrus correlate with image-level face-semblance, while those in the right correlate with categorical face/non-face judgements. (ii) Context exerts significant excitatory/inhibitory influence in the left, but has limited effect on the right. (iii) Face-selectivity persists in the right even after activity on the left has returned to baseline. These results provide important clues regarding the functional architecture of face processing, suggesting that the left hemisphere is involved in processing ‘low-level’ face semblance, and perhaps is a precursor to categorical ‘deep’ analyses on the right.