Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Neisseria meningitidis Fur Mutations on Global Control of Gene Transcription

ABSTRACT The ferric uptake regulator Fur is a well-known iron-responsive repressor of gene transcription, which is used by many bacteria to respond to the low-iron environment that pathogens encounter during infection. In this study we used comparative transcriptome analysis to define the role of the Fur protein in the global control of gene transcription and iron regulation in Neisseria meningitidis. By using the Fur-null mutant and its complemented derivative, we identified 83 genes whose transcription is controlled by Fur. We report that Fur may control differential expression of these genes by binding directly to their promoters or through indirect mechanisms. In addition, mutation of the fur gene resulted in the induction of the heat shock response, and transcription of these genes does not respond to iron limitation. Furthermore, analysis of the iron starvation stimulon in the Fur-null mutant provided evidences of iron-responsive regulation that is independent of Fur. We began to dissect the regulatory networks of Fur and the heat shock (stress) response in N. meningitidis, and the observed interlink between the two circuits is discussed.
A growing body of evidence implicates free radical toxicity , oxidative enzyme impairment and mitochondrial dysfunction in neurodegenerative diseases

Apolipoprotein D (apo D), a lipocalin transporter of small hydrophobic molecules could play an important role in several neurodegenerative diseases. However, its role in those diseases remains unclear. Increments of apo D have been reported in relation with injury and degeneration in the nervous system. Recently increases of apo D level have been reported in schizophrenia, a neuropathologic disease where the oxidative stress and lipid abnormalities may be involved. Apo D could act as a sequestering molecule binding excess of arachidonic acid in cells. In order to determine the relationship between apo D expression and other neurodegenerative pathologies related to oxidative damage, we studied the presence of apo D in the substantia nigra of control and Parkinson disease (PD) subjects. We found dopaminergic neurons were not immunoreactive for apo D, control or PD subjects. However, surrounding glial cells showed immuno-staining for apo D and signal increases in PD cases. These findings support the role of apolipoprotein D in neuroprotection and the importance of glia in the amount of this protein in the central nervous system.
THE SPITZER c2d SURVEY OF NEARBY DENSE CORES. VI. THE PROTOSTARS OF LYNDS DARK NEBULA 1221

Observations of Lynds Dark Nebula 1221 from the Spitzer Space Telescope are presented. These data show three candidate protostars toward L1221, only two of which were previously known. The infrared observations also show signatures of outflowing material, an interpretation which is also supported by radio observations with the Very Large Array. In addition, molecular line maps from the Five College Radio Astronomy Observatory are shown. One-dimensional dust continuum modeling of two of these protostars, IRS1 and IRS3, is described. These models show two distinctly different protostars forming in very similar environments. IRS1 shows a higher luminosity and a larger inner radius of the envelope than IRS3. The disparity could be caused by a difference in age or mass, orientation of outflow cavities, or the impact of a binary in the IRS1 core.
An information-theoretic approach to interest matching

The Internet has brought a new meaning to the term communities. Geography is no longer a barrier to international communications. However, the paradigm of meeting new interesting people remains entrenched in traditional means; meeting new interesting people on the Internet still relies on chance and contacts. This thesis explores a new approach towards matching users in online communities in an effective fashion. Instead of using the conventional feature-vector scheme to profile users, each user is represented by a personalized concept hierarchy (or an ontology) that is learnt from the user's behavior in the system. Each concept hierarchy is then interpreted within the Information Theory framework as a probabilistic decision tree. The matching algorithm uses the Kullback-Leiber distance as a measure of deviation between two probabilistic decision trees. Thus, in an online community, where a personalized concept hierarchy represents each user, the Kullback-Leiber distance imposes a fullorder rank on the level of similarity of all the users with respect to a particular user in question. The validity and utility of the proposed scheme of matching users is then applied in a set of simulations, using the feature-vector-overlap measure as a baseline. The results of the simulations show that the Kullback-Leiber distance, when used in conjunction with the concept hierarchy, is more robust to noise and is able to make a stronger and more distinctive classification of users into similar groups in comparison to the conventional keyword-overlap scheme. A graphical agent system that relies upon the ontology-based interest matching algorithm, called the Collaborative Sanctioning Network, is also described in this thesis. Thesis Supervisor: Peter Szolovits Title: Professor of Electrical Engineering and Computer Science
Bandwidth modeling and optimization of PIN photodiodes

This paper presents a very general and complete numerical model which is used in the calculation and optimization of the bandwidth of PIN photodiodes. The model takes into account not only the transit time effects but also the capacitive effects. These combined effects are seen to limit the bandwidth to a maximum value which decreases when the area and the series resistance of the device increase. The capacitive effects determine the bandwidth of short devices which seems to be independent of bias voltage and the direction of incident light.
HRI observation with My Keepon robot using Kansei Engineering approach

Human-robot interaction (HRI) is an interaction technology which breaks the communication boundaries between humans and robots. HRI research is usually carried out using expensive robots. This has become a limitation in this research area. This study developed a programmed interaction by hacking My Keepon, a toy like-robot to interact with kindergarten children. Feedback on My Keepon features that are attractive to the children during HRI are analysed using Kansei Engineering method to investigate whether the children are attracted towards a simple robot. Four children aged 4 and 5 years old were selected for the HRI observation. The children interacted with My Keepon by touching and responding to sounds from the robot. Video records of the interaction will be analysed by giving scores on eight Kansei emotions. Interview session after the experiment will show how the children emotionally perceive My Keepon. Results will conclude the suitability of My Keepon as a robotic play companion for use in kindergartens.
Regularized sequence-level deep neural network model adaptation

We propose a regularized sequence-level (SEQ) deep neural network (DNN) model adaptation methodology as an extension of the previous KL-divergence regularized cross-entropy (CE) adaptation [1]. In this approach, the negative KL-divergence between the baseline and the adapted model is added to the maximum mutual information (MMI) as regularization in the sequence-level adaptation. We compared eight different adaptation setups specified by the baseline training criterion, the adaptation criterion, and the regularization methodology. We found that the proposed sequence-level adaptation consistently outperforms the crossentropy adaptation. For both of them, regularization is critical. We further introduced a unified formulation in which the regularized CE and SEQ adaptation are the special cases. We applied the proposed approach to speaker adaptation and accent adaptation in a mobile short message dictation task. For the speaker adaptation, with 25 or 100 utterances, the proposed approach yields 13.72% or 23.18% WER reduction when adapting from the CE baseline, comparing to 11.87% or 20.18% for the CE adaptation. For the accent adaptation, with 1K utterances, the proposed approach yields 18.74% or 19.50% WER reduction when adapting from the CE-DNN or the SEQ-DNN. The WER reduction using the regularized CE adaptation is 15.98% and 15.69%, respectively.
A Relation-Based Merging Operator for Qualitative Spatial Data Integration and Conflict Resolution

We describe a distance-based approach to integrate spatial information from different sources that is given in the form of constraint networks over relations from a qualitative spatial calculus. The distance functions are based on the notion of conceptual neighborhood between the spatial relations. We opt for a merging approach that is relationbased instead of model-based and as a result is also able to relax inconsistent networks. We investigate the properties of the proposed merging operators and describe an algorithm for their computation.
Endothelium-Derived Netrin-4 Supports Pancreatic Epithelial Cell Adhesion and Differentiation through Integrins α2β1 and α3β1

Background Netrins have been extensively studied in the developing central nervous system as pathfinding guidance cues, and more recently in non-neural tissues where they mediate cell adhesion, migration and differentiation. Netrin-4, a distant relative of Netrins 1–3, has been proposed to affect cell fate determination in developing epithelia, though receptors mediating these functions have yet to be identified. Methodology/Principal Findings Using human embryonic pancreatic cells as a model of developing epithelium, here we report that Netrin-4 is abundantly expressed in vascular endothelial cells and pancreatic ductal cells, and supports epithelial cell adhesion through integrins α2β1and α3β1. Interestingly, we find that Netrin-4 recognition by embryonic pancreatic cells through integrins α2β1 and α3β1 promotes insulin and glucagon gene expression. In addition, full genome microarray analysis revealed that fetal pancreatic cell adhesion to Netrin-4 causes a prominent down-regulation of cyclins and up-regulation of negative regulators of the cell cycle. Consistent with these results, a number of other genes whose activities have been linked to developmental decisions and/or cellular differentiation are up-regulated. Conclusions/Significance Given the recognized function of blood vessels in epithelial tissue morphogenesis, our results provide a mechanism by which endothelial-derived Netrin-4 may function as a pro-differentiation cue for adjacent developing pancreatic cell populations expressing adhesion receptors α2β1 and α3β1 integrins.
Nonlinear Principal Component Analysis by Neural Networks: Theory and Application to the Lorenz System

A nonlinear generalization of principal component analysis (PCA), denoted nonlinear principal component analysis (NLPCA), is implemented in a variational framework using a five-layer autoassociative feed-forward neural network. The method is tested on a dataset sampled from the Lorenz attractor, and it is shown that the NLPCA approximations to the attractor in one and two dimensions, explaining 76% and 99.5% of the variance, respectively, are superior to the corresponding PCA approximations, which respectively explain 60% (mode 1) and 95% (modes 1 and 2) of the variance. It is found that as noise is added to the Lorenz attractor, the NLPCA approximations remain superior to the PCA approximations until the noise level is so great that the lowerdimensional nonlinear structure of the data is no longer manifest to the eye. Finally, directions for future work are presented, and a cinematographic technique to visualize the results of NLPCA is discussed.
Identification of Noun Phrase with Various Granularities

Since noun phrases are the most popular phrases in texts, noun phrase identification is one of vital subtasks of natural language processing. Generally Chinese noun phrases have hierarchical inner structures. This paper proposes an approach of defining various levels of granularity for noun phrases, catering for different application demands. Three levels of granularity noun phrases are proposed, that is, concept noun phrase, base noun phrase and entire noun phrase. The task of noun phrase identification is to label word sequences with phrase tags. All granularity noun phrase identifications are cast as classification problem under certain encoding schemes. The experimental dataset is acquired empirically from Chinese Penn Treebank 5.1. F, measure of concept noun phrase, base noun phrase and entire noun phrase identification reaches 92.12%, 84.13% and 85.32% respectively.
Breast imaging with fluorine-18-FDG PET: quantitative image analysis.

UNLABELLED This study evaluated various quantitative criteria for analysis of breast imaging with PET using the radiolabeled glucose analog 18F-fluorodeoxyglucose (FDG). METHODS In a prospective study, 73 patients with abnormal mammography or palpable breast masses scheduled for biopsy were investigated with PET. A total of 97 breast tumors were evaluated by histology, including 46 benign and 51 malignant tumors. Using a whole-body PET scanner, attenuation-corrected images were acquired between 40 and 60 min after tracer injection. For Patlak analysis, dynamic data acquisition was obtained in 24 patients. To differentiate between benign and malignant breast tumors, receiver operating characteristic curves were calculated using incrementally increasing threshold values for tumor/ nontumor ratios based on average and maximum activity values per region of interest, standardized uptake values (corrected for partial volume effect, normalized to blood glucose, partial volume effect and blood glucose, using the lean body mass as well as the body surface area) and calculating the FDG influx rate (K) assessed by Patlak analysis. RESULTS Quantification of FDG uptake in breast tumors provided objective criteria for differentiation between benign and malignant tissue with similar diagnostic accuracy as compared with visual analysis. Applying correction for partial volume effect and normalization by blood glucose yielded the highest diagnostic accuracy. CONCLUSIONS These quantitative methods provided accurate evaluation of PET data for differentiating benign from malignant breast tumors. Quantitative assessment is recommended to complement visual image interpretation with the potential benefit of reduced interobserver variability.
Performance analysis of trust based geographic routing protocols for Wireless Sensor Networks

Deploying Wireless Sensor Networks (WSNs) in hostile and remote environments are often lead to vulnerabilities and prone to various security attacks. These attacks can disrupt the regular network operations such that the dependable metrics of routing protocols can be seriously affected. Conventional cryptography techniques have proved their strength in communication security rather than the routing security. To achieve the routing security, numerous researchers have utilized a human behavior patten called trust. In this paper, the authors present a study on the performance of various trust based geographic routing protocols for WSNs. After studying the trust based routing protocols, the performance of these protocols are analyzed using NS-2 simulations. With the extensive simulations, it is noted that each protocol has their own advantages and limitations in discovering and isolating the malicious nodes from the routing process. Finally, with the observations from the simulations, the authors suggests extensions to the trust models to make them more robust against detecting malicious nodes.
Roles and Responsibilities: Creating Moral Subjects

or conceptual, characteristics for which they qualify. Consider Sarah’s child. While the actual, physically determinate child is a concrete subject, nothing about the child’s material measurements is a prerequisite for its occupying the office of ‘Sarah’s child,’ save, perhaps, for the fact that a physical person must occupy this office in order for the office to be filled at all. As long as the future person in question is a person, it matters not what his height, hair color, etc. is. Something or other will be the case regarding his material measurements, but no specific measurement will by itself qualify or disqualify him as Sarah’s future child. Thus, there are inherent and non-inherent attributes of a de dicto entity. The inherent attributes are whatever qualifies a thing to fill the office in question. For our purposes, the office of ‘her child’ must be filled by a person to be filled at all, just like the office of ‘my car’ must be filled by a vehicle with parts that match the description of a car, insofar as it is filled at all. Noninherent attributes are any of the variable physical properties that can be expressed by the things that fill these offices, for example, the weight of a future person at t1, or the make and model of my car. Notice that the inherent attributes are subject to de dicto restrictions. That is, the parts that make up my car could be disassembled, widely dispersed, and certain parts could be melted down to make other objects. Thus, my car would no longer exist, not because the physical parts of my car no longer existed, but because these parts fail to fit the description of a car. This difference between inherent and non-inherent attributes demonstrates that an individual’s physical properties and de dicto properties are actually closely intertwined. To talk 32 That its occupant is physical is often a prerequisite for a de dicto entity, but any further specification of its physical properties usually is not.
Face-to-face versus one-way and two-way videoconferencing: How medium naturalness and personality traits influence achievement and perceived learning?

During the last years, an increasing portion of face-to-face teaching-learning processes in the academia is being replaced with online synchronous sessions. According to the Media Naturalness Theory [1], the effectiveness of e-learning is affected by differences in naturalness level of the communication media. The present study compared between learning in a face-to-face environment and learning in two online conditions (one-way and two-way video conferencing). We examined the influence of medium naturalness and personality traits (extroversion-introversion and emotional stability-neuroticism) on students' achievements and perceived learning. A controlled experiment was conducted with 76 participants attending a lesson that was composed of several teaching-learning components: frontal lecture, instructor-students interactions, students-instructor interactions, and peer interactions. Zoom videoconferencing application, which allows spontaneous participation, was utilized for e-learning. Results indicate that for the face-to-face learning condition, achievement was higher compared to one-way videoconferencing only for fact-related questions, but not for inference questions or overall test score. We also found that low level of medium naturalness was associated with lower scores only for neurotic students. No clear evidence that links extroversion-introversion trait to academic performance was found. All aspects of perceived learning showed consistent advantage for emotionally stable participants and extroverts, compared to neurotic students and introverts.
How to share an object: A fast timing-based solution

We consider the problem of transforming a given sequential implementation of a data structure into a wait-free concurrent implementation. Given the code for different operations of an object that is designed to work under the assumption that only a single process accesses it, we want to construct an implementation that works correctly in a concurrent environment where it may be accessed by many processes. We assume a shared memory model with atomic registers. It is well known that using atomic registers it is impossible to construct concurrent implementations of even very simple objects such as test-and-set bits. However, we show that the knowledge about relative speeds of processes can be used for such implementations. We assume that there is a known upper bound on the time taken by the slowest process to execute a statement involving an access to the shared memory. This timing assumption is very powerful and enables us to construct fast wait-free implementations of data structures such as queues, stacks and synchronization primitives such as test-and-set, compare-and-swap, fetch-and-add, etc. Our transformation works only when the given sequential implementation is bounded, that is, there is a known upper bound on the number of steps required to complete any of the operations it supports. In the absence of contention, it guarantees that there is only a small overhead in the cost of executing the concurrent operations over the sequential ones, namely, only a constant number of accesses to the shared memory. <<ETX>>
Ilp-95, Leuven Mult Icn: an Empirical Multiple Predicate Learner

In this paper, we are interested in empirical multiple predicate learning. The rst solution to this problem that consists in putting together the deenitions obtained by a single predicate learning system is rarely interesting. We explain why and we show how a single predicate learning system has been extended to a multiple predicate learning system called mult icn, which learns deenite logic programs. Our system is based on the notion of exten-sional coverage but during the construction of the program, it builds a set of recursive dependencies that gives information about the mutually recur-sive calls. It has therefore two main advantages. First, it ensures that the learned program is globally consistent and complete, i.e., the learned program does not only extensionally cover the positive examples and reject the negative ones but it does prove that positive examples are true and negative ones are false in the semantics of the learned program. Secondly it uses only the knowledge given by the user to induce deenitions and it is based on an acceptability rate; both enable to reduce the innuence of the order the predicates are learned.
A Comprehensive Expression Profile of MicroRNAs and Other Classes of Non-Coding Small RNAs in Barley Under Phosphorous-Deficient and -Sufficient Conditions

Phosphorus (P) is essential for plant growth. MicroRNAs (miRNAs) play a key role in phosphate homeostasis. However, little is known about P effect on miRNA expression in barley (Hordeum vulgare L.). In this study, we used Illumina's next-generation sequencing technology to sequence small RNAs (sRNAs) in barley grown under P-deficient and P-sufficient conditions. We identified 221 conserved miRNAs and 12 novel miRNAs, of which 55 were only present in P-deficient treatment while 32 only existed in P-sufficient treatment. Total 47 miRNAs were significantly differentially expressed between the two P treatments (|log2| > 1). We also identified many other classes of sRNAs, including sense and antisense sRNAs, repeat-associated sRNAs, transfer RNA (tRNA)-derived sRNAs and chloroplast-derived sRNAs, and some of which were also significantly differentially expressed between the two P treatments. Of all the sRNAs identified, antisense sRNAs were the most abundant sRNA class in both P treatments. Surprisingly, about one-fourth of sRNAs were derived from the chloroplast genome, and a chloroplast-encoded tRNA-derived sRNA was the most abundant sRNA of all the sRNAs sequenced. Our data provide valuable clues for understanding the properties of sRNAs and new insights into the potential roles of miRNAs and other classes of sRNAs in the control of phosphate homeostasis.