Miracle-dz/UniFAR-FTUs huggingface dataset:
Escherichia coli transcriptional regulatory network

Escherichia coli is the most well-know bacterial model about the function of its molecular components. In this review are presented several structural and functional aspects of their transcriptional regulatory network constituted by transcription factors and target genes. The network discussed here represent to 1531 genes and 3421 regulatory interactions. This network shows a power-law distribution with a few global regulators and most of genes poorly connected. 176 of genes in the network correspond to transcription factors, which form a sub-network of seven hierarchical layers where global regulators tend to be set in superior layers while local regulators are located in the lower ones. There is a small set of proteins know as nucleoid-associated proteins, which are in a high cellular concentrations and reshape the nucleoid structure to influence the running of global transcriptional programs, to this mode of regulation is named analog regulation. Specific signal effectors assist the activity of most of transcription factors in E. coli. These effectors switch and tune the activity of transcription factors. To this type of regulation, depending of environmental signals is named the digital–precise-regulation. The integration of regulatory programs have place in the promoter region of transcription units where it is common to observe co-regulation among global and local TFs as well as of TFs sensing exogenous and endogenous conditions. The mechanistic logic to understand the harmonious operation of regulatory programs in the network should consider the globalism of TFs, their signal perceived, coregulation, genome position, and cellular concentration. Finally, duplicated TFs and their horizontal transfer influence the evolvability of members of the network. The most duplicated and transferred TFs are located in the network periphery.
Feasibility of soil moisture estimation using passive distributed temperature sensing

Through its role in the energy and water balances at the land surface, soil moisture is a key state variable in surface hydrology and land‐atmosphere interactions. Point observations of soil moisture are easy to make using established methods such as time domain reflectometry and gravimetric sampling. However, monitoring large‐scale variability with these techniques is logistically and economically infeasible. Here passive soil distributed temperature sensing (DTS) will be introduced as an experimental method of measuring soil moisture on the basis of DTS. Several fiber‐optic cables in a vertical profile are used as thermal sensors, measuring propagation of temperature changes due to the diurnal cycle. Current technology allows these cables to be in excess of 10 km in length, and DTS equipment allows measurement of temperatures every 1 m. The passive soil DTS concept is based on the fact that soil moisture influences soil thermal properties. Therefore, observing temperature dynamics can yield information on changes in soil moisture content. Results from this preliminary study demonstrate that passive soil DTS can detect changes in thermal properties. Deriving soil moisture is complicated by the uncertainty and nonuniqueness in the relationship between thermal conductivity and soil moisture. A numerical simulation indicates that the accuracy could be improved if the depth of the cables was known with greater certainty.
Descending projections from the auditory cortex to the inferior colliculus in the gerbil, Meriones unguiculatus

Corticofugal projections to the auditory midbrain, the inferior colliculus (IC), influence the way in which specific sets of IC neurons process acoustic signals. We used retrograde tracer (Fluorogold, Fluororuby, microbeads) injections in the IC to study the morphology and location of cortico‐collicular projecting neurons and anterograde tracer (dextran biotin) injections in auditory cortical fields to describe the distribution of terminals in the IC. Nissl staining, cytochrome oxidase activity, and neurofilament SMI32 immunostaining were used to delimit the different auditory areas. We defined a primary or “core” auditory cortex and a secondary “caudal” auditory area containing layer V pyramidal neurons that project to the IC. These projections target the central nucleus of the IC (CNIC) ipsilaterally and the IC cortices bilaterally, with the ipsilateral component predominant. Other secondary auditory areas, dorsal and ventral to the core, do not directly participate in this projection. The ventral secondary cortex targets midbrain periaqueductal gray. The projection from the core cortex originates from two classes of layer V pyramidal cells. Cells presenting a tufted apical dendrite in layer I have dense terminal fields in the IC cortices. Pyramids lacking layer I dendritic tufts target the CNIC in a less dense but tonotopic manner. The caudal cortex projection originates from smaller layer V pyramids and targets the IC cortices with dense terminal fields. Descending auditory inputs from the core and caudal areas converge in the dorsal and external cortices of the IC. Descending connections to the gerbil IC form a segregated system in which multiple descending channels originating from different neuronal subpopulations may modulate specific aspects of ascending auditory information. J. Comp. Neurol. 486:101–116, 2005. © 2005 Wiley‐Liss, Inc.
Optimization of network protection against virus spread

The effect of virus spreading in a telecommunication network, where a certain curing strategy is deployed, can be captured by epidemic models. In the N-intertwined model proposed and studied in [1], [2], the probability of each node to be infected depends on the curing and infection rate of its neighbors. In this paper, we consider the case where all infection rates are equal and different values of curing rates can be deployed within a given budget, in order to minimize the overall infection of the network. We investigate this difficult optimization together with a related problem where the curing budget must be minimized within a given level of network infection. Some properties of these problems are derived and several solution algorithms are proposed. These algorithms are compared on two real world network instances, while Erdös-Rényi graphs and some special graphs such as the cycle, the star, the wheel and the complete bipartite graph are also addressed.
A study on Psychological well-being among Elderly

Psychological well-being is closely related to old age. The main purpose of this research was to assess the psychological well-being among the elderly. The study was carried out in Lucknow and the sample was selected following Multistage sampling technique. The sample for this study comprised 120 elderly individuals (60 females and 60 males respectively) comprising of elderly living in home and old age home. Psychological well-being scale developed by Sisodia and Choudhary (1999) was used to asses the psychological well-being level. Significant difference in psychological level was found among the elderly living in the familes and old age home.
Navigating the Alcohol Treatment Pathway: A Qualitative Study from the Service Users' Perspective

Aims Provision of effective treatment for dependent drinkers has been identified as a priority in England yet evidence suggests that access is problematic and there are low levels of retention. This qualitative study explores how the alcohol treatment system is experienced by service users, identifying barriers and facilitators that influence treatment outcomes. Methods A total of 20 semi-structured face-to-face interviews were conducted with patients from community alcohol treatment services in three London boroughs in 2012. Interviews were undertaken one year after initially entering treatment. A thematic analysis was conducted, with the results further abstracted to relate them to specific aspects of the treatment journey. Results Patients journeys were characterized by a perceived lack of control leading to help-seeking, with treatment outcomes influenced by an individuals' self-efficacy and the capabilities and skills of staff in actively engaging and supporting patients on the journey. A focus of services on the detoxification process and fragmented care pathways impacted negatively on engagement. Conclusions Current alcohol care pathways require significant levels of motivation and self-efficacy to navigate that few patients possess. Pathways need to better reflect the capacity and capabilities of patients to be successful in supporting recovery.
An Arbitration Scheme for Concurrency Control in Distributed Groupware

We present a novel algorithm, called dARB, for solving the concurrency control problem in completely distributed collaborative applications. The main advantages of our approach are the simplicity of use and good responsiveness, as there are no lock mechanisms. The algorithm is application independent, which it makes it suitable for general collaborative applications. The only constraint is that the applications use a tree as the internal data structure. The examples applications of the algorithm are implemented in the DISCIPLE collaboration framework. The applications are a group text editor and a collaborative 3D virtual environment called cWorld. We also discuss the use of awareness widgets to increase the efficiency of collaborative work.
Detecting Curved Symmetric Parts Using a Deformable Disc Model

Symmetry is a powerful shape regularity that's been exploited by perceptual grouping researchers in both human and computer vision to recover part structure from an image without a priori knowledge of scene content. Drawing on the concept of a medial axis, defined as the locus of centers of maximal inscribed discs that sweep out a symmetric part, we model part recovery as the search for a sequence of deformable maximal inscribed disc hypotheses generated from a multiscale super pixel segmentation, a framework proposed by LEV09. However, we learn affinities between adjacent super pixels in a space that's invariant to bending and tapering along the symmetry axis, enabling us to capture a wider class of symmetric parts. Moreover, we introduce a global cost that perceptually integrates the hypothesis space by combining a pair wise and a higher-level smoothing term, which we minimize globally using dynamic programming. The new framework is demonstrated on two datasets, and is shown to significantly outperform the baseline LEV09.
DETAILED X-RAY LINE PROPERTIES OF θ2 Ori A IN QUIESCENCE

We investigate X-ray emission properties of the peculiar X-ray source θ2 Ori A in the Orion Trapezium region using more than 500 ks of HETGS spectral data in the quiescent state. The amount of exposure provides tight constraints on several important diagnostics involving O, Ne, Mg, and Si line flux ratios from He-like ion triplets, resonance line ratios of the H- and He-like lines, and line widths. Accounting for the influence of the strong UV radiation field of the O9.5V star, we can now place the He-like line origin well within two stellar radii of the O-star's surface. The lines are resolved with average line widths of 341 ± 38 km s−1. In the framework of standard wind models, this likely implies a rather weak wind with moderate post-shock velocities. The emission measure distribution of the X-ray spectrum, as reported previously, includes very high temperature components which are not easily explained in this framework. The X-ray properties are also not consistent with coronal emissions from an unseen low-mass companion nor with typical signatures from colliding wind interactions. The properties are more consistent with X-ray signatures observed in the massive Trapezium star θ1 Ori C which has recently been successfully modeled with a magnetically confined wind model.
Swift XRT Observations of X-ray Flares in GRB Afterglows

The Swift XRT has been observing GRB afterglows since December 23, 2004. Three-quarters of these observations begin within 300 s of the burst onset, providing an unprecendented look at the behavior of X-ray emission from GRB afterglows in the first few hours after the burst. While most of the early afterglows have smoothly declining lightcurves, a substantial fraction has large X-ray flares on short time-scales. We suggest that these flares provide support for models with extended central engine activity producing late-time internal shocks.
Genetic and Diurnal Variation in the Juvenile Hormone Titer in a Wing‐Polymorphic Cricket: Implications for the Evolution of Life Histories and Dispersal

The wing‐polymorphic cricket, Gryllus firmus, contains (1) a flight‐capable morph (LWf) with long wings and functional flight muscles, (2) a flightless morph with reduced wings and underdeveloped flight muscles (SW), and (3) a flightless morph with histolyzed flight muscles but with fully developed wings (LWh). The LWf morph differed genetically from the SW morph and phenotypically from the LWh morph in the size of flight muscles, ovarian growth during the first week of adulthood, and the hemolymph titer of juvenile hormone (JH). This is the first study to document that phenotypes that differ genetically in morphological aspects of dispersal capability and in ovarian growth also differ genetically in the titer of a hormone that potentially regulates those traits. The JH titer rose 9–100‐fold during the photophase in the flight‐capable LWf morph but did not change significantly during this time in either flightless morph. Prolonged elevation of the in vivo JH titer in flight‐capable females, by topical application of a hormone analogue, caused a substantial increase in ovarian growth and histolysis of flight muscles. The short‐term, diurnal rise in the JH titer in the dispersing morph may be a mechanism that allows JH to positively regulate nocturnal flight behavior, while not causing maladaptive histolysis of flight muscles and ovarian growth. This is the first demonstration of naturally occurring, genetically based variation for diurnal change in a hormone titer in any organism.
The design and implementation of PowerMill

In this paper we discuss the design and implementation of the simulator PowerMill, a novel transistor level simulator for the simulation of current and power behavior in vlsi circuits. With a new transistor modeling technology and a versatile event driven simulation algorithm, PowerMill is capable of simulating detailed current behavior in modern deep-submicron cmos circuits, including sophisticated circuitries such as exclusive-or gates and sense-ampli ers, with speed and capacity approaching conventional gate level simulators. The high accuracy and speed have made it possible for designers to study and verify detailed current behavior of large functional blocks or even an entire chip with a reasonable amount of CPU resources, making it a de facto industry standard for power simulation.
GPS precise positioning with pseudorange evaluation using 3-dimensional maps

The accurate and reliable positions of pedestrians are important and useful information. Although global positioning systems (GPSs) in smartphones are currently the most convenient devices to obtain the positions of pedestrians, GPSs still have problems with their accuracy and reliability because of the performance degradation caused by multipath and non-line-of-sight (NLOS) propagation in urban canyons. This study describes an approach to estimate a position by searching around the reference position. Position candidates are prepared and evaluated based on the similarity between the simulated pseudorange from the candidate and the observed pseudorange. Simulated pseudoranges are calculated on the basis of a ray-tracing simulation. The proposed method was verified through field experiments in urban canyons in Tokyo. It successfully estimates the reflection paths and direct paths so that the estimate appears very close to the ground truth even though the GPS result is far away from the ground truth.
An in-depth exploration of an aphasia care pathway

xvi Chapter 1: INTRODUCTION . 1 1.1 Background to the research . 1 1.2 Stroke : an overview . 2 1.3 Aphasia: an overview . 2 1.3. 1 Incidence and prevalence of aphasia . 3 1.3. 2 Aphasia and stroke: an overview . 3 1.4 Outline of the Thesis . 4 Chapter 2: LITERATURE REVIEW . 6 2.1 The context of aphasia within stoke and its recovery. 6 2.1.1 Living with aphasia . 6 2.1.2 The impact of aphasia on family members and carers. 8 2.1.3 Impact of aphasia on the rehabilitation process . 9 2.2 The role of Health Care Professionals with people with aphasia . 11 2.2.1 The role of SLT with PWA . 12 2.3 Information needs about aphasia . 14
Robust facial emotion recognition using a temporal-reinforced approach

In this paper, a temporal-reinforced approach to enhancing emotion recognition from facial images is presented. Shape and texture models of facial images are computed by using active appearance model (AAM), from which facial feature points and geometrical feature values are extracted. The extracted features are used by relevance vector machine (RVM) to recognize emotional states. We propose a temporal analysis approach to recognizing likelihood of emotional categories, such that more subtle emotion, such as degree and ratio of basic emotional states can be obtained. Furthermore, a method is developed to map the recognition result to the arousal-valence plane (A-V Plane). Experimental results verify that the performance of emotion recognition is enhanced by the proposed method.
Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties

Enterprise distributed systems, such as mission avionic systems, traffic management systems, and shipboard computing environments, are transitioning to next-generation middleware, such as service-oriented middleware (Pezzini & Natis, 2007) and component-based software engineering (Heineman & Councill, 2001). Although nextgeneration middleware is improving enterprise distributed system functional properties (i.e., its operational scenarios), Quality-of-Service (QoS) properties (e.g., end-to-end response time, throughput, and scalability) are not validated until late in the software lifecycle, i.e., during system integration time. This is due in part to the serialized-phasing development problem (Rittel & Webber, 1973). As illustrated in Figure 1, in serialized-phasing development, the infrastructure and applicationlevel system entities, such as components that encapsulate common services, are developed during different phases of the software lifecycle. Software design decisions that affect QoS properties, ABSTRACT
The Resilience of Language : What Gesture Creation in Deaf Children Can Tell Us About How All Children Learn Language

Part 1: The Problem of Language Learning. Out of the Mouths of Babes. How Do Children Learn Language? Language-Learning Across the Globe. Language-Learning by Hand. Does More or Less Input Matter? Part 2: Language Development without a Language Model. Background on Deafness and Language-Learning. How Do We Begin? Words. The Parts of Words. Combining Words Into Simple Sentences. Making Complex Sentences out of Simple Ones: Recursion. Building a System. Beyond the Here-And-Now: The Functions Gesture Serves. How Might Hearing Parents Foster Gesture Creation in Their Deaf Children? Gesture Creation Across the Globe. Part 3: The Conditions that Foster Language and Language-Learning. How Do the Resilient Properties of Language Help Children Learn Language? When Does Gesture Become Language? Is Language Innate? The Resilience of Language. References. Author Index. Subject Index.
Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis

In this paper, we propose an expert selection system that learns online the best expert to assign to each patient depending on the context of the patient. In general, the context can include an enormous number and variety of information related to the patient's health condition, age, gender, previous drug doses, and so forth, but the most relevant information is embedded in only a few contexts. If these most relevant contexts were known in advance, learning would be relatively simple but they are not. Moreover, the relevant contexts may be different for different health conditions. To address these challenges, we develop a new class of algorithms aimed at discovering the most relevant contexts and the best clinic and expert to use to make a diagnosis given a patient's contexts. We prove that as the number of patients grows, the proposed context-adaptive algorithm will discover the optimal expert to select for patients with a specific context. Moreover, the algorithm also provides confidence bounds on the diagnostic accuracy of the expert it selects, which can be considered by the primary care physician before making the final decision. While our algorithm is general and can be applied in numerous medical scenarios, we illustrate its functionality and performance by applying it to a real-world breast cancer diagnosis data set. Finally, while the application we consider in this paper is medical diagnosis, our proposed algorithm can be applied in other environments where expertise needs to be discovered.
Outlier Detection in Structural Time Series Models: The Indicator Saturation Approach

Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general to specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse- and step-indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.
Image Segmentation as a Classification task

We implement two-class classification model for image segmentation. This project draws loosely on [1]. In a preprocessing stage an image is oversegmented into superpixels by normalized cut algorithm. For a segment as a mergence of superpixels, we define two features based on similarity in brightness. We train a logistic regression classifier to combine these features. As a ground truth segmentation, we use the database of segmentation produced by human. Finally we conduct experiments and qualitatively evaluate the results.