Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational methodology for predicting the landscape of the human-microbial interactome region level influence

Microbial communities thrive in close association among themselves and with the host, establishing protein-protein interactions (PPIs) with the latter, and thus being able to benefit (positively impact) or disturb (negatively impact) biological events in the host. Despite major collaborative efforts to sequence the Human microbiome, there is still a great lack of understanding their impact. We propose a computational methodology to predict the impact of microbial proteins in human biological events, taking into account the abundance of each microbial protein and its relation to all other microbial and human proteins. This alternative methodology is centered on an improved impact estimation algorithm that integrates PPIs between human and microbial proteins with Reactome pathway data. This methodology was applied to study the impact of 24 microbial phyla over different cellular events, within 10 different human microbiomes. The results obtained confirm findings already described in the literature and explore new ones. We believe the Human microbiome can no longer be ignored as not only is there enough evidence correlating microbiome alterations and disease states, but also the return to healthy states once these alterations are reversed.
Measuring agreement in method comparison studies

Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.
Seeing the Forest Among the Trees: When Data Do Not Speak Loud Enough

As I read the article by Boyd, Odom, Humphreys, and Sam (2010 [this issue]), entitled “Infant and Toddlers With Autism Spectrum Disorder: Early Identification and Early Intervention,” I reflected on a recent conversation with a colleague who was in the process of finding out that her 18-month-old son is being diagnosed with autism spectrum disorder (ASD). After realizing that her son’s development was atypical and after searching for professionals to diagnosis him and guide her intervention choices, she said,
Construction of Point Process Adaptive Filter Algorithms for Neural Systems Using Sequential Monte Carlo Methods

The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFS and SMC-PPFD , respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFS and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFS algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods
Isolation, characterization, and expression of the murine Wilms' tumor gene (WT1) during kidney development

The human Wilms' tumor predisposition gene, WT1, is a Cys-His zinc finger polypeptide which appears to be a transcription factor controlling gene expression during embryonic kidney development. In order to analyze the role of the WT1 gene in nephroblast differentiation, we have isolated the murine homolog of human WT1. An extremely high level of amino acid sequence conservation (greater than 95%) extends throughout all regions of the predicted mouse and human WT1 polypeptides. Two alternative splices within the WT1 transcript have been conserved between mice and humans, suggesting that these have functional significance. Expression of the mouse WT1 mRNA in fetal kidney increases during late gestation, peaks just prior to or shortly after birth, and declines dramatically by 15 days postpartum. Developmental regulation of WT1 expression appears to be selective for the kidney. The restriction of WT1 expression to a limited number of tissues is in contrast to previously described tumor suppressor genes. In addition, the narrow window of time during which WT1 is expressed at high levels in the kidney is consistent with the origin of Wilms' tumor from primitive nephroblasts and the postulated role of this gene as a negative regulator of growth.
A Highly Accurate Statistical Attack against Searchable Symmetric Encryption

Searchable symmetric encryption schemes(SSE) allow clients to search encrypted data stored on remote servers but ensure efficient retrieval by revealing specific information about the queries, such as access patterns. Honest but curious servers can then use these leakages to infer keywords queried by users. However, most attack schemes can only achieve considerable recovery accuracy (over 30%) under favorable conditions. When the auxiliary information is weak, the inference attack schemes using statistical information can achieve the same or even better recovery accuracy. In this paper, an attack based on statistical information is proposed. Our attack iteratively solves the quadratic recovery query problem using a linear optimization solver. With a tiny number of known queries (which can be deleted by the server), the query recovery accuracy can reach about 95% without knowing the exact background knowledge of the documents stored by the client. This process not only makes our scheme outperforms other attack schemes in accuracy but also makes our attack execution more efficient than other schemes, up to a maximum difference of 1000 seconds. The attack can still achieve considerable accuracy even when the defense is applied to the SSE scheme; defenses can help the SSE scheme obfuscate the pattern leakages.
Guide system based users' intentions for a humanoid robot

In the field of human-robot interaction, it is important to attract user's interests. In this regard, it is useful to use gaze and pointing gesture to read and lead to intentions. However, there are not sufficient to studies that generate behaviors according to recognized users' intentions. Furthermore, it is difficult to select suitable behavior considering changing user's state. In this paper, we propose a method that is to guide user's decision using robot's gaze and pointing gestures.
A Multilevel Systems Model of Leadership

This study uses a control theory to develop a multilevel systems model of leadership. The model outlines the processes that senior leaders can use to influence others across hierarchical levels of management and clarifies the mechanisms that link leadership across levels of management—goal cascading, alignment, and the bypass channel of communication. The authors discuss leadership behaviors needed to effectively influence others across a cybernetic process of leadership. Embedded within the multilevel systems model of leadership is a micro-level cybernetic model that focuses on the temporal process that leaders use to influence individuals in the pursuit of goal achievement.
Effect of using ECG derived respiration (EDR) signal in linear parametric QT-RR modeling

Linear parametric modeling techniques are widely used for modeling the short term QT-RR interaction to explore the factors (i.e. heart rate variability, Autonomic Nervous system) controlling ventricular repolarisation variability. Recent studies established that respiration also has important effect on the ventricular repolarisation process like it has on the heart rate variability. So for the clear understanding of cardiac regulations, respiration signal should be considered for modeling the QT-RR dynamics. Due to several problems in collecting original respiration signal using the traditional recording devices that measure the nasal air flow or abdominal or chest pressure, a lot of research has been done to extract respiration information from the ECG signal called the ECG derived respiration (EDR). In this study we verify the use of EDR signal as a surrogate of original respiratory signal in modeling QT-RR interaction. We collect 10 young subjects' ECG and respiration signal from Fantasia database. We developed linear parametric autoregressive model with multiple exogenous inputs with an autoregressive noise term and check the model performance by using original respiration and EDR signal and found statistically similar result. Our findings showed that EDR can be used as a surrogate of original respiration in QT-RR model for the better understanding of cardiac regulations in young healthy subjects.
OSIS: Obstacle-Sensitive and Initial-Solution-first path planning

The efficiency of informed path planning algorithms is contingent upon how quickly the planner can find the initial solution and the associated overhead involved in collision detection. Existing informed planners do not fully exploit the information contained in historical collision detection results, resulting in additional unnecessary collision detections. Furthermore, they optimize paths through rewiring before discovering an initial solution, which not only hampers the planner’s space exploration, but also generates a superfluous amount of unproductive over-head. To address the shortcomings of existing algorithms, this paper proposes an Obstacle-Sensitive and Initial-Solution-first path planning algorithm (OSIS). OSIS uses historical collision detection results to predict the distribution of obstacles in space and utilizes an initial-solution-first path optimization strategy to avoid useless path optimization. Experiments show that OSIS can efficiently bypass obstacles and converge the cost of the solution compared to existing algorithms.
Calibration between a central catadioptric camera and a laser range finder for robotic applications

This paper presents several methods for estimating the relative position of a central catadioptric camera (including perspective cameras) and a laser range finder in order to obtain depth information in the panoramic image. The problem is analysed from a robotic perspective and according to the available information (visible or invisible laser beam, partial calibration, drift of laser data,.). The feasibility of the calibration is also discussed. The feature extraction process and real data are presented
Cross-Layer Power Allocation in Nonorthogonal Multiple Access Systems for Statistical QoS Provisioning

Power allocation is a critical issue in the physical layer of power-domain nonorthogonal multiple access (NOMA) systems. However, existing power allocation schemes have not considered the delay quality of service (QoS) requirement in the datalink layer of users, and hence may not ensure the desired delay QoS requested by the services in the upper layers. Different from existing works, we apply the statistical QoS theory into NOMA systems and formulate the physical-datalink cross-layer power allocation problem as a stochastic optimization problem under different delay QoS constraints. Also, we show that the formulated problem is quasi-concave and propose a bisection-based cross-layer power allocation algorithm. Simulation results show that the proposed algorithm is able to converge to the optimal solution obtained by exhaustive search. Also, the proposed scheme outperforms existing fixed NOMA and time-division multiple access based schemes in terms of max–min effective capacity.
A novel SMC protein complex in Schizosaccharomyces pombe contains the Rad18 DNA repair protein

In Schizosaccharomyces pombe, rad18 is an essential gene involved in the repair of DNA damage produced by ionizing radiation and in tolerance of UV‐induced DNA damage. The Rad18 protein is a member of the SMC (structural maintenance of chromosomes) superfamily, and we show that, like the other SMC proteins in condensin and cohesin, Rad18 is a component of a high‐molecular‐weight complex. This complex contains at least six other proteins, the largest of which is Spr18, a novel SMC family member closely related to Rad18, and likely to be its heterodimeric partner. SMC proteins have ATP‐binding domains at the N‐ and C‐termini, and two extended coiled‐coil domains separated by a hinge in the middle. We show that the N‐terminal ATP‐binding domain of Rad18 is essential for all functions, and overexpression of an N‐terminal mutant has a dominant‐negative effect. We have identified an important mutation (S1045A) near the C‐terminus of Rad18 that separates its repair and essential roles. Potential models for the role of the Rad18–Spr18 complex during DNA repair are discussed.
Andlantis: Large-scale Android Dynamic Analysis

Analyzing Android applications for malicious behavior is an important area of research, and is made difficult, in part, by the increasingly large number of applications available for the platform. While techniques exist to perform static analysis on a large number of applications, dynamic analysis techniques are relatively limited in scale due to the computational resources required to emulate the full Android system to achieve accurate execution. We present Andlantis, a scalable dynamic analysis system capable of processing over 3000 Android applications per hour. During this processing, the system is able to collect valuable forensic data, which helps reverse-engineers and malware researchers identify and understand anomalous application behavior. We discuss the results of running 1261 malware samples through the system, and provide examples of malware analysis performed with the resulting data.
Scalable MU-MIMO User Scheduling for Next-Generation Wireless Networks

Next-generation wireless networks are expected to be ultra-dense in terms of users and be able to support delaysensitive traffic. Multiple-user, multiple-input, multiple-output (MU-MIMO) offers a potential solution by multiplexing a large number of concurrent data streams in the spatial domain. The MU-MIMO user scheduling process involves allocating the users across the space, and time or frequency resources, such that a performance metric is maximized, and subject to specific (e.g., rate) constraints being met. However, user scheduling is a combinatorial problem, making its optimal solution highly intricate. This paper introduces the orthonormal subspace alignment scheduling (OSAS) approach, designed to be scalable for use in highlydense networks and optimized for low-latency communications. Its design prioritizes users that align to the standard orthonormal basis and features a novel pruning process that enhances the users’ transmission rates. Comparative evaluations reveal that OSAS makes more efficient use of the available resources and offers higher performance than other state-of-the-art techniques, while exhibiting lower complexity.
Increased Level of Extracellular ATP at Tumor Sites: In Vivo Imaging with Plasma Membrane Luciferase

Background There is growing awareness that tumour cells build up a “self-advantageous” microenvironment that reduces effectiveness of anti-tumour immune response. While many different immunosuppressive mechanisms are likely to come into play, recent evidence suggests that extracellular adenosine acting at A2A receptors may have a major role in down-modulating the immune response as cancerous tissues contain elevated levels of adenosine and adenosine break-down products. While there is no doubt that all cells possess plasma membrane adenosine transporters that mediate adenosine uptake and may also allow its release, it is now clear that most of extracellularly-generated adenosine originates from the catabolism of extracellular ATP. Methodology/Principal Findings Measurement of extracellular ATP is generally performed in cell supernatants by HPLC or soluble luciferin-luciferase assay, thus it generally turns out to be laborious and inaccurate. We have engineered a chimeric plasma membrane-targeted luciferase that allows in vivo real-time imaging of extracellular ATP. With this novel probe we have measured the ATP concentration within the tumour microenvironment of several experimentally-induced tumours. Conclusions/Significance Our results show that ATP in the tumour interstitium is in the hundrends micromolar range, while it is basically undetectable in healthy tissues. Here we show that a chimeric plasma membrane-targeted luciferase allows in vivo detection of high extracellular ATP concentration at tumour sites. On the contrary, tumour-free tissues show undetectable extracellular ATP levels. Extracellular ATP may be crucial for the tumour not only as a stimulus for growth but also as a source of an immunosuppressive agent such as adenosine. Our approach offers a new tool for the investigation of the biochemical composition of tumour milieu and for development of novel therapies based on the modulation of extracellular purine-based signalling.
Leakage Reduction in Advanced Image Sensors Using an Improved ${\rm AB}^{2}{\rm C}$ Scheme

Static leakage currents in advanced CMOS processes have become the main source of power consumption in many of today's systems. This is especially true for systems with a large number of devices that are in a stable state for most of their operation time, such as image sensors and memory arrays. This paper introduces an improved adaptive bulk biasing control (AB2C) scheme for reduction of leakage currents during these “standby” periods in serially accessed arrays, while enabling device acceleration during active cycles. We provide a theoretical analysis of the AB2C operation, showing its advantages and limitations. The proposed scheme has been integrated with a test-case advanced wide dynamic range (WDR) image sensor with an on-chip memory. The scheme was applied to both the pixel and bitcell arrays, providing configurable leakage reduction and performance enhancement. An 80 nm test chip was fabricated with a 10 k pixel/bitcell test-case system and successfully tested, showing a 21% power reduction compared to a standard system and up to 44% compared to an accelerated system.
A critical reflection of self in context-first steps towards the professional doctorate

This paper employs a reflexive methodology in order to examine critically the author’s professional context and responsibilities. It represents his first, tentative steps towards the award of professional doctorate in education. His role as a head teacher and, specifically, as an agent of change within his school, is scrutinised within the framework of his own professional practice. Issues surrounding his professional relationships with other members of the school community are analysed. In particular, his position of power and authority is explored and set alongside his perceptions of his broader social responsibility. The initial stages of his search for a fruitful focus for his doctoral studies are contextualised and a potential area for his research identified.
Combined Classification for Extracting Named Entities from Arabic Texts

In this paper, we describe an approach for extracting named entities from Arabic texts. Arabic language is hard to process since its characteristics that influence, even, the NE extraction. For our case, we consider that the named entities extraction can be assimilated to a typical classification problem. Indeed, this extraction consists of searching for text portions that can be classified in a NE class (Person, Locality or Organization). Thus, we choose to use a supervised learning approach and employ the BIO tagging format that can solve the twin problems of segmentation and categorization. In addition, singular classifier cannot give good results for all types of contexts. Thus, we adopt a set of weighted classifiers which we combined through a voting procedure. In order to appreciate properly the performance of our system, we perform two types of tests: with and without morphological attributes. We consider that the results are highly satisfactory especially with a accuracy that exceeds 89% for both Person and Locality classes.
Automated clustering and program repair for introductory programming assignments

Providing feedback on programming assignments is a tedious task for the instructor, and even impossible in large Massive Open Online Courses with thousands of students. Previous research has suggested that program repair techniques can be used to generate feedback in programming education. In this paper, we present a novel fully automated program repair algorithm for introductory programming assignments. The key idea of the technique, which enables automation and scalability, is to use the existing correct student solutions to repair the incorrect attempts. We evaluate the approach in two experiments: (I) We evaluate the number, size and quality of the generated repairs on 4,293 incorrect student attempts from an existing MOOC. We find that our approach can repair 97% of student attempts, while 81% of those are small repairs of good quality. (II) We conduct a preliminary user study on performance and repair usefulness in an interactive teaching setting. We obtain promising initial results (the average usefulness grade 3.4 on a scale from 1 to 5), and conclude that our approach can be used in an interactive setting.
COMMUNITY COLLEGE WITHDRAWAL DECISIONS: STUDENT CHARACTERISTICS AND SUBSEQUENT REENROLLMENT PATTERNS

The Withdrawing / Nonreturning Student Survey was completed by 208 community college students over a six‐semester period. Student responses were analyzed to determine reasons for the withdrawal decision, the relationship of student characteristics to these reasons, and the subsequent implementation of reenrollment intent over 1 to 3 years. Student characteristics, including gender and ethnicity, demonstrated significant relationships to withdrawal decisions, with women reporting more health, family, and emotional reasons and men reporting nonchallenging coursework. Minority withdrawals were related to multiple academic and social factors, as well as transportation problems, which may be institution‐specific or may be associated with lower socioeconomic status. Course completion rates and grade point averages (GPAs) were greater for academically prepared students and stop‐outs. GPAs were lower for men and students reporting budgeting problems. Reenroll‐ment plans at withdrawal did not accurately predict su.