Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential Utility of Urinary Follistatin as a Non-Invasive Indicator of Acute Tubular Damage in Patients with Acute Kidney Injury

Activin A is known to impede tubular repair following renal ischemia, whereas exogenous follistatin, an activin A antagonist, has been shown to ameliorate kidney damage in rats. Despite these findings, the precise role of endogenous follistatin in the kidney has yet to be elucidated. In this study, we investigated the localization of follistatin in the normal human kidney and its potential utility as a marker for acute kidney injury (AKI). In a total of 118 AKI patients and 16 healthy adults, follistatin levels in serum and urine were quantified using ELISA, and correlations with clinical parameters were analyzed. Follistatin-producing cells were positive for Na-Cl co-transporter and uromodulin, but negative for aquaporin 1 and aquaporin 2. Unlike healthy adults, urinary follistatin significantly increased in AKI patients, correlating positively with AKI severity. Urinary follistatin levels were notably higher in patients needing renal replacement therapy. Significant correlations were observed with urinary protein, α1 microglobulin, and urinary NGAL, but not with urinary KIM-1, urinary L-FABP, urinary NAG, urinary β2 microglobulin, or serum creatinine. Interestingly, no correlation between urinary and serum follistatin levels was identified, indicating a renal origin for urinary follistatin. In conclusion, follistatin, produced by distal tubules, is detectable in the urine of AKI patients, suggesting its potential as a valuable marker for monitoring acute tubular damage severity in AKI.
A geometric segmentation approach for the 3D reconstruction of dynamic scenes in 2D video sequences

In this paper, an algorithm is proposed to solve the multi-frame structure from motion (MFSfM) problem for monocular video sequences with multiple rigid moving objects. The algorithm uses the epipolar criterion to segment feature trajectories belonging to the background scene and each of the independently moving objects. As a large baseline length is essential for the reliability of the epipolar geometry, the geometric robust information criterion is employed for key-frame selection within the sequences. Once the features are segmented, corresponding objects are reconstructed individually using a sequential algorithm that is capable of prioritizing the frame pairs with respect to their reliability and information content. The experimental results on synthetic and real data demonstrate that our approach has the potential to effectively deal with the multi-body MFSfM problem.
Translational Regulators Maintain Totipotency in the Caenorhabditis elegans Germline

The molecular mechanisms that maintain totipotency of the germline are not well understood. Here, we show that two conserved translational regulators, MEX-3 and GLD-1, are essential for maintaining totipotency in the Caenorhabditis elegans germline. In mex-3 gld-1 mutants, germ cells transdifferentiate into various somatic cell types such as muscles or neurons. Our findings implicate RNA regulation in the maintenance of totipotency, suggest that multiple mechanisms maintain totipotency at different stages of germline development, and establish a genetically tractable model for studying the development of teratomas.
An empirical study using combination of SVM with PSO based scattering ratio optimization and K-means

One of the most significant practical challenges for face recognition is a likeness of faces which leads to a big problem in classification of different classes. To tackle this problem, we present a novel method based on similarity of each face with other faces using the Pearson correlation coefficients. Besides, another problem is variability in lighting intensity which its physics are difficult for accurate model. In this paper, first, discrete wavelet transform (DWT) is used for feature extraction. Next, with respect to the correlation matrix, two algorithms are employed, namely, K-means clustering and particle swarm optimization (PSO) based scattering ratio matrix of correlation features. Then for each cluster, the process of classification is continued by normalization of the each subset firstly and then the decision making for each subset is performed by support vector machine (SVM). The experiments are performed on the ORL and Yale databases and the results show that there are a significant improvement in 45 features based weighted recognition rate.
Intra‐seasonal rainfall variability in the Amazon basin related to large‐scale circulation patterns: a focus on western Amazon–Andes transition region

This study aims to relate the intra‐seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the Amazon–Andes region. The CPs summarize the intra‐seasonal variability of atmospheric circulation and are defined using daily low‐level winds from the ERA‐Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high‐resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self‐organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day−1, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day−1). This study provides additional information concerning the intra‐seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information.
An Anomaly Detection System Based on Ensemble of Detectors with Effective Pruning Techniques

Anomaly detection systems rely on machine learning techniques to model the normal behavior of the system. This model is used during operation to detect anomalies due to attacks or design faults. Ensemble methods have been used to improve the overall detection accuracy by combining the outputs of several accurate and diverse models. Existing Boolean combination techniques either require an exponential number of combinations or sequential combinations that grow linearly with the number of iterations, which make them difficult to scale up and analyze. In this paper, we propose PBC (Pruning Boolean Combination), an efficient approach for selecting and combining anomaly detectors. PBC relies on two novel pruning techniques that we have developed to aggressively prune redundant and trivial detectors. Compared to existing work, PBC reduces significantly the number of detectors to combine, while keeping similar accuracy. We show the effectiveness of PBC when applying it to a large dataset.
Reachability under uncertainty

The paper studies the problem of reachability for linear systems in the presence of uncertain (unknown but bounded) input disturbances, which may also be interpreted as the action of an adversary in a game-theoretic setting. It defines possible notions of reachability under uncertainty emphasizing the differences between open-loop and closed-loop control. Solution schemes for calculating reachability sets are indicated. The situation when observations arrive at given isolated instances of time leads to problems of anticipative (maxmin) and nonanticipative (minmax) piecewise open-loop control with corrections and to corresponding notions of reachability. As the number of corrections tends to infinity, one comes in both cases to reachability under nonanticipative feedback control. It is shown that the closed-loop reach sets under uncertainty may be found through a solution of the forward Hamilton-Jacobi-Bellman-Isaacs (HJBI) equation. The basic relations are derived through the investigation of superpositions of value functions for appropriate sequential maxmin or minmax problems of control.
An integrated probabilistic model for functional prediction of proteins

We develop an integrated probabilistic model to combine protein physical interactions, genetic interactions, highly correlated gene expression network, protein complex data, and domain structures of individual proteins to predict protein functions. The model is an extension of our previous model for protein function prediction based on Markovian random field theory. The model is flexible in that other protein pairwise relationship information and features of individual proteins can be easily incorporated. Two features distinguish the integrated approach from other available methods for protein function prediction. One is that the integrated approach uses all available sources of information with different weights for different sources of data. It is a global approach that takes the whole network into consideration. The second feature is that the posterior probability that a protein has the function of interest is assigned. The posterior probability indicates how confident we are about assigning the function to the protein. We apply our integrated approach to predict functions of yeast proteins based upon MIPS protein function classifications and upon the interaction networks based on MIPS physical and genetic interactions, gene expression profiles, Tandem Affinity Purification (TAP) protein complex data, and protein domain information. We study the sensitivity and specificity of the integrated approach using different sources of information by the leave-one-out approach. In contrast to using MIPS physical interactions only, the integrated approach combining all of the information increases the sensitivity from 57% to 87% when the specificity is set at 57%-an increase of 30%. It should also be noted that enlarging the interaction network greatly increases the number of proteins whose functions can be predicted.
Population densities of Sulawesi crested black macaques (Macaca nigra) on Bacan and Sulawesi, Indonesia: Effects of habitat disturbance and hunting

Population surveys of Sulawesi crested black macaques (Macaca nigra) were conducted on the Indonesian islands of Sulawesi and Bacan in 1992–1994 to assess the status of natural populations and determine habitat and anthropogenic factors affecting their population densities. We surveyed five sites for primates, including undisturbed and disturbed habitats. Data were collected on habitat structure and composition at two undisturbed and one disturbed forest site in which the primates were surveyed. The highest density of macaques was found in primary forest at Gunung Sibela Nature Reserve on Bacan (170.3 individuals/km2). Population density in logged forest on Bacan was high but significantly less than primary forest (133.4 individuals/km2). The high density of crested black macaques in primary forest on Bacan is best explained by the high carrying capacity found in primary forest. The lower food quantity and quality of food resources found in logged forest correlated with lower primate densities compared to primary forest. However, the large population of macaques in logged forest demonstrates the conservation value of such forest. Densities on Sulawesi at Tangkoko‐Batuangas‐DuaSudara Nature Reserve (TBDS) showed a continuing decline since earlier surveys. Primate densities were highest near the protected center of Tangkoko Reserve (66.7 individuals/km2). The peripheral areas of Batuangas and DuaSudara, even though adjacent and continuous, showed lower population densities of 46.4 and 23.5 individuals/km2, respectively. The best explanation for the continued decline of Macaca nigra populations at TBDS is hunting. Unless conservation measures are implemented immediately, M. nigra on Sulawesi risks extinction in the near future. Am. J. Primatol. 44:89–106, 1998. © 1998 Wiley‐Liss, Inc.
Insider Attacker Detection in Wireless Sensor Networks

Though destructive to network functions, insider attackers are not detectable with only the classic cryptography-based techniques. Many mission-critic sensor network applications demand an effective, light, flexible algorithm for internal adversary identification with only localized information available. The insider attacker detection scheme proposed in this paper meets all the requirements by exploring the spatial correlation existent among the networking behaviors of sensors in close proximity. Our work is exploratory in that the proposed algorithm considers multiple attributes simultaneously in node behavior evaluation, with no requirement on a prior knowledge about normal/malicious sensor activities. Moreover, it is application-friendly, which employs original measurements from sensors and can be employed to monitor many aspects of sensor networking behaviors. Our algorithm is purely localized, fitting well to the large-scale sensor networks. Simulation results indicate that internal adversaries can be identified with a high accuracy and a low false alarm rate when as many as 25% sensors are misbehaving.
Artificial Neural Network-Based Feature Combination for Spatial Voice Activity Detection

For many applications in speech communications and speechbased human-machine interaction, a reliable Voice Activity Detection (VAD) is crucial. Conventional methods for VAD typically differentiate between a target speaker and background noise by exploiting characteristic properties of speech signals. If a target speaker should be distinguished from other speech sources, these conventional concepts are no longer applicable, and other methods, typically exploiting the spatial diversity of the individual sources, are required. Often, it is beneficial to combine several features in order to improve the overall decision. Optimum combinations of features, however, depend strongly on the scenario, especially on the position of the target source, the characteristics of noise and interference and the Signal-to-Interference Ratio (SIR). Moreover, choosing detection thresholds which are robust to changing scenarios is often a difficult problem. In this paper, these issues are addressed by introducing Artificial Neural Networks (ANNs) for spatial voice activity detection, which allow to combine several features with background information. The experimental results show that already small ANNs can significantly and robustly improve the detection rates, offering a valuable tool for VAD.
Bradykinin-related compounds as new drugs for cancer and inflammation.

Bradykinin (BK) (Arg-Pro-Pro-Gly-Phe-Ser-Pro-Phe-Arg) is an important growth factor for small-cell lung cancer (SCLC) and prostate cancer (PC). These cancers have cells of neuroendocrine origin and express receptors for a variety of neuropeptides. BK receptors are expressed on almost all lung cancer cell lines and on many PC cells. Our very potent BK antagonist B9430 (D-Arg-Arg-Pro-Hyp-Gly-lgl-Ser-D-Igl-Oic-Arg) (Hyp, trans-4-hydroxy-L-proline; Ig1, alpha-2-indanylglycine; Oic, octahydroindole-2-carboxylic acid) is a candidate anti-inflammatory drug but does not inhibit growth of SCLC or PC. When B9430 is dimerized by N-terminal cross-linking with a suberimide linker, the product B9870 is a potent growth inhibitor for SCLC both in vitro and in vivo in athymic nude mice. Daily i.p. injection at 5 mg x kg(-1) day(-1) beginning on day 8 after SCLC SHP-77 cell implantation gave 65% inhibition of tumor growth. B9870 stimulates apoptosis in SCLC by a novel "biased agonist" action. We have also developed new small mimetic antagonists. BKM-570 (F5C-OC2Y-Atmp) (F5C, pentafluorocinnamic acid; OC2Y, O-2,6-dichlorobenzyl tyrosine; Atmp, 4-amino-2,2,6,6-tetramethylpiperidine) is very potent for inhibition of SHP-77 growth in nude mice. When injected daily i.p. at 5 mg x kg(-1), M-570 gave 90% suppression of tumor growth. M-570 is more potent than the well-known anticancer drug cisPlatin (60% inhibition) or the recently developed SU5416 (40% inhibition) in this model. M-570 also showed activity against various other cancer cell lines in vitro (SCLC, non-SCLC, lung, prostate, colon, cervix) and inhibited growth of prostate cell line PC3 in nude mice. M-570 and related compounds evidently act in vivo through pathways other than BK receptors. These compounds have clinical potential for treatment of human lung and prostate cancers.
Effect of prey abundance and size on the distribution of demersal fishes

Many demersal fish species rely on benthic prey as food sources for part of, or in some cases, all of their life his- tory. We investigated the relationships between prey and predator abundance and prey size and predator mouth gape size for nine demersal fish species. Of the species analysed, four showed a significant positive increase in abundance with increasing prey abundance. Prey size is thought to be an important parameter for demersal fish that are limited in their feeding poten- tial by their mouth gape size, as it influences consumption rate and energy expenditure while foraging. The relationship be- tween prey size and mouth gape was investigated using both stomach content data and prey availability data. Stomach content analysis revealed positive relationships between maximum prey size and predator mouth gape size for six of the spe- cies. Indications of prey size selectivity were only seen in the environment for European hake (Merluccius merluccius), high- lighting the potential importance of prey size over prey abundance for this species. The results demonstrate that prey abundance and size are of significance for some demersal fish species feeding primarily on benthos and will help in defining habitat requirements of demersal fish species. Resume : Plusieurs poissons demersaux comptent sur les proies benthiques comme sources de nourriture durant une partie et, en certains cas, l'ensemble de leur cycle biologique. Nous etudions la relation entre l'abondance des proies et des preda- teurs, ainsi qu'entre la taille des proies et l'ouverture de la bouche des predateurs, chez neuf especes de poissons demersaux. Parmi les especes analysees, quatre montrent une augmentation positive d'abondance en fonction de l'augmentation de l'a- bondance des proies. On croit que la taille des proies est une variable importante chez les poissons demersaux dont le po- tentiel alimentaire est limite par la taille de l'ouverture de leur bouche, puisqu'elle va influencer le taux de consommation et la depense d'energie durant la recherche de nourriture. Nous examinons la relation entre la taille des proies et l'ouverture de la bouche en utilisant a la fois des donnees sur les contenus stomacaux et des donnees de disponibilite des proies. L'analyse des contenus stomacaux montre une relation positive entre la taille maximale des proies et l'ouverture de la bouche chez six des especes. Nous n'observons d'indications de selection des proies en fonction de la taille dans le milieu que chez le merlu (Merluccius merluccius), ce qui souligne le role plus grand de la taille que de l'abondance des proies chez cette espece. Nos resultats demontrent que l'abondance et la taille des proies sont d'importance pour certains poissons demersaux qui se nour- rissent principalement de benthos et qu'elles pourront servir a definir les besoins d'habitat des especes de poissons demer- saux. (Traduit par la Redaction)
Town Planning Education: A Survey Of Students' Entrance, Performance And Prospects In Auchi Polytechnic, Auchi, Nigeria

Several empirical researches have been carried out to explore factors affecting students' choice of career path and academic performance. This paper examined relationship between modes of entrance into planning studies and academic grade performance, as well as factors that influenced choice of career in town planning education in Auchi polytechnic. Data were provided by 190 male and 70 female on a cross-sectional survey (N=260). SPSS was used to analyse data. Statistically significant relationships albeit, with inverse implication, at . 05 level, were demonstrated in bivariate analysis between mode of entrance and GPA. The Chi -square test statistics revealed that students' academic performance depend on their modes of entrance into the programme (X 2 =198.71, p < 0.05) with Cramer's V = 0.62; concluding that the Pre-ND programme did not impact on the quality of planning education. The paper recommended admission policy that permits change of course to town planning studies should be further encourage, with adequate orientation and career counseling programmes in the department.
Decision Making Strategies for Mobile Collaborative Context Recognition

This paper focuses to a problem domain where several imperfect context recognition devices with restricted resources are located in the same context. The main aim of the paper is to compare different collaborative decision making methods in this domain. In particular, the focus is on certain voting mechanisms and confidence evaluation metrics. Also computational, space, and communication requirements of the discussed methods are discussed briefly. The methods are tested with a real-world dataset containing data from several sport activities. In addition, an online context recognition platform that is capable to make distributed context recognition decisions is introduced briefly.
On error modeling of electrical bugs for post-silicon timing validation

There is great demand for an accurate and scalable metric to evaluate the functional stimuli, testbench checkers, and DfD (Design-for-Debug) structures used in post-silicon timing validation. In this paper, we show the inadequacy of existing methods (due to either inaccuracy or a lack of scalability) and propose an approach that leverages debug engineers' experience to model timing errors efficiently and with sufficient precision. Experimental results demonstrate that the proposed approach produced an error model six times more accurate than the prior art with a negligible simulation overhead.
An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.