Miracle-dz/UniFAR-FTUs huggingface dataset:
Adenosine-Evoked Hyperpolarization of Retinal Ganglion Cells Is Mediated by G-Protein-Coupled Inwardly Rectifying K+ and Small Conductance Ca2+-Activated K+ Channel Activation

Adenosine is a neuromodulator that activates presynaptic receptors to regulate synaptic transmission and postsynaptic receptors to hyperpolarize neurons. Here, we report that adenosine-induced hyperpolarization of retinal ganglion cells is produced by the activation of A1 receptors, which initiates a signaling cascade that activates G-protein-coupled inwardly rectifying K+ (GIRK) channels and small conductance Ca2+-activated K+ (SK) channels. Rat retinal ganglion cells were stimulated by focal ejection of the adenosine receptor agonist 5′-N-ethylcarboxamidoadenosine (NECA) while cell activity was monitored with whole-cell patch recordings and Ca2+ imaging. Focal ejections of NECA evoked outward currents in all cells tested and reduced light- and depolarization-induced spiking. The NECA-evoked current was abolished by the A1 antagonist 1,3-dipropyl-8-cyclopentylxanthine (DPCPX) but was unaffected by A2a, A2b, and A3 antagonists, indicating that the response was mediated entirely by A1 receptors. The GIRK channel blocker rTertiapin-Q diminished the NECA-evoked inhibitory current by 56 ± 12%, whereas the SK channel blocker apamin decreased the NECA-induced current by 42 ± 7%. The SK component of the NECA-evoked current coincided with an increase in intracellular Ca2+ and was blocked by IP3 receptor antagonists and depletion of internal Ca2+ stores, suggesting that A1 receptor activation leads to an increase in IP3, which then elevates intracellular Ca2+ and activates SK channels. This A1-mediated, prolonged SK channel activation has not been described previously. The coactivation of GIRK and SK channels represents a novel mechanism of adenosine-mediated neuromodulation that could contribute to the regulation of retinal ganglion cell activity.
The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction

MOTIVATION Rapidly expanding repositories of highly informative genomic data have generated increasing interest in methods for protein function prediction and inference of biological networks. The successful application of supervised machine learning to these tasks requires a gold standard for protein function: a trusted set of correct examples, which can be used to assess performance through cross-validation or other statistical approaches. Since gene annotation is incomplete for even the best studied model organisms, the biological reliability of such evaluations may be called into question. RESULTS We address this concern by constructing and analyzing an experimentally based gold standard through comprehensive validation of protein function predictions for mitochondrion biogenesis in Saccharomyces cerevisiae. Specifically, we determine that (i) current machine learning approaches are able to generalize and predict novel biology from an incomplete gold standard and (ii) incomplete functional annotations adversely affect the evaluation of machine learning performance. While computational approaches performed better than predicted in the face of incomplete data, relative comparison of competing approaches-even those employing the same training data-is problematic with a sparse gold standard. Incomplete knowledge causes individual methods' performances to be differentially underestimated, resulting in misleading performance evaluations. We provide a benchmark gold standard for yeast mitochondria to complement current databases and an analysis of our experimental results in the hopes of mitigating these effects in future comparative evaluations. AVAILABILITY The mitochondrial benchmark gold standard, as well as experimental results and additional data, is available at http://function.princeton. edu/mitochondria.
An algorithm for the analysis of fetal ECGs from 4-channel non-invasive abdominal recordings

The fetal ECG (fECG) is one of the most valuable tools for monitoring the health of the fetus throughout pregnancy. However, its clinical use has been limited by the difficulty in analysing such non-invasive fECG recordings. The aim of this study was to develop a robust algorithm for the analysis of 4-channel abdominal fECG recordings and test its performance in the Computing in Cardiology Physionet Challenge 2013. Signals were pre-processed by a combination of frequency filtering and wavelet de-noising. Adaptive cancellation of the maternal ECG (mECG) was performed using maternal QRS time markers obtained from the principal component containing the largest mECG. Following further wavelet de-noising of the residuals, the fetal QRS time markers were computed with a local peak detection algorithm from the first principal component. The derived fetal HR (event 4) and fetal RR (event 5) time series were compared to the reference values obtained from a scalp electrode signal. This algorithm scored 223.23 for Challenge event 4 and 19.34 for Challenge event 5, outperforming the sample algorithm.
Feature Ranking through Weights Manipulations for Artificial Neural Networks-Based Classifiers

Artificial Neural Networks (ANNs) are often viewed as black box. This limits the comprehensive understanding on how it deals with input neuron/data, as well as how it reached a particular decision. Input significance analysis (ISA) refers to the process of understanding these input neurons/data. And since this work is on classification problem, hence similarly, this process can also be called feature selection; where the goal is to have a classifier that can predict accurately and at the same time, its structure is as simple as possible. This work is particularly interested with ISA methods that manipulate weights, where separately, correlations are also applied. The goal is to create feature ranking list that performed the best in the selected classifiers. For validation methods, memory recall validation and K-Fold cross-validation methods are used. The results show one classifier that uses one of the ISA methods are performing well for both validation methods.
A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output.
A Robust Finite-Time Converse Theorem for Inductive Safety Certificates of Ordinary Differential Equations

This paper is motivated by safety verification---the task of proving that a given system always stays in a given set of states considered to be safe. For proving this, one can use a certificate in the form of a set of states that contains all initial states, that is inductive (i.e., no solution leaves this set of states), and that contains only safe states. The set of reachable states always forms such a certificate, but may be difficult to compute. In this paper, under some boundedness and robustness assumptions, we prove the existence of such a certificate that is formed by a finite-time reach set, and that is again robust.
High Aspirations Amidst Challenging Situations: Narratives of Six Vulnerable Primary School Children in Swaziland

Informed by social constructionism, this article explores the educational aspirations, fears and support mechanisms required to enhance the schooling experiences of vulnerable children in one rural school in Swaziland. It uses data from semi-structured interviews and photovoice based on a qualitative study of six vulnerable children, aged between 11 and 15 years. Vulnerable children viewed education as a vehicle for their aspired better adult life. These children held anxieties regarding anticipated lack of support to complete further education. Support mechanisms included the need for the community and teachers to assist with basic survival necessities like candles, clothing, and general parental guidance.
Optimal linear estimation fusion. Part V. Relationships

For pt.IV see proc. 2001 International Conf on Information Fusion. In this paper, we continue our study of optimal linear estimation fusion in. a unified, general, and systematic setting. We clarify relationships among various BLUE and WLS fusion rules with complete, incomplete, and no prior information presented in Part I before; and we quantify the effect of prior information and data on fusion performance, including conditions under which prior information or data are redundant.
Selecting Habitat to Survive: The Impact of Road Density on Survival in a Large Carnivore

Habitat selection studies generally assume that animals select habitat and food resources at multiple scales to maximise their fitness. However, animals sometimes prefer habitats of apparently low quality, especially when considering the costs associated with spatially heterogeneous human disturbance. We used spatial variation in human disturbance, and its consequences on lynx survival, a direct fitness component, to test the Hierarchical Habitat Selection hypothesis from a population of Eurasian lynx Lynx lynx in southern Norway. Data from 46 lynx monitored with telemetry indicated that a high proportion of forest strongly reduced the risk of mortality from legal hunting at the home range scale, while increasing road density strongly increased such risk at the finer scale within the home range. We found hierarchical effects of the impact of human disturbance, with a higher road density at a large scale reinforcing its negative impact at a fine scale. Conversely, we demonstrated that lynx shifted their habitat selection to avoid areas with the highest road densities within their home ranges, thus supporting a compensatory mechanism at fine scale enabling lynx to mitigate the impact of large-scale disturbance. Human impact, positively associated with high road accessibility, was thus a stronger driver of lynx space use at a finer scale, with home range characteristics nevertheless constraining habitat selection. Our study demonstrates the truly hierarchical nature of habitat selection, which aims at maximising fitness by selecting against limiting factors at multiple spatial scales, and indicates that scale-specific heterogeneity of the environment is driving individual spatial behaviour, by means of trade-offs across spatial scales.
High Security for MANET using Intrusion Detection and Authentication with Data Fusion on Leaders

This paper proposes a novel architecture for security in Mobile Adhoc Networks using Intrusion Detection and Authentication with Data Fusion along with clustering technique and leader election model. Here clusters are formed through fixed width algorithm and it elects the leader node in each cluster with the help of neighbor ranking mechanism. Leader helps in finding out the intruder in the cluster and sends them out of the network by using Intrusion Detection System and Authentication device that are implemented only in leader node, so leader node is able to detect the intruder. To overcome the demerits in unimodal biometric system, Multimodal biometrics is set to work with IDS. Each and every device has dimensions and estimation limitations, many devices to be selected with the help of Dempster-Shafter theory for data fusion. Based on the security posture, system concludes which biosensor (IDS) to select and decide user need authentication (or IDS input) is essential. By every authentication device and Intrusion Detection System (IDS), the decisions are made in a fully distributed manner. Simulation results demonstrate that the novel architecture is efficient and accurate.
Cochlear Compression: Perceptual Measures and Implications for Normal and Impaired Hearing

This article provides a review of recent developments in our understanding of how cochlear nonlinearity affects sound perception and how a loss of the nonlinearity associated with cochlear hearing impairment changes the way sounds are perceived. The response of the healthy mammalian basilar membrane (BM) to sound is sharply tuned, highly nonlinear, and compressive. Damage to the outer hair cells (OHCs) results in changes to all three attributes: in the case of total OHC loss, the response of the BM becomes broadly tuned and linear. Many of the differences in auditory perception and performance between normal-hearing and hearing-impaired listeners can be explained in terms of these changes in BM response. Effects that can be accounted for in this way include poorer audiometric thresholds, loudness recruitment, reduced frequency selectivity, and changes in apparent temporal processing. All these effects can influence the ability of hearing-impaired listeners to perceive speech, especially in complex acoustic backgrounds. A number of behavioral methods have been proposed to estimate cochlear nonlinearity in individual listeners. By separating the effects of cochlear nonlinearity from other aspects of hearing impairment, such methods may contribute towards identifying the different physiological mechanisms responsible for hearing loss in individual patients. This in turn may lead to more accurate diagnoses and more effective hearing-aid fitting for individual patients. A remaining challenge is to devise a behavioral measure that is sufficiently accurate and efficient to be used in a clinical setting.
The Influence of Service-Learning on the Civic Attitudes and Skills of Japanese Teacher Education Candidates

This study examined the effectiveness of integrating a service-learning project into the undergraduate curriculum for students majoring in education at an urban university in Japan. These teacher candidates engaged in service-learning by supporting high school students in the development and implementation of social action projects. The study employed pre and post seminar data from the Civic Attitudes and Skills Questionnaire (CASQ) and student reflection papers to examine the influence of service-learning participation on the civic attitudes and skills of the teacher candidates. Results indicate that while the teacher candidates experienced a variety of benefits from their participation in service-learning, including significant gains in their civic action and political awareness, their self- perception of leadership skills declined significantly. Discussion centers on differences in the understanding and application of personal skills, such as leadership, between the culture of Japan, where the study took place, and the culture of the United States, where the CASQ was developed. Cultural differences need to be taken into account in order to appropriately interpret the results of the CASQ leadership scale when applied in a Japanese setting.
An examination of Hispanic college enrollment and graduation: Has the Texas Closing the Gaps plan been successful?

ABSTRACT The purpose of this study is to assess the effectiveness of the Texas “Closing the Gaps by 2015” plan to increase Hispanic enrollment into and graduation from institutions of higher education. This study uses Interrupted Time Series with Comparison Group to analyze 26 years of student cohorts in Texas universities. Controlling for historical enrollment and graduation growth as well as population growth, the results show that Hispanic college enrollment significantly increased as a result of the plan, but graduation did not. Several recommendations are offered for those states that wish to emulate Texas policies of increasing college enrollment and graduation of Hispanics.
Back to Baseline in Britain: Adaptation in the BHPS

We look for evidence of adaptation in well-being to major life events using eighteen waves of British panel data. Adaptation to marriage, divorce, birth of a child and widowhood appears to be rapid and complete, whereas this is not the case for unemployment. These findings are remarkably similar to those in previous work on German panel data. Equally, the time profiles with life satisfaction as the well-being measure are very close to those using a twelve-item scale of psychological functioning. As such, the phenomenon of adaptation may be a general one, rather than being only found in German data or using single-item well-being measures.
Proposal-Robot Learning

Introduction The move of robots from industrial to everyday environments like hospitals and homes has introduced a number of powerful capabilities to handle uncertainty and changing external conditions. Still, most robots are constructed to perform pre-defined tasks, defined by a researcher or engineer. However, most robots of tomorrow will have to be constructed for a large variety of tasks to become economically attractive. The adaptation to new tasks can not be expected to be made with regular end-user programming. Rather, the robot has to be delivered with advanced capability to learn new tasks and new working conditions, by its new user in its new environment. Robot learning is for this reason a very active research area.
Estimating quality weights for EQ-5D health states with the time trade-off method in South Korea.

OBJECTIVE To estimate quality weights of EQ-5D health states with the time trade-off (TTO) method in the general population of South Korea. METHODS A total of 500 respondents valued 42 hypothetical EQ-5D health states using the TTO and visual analog scale. The quality weights for all EQ-5D health states were estimated by a random effects model and compared with those from studies in other countries. RESULTS Overall estimated quality weights for all EQ-5D health states from this study were highly correlated with those from previous studies, but quality weights of individual states were substantially different from those of their corresponding states in other studies. CONCLUSIONS The Korean value set differed from value sets from other countries. Special caution is needed when a value set from one country is applied to another with a different culture.
UTTG – 04 – 05 ITP – UH – 09 / 05 Drinfeld-Twisted Supersymmetry and Non-Anticommutative Superspace

We extend the analysis of hep-th/0408069 on a Lorentz invariant interpretation of noncommutative spacetime to field theories on non-anticommutative superspace with half the supersymmetries broken. By defining a Drinfeld-twisted Hopf superalgebra, it is shown that one can restore twisted supersymmetry and therefore obtain a twisted version of the chiral rings along with certain Ward-Takahashi identities. Moreover, we argue that the representation content of theories on the deformed superspace is identical to that of their undeformed cousins and comment on the consequences of our analysis concerning non-renormalization theorems. PACS: 11.10. Nx, 11.30. Pb, 12.60. Jv
Extracting Metadata from Biological Experimental Data

The process of automatically extracting metadata from an experiment's dataset is an important stage in efficiently integrating this dataset with data available in public bioinformatics data sources. Metadata extracted from the experiment's dataset can be stored in databases and used to verify data extracted from other experiments' datasets. Moreover, the biologist can keep track of the dataset so that it can be easily retrieved next time. The extracted metadata can be mined to discover useful knowledge as well as integrated with other information using domain ontology to reveal hidden relationships. The experiment's dataset may contain several kinds of metadata that can be used to add semantic value to linked data. This paper describes an approach for extracting metadata from an experiment's dataset. This system has been used in a preliminary investigation of aging across species
Group norm violations in an online environmental social network: Effects on impression formation and intergroup judgments

Much research has demonstrated the power of social norms to affect proenvironmental behavior and conservation-related attitudes and beliefs in traditional “offline” social contexts. With the emergence of social media and citizen science platforms that allow for socially coordinated conservation efforts at scale comes a need to better understand the influence of social norms in online contexts. The present experiment explored effects of norm violations on impression formation and intergroup judgments within the context of Habitat Network, a socially networked mapping application where users create and share virtual representations (maps) of their properties. Results revealed that when participants viewed a map depicting the violation of a strongly held group norm—namely, the presence of an outdoor (vs. indoor) pet cat—they judged the map owner as significantly less likely to engage in a variety of proenvironmental behaviors. Importantly, this effect emerged despite evidence that the owner was already engaging in various sustainable practices. Moreover, the effect was mediated by the perceived quality of wildlife habitat represented by the map and moderated by participants’ group membership status (as a cat owner) in a manner consistent with theories of impression formation and intergroup judgments. We discuss implications for social cognition and intergroup relations in proenvironmental online contexts.
We May Not Know What We Want, But Do We Know What We Need? Examining the Ability to Forecast Need Satisfaction in Goal Pursuit

Do we have the necessary perceptual abilities to set goals that are congruent with our own values and needs? In a prospective study, participants (n =185) identified three goals that they planned to pursue throughout the week. For each goal, they then rated their motivation for pursuing it and made predictions about the extent to which goal attainment would satisfy their needs for autonomy, competence, and relatedness. One week later, participants rated their progress on each goal, as well as the actual need satisfaction they experienced. Using Bayesian analysis, we found support for our (null) hypothesis that participants predicted that their goals would satisfy their psychological needs, irrespective of goal self-concordance. While people sometimes overestimated need satisfaction, we found that people who pursued more self-concordant goals actually benefited more from their pursuits, both compared to others who pursued less concordant goals and among their own goals.