Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Genetic Interactions Using Machine Learning and Network Properties

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.
Protective Effects of Hydrogen Sulfide in the Ageing Kidney

Aims. The study aimed to examine whether hydrogen sulfide (H2S) generation changed in the kidney of the ageing mouse and its relationship with impaired kidney function. Results. H2S levels in the plasma, urine, and kidney decreased significantly in ageing mice. The expression of two known H2S-producing enzymes in kidney, cystathionine γ-lyase (CSE) and cystathionine-β-synthase (CBS), decreased significantly during ageing. Chronic H2S donor (NaHS, 50 μmol/kg/day, 10 weeks) treatment could alleviate oxidative stress levels and renal tubular interstitial collagen deposition. These protective effects may relate to transcription factor Nrf2 activation and antioxidant proteins such as HO-1, SIRT1, SOD1, and SOD2 expression upregulation in the ageing kidney after NaHS treatment. Furthermore, the expression of H2S-producing enzymes changed with exogenous H2S administration and contributed to elevated H2S levels in the ageing kidney. Conclusions. Endogenous hydrogen sulfide production in the ageing kidney is insufficient. Exogenous H2S can partially rescue ageing-related kidney dysfunction by reducing oxidative stress, decreasing collagen deposition, and enhancing Nrf2 nuclear translocation. Recovery of endogenous hydrogen sulfide production may also contribute to the beneficial effects of NaHS treatment.
Tropical and extratropical SST effects on the midlatitude storm track

Effects of zonal variations in sea surface temperature (SST) on synoptic eddy activity are examined using an atmospheric general circulation model under the perpetual January and the aqua-planet conditions. In the presence of zonal variations in tropical SST, upper tropospheric zonal wind displays large zonal asymmetry and the storm track is located downstream of the maximum westerly speed. When only extratropical SST varies in the east-west direction, a storm track develops along the sharp meridional SST gradient despite a nearly zonally uniform westerly jet in the upper troposphere. The latter result suggests that zonal variations in upper tropospheric westerlies contribute to but are not necessary for zonally confined storm tracks.
Rhythm and meter in spoken-word perception 1 Running head : RHYTHM AND METER IN SPOKEN-WORD PERCEPTION Rhythmic regularity and metrical expectancy in spoken-word perception

Certain types of speech, e.g. lists of words or numbers, are usually spoken with a clear speech rhythm. Salient, stressed vowels are aligned to rhythmic points within the phrase period. The main hypothesis of this study (derived from the Dynamic Attending Theory; M.R. Jones (1976), Psych. Rev. 83, 323–355) is that listeners attend in particular to speech events at these rhythmic time points. Better rhythmic regularity should improve spoken-word perception. Previous studies that suggested only a weak effect of speech rhythm on spoken-word perception, suffered from poor control over speech rhythm in their stimuli. A phoneme monitoring experiment is reported, in which listeners heard lists of disyllabic words in which the rhythm of the stressed vowels was either regular (with equidistant inter-stress intervals) or jittery. In addition, metrical expectancy was controlled by varying the stress pattern of the target word, as either the same or the opposite of the stress pattern in its preceding words. Resulting RTs show a main effect of rhythmic regularity, but not of metrical expectancy. Rhythmic regularity had its strongest effect in the perception of iambic words. These results suggest that listeners employ attentional rhythms in spoken-word perception, and that a clear rhythm improves speech communication.
Understanding Institutions: The Science and Philosophy of Living Together

Social ontology is a meeting ground for travellers of various disciplines from all directions. The perspectives are as diverse as modelling social phenomena in game theory, explaining cooperation using theoretical biology, drawing on metaphysics to clarify the nature of groups, or criticizing political theory with accounts of gender and power. Francesco Guala achieves similar breadth in his book. Understanding Institutions illustrates how institutions emerge, how they maintain their existence, how their names acquire meaning, how their nature can be investigated, and how they change and cease to exist. Guala introduces the reader to coordination games, to Ian Hacking’s account of social kinds, and to different views of social construction. To non-specialists, the book is attractive because Guala writes so accessibly, inviting his readers to join him on an inspiring journey through some of the major issues in the field. To specialists, Guala instructively outlines a coherent overall view on the nature of our social world and how this world can be explored. The book is a reminder of the deep relations between seemingly disparate issues. Understanding Institutions has two parts. In the first part entitled “Unification,” Guala presents his account of institutions as rules-in-equilibrium that he has developed together with Frank Hindriks. In the second part entitled “Application,” Guala explores the implications of this account on the methods of social science, on the demarcation between the social and natural science, and on the changing meaning of an institution in the example of “marriage. ” Wedged between these two parts is an “Interlude” of two chapters dealing with theories of mindreading and collective intentions. Guala pursues three main questions in this book. First, what are institutions? His answer to this first question is the rules-in-equilibrium approach to which I turn shortly. The second is how can social scientists investigate institutions? Guala defends a kind of naturalism, as I will also explain. Third, how can institutions change? Here Guala argues that institutions appear to change because we never conclusively know what institutions are in the first place. These three questions build on one another, so combining them in a book makes good sense.
Photometric Stereo via Computer Screen Lighting for Real-time Surface Reconstruction

We introduce a method which uses the light emitted by a computer screen to illuminate an object such as a human face from multiple directions, simultaneously capturing images with a webcam in order to perform photometric stereo. Dominant eigenvectors of the captured images provide surface normals, which are integrated into a 3D surface using Gauss-Seidel relaxation. The system runs at 10 frames per second on a consumer laptop computer.
Relationship between Structure and Conformational Change of the Vitamin D Receptor Ligand Binding Domain in 1α,25-Dihydroxyvitamin D3 Signaling

Vitamin D Receptor (VDR) belongs to the nuclear receptor (NR) superfamily. Whereas the structure of the ligand binding domain (LBD) of VDR has been determined in great detail, the role of its amino acid residues in stabilizing the structure and ligand triggering conformational change is still under debate. There are 13 α-helices and one β-sheet in the VDR LBD and they form a three-layer sandwich structure stabilized by 10 residues. Thirty-six amino acid residues line the ligand binding pocket (LBP) and six of these residues have hydrogen-bonds linking with the ligand. In 1α,25-dihydroxyvitamin D3 signaling, H3 and H12 play an important role in the course of conformational change resulting in the provision of interfaces for dimerization, coactivator (CoA), corepressor (CoR), and hTAFII 28. In this paper we provide a detailed description of the amino acid residues stabilizing the structure and taking part in conformational change of VDR LBD according to functional domains.
Approximation and Limit Results for NonlinearFilters Over an In nite Time

The paper is concerned with approximations to nonlinear ltering problems that are of interest over a very long time interval. Since the optimal lter can rarely be constructed, one needs to compute with numerically feasible approximations. The signal model can be a jump{diiusion or just a process that is approximated by a jump{diiusion. The observation noise can be either white or of wide bandwidth. The observations can be taken either in discrete or continuous time. The cost of interest is the pathwise error per unit time over a long time interval. It is shown under quite reasonable conditions on the approximating lter and the signal and noise processes that (as time, bandwidth, process and lter approximation , etc.) go to their limit in any way at all, the limit of the pathwise average costs per unit time is just what one would get if the approximating processes were replaced by their ideal values and the optimal lter were used. Analogous results are obtained (with appropriate scaling) if the observations are taken in discrete time, and the sampling interval also goes to zero. For these cases, the approximating lter is a numerical approximation to the optimal lter for the presumed limit (signal, observation noise) problem.
Sentiment Analysis of Political Tweets: Towards an Accurate Classifier

We perform a series of 3-class sentiment classification experiments on a set of 2,624 tweets produced during the run-up to the Irish General Elections in February 2011. Even though tweets that have been labelled as sarcastic have been omitted from this set, it still represents a difficult test set and the highest accuracy we achieve is 61.6% using supervised learning and a feature set consisting of subjectivity-lexicon-based scores, Twitter- specific features and the top 1,000 most dis- criminative words. This is superior to various naive unsupervised approaches which use subjectivity lexicons to compute an overall sentiment score for a pair.
Poor Reading in French Elementary School: The Interplay of Cognitive, Behavioral, and Socioeconomic Factors

Background: Reading impairment is the major learning disability in childhood. Most previous studies were done on English-speaking populations. Yet, it has been argued that the English writing system exacerbates phonological deficits because of its exceptionally high inconsistency between spelling and sound. Thus, cross-language studies are needed to explore the universal versus language-specific factors underlying reading impairment. The goal of the present research was to study biological, socioeconomic, cognitive, and behavioral factors underlying poor reading in French-speaking second grade children. Methods: A total of 1062 children from 20 different schools in the city of Paris participated in the study. After an initial test phase, children with a suspected impairment in reading acquisition were assessed individually. Subsequently, 100 poor readers and 50 controls were matched for sex, age, school, and neighborhood socioeconomic status (SES). They underwent comprehensive medical, cognitive, and behavioral assessment complemented by individual socioeconomic data. Results: The average prevalence of reading impairment was around 12% in our sample. It was highly influenced by neighborhood SES, varying from 3.3% in high SES to 24.2% in low SES areas. Among the individual SES variables, low maternal education significantly distinguished poor from typical readers. Multiple regression analyses showed that reading outcome was best predicted by phonological awareness skills and attention deficits. Conclusion: The majority of poor readers come from low SES areas. As in the English literature, the most robust predictor for reading impairment is phonological awareness. In addition, behavioral problems, such as attention deficits, seem to aggravate reading deficits for children with weak phonological awareness skills.
Local Pyramidal Descriptors for Image Recognition

In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one's bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement. We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.
The Role of Energy Availability in Mammalian Hibernation: A Cost‐Benefit Approach

Hibernation is widely regarded as an adaptation to seasonal energy shortage, but the actual influence of energy availability on hibernation patterns is rarely considered. Here we review literature on the costs and benefits of torpor expression to examine the influence that energy may have on hibernation patterns. We first establish that the dichotomy between food‐ and fat‐storing hibernators coincides with differences in diet rather than body size and show that small or large species pursuing either strategy have considerable potential scope in the amount of torpor needed to survive winter. Torpor expression provides substantial energy savings, which increase the chance of surviving a period of food shortage and emerging with residual energy for early spring reproduction. However, all hibernating mammals periodically arouse to normal body temperatures during hibernation. The function of these arousals has long been speculated to involve recovery from physiological costs accumulated during metabolic depression, and recent physiological studies indicate these costs may include oxidative stress, reduced immunocompetence, and perhaps neuronal tissue damage. Using an optimality approach, we suggest that trade‐offs between the benefits of energy conservation and the physiological costs of metabolic depression can explain both why hibernators periodically arouse from torpor and why they should use available energy to minimize the depth and duration of their torpor bouts. On the basis of these trade‐offs, we derive a series of testable predictions concerning the relationship between energy availability and torpor expression. We conclude by reviewing the empirical support for these predictions and suggesting new avenues for research on the role of energy availability in mammalian hibernation.
Testing for Structural Breaks in Variance with Additive Outliers and Measurement Errors

This paper discusses the asymptotic and finite-sample properties of CUSUM-based tests for detecting structural breaks in volatility. Our aim is to analyze formally the effects of stochastic contamination, such as additive outliers or measurement errors. This analysis is particularly relevant for financial data, for which these tests are the most common way to detect variance breaks. In particular, we focus on the tests by Inclan and Tiao (1994) [IT] and Kokoszka and Leipus (1998, 2000) [KL], which have been intensively used in the applied literature. Our results are extensible to related procedures. We show that the asymptotic distribution of the IT test can largely be affected by sample contamination, whereas the distribution of the KL test remains invariant. Furthermore, the break-point estimator of the KL test renders consistent estimates. In spite of the good large-sample properties for this test, we discuss that large additive outliers tend to generate power distortions or wrong break-date estimates in small samples.
Moderating role of religiosity on Zakat compliance behavior in Nigeria

Purpose This paper examines the moderating effect of religiosity in the theory of reasoned action (TRA) on intention to comply with the Zakat on employment income (ZEI). Design/methodology/approach A survey of civil servants in Kano State, Nigeria was carried out and 474 valid observations were retrieved from the 700 distributed questionnaires. Partial least squares structural equation modeling (PLS-SEM) was used to validate the instrument that measures the variables, examined the structural model and the predictive relevance of the study’s model. Findings The results revealed the suitability of both the TRA and the PLS-SEM in the study of ZEI behavioral intention in a developing country. All the hypothesized direct relationships were supported on the one hand. On the other hand, one of the two indirect relationships, subjective norm and behavioral intention moderated by religiosity was supported, but the other tests for moderating effects of religiosity on the relationship between attitude and behavioral intention was not. Practical implications Public and private Zakat institutions in Muslim majority societies in Nigeria and in other Muslim countries may use the findings to focus their attention on the formulation of policies based on the findings of the study to strengthen eligible Zakat payers’ intention to comply to further boost their Zakat collections. Originality/value This study extends the TRA in the context of ZEI by proposing religiosity as a moderator in the predictive capacity of TRA on ZEI. Moreover, the suitability for the use of PLS-SEM as a statistical tool in investigating the extended TRA with religiosity as a moderating variable as well as its implications for theory and practice were also discussed.
Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers

This thesis (1) characterizes the gender and skin type distribution of IJB-A, a government facial recognition benchmark, and Adience, a gender classification benchmark, (2) outlines an approach for capturing images with more diverse skin types which is then applied to develop the Pilot Parliaments Benchmark (PPB), and (3) uses PPB to assess the classification accuracy of Adience, IBM, Microsoft, and Face++ gender classifiers with respect to gender, skin type, and the intersection of skin type and gender. The datasets evaluated are overwhelming lighter skinned: 79.6% 86.24%. IJB-A includes only 24.6% female and 4.4% darker female, and features 59.4% lighter males. By construction, Adience achieves rough gender parity at 52.0% female but has only 13.76% darker skin. The Parliaments method for creating a more skin-type-balanced benchmark resulted in a dataset that is 44.39% female and 47% darker skin. An evaluation of four gender classifiers revealed a significant gap exists when comparing gender classification accuracies of females vs males (9 20%) and darker skin vs lighter skin (10 21%). Lighter males were in general the best classified group, and darker females were the worst classified group. 37% 83% of classification errors resulted from the misclassification of darker females. Lighter males contributed the least to overall classification error (.4% 3%). For the best performing classifier, darker females were 32 times more likely to be misclassified than lighter males. To increase the accuracy of these systems, more phenotypically diverse datasets need to be developed. Benchmark performance metrics need to be disaggregated not just by gender or skin type but by the intersection of gender and skin type. At a minimum, human-focused computer vision models should report accuracy on four subgroups: darker females, lighter females, darker males, and lighter males. The thesis concludes with a discussion of the implications of misclassification and the importance of building inclusive training sets and benchmarks. Thesis supervisor: Ethan Zuckerman Title: Associate Professor of the Practice in Media Arts and Sciences Director, Center for Civic Media 4 Gender Shades: Intersectional Phenotypic and Demographic Evaluation of Face Datasets and Gender Classifiers Author: Joy Adowaa Buolamwini
Difference of Self-identity Levels between Strabismus Patients and Normal Controls

Purpose To evaluate differences in self-identity in patients diagnosed with strabismus, patients who underwent strabismus surgery, and healthy control individuals. Methods Self-identity testing was done during a military service physical examination. There were three subject groups: subjects with strabismus (group 1), subjects who had undergone corrective strabismus surgery (group 2), and subjects free of strabismus (group 3). The self-identity test was comprised of six sub-sections (subjectivity, self-acceptance, future confidence, goal orientation, initiative, and familiarity). Statistical significance of the sub-sections was compared across the three groups. Correlations in age at the time of surgery and across the six sub-sections were investigated in group 2. Results A total of 351 subjects were enrolled in the study; 96 subjects were in group 1, 108 subjects were in group 2, and 147 subjects were in group 3. Significant differences were evident in subjectivity, self-acceptance, initiative and familiarity between groups 1 and 3. No significant differences were found between groups 2 and 3. In group 2, statistical significance was evident between age at surgery and initiative and familiarity (r = −0.333, p < 0.001; r = −0.433, p < 0.001, respectively). Conclusions Self-identity is greater in non-strabismus subjects than strabismus subjects. Correction of strabismus may increase self-identity levels.