Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of In2Care mosquito stations for suppression of the Australian backyard mosquito, Aedes notoscriptus (Diptera: Culicidae)

Aedes notoscriptus (Skuse) is a container-inhabiting mosquito endemic to Australia that vectors arboviruses and is suspected to transmit Mycobacterium ulcerans, the cause of Buruli ulcer. We evaluated the effectiveness of the In2Care station, which suppresses mosquito populations via the entomopathogenic fungus, Beauveria bassiana, and the insect growth regulator pyriproxyfen, the latter of which is autodisseminated among larval habitats by contaminated mosquitoes. A field trial was conducted using 110 In2Care stations in a 50,000 m2 area and results were compared to 4 control areas that did not receive the treatment. Efficacy was evaluated by comparing egg counts and measuring larvicidal impact in surrounding breeding sites. Laboratory experiments validated the effect of B. bassiana on adult survival. Results of this field trial indicate that, 6 wk after the In2Care stations were deployed, treatment site ovitraps contained 43% fewer eggs than control site ovitraps, and 33% fewer eggs after 10 wk, suggesting that the In2Care station was able to reduce the egg density of Ae. notoscriptus. Population reduction remained evident for up to 3 wk after In2Care stations were removed. Treatment site ovitraps had significantly fewer Ae. notoscriptus eclosing than control site ovitraps, confirming the pyriproxyfen autodissemination feature of the stations. An average reduction of 50% in adult eclosion was achieved. Exposure to B. bassiana resulted in four-times higher mortality among adult mosquitoes. Additionally, using fresh In2Care nettings led to an 88% decrease in average survival compared to 4-wk-old nettings. The use of In2Care stations has potential for suppressing Ae. notoscriptus egg density.
NCOurd: modelling length distributions of NCO events and gene conversion tracts

Abstract Motivation Meiotic recombination is the main driving force of human genetic diversity, along with mutations. Recombinations split into crossovers, separating large chromosomal regions originating from different homologous chromosomes, and non-crossovers (NCOs), where a small segment from one chromosome is embedded in a region originating from the homologous chromosome. NCOs are much less studied than mutations and crossovers as NCOs are short and can only be detected at markers heterozygous in the transmitting parent, leaving most of them undetectable. Results The detectable NCOs, known as gene conversions, hide information about NCOs, including their number and length, waiting to be unveiled. We introduce NCOurd, software, and algorithm, based on an expectation–maximization algorithm, to estimate the number of NCOs and their length distribution from gene conversion data. Availability and implementation https://github.com/DecodeGenetics/NCOurd
Methods in Computational Physics Vol l0

B Alder, S Fernbach and M Rotenberg (Eds) New York: Academic Press 1971 pp xi + 341 price £1025 This volume, the tenth in the series edited by Alder et al, is devoted to quantum atomic and molecular scattering including electron-atom collisions. It comes at a useful time when workers in various fields may wish to draw on the expertise built up around atomic scattering.
Frequent Ordered Subtree Discovery Algorithm Using Left-Right Tree Join

Recently many studies have been conducted to discover the frequent subtrees in the tree data, as data mining on semi-structured data such as HTML or XML. In this paper, we propose a fast algorithm to discover all frequent trees in the tree data, using Left-Right Tree Join for effecient generation of the set of candidate tree with size k + 1 from the set of frequent tree with size k. In the experiments with artificial tree data and XML data, proposed algorithm can extract all frequent subtrees without any duplicates for the linear time compexity with the size of the tree data. Our method outperforms FREQT, typical frequent subtree discovery algorithm, by up to several times in the execution time.
Genomes of two type strains of the Rhizobium tropici group: R. calliandrae CCGE524T and R. mayense CCGE526T

ABSTRACT The genome sequences of two nitrogen-fixing type strains of the Rhizobium tropici group were obtained: Rhizobium calliandrae CCGE524T and R. mayense CCGE526T. Genomic analyses confirmed their taxonomic position and identified three complete sequences of the repABC genes, indicative of three plasmids, one of them carrying symbiotic genes.
π-Calculus, structured coalgebras, and minimal HD-automata

The coalgebraic framework developed for the classical process algebras, and in particular its advantages concerning minimal realizations, does not fully apply to the π-calculus, due to the constraints on the freshly generated names that appear in the bisimulation. In this paper we propose to model the transition system of the π-calculus as a coalgebra on a category of name permutation algebras and to define its abstract semantics as the final coalgebra of such a category. We show that permutations are sufficient to represent in an explicit way fresh name generation, thus allowing for the definition of minimal realizations. We also link the coalgebraic semantics with a slightly improved version of history dependent (HD) automata, a model developed for verification purposes, where states have local names and transitions are decorated with names and name relations. HD-automata associated with agents with a bounded number of threads in their derivatives are finite and can be actually minimized. We show that the bisimulation relation in the coalgebraic context corresponds to the minimal HD-automaton.
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data

We present further empirical and qualitative results for both image and video description. For the image description task, we explore averaging weight vectors before transfer, illustrate errors made by the model when no unpaired text data is used during training and provide descriptions generated by DCC for a large variety of novel object categories in ImageNet. For the video description task, we include results when training the language model with an external text corpora and more DCC descriptions of novel objects in video
Parental rejection and control: Potential risks for excessive Internet usage among adolescents

Background: Internet addiction is an emergent health hazard among adolescents, especially after COVID19. Parenting factors in a family environment specifically, have potential roles in either causing excessive internet usage, or in protecting teenagers from internet addiction. Aim: The aim of the study was to understand factors in parent-adolescent relationship that determine low and excessive internet usage. Methods: A total of 102 adolescents within the age group of 13 to 18 years from two different schools in urban Bengaluru, India were recruited. Data was collected using three instruments namely semi structured tool to assess socio-demographic characteristics, Young’s internet addiction test for internet usage and parental bonding instrument for adolescent perception about parenting dimensions. Results: Half of the participants were found to have mild to severe levels of internet addiction, and the remaining half had normal levels of internet usage. The results showed that decreased care and increased control from the mother, high autonomy from father and increased rejection from both parents as risk factors associated with adolescent internet excessive use. No other significant associations were found between adolescents’ demographic, academic, peer and school profile and their internet addiction. Conclusion: As internet has become an inevitable part of our life, parent-adolescent relationship forms an essential moderating factor in adolescents’ internet usage.
Background subtraction in Fourier-domain mobility spectrum analysis for resolving low-mobility carriers

Narrow gap semiconductors and superlattices relevant to optoelectronics often host multiple conducting species, such as electrons and holes, requiring a mobility spectral analysis (MSA) method to separate contributions to the conductivity. Building on the Fourier-domain MSA (FMSA) method proposed previously by the authors, we introduce a background subtraction step prior to FMSA to more accurately extract the spectral mobilities below what is normally considered the low mobility threshold μth = 1/Bmax, where Bmax is the maximum magnetic field of the experimental data under analysis. This preliminary step subtracts a linear background from the magnetotransport data and enables a more accurate fit in the low mobility range by several orders of magnitude. Background subtraction extends the useful low-mobility limit by a factor of 5 to μmin = 1/5Bmax, and can be easily applied in other MSA techniques where low mobilities are of interest.
Comparative cost analysis of implanting devices in different cardiac resynchronization therapeutic strategies

Cardiac resynchronization therapy (CRT) is an established treatment option for heart failure patients. However, the implementation of triple‐chamber pacemakers can be cost‐prohibitive. His‐Purkinje system pacing (HPSP) can also enable cardiac resynchronization, and it can be achieved with relatively inexpensive conventional pacemakers.
Revisiting signatures of thermal axions in nonstandard cosmologies

We revisit the formation of a thermal population of hadronic axions in nonstandard cosmologies, in light of the recent developments in obtaining continuous and smooth interaction rates for both the gluon and photon couplings. For certain cosmological histories, such as low-temperature reheating (LTR) and kination-like scenarios, the thermalization of the axion can be severely delayed to higher masses. In the case that thermal equilibrium is achieved, we improve the constraints on LTR for axion masses around the eV scale with respect to previous works and we constrain for the first time early matter-dominated (EMD) cosmologies. We also point out the possibility of having the co-existence of cold and warm dark matter populations of axions in kination-like scenarios in the eV mass range.
Kappa opioid receptor agonist U50,488 inhibits dopamine more in caudal than rostral nucleus accumbens core

The nucleus accumbens (NAc) core is involved in regulating stress and shaping reward seeking behaviours. Multiple neuromodulators, including dynorphin/kappa opioid receptor (KOR) and dopamine systems, converge in this area to influence behavioural outcomes. KOR activation acutely inhibits dopamine release and chronically depresses overall dopamine transmission. Recently, studies in the NAc shell have revealed that the impact of KOR activation on behaviour is regionally specific, and these rostro‐caudal differences are likely driven by greater control of KORs over dopamine inhibition in the caudal compared with rostral subregion. Given the importance of NAc core, particularly the interaction between KORs and dopamine in regulating reward seeking behaviours, we examined the impact of KOR activation on dopamine release and uptake along the rostro‐caudal axis in the NAc core of male and female mice. Using ex vivo fast scan cyclic voltammetry, we observed that KOR mediated inhibition of dopamine release was significantly greater in caudal compared with rostral NAc core with no significant sex differences observed. These data suggest that KORs regulate dopamine release differentially along the rostro‐caudal axis, providing a new axis on which to examine the process by which the KOR/dopamine system controls reward encoding.
Multiple-shrinkage principal component regression

SUMMARY Consider the multiple-regression framework where the goal is 'black box' prediction and the explanatory variables are (approximately) multicollinear. For this situation we propose a multiple-shrinkage estimator which adaptively mimics the best principal components shrinkage estimator. The estimator proposed is a variant of the Stein estimator which is also guaranteed to give lower prediction mean-squared error than the least squares estimator, thus avoiding the danger of overshrinking when using principal components regression. The predictive performance of this estimator is illustrated and compared with that of principal components regression on three data sets by using random crossvalidation.
Optical intensity scintillation in the simulated atmospherical environment

There are several parameters of the atmospheric environment which have an effect on the optical wireless connection. Effects like fog, snow or rain are ones of the effects which appears tendentiously and which are bound by season, geographic location, etc. One of the effects that appear with various intensity for the whole time is airflow. The airflow changes the local refractive index of the air and areas with lower or higher refractive index form. The light going through these areas refracts and due to the optical intensity scintillates on the detector of the receiver. The airflow forms on the basis of two effects in the atmosphere. The first is wind cut and flowing over barriers. The other is thermal flow when warm air rises to the higher layers of the atmosphere. The heart of this article is creation such an environment that will form airflow and the refractive index will scintillate. For the experiment, we used special laboratory box with high-speed ventilators and heating units to simulate atmospheric turbulence. We monitor the impact of ventilator arrangement and air temperature on the scintillation of the gas laser with wavelength 633 nm/15 mW. In the experiment, there is watched the difference in behavior between real measurement and flow simulation with the same peripheral conditions of the airflow in the area of 500 x 500 cm.
Test of treatment effect using the Cox proportional hazards model with imputed covariate data

Missing covariate data is a common problem in clinical trials and the analysis of survival time data. Imputing values for the missing covariate data is equivalent to omitting the differences between the true and imputed values from the model. If the model for the hazard rate given the true values is proportional hazards, then the model using imputed values will not be. In this dissertation the distribution of the score statistic for the test of treatment effect using imputed data is derived for the correct and misspecified general parametric models and for the misspecified Cox nonparametric prop·ortional hazards model (PHM). The robust variance estimator for the score statistic When the Cox model is misspecified proposed by Lin and Wei (1989) is examined for this test under the null hypothesis. The asymptotic relative efficiency of the misspecified Cox PHM with imputed data relative to the complete data Cox PHM and relative to the correct parametric model for complete and incomplete data, assuming a PHM for the complete data, is derived. Imputing values for the missing covariate data and ' . misspecifying the Cox PHM is very efficient unless there is a moderate to high percentage of missing data for a covariate with a strong effect on survival time which cannot be imputed well and there is little censoring. The procedure is easily implemented using standard statistical software and a SAS macro. The implementation of the procedure and the evaluation of its efficiency is illustrated.
Business Cycles: Theory, History, Indicators, and Forecasting

Acknowledgments Preface I: Theories and Evidence 1: Macroeconomics and Business Cycles: An Overview 2: Recent Work on Business Cycles in Historical Perspective 3: Facts and Factors in the Modern Evolution of U.S. Economic Fluctuations 4: Cyclical Aspects of Cost and Price Movements 5: Research during the First 50 Years of the National Bureau II: History and Measurement 6: How Trends and Fluctuations Are Observed, Modeled, and Simulated: An Introduction 7: Business Cycles and Growth 8: The Regularity of Business Cycles 9: Econometric Model Simulations and the Cyclical Characteristics of the Economy III: Indicators 10: Cyclical Indicators: Structure, Significance, and Uses 11: Composite Indexes of Leading, Coincident, and Lagging Indicators 12: Major Macroeconomic Variables and Leading Indexes Phillip Braun IV: Forecasting 13: On Short-Term Predictions of General Economic Conditions 14: An Analysis of Annual and Multiperiod Quarterly Aggregate Forecasts 15: The Accuracy of Individual and Group Forecasts 16: Rational Expectations and Macroeconomic Forecasts 17: Consensus and Uncertainty in Economic Prediction Louis A. Lambros 18: The Record and Improvability of Economic Forecasting References Author Index Subject Index
Processus de Markov Longitudinal Latent Rasch Observable

On etudie dans cet article, une classe de processus latents ou a tout instant, la variable latente est supposee observable indirectement a travers un ensemble de variables binaires. Nous supposons que le modele de mesure qui relie a chaque instant, les items observes et la variable latente est un modele de Rasch. Apres avoir decrit ces modeles, nous presentons l'estimation de leurs parametres par la methode du maximum de vraisemblance via l'algorithme EM. Nous illustrons cette etude a travers deux classes de distribution pour ces processus latents longitudinaux: i) le modele general de processus de Markov latent qui generalise le cas particulier, plus facile a manipuler ii) le processus autoregressif latent d'ordre un.
Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories

We introduce a Probabilistic Grammar-Markov Model (PGMM) w hich couples probabilistic context free grammars and Markov Random Fields. We propose an unsupervis ed structure induction method for learning a PGMM for object categories. Our approach is invariant to the scale and rotation of the objects. We illustrate our approach using thirteen objects from the Caltech 101 databa se. In addition, we learn the model of a hybrid object class where we do not know the specific object or its position, scale or pose. This is illustrated by learning a hybrid class consisting of faces, motorbikes, and airplanes. The i ndividual objects can be recovered as different aspects of the grammar for the object class. In all cases, we validate ou r results by learning the probabilistic grammars from training datasets and evaluating them on the test datasets. W compare our method to alternative approaches. The advantages of our approach is the speed of inference (under o e second), the parsing of the object, and increased accuracy of performance. Moreover, our approach is very gen eral and can be applied to a large range of objects and structures.
Unsupervised Learning of Probabilistic Grammar-Markov Models for Object Categories Long (

We introduce a Probabilistic Grammar-Markov Model (PGMM) which couples probabilistic context free grammars and Markov Random Fields. These PGMMs are generative models defined over attributed features and are used to detect and classify objects in natural images. PGMMs are designed so that they can perform rapid inference, parameter learning, and the more difficult task of structure induction. PGMMs can deal with unknown pose (position, orientation, and scale) in both inference and learning, different appearances, or aspects, of the model. They are learnt in an unsupervised manner where the input is a set of images with the object somewhere in the image (at different poses) and with variable background. We also extend this to learning PGMMs for classes of objects (where an unknown object from the class is present in each image). The goal of this paper is theoretical but, to provide proof of concept, we demonstrate results from this approach on a subset of the Caltech 101 dataset (learning on a training set and evaluating on a testing set). Our results are generally comparable with the current state of the art, and our inference is performed in less than five seconds.
Modeling Missing Data in Clinical Time Series with RNNs Modeling Missing Data in Clinical Time Series with RNNs

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1 significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.
Synthesis and characterization of uncoated and cysteamine-coated gold nanoparticles by pulsed laser ablation

Abstract. Gold nanoparticles and cysteamine-coated gold nanoparticles are physically synthesized using the pulsed laser ablation in liquid technique, which is rapid, simple, and efficient one-step synthesis. UV-Vis analysis shows that the absorption band after cysteamine coating is shifted toward the lower wavelength range around ∼234 nm as compared to gold nanoparticles’ absorption band around ∼520 nm. Moreover, the change in the color of the solution is due to the aggregation of nanoparticles after cysteamine coating. From transmission electron microscopy, cysteamine coating of ∼1 nm thickness on gold nanoparticles is successfully carried out with narrow pore size distributions. Energy-dispersive x-ray spectroscopy analysis confirms the presence of cysteamine coating. Attenuated total reflectance Fourier transform infrared spectroscopy analysis also confirms the bond linkage between gold and cysteamine species.
The control of an active seat with vehicle suspension preview information

This paper presents a novel, simple and reliable control strategy for an active seat suspension, intended for use in a vehicle, which attenuates the harmful low-frequency vertical vibration at the driver’s seat. An advantage of this strategy is that it uses measurable preview information from the vehicle suspension. The control force is calculated from this preview information and controller gains obtained by optimising an objective function using a genetic algorithm (GA) approach. The objective function optimises ride comfort in terms of the Seat Effective Amplitude Transmissibility factor, taking into account constraints on both the allowable seat suspension stroke and actuator force capacity. This new controller is evaluated using both simulation and experimental tests in both the frequency and time domains. The simulation model is based upon a linear quarter vehicle model and a single degree of freedom seat suspension. Experimental tests are performed using a multi-axis simulation table and an active seat suspension. Finally, the performance of the active seat suspension is analysed and compared to a passive system, demonstrating significant acceleration attenuation of more than 10 dB across a broad frequency range. Consequently, this has the potential to improve ride comfort and hence reduce the driver’s fatigue using a reliable and cost-effective control method.