Miracle-dz/UniFAR-FTUs huggingface dataset:
eCommons@AKU eCommons@AKU

Coronavirus disease 2019 (COVID-19) has been a pandemic disease reported in almost every country and causes life-threatening, severe respiratory symptoms. Recent studies showed that various environmental selection pressures challenge the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infectivity and, in response, the virus engenders new mutations, leading to the emergence of more virulent strains of WHO concern. Advance prediction of the forthcoming virulent SARS-CoV-2 strains in response to the principal environmental selection pressures like temperature and solar UV radiation is indispensable to overcome COVID-19. To discover the UV-solar radiation-driven genomic adaption of SARS-CoV-2, a curated dataset of 2,500 full-grade genomes from ﬁve different UVindex regions (25 countries) was subjected to in-depth downstream genome-wide analysis. The recurrent variants that best respond to UV-solar radiations were extracted and extensively annotated to determine their possible effects and impacts on gene functions. This study revealed 515 recurrent single nucleotide variants (rcntSNVs) as SARS-CoV-2 genomic responses to UV-solar radiation, of which 380 were found to be distinct. For all discovered rcntSNVs, 596 functional effects (rcntEffs) were detected, containing 290 missense, 194 synonymous, 81 regulatory, and 31 in the intergenic region. The highest counts of missense rcntSNVs in spike (27) and nucleocapsid (26) genes explain the SARS-CoV-2 genomic adjustment to escape immunity and prevent UV-induced
Effects of Ca2 channel blockers, low Ca2 medium and glycine on cell Ca2 and injury in anoxic rabbit proximal tubules

Effects of Ca2 channel blockers, low Ca2 medium and glycine on cell Ca2 and injury in anoxic rabbit proximal tubules. L-type Ca2 channel blockers (CCBs) have been shown to be protective against ischemia-induced injury of the kidney, suggesting that increased intracellular Ca2 levels ([Ca2}) play an important role in the pathogenesis of ischemic cell injury. To assess the role of [Ca2} in anoxic injury of the proximal tubule (PT) and the protective effect of CCBs, digital imaging fluorescence microscopy was used to monitor [Ca2]1 in individual PT cells during 60 minutes of anoxia. [Ca2]1 started to rise within 10 minutes and reached maximal levels between 30 to 45 minutes of anoxia. The onset of this increase and the maximal levels reached varied markedly among individual cells. The mean values for initial and maximal anoxic [Ca2J1 were 109 2 (N = 209) and 422 14 (N = 240) nM, respectively. Methoxyverapamil (D600; I sM) significantly reduced anoxic
Modeling functional cell types in spike train data

A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this “simultaneous” method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to in vitro neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons. Author Summary Computational neuroscience has its roots in fitting and interpreting predictive models of the activity of individual neurons. In recent years, more attention has focused on models of how ensembles of neurons work together to perform computations. However, fitting these more complex models to data is challenging, limiting our ability to use them to understand real neural systems. One idea that has improved our understanding of populations of neurons is cell types, where neurons of the same type have similar properties. While the idea of cell types is old, recent work has focused on functional cell types, where the properties of interest are derived from fitted predictive models of the activity of single neurons. In this work, we develop a method that simultaneously fits a predictive model of each neuron’s activity and groups neurons into functional cell types. Compared to existing techniques, this method produces more accurate models of single-cell neural activity and better groupings of neurons into types. This method can thus contribute in using cell types to better understanding the components of neural systems based on our increasingly rich observations of their functional responses.
Stragglers’ Detection in Big Data Analytic Systems: The Impact of Heartbeat Arrival

—Speculative execution can signiﬁcantly improve the performance of Big Data applications by launching other copies of stragglers (slow tasks). Stragglers detection plays an important role in the effectiveness of speculative execution. The methods employed to detect stragglers use the information extracted from the last received heartbeats which may be outdated when triggering detection. This, in turn, can mislead Big Data analytic systems to make wrong detection with high inaccuracy. To shed the light on this issue, we carry out extensive simulations to identify how heartbeat arrival, task starting times, and detection methods impact the accuracy of stragglers detection in Big Data analytic systems. We reveal that the asynchrony in heartbeat arrivals not only lead to marking normal tasks as stragglers (false positives) but can also result in overlooking real stragglers (false negatives).
NUOVA OFFICINA ASSERGI: future perspectives beyond DarkSide-20k

The Nuova Officina Assergi (NOA) is a functional Research and Technological Development Unit realized at LNGS and operational starting from February 2023. Flagship of INFN for the production and integration of silicon devices operating at cryogenic temperatures, NOA occupies an area of 420 m 2 with reduced radon concentration and is equipped with cutting-edge technology machines: a cryogenic Silicon device probe, a semi-automatic dicing system, a high-speed dual bond head flip-chip bonder and an ultrasonic wedge-wedge and ball-wedge wire bonder. The next 2 years the facility will host the DarkSide-20k activities for the packaging and assembly of more than 21 m 2 of cryogenic photosensors
Psychotic disorders in the Lundby population 1947-1997: Incidence, life-time prevalence and predictors related to personality and behaviour

The aims were to analyze the first incidence, age-at-onset, prevalence and personality related predictors of psychotic disorders in the Lundby population (n=3563) – a community population followed through four waves of field study 1947-1997. Case ascertainment included interviews, registers, case notes and key informants. Consensus diagnoses of mental illness were assessed according to a classification adapted to the Lundby Study, but in 1972-1997 also according to the DSM-IV. Mental illness 1947-1972 was rediagnosed according to the DSM-IV. Follow-ups were feasible in the majority of subjects; 99% in 1947-1972 and 94% in 1972-1997. The incidence rate was higher in males than in females for ‘substance-induced psychoses’. The incidence rates of the other psychoses did not differ between the sexes. The male mean age-at-onset was lower than that for females for ‘psychotic disorder due to a general medical condition’, ‘nonaffective psychoses’ and ‘schizophrenia’. Males and females had different incidences by age patterns for ‘nonaffective psychoses’, ‘schizophrenia’ and ‘other nonaffective psychoses’. The period prevalence 1947-1997 was higher in males than in females for ‘substance-induced psychoses’. For the other psychoses the period prevalences did not differ between the sexes. The lifetime prevalence in 1997 was higher in males than in females for the general group including any psychotic or bipolar disorder. For the specific psychoses the lifetime prevalences in 1997 did not differ significantly between the sexes. The influence of constructed dichotomous predictor variables related to premorbid personality traits in the subjects on time to occurrence of ‘functional psychosis’ and ‘schizophrenia’, respectively, was assessed by Cox regression. In multivariate and/or univariate models the predictors nervous-tense, down-semidepressed, blunt-deteriorated, paranoid-schizotypal, sensitive-frail, easily hurt and tired-distracted were associated with ‘functional psychosis’. When ‘schizophrenia’ was analyzed separately, nervous-tense remained significant and abnormal-antisocial reached significance. The findings suggest similar overall risks in males and females to contract psychosis during the life course, but with putative disorder delaying mechanisms in females and/or different aetiologies in males and females. Certain personality traits – related to anxiety-proneness, affective/cognitive blunting, poor concentration, personality cluster-A like traits and interpersonal sensitivity – may be associated with general psychosis vulnerability. Limitations: changing sample representativeness, small sample size, attrition, recall bias, changing data sources, inter-rater reliability and changing diagnostic systems during the study.
Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods

Quantifying the uncertainty of machine learning models is a promising way to make better-informed decisions in digital agriculture. Efforts have been made to address this, ranging from understanding and segregating sources of uncertainty to utilizing diverse approaches for quantifying the cumulative amount. However, in order to fully realize the potential of uncertainty quantification in digital agriculture, more research is needed to compare and contrast different methods and determine which are most effective in different contexts. In this paper, we investigate inductive conformal prediction as another family of machine learning methods besides the commonly used softmax outputs and Monte Carlo dropout. Inductive conformal prediction constructs valid prediction sets by selecting a pre-defined level of predictive confidence in the system. In our experiments, we analyze this method for an image-based harvest-readiness classification task of cauliflower plants, and compare the results to softmax outputs and uncertainties derived from Monte Carlo dropout. Inductive conformal prediction turns out as a complementary tool offering distinct advantages and providing another level of information for decision support.
Model Predictive Control based Driver Support for Docking of Articulated Vehicles at Logistics Areas

Bi-directional maneuvering of articulated vehicles at distribution centers is a complex task even for an experienced driver given (i) the unstable nature of the vehicle combination whilst reversing, (ii) a limited field of view, and (iii) a constrained maneuverability space. To support the driver, a novel driver-assist system is established, which consists of a computer vision-based localization module, a vehicle navigation system, and a human machine interface (HMI). This paper focuses on a fundamental module for the vehicle navigation system, that is, the design of a model-predictive-control(MPC)-based tracking controller. This controller is responsible for providing an input for the HMI, based on a known reference path and the actual vehicle pose resulting from driver-vehicle interaction. The controller is validated on a scaled real vehicle and in a virtual reality simulator with human-drivers in the loop.
lactat-Characteristics of dairy cows with a greater or lower risk of subacute ruminal acidosis: Volatile fatty acid absorption, rumen digestion, and expression of genes in rumen epithelial cells

The objective of this study was to examine whether lactating dairy cows with a greater or lower risk of subacute ruminal acidosis (SARA) have differences in volatile fatty acid (VFA) absorption rate, expression of genes involved in VFA metabolism and intracellular pH regulation in rumen epithelial cells, and in situ carbohydrate digestibility in the rumen. We fed 14 ruminally cannulated mid-lactating dairy cows (119 ± 47.2 d in milk; body weight 640 ± 47.9 kg) a high-grain diet consisting of 30% forage ad libitum, with an 18-d diet adaptation and a 7-d sample and data collection period. Eight cows with the lowest acidosis index [area below pH 5.8 normalized for dry matter intake (DMI); 0.10 ± 0.16 pH × min/kg of DMI] and 5 with the highest acidosis index (3.72 ± 0.19 pH × min/kg of DMI) were classified as animals with lower risk (LS) and higher risk (HS) of SARA, respectively. Minimum (5.75 vs. 5.33) and mean rumen pH (6.33 vs. 5.98) were higher for LS than for HS cows. In addition, the duration and area of rumen pH below 5.8 was lower in LS cows (24.9 vs. 481 min/d; 2.94 vs. 102 pH × min/d). Although DMI, milk yield, and milk component yields did not differ, milk fat concentration tended to be higher for LS cows than for HS cows (3.36 vs. 2.93%). However, we observed no difference in VFA absorption rate between LS and HS cows. In situ starch and neutral detergent fiber digestibility were not different between LS and HS cows, but the relative mRNA abundance of lanosterol synthase ( LSS ) was higher for LS cows than for HS cows. In addition, the mRNA abundance of hydroxy-3-methylglutaryl-CoA synthase 1 ( HMGCS1 ) tended to be higher for LS cows than for HS cows. These results suggested that VFA absorption rate might not explain the difference in rumen pH between LS and HS cows in the current study, even though expression of some genes related to VFA metabolism in rumen epithelium may be associated with variation in the risk of SARA among lactating cows. This variation in the risk of SARA may not be attributed to differences in the capacity of rumen microbes to ferment carbohydrates, because in situ carbohydrate digestibility in the rumen was not different between cows with higher and lower risk of SARA.
Landmark Recognition using Deep Learning in a Virtual Space

It is a very important issue to simulate human behavior in a virtual space for emergency evacuation in the real world. Humans take actions using their own eyes and memory. Then, the identiﬁcation of the scene that virtual humans are looking at in a town is one of the key elements of the behavior, and image-based pattern matching is usually used; however, the accuracy is affected by the view angle and the length between the target object and the position at which the image is taken. This paper proposes a method to identify the images of landmarks that are placed at the corners in an intersection in a virtual space using a deep learning method and reports the relationship between the accuracy and the area rate that the landmark object occupies in the image.
Modification of Turbolent Flow Using Distributed Suction

The long-term objective of this investigation is the development of techniques for re-arrangement and control of turbulent flows by using a suitably roughened wall. As a preliminary step towards this goal, we describe in the present paper the analysis of the turbulent flow in a plane channel modified by a sinusoidal distribution of suction/blowing at the wall with zero net mass flux. The tool by which the whole investigation is carried out is the Direct Numerical Simulation (DNS) of the incompressible Navier–Stokes equations. The fundamental result of this preliminary study is that the suction wavelength has a dramatic effect on the modification induced on the turbulent flow by the suction at the wall. Large wavelengths produce very high increases in the friction drag, whereas smaller wavelengths can lead to a friction decrease up to 4-5%.
Three Lenses for Improving Programmer Productivity: From Anecdote to Evidence

Overview: We propose a series of algorithms and theoretically-grounded interventions to enhance programmer productivity. By combining large-scale exploratory empirical investigations and controlled human-focused experimental design, we propose to both build mathematical models of the impact of understudied features on programmer productivity and to also provide actionable and evidence-backed interventions that improve productivity in practice for targeted diverse programmer groups. We propose three primary lenses: developing efficient and usable bug-fixing tools for non-traditional novices, designing effective programming training informed by objective measures of programming cognition, and understanding the impact of external factors , such as psychoactive substance use. Intellectual Merit: In this proposal, we argue not only that varied external support can improve developer productivity, but we also specify which support can best do so. We contend that understudied factors and potential interventions can be identified through large-scale exploratory analyses. In addition, the impact of targeted interventions can be measured via causal experimental designs and large-scale human evaluations, even for factors impacting diverse populations that have previously only been considered anecdotally. We propose three research lenses into improving programmer productivity, each targeting a different type of support and programmer population.
eCommons@AKU eCommons@AKU

Background HIV outbreaks in the Former Soviet Union (FSU) countries were characterized by repeated transmission of the HIV variant AFSU, which is now classified as a distinct subtype A sub-subtype called A6. The current study used phylogenetic/phylodynamic and signature mutation analyses to determine likely evolutionary relationship between subtype A6 and other subtype A sub-subtypes.
Seasonal climate summary southern hemisphere (autumn 2013): significant heat across Australia

Southern hemisphere circulation patterns and associated anomalies for the austral autumn 2013 period are reviewed, with emphasis on Pacific Basin climate indicators and Australian rainfall and temperatures. Autumn 2013 saw the continuation of neutral El Niño-Southern Oscillation (ENSO) conditions that had been in place since mid-2012. ENSO indices, which include the Southern Oscillation Index, showed near neutral conditions throughout the autumn period. In the tropical Indian Ocean, warmer-than-average sea surface temperatures developed in the east, while sea surface temperatures in the western Indian Ocean cooled during the latter half of May. As a result of this pattern, the end of autumn saw the development of a negative Indian Ocean Dipole and led to above average rainfall for May, particularly across parts of southern Australia. Maximum temperatures were above average during autumn for most of Australia, with the occurrence of several heatwave events throughout the season. A heatwave event in early March saw record breaking temperatures across southeast Australia. Minimum tempera-tures were above average across most of the country, but near average across large parts of New South Wales. Averaged across the country as a whole, seasonal minimum temperatures ranked 2013 as the tenth warmest autumn in 104 years of record and seasonal maximum temperatures ranked as seventh warmest.
The high official Harkhuf and the inscriptions of his tomb in Aswan (Egypt). An integrated methodological approach.

TECH project (Technology for the Egyptian Cultural Heritage) aimed to document an Egyptian monument for Egyptological studies and researches but, at the same time, to check a new methodological approach for conservation, valorisation and enhancement. In particular, the CNR mission focused the attention on the tomb of Harkhuf, a high official of the VI dynasty (XXIII century BC), who led trading and military expeditions into Nubia. The hieroglyphic texts inscribed on the facade of his tomb are very important and famous documents. The team checked an innovative and integrated methodology. The methodology has been focused mainly on the use of digital photogrammetric systems in order to generate an accurate numerical model (3D) and to facilitate the epigraphic study. Different procedures have been established in the processing and representation steps in order to accomplish the final communication of the results. Moreover climatic measurements have been carried out in order to understand the role of environmental factors on the deterioration of the monument. Finally the data have been crossed in order to check the environmental impact and the decay.
About effect of dynamic loading caused by concrete pump on bending moments, anchor forces and deformations in single level anchored retaining wall

This paper explore effect of dynamic loading caused by concrete pump on bending moments, anchor forces and deformations in single level anchored retaining wall. Dynamic loading is obtained by using a special developed dynamic mathematical model of truck-mounted concrete pump, by means of which are calculated the maximal dynamic loadings (acceleration and forces), which are transferred from concrete pump to the soil, and from there accordingly – from soil to the retaining wall under consideration. After that is performed a detailed finite element analysis of the retaining wall, by using software product Plaxis 3D, during which are analyzed the variation of bending moments, anchor forces and deformations in single level anchored retaining wall and after dynamic loading applying. There is also a numerical example considered, for which are calculated the maximal dynamic loadings, which are transferred from concrete pump to the soil, and from ground supports to the retaining wall. An amplification factor (dynamic factor) due to dynamic effects is calculated, by which is necessary to be multiplied the static loadings caused by concrete pump on soil. In the conclusion is made a qualitative assessment of the dynamic influence of concrete pump on the soil and the retaining wall section and ground anchor.
A Novel Soft Ensemble Model for Financial Distress Prediction with Different Sample Sizes

This work presents a novel soft ensemble model (ANSEM) for financial distress prediction with different sample sizes. It integrates qualitative classifiers (expert system method, ES) and quantitative classifiers (convolutional neural network, CNN) based on the uni-int decision making method of soft set theory (UI). We introduce internet searches indices as new variables for financial distress prediction. By constructing a soft set representation of each classifier and then using the optimal decision on soft sets to identify the financial status of firms, ANSEM inherits advantages of ES, CNN, and UI. Empirical experiments with the real data set of Chinese listed firms demonstrate that the proposed ANSEM has superior predicting performance for financial distress on accuracy and stability with different sample sizes. Further discussions also show that internet searches indices can offer additional information to improve predicting performance.