Miracle-dz/UniFAR-FTUs huggingface dataset:
The Australian digital Online Farm Trials database increases the quality of systematic reviews and meta-analyses in grains crop research

Abstract. Synthesis and analysis of past cropping research can provide valuable information to direct future decisions around crop management. Systematic reviews and meta-analyses are considered gold standards in the synthesis and analysis of scientific research because they distil large amounts of information about complex issues, provide a summary of knowledge to date, and identify knowledge gaps. However, several issues concerning the methodologies employed to conduct systematic reviews have been identified; among them is the risk of publication bias when a review relies too heavily on ‘white’ literature from published academic sources and in so doing fails identify relevant ‘grey’ literature. Grey literature is inherently difficult to identify and collect, but forms a large portion of information available in many fields including agricultural-based research within Australia. The Online Farm Trials (OFT) database is a digital database of crop research field trial data from across Australia that has the potential for use as a discipline-specific source of grey literature to inform systematic reviews and meta-analyses. Using a case study approach to investigate the amount of information available on time of sowing (sowing date) on crop yield across Australia, we demonstrate that the OFT database provides easy access to transparent and reproducible search results similar to other commonly used academic databases.
Prevalence of Eimeria sp. in Broiler Poultry Houses with Positive and Negative Pressure Ventilation Systems in Southern Brazil

SUMMARY. Coccidiosis is an enteric disease caused by protozoa of the Eimeria genus and is of great economic relevance in industrial aviculture. The objective of this work was to determine the prevalence of Eimeria sp. in broiler poultry houses with positive (System 1) and negative (System 2) pressure ventilation and assess the associated factors. A transversal study was conducted using 8 random broiler chickens from 64 houses (n = 512) and macroscopic and histologic evaluation of the intestines, as well as PCR for Eimeria sp. The prevalence of Eimeria sp. was 90.6% (95% confidence interval [CI]: 97.8–83.5), with 93.8% (95% CI: 100–85.4) in System 1 and 87.5% (95% CI: 99.0–76.0) in System 2. The most prevalent species was Eimeria acervulina, and the most common combination was Eimeria acervulina, Eimeria maxima, and Eimeria tenella. System 2 and the negative Eimeria subgroup showed the best results for feed conversion and daily weight gain. By evaluating litter treatment, we found that quicklime reduced the risk of presence of Eimeria maxima and Eimeria tenella. In conclusion, Eimeria sp. had a high prevalence in both systems, with a predominance of mixed infections. System 2 and negative flocks showed the best zootechnical results.
Trimming Data Sets: a Verified Algorithm for Robust Mean Estimation

The operation of trimming data sets is heavily used in AI systems. Trimming is useful to make AI systems more robust against adversarial or common perturbations. At the core of robust AI systems lies the concept that outliers in a data set occur with low probability, and therefore can be discarded with little loss of precision in the result. The statistical argument that formalizes this concept of robustness is based on an extension of the Chebyshev’s inequality first proposed by Tukey in 1960. In this paper we present a mechanized proof of robustness of the trimmed mean algorithm, which is a statistical method underlying many complex applications of deep learning. For this purpose we use the Coq proof assistant to formalize Tukey’s extension to Chebyshev’s inequality, which allows us to verify the robustness of the trimmed mean algorithm. Our contribution shows the viability of mechanized robustness arguments for algorithms that are at the foundation of complex AI systems.
Automatic analysis of ECG signals based on their fractal and multifractal properties

Automatic analysis of electrocardiographic (ECG) signals, including the heart rate variability (HRV), makes it possible to assess the health status of patients, by reducing the likelihood of human error and providing an optimal and relatively accurate result. HRV is an important information indicator for diagnosing and predicting cardiovascular disease, which is based on measuring the intervals between heartbeats (known as RR-time intervals) derived from ECG signals. HRV has been used for research in various scientific fields, including information technology where it is used to create software products for automatic analysis of ECG signals in order to gain additional knowledge about the behavior of RR fluctuations in normal and disease states. ECG signals are non-stationary and one of the most suitable methods for analysis are the fractal methods. This article presents the results of the study of the fractal and multifractal properties of real ECG signals, combined into two groups: ECG signals of healthy subjects as well as patients with cardiovascular disease (arrhythmia), using the methods: Rescaled range (R/S) analysis and Multifractal Detrended Fluctuation Analysis (MFDFA). The obtained values of the studied parameters are used to distinguish healthy subjects from sick ones, by applying statistical analysis. The statistical analysis is performed by applying a t-test to determine the statistical significance of the studied ECG signals and Receiver Operating Characteristic (ROC) analysis to assess the quality of the selected methods. The obtained results show that the fractal methods used are suitable for analysis of the dynamics of RR intervals and for distinguishing the healthy subjects from those with pathological diseases.
iTOP: Automating Counterfeit Object-Oriented Programming Attacks

Exploiting a program requires a security analyst to manipulate data in program memory with the goal to obtain control over the program counter and to escalate privileges. However, this is a tedious and lengthy process as: (1) the analyst has to massage program data such that a logical reliable data passing chain can be established, and (2) depending on the attacker goal certain in-place fine-grained protection mechanisms need to be bypassed. Previous work has proposed various techniques to facilitate exploit development. Unfortunately, none of them can be easily used to address the given challenges. This is due to the fact that data in memory is difficult to be massaged by an analyst who does not know the peculiarities of the program as the attack specification is most of the time only textually available, and not automated at all. In this paper, we present indirect transfer oriented programming (iTOP), a framework to automate the construction of control-flow hijacking attacks in the presence of strong protections including control flow integrity, data execution prevention, and stack canaries. Given a vulnerable program, iTOP automatically builds an exploit payload with a chain of viable gadgets with solved SMT-based memory constraints. One salient feature of iTOP is that it contains 13 attack primitives powered by a Turing complete payload specification language, ESL. It also combines virtual and non-virtual gadgets using COOP-like dispatchers. As such, when searching for gadget chains, iTOP can respect, for example, a previously enforced CFI policy, by using only legitimate control flow transfers. We have evaluated iTOP with a variety of programs and demonstrated that it can successfully generate exploits with the developed attack primitives.
Fast direct calibration of interest rate derivatives pricing models

To price complex derivative instruments and to manage the associated financial risk, investment banks typically model the underlying asset price dynamics using parametric stochastic models. Model parameters are calibrated by fitting cross sections of option prices on the relevant risk factors. It is fundamental for a calibration method to be accurate and fast and, to this end, Deep Learning techniques have attracted increasing attention in recent years. In this paper, the aim is to propose a Neural Network based calibration of a pricing model, where learning is directly performed on market data by using a non-trivial loss function, which includes the financial model adopted. In particular, the model chosen is the two-additive factor Gaussian Interest Rates model in a multi-curve framework calibrated on at-the-money European swaptions. The main advantage lies in the independence from an external calibrator and in the calibration time, reduced from several seconds to milliseconds, achieved by offloading the computational-intensive tasks to an offline training process, while the online evaluation can be performed in a considerably shorter time. Finally, the efficiency of the proposed approach is tested in both a single-currency and a multi-currency framework.
Characterization of Nothacrobeles lanceolatus Abolafia & Peña-Santiago, 2003 (Rhabditida, Cephalobidae), a typical species of coastal dunes from Spain

Abstract The nematode Nothacrobeles lanceolatus was described in arid soils from Cabo de Gata-Níjar Natural Park, province of Almería (Spain). Since then this species has not been found again. In this study, new data on the distribution and morphological, morphometrical and molecular characteristics (based on 18S and 28S ribosomal DNA) of N. lanceolatus is provided. This species is characterized by having a tessellated cuticle, with its inner layer bearing two rows of cuticular punctations per annulus, lips quadrangular with eight pinnae, labial probolae bifurcate with divergent prongs surrounded by pinnae, pharynx with corpus 2.5–4.0 times isthmus length, post-vulval uterine sac 1.4–2.2 times the corresponding body diameter, and female and male tails conical, bearing a conical mucro with acute terminus. The phylogenetic position of this and related species of the genus Nothacrobeles is discussed. Additionally, bacteria (Pasteuria) and fungi (Catenaria, Nematocida) feeding on N. lanceolatus are showed including light microscopy and scanning electron microscopy illustrations.
Bottom-up and top-down effects of temperature on body growth, population size spectra and yield in a size-structured food web

Resolving the combined effect of climate warming and exploitation in a food web context is key for predicting future biomass production, size-structure, and potential yields of marine fishes. Previous studies based on mechanistic size-based food web models have found that bottom-up processes are important drivers of size-structure and fisheries yield in changing climates. However, we know less about the joint effects of ‘bottom-up’ and ‘top-down’ effects of temperature: how do temperature effects propagate from individual-level physiology through food webs and alter the size-structure of exploited species in a community? Here we assess how a species-resolved size-based food web is affected by warming through both these pathways, and by exploitation. We parameterize a dynamic size spectrum food web model inspired by the offshore Baltic Sea food web, and investigate how individual growth rates, size-structure, relative abundances of species and yields are affected by warming. The magnitude of warming is based on projections by the regional coupled model system RCA4-NEMO and the RCP 8.5 emission scenario, and we evaluate different scenarios of temperature dependence on fish physiology and resource productivity. When accounting for temperature-effects on physiology in addition to on basal productivity, projected size-at-age in 2050 increases on average for all fish species, mainly for young fish, compared to scenarios without warming. In contrast, size-at-age decreases when temperature affects resource dynamics only, and the decline is largest for young fish. Faster growth rates due to warming, however, do not always translate to larger yields, as lower resource carrying capacities with increasing temperature tend to result in declines in the abundance of larger fish and hence spawning stock biomass – the part of the population exposed to fishing. These results show that to understand how global warming impacts the size structure of fish communities, both direct metabolic effects and indirect effects of temperature via basal resources must be accounted for.
A class of identifiable phylogenetic birth–death models

In a striking result, Louca and Pennell (2020) recently proved that a large class of phylogenetic birth-death models are statistically unidentifiable from lineage-through-time (LTT) data: any pair of sufficiently smooth birth and death rate functions is “congruent” to an infinite collection of other rate functions, all of which have the same likelihood for any LTT vector of any dimension. As Louca and Pennell argue, this fact has distressing implications for the thousands of studies that have utilized birth-death models to study evolution. In this paper, we qualify their finding by proving that an alternative and widely used class of birth-death models is indeed identifiable. Specifically, we show that piecewise constant birth-death models can, in principle, be consistently estimated and distinguished from one another, given a sufficiently large extant time tree and some knowledge of the present-day population. Subject to mild regularity conditions, we further show that any unidentifiable birth-death model class can be arbitrarily closely approximated by a class of identifiable models. The sampling requirements needed for our results to hold are explicit, and are expected to be satisfied in many contexts such as the phylodynamic analysis of a global pandemic.
Weight Perturbation Learning Performs Similarly or Better than Node Perturbation on Broad Classes of Temporally Extended Tasks

Biological constraints often impose restrictions for plausible plasticity rules such as locality and reward-based rather than supervised learning. Two learning rules that comply with these restrictions are weight (WP) and node (NP) perturbation. NP is often used in learning studies, in particular as a benchmark; it is considered to be superior to WP and more likely neurobiologically realized, as the number of weights and therefore their perturbation dimension typically massively exceeds the number of nodes. Here we show that this conclusion no longer holds when we take two biologically relevant properties into account: First, tasks extend in time. This increases the perturbation dimension of NP but not WP. Second, tasks are low dimensional, with many weight configurations providing solutions. We analytically delineate regimes where these properties let WP perform as well as or better than NP. Furthermore we find that the changes in weight space directions that are irrelevant for the task differ qualitatively between WP and NP and that only in WP gathering batches of subtasks in a trial decreases the number of trials required. This may allow to experimentally distinguish which of the two rules underlies a learning process. Our insights suggest new learning rules, which combine for specific task types the advantages of WP and NP. If the inputs are similarly correlated, temporally correlated perturbations improve NP. Using numerical simulations, we generalize the results to networks with various architectures solving biologically relevant and standard network learning tasks. Our findings, together with WP’s practicability suggest WP as a useful benchmark and plausible model for learning in the brain.
Topological properties accurately predict cell division events and organization of Arabidopsis thaliana’s shoot apical meristem

Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues. Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology. Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level. We show that a classifier based on the SAM network properties is predictive of cell division events, with validation accuracy of 82%, on par with that based on cell size alone. Further, we demonstrate that the combination of topological and biological properties, including: cell size, perimeter, distance, and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division. Using our classifiers, we demonstrate the importance of microtubule mediated cell-to-cell growth coordination in influencing tissue-level topology. Altogether, the results from our network-based analysis demonstrates a feedback mechanism between tissue topology and cell division in A. thaliana’s SAMs. Summary statement we use a network representation of live cell imaging data from SAMs in Arabidopsis thaliana to predict cell division events and their consequences at a tissue level.
Prefrontal neural ensembles encode an internal model of visual sequences and their violations

Theories of predictive coding hypothesize that cortical networks learn internal models of environmental regularities to generate expectations that are constantly compared with sensory inputs. The prefrontal cortex (PFC) is thought to be critical for predictive coding. Here, we show how prefrontal neuronal ensembles encode a detailed internal model of sequences of visual events and their violations. We recorded PFC ensembles in a visual “local-global” sequence paradigm probing low and higher-order predictions and mismatches. PFC ensembles formed distributed, overlapping representations for all aspects of the dynamically unfolding sequences, including information about image identity as well as abstract information about ordinal position, anticipated sequence pattern, mismatches to local and global structure, and model updates. Model and mismatch signals were mixed in the same ensembles, suggesting a revision of predictive processing models that consider segregated processing. We conclude that overlapping prefrontal ensembles may collectively encode all aspects of an ongoing visual experience, including anticipation, perception, and surprise.
Video-evoked fMRI BOLD responses are highly consistent across different data acquisition sites

Naturalistic imaging paradigms, in which participants view complex videos in the scanner, are increasingly used in human cognitive neuroscience. Videos evoke temporally synchronized brain responses that are similar across subjects as well as within subjects, but the reproducibility of these brain responses across different data acquisition sites has not yet been quantified. Here we characterize the consistency of brain responses across independent samples of participants viewing the same videos in fMRI scanners at different sites (Indiana University and Caltech). We compared brain responses collected at these different sites for two carefully matched datasets with identical scanner models, acquisition, and preprocessing details, along with a third unmatched dataset in which these details varied. Our overall conclusion is that for matched and unmatched datasets alike, video-evoked brain responses have high consistency across these different sites, both when compared across groups and across pairs of individuals. As one might expect, differences between sites were larger for unmatched datasets than matched datasets. Residual differences between datasets could in part reflect participant-level variability rather than scanner- or data-related effects. Altogether our results indicate promise for the development and, critically, generalization of video fMRI studies of individual differences in healthy and clinical populations alike.
DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis

Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with an accuracy comparable to that of a trained human being. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping of aging cells and further quantitative division tracking assays in other eucaryotic models.
The role of phenotypic plasticity in the establishment of range margins

It has been argued that adaptive phenotypic plasticity may facilitate range expansions over spatially and temporally variable environments. However, plasticity may induce fitness costs. This may hinder the evolution of plasticity. Earlier modelling studies examined the role of plasticity during range expansions of populations with fixed genetic variance. However, genetic variance evolves in natural populations. This may critically alter model outcomes. We ask: How does the capacity for plasticity in populations with evolving genetic variance alter range margins that populations without the capacity for plasticity are expected to attain? We answered this question using computer simulations and analytical approximations. We found a critical plasticity cost above which the capacity for plasticity has no impact on the expected range of the population. Below the critical cost, by contrast, plasticity facilitates range expansion, extending the range in comparison to that expected for populations without plasticity. We further found that populations may evolve plasticity to buffer temporal environmental fluctuations, but only when the plasticity cost is below the critical cost. Thus, the cost of plasticity is a key factor involved in range expansions of populations with the potential to express plastic response in the adaptive trait.