Miracle-dz/UniFAR-FTUs huggingface dataset:
Positive Almost-Sure Termination: Complexity and Proof Rules

We study the recursion-theoretic complexity of Positive Almost-Sure Termination (PAST) in an imperative programming language with rational variables, bounded nondeterministic choice, and discrete probabilistic choice. A program terminates positive almost-surely if, for every scheduler, the program terminates almost-surely and the expected runtime to termination is finite. We show that PAST for our language is complete for the (lightface) co-analytic sets (Π11-complete). This is in contrast to the related notions of Almost-Sure Termination (AST) and Bounded Termination (BAST), both of which are arithmetical (Π20- and Σ20-complete respectively). Our upper bound implies an effective procedure to reduce reasoning about probabilistic termination to non-probabilistic fair termination in a model with bounded nondeterminism, and to simple program termination in models with unbounded nondeterminism. Our lower bound shows the opposite: for every program with unbounded nondeterministic choice, there is an effectively computable probabilistic program with bounded choice such that the original program is terminating if, and only if, the transformed program is PAST. We show that every program has an effectively computable normal form, in which each probabilistic choice either continues or terminates execution immediately, each with probability 1/2. For normal form programs, we provide a sound and complete proof rule for PAST. Our proof rule uses transfinite ordinals. We show that reasoning about PAST requires transfinite ordinals up to ω1CK; thus, existing techniques for probabilistic termination based on ranking supermartingales that map program states to reals do not suffice to reason about PAST.
Palaeogeography and crustal evolution of the Ossa–Morena Zone, southwest Iberia, and the North Gondwana margin during the Cambro-Ordovician: a review of isotopic evidence

ABSTRACT Cambro-Ordovician palaeogeography and fragmentation of the North Gondwana margin is still not very well understood. Here we address this question using isotopic data to consider the crustal evolution and palaeogeographic position of the, North Gondwana, Iberian Massif Ossa–Morena Zone (OMZ). The OMZ preserves a complex tectonomagmatic history: late Neoproterozoic Cadomian orogenesis (ca. 650–550 Ma); Cambro-Ordovician rifting (ca. 540–450 Ma); and Variscan orogenesis (ca. 390–305 Ma). We place this evolution in the context of recent North Gondwana Cambro-Ordovician palaeogeographic reconstructions that suggest more easterly positions, adjacent to the Sahara Metacraton, for other Iberian Massif zones. To do this we compiled an extensive new database of published late Proterozoic–Palaeozoic Nd model ages and detrital and magmatic zircon age data for (i) the Iberian Massif and (ii) North Gondwana Anti-Atlas West African Craton, Tuareg Shield, and Sahara Metacraton. The Nd model ages of OMZ Cambro-Ordovician crustal-derived magmatism and Ediacaran-Ordovician sedimentary rocks range from ca. 1.9 to 1.6 Ga, with a mode ca. 1.7 Ga. They show the greatest affinity with the Tuareg Shield, with limited contribution of more juvenile material from the Anti-Atlas West African Craton. This association is supported by detrital zircons that have Archaean, Palaeoproterozic, and Neoproterozoic radiometric ages similar to the aforementioned Iberian Massif zones. However, an OMZ Mesoproterozoic gap, with no ca. 1.0 Ga cluster, is different from other zones but, once more, similar to the westerly Tuareg Shield distribution. This places the OMZ in a more easterly position than previously thought but still further west than other Iberian zones. It has been proposed that in the Cambro-Ordovician the North Gondwana margin rifted as the Rheic Ocean opened diachronously from west to east. Thus, the more extensive rift-related magmatism in the westerly OMZ than in other, more easterly, Iberian Massif zones fits our new proposed palaeogeographic reconstruction.
NeoCOMM: A Neocortical Neuroinspired Computational Model for the Reconstruction and Simulation of Epileptiform Events

Understanding the pathophysiological dynamics which underline interictal epileptiform events (IEEs) such as epileptic spikes, spike-and-waves or High-frequency oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in local field potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. Accordingly, a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.
Predicting RNA Sequence-Structure Likelihood via Structure-Aware Deep Learning

Motivation The active functionalities of RNA are recognized to heavily dependent on the structure and sequence. Therefore, A model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many researchers. Machine learning methods have been explored to develop such tools, showing promising results. However, two key issues remain. Firstly, the performance of machine learning models is affected by the features used to characterize RNA. Currently, there is no consensus on which features are the most effective for characterizing RNA sequence-structure pairs. Secondly, most existing machine learning methods extract features describing entire RNA molecule. We argue that it is essential to define additional features that characterize nucleotides and specific sections of RNA structure to enhance the overall efficacy of the RNA design process. Results We develop two deep learning models for evaluating RNA sequence-structure pairs. The first model, NU-ResNet, uses a convolutional neural network architecture that solves the aforementioned problems by explicitly encoding RNA sequence-structure information into a 3D matrix. Building upon NU-ResNet, our second deep learning model, NUMO-ResNet, incorporates additional information derived from the characterizations of RNA, specifically the 2D folding motifs. In this work, we introduce an automated method to extract these motifs based on fundamental secondary structure descriptions. To assess the robustness of our models, we conduct 10-fold cross validation. Furthermore, we evaluate the performance of both models on two independent testing datasets. Our proposed models demonstrate excellent performance across both datasets and surpass the performance of the ENTRNA approach. Availability and Implementation The corresponding source code and data for this research is available at https://github.com/yzhou617/NU-ResNet_and_NUMO-ResNet. Contact Giulia.Pedrielli@asu.edu Supplementary information Supplementary data are available at Bioinformatics online.
The topography of DNA replication origins in Eukarya: GGN clusters, landmark nucleosomes, CDC6 and G4 structures

Background We recently identified tandem repeats of GGN triplets as a sequence motif strongly enriched in the DNA replication origins (ORIs) of the Arabidopsis thaliana genome, where it correlates with the ORI strength quantified through short nascent strands sequencing. Results Here we show that clusters of four or more GGN repeats separated by short tracts (<7 nucleotides) are present in more than 65% ORIs of six model eukaryotic organisms: Leishmania major, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens. The measured percentages vary with the experimental technique adopted for ORI determination. For all studied techniques and organisms, ORIs are significantly prone to be located within or near strong GGN clusters, although these are neither necessary nor sufficient for ORI activation. Interestingly, for same experimental technique, the association between ORIs and GGN clusters was stronger al later developmental stages for all organisms for which we could perform this comparison. In accordance with a biophysical model of nucleosome positioning, the GGN clusters strongly favor nucleosome occupancy. At the same time, both GGN clusters and ORIs occur frequently within 1kb from nucleosome-depleted regions (NDR). We propose a structural model based on chromatin secondary structure in which the NDR and the well-positioned nucleosome at the GGN are close in space, which may favor functional interactions. We hypothesize that the presence of GGN at ORIs arose in part for promoting the above nucleosome organization Moreover, at least in Arabidopsis, the replication protein CDC6 is very strongly associated with GGN clusters. NMR experiments showed that clusters of at least four GGN can form G-quadruplex (G4) in vitro. Our data support the view that GGN clusters are formed through the interplay of mutational processes (GC skew at ORIs plus triplet expansion), and that similar mutational processes, i.e. AT skew at transcription start sites (TSS) and ORIs, might facilitate the formation of the NDR, thus favoring the evolvability of chromatin. Conclusions GGN clusters are easily evolvable sequence motifs enriched at ORIs of eukaryotic genomes. They favor G4 secondary structure and a nucleosome organization that may unify the apparent discrepancy between ORIs of higher eukaryotes and yeast.
The Regulatory-Associated Protein of the Target of Rapamycin Complex, RAPTOR1B, interconnects with the photoperiod pathway to promote flowering in Arabidopsis

Transition from vegetative to reproductive growth (floral transition) is a strictly regulated energy-demanding process. In Arabidopsis, light perception coupled with internal circadian rhythms allows sensing changes in the duration of the light period (photoperiod) to accelerate flowering under long days (LD) in spring. This photoperiod-mediated floral induction relies on the accumulation of CONSTANS (CO) at dusk, a transcription factor that upregulates FLOWERING LOCUS T (FT) in leaves. Subsequently, FT protein moves into the shoot apical meristem to trigger the floral transition. Light and circadian clock-related signals are known to control CO at the genetic and protein levels; however, less is known about how energy sensing regulates components of the photoperiod pathway to modulate flowering. Here, we found that RAPTOR, a component of the Target Of Rapamycin complex (TORC), contributes to the induction of specific flowering genes that are under CO control. While transcription of CO remains intact in raptor mutants, its protein levels are reduced at dusk compared to wild-type (Col-0). This is due to increased protein degradation. Remarkably, GIGANTEA (GI) protein levels, which contributes to CO stabilization at dusk, are likewise hampered in the mutant. We show that RAPTOR interacts with and co-localizes at the nucleus with GI, altering GI levels through an unknown posttranscriptional mechanism. Phenotypic and molecular analysis of genetic crosses placed RAPTOR upstream of CO and GI. Since TORC is an energy sensor, our work suggests that RAPTOR could convey energy status information into the photoperiod sensing mechanism to fine-tune flowering behavior. Significance statement For annual plants, such as Arabidopsis, the correct timing of flowering in spring/summer is critical for reproductive success. Molecular mechanisms through which plants perceive and integrate day length with internal rhythms to accelerate flowering under long days are well described. However, little is known about the pathways sensing and conveying energy availability to the flowering programs. We found that RAPTOR, the regulatory unit of Target of Rapamycin complex (TORC), regulates CONSTANS post-transcriptionally through GIGANTEA. Both proteins are components of the photoperiod pathway of the flowering network and their miss-regulation in raptor mutants hampers the upregulation of genes promoting flowering. Our work suggests that the high energy available in long days is sensed and integrated into the photoperiod pathway by TORC.
TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus posing significant obstacles in multi-omics data integration. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation method and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks, then applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we design a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.
Experiential Self-Referential Processing & Experiential Self-Referential Processing & Autobiographical Memory Retrieval: A Preliminary Autobiographical Memory Retrieval: A Preliminary Look Look

Self-referential processing can be defined as the process of experiencing stimuli as they relate to one’s self (Northoff, 2006). Two distinct modes of self-referential processing, an “experiential mode” of self-referential processing, and a “narrative mode,” have been proposed, and subsequently supported by neuroimaging research (
Butanolides from the Stem of Cinnamomum Kotoense

Kotomolide (1), obtusilactone A (2), isoobtusilactone A (3), secokotomolide (4), kaempferol (5), quercetin (6), (-)-catechin (7), (-)-epicatechin (8), (-)-sesamin (9), isoeugenol (10), trans-ferulic acid (11), benzoic acid (12), p-hydroxybenzoic acid (13), p-hydroxybenzaldehyde (14), a mixture of β-sitosterol (15) and stigmasterol (16), and a mixture of β-sitosterol-D-glucoside (17) and stigmasterol-D-glucoside (18) were isolated from the stems of Cinnamomum kotoense Kanehira & Sasaki. Among them, 1 is a new compound. The structures of these compounds were characterized and identified by spectral analyses.
The Medical Journal of Australia

The Medical Journal of Australia ISSN: 0025729X 1 October 2007 187 7 S35-S39 ©The Medical Journal of Australia 2007 www.mja. com.au Getting there: proto-psychiatry large study from the United States reported that half mental disorders start by 14 years of age, and three-q years of age. In Australia, the National Survey of M and Wellbeing (NSMHWB) found a prevalence of m of 27% for ose in the 18–24-year age range. people have the greatest need for mental health inter are the least likely to seek help.
Generation and Detection of Cranial Landmark

Purpose When a surgeon examines the morphology of skull of patient, locations of craniometric landmarks of 3D computed tomography(CT) volume are one of the most important information for surgical purpose. The locations of craniometric landmarks can be found manually by surgeon from the 3D rendered volume or 2D sagittal, axial, and coronal slices which are taken by CT. Since there are many landmarks on the skull, finding these manually is time-consuming, exhaustive, and occasionally inexact. These inefficiencies raise a demand for a automatic localization technique for craniometric landmark points. So in this paper, we propose a novel method through which we can automatically find these landmark points, which are useful for surgical purpose. Materials and Methods At first, we align the experimental data (CT volumes) using Frankfurt Horizontal Plane (FHP) and Mid Sagittal Plane(MSP) which are defined by 3 and 2 cranial landmark points each. The target landmark of our experiment is the anterior nasal spine. Prior to constructing a statistical cubic model which would be used for detecting the location of the landmark from a given CT volume, reference points for the anterior nasal spine were manually chosen by a surgeon from several CT volume sets. The statistical cubic model is constructed by calculating weighted intensity means of these CT sets around the reference points. By finding the location where similarity function (squared difference function) has the minimal value with this model, the location of the landmark can be found from any given CT volume. Results In this paper, we used 5 CT volumes to construct the statistical cubic model. The 20 CT volumes including the volumes, which were used to construct the model, were used for testing. The range of age of subjects is up to 2 years (24 months) old. The found points of each data are almost close to the reference point which were manually chosen by surgeon. Also it has been seen that the similarity function always has the global minimum at the detection point. Conclusion Through the experiment, we have seen the proposed method shows the outstanding performance in searching the landmark point. This algorithm would make surgeons efficiently work with morphological informations of skull. We also expect the potential of our algorithm for searching the anatomic landmarks not only cranial landmarks.
THE APEX MODEL

The Agricultural Policy/Environmental eXtender (APEX) model was developed for use in whole farm/small watershed management. The model was constructed to evaluate various land management strategies considering sustainability, erosion (wind, sheet, and channel), economics, water supply and quality, soil quality, plant competition, weather and pests. Management capabilities include irrigation, drainage, furrow diking, buffer strips, terraces, waterways, fertilization, manure management, lagoons, reservoirs, crop rotation and selection, pesticide application, grazing, and tillage. Besides these farm management functions, APEX can be used in evaluating the effects of global climate/CO2 changes; designing environmentally safe, economic landfill sites; designing biomass production systems for energy; and other spin off applications. The model operates on a daily time step (some processes are simulated with hourly or less time steps) and is capable of simulating hundreds of years if necessary. Farms may be subdivided into fields, soil types, land scape positions, or any other desirable configuration.
Host responses to cycles of climate change shape parasite diversity across North America's Intermountain West

Abstract. Host-parasite cospeciation, in which parasite divergence occurs in response to host divergence, is commonly proposed as a driver of parasite diversification, yet few empirical examples of strict cospeciation exist. Host-parasite co-evolutionary histories commonly reflect complex mosaics of cospeciation, dispersal, lineage extinction and other phenomena. The episodic host-switching model of parasite diversification accounts for complexity by suggesting that diversification and faunal assembly is a consequence of fluctuation between environmental disruption and environmental stability. The phylogeographic predictions of the strict cospeciation and episodic host-switching models were tested using the North American pika/parasite assemblage, with a primary focus on the American pika, Ochotona princeps (Richardson, 1828), and a suite of its endoparasitic cestodes and nematodes. This approach integrating phylogeographic and demographic methods with inferences drawn from species distribution modelling revealed that the parasite community of pikas has been shaped by climate-driven range fluctuation of hosts and bouts of geographic and host colonization by parasites associated with transitions between glacial and interglacial phases.
Identiﬁability and data-informativity for single module identiﬁcation in dynamic networks (cid:63)

: Identifying single modules that are embedded in a linear dynamic network is an identiﬁcation problem that has been addressed with diﬀerent techniques. Prediction error identiﬁcation methods provide suﬃcient conditions in terms of the selection of an appropriate set of node signals to be measured, an appropriate identiﬁcation method (e.g. direct or indirect method), and a data-informativity condition, for arriving at consistent and possibly minimum variance module estimates. The question whether at all it is possible to identify a unique module estimate from data, is treated by the concept of identiﬁability, that is independent of the identiﬁcation method chosen, and independent of the particular experiment that is done, i.e. independent of the design of the signals. In this presentation we will analyse the relation between these two principal concepts, and show that the data-informativity conditions that are used for a particular identiﬁcation method are composed of two components: an identiﬁability-induced component, that is independent of the identiﬁcation method, and a data-informativity condition that is induced by the particular identiﬁcation method.