Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic instability analysis in DNA from Papanicolaou test provides proof-of-principle early diagnosis of high-grade serous ovarian cancer

Late diagnosis and the lack of screening methods for early detection define high-grade serous ovarian cancer (HGSOC) as the gynecological malignancy with the highest mortality rate. In the work presented here, we investigated a retrospective and multicentric cohort of 250 archival Papanicolaou (Pap) test smears collected during routine gynecological screening. Samples were taken at different time points (from 1 month to 13.5 years before diagnosis) from 113 presymptomatic women who were subsequently diagnosed with HGSOC (pre-HGSOC) and from 77 healthy women. Genome instability was detected through low-pass whole-genome sequencing of DNA derived from Pap test samples in terms of copy number profile abnormality (CPA). CPA values of DNA extracted from Pap test samples from pre-HGSOC women were substantially higher than those in samples from healthy women. Consistently with the longitudinal analysis of clonal pathogenic TP53 mutations, this assay could detect HGSOC presence up to 9 years before diagnosis. This finding confirms the continual shedding of tumor cells from fimbriae toward the endocervical canal, suggesting a new path for the early diagnosis of HGSOC. We integrated the CPA score into the EVA (early ovarian cancer) test, the sensitivity of which was 75% (95% CI, 64.97 to 85.79), the specificity 96% (95% CI, 88.35 to 100.00), and the accuracy 81%. This proof-of-principle study indicates that the early diagnosis of HGSOC is feasible through the analysis of genomic alterations in DNA from endocervical smears. Quantification of overall genomic instability in Pap smears is a promising early detection test for high-grade serous ovarian cancer. Editor’s summary Ovarian cancer is often diagnosed at a late stage, which leads to increased patient mortality in part because of lack of early detection tests. Here, Paracchini et al. evaluated the use of archival Papanicolaou test smears as a source of tissue for early detection of ovarian cancer. The authors obtained DNA from these samples from which they performed shallow whole-genome sequencing and identified high copy number profile abnormality (CPA) correlated to ovarian cancer development. They created an early ovarian cancer test (EVA), providing a proof of principle for using Pap smears and CPA for early diagnosis of ovarian cancer. —Dorothy Hallberg
Subjective Evaluation on the Sense of "Being There" using Augmented reality Room Acoustic Estimator (ARAE) Platform

This paper presents a subjective evaluation of spatial acoustic rendering in audio augmented reality (AAR) using the Augmented reality Room Acoustic Estimator (ARAE) platform. AAR aims to enhance user experiences by overlaying spatially and contextually appropriate sounds in real-world environments, creating the illusion that virtual sounds exist within the physical space. However, the current limitations in hardware and algorithms for AAR rendering present challenges in achieving a truly immersive "being there" sensation. The research paper performs a comparative analysis of two commonly used audio delivery algorithms in AAR applications: dummy head recording and stereo panning. These algorithms are evaluated against LiDAR-based spatial acoustics facilitated by ARAE. The main objective is to understand the different impressions induced by each audio algorithm within the acoustic experience of the AAR application and to identify the essential elements that contribute to the sense of "being there. " The results highlight several challenges in ARAE’s acoustic processing, such as inaccuracies in early reflection calculations and localization processing of virtual sounds. To foster a genuine sense of "being there" in the AAR framework, it is crucial to tackle and overcome these challenges through technical advancements and comprehensive psychoacoustic experiments.
A Machine Learning-Based Anomaly Packets Detection for Smart Home

The advent of smart homes has revolutionized residential living, integrating advanced technologies and intelligent devices to create secure, comfortable, and efficient environments. However, this integration of diverse smart devices has brought significant cybersecurity challenges. Detecting and analyzing abnormal network packets have become paramount, signifying potential intrusions, malicious activities, or system errors and ensuring the security and stability of smart home systems. Machine learning techniques, such as Decision Trees, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Random Forests, have shown promise in addressing these challenges. However, most research has concentrated on anomaly detection rather than malicious activity in smart homes. The vast datasets collected from various scenarios pose methodological and algorithmic challenges for applying machine learning techniques. To fill these research gaps, our study introduces traditional machine-learning methods for detecting abnormal network packets in smart homes using the IoT-23 dataset. It involves preprocessing the dataset, extracting relevant features, and training various machine learning models. The correlation matrix helps validate the feature selection of the best models based on performance metrics like precision, F1-score, recall, accuracy ratio, training score, and training time cost. Additionally, the study classifies 12 types of malicious malware across different machine learning models, considering performance within the context of smart home devices. This study implements real-time anomaly detection on the Raspberry Pi using packet captures and Zeek flowmeter methods. The findings contribute insights into models suitable for smart home security. In addition, our research enhances the understanding and application of machine learning methods for bolstering security in smart homes.
Simulation of regional soil moisture deficits on a European scale

A simple deterministic simulation model for landscape-scale soil water deficits has been developed. Standard weather station data are used to estimate ‘daily’ precipitation and actual (regional) evapotranspiration (AET). The balance is added to soil moisture, which is accounted by a simple bucket model. AET is the daily integral of the lesser of supply and demand: supply is governed by current soil moisture, demand by estimated net radiation and temperature. The model estimates geographical patterns of soil water deficit at a scale suitable for comparison with the distributions of plant species and vegetation types. The model provides a way of quantifying soil moisture stress in biogeographical research.
Eff ect of temperature on the volume of gas emissions

Summary. In the full mould process, the polystyrene pattern which (cid:222) lls the mould cavity is subjected to pouring of this mould with liquid metal to the effect of high temperature (1600 0 C) and passes from the solid state into liquid and gas. During this process some solid and gaseous products of the thermal destruction of the pattern are created. The kinetics of this process depends on the temperature of evaporation and, though to a smaller extent, on the pattern density, which, com-bined with the mould parameters, parameters of a ceramic coating, i.e. its thickness, permeability, and resistance to the effect of high temperatures, parameters of a granular material (cid:222) lling the mould and parameters of a technological process, including the technique of pouring and design. The mould cavity is (cid:222) lled with a pattern made of foamed polystyrene which on pouring of mould with liquid metal undergoes total destruction.
Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Abstract. Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m−3 in the North China Plain (NCP) and 23 µg m−3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.
UvA-DARE ( Digital Academic Repository ) Prior-informed distant supervision for temporal evidence classification

Temporal evidence classification, i.e., finding associations between temporal expressions and relations expressed in text, is an important part of temporal relation extraction. To capture the variations found in this setting, we employ a distant supervision approach, modeling the task as multi-class text classification. There are two main challenges with distant supervision: (1) noise generated by incorrect heuristic labeling, and (2) distribution mismatch between the target and distant supervision examples. We are particularly interested in addressing the second problem and propose a sampling approach to handle the distribution mismatch. Our prior-informed distant supervision approach improves over basic distant supervision and outperforms a purely supervised approach when evaluated on TAC-KBP data, both on classification and end-to-end metrics.
Managing growth and environmental impacts in recreational scuba diving

Recreational scuba diving is a rapidly growing sport both worldwide and in Australia. Increasing use of marine protected areas for pursuits such as recreational scuba diving may, however, lead to biological damage and reduced amenity values in popular locations. The relationships between biological and amenity values are reviewed, as are ideas about carrying capacities and biological thresholds at dive sites. The carrying capacity concept and the critical thresholds approach are found to be constrained by a number of limitations on their effectiveness as resource management tools. Possible approaches to managing heavily used dive sites, and to reallocating diver activity away from those sites to lesser used areas, are examined. It is concluded that a judicious blend of regulation and the use of economic instruments will be required to overcome open access and boundary problems, and will provide for better overall management of popular marine recreational sites than is presently the case. Education will also have a significant role to play by increasing environmental awareness and reducing the damaging impacts of the users of those popular sites.
Human Neuroscience

flexibility on the other. The idea is that higher prefrontal DA levels promote cognitive stability by supporting working memory and increasing the resistance against distractors, whereas higher striatal DA levels promote cognitive flexibility by allowing the updating of novel relevant representations or the processing of goal-unrelated but nevertheless important information. Even though both stability and flexibility are important for proper cognitive functioning, shifting to one or the other extreme can be very counterproductive: too much stability would result in rigidity and too much flexibility in distractibility, either of which poses a major control dilemma (Goschke, 2000). Keeping stability and flexibility in proper balance, according to Cools and D'Esposito (2009), relies on continuous reciprocal adjustments of the prefrontal and striatal DA levels that are assumed to underlie these control functions. In terms of this assumed reciprocal relationship, 9-repeat carriers of the DAT1 gene with their higher striatal DA levels would be expected to be more biased towards the flexibility pole of the stability–flexibility dimension than 10-repeat carriers would be. In the present study, we tested this hypothesis by applying it to the inhibition of return (IOR) effect (Posner and Cohen, 1984), a particularly reliable phenomenon in human attention that has been hypothesized to promote flexibility. It is observed when people attend sequential displays or scan complex visual scenes (Klein, 1988), or in other circumstances where they move their attentional focus from one object to another until an interesting or searched-for object has been found. As Posner and Cohen (1984) have shown, cuing the location of a visual target by means of a randomly varying, peripheral stimulus (e.g., a flash at the location where the target will occur) can improve performance at very short stimulus-onset asynchronies (SOAs) but impairs performance at longer SOAs. Once a given location has been inspected and
A Bayesian foundation for individual learning under uncertainty

learning because the requisite updating of conditional probabilities is described by Bayes’ theorem. Since a Bayesian learner processes information optimally, it should have an evolutionary advantage over other types of agents, and one might therefore expect the human brain to have evolved such that it implements an ideal Bayesian learner (Geisler and Diehl, 2002). Indeed, there is substantial evidence from studies on various domains of learning and perception that human behavior is better described by Bayesian models than by other theories (e.g., Kording and Wolpert, 2004; Bresciani et al., 2006; Yuille and Kersten, 2006; Behrens et al., 2007; Xu and Tenenbaum, 2007; Orbán et al., 2008; den Ouden et al., 2010). However, there remain at least three serious difficulties with the hypothesis that humans act as ideal Bayesian learners. The first problem is that in all but the simplest cases, the application of Bayes’ theorem involves complicated integrals that require burdensome and time-consuming numerical calculations. This makes online learning a challenging task for Bayesian models, and any evolutionary advantage conferred by optimal learning might be outweighed by these computational costs. A second and associated problem is how ideal Bayesian learning, with its requirement to evaluate high-dimensional integrals, would be implemented IntroductIon Learning can be understood as the process of updating an agent’s beliefs about the world by integrating new and old information. This enables the agent to exploit past experience and improve predictions about the future; e.g., the consequences of chosen actions. Understanding how biological agents, such as humans or animals, learn requires a specification of both the computational principles and their neurophysiological implementation in the brain. This can be approached in a bottom-up fashion, building a neuronal circuit from neurons and synapses and studying what forms of learning are supported by the ensuing neuronal architecture. Alternatively, one can choose a top-down approach, using generic computational principles to construct generative models of learning and use these to infer on underlying mechanisms (e.g., Daunizeau et al., 2010a). The latter approach is the one that we pursue in this paper. The laws of inductive inference, prescribing an optimal way to learn from new information, have long been known (Laplace, 1774, 1812). They have a unique mathematical form, i.e., it has been proven that there is no alternative formulation of inductive reasoning that does not violate either consistency or common sense (Cox, 1946). Inductive reasoning is also known as Bayesian A Bayesian foundation for individual learning under uncertainty
A fast neural signature of motivated attention to consumer goods separates the sexes

suggested that emotional stimuli receive enhanced processing in occipito–temporo–parietal regions, particularly pronounced for right-hemispheric regions (Junghöfer et al., 2001; Schupp et al., 2003). The measurement of event-related field potentials (derived from MEG) provides a complementary look at the spread of activation in the visual processing stream. The magnetic counterpart of the EPN consists of two distinct peaks of activation. Strongest effect sizes were observed for the first EPNm peak between 110 and 170 ms indicating the amplified processing of emotional pictures in occipito–parieto–temporal visual processing areas (Peyk et al., 2009). In combination, these findings have been considered from the perspective of “natural selective attention”, proposing that stimulus perception and evaluation are in part directed by underlying motivational systems of avoidance and approach (Lang et al., 1998). It is an enduring challenge to determine the sensitivity of the EPN component to distinct classes of emotional stimuli. There is accumulating evidence that the EPN component is not only modulated by comparatively high-arousing IAPS pictures but also by less intense emotional stimuli. Facial expression provides important information in social interaction and previous research revealed a larger EPN component to threatening and fearful as compared to neutral facial expressions (Schupp et al., 2004; Leppänen et al., IntroductIon Emotional cues guide selective visual attention and receive enhanced processing (Lang et al., 1997; Öhman et al., 2000; Tucker et al., 2000; Vuilleumier, 2005). Functional magnetic resonance imaging (fMRI) revealed increased blood oxygen level dependent (BOLD) signals in associative visual regions (extrastriate, occipito-parietal, and inferior temporal cortex) and subcortical limbic structures when viewing emotionally arousing compared to neutral pictures (e.g., Bradley et al., 2003; Junghöfer et al., 2005; Sabatinelli et al., 2005). Event-related brain potential studies are particularly informative with regard to the temporal dynamics of emotion processing in the visual brain. Using naturalistic scenes from the international affective picture system (IAPS, Lang et al., 2005), a difference in processing emotional (pleasant and unpleasant) compared to neutral pictures is reflected by an enhanced negativity over temporooccipital sites (Junghöfer et al., 2001; Keil et al., 2002; Schupp et al., 2003; Wieser et al., 2006). This early posterior negativity (EPN) develops around 120 ms after stimulus onset and lasts until about 300 ms. EPN modulation is most accentuated for pictures depicting evolutionary relevant stimulus contents such as reproduction and defense (Schupp et al., 2003), which also provoke reliable somatic, autonomic, and humoral responses (Bradley et al., 2001). Estimates of the generating sources for these differential ERP activities A fast neural signature of motivated attention to consumer goods separates the sexes
Human Neuroscience

2006), the ability to narrow the focus of visual attention (Pastò and Burack, 1997), and the ability to use deliberate task strategies (Bjorklund and Douglas, 1997), to name a few, have been shown to improve across childhood and even into adolescence in some cases (see also Flavell, 1985). Consequently, whether age-related improvement on face processing tasks is specifi c to our ability to perceive faces per se (Carey and Diamond, 1977; Carey, 1992), or rather a product of age-related improvements in general sensory or cognitive functions, or general visual pattern recognition (Want et al., 2003; Crookes and McKone, 2009) is still highly debated. Event-related potentials (ERPs) recorded from the scalp are particularly promising for clarifying this issue, and for understanding the developmental course of human face processing in particular for at least two reasons. First, a behavioral response refl ects the output of a large number of sensory, perceptual, cognitive and motor processes, so that variations in response time (RT) and accuracy are diffi cult to attribute to variations in a specifi c process (e.g., high-level perceptual processes) (Luck, 2005). In contrast, the excellent temporal resolution of ERPs provides a continuous measure of processing between a stimulus and a response, making it possible to more precisely determine which stage(s) of processing
Human Neuroscience

rons in gamma generation is more ambiguous. While the presence of sufficient glutamatergic activity to recruit IN activity is essential in most models and in vitro preparations, the decay rate of synaptic excitation is typically thought to be less important. In contrast, the origin of low-frequency cortical activity (e.g., 8 Hz – " alpha " band activity) has been tied to intrinsic biophysical properties of corti-cal pyramidal neurons (Silva et al. Optogenetic tools provide a novel avenue for investigating the cellular mechanisms controlling neocortical rhythmicity in vivo, by permitting selective drive of distinct neural populations. Using the light-sensitive cation channel Channelrhodopsin-2 (ChR2; Deisseroth et al., 2006) to drive either FS or RS in neo-cortex, we recently found that selective FS stimulation from 8 to 200 Hz led to increased local field potential (LFP) power above baseline at the driving frequency, specifically in the gamma band (most pronounced at 32–64 Hz) (Cardin et al., 2009). In contrast, selective RS activation showed a peak in enhanced power at 8 Hz, with significantly weaker higher frequency recruitment (Figure 1A).
Human Neuroscience

The observation that children from different cultures may show differences in EF abilities raises the question of whether children's brain function varies in systematic ways across cultures, and if so, to what extent. There is currently considerable interest in the specifi c neural correlates of EF (e.g., Bunge and Crone, 2009), but as with behavioral research on this topic, nearly all of this work has been conducted with Western samples, and indeed, to date, there have been no published cross-cultural comparisons of these neural correlates. It seems plausible, however, that genetic and/or environmental infl uences associated with culture may be refl ected not only in children's behavior, but also in their neural function. Recent research on neural plasticity, for example, supports the suggestion that neural development is experience dependent (e.g., Neville, 1993; Huttenlocher, 2002), and indeed, interventions designed to promote EF have been found to produce changes in neural function related to EF (Olesen et al., 2004; Rueda et al., 2005). One task that has been used to examine the neural correlates of EF in young children is the go/no-go task, in which children must respond on the majority of trials (go stimuli) but inhibit responding on certain trials (no-go stimuli) (e.g., Davis et al., 2003). Event-related fMRI research comparing adults and school-age children on go/no-go-type tasks indicates that several regions of prefrontal cortex, including ventrolateral prefrontal cortex and