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Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality. [ABSTRACT FROM AUTHOR]

Cross-national surveys run the risk of differential survey errors, where data collected vary in quality from country to country. Responsive and adaptive survey designs (RASDs) have been proposed as a way to reduce survey errors, by leveraging auxiliary variables to inform fieldwork efforts, but have rarely been considered in the context of cross-national surveys. Using data from the European Social Survey, we simulate fieldwork in a repeated cross-national survey using RASD where fieldwork efforts are ended early for selected units in the final stage of data collection. Demographic variables, paradata (interviewer observations), and contact data are used to inform fieldwork efforts. Eight combinations of response propensity models and selection mechanisms are evaluated in terms of sample composition (as measured by the coefficient of variation of response propensities), response rates, number of contact attempts saved, and effects on estimates of target variables in the survey. We find that sample balance can be improved in many country-round combinations. Response rates can be increased marginally and targeting high propensity respondents could lead to significant cost savings associated with making fewer contact attempts. Estimates of target variables are not changed by the case prioritizations used in the simulations, indicating that they do not impact nonresponse bias. We conclude that RASDs should be considered in cross-national surveys, but that more work is needed to identify suitable covariates to inform fieldwork efforts. [ABSTRACT FROM AUTHOR]

The presentation of survey requests represents an easily modifiable feature of survey communications that can in some contexts affect response propensities. Here, we examine how two features: the framing of the participation request (informed by prospect theory) and the inclusion of targeted appeals based on demographic background (age or electoral district), affect participation rates in the 2021 Icelandic National Election Study, a mixed-mode cross-sectional survey. We find that these features generally fail to affect response propensities and sample composition. However, framed requests produced marginally higher response rates for a subset of the sample that was invited to complete the survey online. This suggests that modifying the presentation of the survey request may be less effective in cross-sectional surveys than in longitudinal ones, where more information is available to inform the content of targeted messages. [ABSTRACT FROM AUTHOR]

Nonprobability online panels are commonly used in the social sciences as a fast and inexpensive way of collecting data in contrast to more expensive probability-based panels. Given their ubiquitous use in social science research, a great deal of research is being undertaken to assess the properties of nonprobability panels relative to probability ones. Much of this research focuses on selection bias, however, there is considerably less research assessing the comparability (or equivalence) of measurements collected from respondents in nonprobability and probability panels. This article contributes to addressing this research gap by testing whether measurement equivalence holds between multiple probability and nonprobability online panels in Australia and Germany. Using equivalence testing in the Confirmatory Factor Analysis framework, we assessed measurement equivalence in six multi-item scales (three in each country). We found significant measurement differences between probability and nonprobability panels and within them, even after weighting by demographic variables. These results suggest that combining or comparing multi-item scale data from different sources should be done with caution. We conclude with a discussion of the possible causes of these findings, their implications for survey research, and some guidance for data users. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored the use of mobile phone sensors to accurately classify the gaits of five-gaited horses. The data were collected from horses and riders using a mobile phone in the rider's pocket and an existing multi-sensor gait classification system. A machine learning model was then trained to classify the gaits using input from the phone's accelerometer and gyroscope, achieving an accuracy of 94.4%. This research demonstrates that mobile phones can be used to gather data on horse gaits, reducing the cost of large-scale studies. This efficient method for acquiring labelled data will be invaluable for ongoing research into horse riding activities. Automated gait classification has traditionally been studied using horse-mounted sensors. However, smartphone-based sensors are more accessible, but the performance of gait classification models using data from such sensors has not been widely known or accessible. In this study, we performed horse gait classification using deep learning models and data from mobile phone sensors located in the rider's pocket. We gathered data from 17 horses and 14 riders. The data were gathered simultaneously from movement sensors in a mobile phone located in the rider's pocket and a gait classification system based on four wearable sensors attached to the horse's limbs. With this efficient approach to acquire labelled data, we trained a Bi-LSTM model for gait classification. The only input to the model was a 50 Hz signal from the phone's accelerometer and gyroscope that was rotated to the horse's frame of reference. We demonstrate that sensor data from mobile phones can be used to classify the five gaits of the Icelandic horse with up to 94.4% accuracy. The result suggests that horse riding activities can be studied at a large scale using mobile phones to gather data on gaits. While our study showed that mobile phone sensors could be effective for gait classification, there are still some limitations that need to be addressed in future research. For example, further studies could explore the effects of different riding styles or equipment on gait classification accuracy or investigate ways to minimize the influence of factors such as phone placement. By addressing these questions, we can continue to improve our understanding of horse gait and its role in horse riding activities. [ABSTRACT FROM AUTHOR]

We study a variant of the classical bootstrap percolation process on Erdős Rényi random graphs. The graphs we consider have inhibitory vertices obstructing the diffusion of activity and excitatory vertices facilitating it. We study both a synchronous and an asynchronous version of the process. Both begin with a small initial set of active vertices, and the activation spreads to all vertices for which the number of excitatory active neighbors exceeds the number of inhibitory active neighbors by a certain amount. We show that in the synchronous process, inhibitory vertices may cause unstable behavior: tiny changes in the size of the starting set can dramatically influence the size of the final active set. We further show that in the asynchronous model the process becomes stable and stops with an active set containing a nontrivial deterministic constant fraction of all vertices. Moreover, we show that percolation occurs significantly faster asynchronously than synchronously. [ABSTRACT FROM AUTHOR]

We study unbiased (1 + 1) evolutionary algorithms on linear functions with an unknown number n of bits with non-zero weight. Static algorithms achieve an optimal runtime of O (n (ln ⁡ n) 2 + ε) , however, it remained unclear whether more dynamic parameter policies could yield better runtime guarantees. We consider two setups: one where the mutation rate follows a fixed schedule, and one where it may be adapted depending on the history of the run. For the first setup, we give a schedule that achieves a runtime of (1 ± o (1)) β n ln ⁡ n , where β ≈ 3.552 , which is an asymptotic improvement over the runtime of the static setup. Moreover, we show that no schedule admits a better runtime guarantee and that the optimal schedule is essentially unique. For the second setup, we show that the runtime can be further improved to (1 ± o (1)) e n ln ⁡ n , which matches the performance of algorithms that know n in advance. Finally, we study the related model of initial segment uncertainty with static position-dependent mutation rates, and derive asymptotically optimal lower bounds. This answers a question by Doerr, Doerr, and Kötzing. [ABSTRACT FROM AUTHOR]

The sharp wave ripple complex in rodent hippocampus is associated with a network burst in CA3 (NB) that triggers a synchronous event in the CA1 population (SE). The number of CA1 pyramidal cells participating in a SE has been observed to follow a lognormal distribution. However, the origin of this skewed and heavy‐tailed distribution of population synchrony in CA1 remains unknown. Because the size of SEs is likely to originate from the size of the NBs and the underlying neural circuitry, we model the CA3‐CA1 circuit to study the underlying mechanisms and their functional implications. We show analytically that if the size of a NB in CA3 is distributed according to a normal distribution, then the size of the resulting SE in CA1 follows a lognormal distribution. Our model predicts the distribution of the NB size in CA3, which remains to be tested experimentally. Moreover, we show that a putative lognormal NB size distribution leads to an extremely heavy‐tailed SE size distribution in CA1, contradicting experimental evidence. In conclusion, our model provides general insight on the origin of lognormally distributed network synchrony as a consequence of synchronous synaptic transmission of normally distributed input events. [ABSTRACT FROM AUTHOR]

Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity. [ABSTRACT FROM AUTHOR]

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. [ABSTRACT FROM AUTHOR]

Aims Methods and results Conclusion Heart failure (HF) with preserved ejection fraction (HFpEF) is characterized by growing incidence and poor outcomes. A large majority of HFpEF patients are cared by non‐cardiologists. The availability of sodium–glucose cotransporter 2 inhibitors (SGLT2i) as recommended therapy raises the importance of prompt and accurate identification and treatment of HFpEF across diverse healthcare settings. We evaluated HFpEF management across specialties through a survey targeting cardiologists, HF specialists, and non‐cardiologists.An independent web‐based survey was distributed globally between May and July 2023. We performed a post‐hoc analysis, comparing cardiologists, HF specialists, and non‐cardiologists. A total of 1460 physicians (61% male, median age 41[34–49]) from 95 countries completed the survey; 20% were HF specialists, 65% cardiologists, and 15% non‐cardiologists. Compared with HF specialists, non‐cardiologists and cardiologists were less likely to use natriuretic peptides (p = 0.003) and HFpEF scores (p = 0.004) for diagnosis, and were also less likely to have access to or consider specific echocardiographic parameters (p < 0.001) for identifying HFpEF. Diastolic stress tests were used in less than 30% of the cases, regardless of the specialty (p = 1.12). Multidrug treatment strategies were similar across different specialties. While SGLT2i and diuretics were the preferred drugs, angiotensin receptor blockers and angiotensin receptor–neprilysin inhibitors were the least frequently prescribed in all three groups. However, when constrained to choose one drug, the proportion of physicians favoring SGLT2i varied significantly among specialties (66% HF specialists, 52% cardiologists, 51% non‐cardiologists). Additionally, 10% of non‐cardiologists and 8% of cardiologists considered beta blocker the drug of choice for HFpEF.Significant differences among specialty groups were observed in HFpEF management, particularly in the diagnostic work‐up. Our results highlight a substantial risk of underdiagnosis and undertreatment of HFpEF patients, especially among non‐HF specialists. [ABSTRACT FROM AUTHOR]

Aims: This study aims to evaluate the worldwide variations in the diagnosis and treatment of heart failure with preserved ejection fraction (HFpEF), using an HF survey distributed internationally to physicians, including both cardiologists and non-cardiologists., Methods and Results: A group of HF specialists designed an independent, academic web-based survey focusing on HFpEF care and diagnosis, which was distributed via scientific societies and various social networks between 1 May 2023 and 1 July 2023. The survey included 1459 physicians (1242 cardiologists and 217 non-cardiologists) from 91 countries, with a mean age of 42 (34-49) years and 61% male. Most physicians (89.2%) defined HFpEF as left ventricular ejection fraction ≥50%. Significant regional variations were observed in HFpEF management (P < 0.001 for all comparisons unless stated otherwise). Cardiologists managed 63.1% of HFpEF patients overall, with significant variability across regions (P < 0.001). The estimated HFpEF prevalence was highest in Eastern Asia and Western Europe and lowest in Africa and South America. Diagnostic practices varied: natriuretic peptide use ranged from 70%-74% in Africa to 95%-97% in Southern/Western Europe. Echocardiographic parameters showed regional differences, with diastolic stress testing used most in South-Eastern Asia (47% vs. 13-36% elsewhere). HFpEF scoring systems were most common in South-Eastern Asia (78%) and least in Africa (30.1%). Coronary artery disease screening approaches differed, with Eastern Asian physicians more likely to always perform routine angiograms (52%) compared with Northern Europeans (12%). Treatment preferences also varied regionally. Sodium glucose co-transporter-2 inhibitors (SGLT2i) was the preferred first-line treatment (45%-70% across regions), followed by diuretics. In an ideal setting, 52% would primarily use SGLT2i, 33% loop diuretics, and 22% beta-blockers. Drug availability differed significantly: SGLT2i was most available (88% overall), while ARNI was least available (61%). South America and Middle Eastern/Northern Africa reported lower availability of guideline-directed therapies. Multidisciplinary HF programmes were most common in Asia (70%) and least in Africa (24%). The perceived benefit of atrial flow regulator devices also showed significant regional differences., Conclusions: There are considerable global variations in the diagnosis and management of HFpEF. Most physicians favour SGLT2i despite regional disparities in health care resources and guideline adherence. Harmonized practices and improved access to comprehensive care can enhance outcomes of HFpEF patients worldwide., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Introduction: The incidence of heart failure (HF) is increasing, largely because populations are both ageing and growing. Most clinical HF treatment trials are conducted on selected cohorts, only a few of which include elderly patients, among whom HF is common. HF registries can include all HF patients, independent of age or comorbidity profile, and thus reflect reality in healthcare management., Methods: The Icelandic Heart Failure Registry (IHFR) was created in the autumn of 2019 and has operated since 1 January 2020. Based on the Swedish Heart Failure Registry (SwedeHF), it quickly acquired several extensions. All patients admitted for HF to the Department of Cardiology (DC) at Landspítali - The National University Hospital of Iceland are included. Several variables are collected, including the aetiology of HF, comorbidities, clinical assessment at admission, blood tests, imaging results, treatment given and medical therapy at discharge., Results: During the 3 years from 2020 to 2022, the DC admitted 1890 patients. As some were readmitted during the study period, the true total was 2384 admissions. Because the IHFR 2023 edition includes 327 variables, automation of many of them is imperative for data collection., Conclusion: HF is a heterogenous disease with numerous underlying factors. HF management differs among HF phenotypes. A registry can serve as an unbiased indicator of care quality and has the potential to be studied in the future to assess the long-term effects of HF treatment. A registry like the IHFR, therefore, can impact the treatment of all patients recorded in it, reduce the rate of readmissions and even optimize HF management costs., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Importance: Recurrent pericarditis is a treatment challenge and often a debilitating condition. Drugs inhibiting interleukin 1 cytokines are a promising new treatment option, but their use is based on scarce biological evidence and clinical trials of modest sizes, and the contributions of innate and adaptive immune processes to the pathophysiology are incompletely understood., Objective: To use human genomics, transcriptomics, and proteomics to shed light on the pathogenesis of pericarditis., Design, Setting, and Participants: This was a meta-analysis of genome-wide association studies of pericarditis from 5 countries. Associations were examined between the pericarditis-associated variants and pericarditis subtypes (including recurrent pericarditis) and secondary phenotypes. To explore mechanisms, associations with messenger RNA expression (cis-eQTL), plasma protein levels (pQTL), and CpG methylation of DNA (ASM-QTL) were assessed. Data from Iceland (deCODE genetics, 1983-2020), Denmark (Copenhagen Hospital Biobank/Danish Blood Donor Study, 1977-2022), the UK (UK Biobank, 1953-2021), the US (Intermountain, 1996-2022), and Finland (FinnGen, 1970-2022) were included. Data were analyzed from September 2022 to August 2023., Exposure: Genotype., Main Outcomes and Measures: Pericarditis., Results: In this genome-wide association study of 4894 individuals with pericarditis (mean [SD] age at diagnosis, 51.4 [17.9] years, 2734 [67.6%] male, excluding the FinnGen cohort), associations were identified with 2 independent common intergenic variants at the interleukin 1 locus on chromosome 2q14. The lead variant was rs12992780 (T) (effect allele frequency [EAF], 31%-40%; odds ratio [OR], 0.83; 95% CI, 0.79-0.87; P = 6.67 × 10-16), downstream of IL1B and the secondary variant rs7575402 (A or T) (EAF, 45%-55%; adjusted OR, 0.89; 95% CI, 0.85-0.93; adjusted P = 9.6 × 10-8). The lead variant rs12992780 had a smaller odds ratio for recurrent pericarditis (0.76) than the acute form (0.86) (P for heterogeneity = .03) and rs7575402 was associated with CpG methylation overlapping binding sites of 4 transcription factors known to regulate interleukin 1 production: PU.1 (encoded by SPI1), STAT1, STAT3, and CCAAT/enhancer-binding protein β (encoded by CEBPB)., Conclusions and Relevance: This study found an association between pericarditis and 2 independent sequence variants at the interleukin 1 gene locus. This finding has the potential to contribute to development of more targeted and personalized therapy of pericarditis with interleukin 1-blocking drugs.

Automated gait classification has traditionally been studied using horse-mounted sensors. However, smartphone-based sensors are more accessible, but the performance of gait classification models using data from such sensors has not been widely known or accessible. In this study, we performed horse gait classification using deep learning models and data from mobile phone sensors located in the rider's pocket. We gathered data from 17 horses and 14 riders. The data were gathered simultaneously from movement sensors in a mobile phone located in the rider's pocket and a gait classification system based on four wearable sensors attached to the horse's limbs. With this efficient approach to acquire labelled data, we trained a Bi-LSTM model for gait classification. The only input to the model was a 50 Hz signal from the phone's accelerometer and gyroscope that was rotated to the horse's frame of reference. We demonstrate that sensor data from mobile phones can be used to classify the five gaits of the Icelandic horse with up to 94.4% accuracy. The result suggests that horse riding activities can be studied at a large scale using mobile phones to gather data on gaits. While our study showed that mobile phone sensors could be effective for gait classification, there are still some limitations that need to be addressed in future research. For example, further studies could explore the effects of different riding styles or equipment on gait classification accuracy or investigate ways to minimize the influence of factors such as phone placement. By addressing these questions, we can continue to improve our understanding of horse gait and its role in horse riding activities.

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.