Reporting on COVID-19 – or not? Annual report disclosure of the pandemic as a subsequent event

PurposeThis paper focuses on the extent to which Swedish municipalities identified and communicated risks due to the COVID-19 outbreak early on. The purpose of this paper is to explore to what extent the situational factors of the COVID-19 pandemic influenced the likelihood of municipalities disclosing COVID-19 information as a subsequent event in the annual reports of 2019.Design/methodology/approachLogistic regression models were used to estimate COVID-19 disclosure as a subsequent event. Data were handpicked from annual reports, audit reports and meeting minutes, or were retrieved from publicly available sources.FindingsRegression results indicate that municipalities issuing their annual report in a later stage of the pandemic, in regions with a higher number of confirmed COVID-19 cases, were more likely to disclose COVID-19 information as a subsequent event. However, the municipal factors used to capture the risk of a severe impact of the COVID-19 outbreak were not of major importance. In line with previous research, this study shows that political and institutional factors have explanatory power in predicting and explaining accounting disclosure choices.Originality/valueThis paper contributes to research on accounting disclosures in urgent crises and on the specific topic of subsequent events in the public sector. Few studies address subsequent events in a corporate setting and, to the best of the authors’ knowledge, none do so in the context of the public sector. This paper also offers insight into how explanatory factors, previously tested under normal conditions and circumstances, influence disclosure choices in an early stage of a health crisis characterized by uncertainty regarding both occurrence and consequences.

Predictive Uncertainty Underlies Auditory Boundary Perception

Anticipating the future is essential for efficient perception and action planning. Yet the role of anticipation in event segmentation is understudied because empirical research has focused on retrospective cues such as surprise. We address this concern in the context of perception of musical-phrase boundaries. A computational model of cognitive sequence processing was used to control the information-dynamic properties of tone sequences. In an implicit, self-paced listening task ( N = 38), undergraduates dwelled longer on tones generating high entropy (i.e., high uncertainty) than on those generating low entropy (i.e., low uncertainty). Similarly, sequences that ended on tones generating high entropy were rated as sounding more complete ( N = 31 undergraduates). These entropy effects were independent of both the surprise (i.e., information content) and phrase position of target tones in the original musical stimuli. Our results indicate that events generating high entropy prospectively contribute to segmentation processes in auditory sequence perception, independently of the properties of the subsequent event.

Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

During the operation of modern technical systems, the use of the LSTM model for the prediction of process variable values and system states is commonly widespread. The goal of this paper is to expand the application of the LSTM-based models upon obtaining information based on prediction. In this method, by predicting transition probabilities, the output layer is interpreted as a probability model by creating a prediction tree of sequences instead of just a single sequence. By further analyzing the prediction tree, we can take risk considerations into account, extract more complex prediction, and analyze what event trees are yielded from different input sequences, that is, with a given state or input sequence, the upcoming events and the probability of their occurrence are considered. In the case of online application, by utilizing a series of input events and the probability trees, it is possible to predetermine subsequent event sequences. The applicability and performance of the approach are demonstrated via a dataset in which the occurrence of events is predetermined, and further datasets are generated with a higher-order decision tree-based model. The case studies simply and effectively validate the performance of the created tool as the structure of the generated tree, and the determined probabilities reflect the original dataset.

“New York City Closed to all Traffic”

This chapter examines the transportation shutdowns that quickly ricocheted out beyond the New York area following the attacks of 9/11. Each subsequent event amplified the crisis unfolding at the World Trade Center, intensifying the fear and panic and increasing the numbers of people directly caught up in the catastrophe. With the avalanche of toxic dust and debris came terror. Bridges and tunnels were closed, streets were clogged with stalled traffic, and no trains were moving. Suddenly, hundreds of thousands of visitors, residents, and commuters found themselves trapped in Lower Manhattan, struggling to grasp what was happening and trying to answer one question: How could they get off the island? Passengers then arrived in waves at the World Financial Center ferry terminal.

Survival Analysis of Cancer Patients at Teaching Hospitals in Ethiopia

Survival analysis today is widely implemented in the fields of medical and biological sciences. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. This study aims to analysis the survival status of cancer patients, to identify the main cancer in women at study area and to determine the survival time of women with cancer after undergoing certain treatments in the selected teaching hospital(s). A random sample of 460 cancer patients was selected using purposive sampling from the study area, in 2016 to 2018. Parametric and non-parametric models, Logistic regression models, Statistical distributions and tests methods of data analysis were used in this study. The demographic information: age, educational level, treatments, economic level, marital status and stages of cancer were included and Comparisons were made among major cancers in patients. The findings of the study suggest that among major types of cancer in patients, breast cancer was the highly affecting the females in the study area. There are 62.15 % survival and 37.35 % of not survived patients in the data and the majority of patients (about 88 %) are diagnosed when they are in stages 1 and 2 and very few (about 8 %) of them are diagnosed in advanced stage of cancer. It also reveals that the average tumor sizes are significantly different for all ages and stages. By grouping ages into groups of 5, I also stratified the number of patients diagnosed with cancer in different stages and the diagnoses at different stages were different. Finally, Results of survival time shows that the surviving of cancer patient were different at different ages and treatments level.

Learning to use episodic memory for event prediction

When should episodic memories be stored and retrieved to support event understanding? Traditional list-learning memory experiments make it obvious when to store and retrieve memories, but it is less obvious when to do this in naturalistic settings. To address this question, we trained a memory-augmented neural network to predict upcoming events, in an environment where situations (sets of parameters governing transitions between events) sometimes reoccurred. The model was allowed to learn a policy for when to consult episodic memory, and we explored how this learned policy varied as a function of the task environment. We found that the learned retrieval policy is shaped by internal uncertainty about upcoming events, the level of penalty associated with incorrect predictions, the confusability of stored memories, the presence of a “familiarity signal” indicating the availability of relevant memories, and the presence of statistical regularities (prototypical events). With regard to encoding policy, we found that selectively storing episodic memories at the end of an event (but not mid-event) leads to better subsequent event prediction performance and less incorrect recall. Additionally, we found that the model can integrate information over long timescales even without the hippocampus; it can link information over many event segments via episodic memory; and it shows classic schema-consistent memory effects when the upcoming time point has a prototypical event. Overall, these modeling results provide a normative explanation of several existing behavioral and neuroimaging findings regarding the use of episodic memory in naturalistic settings, and lead to a wide range of testable predictions.

Impact of pulmonary wave reflection in heart failure: right ventricular-arterial coupling

Background Pulmonary wave reflection, an important loading component on RV, would help better describe right ventricular (RV) – arterial uncouplingin type II pulmonary hypertension (PH). Methods This study included 105patients with type II PH (age = 72±13 years old, ejection fraction = 47±21%). Pulmonary wave reflection was characterised by estimating pressure and velocity profiles from Doppler measurement and separating pressure waveform into forward (Pf) and backward pressure (Pb) waves based on the concept of wave intensity. The relationship of RV strain against Pb or pulmonary artery systolic pressure (PASP) was compared between patients with and without subsequent event of worsening heart failure, using discriminant analysis. Results Sample profiles of pressure waveforms illustrated Pb accounted for a significant proportion of total pressure (figure A). When RV strain was plotted against Pb, the distribution of both group were clearly separated, indicating significant uncoupling in patients with events, whereas when plotted against PASP, the both groups were overlapped (figure B and C). Conclusions Pulmonary wave reflection can better describe RV-arterial uncoupling in type II PH. Funding Acknowledgement Type of funding source: None

Disease burden of subsequent events among patients with atherosclerotic cardiovascular disease in Taiwan

Background/Introduction Patients with a history of cardiovascular disease are considered at high risk for cardiovascular (CV) events. Existing studies have indicated that a high proportion of patients developed secondary or tertiary atherosclerotic cardiovascular disease (ASCVD) events within a relatively short period of time. However, the whole picture regarding when and how these subsequent CV events occur is not well understood, particularly in Asia. Purpose To estimate the incidences, characteristics and mortality of subsequent ASCVD events among those with new-onset ASCVD events (index events). Methods We utilized National Health Insurance Research Database in Taiwan to identify patients with new-onset ASCVD events (index events), and further categorized them into those with incident coronary heart disease (CHD), cerebrovascular disease (CBVD) and peripheral artery disease (PAD) during 2012–2014. Re-admission due to ASCVD after the index event, defined as subsequent ASCVD event, was our main outcome of interest. All subsequent ASCVD events within 3-year period after the index ASCVD events were identified. Particularly, we intended to sequentially identify first and second subsequent ASCVD events. Descriptive statistics regarding proportion of developing subsequent ASCVD events as well as the type of subsequent ASCVD events were estimated. We also used Kaplan-Meier method to estimate crude survival curve of all-cause mortality following each subsequent ASCVD event. Results We identified 97,321, 120,914 and 14,794 patients with new-onset CHD, CBVD and PAD, respectively. The proportion of developing subsequent events increased with sequence of events occurred (for the first three subsequent event, the proportions of developing subsequent event were: 22.5, 25.6 and 30.9% for CHD, 21.0, 26.2 and 32.4% for CBVD, and 40.2, 41.4 and 43.6% for PAD). The majority of patients had the same type of ASCVD for their subsequent events to the primary event (proportions of having the same ASCVD type of subsequent event to the primary event ranged: 66–81% for CHD, 80–84% for CBVD, and 76–78% for PAD). The 1-year readmission rates increased if patients encountered more subsequent events. Among patients with new-onset CHD, the 1-year readmission rates following new-onset events, first subsequent events and second subsequent events were 43.1%, 47.6% and 55.3%, respectively. More subsequent events also worsened the survival. The 1-year survival rates following new-onset CHD events, first subsequent events and second subsequent events were 85.9%, 84.3% and 79.8%, respectively. Similar trend was observed for CBVD and PAD patients as well. Conclusions Compared with new-onset ASCVD events, subsequent ASCVD events posed a heavier disease burden in terms of readmission and mortality. These results highlighted the importance of prevention of secondary or tertiary ASCVD events. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen Taiwan Limited

Causal conflicts produce domino effects

Inconsistent beliefs call for revision—but which of them should individuals revise? A long-standing view is that they should make minimal changes that restore consistency. An alternative view is that their primary task is to explain how the inconsistency arose. Hence, they are likely to violate minimalism in two ways: they should infer more information than is strictly necessary to establish consistency and they should reject more information than is strictly necessary to establish consistency. Previous studies corroborated the first effect: reasoners use causal simulations to build explanations that resolve inconsistencies. Here, we show that the second effect is true too: they use causal simulations to reject more information than is strictly necessary to establish consistency. When they abandon a cause, the effects of the cause topple like dominos: Reasoners tend to deny the occurrence of each subsequent event in the chain. Four studies corroborated this prediction.

Auxin-degron system identifies immediate mechanisms of Oct4

The pluripotency factor Oct4 is essential for the maintenance of naïve pluripotent stem cells in vitro and in vivo. However, the specific role of Oct4 in this process remains unknown. Here, we developed a rapid protein-level Oct4 depletion system that demonstrates that the immediate downstream response to loss of Oct4 is reduced expression of key pluripotency factors. Our data show a requirement for Oct4 for the efficient transcription of several key pluripotency factors, and suggest that expression of trophectoderm markers is a subsequent event. Additionally, we find that Nanog is competent to bind to the genome in the absence of Oct4, and this binding is in fact enhanced. Globally, however, active enhancer associated histone mark H3K27ac is depleted. Our work establishes that while Oct4 is required for the maintenance of the naïve transcription factor network, at a normal ESC level it antagonises this network through inhibition of Nanog binding.