Random Occurrence Research Articles

Getting Cancer Is "Just Bad Luck": Exploring Bereaved Emerging and Young Adults' Cancer Risk Uncertainty After Caring for a Parent With Advanced Cancer.

Emerging and young adult caregivers (EYACs) who provide care to a parent with advanced cancer are underrepresented in caregiving scholarship, and yet, are not uncommon. Little is known about the psychosocial impacts of caring for a parent at this age or how EYACs manage their uncertainty regarding their own, potentially elevated, future cancer risk. To employ Uncertainty Management Theory (UMT) to examine how bereaved EYACs of a parent who died of advanced cancer appraise and manage their uncertainty regarding their personal cancer risk. We conducted a secondary analysis of in-depth, semi-structured interviews with EYACs (age 18-35) who cared for a parent who died of advanced cancer (n=33)<5years prior. The interviews were transcribed and thematically analyzed. Some EYACs described appraising their cancer risk uncertainty as an opportunity and were motivated to reduce their risks through behavior choices. Others appraised it as a danger and experienced anxiety, paranoia, and fatalism about their risk. Others described their parents' cancer as "just bad luck," believing it to be a random anomaly that could not impact their cancer risk and reported no changes in their appraisal of their cancer risk uncertainty. EYACs' opportunity and danger appraisals align with studies of high hereditary risk populations but reporting no change in cancer risk uncertainty is unique. The long-term health implications of appraising their parent's cancer as a random occurrence, disconnected from their personal risk, remain unknown. Future research should seek to help both bereaved and active EYACs better understand their cancer risk and manage their uncertainty.

Optimal promise time for customer orders in imperfect discrete production systems with earliness and lateness penalties

Discrete production systems are very common in industry. This paper studies the case where the system may generate imperfect units and rework is performed immediately before starting a new unit. These systems have not received enough attention in the existing literature. We consider make-to-order imperfect discrete production systems where the manufacturer quotes the customer a promise time for delivering the ordered quantity. This is a very realistic problem that arises often in practice. The random occurrence of imperfect units results in a huge number of possible outcomes. This difficulty is resolved by defining new quantities, and success times, which are possible points of time where an imperfect unit could be generated. These quantities are used efficiently to compute the probability of having a given number of perfect units in a given time, which in turn is used in determining the optimal promise time that minimises the costs associated with late and early production of the demand. This paper examines three cases: imperfect rework, perfect rework, and non-reworkable/scrapped units. It is expected that this work will generate interest in this type of production system.

Modeling the Microsurgical Learning Curve Using a Poisson-Based Statistical Approach for Skill Assessment.

The learning curve (LC), a multifaceted concept, plays a pivotal role in evaluating surgical training. This study aimed to define critical inflection points in the microsurgical learning curve, develop a reliable index for skill assessment, and statistically validate this approach using Poisson distribution theory. A standardized microsurgical training protocol was employed using a biological simulator. Data regarding time to complete the task and error rates were collected over 132 attempts by a single operator. The primary outcome variable, the major mistake average (MMA), was used to generate a learning curve. Its progression was analyzed using autoregressive integrated moving average (ARIMA) modeling and validated with Poisson dispersion theory to determine the randomness of error occurrence at advanced stages of training. The entire trial was conducted by a single operator, a consultant neurosurgeon from our institution, who had been properly instructed on the protocol and the corresponding operator's manual. Task completion time (TCT) ranged from 860 to 3,054 seconds (mean: 1,472 seconds; R² = 0.561). MMA peaked at the 19th attempt (0.263) and decreased progressively, reaching 0.091 by the 132nd attempt (R² = 0.835). Three distinct phases of learning were identified, culminating in a plateau phase during which major mistakes followed a Poisson distribution (Chi² = 3.841), suggesting random occurrence independent of skill deficits. The MMA was found to be a robust and objective indicator of microsurgical proficiency. Its statistical validation using Poisson distribution theory supports its utility in skill assessment and training programs. Further studies involving multiple operators are warranted to confirm these findings.

Towards a definition of a meteor cluster

Context. As of today, there is no official definition of a meteor cluster. It is usually identified as a large number of meteors sharing a similar radiant and velocity, all occurring within a few seconds. Only eight clusters have been reported so far, from single-camera or camera network observations. However, a cluster may be observed by several distant networks and remain unnoticed simply because each network is recording a small portion of the cluster. Aims. We aim to provide an overview of meteor clusters to help define what constitutes a cluster by potentially adding more to the already identified ones and determining their common parameters. Methods. A search for new clusters was performed in publicly available International Astronomical Union meteor databases with the DBSCAN algorithm. Then, a statistical significance method was applied to derive the most promising cluster candidates. However, the method still lacks a way to debias the atmospheric area surveyed by the cameras due to a lack of publicly available data. Results. A set of 16 statistically significant potential clusters is identified, involving four to seven fragments. The 90th percentile includes a duration of 8 seconds, a velocity difference of 2.2 km/s, and a radiant spread of nearly 4 degrees. The velocity difference may arise from the method used for orbit computation. Conclusions. Meteor clusters might be more frequent than currently reported. However, we recommend that future meteor orbit databases also include a way to estimate the surveyed area by the cameras involved in the detection. This would strengthen the veracity of the 16 identified cluster candidates and ultimately allow scientists to fully debias the number of clusters, and hence derive the physical lifetime expectancy of meteoroids, which is often overlooked due to the focus on collisional lifetime estimates only. We also recommend that any future cluster observation report include the expected number of random occurrences and consider the event to be real if this value is below 0.1.

The use of wastewater surveillance to estimate SARS-CoV-2 fecal viral shedding pattern and identify time periods with intensified transmission

BackgroundWastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants.ObjectivesWe utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves.MethodsWe collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 – August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission.ResultsModels suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 – 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by around 20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of the city's 14 sewersheds. These anomalies disproportionally occurred during late January, late April—early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test).DiscussionThese estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

Detection of mRNA Transcript Variants.

Most eukaryotic genes express more than one mature mRNA, defined as transcript variants. This complex phenomenon arises from various mechanisms, such as using alternative transcription start sites and alternative post-transcriptional processing events. The resulting transcript variants can lead to synthesizing proteins that possess distinct functional domains or may even generate noncoding RNAs, each with unique roles in cellular processes. The generation of these transcript variants is not merely a random occurrence; it is cell-type specific and varies with developmental stages, aging processes, or pathogenesis of diseases. This highlights the biological significance of transcript variants in regulating gene expression and their potential impact on cellular functionality. Despite the biological importance, investigating transcript variants has been hampered by challenges associated with detecting their expression. This review article addresses the advancements in molecular techniques in detecting transcript variants. Traditional methods such as RT-PCR and RT-qPCR can easily detect known transcript variants using primers that target unique exons associated with the variants. Other techniques like RACE-PCR and hybridization-based methods, including Northern blotting, RNase protection assays, and microarrays, have also been utilized to detect transcript variants. Nevertheless, RNA sequencing (RNA-Seq) has emerged as a powerful technique for identifying transcript variants, especially those with previously unknown sequences. The effectiveness of RNA sequencing in transcript variant detection depends on the specific sequencing approach and the precision of data analysis. By understanding the strengths and weaknesses of each laboratory technique, researchers can develop more effective strategies for detecting mRNA transcript variants. This ability will be crucial for our comprehensive understanding of gene regulation and the implications of transcript diversity in various biological contexts.

Self-Defense in Medical Malpractice: How to Derail a Frivolous Malpractice Lawsuit

Every physician in the United States has an 8.5% risk for a medical malpractice lawsuit each year. A patient who is the victim of an adverse outcome understandably misconstrues an error of nature as a medical error and retains an attorney who hires a medical expert. By using statistical significance, a complication that follows any medical intervention can be validated as an error of nature or a medical error with 95% confidence, thus making the malpractice suit frivolous. Therefore, ensuring 18 words appear in a patient’s chart — “With the exception of random occurrences, there is 95% confidence that this medical intervention is safe and effective” — a plaintiff medical expert must demonstrate a statistically significant difference between the “incident risk” of a medical error representing the medical intervention and the “background risk” of an error-of-nature representing the standard of care. Statistical significance is a professional standard that any physician who professes to be a medical expert should be able to determine.

Familial polyglandular deficiencies with no AIRE mutation: random occurrence or genetic predisposition yet to be discovered?

Familial polyglandular deficiencies with no AIRE mutation: random occurrence or genetic predisposition yet to be discovered?

Spatial-Temporal Evolution of Maritime Accident Hot Spots in the East China Sea: A Space-Time Cube Representation

As public concern for maritime safety grows, there is a pressing need to delve deeper into the root causes of maritime accidents and develop effective preventive strategies. Spatial-temporal analysis stands out as a powerful approach to pinpointing accident hot spots. While previous research has shed light on the spatial aspects of these incidents, a comprehensive understanding of their temporal dimensions remains elusive. This paper bridges this gap by leveraging the Space-Time Cube tool in conjunction with traditional Kernel Density analysis to chart the spatial-temporal dynamics of maritime accident hot spots. Focusing on the East China Sea, a region notorious for its high incidence of maritime accidents and home to numerous world-class ports, we present a case study that offers fresh insights. Data spanning from 1994 to 2020, sourced from the Lloyd’s List Intelligence (LLI) database, reveal the evolving landscape of maritime accidents in the area. Notably, since 2005, the Yangtze River Delta Region in China has emerged as a persistent hot spot for accidents, underscoring its significance in maritime safety discourse. Furthermore, our analysis from the 2010s detects a new hot spot expanding towards the southwest of Kaohsiung Port, China, signaling a burgeoning area of concern for maritime safety. While the Fujian coast of China has seen its share of accidents, it is not qualified as a hot spot zone. The Space-Time Cube proves to be an indispensable tool in unraveling the progression of maritime accidents, and our findings indicate that maritime accidents in certain areas may not be merely random occurrences but exhibit intricate patterns.

Additional dietary records of the arboreal kinkajou (Potos flavus)

The kinkajou (Potos flavus) is a medium-sized arboreal mammal that primarily feeds on fruits, flowers, nectar, leaves, and occasionally arthropods. While interactions between P. flavus and plants are well-documented with the species known to consume fruits and other parts of 119 plant species across 50 families, reports of frugivory involving kinkajous and Araceae plants are scarce. To date, only a single instance of frugivory on Philodendron crassispathum has been recorded. During a field trip in March 2022 to Los Tuxtlas, Veracruz, México, we documented the first observed consumption of Monstera egregia by kinkajous using a video camera. The kinkajou was observed perched on a leaf stem of the same M. egregia, approximately 6 m above the ground. The individual gripped the stem with its feet and prehensile tail while using its hands to manipulate the infructescence. The animal was seen biting into the infructescence and picking individual fruits, which it brought to its mouth. Although Araceae plants have evolved mechanisms to deter herbivory, the consumption of M. egregia by kinkajous appears to be a deliberate behavior rather than a random occurrence. Interactions between Araceae species and kinkajous, as well as with other frugivorous mammals, may represent an important ecological relationship deserving of further investigation.

DNA markers for early detection of somaclonal variation in Berangan banana

Banana is Malaysia’s second most cultivated fruit, with 50% of the cultivation focusing on Berangan and Cavendish types. Bananas have been identified as one of the important fruits in the National Agrofood Policy (2011-2020) due to their demand in the domestic and export markets. Thus, FGV is committed to achieving a 50% market share of local banana production through tissue culture propagation techniques. This technique enables the mass production of banana planting material in a short period. However, one downside of this technique is the random occurrence of somaclonal variation, which has been reported for various crops. FGV’s clonal propagation aims to produce true-to-type banana planting materials, therefore, somaclonal variants are undesired. Hence, we aimed to establish a marker-based screening method for early detection and elimination of Berangan somaclonal variants prior to field planting. Berangan leaf samples with and without somaclonal variation were collected from different sites. The samples’ DNA was extracted using an in-house cetyltrimethylammonium bromide (CTAB) extraction method and screened using 145 Random Amplified Polymorphic DNA (RAPD) markers. RAPD markers that could distinguish somaclonal variation in the Berangan samples were developed into Sequence Characterized Amplified Region (SCAR) markers for routine screening. The SCAR markers were validated for their ability to reproduce the specific bands from the RAPD results. Of the 145 RAPD markers, three markers do not produce any bands, 47 RAPD markers produce monomorphic bands and 95 RAPD markers produce polymorphic bands. Three out of the 95 polymorphic RAPD markers were able to selectively distinguish the somaclonal variation in samples from similar sources. The three RAPD markers were developed into SCAR markers, and they successfully reproduced the specific bands from their respective RAPD results. Hence, the developed SCAR markers will be utilized for the early detection and elimination of Berangan somaclonal variants from selected sources.

Enhancing Production and Logistics Efficiency Through Dynamic Simulation: A Case Study in Transport System Optimization

Abstract Dynamic computer simulation verifies before implementation how the production and logistics process system will work after implementation It is equally important in process automation when bottlenecks in processes are found and need to be optimised. Simulation can be used to verify the outcome of changes in production, logistics, warehouse or management processes before they are implemented in the real system. In the digital environment, experiments are conducted with internally deployed systems or processes, for example, the impact of process sequencing, orders, random occurrence of equipment failures, their retyping, the impact of logistics on production performance, and others. The customer thus avoids potential high financial and capacity losses resulting from additional modifications to the real system and gains certainty in decision-making in the planning phase. The aim of this paper is to provide a detailed analysis of the transport system in industry and to develop a simulation case study of a transport system. The simulation will be used to verify the proposed innovations in the case study.

Magnetically Guided Diagnosis of Current Flow Patterns inside Fault-Simulated Li-Ion Batteries

As the energy industry shifts towards cleaner and more electrified solutions, there's a growing demand for improved batteries, especially for electric vehicles (EVs) and stationary energy storage systems (ESSs). Lithium-ion batteries (LIBs) have been dominant due to their advantages such as high energy/power density, long lifespan, and fast charging capabilities. Despite advancements in cell design making them more efficient and compact, current LIBs face new challenges, particularly in improving reliability and addressing safety concerns. Recent instances of battery recalls due to explosions and fires have underscored the need to address potential failures stemming from internal faults in large-format cell designs and manufacturing, or from "latent" defects arising from misuse or aging under extreme conditions. Therefore, understanding failure scenarios and developing diagnostic tools are crucial for facilitating smoother troubleshooting for battery manufacturers and ensuring the development of safer battery technologies.Advanced imaging methods, both postmortem and in situ, have been developed to reveal the underlying causes of battery failures at the material level. These techniques help identify physical and chemical defects in active materials, degradation of electrolytes, and structural damage in inactive components like separators and current collectors. However, connecting these material-level defects with overall cell failure is difficult due to their random occurrence within complex cell structures. Additionally, many microscale imaging tools designed for customized cells are impractical for commercial cells, making it nearly impossible to detect small errors in large-scale cells during postmortem analysis. Therefore, global imaging techniques are more effective for identifying abnormalities and examining local faults. One powerful tool for this purpose is cell-penetrating X-ray computed tomography, which provides 3D images of the cell's internal structures, although it is time-consuming. However, this method may struggle to detect invisible errors that can lead to abnormal current flow in areas where latent defects exist. Thermal imaging is another useful tool for detecting abnormal heat generation in batteries caused by current flow, but without information on current direction, determining the type and extent of defects and cell degradation may be limited.This research introduces a real-time, noninvasive method for imaging magnetic fields (MFI) generated by current flow within Li-ion pouch cells. This technique provides fast scanning speeds (~100 mm/min), high spatial resolution (0.161 mm2), and a simple setup without the need for magnetic field shielding. Initially, we verified the correlation between magnetic fields and current by conducting MFI studies on 1D straight wires and 2D sheets carrying current. Subsequently, we identified an appropriate pouch cell geometry for analyzing current flow patterns using MFI, cross-referencing with current distribution maps generated by COMSOL simulations. To illustrate the effectiveness of current pattern analysis in identifying failure modes, we analyzed MFI data from fault-simulated batteries (FSBs) deliberately manufactured with faults like lead-tab contact failure, electrode misalignment, and stacking process folds. By using MFI destructive interference with a countercurrent-carrying cell-shaped 2D sheet below, we could selectively enhance areas of failure where abnormal current flows occur. This MFI-guided visualization of current distribution patterns in FSBs provides a nondestructive, immediate diagnostic approach to identify fault types that may not be discernible through electrochemical analysis alone.

Synthesis, characterization, quantum mechanical calculations and biomedical docking studies on curcumin analogs: 2, 6-(Difurfurylidene) cyclohexanone and 2, 6 – Bis (2,6-Dichloro Benzylidene) Cyclohexanone

Synthesis, characterization, quantum mechanical calculations and biomedical docking studies on curcumin analogs: 2, 6-(Difurfurylidene) cyclohexanone and 2, 6 – Bis (2,6-Dichloro Benzylidene) Cyclohexanone

Effect of controlling residual moisture in atmospheric plasma spray-Y2O3 coatings on random defect generation by halogen-based plasma

Effect of controlling residual moisture in atmospheric plasma spray-Y2O3 coatings on random defect generation by halogen-based plasma

Reliability assessment of additively manufactured TB6 titanium alloy wheel hubs

Reliability assessment of additively manufactured TB6 titanium alloy wheel hubs

A new spatiotemporal convolutional neural network model for short-term crash prediction

Predicting short-term traffic crashes is challenging due to an imbalanced data set characterized by excessive zeros in noncrash counts, random crash occurrences, spatiotemporal correlation in crash counts, and inherent heterogeneity. Existing models struggle to effectively address these distinct characteristics in crash data. This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction. The model was trained and tested using real-world, highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019, along with crash data extracted from the UK National Accident Database for the same year. The short-term is defined as a 30-min interval, providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards. The year was segmented into 30-min intervals, resulting in a highly imbalanced data set with over 99.99% noncrash samples. The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min. The findings revealed that 75.3% of crashes and 81.6% of noncrash events were correctly predicted in the southbound direction. In the northbound direction, 78.1% of crashes and 80.2% of noncrash events were accurately captured. Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes. The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

Evidence of spontaneous cardiac-locomotor coupling during daily activities in healthy adults.

One way to improve exercise performance and protect heart health is the extended synchronization of the stepping with the diastolic phase of the cardiac cycle. Cardiac-locomotor coupling (CLC) happens when the step rate (SR) equals the heart rate (HR). The extent of CLC in daily life is unknown. This study aims to analyze spontaneous occurrences of CLC during daily activities. A retrospective analysis of daily life recordings from a wrist-worn sensor was undertaken (PMData, N = 16, 5months duration). The deviation between HR and SR was used to define CLC (deviation ≤ 1%) and weak CLC (1%< deviation ≤ 10%). The occurrence and the probability of CLC during everyday life were computed from the recordings. The CLC occurrences were stratified depending on the duration and intensity of the physical activity. Finally, a Monte Carlo simulation was run to evaluate the probability of random occurrences of CLC vs. the observed recordings. Participants couple for 5% and weakly couple for 35% of the observational period. The ratio of 1:1 between HR and SR is the dominating occurrence across the study population and this overrepresentation is significant. CLC occurs mostly for long activities. The extent of CLC for various intensities of activity is subject-dependent. The results suggest that CLC is feasible for most people. CLC occurs spontaneously during unsupervised daily activity in everyone in our cohort, which suggests a mechanistic interaction between the cardiac and the locomotor systems. This interaction should be investigated for medical rehabilitation and sports applications in the future.

The Use of Wastewater Surveillance to Estimate SARS-CoV-2 Fecal Viral Shedding Pattern and Identify Time Periods with Intensified Transmission.

Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants. We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves. We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 ( N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission. Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ∼20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence ( P < 0.05; bootstrapping test). These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

The Phenomenon of COVID-19 Conspiracy Theory Narratives through Virtual Ethnography on Instagram

This study aims to investigate the conspiracy phenomenon of the COVID-19 pandemic as disseminated by the Instagram account @unexplnd. This account boasts 842 thousand followers and stands as one of the largest social media propagating the concept of COVID-19 conspiracy theories in Indonesia. Primary data is derived from posts on this account, and secondary data is obtained through interviews with four followers who actively engage in commenting and harbor beliefs in the existence of conspiracies. The research corpus is subsequently scrutinized employing the conceptual framework delineated by Douglas, Sutton, and Cichocka (2017), which delineates the emergence of conspiracies through epistemic, existential, and social motives. The findings of this investigation reveal that incidents perceived as random chance occurrences, motivated by the desire to preserve comfort and freedom, along with suspicion or resentment towards particular entities, serve as grounds for endorsing COVID-19 pandemic conspiracy theories. Furthermore, exposure to media presenting conspiracy theories, such as the Instagram account @unexplnd, contributes to an augmented belief in such theories among proponents. Conspiracy theorists desire to raise awareness among the general populace regarding conspiracy theories by disseminating information containing conspiracy narratives across various media channels.