Streamlining remote nanopore data access with slow5curl.

BACKGROUND: As adoption of nanopore sequencing technology continues to advance, the need to maintain large volumes of raw current signal data for reanalysis with updated algorithms is a growing challenge. Here we introduce slow5curl, a software package designed to streamline nanopore data sharing, accessibility, and reanalysis. RESULTS: Slow5curl allows a user to fetch a specified read or group of reads from a raw nanopore dataset stored on a remote server, such as a public data repository, without downloading the entire file. Slow5curl uses an index to quickly fetch specific reads from a large dataset in SLOW5/BLOW5 format and highly parallelized data access requests to maximize download speeds. Using all public nanopore data from the Human Pangenome Reference Consortium (>22 TB), we demonstrate how slow5curl can be used to quickly fetch and reanalyze raw signal reads corresponding to a set of target genes from each individual in large cohort dataset (n = 91), minimizing the time, egress costs, and local storage requirements for their reanalysis. CONCLUSIONS: We provide slow5curl as a free, open-source package that will reduce frictions in data sharing for the nanopore community: https://github.com/BonsonW/slow5curl.

Randomness impacts the building of specific priors, visual exploration, and perception in object recognition.

Recognising objects is a vital skill on which humans heavily rely to respond quickly and adaptively to their environment. Yet, we lack a full understanding of the role visual information sampling plays in this process, and its relation to the individual's priors. To bridge this gap, the eye-movements of 18 adult participants were recorded during a free-viewing object-recognition task using Dots stimuli1. Participants viewed the stimuli in one of three orders: from most visible to least (Descending), least visible to most (Ascending), or in a randomised order (Random). This dictated the strength of their priors along the experiment. Visibility order influenced the participants' recognition performance and visual exploration. In addition, we found that while orders allowing for stronger priors generally led participants to visually sample more informative locations, this was not the case of Random participants. Indeed, they appeared to behave naïvely, and their use of specific object-related priors was fully impaired, while they maintained the ability to use general, task-related priors to guide their exploration. These findings have important implications for our understanding of perception, which appears to be influenced by complex cognitive processes, even at the basic level of visual sampling during object recognition.

Video laryngoscopy versus direct laryngoscopy in achieving successful emergency endotracheal intubations: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Intubating a patient in an emergent setting presents significant challenges compared to planned intubation in an operating room. This study aims to compare video laryngoscopy versus direct laryngoscopy in achieving successful endotracheal intubation on the first attempt in emergency intubations, irrespective of the clinical setting. METHODS: We systematically searched PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials from inception until 27 February 2023. We included only randomized controlled trials that included patients who had undergone emergent endotracheal intubation for any indication, regardless of the clinical setting. We used the Cochrane risk-of-bias assessment tool 2 (ROB2) to assess the included studies. We used the mean difference (MD) and risk ratio (RR), with the corresponding 95% confidence interval (CI), to pool the continuous and dichotomous variables, respectively. RESULTS: Fourteen studies were included with a total of 2470 patients. The overall analysis favored video laryngoscopy over direct laryngoscopy in first-attempt success rate (RR = 1.09, 95% CI [1.02, 1.18], P = 0.02), first-attempt intubation time (MD = - 6.92, 95% CI [- 12.86, - 0.99], P = 0.02), intubation difficulty score (MD = - 0.62, 95% CI [- 0.86, - 0.37], P < 0.001), peri-intubation percentage of glottis opening (MD = 24.91, 95% CI [11.18, 38.64], P < 0.001), upper airway injuries (RR = 0.15, 95% CI [0.04, 0.56], P = 0.005), and esophageal intubation (RR = 0.37, 95% CI [0.15, 0.94], P = 0.04). However, no difference between the two groups was found regarding the overall intubation success rate (P > 0.05). CONCLUSION: In emergency intubations, video laryngoscopy is preferred to direct laryngoscopy in achieving successful intubation on the first attempt and was associated with a lower incidence of complications.

triact package for R: analyzing the lying behavior of cows from accelerometer data.

Accelerometers are sensors proven to be useful to analyze the lying behavior of cows. For reasons of algorithm transparency and control, researchers often prefer to use their own data analysis scripts rather than proprietary software. We developed the triact R package that assists animal scientists in analyzing the lying behavior of cows from raw data recorded with a triaxial accelerometer (manufacturer agnostic) attached to a hind leg. In a user-friendly workflow, triact allows the determination of common measures for lying behavior including total lying duration, the number of lying bouts, and the mean duration of lying bouts. Further capabilities are the description of lying laterality and the calculation of proxies for the level of physical activity of the cow. In this publication we describe the functionality of triact and the rationales behind the implemented algorithms. The triact R package is developed as an open-source project and freely available via the CRAN repository.

Should Researchers Destroy Audio or Video Recordings?

It is a common practice in qualitative research to transcribe audio or video files from interviews or focus groups and then destroy the files at some future time, usually after validating the transcript or concluding the research. We argue that it is time to rethink this practice and that retention of original qualitative data-including audio and video recordings-should be the default stance in most cases.

Cross-border forensic profiling of fraudulent identity and travel documents: A pilot project between France and Switzerland.

The serial character of document fraud and its connection to organised crime groups who produce, sell and/or use fraudulent documents is a challenge for security and crime fighting. As a response, the added value of forensic intelligence is increasingly recognised. Using a forensic profiling method and a dedicated system deployed in Switzerland, document examiners can detect series (i.e., documents that share a common source) of fraudulent documents conveniently and efficiently. This detection can trigger or orientate investigations, supports crime intelligence efforts, and facilitates cross-jurisdictional cooperation. This study aims to assess the suitability of the forensic profiling system for international purpose and the efficiency of the method to detect cross-border series. The forensic profiling system has been deployed in France in the framework of a cross-border pilot project conducted by the School of Criminal Justice from the University of Lausanne and the French National Police (Division Nationale de Lutte contre la Fraude Documentaire et à l'Identité) over the period July 2019-May 2020. Data from the Swiss and French forensic profiling systems were compared to each other to detect cross-border series. The study sought to create operating conditions as close as possible to the real-life conditions of the profiling systems. The results are extremely positive both quantitatively and qualitatively. They demonstrate the benefit of setting up a systematic exchange of forensic data issued from profiling systems for fraudulent identity documents between France and Switzerland, let alone between any other countries. The results open up a very promising prospect for a sustained operational implementation by the police services of both countries and the extension of the exchanges internationally.

Design, construction and field testing of a manually feeding semiautomatic sugarcane dud chipper.

Sugarcane is the main sugar crop, and sugar is an important agricultural product in Egypt. There are many problems with the technology used in the current planting method of sugarcane, which has a great impact on the planting quality of sugarcane, which have a series of problems, such as low cutting efficiency and poor quality. Therefore, the aim of the current study was to design, construct, and field testing of a semiautomatic sugarcane bud chipper assisted with pivot knives for cutting sugarcane buds and germinating them in plastic trays inside a greenhouse until they reached an average length of 35 cm, and then planting them in the field. In the field tests five cutting speeds (35, 40, 45, 50, and 56 rpm. (Revolution Per minute), three cutting knives (1.5, 2.0, and 2.5 mm) were used for cutting sugarcane stalks with four different diameters (1.32, 1.82, 2.43, and 2.68 cm). The obtained results showed that the values of the damage index and invisible losses were within acceptable limits (ranging between - 1.0 and 0.0) for all the variables under the test. Still, the lowest damage index and invisible losses were recorded with the buds that were cut with a knife of 1.5 mm thickness and cutting speeds less than 50 rpm. The skipping rate increases with the increase in cutting speed and stalk diameter, ranging between 0.0 to 13%. The maximum machine productivity was 110 Buds per minute at a cutting speed of 35 rpm and stalk diameter of 1.32 cm. The paper's findings have important application values for promoting the designing and development of sugarcane bud chipper and sugarcane planting technology in the future.

Z2F: Heterogeneous graph-based Android malware detection.

Android malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people's lives. Most existing Android malware detection methods extract Android features from the original application files without considering the high-order hidden information behind them, but these hidden information can reflect malicious behaviors. To solve this problem, this paper proposes Z2F, a detection framework based on multidimensional Android feature extraction and graph neural networks for Android applications. Z2F first extracts seven types of Android features from the original Android application and then embeds them into a heterogeneous graph. On this basis, we design 12 kinds of meta-structures to analyze different semantic spaces of heterogeneous graphs, mine high-order hidden semantic information, and adopt a multi-layer graph attention mechanism to iteratively embed and update information. In this paper, a total of 14429 Android applications were detected and 1039726 Android features were extracted, with a detection accuracy of 99.7%.

Toward viewing behavior for aerial scene categorization.

Previous work has demonstrated similarities and differences between aerial and terrestrial image viewing. Aerial scene categorization, a pivotal visual processing task for gathering geoinformation, heavily depends on rotation-invariant information. Aerial image-centered research has revealed effects of low-level features on performance of various aerial image interpretation tasks. However, there are fewer studies of viewing behavior for aerial scene categorization and of higher-level factors that might influence that categorization. In this paper, experienced subjects' eye movements were recorded while they were asked to categorize aerial scenes. A typical viewing center bias was observed. Eye movement patterns varied among categories. We explored the relationship of nine image statistics to observers' eye movements. Results showed that if the images were less homogeneous, and/or if they contained fewer or no salient diagnostic objects, viewing behavior became more exploratory. Higher- and object-level image statistics were predictive at both the image and scene category levels. Scanpaths were generally organized and small differences in scanpath randomness could be roughly captured by critical object saliency. Participants tended to fixate on critical objects. Image statistics included in this study showed rotational invariance. The results supported our hypothesis that the availability of diagnostic objects strongly influences eye movements in this task. In addition, this study provides supporting evidence for Loschky et al.'s (Journal of Vision, 15(6), 11, 2015) speculation that aerial scenes are categorized on the basis of image parts and individual objects. The findings were discussed in relation to theories of scene perception and their implications for automation development.

Demography, commonly diagnosed disorders and mortality of guinea pigs under primary veterinary care in the UK in 2019-A VetCompass study.

INTRODUCTION: Guinea pigs are popular as domestic pets but there is limited information on the health of the wider pet population. This study aimed to report demography, commonly diagnosed disorders and mortality of guinea pigs under UK primary veterinary care. METHODS: Diagnosis and mortality information on guinea pigs was extracted from anonymised UK primary-care clinical records in VetCompass. RESULTS: From 51,622 guinea pigs under primary veterinary care during 2019, a specific breed was not recorded in 50,098 (97.05%). Of guinea pigs with information recorded, 23,206 (47.33%) were female and 25,828 (52.67%) were male. There were 1,020 (2.08%) neutered and 48,014 (97.92%) entire. Median adult bodyweight overall was 1.05kg (interquartile range [IQR] 0.90-1.19, range 0.40-2.66). From a random sample of 3,785/51,622 (7.33%) guinea pigs, the most prevalent disorders were overgrown nail(s) (n = 1,005, 26.55%, 95% confidence interval [CI] 25.15-27.99), dermatophytosis (228, 6.02%, 95% CI 5.29-6.83) and corneal ulceration (189, 4.99%, 95% CI 4.32-5.74). Among the 30 most common disorders, females showed predisposition for 3 disorders and males showed predisposition for 5 disorders. The disorder with the youngest age of affected animals was dermatophytosis (1.11 years) while weight loss had the oldest age of affected animals (4.64 years). From 757 recorded deaths, the median age at death overall was 4.03 years (IQR 2.56-5.44, range 0.17-10.00). Among deaths with a recorded cause, the most common causes of death were anorexia (n = 82, 13.87%, 95% CI 11.19-16.93), collapsed (58, 9.81%, 95% CI 7.54-12.50) and peri-anaesthetic death (20, 3.38%, 95% CI 2.08-5.18). CONCLUSIONS: These results can assist veterinarians and owners by providing demographic, disorder and mortality benchmarks that support improved clinical care and welfare outcomes in guinea pigs. Many common disorders in guinea pigs were husbandry related.

Anomaly based multi-stage attack detection method.

Multi-stage attacks are one of the most critical security threats in the current cyberspace. To accurately identify multi-stage attacks, this paper proposes an anomaly-based multi-stage attack detection method. It constructs a Multi-Stage Profile (MSP) by modeling the stable system's normal state to detect attack behaviors. Initially, the method employs Doc2Vec to vectorize alert messages generated by the intrusion detection systems (IDS), extracting profound inter-message correlations. Subsequently, Hidden Markov Models (HMM) are employed to model the normal system state, constructing an MSP, with relevant HMM parameters dynamically acquired via clustering algorithms. Finally, the detection of attacks is achieved by determining the anomaly threshold through the generation probability (GP). To evaluate the performance of the proposed method, experiments were conducted using three public datasets and compared with three advanced multi-stage attack detection methods. The experimental results demonstrate that our method achieves an accuracy of over 99% and precision of 100% in multi-stage attack detection. This confirms the effectiveness of our method in adapting to different attack scenarios and ultimately completing attack detection.

Weights, resistance bands, and rest days are best for tendinopathy.

The studyPavlova AV, Shim JSC, Moss R, et al. Effect of resistance exercise dose components for tendinopathy management: a systematic review with meta-analysis. Br J Sports Med 2023;57:1327-34.To read the full NIHR Alert, go to: https://evidence.nihr.ac.uk/alert/weights-resistance-bands-rest-days-best-tendinopathy/.

REDCapDM: An R package with a set of data management tools for a REDCap project.

BACKGROUND: Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management. RESULTS: The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data. CONCLUSION: The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.

Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

How do bicyclists respond to vehicles with adaptive headlamp systems? A nighttime study in an immersive virtual environment.

INTRODUCTION: The risk of motor vehicle-bicyclist crashes and fatalities is greater during nighttime than daytime lighting conditions, even though there are fewer cyclists on roadways at night. Vehicle Adaptive Headlamp Systems (AHS) aim to increase the visibility of bicyclists for drivers by directing a spotlight to illuminate bicyclists on or near the roadway. AHS technology also serves to alert bicyclists to the approaching vehicle by illuminating the road beneath the rider and by projecting a warning icon on the roadway. METHOD: Here, we examined how bicyclists respond to different AHS designs using a large screen, immersive virtual environment. Participants bicycled along a virtual road during nighttime lighting conditions and were overtaken by vehicles with and without an AHS system. The experiment included five treatment conditions with five different AHS designs. In each design a box of white light was projected beneath the rider; in four of the designs an icon was also projected on the road that varied in color (white or red) and position (to the left of the rider at midline or to the left of the front wheel). Participants in the control condition experienced only non-AHS vehicles. RESULTS: We found that riders in all AHS treatment conditions moved significantly farther away from overtaking vehicles with AHS systems, whereas riders in the control condition did not significantly move away from overtaking vehicles without AHS systems. PRACTICAL APPLICATIONS: The experiment demonstrates that AHS has potential to increase bicycling safety by influencing riders to steer away from overtaking vehicles.

A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species.

The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

Visual features are processed before navigational affordances in the human brain.

To navigate through their immediate environment humans process scene information rapidly. How does the cascade of neural processing elicited by scene viewing to facilitate navigational planning unfold over time? To investigate, we recorded human brain responses to visual scenes with electroencephalography and related those to computational models that operationalize three aspects of scene processing (2D, 3D, and semantic information), as well as to a behavioral model capturing navigational affordances. We found a temporal processing hierarchy: navigational affordance is processed later than the other scene features (2D, 3D, and semantic) investigated. This reveals the temporal order with which the human brain computes complex scene information and suggests that the brain leverages these pieces of information to plan navigation.

'Forgetting' or 'Precipitation': Literary inquisition in Qing Dynasty and modern enterprise risk preference.

This paper takes the risk preference of modern listed companies as the research object, uses the financial data of Chinese listed companies combined with the literary inquisition file in Qing Dynasty to conduct an empirical study, and examines the influence of literary inquisition on the risk preference of modern corporate CEOs in Qing Dynasty. The study found that the literary inquisition incident in Qing Dynasty significantly affected and reduced the risk preference of modern enterprises. The competitive hypothesis of the influence of Confucian culture and China City Commercial Credit Environment Index (CEI) on CEOs' risk preference is excluded. In addition, through the study of heterogeneity, this paper also verifies that the influence of literary inquisition is more significant in areas with a higher degree of marketization, indicating that the influence of informal institutions depends on the establishment of formal institutions. Finally, in the mechanism study, this paper points out that the rulers' suppression of ideas will change long-term social capital and lead to the decrease of general trust in society, which will make the enterprise managers born in the region tend to be conservative in their risk preference.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs.

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Improving the response of animals to environmental disturbances in terms of robustness is a key element to face the new breeding constraints related to climate change and the agroecological transition. Characterizing the disturbances that an animal experiences is a necessary first step to correctly evaluate its robustness. We propose a new method to do so based on the analysis of high-throughput phenotyping data. Using simulated data, we demonstrate that this method is effective for detecting and characterizing unknown disturbances and is thus helpful in correctly evaluating an animal's robustness. Applied to real growing pig data, it allowed us to obtain new measurements of robustness and to estimate their heritability in order to consider including these new traits for selection.