Dixon-based B0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging.

PURPOSE: To develop a Dixon-based B 0 $$ {\mathrm{B}}_0 $$ self-navigation approach to estimate and correct temporal B 0 $$ {\mathrm{B}}_0 $$ variations in radial stack-of-stars gradient echo imaging for quantitative body MRI. METHODS: The proposed method estimates temporal B 0 $$ {\mathrm{B}}_0 $$ variations using a B 0 $$ {\mathrm{B}}_0 $$ self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. The B 0 $$ {\mathrm{B}}_0 $$ self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodic B 0 $$ {\mathrm{B}}_0 $$ variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporal B 0 $$ {\mathrm{B}}_0 $$ variations on the field-map, proton density fat fraction (PDFF) and T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ map, and to validate the proposed method. RESULTS: Temporal B 0 $$ {\mathrm{B}}_0 $$ variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation of T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ , while PDFF mapping was less affected. The B 0 $$ {\mathrm{B}}_0 $$ self-navigator captured slowly varying temporal B 0 $$ {\mathrm{B}}_0 $$ drifts and temporal variations caused by respiratory motion. While the 1D correction effectively corrected B 0 $$ {\mathrm{B}}_0 $$ drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporal B 0 $$ {\mathrm{B}}_0 $$ variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map and T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ and reduced image artifacts. CONCLUSION: Temporal B 0 $$ {\mathrm{B}}_0 $$ variations particularly affect T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct for B 0 $$ {\mathrm{B}}_0 $$ drift and physiological motion-induced B 0 $$ {\mathrm{B}}_0 $$ variations, especially in the presence of fat.

A study on the determination of the metabolites of 2,4,6-Trinitrotoluene using a dual-drift tube ion mobility spectrometer.

2,4,6-Trinitrotoluene (TNT) and its four metabolites, namely 2-ADNT, 4-ADNT, 2,4-DANT, and 2,6-DANT, are highly toxic substances. These metabolites also serve as biomarkers for assessing the health of individuals exposed to TNT. In this study, a homemade DDT-IMS apparatus was utilized to detect these metabolites. Under negative detection mode, the drift times of 2-ADNT and 4-ADNT showed subtle shifts within a drift tube temperature range of 100 °C-120 °C, aiding in their differentiation. In positive detection mode for 2,4-DANT and 2,6-DANT, significant variations were observed in both the number and drift time of their positive product ions across a drift tube temperature range of 80 °C-120 °C. Consequently, optimal analytical performance for these metabolites was achieved at approximately 100 °C. Evaluation of the instrumental response during the measurement of the four metabolites in both positive and negative modes revealed that negative detection mode offered greater advantages of detecting these compounds. The working ranges for measuring the four metabolites spanned two orders of magnitude, with detection limits for each metabolite nearly below 1 ng. Notably, clear identification of the signals for these metabolites was achieved even when samples were mixed in urine, highlighting the ability of the DDT-IMS in detecting TNT metabolites. The developed DDT-IMS detection method has significant potential for enhancing environmental risk assessment and biological hazard evaluation, particularly in relation to human exposure to TNT.

Evolutionary and Ecological Processes Determining the Properties of the G Matrix.

AbstractThe G matrix is the matrix of additive genetic variances and covariances for a vector of phenotypes. Here we apply the classical theory for the balance among selection, genetic drift, and mutations to find the contributions to G from each locus for populations at stasis. The fitness is approximated by a linear function of phenotypes, with coefficients affected by environmental fluctuations. We show that the G matrix can be decomposed into four additive components generated by selection, drift, mutations, and environmental fluctuations. Selection is on average counteracted by the other three processes included in Fisher's concept of deterioration of the environment, generating considerable changes in mean phenotypes. The theory illustrates that neither Fisher's fundamental theorem nor Lande's classical gradient formula is sufficient for assessing adaptive changes through time unless the deteriorations are corrected for. This applies for populations at stasis, but also for populations that are subject to long-term evolutionary changes. The theory also indicates several possible comparative studies for investigations of deteriorating effects. Our analyses also suggest that the factor loadings to the eigenvector of the G matrix with the lowest eigenvalue will rather accurately indicate the relative contributions from different phenotype components to fitness. This is information notoriously difficult to obtain in natural populations.

Wheatstone Bridge MEMS Hydrogen Sensor with ppb-Level Detection Limit Based on the Palladium-Gold Alloy.

On some occasions, such as new energy clinics, monitoring the trace hydrogen at the ppb level is necessary. The traditional resistive hydrogen sensors based on the Pd alloys are very difficult to realize such an extremely low detection limit. To achieve a detection limit at the ppb level and also ensure good stability, a MEMS hydrogen sensor was designed in a suspended Wheatstone bridge structure, with all four resistive arms defined on a sputtered Pd-Au alloy thin film. For the Wheatstone bridge sensor, absolute response (Ra) and relative response (Rs) are defined to describe the sensitivity of the sensor, and the effect of annealing temperature on baseline drift is investigated using the baseline zero drift parameter (DBZD). By testing the sensors across a hydrogen concentration range of 20 ppb to 3 v/v%, the optimal annealing temperature (250 °C) and operating temperature (60 °C) were identified. Under these conditions, the sensor exhibited a detection limit as low as 20 ppb with a power consumption of only 4.6 mW. At the same time, the response and recovery times of the sensor were 6 and 19 s, respectively, toward 3 v/v% hydrogen. After testing over a 100-day period, Ra fluctuated only 0.0026%, indicating that the hydrogen sensor had good long-term stability for low-concentration detection. More results also showed that the sensor has good repeatability, selectivity, and humidity resistance. With the wide measurement range (20 ppb to 3 v/v%), the sensor has the potential to meet hydrogen detection requirements in multiple scenarios.

Effects of Simulated Glyphosate Drift to Native Prairie Plants and Canola-Compatible Brassicaceae Species of North Dakota, United States.

This study evaluated effects to native plant and weedy Brassicaceae species growing in areas potentially affected by drift of glyphosate used with glyphosate-resistant canola (Brassica napus). Ten native grass and forb species were selected based on importance in prairie areas of North Dakota, US; and four introduced Brassicaceae species (Brassica juncea, Brassica nigra, Brassica rapa, and Sinapis arvensis ssp. arvensis) were selected based on their ability to cross with B. napus. Greenhouse-grown seedlings were treated with 0 (carrier control), 0.00056, 0.0032, 0.018 and 0.1 × a field application rate (FAR) of 829 g ha-1 acid glyphosate (g acid equivalent or a.e. ha-1), along with no spray plants; with each treatment repeated in two experiments. Shoot dry weight and height were measured 14 days after treatment, and data were subjected to analysis of variance or covariance followed by a Dunnett's multiple comparison test to obtain No-Observed-Adverse-Effect-Rates (NOAERs) for both parameters. A Weibull regression was used to obtain the rate producing a 25% reduction (ER25) for shoot dry weight or height for a limited number of species and experiment combinations. Based on NOAER values for both shoot dry weight and height, most native species had reductions in growth with 0.1 x FAR resulting in NOAERs of 0.018 × FAR for at least one experiment. Nassella viridula was the most sensitive native species, with a NOAER of 0.0032 x FAR for shoot dry weight and one experiment for height. The Brassicaceae species responded similarly to glyphosate as the native species, with NOAER values ranging from 0.0032 to 0.018 × FAR. Only four species had valid regression analyses for shoot dry weight or height resulting in ER25 values between 0.007 and 0.054 x FAR. Pascopyrum smithii and Schizachyrium scoparium were not affected by glyphosate as indicated by NOAER values. This study indicated that drift concentrations between approximately 0.003, but more commonly ≥ 0.1 × FAR (2.49 and 82.9 g ha-1 acid glyphosate, respectively) may affect the growth and potential competitiveness of selected native plant species, and Brassicaceae species sexually compatible with glyphosate-resistant B. napus in North Dakota.

Anticipative Bayesian classification for data streams with verification latency.

Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classification under verification latency only few approaches exist. Most of them assume clustered data or homogeneous drift in all features, which limits their applicability. We address this by proposing Anticipative Bayesian stream Classifier (ABClass), an approach that is capable of integrating and automatically selecting from different components. In its Bayesian classification framework, ABClass combines density estimation techniques, extended to extrapolate drift patterns over time, with unsupervised parameter tuning and unsupervised model selection. ABClass allows for multivariate density estimation and extrapolation techniques. In this work, we assume conditional independence between features given the class label for modelling feature-specific drift patterns. ABClass is generative and can also be used for explaining and visualising concept drift patterns. It is generic, making it easy to include further types of drift models, both for the class-conditional feature distribution and for the class prior distribution. The experimental evaluation on several real-world data streams shows its competitiveness compared to other state-of-the-art approaches. ABClass is in most cases ten- to hundred-times faster than its competitors, both for model fitting and for prediction.

Rhizobial variation, more than plant variation, mediates plant symbiotic and fitness responses to herbicide stress.

Symbiotic mutualisms provide critical ecosystem services throughout the world. Anthropogenic stressors, however, may disrupt mutualistic interactions and impact ecosystem health. The plant-rhizobia symbiosis promotes plant growth and contributes to the nitrogen (N) cycle. While off-target herbicide exposure is recognized as a significant stressor impacting wild plants, we lack knowledge about how it affects the symbiotic relationship between plants and rhizobia. Moreover, we do not know whether the impact of herbicide exposure on symbiotic traits or plant fitness might be ameliorated by plant or rhizobial genetic variation. To address these gaps, we conducted a greenhouse study where we grew 17 full-sibling genetic families of red clover (Trifolium pratense) either alone (uninoculated) or in symbiosis with one of two genetic strains of rhizobia (Rhizobium leguminosarum) and exposed them to a concentration of the herbicide dicamba that simulated "drift" (i.e., off-target atmospheric movement) or a control solution. We recorded responses in immediate vegetative injury, key features of the plant-rhizobia mutualism (nodule number, nodule size, and N fixation), mutualism outcomes, and plant fitness (biomass). In general, we found that rhizobial variation more than plant variation determined outcomes of mutualism and plant fitness in response to herbicide exposure. Herbicide damage response depended on plant family, but also whether plants were inoculated with rhizobia and if so, with which strain. Rhizobial strain variation determined nodule number and size, but this was herbicide treatment-dependent. In contrast, strain and herbicide treatment independently impacted symbiotic N fixation. And while herbicide exposure significantly reduced plant fitness, this effect depended on inoculation state. Furthermore, the differential fitness benefits that the two rhizobial strains provided plants seemed to diminish under herbicidal conditions. Altogether, these findings suggest that exposure to low levels of herbicide impact key components of the plant-rhizobia mutualism as well as plant fitness, but genetic variation in the partners determines the magnitude and/or direction of these effects. In particular, our results highlight a strong role of rhizobial strain identity in driving both symbiotic and plant growth responses to herbicide stress.

Mitigating risks from atrazine drift to soybeans through foliar pre-spraying with a degrading bacterium.

Herbicides play a crucial role in managing weeds in agriculture, ensuring the productivity and quality of crops. However, herbicide drift poses a significant threat to sensitive plants, necessitating the consideration of ecosystem-based solutions to address this issue. In this study, foliar pre-spraying of atrazine-degrading Paenarthrobacter sp. AT5 was proposed as a new approach to mitigate the risks associated with atrazine drift on soybeans. Exposure to atrazine reduced chlorophyll levels and disturbed the antioxidant system and metabolic processes in soybean leaves, ultimately causing leaves to turn yellow. However, by pre-spraying, strain AT5 successfully colonized the surface of soybean leaves and mitigated the harmful effects of atrazine. This was achieved by slowing down atrazine absorption, expediting its reduction (half-life decreased from 2.22 d to 0.86 d), altering its degradation pathway (enhancing hydroxylation while weakening alkylation), and enhancing the interaction within phyllosphere bacteria communities. This study introduces a new approach that is both eco-friendly and user-friendly for reducing the risks of herbicide drift to sensitive crops, hence promoting the development of mixed cropping.

Parameter Optimization of Semiconductor Gas Sensor under AC Impedance Measurement.

Semiconductor gas sensors were confirmed to perform high linearity and a stable baseline under alternating current (AC) impedance measurements. However, a procedure to determine the optimal parameters of AC impedance measurements is still lacking. Taking the detection of SF6 decomposition gas as an example, this work has established a model of semiconductor gas sensors under AC impedance measurement. Employing four types of sensors to detect three gases (H2S, SO2, and CO), the effectiveness of the optimization method has been validated, as well. With the high linearity and stable baseline obtained from AC impedance measurement, it enables rapid correction of temperature drift within environmental temperatures ranging from 10 to 30 °C. Overall, the proposed method can provide a novel approach to inhibit the drift failure of semiconductor gas sensors.

Psychometrics of drift-diffusion model parameters derived from the Eriksen flanker task: Reliability and validity in two independent samples.

The flanker task is a widely used measure of cognitive control abilities. Drift-diffusion modeling of flanker task behavior can yield separable parameters of cognitive control-related subprocesses, but the parameters' psychometrics are not well-established. We examined the reliability and validity of four behavioral measures: (1) raw accuracy, (2) reaction time (RT) interference, (3) NIH Toolbox flanker score, and (4) two drift-diffusion model (DDM) parameters-drift rate and boundary separation-capturing evidence accumulation efficiency and speed-accuracy trade-off, respectively. Participants from two independent studies - one cross-sectional (N = 381) and one with three timepoints (N = 83) - completed the flanker task while electroencephalography data were collected. Across both studies, drift rate and boundary separation demonstrated comparable split-half and test-retest reliability to accuracy, RT interference, and NIH Toolbox flanker score, but better incremental convergent validity with psychophysiological measures (i.e., the error-related negativity; ERN) and neuropsychological measures of cognitive control than the other behavioral indices. Greater drift rate (i.e., faster and more accurate responses) to congruent and incongruent stimuli, and smaller boundary separation to incongruent stimuli were related to 1) larger ERN amplitudes (in both studies) and 2) faster and more accurate inhibition and set-shifting over and above raw accuracy, reaction time, and NIH Toolbox flanker scores (in Study 1). Computational models, such as DDM, can parse behavioral performance into subprocesses that exhibit comparable reliability to other scoring approaches, but more meaningful relationships with other measures of cognitive control. The application of these computational models may be applied to existing data and enhance the identification of cognitive control deficits in psychiatric disorders.

Contrasting Genomic Diversity and Inbreeding Levels Among Two Closely Related Falcon Species With Overlapping Geographic Distributions.

Genomic resources are valuable to examine historical demographic patterns and their effects to better inform management and conservation of threatened species. We evaluated population trends and genome-wide variation in the near-threatened Orange-breasted Falcon (Falco deiroleucus) and its more common sister species, the Bat Falcon (F. rufigularis), to explore how the two species differ in genomic diversity as influenced by their contrasting long-term demographic histories. We generated and aligned whole genome resequencing data for 12 Orange-breasted Falcons and 9 Bat Falcons to an annotated Gyrfalcon (F. rusticolus) reference genome that retained approximately 22.4 million biallelic autosomal SNPs (chromosomes 1-22). Our analyses indicated much lower genomic diversity in Orange-breasted Falcons compared to Bat Falcons. All sampled Orange-breasted Falcons were significantly more inbred than the sampled Bat Falcons, with values similar to those observed in island-mainland species comparisons. The distribution of runs of homozygosity showed variation suggesting long-term low population size and the possibility of bottlenecks in Orange-breasted Falcons contrasting with consistently larger populations in Bat Falcons. Analysis of genetic load suggests that Orange-breasted Falcons are less likely to experience inbreeding depression than Bat Falcons due to reduced inbreeding load but are at elevated risk from fixation of deleterious gene variants and perhaps a reduced adaptive potential. These genomic analyses highlight differences in the historical demography of two closely related species that have influenced their current genomic diversity and should result in differing strategies for their continued conservation.

Apathy and effort-based decision-making in Alzheimer's disease and subjective cognitive impairment.

INTRODUCTION: Apathy is a significant feature in Alzheimer's disease (AD) and subjective cognitive impairment (SCI), though its mechanisms are not well established. METHODS: An effort-based decision-making (EBDM) framework was applied to investigate apathy in 30 AD patients, 41 SCI participants, and 55 healthy controls (HC). Data were analyzed using a drift-diffusion model (DDM) to uncover latent psychological processes. RESULTS: SCI participants reported higher apathy than AD patients and HC. However, informant reports of apathy in AD patients were higher than self-reports and indicated significant apathy compared to HC. Both the AD and SCI groups showed reduced sensitivity to effort changes, linked to executive dysfunction in AD and apathy in SCI. Increased resting functional cortical connectivity with the nucleus accumbens (NA) was associated with higher apathy in SCI. DISCUSSION: These results highlight a similar disruption of EBDM in AD and SCI, differentially related to executive functioning in AD and apathy in SCI. Highlights: This is the first study investigating apathy using an effort-based decision-making (EBDM) framework in Alzheimer's disease (AD) and subjective cognitive impairment (SCI).Self-reports underestimate apathy in AD patients when compared to informant reports and healthy controls (HC). SCI participants, in whom self and informant reports were more concordant, also showed higher degrees of apathy.Both AD and SCI groups showed reduced sensitivity to effort.Reduced sensitivity to effort correlates with executive dysfunction in AD and apathy, but not depression, in SCI.Increased nucleus accumbens (ventral striatum) connectivity with the frontoparietal network was associated with higher apathy scores in SCI.The results thus suggest that while AD and SCI can have similar deficits in EBDM, these deficits correlate with distinct clinical manifestations: executive dysfunction in AD and apathy in SCI.

Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors.

In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected.

[Inequalities ed equity in health]. / Disuguaglianze ed equità di salute.

The relationship between inequalities and health has been widely studied. Several theories have been proposed to define the role of the factors that act on the health levels, their strength and the determination profile. This review recalls the main theories and interpretation proposed by different fields of knowledge and highlights that there is not a single way to generate inequalities in health. Deprivation, disadvantage drift, empowerment, structure and social capital, status syndrome, and embodiment are some of the concepts recalled and explored. Some theories consolidate each other, and some remain more isolated. To increase the knowledge on the mechanisms to define the disease distributions among individuals in the population can help to define new and greater equity intervention policies.

Semi-supervised comparative learning compensation method for chemical gas sensor drift.

The gradual and unpredictable variation in chemo-sensory signal responses when exposed to the same analyte under identical conditions, commonly referred to as sensor drift, has long been recognized as one of the most serious challenges faced by chemical sensors. The traditional drift compensation method is both labor-intensive and expensive, as it requires frequent collection and labeling of gas samples for recalibration. Introducing a small number of meaningful drift calibration samples can be an attractive strategy to reduce the computational load and improve the performance of the updated classifier. However, under the influence of drift, new challenges arise due to the difference in the distribution of source and target domain data. This paper proposes a novel algorithm framework called semi-supervised contrastive learning drift compensation (SSCLDC). The framework automatically extracts high-level abstract features based on a multilayer perceptron to better represent the structure of the source data. In addition, to address the issue of data distribution differences caused by drift between the source and target domains. We add a small number of reference sample pairs into the training for semi-supervised learning. Combining a contrastive loss function that can represent the matching degree of paired samples effectively overcomes the problem of sensor drift. The Kennard-Stone sequential algorithm is used to select the representative reference sample from the set of candidate reference samples. Experiments conducted on a widely used long-term chemical gas sensor drift dataset demonstrate that the proposed method outperforms several classic drift compensation techniques, highlighting its effectiveness and practical applicability.

Direct observation of the neural computations underlying a single decision.

Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons. Neurons in the parietal and prefrontal cortex are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound. Critically, it is the unobserved stochastic component that is thought to confer variability in both choice and decision time. Here, we elucidate this drift-diffusion signal on individual decisions. We recorded simultaneously from hundreds of neurons in the lateral intraparietal cortex of monkeys while they made decisions about the direction of random dot motion. We show that a single scalar quantity, derived from the weighted sum of the population activity, represents a combination of deterministic drift and stochastic diffusion. Moreover, we provide direct support for the hypothesis that this drift-diffusion signal approximates the quantity responsible for the variability in choice and reaction times. The population-derived signals rely on a small subset of neurons with response fields that overlap the choice targets. These neurons represent the integral of noisy evidence. Another subset of direction-selective neurons with response fields that overlap the motion stimulus appear to represent the integrand. This parsimonious architecture would escape detection by state-space analyses, absent a clear hypothesis.

Tracking the evolution of a single choice.

Measuring the activity of hundreds of neurons in macaque brains simultaneously provides further evidence that drift-diffusion dynamics underlie how decisions are made in the brain.

A clinical investigation of force plate drift error on predicted joint kinetics during gait.

Inverse dynamic analysis is a technique used during gait analysis to estimate intersegmental forces and net joint moments. Inverse dynamic calculations are susceptible to various forms of error. One such error is force plate drift, often produced by humidity condensing within the input connectors and electronics, causing an undesired change in output over time. This can be particularly concerning for movement laboratories where inverse dynamics are considered in clinical decision-making processes. Manufacturers will provide tolerance levels for drift. However, levels of acceptable drift are rarely considered from a clinical perspective. Therefore, this study aims to establish clinically acceptable limits of force plate drift error, induced by applying systematic errors to force plate channels, on predicted lower limb joint moments during gait. Gait data of 10 children with typical development were analysed and induced errors of 0.5 N, 1 N, 1.5 N, 3 N, 6 N and 12 N were incrementally applied to the horizontal and vertical force channels. Data were recalculated for each increment and mean profiles compared to an error free mean (±1SD) band. Error was deemed clinically significant when moments fell outside the mean (±1SD) band. Induced error at 6 N and above was sufficient to cause a clinically significant change. Sagittal and coronal plane moments at the hip were most affected, followed by the knee and then the ankle. While manufacturer guidelines for acceptable drift are usually well below 6 N, care is needed when using force plates over several minutes or more as drift may eventually exceed clinically acceptable limits.

Physically stressed bees expect less reward in an active choice judgement bias test.

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias.

Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.