Set Of Experiments Research Articles

Microstructure and Properties Variation of High-Performance Grey Cast Iron via Small Boron Additions

A study was undertaken to investigate the effects of small boron additions on the solidification and microstructure of hypo-eutectic alloyed grey cast iron. The characteristic temperatures upon crystallisation of the treated metal melt were recorded, specifically those concerning small boron addition by using thermal analysis with the ATAS system. Additionally, a standardised wedge test was set to observe any changes in chill performance. The microstructures of thermal analysis samples were analysed using a light optical microscope (LOM) and field emission scanning electron microscopy (FE-SEM) equipped with energy dispersive spectroscopy (EDS), which reveal variations in graphite count number with the addition of boron within observed random and undercooled flake graphite. The effect of boron was estimated by the classical analytical and statistical approach. The solidification behaviour under equilibrium conditions was predicted by a thermodynamic approach using Thermo-Calc. Based on all gathered data, a response model was set with boron for given melt quality and melt treatment using the experimentally determined data. The study reveals that boron as a ferrite and carbide-promoting element under the experimental set shows weak nucleation potential in synergy with other heterogenic nuclei at increased solidification rates, but no considerable changes were observed by the TA samples solidified at slower cooling rates, indicating the loss of the overall inoculation effect. The potential presence of boron nitride as an inoculator for graphite precipitation for a given melt composition and melt treatment was not confirmed in this study. It seems that boron at increased solidification rates can contribute to overall inoculation, but at slower cooling rates these effects are gradually lost. In the last solidification range, an increased boron content could have a carbide forming nature, as is usually expected. The study suggests that boron in traces could affect the microstructure and properties of hypo-eutectic alloyed grey cast iron.

Bovine Serum Albumin Rejection by an Open Ultrafiltration Membrane: Characterization and Modeling.

The classic application of ultrafiltration (UF) is for the complete retention of proteins, and in that situation, the transport behavior is well established. More open membranes with fractional retention are used when separating different proteins. However, protein transport has not been well documented yet in the literature. The bovine serum albumin (∼69 kDa) observed rejection ranges from 0.65 to 1 using a 300 kDa molecular weight cut-off membrane at different pH, ionic strength, and pressure. We demonstrated that, especially with open UF, the transport of proteins through the membrane is dominated by advection, with insignificant diffusion effects (p value > 0.05). We showed that with open UF, retention is not only caused by size exclusion but also to a large extent by electrostatic interactions and oligomerization of the proteins. Mass transfer in the polarization layer was relatively independent of the pH and ionic strength. It was underestimated by common Sherwood relations due to a relatively large contribution of the reduction in the flow turbulence near the membrane by the removal of fluid through the membrane. We propose a model that allows relatively quick characterization of the rejection of proteins without prior knowledge of the pore sizes and charges based on just a limited set of experiments. Therefore, protein rejection with the open UF system can be targeted by tuning the processing conditions, which might be useful for designing protein fractionation processes.

A NOVEL HYBRID HARMONY SEARCH (HS) WITH WAR STRATEGY OPTIMIZATION (WSO) FOR SOLVING OPTIMIZATION PROBLEMS

The usage of nature-inspired meta-heuristic algorithms is increasing due to their simplicity and versatility. These algorithms are widely used in numerous domains, especially in scientific fields such as operations research, computer science, artificial intelligence, and mathematics. Based on the core principles of exploration and exploitation, they provide flexible problem-solving abilities. This study presents a novel method to improve the effectiveness of the War Strategy Optimization (WSO) algorithm for optimization issues. The suggested approach combines the WSO technique with the Harmony Search (HS) algorithm, resulting in a hybrid algorithm called H-WSO. The aim is to enhance the overall optimization performance by leveraging the capabilities of both algorithms through the integration of swarm intelligence approaches. In order to assess the effectiveness of the recently suggested H-WSO algorithm, a set of experiments was carried out on 50 benchmark test functions. These functions included both unimodal and multimodal functions and spanned across different dimensions. The findings from these studies clearly showed a notable enhancement in the efficiency of the H-WSO algorithm when compared to the original WSO algorithm. Various metrics were utilized to evaluate the effectiveness of the proposed algorithm, including the optimal fitness function value (Mean), Standard Deviation (St.d), and Median. The H-WSO algorithm regularly shows higher efficiency than the WSO algorithm, making it a promising and practical approach for addressing complicated optimization challenges

Electrochemical silica removal from carbon nanotube surfaces: Energy consumption and removal mechanisms

Inorganic fouling, or scaling, is a major challenge for engineered systems exposed to aqueous environments, particularly in water treatment, desalination, or thermal systems where the high salinity and temperature conditions favor surface scaling. Silica, in particular, is hard to remove once a scaling layer forms on the surface. Current scaling mitigation strategies include the addition of antiscalants, physical cleaning by flushing or backwashing, and pretreatment of the feed water. These mitigation strategies increase operation costs due to increased chemical use and system downtime, as well increase the complexity of the process. In this work, we investigated the ability of carbon nanotube (CNT) coatings to disperse silica from the surface via electrochemical reactions, providing a chemical-free approach to scaling control. Silica scaling was induced and then dispersed from CNT coupons upon the application of a potential difference across the experimental set up, with the CNT coupon acting as a cathode or anode. Scale removal was measured using optical coherence tomography at different applied current densities. In this study, a cathodic current of 2.5 A/m2 was found to achieve the highest silica removal: > 75% after 30 min. Control experiments conducted using pH-buffered solutions and pH-insensitive gypsum as the scaling mineral suggest that silica dispersion from the CNT surface was due to a combination of gas evolution and interfacial pH change. These results provide a better understanding of the potential of using electrochemical coatings for the removal of inorganic fouling from surfaces.

Attention control in preterm and term 5-month-old infants: Cross-task stability increases with gestational age.

Cross-task stability refers to performance consistency across different settings and measures of the same construct. Cross-task stability can help us understand developmental processes, including how risks such as preterm birth affect outcomes. We investigated cross-task stability of attention control in 32 preterm and 39 term infants. All infants had the same chronological age at time of testing (5months) but varied in gestational age (GA) at birth (30-42weeks). Infants completed an experimental attention following task with a researcher and a naturalistic play observation with their mothers. Both preterm and term infants demonstrated attention following in the experimental task. GA and flexibility of attention were related: the likelihood of no turn trials decreased with increasing GA. To evaluate cross-task stability, we compared attention performance in the experimental and naturalistic settings. Flexible attention shifts on the experimental task were positively related to attention to objects in the naturalistic observation. Furthermore, the association between flexible attention shifts on the experimental task and attention to objects in the naturalistic observation was moderated by GA. Our study provides initial evidence that the consolidation of attention control increases with GA. These findings highlight the value of comparing experimental and observational measures of attention.

Liquid to solid phase transition detection by using a vibrating tube densimeter along with densities up to 137 MPa of beef tallow fatty acid alkyl esters

Phase behavior and thermophysical properties of biodiesel at high pressure and high temperature are limited in the literature. These are useful to develop models and to understand their behavior having in mind the use of biodiesel in engines. Liquid to solid phase transitions, by using a vibrating tube densimeter, along with densities of three beef tallow fatty acid alkyl esters were studied in this work. Measurements of the oscillating period for each biofuel were carried out in a modular array constituted by two vibrating tube densimeters coupled in series and connected to a single loading cell. The oscillating period was converted to density by the forced path mechanical calibration (FPMC) method. Experimental liquid density sets for three beef tallow biodiesel samples based on fatty acid alkyl esters (FAAEs) are reported for the first time for pressures up to 137 MPa. These biofuels, named fatty acid methyl esters (FAMEs), fatty acid ethyl esters (FAEEs) and fatty acid butyl esters (FABEs), were measured in the temperature range of 286–393 K. The detection procedure for the beginning of the high-pressure solidification was focused on the beef tallow FABEs sample based upon its lower cloud point (CPFABEs = 281.15 K) in comparison with the reported property for the other esters (CPFAEEs = 289.15 K, CPFAMEs = 291 K) with the same feedstock; therefore, the conditions for the metastable liquid to solid phase transition associated to the effect of high pressure was detailed for waste beef tallow butyl ester biodiesel by performing step-by-step compressions in the interval of 286.20–298.13 K and with the help of the vibrating tube densimeters. FABEs had the lower density in contrast with FAEEs and FAMEs, this one being the denser biofuel. Modeling of liquid density data was performed by the perturbed-chain statistical associating fluid theory (PC-SAFT) Equation of State and the Tammann-Tait equation with maximum absolute average deviations of 0.15 % and 0.05 %, respectively. Both models were applied for correlating the thermodynamic derived properties of the fluid phase: isobaric thermal expansivity and isothermal compressibility.

Predicting rapid intensification of tropical cyclones in the western North Pacific: a machine learning and net energy gain rate approach

In this study, a machine learning (ML)-based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs. It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms. This ensemble approach showed a noteworthy improvement of approximately 10% in the skill score of RI prediction when NGR was included. The findings of this study emphasize the potential of NGR in refining TC intensity prediction and underline the effectiveness of ensemble ML models in RI event detection.

Tripolar configuration and pulse shape in cochlear implants reduce channel interactions in the temporal domain

The present study investigates effects of current focusing and pulse shape on threshold, dynamic range, spread of excitation and channel interaction in the time domain using cochlear implant stimulation. The study was performed on 20 adult guinea pigs using a 6-channel animal cochlear implant, recording was performed in the auditory midbrain using a multielectrode array. After determining the best frequencies for individual recording contacts with acoustic stimulation, the ear was deafened and a cochlear implant was inserted into the cochlea. The position of the implant was controlled by x-ray. Stimulation with biphasic, pseudomonophasic and monophasic stimuli was performed with monopolar, monopolar with common ground, bipolar and tripolar configuration in two sets of experiments, allowing comparison of the effects of the different stimulation strategies on threshold, dynamic range, spread of excitation and channel interaction. Channel interaction was studied in the temporal domain, where two electrodes were activated with pulse trains and phase locking to these pulse trains in the midbrain was quantified. The results documented multifactorial influences on the response properties, with significant interaction between factors. Thresholds increased with increasing current focusing, but decreased with pseudomonophasic and monophasic pulse shapes. The results documented that current focusing, particularly tripolar configuration, effectively reduces channel interaction, but that also pseudomonophasic and monophasic stimulation and phase duration intensity coding reduce channel interactions.

Not Batting an Eye: Figurative Meanings of L2 Idioms Do Not Interfere with Literal Uses

Encountering idioms (hit the sack = “go to bed”) in a second language (L2) often results in a literal-first understanding (“literally hit a sack”). The figurative meaning is retrieved later, subject to idiom familiarity and L2 proficiency, and typically at a processing cost. Intriguingly recent findings report the overextension of idiom use in inappropriate contexts by advanced L2 users, with greater L2 proficiency somewhat mitigating this effect. In this study, we tested the tenability of this finding by comparing eye-movement patterns for idioms used literally, vs. literal control phrases (hit the dirt) in an eye-tracking-while-reading paradigm. We hypothesised that if idiom overextension holds, processing delays should be observed for idioms, as the (over)activated but contextually irrelevant figurative meanings would cause interference. In contrast, unambiguous control phrases should be faster to process. The results demonstrated undifferentiated processing for idioms used literally and control phrases across measures, with L2 proficiency affecting both similarly. Therefore, the findings do not support the hypothesis that advanced L2 users overextend idiom use in inappropriate contexts, nor that L2 proficiency modulates this tendency. The results are also discussed in light of potential pitfalls pertaining to idiom priming under typical experimental settings.

Diurnal patterns in Twitter sentiment in Italy and United Kingdom are correlated.

Diurnal variations in indicators of emotion have been reliably observed in Twitter content, but confirmation of their circadian nature has not been possible due to the many confounding factors present in the data. We report on correlations between those indicators in Twitter content obtained from 9 cities of Italy and 54 cities in the United Kingdom, sampled hourly at the time of the 2020 national lockdowns. This experimental setting aims at minimizing synchronization effects related to television, eating habits, or other cultural factors. This correlation supports a circadian origin for these diurnal variations, although it does not exclude the possibility that similar zeitgebers exist in both countries including during lockdowns.

Quantum fluctuations drive nonmonotonic correlations in a qubit lattice

Fluctuations may induce the degradation of order by overcoming ordering interactions, consequently leading to an increase of entropy. This is particularly evident in magnetic systems characterized by nontrivial, constrained disorder, where thermal or quantum fluctuations can yield counterintuitive forms of ordering. Using the proven efficiency of quantum annealers as programmable spin system simulators, we present a study based on entropy postulates and experiments on a platform of programmable superconducting qubits to show that a low level of uncertainty can promote ordering in a system impacted by both thermal and quantum fluctuations. A set of experiments is proposed on a lattice of interacting qubits arranged in a triangular geometry with precisely controlled disorder, effective temperature, and quantum fluctuations. Our results demonstrate the creation of ordered ferrimagnetic and layered anisotropic disordered phases, displaying characteristics akin to the elegant order-by-disorder phenomenon. Extensive experimental evidence is provided for the role of quantum fluctuations in lowering the total energy of the system by increasing entropy and defect clustering. Our thorough and comprehensive application of an intentionally introduced noise on a quantum platform provides insight into the dynamics of defects and fluctuations in quantum devices, which may help to reduce the cost associated with quantum processing.

Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

BackgroundModeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.ResultsWe propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).ConclusionHere we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

Anticipating disruption: artificial intelligence and minor experiments in education policy

ABSTRACT The growing use of artificial intelligence in education extends and intensifies technologies of governing, including datafication, performativity and accountability. In this article, we outline how the use of AI and data science has the disruptive potential to create new norms in education policy and governance. We report on an ethnographic investigation into an education department initiative that involved data scientists using machine learning to identify causal links between different school-based and non-school based factors, and educational outcomes. The article proposes that machine learning is being introduced into this education department as a set of minor experiments in education governance. We focus on issues of changing expertise and evidence and differences in method between new data science approaches and established statistical expertise in this education department, in order to highlight minor but important new governance practices. We conclude that the increasing use of AI in education, as part of new cognitive infrastructures, is quietly shifting the ways in which education systems operate.

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement.

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

Machine learning tools to improve nonlinear modeling parameters of RC columns

Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental data sets: identifying the most likely mode of failure of structural components, and implementing data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It was found that machine learning regression models and machine learning black-boxes were more accurate than current provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized Linear Regression was the most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black-box models evaluated, namely the Gaussian Process Regression and the Neural Network (NN), provided the most accurate estimates of MPs a and b. The NN model was the most accurate machine learning tool of all evaluated. A multi-class classification tool from the Scikit-learn machine learning library correctly identified column mode of failure with 79 % accuracy for rectangular columns and with 81 % accuracy for circular columns, a substantial improvement over the classification rules in ASCE 41-13.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.

To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Increasing striatal dopamine release through repeated bouts of theta burst transcranial magnetic stimulation of the left dorsolateral prefrontal cortex. A 18F-desmethoxyfallypride positron emission tomography study.

Transcranial Magnetic Stimulation (TMS) can modulate fronto-striatal connectivity in the human brain. Here Positron Emission Tomography (PET) and neuro-navigated TMS were combined to investigate the dynamics of the fronto-striatal connectivity in the human brain. Employing 18F-DesmethoxyFallypride (DMFP) - a Dopamine receptor-antagonist - the release of endogenous dopamine in the striatum in response to time-spaced repeated bouts of excitatory, intermittent theta burst stimulation (iTBS) of the Left-Dorsolateral Prefrontal Cortex (L-DLPFC) was measured. 23 healthy participants underwent two PET sessions, each one with four blocks of iTBS separated by 30 minutes: sham (control) and verum (90% of individual resting motor threshold). Receptor Binding Ratios were collected for sham and verum sessions across 37 time frames (about 130 minutes) in striatal sub-regions (Caudate nucleus and Putamen). Verum iTBS increased the dopamine release in striatal sub-regions, relative to sham iTBS. Dopamine levels in the verum session increased progressively across the time frames until frame number 28 (approximately 85 minutes after the start of the session and after three iTBS bouts) and then essentially remained unchanged until the end of the session. Results suggest that the short-timed iTBS protocol performed in time-spaced blocks can effectively induce a dynamic dose dependent increase in dopaminergic fronto-striatal connectivity. This scheme could provide an alternative to unpleasant and distressing, long stimulation protocols in experimental and therapeutic settings. Specifically, it was demonstrated that three repeated bouts of iTBS, spaced by short intervals, achieve larger effects than one single stimulation. This finding has implications for the planning of therapeutic interventions, for example, treatment of major depression.

Determining critical nodes in optimal cost attacks on networked infrastructures

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

The fatigue-inducing effects of cancer and its therapy are characterized by decreased physical activity in the absence of any motivational deficit

Although cancer and its therapy are well known to be associated with fatigue, the exact nature of cancer-related fatigue remains ill-defined. We previously reported that fatigue-like behavior induced independently by tumor growth and by the chemotherapeutic agent cisplatin is characterized by reduced voluntary wheel running and an intact motivation to expand effort for food rewards. The present set of experiments was initiated to characterize the functional consequences of fatigue induced by chemoradiotherapy in tumor-bearing mice and relate them to changes in the expression of genes coding for inflammation, mitochondria dynamics and metabolism. Two syngeneic murine models of cancer were selected for this purpose, a model of human papilloma virus-related head and neck cancer and a model of lung cancer. In both models, tumor-bearing mice were submitted to chemoradiotherapy to limit tumor progression. Two dimensions of fatigue were assessed, the physical dimension by changes in physical activity in mice trained to run in wheels and the motivational dimension by changes in the performance of mice trained to nose poke to obtain a food reward in a progressive ratio schedule of food reinforcement. Chemoradiotherapy reliably decreased wheel running activity but had no effect on performance in the progressive ratio in both murine models of cancer. These effects were the same for the two murine models of cancer and did not differ according to sex. Livers and brains were collected at the end of the experiments for qRT-PCR analysis of expression of genes coding for inflammation, mitochondria dynamics, and metabolism. The observed changes were mainly apparent in the liver and typical of activation of type I interferon and NF-κB-dependent signaling, with alterations in mitochondrial dynamics and a shift toward glycolysis. Although the importance of these alterations for the pathophysiology of cancer-related fatigue remains to be explored, the present findings indicate that fatigue brought on by cancer therapy in tumor-bearing mice is more physical than motivational.

High-Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data

Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. In recent years, generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. In this study, for the first time, a small dataset of rosacea with 300 full-face images was utilized to further investigate the possibility of generating synthetic data. Our experimentation demonstrated that the strength of R1 regularization is crucial for generating high-fidelity rosacea images using a few hundred images. This was complemented by various experimental settings to ensure model convergence. We successfully generated 300 high-quality synthetic images, significantly contributing to the limited pool of rosacea images for computer-aided diagnosis. Additionally, our qualitative evaluations by 3 expert dermatologists and 23 non-specialists highlighted the realistic portrayal of rosacea features in the synthetic images. We also provide a critical analysis of the quantitative evaluations and discuss the limitations of solely relying on validation metrics in the field of computer-aided clinical image diagnosis.