Qualitative Features Research Articles

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Abstract A008: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry

Abstract Background: Cancer disparities exist within the Hispanic/Latino/a/x (H/L) population by nativity status. Non-US born H/L colorectal cancer (CRC) patients and Prostate Cancer (PCa) patients in California have a statistically significant lower risk of cancer-specific survival when compared to Non-Hispanic White patients after controlling for confounders. However, birthplace information is largely incomplete in population-based cancer registries. For example, more than 40% of H/L patients have missing information on nativity in the California Cancer Registry (CCR) database. With the goal of better imputing missing birthplace, we built a Machine Learning (ML) model to predict nativity among CCR H/L cancer patients. Methods: H/L cases with primary invasive CRC (n = 75,613) and PCa (n = 93,042) at least 18 years old, diagnosed between 1988 and 2021 contained in the CCR research file were analyzed (46.36% had missing birthplace). A binary indicator variable assigning cases with known birthplace to US-born vs. Non-US born was constructed as the target for prediction. A stratified split by levels of the outcome was used to generate training (70%, n = 63,323) and testing (30%, n = 27,139) datasets. We used a pruned classification tree to perform feature selection, with the 10 features with the highest importance (measured by the overall reduction in impurity for any split containing each feature) used to predict nativity. Missing observations of the quantitative and qualitative features were mean, or mode imputed, respectively. The classification methods considered were classification trees, Random Forest (RF), and Boosting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and the rate of correctly classified observations. Results: The top three features with the highest importance were the NHIA (NAACCR Hispanic/Latino Identification Algorithm) label, the Hispanic/Spanish identity, and the year of social security number issuance (SSN); the latter is part of a current SSN-based algorithm used to impute nativity status in this population. Classification models showed similar prediction performance. The highest AUC was reported by the RF (AUC = 0.9822), followed by Boosting (AUC = 0.9785), and the pruned classification tree (AUC = 0.9647). A similar rate of correctly classified observations was reported for the Boosting and RF models (∼94%). The RF model ranked feature importance in a similar order as the pruned classification tree. Ultimately, the RF model was the best performing classifier with a sensitivity and specificity of 0.61 and 0.90, respectively. This classifier substantially outperforms the SSN-based algorithm, which reported a correct classification rate of 81%, a sensitivity = 0.54, and a specificity = 0.41 in this dataset. Conclusions: This proof-of-concept analysis showcases a potential for developing a ML model to overcome missing nativity status in H/L cancer patients to enable more accurate classification by nativity in future research using CCR data. Citation Format: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry. Joel Sanchez Mendez, Lihua Liu, Juan P. Lewinger, Laura Fejerman, Mariana C. Stern [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A008.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens’ PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 − 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a “Capsular” morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed “Unipolar” morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and “Capsular” enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Sensory Evaluation and Consumers' Acceptance of a Low Glycemic and Gluten-Free Carob-Based Bakery Product.

Carob pulp flour has antidiabetic and antioxidant activities, is naturally sweet, and is rich in fibers. It is obtained from carob pod pulp from the evergreen tree Ceratonia siliqua L., which is grown in Mediterranean areas and is known for locust bean gum production. Despite its valuable effects on health, such as the modulation of the glycemic index, this ingredient has a tremendous impact on technological and hedonic features, mainly on color, flavor, and texture. In this paper, the qualitative features and consumers' acceptance of a carob-based gluten-free bakery product where rice flour was substituted at 40% with carob pulp flour were studied. A panel group of experts described the bread as dark, quite dense, sweet, aromatic, and with a limited bubble dispersion. On the other hand, the sensory assessment and the willingness to pay of consumers were assessed in two groups (a fully informed one about heathy attributes of the food and a blind one). The results indicated a moderate appreciation of the overall quality of the product (average score between 4 and 5 points on a 9-point Likert scale). The information about the food's healthy properties and the ability to maintain a low glycemic index did not enhance the consumers' perception of the product, while previous knowledge and involvement in the product consumption were perceived to have primary importance regarding the final consumers' choice. Finally, an accelerated shelf-life test was run on the packaged snack to evaluate the general quality and stability. The protective packaging helped in limiting bread decay and maintaining the textural characteristics.

Neural networks in oncourology

In recent decades, neural networks have been widely applied in many fields of science and medicine. Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. This allows for more precise and timely diagnoses. Moreover, they can be used to predict tumour progression, evaluate treatment effectiveness, and optimise treatment plans for each patientIn oncourology, the use of neural networks offers new perspectives for the diagnosis, prognosis, and treatment of various cancer conditions related to the urinary tract and male reproductive system. This review article explores how neural networks are being used in this field and present research into the use of neural networks for diagnosing, predicting the course and treating urological oncological diseases. The advantages and limitations of using neural networks in this field are demonstrated, and possible directions for future research are suggested. The application of neural networks in oncourology opens new horizons for the development of a personalised approach to diagnosing and treating oncological diseases. Artificial intelligence has the potential to become a powerful tool for improving the accuracy of patient outcome predictions and reducing undesirable side effects of therapy. Introducing neural networks into oncourological practice creates new opportunities for enhancing the work of healthcare organisations and improving the quality of care provided to patients. This can lead to better treatment outcomes and improved patient satisfaction.

EFFECTS IN DISCRETE AND CONTINUOUS STRENGTHENING OF MACHINES ELEMENTS

The work describes approaches to problem solving of operational and effective improvement of the technical and tactical characteristics of military and civilian machines: tanks, armored vehicles, diesel locomotives, turbo-expander power plants, autonomous power plants of strategic objects, etc. For this purpose, fundamentally new methods of strengthening of contacting machines elements are being developed. These methods combine the advantages of discrete and continuous strengthening methods, which have largely exhausted their capabilities by now. Qualitative features and advantages of proposed discrete-continuous strengthening methods require effective implementation. We are talking about the justification of progressive technical solutions based on the criteria of strength and durability. For this purpose, the methodology of multi-criteria synthesis in the expanded space of design and technological parameters will be improved. In addition, as a basis for substantiating advanced technological solutions, a set of related problems of analysis of the stress-strain state, friction and wear is formed. Therefore, based on the improved system approach, a much higher level of strength and durability of the reinforced structural parts is achieved than with traditional methods. The results of test problems solving are illustrated. In the result, micro-macromodels are generalized to determine the joint influence of structural and technological factors, microstructure, physical and mechanical properties of materials of reinforced layers on the contact interaction and stress-strain state of representative fragments of the system of discrete-continuous reinforced bodies. The results of analysis of combined influence of structural and technological factors, microstructure, physical and mechanical properties of materials reinforced layers have been established on the contact interaction and stress-strain state of test representative fragments of the system of discrete-continuously reinforced bodies. At the same time, a significant influence of reinforced layers properties was established on strength of contacting bodies.

Comparison between pancreatoblastoma (PB) and solid pseudopapillary neoplasm (SPN) in pediatric patients with enhanced CT

Rationale and objectivesTo investigate whether computed tomography features can differentiate pancreatoblastoma (PB) from solid pseudopapillary tumor (SPN) in children. Materials and methodsClinical and imaging data of 18 cases of PB and 61 cases of SPN confirmed by surgery or biopsy were retrospectively analyzed. All enrolled patients underwent 3 phases (non-contrast, arterial, and portal venous phases) of CT scanning. Qualitative CT analysis (location, margin, solid/cystic component proportion, calcification, hemorrhage, peritumoral vascularity, bile duct dilatation, pancreatic duct dilatation, pancreatic atrophy, vascular invasion, peripancreatic invasion, and distant metastases) and quantitative analysis (maximum tumor diameter, interface between tumor and parenchyma [delta], arterial enhancement ratio [AER], and portal enhancement ratio [PER]) were performed. The general CT morphologic features, age and tumor markers were compared also compared between the groups. Univariate analysis and the F test were conducted to identify features of PB. Then logistic Regression classifier was trained using the top five features with the highest F-value. Moreover, we used 5-fold cross-validation techniques for the validation of our model. ResultsPB exhibited a significantly higher frequency of location in the body/tail, larger tumor size, poorly defined margins, calcification, peritumoral vascularity, pancreatic atrophy, and less hemorrhage. In addition, PB had higher AER, PER and lower delta relative to SPN (p < 0.05). PB presented a younger age and higher levels of AFP. Results of the F test indicated that AFP, AER, Age, calcification and pancreatic atrophy were the top five features included in the model that could differentiate pediatric PB from SPN. The combined model of CT and clinical features performed well in differentiating PB from SPN, with an AUC of 0.981 in the training cohort and 0.953 in the validation cohort. ConclusionsAFP, AER, age, calcification and pancreatic atrophy are robust CT and clinical features for differentiating pediatric PB from SPN. A combination of qualitative and quantitative CT features may provide good diagnostic accuracy in differentiating PB from SPN in children.

Detection and classification of electrocardiography using hybrid deep learning models

ObjectiveElectrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. MethodsPrecision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared. ResultsThe classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07 and 1.87, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record. ConclusionWhen compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.

Leaf morphometric analysis of the Mexican species of Forestiera (Oleaceae) using machine learning

Forestiera includes woody plants distributed from the United States to Ecuador, with a wide distribution in Mexico. However, basic knowledge about its biology and taxonomy is still limited. In the present study, a leaf morphometric analysis of the 14 Mexican Forestiera species, including F. neomexicana, was carried out to determine if leaf characters allow classifying the species and to evaluate if the inclusion of qualitative features in statistical analyses improves the fit of used methods. Four quantitative and four qualitative variables from herbarium specimens, were evaluated. A matrix was generated which was submitted to multivariate analysis, including Linear Discriminant Analysis and Random Forest. Results revealed that the species are correctly classified, with Random Forest being the method with the best classifying capacity. The most important variables to differentiate the species are the shape of the base, width and margin of the leaves. All species were classified as different entities including F. pubescens and F. neomexicana, currently considered synonyms, were classified by Random Forest as different entities.

Association Between Baseline Macular Morphologic Features on Optical Coherence Tomography and Visual Outcomes in Patients with Vogt-Koyanagi-Harada Disease.

The choroidal thickening and serous retinal detachments that characterize Vogt-Koyanagi-Harada (VKH) disease can be imaged in detail using spectral domain optical coherence tomography (SD-OCT). Whether specific qualitative and quantitative SD-OCT features at presentation were associated with visual outcomes in a randomized controlled trial comparing methotrexate to mycophenolate for steroid-sparing control of uveitis were evaluated. An exploratory subanalysis of data from the FAST trial in which SD-OCT images from VKH participants were analyzed for presence/absence of bacillary detachments, retinal pigment epithelium (RPE) folds, and internal limiting membrane (ILM) fluctuations was performed. A modified RPE undulation index was calculated to provide a quantifiable surrogate marker for choroidal folds. SD-OCT images were available from 158 eyes with VKH. At baseline, bacillary detachments were present in 23.5% of eyes, RPE folds in 22.8% of eyes, and ILM fluctuations in 35.2% of eyes. For each 0.1 unit increase in modified RPE undulation index, there was an associated 0.13 increase in mean logMAR BSCVA at baseline. None of the SD-OCT features were associated with BSCVA at the 6-month primary endpoint. Indeed, mean final BSCVA was similar in those with and without the SD-OCT features of interest at baseline, and was between 0.1 and 0.2 logMAR (Snellen visual acuity 20/25 to 20/30). While eyes with VKH may present with a variety of SD-OCT imaging pathology prior to starting immunosuppression with methotrexate or mycophenolate mofetil, final visual outcome in our study was excellent. With appropriate immunosuppression, good visual outcomes are possible in VKH.ClinicalTrials.gov Identifier NCT01829295Date of Registration: April 11, 2013.

Differences In The Qualitative Features Of Dermatoglyphs Between Persons With Turner And Klinefelter Syndromes And Those With A Normal Karyotype

: Individuals with numerical abnormalities in the X and Y chromosomes are ideal candidates for studying the impact of these chromosomes on the development of specific dermatoglyphic characteristics in humans. The aim of this study is to identify differences in the qualitative features of dermatoglyphs in the Albanian population of Kosovo between women with Turner syndrome and women with a normal karyotype, as well as between men with Klinefelter syndrome and men with a normal karyotype.

NEW METHODOLOGIES FOR ASSESSING CAVITATION LOAD AND RESISTANCE OF STRUCTURAL MATERIALS AND PROTECTIVE COATINGS IN NUCLEAR POWER INDUSTRY. PART I. MONOFRACTIONAL APPROACH

The paper presents a new method for assessing the cavitation load and durability of structural materials and protective coatings. To determine the cavitation load on a rotating disk or other test rig, a new method for extracting erosion loads based on tests of reference materials on a reference cavitation rig is proposed. Direct measurements of the cavitation load in a cavitation tunnel with a slot cavitator, performed using piezoelectric sensors, made it possible to obtain information on the spatial distribution of the impact load. As an analytical function for modeling cumulative erosion curves in the monofractional approach, the logarithmic formula of L. Sitnik is used, assuming that cavitation erosion is a stochastic process described using fatigue theory. Armco iron is chosen as a reference material, in view of the weak dependence of its erosion rate on cavitation qualitative features. Therefore the local load value could have been extracted from the eroded surface profile of the Armco iron sample. Using the example of TiN coating analysis on stainless steel and VT3-1 titanium alloy, the time dependencies and threshold characteristics of their resistance were determined depending on the parameter of the density of the supplied energy.

Firing rate models for gamma oscillations in I-I and E-I networks.

Firing rate models for describing the mean-field activities of neuronal ensembles can be used effectively to study network function and dynamics, including synchronization and rhythmicity of excitatory-inhibitory populations. However, traditional Wilson-Cowan-like models, even when extended to include an explicit dynamic synaptic activation variable, are found unable to capture some dynamics such as Interneuronal Network Gamma oscillations (ING). Use of an explicit delay is helpful in simulations at the expense of complicating mathematical analysis. We resolve this issue by introducing a dynamic variable, u, that acts as an effective delay in the negative feedback loop between firing rate (r) and synaptic gating of inhibition (s). In effect, u endows synaptic activation with second order dynamics. With linear stability analysis, numerical branch-tracking and simulations, we show that our r-u-s rate model captures some key qualitative features of spiking network models for ING. We also propose an alternative formulation, a v-u-s model, in which mean membrane potential v satisfies an averaged current-balance equation. Furthermore, we extend the framework to E-I networks. With our six-variable v-u-s model, we demonstrate in firing rate models the transition from Pyramidal-Interneuronal Network Gamma (PING) to ING by increasing the external drive to the inhibitory population without adjusting synaptic weights. Having PING and ING available in a single network, without invoking synaptic blockers, is plausible and natural for explaining the emergence and transition of two different types of gamma oscillations.

Analysis of remissions in patients with combined addiction to psychostimulants and other psychoactive substances and comorbid psychiatric pathology after rehabilitation

Despite the annual increase in the number of patients with psychostimulant addiction and the presence of comorbid psychiatric pathology, the question of finding effective therapies for these disorders remains open. When developing therapeutic remission, specialists often do not take into account the peculiarities of its duration and quality in these patients. The purpose of the study is to assess the duration and quality of therapeutic remissions in patients with addiction to psychostimulants, combined use of other psychoactive substances, and comorbid psychiatric pathology. Materials and methods. 325 patients with psychostimulant addiction, who underwent a course of medical and social rehabilitation in the Moscow Research and Practical Centre for Narcology of the Moscow Healthcare Department, were examined. Methods: clinical-psychopathological, psychometric ("Addiction Severity Index: Treatnet Version"), anamnestic, catamnestic, and statistical. The results of the study. It was found that the longest remissions were noted in patients only with psychostimulant addiction. Patients with combined addiction to psychostimulants and the presence of comorbid psychiatric pathology are characterized by short remissions. Patients only with psychostimulant addiction have the best dynamics of remission quality. Conclusion. The revealed clinical features of patients, together with quantitative and qualitative features of remissions, will help to increase the effectiveness of care for these patients.

An innate immune signature induced by AS01- or AS03-adjuvanted vaccines predicts the antibody response magnitude and quality consistently over time.

Antibody-mediated protection can depend on mechanisms varying from neutralization to Fc-dependent innate immune-cell recruitment. Adjuvanted vaccine development relies on a holistic understanding of how adjuvants modulate the quantity/titer and quality of the antibody response. A Phase 2 trial (ClinicalTrials.gov: NCT00805389) evaluated hepatitis B vaccines formulated with licensed adjuvants (AS01B, AS01E, AS03, AS04 or Alum) in antigen-naïve adults. The trial investigated the role of adjuvants in shaping antibody-effector functions, and identified an innate transcriptional response shared by AS01B, AS01E and AS03. We integrated previously reported data on the innate response (gene expression, cytokine/C-reactive protein levels) and on quantitative/qualitative features of the mature antibody response (Fc-related parameters, immunoglobulin titers, avidity). Associations between the innate and humoral parameters were explored using systems vaccinology and a machine-learning framework. A dichotomy in responses between AS01/AS03 and AS04/Alum (with the former two contributing most to the association with the humoral response) was observed across all timepoints of this longitudinal study. The consistent patterns over time suggested a similarity in the impacts of the two-dose immunization regimen, year-long interval, and non-adjuvanted antigenic challenge given one year later. An innate signature characterized by interferon pathway-related gene expression and secreted interferon-γ-induced protein 10 and C-reactive protein, which was shared by AS01 and AS03, consistently predicted both the qualitative antibody response features and the titers. The signature also predicted from the antibody response quality, the group of adjuvants from which the administered vaccine was derived. An innate signature induced by AS01- or AS03-adjuvanted vaccines predicts the antibody response magnitude and quality consistently over time.

Регуляция нейрокогнитивных функций у подростков с печеночной энцефалопатией

&lt;p&gt;&lt;strong&gt;The relevance&lt;/strong&gt; of the study is due to the need to study the regulatory functions of adolescents with portal hypertension, which is accompanied by the development of a generalized intoxication effect on the nervous system affecting the cortical structures of the prefrontal parts of the brain. &lt;strong&gt;Objective: &lt;/strong&gt;to study the specifics of prognostic regulation in adolescents with hepatic encephalopathy with portal hypertension. &lt;strong&gt;Materials and methods:&lt;/strong&gt; the total sample size is 60 adolescents. The experimental group (EG) included adolescents in the number of 30 people (14 men; 16 women) with a diagnosis established by a gastroenterologist K76.6 "Portal hypertension" according to ICD-10. The control group (KG) included 30 respondents (14 men; 16 women) with a normative somatic status. The experimental and control groups were equalized by gender, age and social status. The empirical study included motivation, instruction, and psychological examination with the use of neurocognitive and test methods for assessing regulation: The Line Tracing Test, Digit-symbol Test, Number Connection Test A/B, methodology &amp;laquo;Style of self&amp;ndash;regulation of behavior - SSP-98&amp;raquo; by V.I. Morosanova. &lt;strong&gt;Results.&lt;/strong&gt; Low values of the general level of voluntary self-regulation of behavior of adolescents with hepatic encephalopathy are due to a decrease in the severity of regulatory processes of flexibility and independence. The results obtained allow us to conclude that the reduction of planning processes, flexibility and independence, providing a low overall level of arbitrary self-regulation, provide a significant decrease in neurocognitive indicators of prognostic regulation. &lt;strong&gt;Conclusions.&lt;/strong&gt; The revealed qualitative and quantitative features of the regulatory sphere can be considered as one of the predictors in the genesis of hepatic encephalopathy, which make it possible to carry out prognostic diagnosis of the symptoms of the disease in adolescents at the earliest stages of its development, as well as to carry out differential diagnosis of the disease.&lt;/p&gt;

Histological features of immune cell infiltrate in lesional skin correlate with therapeutic response to dupilumab.

In the last decade, dupilumab, a monoclonal human antibody inhibiting IL-4/IL-13 signaling, has revolutionized the therapeutic management of moderate-to-severe atopic dermatitis (AD), permitting a long-term control of its signs and symptoms. The aim of this study was to identify histologic predictors of dupilumab efficacy after 16 weeks of treatment in a cohort of forty adult patients with moderate to severe AD who had undergone a skin biopsy for diagnostic purposes prior to treatment initiation. We found that EASI 75 and EASI 90 responses at week 16 were significantly associated with perivascular localization (OR=17.6, p=0.038) and lichenoid distribution (OR=31.8, p=0.025) of the immune infiltrate. Moreover, for each unit increase in the number (cells/m2) of CD4+ cells, the likelihood to achieve EASI75 response decreased by 1% (OR=0.99, p=0.037). In conclusion our study suggested a few pre-treatments qualitative and quantitative immunohistochemical features as promising markers predicting dupilumab response in AD patients.

Role of grain size and anisotropy of neighboring grains in hydrogen‐assisted intergranular fatigue crack initiation in austenitic stainless steel

AbstractThis study explores the impact of microstructural features on fatigue crack initiation in poly‐crystalline materials, emphasizing hydrogen‐induced complexities. Grain anisotropy, misorientations, grain size variations, and elastic–plastic inhomogeneities concentrate stress at grain boundaries, making them susceptible to crack initiation during fatigue loading. The presence of hydrogen compounds this process, due to complications of characterization of local hydrogen content and activating embrittling mechanisms. Building upon a model for nickel, this research investigates 316L austenitic stainless steel specimens with varying grain sizes, both uncharged and hydrogen‐charged. In situ low‐cycle fatigue loading experiments establish correlations between fatigue crack initiation and microstructural features. The study reveals specific combinations of features crucial in the initiation process, undergoing alterations in the presence of hydrogen. A proposed qualitative model links microstructural features with accumulated plastic shear strain during fatigue and prevalent hydrogen embrittlement mechanisms like hydrogen‐enhanced local plasticity and hydrogen‐enhanced decohesion.

Guar gum and essential oil-based composite coatings preserve antioxidant enzymes activity and postharvest quality of kinnow Mandarin

Kinnow mandarin is a perishable fruit and is prone to various postharvest losses during storage and transportation. To address this issue, the effect of composite coatings of guar gum (GG) and lemongrass essential oil (LGO) on the quality and longevity of Kinnow mandarins was investigated. The findings showed that as compared to uncoated fruits, the GG 1.0% + LGO 1.0% coated fruits had less spoilage (3.01%), weight loss (7.21%), and lipid peroxidation rate (1.10 mmol g−1) while retaining higher levels of ascorbic acid, sensory quality, total soluble solids, flavonoids, and total antioxidant activity at the end of storage. The coated fruits also exhibited higher enzymatic activities of the superoxide dismutase (42.65 U min−1 g−1), phenylalanine ammonia-lyase (224 U h−1 g−1), peroxidase (35.21 U min−1 g−1), and catalase (47.65 U min−1 g−1). Furthermore, scanning electron microscopy (SEM) revealed a smooth and even layer in the composite-coated fruits, whereas uncoated fruits showed open stomata and cracks. These results indicate that coatings made of GG 1.0% and LGO 1.0% have the potential to preserve the qualitative features of Kinnow fruits stored at low temperatures.

Hayden-Preskill recovery in chaotic and integrable unitary circuit dynamics

The Hayden-Preskill protocol probes the capability of information recovery from local subsystems after unitary dynamics. As such it resolves the capability of quantum many-body systems to dynamically implement a quantum error-correcting code. The transition to coding behavior has been mostly discussed using effective approaches, such as entanglement membrane theory. Here, we present exact results on the use of Hayden-Preskill recovery as a dynamical probe of scrambling in local quantum many-body systems. We investigate certain classes of unitary circuit models, both structured Floquet (dual-unitary) and Haar-random circuits. We discuss different dynamical signatures corresponding to information transport or scrambling, respectively, that go beyond effective approaches. Surprisingly, certain chaotic circuits transport information with perfect fidelity. In integrable dual-unitary circuits, we relate the information transmission to the propagation and scattering of quasiparticles. Using numerical and analytical insights, we argue that the qualitative features of information recovery extend away from these solvable points. Our results suggest that information recovery protocols can serve to distinguish chaotic and integrable behavior, and that they are sensitive to characteristic dynamical features, such as long-lived quasiparticles or dual-unitarity.