Backward Model Research Articles

Application of Principal Component Analysis as a Prediction Model for Feline Sporotrichosis.

Sporotrichosis is a worldwide zoonotic disease that is spreading and causing epidemics in large urban centers. Cats are the most susceptible species to develop the disease, which could cause significant systemic lesions. The aim was to investigate and to identify predictive indicators of disease progression by correlations between the blood profile (hematological and biochemical analytes) and cutaneous lesion patterns of 70 cats diagnosed with Sporothrix brasiliensis. The higher occurrence in male cats in this study could be related to being non-neutered and having access to open spaces. Principal component analysis (PCA) with two principal components, followed by binary logistic regression, and binary logistic regression analysis, with independent variables and backward elimination modeling, were performed to evaluate hematological (n = 56) and biochemical (n = 34) analytes, including red blood cells, hemoglobin, hematocrit, leukocytes, segmented neutrophils, band neutrophils, eosinophils, lymphocytes, monocytes, total plasma protein, albumin, urea, creatinine, and alanine aminotransferase. Two logistic regression models (PCA and independent variables) were employed to search for a predicted model to correlate fixed (isolated) and disseminated cutaneous lesion patterns. Total plasma protein concentration may be assessed during screening diagnosis as it has been recognized as an independent predictor for the dissemination of cutaneous lesion patterns, with the capability of serving as a predictive biomarker to identify the progression of cutaneous lesions induced by S. brasiliensis infections in cats.

Neuroplasticity in Diffuse Low-grade Gliomas: Backward Modelling of Brain-tumor Interactions Prior to Diagnosis is Needed to Better Predict Recovery after Treatment.

In low-grade glioma (LGG), besides the patient's neurological status and tumor characteristics on neuroimaging, current treatment guidelines mainly rely on the glioma's genetics at diagnosis to define therapeutic strategy, usually starting with surgical resection. However, this snapshot in time does not take into account the antecedent period of tumor progression and its interactions with the brain before presentation. This article reviews new concepts that pertain to reconstruct the history of previous interplay between the LGG's course and adaptive changes in the connectome within which the glioma is embedded over the years preceding the diagnosis. Microscale and macroscale parameters helpful for extrapolating backward in time are considered, both for the glioma (kinetics, migration vs. proliferation profile, metabolism with possible intratumoral heterogeneity, relationships with surrounding cerebral pathways) and for patterns of reconfiguration within and across neural networks in reaction to the LGG leading to considerable interindividual cerebral variability. Modelling these continuous variations at the time of LGG diagnosis is a prerequisite to predict recovery from treatment(s). It is important to go beyond the biology of the LGG at a given moment of its history, and instead construct a more comprehensive picture of the past and present dynamics of glioma-brain interactions, and their ongoing evolution, as a necessary stage to optimize a personalized management plan by thinking several steps ahead.

Psychometric properties of the health literacy questionnaire tested in Vietnamese adults with chronic diseases.

The Health Literacy Questionnaire (HLQ) is an increasingly used health literacy instrument that has been translated into many languages. The HLQ has 44 items and comprises 9 scales assessing the multidimensional construct of health literacy. This study reports the HLQ reliability and construct validity tested in people with chronic diseases living in Vietnam. Adults (n = 600) hospitalized with chronic disease in Vietnam completed the HLQ. Floor and ceiling effects, item, and scale difficulty levels were assessed. Generalized linear models with backward modeling techniques were performed to test key variables associated with each HL domain. Confirmatory factor analyses (CFA) testing nine one-factor models were fitted to test the structure of each scale, and a nine-factor model tested the hypothesized structure of the HLQ, followed by the calculation of scale reliability using Cronbach's alpha. No item had floor effects, and only eight items showed ceiling effects. Two scales that had the most difficult tasks to complete (highest difficulty level) were 8 "Ability to find good information" and 9 "Understanding enough to know what to do". Variables associated with health literacy were education, income, age, residential area, main support persons and comorbidity index (associated with 7, 7, 4, 3, 2 and 2 out of 9 scales, respectively). Each HLQ scale demonstrated a robust unidimensional construct with all CFI ≥ 0.95, RMSEA varied from 0 to 0.07. The nine-factor CFA model demonstrated satisfactory fit indices: X2 = 5537.4, 866 df, p < 0.001, CFI = 0.98, NFI = 0.98, RMSEA = 0.09, 90% CI (0.093, 0.097), PCLOSE < 0.001. The highest scores were rated on scales 4 "Social support for health" and 6 "Ability to engage with healthcare providers". The reliability of all nine scales ranged from 0.81 to 0.89. The Vietnamese version of the HLQ demonstrated psychometrically robust properties with high reliability and satisfactory construct validity indexes. This instrument will enable researchers, clinicians, and policymakers to assess health literacy abilities in Vietnam which could inform improvements in healthcare services and clinician practice.

Exploring Summer Precipitation Stable Isotopes in the Monsoon Border Zone: Environmental Drivers and Moisture Source Tracing

ABSTRACTPrecipitation processes in the monsoon border zone (MBZ) of China remain unclear because of the complex hydro‐climatic interactions. This study analyzes the temporal variation of δ2H and δ18O in precipitation and its influencing factors based on samples of precipitation events in Lake Dali during summer from 2018 to 2020. The results showed significant monthly isotopic variability, characterised by lower δ2H and δ18O values in July with averages of −103.62‰ and −14.87‰, respectively. This clear isotopic variability is largely related to the East Asian summer monsoon (EASM) activity during the prevailing summer monsoon season and hydrometeorological processes. The isotopic composition of precipitation is mainly determined by the amount of precipitation and relative humidity, but not related to temperature. The results of backward trajectory modelling show that precipitation primarily originates from northern China, inland Xinjiang, and the western Pacific with different monthly contributions. The increase in δ2H and δ18O isotopes from June to August indicates sub‐cloud evaporation. The mean sub‐cloud evaporation rate was 3.5% during the summer. Sub‐cloud evaporation is associated with relative humidity and precipitation. Recycled moisture accounts for 12% of local precipitation and 2.5%–31.6% of total monthly precipitation. After considering the contribution of evaporation, the estimated average recycling ratio increased from 12% to 24%, suggesting the important role of evaporation processes in lakes in the formation of precipitation.

Free Fasciocutaneous versus Muscle Flaps in Lower Extremity Reconstruction: Implications for Functionality and Quality of Life.

Free tissue transplantations are commonly used to treat complex lower extremity defects caused by trauma, vascular disease, or malignancy, particularly when vital structures are exposed. This study aimed to expand the knowledge on patient-reported outcomes by comparing fasciocutaneous and muscle flaps, with the goal of facilitating patient counseling. Additionally, patient-level risk factors associated with decreased functioning and health-related quality of life were identified. This retrospective, cross-sectional, multicenter study included patients who underwent microsurgical lower extremity reconstruction using free fasciocutaneous or muscle flaps between 2003 and 2021, with a minimum follow-up of 12 months. Data were collected from medical records, pain scores, Short-Form 36 (SF-36), and Lower Extremity Functional Scale (LEFS). Mean scores were compared between flap types and predictors of LEFS, SF-36 mental component summary (MCS), and SF-36 physical component summary (PCS) scores were determined using a backward stepwise regression model. Of the 206 patients eligible, 100 (49%) were included in the retrospective part. A total of 89 (43%) responded to the questionnaires, with 62 treated using a fasciocutaneous flap and 27 with a muscle flap. No significant differences in total LEFS, SF-36 PCS, or MCS scores were found between the two flap type. Pain was a significant predictor of decreased functional outcomes for both flap types and was also linked to poorer mental health in patients treated with fasciocutaneous flaps. Other predictors of low patient-reported outcome scores included obesity, diabetes, poorer mental health, and a follow-up of less than 2 years. Patients treated with fasciocutaneous and muscle flaps experience comparable levels of functionality and quality of life after surgery. Flap selection should be based on defect characteristics, along with the surgeon's individual skills and preferences. A comprehensive approach that considers physical comorbidities, pain, and mental health is essential, as these factors significantly impact patient functionality and quality of life.

Analysis of Ozone Pollution Characteristics and Transmission Pathways in the Main Urban Area of Urumqi

Global climate change is becoming increasingly crucial. With the continuous strengthening of environmental supervision and governance in China, the haze （O3） weather mainly caused by PM2.5 pollution has gradually improved, while the emissions of invisible ozone pollutants have been increasing annually. In particular, the problem of near-ground ozone pollution is more crucial, which poses a severe threat to urban production, life, and health of people. To gain a deeper understanding of the pollution characteristics of ozone, this study was conducted based on ozone monitoring, ground meteorological, and global data assimilation system （GDAS） meteorological data from January 2020 to February 2023 in Tianshan District, Urumqi City, to explore the characteristics of ozone mass concentration changes and potential source areas in Urumqi City at different time scales using the cluster analysis of the backward trajectory model （HYSPLIT） and trajectory （Tianshan District） and the concentration weight trajectory analyses （CWT）. The results showed that： ① The maximum 8-hour O3 （O3-8h） mass concentration in Tianshan District of Urumqi City had been increasing yearly from 2020 to 2022 and had obvious seasonal variation characteristics, with summer （110.33 μg·m-3） &gt; spring （87.67 μg·m-3） &gt; autumn （64.33 μg·m-3） &gt; winter （39.67 μg·m-3）. ② The temperature, relative humidity, and air pressure of each season had a significant impact on O3-8h mass concentration, with correlation coefficients r of 0.871,-0.752, and 0.644 （P &lt; 0.01）, respectively. The annual wind speed variation was relatively low, which was a secondary meteorological factor and had no significant impact on ozone mass concentration. ③ The HYSPLIT clustering analysis results showed that overseas long-distance transportation had less impact on the O3-8h mass concentration in Tianshan District, Urumqi City. The CWT results indicated that ozone pollution sources were mainly concentrated in the northern area of Urumqi in Xinjiang and the high-value areas in summer [O3-8h mass concentration &gt; 70 μg·m-3] mainly appeared in areas with higher temperatures in Urumqi, Changji, Altay, and Tacheng. ④ Especially in autumn, the pollutants emitted by local anthropogenic activities had a great influence. To summarize, spatiotemporal concentration changes were observed in ozone pollution in Urumqi and the prevention and control of urban ozone pollution in Urumqi requires strengthening the emission of ozone precursors while considering implementing coordinated emission reduction measures for surrounding areas.

Evaluating restoration success using metric‐based indicators of ecosystem recovery in tidal marshes along the northern Gulf of Mexico

Abstract Habitat restoration is commonly used to recover ecosystem services, but due to resource constraints, post‐project monitoring often fails to fully evaluate the recovery of important ecosystem functions. Metric‐based indicators use simple‐to‐measure variables to assess ecosystem health and function, thereby providing a time‐ and cost‐effective method to improve monitoring. We used a tidal‐marsh data set to develop metric‐based indicators of ecosystem recovery. In 2021 and 2022, we surveyed eight restored/created and three natural reference tidal marshes in the northern Gulf of Mexico to assess the recovery of ecosystem attributes (e.g. above‐ and below‐ground biomass, soil organic matter [SOM] and sediment total carbon [C] and total nitrogen [N]). To determine what combinations of variables best predicted recovery, we split our data into model training and testing data sets, used backwards model selection and then created and tested a metric‐based indicator of ecosystem recovery. Recovery of plant above‐ and below‐ground biomass and sediment structure (i.e. SOM, C and N)—important measures of wetland carbon sink capacity and biogeochemical functioning—could be predicted through a combination of simpler to measure variables, such as time since restoration, percent plant cover and sediment bulk density. The indicator constructed from these relationships was highly effective in predicting the development of ecosystem attributes (r = 0.85, p &lt; 0.001). Synthesis and applications. This indicator approach provides an effective but simple method to assess the recovery of ecosystem attributes in tidal marshes, and it can be used to develop similar indicators in other ecosystems. By overcoming resource constraints of post‐project monitoring, metric‐based indicators of ecosystem recovery may serve as a key strategy to improve restoration outcomes.

Algebraic Modelling of Experiments on the Example of Proton Therapy

Introduction. Despite the rapid development of the chemical industry and science, discoveries in the field of health care, the emergence of drugs and therapeutics based on nanotechnology and the development of radiation therapy technologies, the safety of biomedical applications of the latest products, and the search for new methods and approaches to the diagnosis and treatment of cancer are an open issue. One of the safest and fastest methods for researching the behaviour of new materials and tools and selecting the best candidates is the modelling of relevant processes, particularly computer molecular modelling based on mathematical models. However, despite a large number of available methods and modelling tools, for most of them, the successful application is possible only for a narrow range of tasks and experiments. As one of the possible solutions to this problem, we propose a new approach to computer molecular modelling based on the synergy of the algebraic approach, namely, algebraic modelling and biological knowledge at different levels of abstraction, starting from quantum interactions to interactions of biological systems. We see one of the directions of application of this approach in the possibilities of modelling the radiation therapy process – starting from modelling the accelerators’ work and ending with modelling the interaction of the particles’ beam with the matter at the level of quantum interactions. In particular, in the article, we consider the possibilities of forward (specific and symbolic) and backward (symbolic) algebraic modelling on the example of models of the higher level of abstraction, which allows us to visualize certain interactions and to build charts of dependencies for specific models, and to determine the presence of the desired scenarios (forward modelling) or a set of initial environment parameters (backward modelling) in symbolic form.

Prognosticators of Optic Nerve Imaging in Malignant Pseudotumor Cerebri Syndrome.

Pseudotumor cerebri (PTC) syndrome is a disorder of increased intracranial pressure, most commonly affecting overweight women of childbearing age. Malignant PTC (MPTC) is a rare presentation that involves rapidly worsening vision, often necessitating surgical intervention to prevent permanent vision loss. The goal of this study was to determine whether radiographic findings of PTC are predictive of MPTC and the final visual outcome. Charts of patients diagnosed with PTC based on the modified Dandy criteria were reviewed. Snellen best-corrected visual acuity (BCVA), automated perimetry mean deviation (APMD), and papilledema grade were documented from the initial and final follow-up examinations. The MRI images were read by 2 neuroradiologists blinded to the clinical diagnosis, and consensus findings were obtained. The patients were classified into malignant and nonmalignant PTC groups. Malignant was defined as BCVA ≤ 20/200 or APMD ≤ -7 dB, and acute onset of symptoms. The sensitivity and specificity of each MRI finding for detecting MPTC was determined. Generalized linear model with random effect and backward model selection was performed to identify which radiographic findings were predictive of final BCVA, APMD, and papilledema grade. Overall, 114 eyes (57 malignant/57 nonmalignant) were included. No MRI finding has both sensitivity and specificity for MPTC reaching 60%. The highest sensitivity (86%) was flattening of the posterior globes and unilateral or bilateral transverse sinus stenosis. The highest specificities were completely empty sella (86%), enhancement (84%), and diffusion restriction (81%) of the prelaminar optic nerves. The presence of vertical tortuosity of the optic nerves was predictive of better final BCVA (P = 0.003) and better final APMD (P = 0.011). While papilledema grade was higher in MPTC, no MRI finding was predictive of papilledema grade. No single MRI finding has both high sensitivity and specificity for diagnosing MPTC. Vertical tortuosity of the optic nerves may be predictive of better final BCVA and APMD.

MiR-200 family as new potential prognostic factor of overall survival of patients with WHO G2 and WHO G3 brain gliomas

Gliomas are the predominant cause of cancer-related deaths among the young population. Even after incorporation of IDH1/2 mutations and 1p19q codeletion there are doubts regarding adjuvant treatment in WHO G2/G3 gliomas. miRNA molecules control about 30% of all genes, also many oncogenes, tumor suppressor genes and genes responsible for the response to ionizing radiation and systemic treatment. Patients with brain gliomas exhibit miRNA disorders. We aimed to evaluate the expression of miR-200 family members in relation to selected clinico-pathological factors and their prognostic value. We enrolled 53 patients diagnosed with WHO G2/G3 brain gliomas treated between 2012–2016. RT-qPCR based expression of miR-200 family was assessed in tumor and surrounding non-cancerous tissue. An analysis of selected clinico-pathological features was carried out. A logistic regression model was prepared for the miRNA signature. The predictive potential of the signature was assessed using the ROC curve. A stepwise backward regression model was used to select variables with a significant predictive potential related to OS. It was shown that miR-200a-3p, miR-200a-5p, miR-200c-5p, miR-141-3p and miR-429 can be independent predictors of survival. Better 2- and 5-year OS was associated with higher expression of miR-200a-3p, miR141-3p and lower expression of miR-200a-5p, miR-200c-5p, miR-429. The strongest predictors of survival were miR-200a-5p, miR-200b-3p, miR-200c-5p, miR-141-3p, miR-429, tumor volume and CTV. Members of the miR-200 family exhibit prognostic value for 2- and 5-year OS. Presented predictive models of survival may be clinically useful for treatment optimization.

The perivascular spaces in young and middle-aged stroke: A single-center analysis integrating clinical and Doppler ultrasound findings.

Purpose: This research seeks to investigate correlations between enlarged Perivascular Spaces (PVSs) and clinical/imaging data, such as information obtained through Doppler analysis, in a population with young and middle-age stroke. Materials and methods: We retrospectively reviewed demographics, clinical and MRI data, of 163 patients, with MRI confirmed stroke. All patients underwent ECD TSA (Eco-Color-Doppler of the Supra-aortic Trunks) and TCCD (Transcranial Eco-color-Doppler), to study extra or intracranial stenosis, presence and composition of plaques. Severity of PVS was evaluated on T2-weighted images according to the Potter scale. To identify potential predictors of PVSs, an exploratory backward stepwise ordinal regression model was developed, including clinical and Doppler US variables. Results: In the stepwise ordered logistic regression analysis with PVSs at BG (Basal Ganglia) as the outcome, hemodynamically significant stenosis in any vessel ipsilateral to the ischemic lesion displayed a significant positive association with a higher outcome value. Similar results were observed for ESUS (Embolic Stroke of Undetermined Source). Fibrolipid plaques in any vase exhibited a significant negative association. At MB (Midbrain), male patients and subjects with hypertension exhibited a higher value of PVSs. Dyslipidemia demonstrated a significant negative effect. When PVSs were investigated in the CS (Centrum Semiovale), no statistically significant association with the extent of PVSs emerged. Conclusion: These insights not only enhance our understanding of the role of PVSs in cerebrovascular health in a young and middle-age population but also highlight the potential of PVSs as a biomarker in neuroimaging studies, warranting further research to elucidate their clinical implications and underlying pathophysiological mechanisms.

Food safety knowledge and attitudes among fish vendors in informal markets in Ilorin, Nigeria: a cross-sectional study

BackgroundWith the rising demand for fish and increasing concerns about foodborne illnesses, understanding vendors' perspectives is critical for enhancing food safety standards. This cross-sectional study assessed food safety knowledge and attitudes among fish sellers operating in informal fish markets in Ilorin Metropolis, Nigeria, using a structured questionnaire administered via face-to-face interviews between October and December 2023.MethodsA total of 240 fish vendors completed the survey. A stepwise backward binary logistic regression model was used to identify the independent factors associated with good knowledge or satisfactory attitudes toward food safety among fish vendors.ResultsOver 62.5% of the respondents had good food safety knowledge, particularly in areas such as personal hygiene and cross-contamination. However, misconceptions about foodborne diseases persist. While attitudes toward food safety were generally positive, certain practices such as wearing protective gear were less endorsed. Demographic factors significantly influenced both knowledge and attitudes. Logistic regression analysis revealed that respondents in older age groups were at least 11 times more likely to have satisfactory attitudes (OR: 10.5, 95% CI: 1.8–62.8, p = 0.015) toward food safety. Additionally, participants with smaller family sizes were four times more likely to display satisfactory attitudes toward fish safety.ConclusionThese findings underscore the need for targeted interventions, including training programs and government regulations, to improve fish vendors’ food safety practices. Strengthening education and enforcement measures is crucial for safeguarding public health and enhancing the quality of fish products in informal markets in Ilorin.

Psychological distress among students in Egypt and Jordan during the initial months of the Gaza war

BackgroundPsychological distress has significantly impacted students in Egypt and Jordan. These countries have faced many challenges, including the COVID-19 pandemic, the fallout from the conflict in Syria, and the war in Ukraine. These crises have had far-reaching consequences, affecting the economy, food security, and energy supplies, particularly with the increased number of refugees in these countries. Amid these existing complexities, the ongoing war in Gaza further exacerbates the situation, compounding mental health challenges. Therefore, this study aimed to analyze how the war in Gaza impacted the mental health of students in Egypt and Jordan.MethodsWe conducted a cross-sectional study involving students from Egypt and Jordan between December 2023 and January 2024. The questionnaire incorporated the Brief Symptom Inventory 18 (BSI-18) to assess the overall general distress score, as well as the domains of anxiety, somatization, and depression.ResultsA total of 1509 Jordanian and Egyptian students were included in the study, of which 66% were female. Approximately 18% of the students had an elevation (≥ 50) in their BSI-18 total score. Females scored significantly higher in general distress [30 (18–44.25) vs. 24 (11–41), p < 0.001], and all three dimensions. The univariate predictors for elevated general distress (≥ 50) were being Egyptian (OR 1.49, 95% CI 1.08 to 2.08, p = 0.014), watching social media multiple times a day (OR 3.04, 95% CI 1.97 to 4.69, p < 0.001), and having a Palestinian connection (friend, neighbor, colleague, or relative) (OR 1.63, 95% CI 1.23 to 2.16, p < 0.001). These three predictors were retained in the backward stepwise multivariate regression analysis model. The univariate predictors for moderate and marked general distress (≥ 57) were watching social media multiple times a day (OR 3.26, 95% CI 1.78 to 5.99, p < 0.001) and having a Palestinian connection (OR 1.49, 95% CI 1.05 to 2.14, p = 0.026). Only the former was retained in the backward stepwise multivariate regression analysis.ConclusionGeneral distress has increased among students in Egypt and Jordan throughout the War in Gaza. Mental, psychological, and social support activities are necessary for these students, particularly those watching social media multiple times daily. The development of coping skills to manage the additional stressors of war and socioeconomic status necessitates further research within this group of students.

Forward and backward modeling of cultural evolutionary processes

Modern cultural evolution theory adopts a variety of concepts and methods developed in mathematical biology, in particular population genetics theory. In addition to forward-looking approaches such as two-locus models, backward-looking approaches such as coalescent theory, which describe ancestral states of the current population, have played an important role in population genetics. Here, we show how forward and backward approaches can be applied to two examples in cultural evolution. The first example deals with the number of cultural traits, which can be analyzed by a backward approach. Cultural coalescent theory illustrates many unique aspects of cultural processes such as information transfer (i.e., social learning) from many donors ("cultural parents"), including or excluding the biological parents. Theory predicts that many cultural traits of intermediate (or higher) frequency (popularity) can exist that are surprisingly old. Many unsolved issues remain, however, such as how to incorporate social structure as well as natural and/or cultural selection, which we believe to be an urgent agenda. The second example is the punishment of sibling incest, a problem to which cultural coalescent theory cannot currently be usefully applied. By analyzing forward recursions, we show that punishment is ineffective in suppressing incest, unless the incestuous inclination is diminished by the presence of punishers. Based on these examples, we discuss the merits and demerits of forward and backward approaches.

Deciphering the Milky Way disc formation time encrypted in the bar chrono-kinematics

Abstract We present a novel method to constrain the formation time of the Milky Way disc using the chrono-kinematic signatures of the inner Galaxy. We construct an O-rich Mira variable sample from the Gaia Long-period Variable catalogue to study the kinematic behaviour of stars with different ages in the inner Galaxy. From the Auriga suite of cosmological zoom-in simulations, we find that the age of the oldest stellar population with imprints of the bar in density and kinematics matches the disc spin-up epoch. This is because stars born before the spin-up show insufficient rotation and are not kinematically cold enough to be efficiently trapped by the bar. We find that the bar kinematic signature disappears for Mira variables with a period shorter than 190 days. Using the period-age relation of Mira variables, we constrain the spin-up epoch of the Milky Way to be younger than ∼11 − 12 Gyr (redshift ∼3). We also discuss and compare our method and result to other evidence of the Milky Way spin-up epoch under the context of a realistic age uncertainty. Age uncertainty leads to an overestimation of the disc formation time when performing backward modelling. Our constrain of the spin-up epoch is independent from previous studies because it relies on the kinematics of the inner Galaxy instead of the solar vicinity.

Backward test particle simulation of nonlinear cyclotron wave-particle interactions in the radiation belts

Wave-particle interaction plays a crucial role in the dynamics of the Earth’s radiation belts. Cyclotron resonance between coherent whistler mode electromagnetic waves and energetic electrons of the radiation belts is often called a coherent instability. Coherent instability leads to wave amplification/generation and particle acceleration/scattering. The effect of wave on particle’s distribution function is a key component of the instability. In general, whistler wave amplitude can grow over threshold of quasi-linear (linear) diffusion theory which analytically tracks the time-evolution of a particle distribution. Thus, a numerical approach is required to model the nonlinear wave induced perturbations on particle distribution function. A backward test particle model is used to determine the energetic electrons phase space dynamics as a result of coherent whistler wave instability. The results show the formation of a phase space features with much higher resolution than is available with forward scattering models. In the nonlinear regime the formation of electron phase space holes upstream of a monochromatic wave is observed. The results validate the nonlinear phase trapping mechanism that drives nonlinear whistler mode growth. The key differences in phase-space perturbations between the linear and nonlinear scenarios are also illustrated. For the linearized equations or for low (below threshold) wave amplitudes in the nonlinear case, there is no formation of a phase-space hole and both models show features that can be characterized as linear striations or ripples in phase-space.

Feasibility study on machine learning methods for prediction of process-related parameters during WAAM process using SS-316L filler material

The geometry of objects by means of wire arc additive manufacturing technology (WAAM) is a function of the quality of the deposited layers. The process parameters variation and heat flow affect the geometric precision of the parts, when compared to the actual dimensions. Therefore, in situ geometry monitoring which is integrated in such a way to enable a backward control model is essential in the WAAM process. In this article, an attempt is made to study the effect of four input variables, namely voltage (U), welding current (I), travel speed and wire feed rate on the output function in the form of two geometrical characteristics of a single weld bead. These output functions which are determinant of the weld quality are width of weld bead (BW) and height of weld bead (BH). A machine learning approach is utilised to predict the bead dimensions based on the input parameters and to predict the parameters by assigning suitable scores. For predicting the bead dimensions, two models, namely linear regression and random forest, shall be utilised, whereas for the purpose of classification based on weld parameters, k-nearest neighbours model shall be employed. Through this work, a wide dataset of parameters in the form of input variable and output in the form bead dimensions are generated for 316LSi filler material which shall be used as a training data for a machine learning algorithm. Subsequently, the predicted parameters shall be cross-checked with actual parameters.

Lower modified rodnan skin score values and complement 4 levels are associated with arterial endothelial dysfunction in patients with systemic sclerosis

Abstract Background Systemic sclerosis (SSc) is a multisystem autoimmune disease affecting skin and multiple organs. Although immunopathological evidence supports direct arterial involvement leading to tissue ischemia in SSc, factors associated with arterial involvement are not precisely determined due to the rarity of SSc. In this study, we aimed to investigate aortic stiffness and endothelial functions in patients with SSc. Methods Patients under treatment of our Rheumatology department and healthy controls have been included in our study. Aortic stiffness was examined by pulse wave velocity (PWV) exam and endothelial functions were assessed by flow mediated dilation (FMD). Individuals with known atherosclerotic risk factors were excluded from our study. A thorough biochemical examination of all individuals and immunologic markers of patients with SSc was completed. All of the organ involvements in patients with SSc were noted and modified Rodnan skin score (MRSS) was calculated. Results A total of 61 patients and 75 healthy controls were included in our study. Brachial-ankle PWV values were significantly higher (6.89±0.93 vs 6.49±0.22 m/sec; p:0.001) and brachial FMD values were significantly lower (16.7±6.7 % vs 18.7±3.8 %; p: 0.03) in patients with SSc. We have also grouped individuals according to severe FMD impairment (FMD &amp;lt; 10%). None of the healthy controls had severe impairment whereas 15 patients with SSc had FMD values lower than 10%. Higher anti-smooth muscle (Sm) antibody positiveness (40 % vs 15.4 %), lower complement 4 levels (23.6±16.7 vs 31.1±11.1 mg/dL), higher plasma iron values (70.3±43.6 vs 31.4±45.2 mcg/dL) and lower MRSS values (11.4±4.8 vs 20.7±10.9) were observed in SSc patients with severe FMD impairment. When we have put these variables in multivariate logistic regression analysis (backward elimination model), we have found an independent association of complement 4 levels (OR: 0.03, [95 % CI: 0.01-0.491], p:0.04) and MRSS values (OR: 0.853, [95 % CI: 0.761-0.955], p:0.006) with significant FMD impairment in patients with SSc. Conclusion Arterial stiffness is significantly increased and endothelial functions, as assessed by FMD is significantly decreased in patients with SSc, when compared to healthy controls. Lower complement 4 levels and lower modified Rodnan skin scores seem to be independently associated with significant severe FMD impairment. Although our study included a remarkable amount of patients with SSc, an infrequent autoimmune disease, number of patients included in our study is not adequate to make more precise comments. Further prospective studies with higher amount of patients are required for certain results.

A novel Shannon entropy-based backward cloud model and cloud K-means clustering

A novel Shannon entropy-based backward cloud model and cloud K-means clustering

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.