Dominant Feature Research Articles

#2900 Appetite changes as a predictor of mortality among end-stage renal disease patients (ESRD) on maintenance hemodialysis: one year survival analysis

Abstract Background and Aims End-stage renal disease (ESRD) has become a major health problem worldwide. Appetite loss is a dominant feature of the disease which plays an important role in protein energy wasting and malnutrition, with subsequent higher rates of mortality. The study aimed to examine appetite changes among patients with ESRD on maintenance hemodialysis and its association with one-year mortality. Method This is a prospective observational study that was conducted at Mansoura Nephrology and Dialysis Unit (MNDU), Mansoura University and Aga Hospital Dialysis Unit over a period of one-year Patients who fulfilled inclusion and exclusion criteria were included. The study was explained to all participants and informed consent was obtained. All demographic, clinical, and laboratory data were collected. Appetite was assessed using the following 4 scores: subjective assessment of appetite, the self-assessment of appetite changes for 30 days, The Visual Analog Scale (VAS), and The Functional Assessment of Anorexia/Cachexia Therapy (FAACT). All patients were followed up for one year to report the mortality events. Results The current study included 162 hemodialysis patients whose age (SD) was 48.72±12.64 years, and males constituted 57.4% of them. Around 44.4% of the cases reported having good appetite using the subjective assessment score, and 13% reported decreased appetite using the self-assessment of appetite score. The median VAS score was 80, and the FAACT score ranged from 7 to 32 with a mean value of 15.85. After one year of observation, death occurred in 24/162 (14.8%). There were statistically significant differences in mean values of three appetite scores among both dead and alive groups with lower values reported in the deceased one [self-assessment of appetite (p=0.019), VAS score (p=0.001), and FACCT (p=<0.001)]. The ROC analysis shows that FACCT & VAS scores were able to determine patients who destinated to mortality with area under curve (AUC) of (0.774 &0.704), at a cut-off point of (14.5%, and 75), with sensitivity of (83.3% &and 66.7%) and specificity of (61.6%, and 53.6%), respectively. Kaplan Meier survival analysis demonstrated that VAS & FACCT scores had a statistically significant effect on median survival time among studied cases. Lower median survival time was detected among cases with decreased appetite by self-assessment tool, VAS score less than 80, and FACCT score less than 15. Kaplan-Meier and hazard curves for the included sample showed lower Median survival time (95% CI) among patients with diminished appetite by the 4 scores: 311.67(265.98-357.36) by self-assessment score (p=0.008), 344.28 (329.57-358.9) days by subjective assessment of appetite (p=0.28), 342.33(330.46-354.19) days by VAS score (p=0.016), and 332.01(314.6-349.43) days by FAACT score (p<0.0001). Cox regression analysis demonstrated that VAS and FACCT scores were statistically significant predictors of median survival time among studied cases (p<0.001, <0.001) and hazard ratio (HR) of (0.968 & 0.734), respectively. Conclusion Diminished appetite is prevalent among hemodialysis patients. Poor appetite is associated with higher mortality. Lower appetite score values can predict mortality among such patients.

Monitoring Long-Term Land Cover Change in Central Yakutia Using Sparse Time Series Landsat Data

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Politico vivere in Niccolò Machiavelli and Donato Giannotti: Monarchy, Republicanism and Mixed Government in Florence

ABSTRACT The tensions between monarchy and republicanism are a dominant feature of Machiavelli’s political works, and both the so-called ‘monarchical’ work, The Prince, and the more overtly republican Discourses laud the benefits of republicanism and warn against relying on hereditary monarchy. This article compares Machiavelli’s proposals, advanced in 1520, for a mixed constitution for the city of Florence with those of his younger compatriot, Donato Giannotti, who became secretary to the Ten in the last Florentine republican government of 1527-30. As the historical context changed, and Florence progressed from republic to absolutist duchy under the later Medici, Giannotti was exiled from the city and the article examines how his proposed reforms of 1528 and then his major work, Della republica fiorentina, drafted and redrafted between 1531 and 1538 as the politics of Florence changed, reinforce and expand Machiavelli’s views of politico vivere in a republican polity like Florence whose constitution was being dramatically rewritten.

The Utility of Contrast-Enhanced Ultrasound (CEUS) in Assessing the Risk of Malignancy in Thyroid Nodules.

Ultrasonography is a primary method used in the evaluation of thyroid nodules, but no single feature of this method predicts malignancy with high accuracy. Therefore, this paper aims to assess the utility of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of thyroid nodules. The study group comprised 188 adult patients (155 women and 33 men) who preoperatively underwent CEUS of a thyroid nodule classified as Bethesda categories II-VI after fine-needle aspiration biopsy. During the CEUS examination, 1.5 mL of SonoVue contrast was injected intravenously, after which 15 qualitative CEUS enhancement patterns were analysed. The histopathologic results comprised 65 benign thyroid nodules and 123 thyroid carcinomas. The dominant malignant CEUS features, such as hypo- and heterogeneous enhancement and slow wash-in phase, were evaluated, whereas high enhancement, ring enhancement, and a slow wash-out phase were assessed as predictors of benign lesions. Two significant combinations of B-mode and CEUS patterns were noted, namely, hypoechogenicity with heterogeneous enhancement and non-smooth margins with hypo- or iso-enhancement. The preliminary results indicate that CEUS is a useful tool in assessing the risk of malignancy of thyroid lesions. The combination of the qualitative enhancement parameters and B-mode sonographic features significantly increases the method's usefulness.

Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefore, eight machine learning models (linear regression (LR), Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regressor (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA) optimizer were developed and evaluated for prediction of H2, CO, CO2, and CH4 gas yields from SCWG of lignocellulosic biomass. A total of 12 input features of SCWG process conditions (temperature, time, concentration, pressure) and biomass properties (C, H, N, S, VM, moisture, ash, real feed) were utilized for the prediction of gas yields using 166 data points. Among machine learning models, boosting ensemble tree models such as XGB and CatBoost demonstrated the highest power for the prediction of gas yields. PSO-optimized XGB was the best performing model for H2 yield with a test R2 of 0.84 and PSO-optimized CatBoost was best for prediction of yields of CH4, CO, and CO2, with test R2 values of 0.83, 0.94, and 0.92, respectively. The effectiveness of the PSO optimizer in improving the prediction ability of the unoptimized machine learning model was higher compared to the GA optimizer for all gas yields. Feature analysis using Shapley additive explanation (SHAP) based on best performing models showed that (21.93%) temperature, (24.85%) C, (16.93%) ash, and (29.73%) C were the most dominant features for the prediction of H2, CH4, CO, and CO2 gas yields, respectively. Even though temperature was the most dominant feature, the cumulative feature importance of biomass characteristics variables (C, H, N, S, VM, moisture, ash, real feed) as a group was higher than that of the SCWG process condition variables (temperature, time, concentration, pressure) for the prediction of all gas yields. SHAP two-way analysis confirmed the strong interactive behavior of input features on the prediction of gas yields.

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.

Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors

With the rapid advancement of urbanization, the development and utilization of urban underground space resource (UUSR) has become one of the dominant features. However, in certain areas, the development of UUSR may cause disasters and accidents, such as ground collapse, settlements, and tunnel water gushing. Geological environmental factors (GEFs) are recognized as the fundamental constraining factor of UUSR development. In this paper, quality based on GEFs is defined to assess the development difficulty degree of UUSR. A 3D assessment framework is proposed based on 3D geological modelling and the interval continuous mathematical model (ICMM). The subjective and objective joint weight method of analytic hierarchy process and entropy weight method (AHP–EWM) is utilized to determine the weight of each indicator. The quality index (QI) of each spatial node of the 3D geological model is calculated by the ICMM mathematical model. A case study conducted in the Jiangbei New District of Nanjing, China, serves as a demonstration of the UUSR assessment. The results clearly illustrate the 3D distribution characteristics of the quality in the study area, offering valuable insights for future 3D urban underground space planning.

Surface morphology of API 5L X65 pipeline steel processed by ultrasonic impact peening: An integrated experimental and computational study

Ultrasonic impact peening (UIP) is a highly effective surface engineering technique to improve material durability through peening-induced surface plastic deformation. These improvements are determined by not only the generation of surface compressive residual stresses but also the alteration of surface morphological features. In literature, researchers have focused primarily on the average roughness (Sa) to understand the morphology changes induced by UIP. From the surface engineering perspective, in addition to Sa, other surface morphological characteristics play key roles in determining material durability, particularly surface skewness (Ssk) and surface kurtosis (Sku), which describe the asymmetrical distribution of surface profiles and the sharpness of peak features, respectively. In this study, surface morphological characteristics of API 5L X65 pipeline steel processed by UIP are investigated with a focus on the effects of processing parameters on Sa, Ssk, and Sku. The results indicate that the optimized UIP conditions lead to the preparation of surfaces with valley structures as the dominant feature accompanied by blunt peaks, resulting in the elimination of stress concentration sites towards enhanced material durability. In addition, surface hardness, residual stresses, and peening-induced deformation depth are measured. Furthermore, to gain a comprehensive understanding of the material response and the underlying mechanisms driving surface morphological alterations by UIP, a finite element method (FEM)-based model is developed. By investigating the effect of process parameters (peening amplitude, peening cycles, and pin size) on the surface deformation depth and stress/strain distribution, a correlation between these process variables with surface characteristics is established.

Shifting between Pragmatism and Ideology: Communist Czechoslovakia and Haile Selassie's Ethiopia during the Cold War, 1955–1974

Abstract Czechoslovakia was one of the most active members of the Soviet bloc in sub-Saharan Africa, with a policy oscillating between euphoric activism and pragmatism. Officials in Prague sought to establish good relations with many African countries, generally on a pragmatic economic basis, as in the case of Ethiopia. The Czechoslovak Communists gave high priority to Ethiopia because of the country's strategic position and its long-standing cooperation with Czechoslovakia. Although ideological factors played some role, economic pragmatism was the dominant feature of Czechoslovak policy in Africa in the 1950s and early 1960s. Not until the mid-1960s did the Soviet Union become the primary architect of the Soviet bloc's “Africa policy.” After the 1974 coup in Ethiopia, which saw the overthrow of the Haile Selassie regime, Czechoslovakia's relations with Ethiopia markedly improved for both ideological and pragmatic reasons.

Social Media and Me: How Community Identity Influences Click Speech

ABSTRACT Social media has become an integral part of our society. So did participation in social media communities. Click-speech, the simple expression of opinions via a click in the form of likes and shares, emerged as a dominant feature of various social media platforms. In this research, we investigate how social media users use click-speech within social media communities through the lens of community identity and how it shapes click-speech behavior. Using a scenario-based study, we show that community identity strongly influences click-speech to the point where content is liked and shared even when the community member disagrees with the post or content. We contextualize the construct of fear of isolation from the spiral of silence theory within identity theory. In this context, the fear of being isolated from the community is salient, and click-speech evolves from a function of endorsing content to a socially driven behavior of fitting in.

Heat transfer analysis in hybrid nano-composite flow in a stretchable convergent/divergent channel in the preaence of Darcy-Forchheimer law and Lorentz force

The hybrid nano-composite fluid transportation in Jaffery-Hemal flow (JHF) has important uses in various technologies, like converging dies, hydrology, automobiles, etc. Such significant applications motivated us to work on the current problem. Therefore, the purpose of the current work is to inspect the energy efficiency analysis of hybrid nanofluids via convergent/divergent channels using porous space. The hybrid nanofluid consists of polyethylene glycol water, nanoparticles ZrO2 and MgO. To understand the porosity features, the Darcy-Forchheimer law is used. Solar radiation is considered for a more comprehensive analysis of the thermal field. The strong ODEs are obtained using a suitable similarity transformation. The NDSolve technique is used to simulate numerical results, which are then compared with previous published results. Plots and tables are used to report physical investigations of the related parameters. For higher solid volume fractions, the divergent channel’s velocity drops. Whereas it increases for a convergent channel. In the presence of variables, Eckert number, and porosity parameter, skin friction increases in divergent channels and decreases in convergent channels for both nanofluids and hybrid nanofluids. Furthermore, it is noticed that hybrid nanocomposite has more dominant features than nanocomposite for both scenarios of narrowing/expanding channels.

The Predictive Power of Bosniak 3 and 4 Cystic Renal Lesion Categorization Using Contrast-Enhanced Ultrasound.

Contrast-enhanced ultrasound (CEUS) is increasingly utilized for the noninvasive assessment of renal cystic lesions, using the Bosniak grading system. Bosniak 3-4 lesions require surgical referral, which allows correlation with the histopathological outcome. In this single-center, retrospective study we evaluated renal CEUS exams conducted with SonoVue® with a diagnosis of a Bosniak 3 or 4 lesion between 2019 and 2022. A total of 49 patients and 50 lesions met the inclusion criteria, 31 lesions had available histopathological results. Patient demographics, cyst morphology, and dominant imaging features were registered. The histopathological diagnosis was considered a reference standard. Positive predictive power (PPV) for neoplastic lesions was comparable in the Bosniak 3 and 4 categories (75 vs 93.3%, P = .33), while PPV for histopathologically malignant lesion was considerably higher in the latter group (25 vs 93.33%, P = .0002). None of the lesions which had vividly enhancing thin septa as their dominant CEUS feature were malignant. Oncocytoma, multilocular cystic renal neoplasm of low malignant potential, and cystic nephroma were the major benign entities among Bosniak 3 lesions. Localized cystic kidney disease and hemorrhagic cysts were found to be the primary mimickers leading to false positive imaging findings. CEUS has a high predictive power for malignancy in the Bosniak 4 category, which is not maintained in the Bosniak 3 group due to the large proportion of benign lesions. Adherence to rigorous rule-in criteria and active surveillance strategies need to be considered for equivocal CEUS Bosniak 3 lesions.

Data-guided for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete

This study aims to present a data-guided intelligent design framework for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete (RHC). First, gradient boosting (GB) models were developed to predict compressive strength of RHC based on a dataset with 1150 samples. The CatBoost model outperforms XGBoost and LightGBM models on the test set, and its determination of coefficient (R2), root mean squared error (RMSE), and mean absolute error (MAE) are 0.984, 3.270 MPa, and 2.290 MPa respectively. With the best prediction model, SHapley Additive exPlanations (SHAP) was utilized to shed light on the model outputs. The curing age is the most dominant features effecting the compressive strength of RHC, and the next two are the contents of cement and water. Then, a computational design framework was proposed by integrating the machine learning-based model and the evolutionary algorithm. Considering compressive strength, materials cost, embodied carbon, and embodied energy, the multi-objective optimization was conducted leveraging the improved adaptive geometry estimation based many-objective evolutionary algorithm (AGE-MOEA II). The optimal solutions of the different design scenarios were achieved using the multi-criteria decision-making method. The research can provide a theoretical reference for the capability of developing RHC mixture using data-guided approachesto promote the sustainable development of concrete industry.

Ecological niche divergence or ecological niche partitioning in a widespread Neotropical bird lineage.

Ecological niche divergence is generally considered to be a facet of evolution that may accompany geographic isolation and diversification in allopatry, contributing to species' evolutionary distinctiveness through time. The null expectation for any two diverging species in geographic isolation is that of niche conservatism, wherein populations do not rapidly shift to or adapt to novel environments. Here, I test ecological niche divergence for a widespread, pan-American lineage, the avian genus of martins (Progne). The genus Progne includes migrant and resident species, as well as geographically restricted taxa and widespread, intercontinentally distributed taxa, thus providing an ideal group in which to study the nature of niche divergence within a broad geographic mosaic. I obtained distributional information for the genus from publicly available databases and created ecological niche models for each species to create pairwise comparisons of environmental space. I combined these data with the most up-to-date phylogeny of Progne currently available to examine the patterns of niche evolution within the genus. I found limited evidence for niche divergence across the breeding distributions of Progne, and much stronger support for niche conservatism with patterns of niche partitioning. The ancestral Progne had a relatively broad ecological niche, like extant basal Progne lineages, and several geographically localized descendant species occupy only portions of this larger ancestral niche. I recovered strong evidence of breeding niche divergence for four of 36 taxon pairs but only one of these divergent pairs involved two widespread species (Southern Martin P. elegans vs. Gray-breasted Martin P. chalybea). Potential niche expansion from the ancestral species was observed in the most wide-ranging present-day species, namely the North American Purple Martin P. subis and P. chalybea. I analyzed populations of P. subis separately, as a microcosm of Progne evolution, and again found only limited evidence of niche divergence. This study adds to the mounting evidence for niche conservatism as a dominant feature of diversifying lineages, and sheds light on the ways in which apparently divergent niches may arise through allopatry while not involving any true niche shifts through evolutionary time. Even taxa that appear unique in terms of habitat or behavior may not be diversifying with respect to their ecological niches, but merely partitioning ancestral niches among descendant taxa.

The mediating effect of dispositional mindfulness on the association between UPPS-P impulsivity traits and gaming disorder among Asia-Pacific young adults

BackgroundLittle evidence is available to verify the mediating effect of dispositional mindfulness on the association between gaming disorder and various impulsivity traits. The present study aimed to investigate the mediating effect of dispositional mindfulness on the association between the five UPPS-P impulsivity traits and the risk of gaming disorder among young adults.MethodsIt was an inter-regional cross-sectional study using online survey in Australia, Japan, The Philippines and China. Impulsivity measured by the UPPS-P Impulsive Behavior Scale–Short version; dispositional mindfulness measured by the Mindfulness Attention Awareness Scale; and the risk of gaming disorder measured by the Internet Gaming Disorder Scale were collected in the focal regions. Structural equation modeling was performed by SPSS AMOS version 26 to verify the study hypotheses. Bootstrapped 95% confidence interval was reported. Statistical significance was indicated by the p-value below 0.05.ResultsAmong the 1,134 returned questionnaires, about 40% of them aged 18–20 years and 21–23 years, respectively. 53.8% were male. 40.7% had been playing digital and video games for over 10 years. The prevalence of gaming disorder was 4.32%. The model fitness indices reflected that the constructed model had an acceptable model fit (χ2(118) = 558.994, p < 0.001; χ2/df = 4.737; CFI = 0.924; TLI = 0.890; GFI = 0.948; RMSEA = 0.058; SRMR = 0.0487). Dispositional mindfulness fully mediated the effect of positive urgency and negative urgency on the risk of gaming disorder. The effect of lack of premeditation on the risk of gaming disorder was partially mediated by dispositional mindfulness. However, dispositional mindfulness did not mediate the effect of sensation seeking on the risk of gaming disorder.ConclusionsThe varied associations between dispositional mindfulness and the five impulsivity traits hints that improving some impulsive traits may increase dispositional mindfulness and so lower the risk of gaming disorder. Despite further studies are needed to verify the present findings, it sheds light on the need to apply interventions on gamers based on their impulsivity profile. Interventions targeting at emotion regulation and self-control such as mindfulness-based interventions seem to be effective to help gamers with dominant features of urgency and lack of premeditation only. Other interventions shall be considered for gamers with high sensation seeking tendency to enhance the effectiveness of gaming disorder prevention.

The Effect of Combined Synthetic Tabular Data Generated Using CTGAN Model with Actual Data on Performance of DHF, Varicella, and COVID-19 Recognition Model

There are several quickly spreading illnesses such as DHFs spread by mosquitoes, COVID-19 spreads through respiratory droplets and contact with contaminated surfaces, and Varicella spreads by direct touch. The transmission rate of these diseases can be reduced if medical services can identify them early. However, the performance of the prediction model based on the machine learning approach is limited by the availability of labeled patient datasets. This study showed some empirical evidence of the use of synthetic data generated using actual medical records as the basis to improve the performance of the prediction model. The empirical results showed that the Decision Tree algorithm which is trained using a mixed synthetic and actual dataset can achieve 91.98% average accuracy which is higher than model performance which is trained using real dataset only. The results of model interpretation using Shapley Additive Explanations have the advantage of measuring the overall dominant features and indicating that the top five most important features are vThrombocyte, vTemp, vCough, vSpot, and vNauseous.

Colon impairments and inflammation driven by an altered gut microbiota leads to social behavior deficits rescued by hyaluronic acid and celecoxib.

The exact mechanisms linking the gut microbiota and social behavior are still under investigation. We aimed to explore the role of the gut microbiota in shaping social behavior deficits using selectively bred mice possessing dominant (Dom) or submissive (Sub) behavior features. Sub mice exhibit asocial, depressive- and anxiety-like behaviors, as well as systemic inflammation, all of which are shaped by their impaired gut microbiota composition. An age-dependent comparative analysis of the gut microbiota composition of Dom and Sub mice was performed using 16S rRNA sequencing, from early infancy to adulthood. Dom and Sub gastrointestinal (GI) tract anatomy, function, and immune profiling analyses were performed using histology, RT-PCR, flow cytometry, cytokine array, and dextran-FITC permeability assays. Short chain fatty acids (SCFA) levels in the colons of Dom and Sub mice were quantified using targeted metabolomics. To support our findings, adult Sub mice were orally treatedwith hyaluronic acid (HA) (30 mg/kg) or with the non-steroidal anti-inflammatory agent celecoxib (16 mg/kg). We demonstrate that from early infancy the Sub mouse gut microbiota lacks essential bacteria for immune maturation, including Lactobacillus and Bifidobacterium genera. Furthermore, from birth, Sub mice possess a thicker colon mucin layer, and from early adulthood, they exhibit shorter colonic length, altered colon integrity with increased gut permeability, reduced SCFA levels and decreased regulatory T-cells, compared to Dom mice. Therapeutic intervention in adult Sub mice treated with HA, celecoxib, or both agents, rescued Sub mice phenotypes. HA treatment reduced Sub mouse gut permeability, increased colon length, and improved mouse social behavior deficits. Treatment with celecoxib increased sociability, reduced depressive- and anxiety-like behaviors, and increased colon length, and a combined treatment resulted in similar effects as celecoxib administered as a single agent. Overall, our data suggest that treating colon inflammation and decreasing gut permeability can restore gut physiology and prevent social deficits later in life. These findings provide critical insights into the importance of early life gut microbiota in shaping gut immunity, functionality, and social behavior, and may be beneficial for the development of future therapeutic strategies.

A novel spectral super-resolution network with dominant information between spatial and spectral domains

Existing spectral super-resolution (SSR) methods have achieved satisfactory performance by designing complicated deep convolution neural networks (DCNNs) to extract spectral and spatial features. However, these methods ignore the fact that the significance of spatial and spectral information in each hyperspectral image (HSI) is different, and most of them directly fuse two kinds of information with concatenation and convolution operation, which resulting in generating redundant information and have negative effects on reconstruction. To address such inadequacies, this paper proposes a novel adaptive spatial–spectral modulation network (ASSM-Net). Specifically, we propose a new adaptive feature fusion module (AFFM) to replace traditional convolutional fusion schemes. Through explicitly measuring the weights of spatial information and spectral features of HSI, AFFM can select dominant features for each pixel to modulate spatial and spectral information. Additionally, we develop a pixel-weighted aware attention (PAA) mechanism to enhance the feature interdependences for recovering finer structure information. Finally, a large number of quantitative and qualitative experiments reveal that the proposed network achieves competitive reconstruction results on three benchmark datasets (NTIRE 2022, CAVE and Harvard).

Function of the hand as a predictor of early diagnosis and progression of Alzheimer's dementia: A systematic review.

The dominant feature of Alzheimer's dementia (AD) is gradual cognitive decline, which can be reflected by reduced finger dexterity. This review analyzed reports on hand function in AD patients to determine the possibility of using it for an early diagnosis and for monitoring the disease progression of AD. PubMed, Web of Science, EMBASE, and Cochrane library were searched systematically (search dates: 2000-2022), and relevant articles were cross-checked for related and relevant publications. Seventeen studies assessed the association of the handgrip strength or dexterity with cognitive performance. The hand dexterity was strongly correlated with the cognitive function in all studies. In the hand dexterity test using the pegboard, there was little difference in the degree of decline in hand function between the healthy elderly (HE) group and the mild cognitive impairment (MCI) group. On the other hand, there was a difference in the hand function between the HE group and the AD group. In addition, the decline in hand dexterity is likely to develop from moderate to severe dementia. In complex hand movements, movement speed variations were greater in the AD than in the HE group, and the automaticity, regularity, and rhythm were reduced. HE and AD can be identified by a simple hand motion test using a pegboard. The data can be used to predict dementia progression from moderate dementia to severe dementia. An evaluation of complex hand movements can help predict the transition from MCI to AD and the progression from moderate to severe dementia.

Colorectal Polyps among Sudanese Patients: A Single-Center Experience (Eyes Study)

Introduction: A gastrointestinal polyp is an abnormal protrusion from the mucosal surface of the gastrointestinal lumen. The objectives of the present study were to evaluate the clinical features, possible risk factors, endoscopic features, and histopathological patterns of gastrointestinal polyps among adult Sudanese patients. Methods: This was a retrospective, descriptive, cross-sectional hospital-based study conducted at the Soba University Hospital Department of Endoscopy from March 2017 to March 2020. This study was conducted on patients with gastrointestinal polyps found upon colonoscopy or flexible sigmoidoscopy. Results: The incidence of colonic polyps was 11.7% (237/2019). The mean age was 51 ± 18 years, and the male-to-female ratio was 1.5:1. The indications for colonoscopy were bleeding per rectum (28.7%; n=68), chronic constipation (16.5%; n=39), a change in bowel habits (11%; n=26), anaemia (8.8%; n=20), melena (3.8%; n=9), diarrhoea (3.8%; n=9%) and surveillance (20%; n=48%). A family history of cancer was directly correlated with the size of the colon polyp on endoscopy and the grade of dysplasia on histopathology (p values=0.01 and 0.02, respectively). The use of NSAIDs was significantly correlated with the site of colonic polyps, while no significant correlation was found with smoking status, alcohol consumption, or family history of polyps (p value=0.02). The main site of polyps for colonic polyps was the left colon (67.4%). Histopathology reports were available for 65.4% (n=155) of patients with polyps. The predominant histopathological type in the lower gastrointestinal tract was adenomatous in 46.4% (n=72) of patients. Conclusion: The frequency of colonic polyps was 11.7%. The main presentation for colonic polyps was bleeding per rectum. A small size &lt; 0.5 cm and a sessile shape were the dominant endoscopic features of the lower gastrointestinal polyps. However, adenomatous polyps were the most common histopathological type of polyp among ......