bluuebunny/crossref_metadata_2025 huggingface dataset:
Creation of crystal structure reproducing X-ray diffraction pattern without using database
Authors: Lee Joohwi , Oba Junpei , Ohba Nobuko , Kajita Seiji
Published: August 2023

When a sample’s X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. However, if a similar XRD pattern is not found, it is tremendously laborious to identify the crystal structure even for experts. This case commonly happens when researchers develop novel and complex materials. In this study, we propose a crystal structure creation scheme that reproduces a given XRD pattern. We employed a combinatorial inverse design method using an evolutionary algorithm and crystal morphing (Evolv&Morph) supported by Bayesian optimization, which maximizes the similarity of the XRD patterns between target one and those of the created crystal structures. For sixteen different crystal structure systems with twelve simulated and four powder target XRD patterns, Evolv&Morph successfully created crystal structures with the same XRD pattern as the target (cosine similarity 99% for the simulated ones and >96% the experimentally measured ones). Furthermore, the present method has merits in that it is an automated crystal structure creation scheme, not dependent on a database. We believe that Evolv&Morph can be applied not only to determine crystal structures but also to design materials for specific properties.
Hierarchical quantum circuit representations for neural architecture search
Authors: Lourens Matt , Sinayskiy Ilya , Park Daniel K. , Blank Carsten , Petruccione Francesco
Published: August 2023

Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts to automate neural network design through learning network architecture and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using techniques from NAS, which enables search space design and architecture search. We use this framework to justify the importance of circuit architecture in quantum machine learning by generating a family of Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset, GTZAN. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. Finally, we implement the framework as an open-source Python package to enable dynamic circuit creation and facilitate circuit search space design for NAS.
Hyperlocal environmental data with a mobile platform in urban environments
Authors: Wang An , Mora Simone , Machida Yuki , deSouza Priyanka , Paul Sanjana , Oyinlola Oluwatobi , Duarte Fábio , Ratti Carlo
Published: August 2023

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.
Gene expression genetics of the striatum of Diversity Outbred mice
Authors: Philip Vivek M. , He Hao , Saul Michael C. , Dickson Price E. , Bubier Jason A. , Chesler Elissa J.
Published: August 2023

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.
FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs
Authors: Abedeen Iftekharul , Rahman Md. Ashiqur , Prottyasha Fatema Zohra , Ahmed Tasnim , Chowdhury Tareque Mohmud , Shatabda Swakkhar
Published: August 2023

Digital radiography is one of the most common and cost-effective standards for the diagnosis of bone fractures. For such diagnoses expert intervention is required which is time-consuming and demands rigorous training. With the recent growth of computer vision algorithms, there is a surge of interest in computer-aided diagnosis. The development of algorithms demands large datasets with proper annotations. Existing X-Ray datasets are either small or lack proper annotation, which hinders the development of machine-learning algorithms and evaluation of the relative performance of algorithms for classification, localization, and segmentation. We present FracAtlas, a new dataset of X-Ray scans curated from the images collected from 3 major hospitals in Bangladesh. Our dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists and an orthopedist using the open-source labeling platform, makesense.ai. There are 717 images with 922 instances of fractures. Each of the fracture instances has its own mask and bounding box, whereas the scans also have global labels for classification tasks. We believe the dataset will be a valuable resource for researchers interested in developing and evaluating machine learning algorithms for bone fracture diagnosis.
PP2A-B55alpha controls keratinocyte adhesion through dephosphorylation of the Desmoplakin C-terminus
Authors: Perl Abbey L. , Koetsier Jennifer L. , Green Kathleen J.
Published: August 2023

Critical for the maintenance of epidermal integrity and function are attachments between intermediate filaments (IF) and intercellular junctions called desmosomes. The desmosomal cytoplasmic plaque protein desmoplakin (DP) is essential for anchoring IF to the junction. DP-IF interactions are regulated by a phospho-regulatory motif within the DP C-terminus controlling keratinocyte intercellular adhesion. Here we identify the protein phosphatase 2A (PP2A)-B55α holoenzyme as the major serine/threonine phosphatase regulating DP’s C-terminus and consequent intercellular adhesion. Using a combination of chemical and genetic approaches, we show that the PP2A-B55α holoenzyme interacts with DP at intercellular membranes in 2D- and 3D- epidermal models and human skin samples. Our experiments demonstrate that PP2A-B55α regulates the phosphorylation status of junctional DP and is required for maintaining strong desmosome-mediated intercellular adhesion. These data identify PP2A-B55α as part of a regulatory module capable of tuning intercellular adhesion strength and a candidate disease target in desmosome-related disorders of the skin and heart.
Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting
Authors: Araza Arnan , de Bruin Sytze , Hein Lars , Herold Martin
Published: August 2023

Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010–2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6–47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9–39 Mg C $$\textrm{ha}^{-1}$$

ha

-
1

and $$\textrm{R}^{2}$$

R
2

=0.16–0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.
Hyper-fidelity depletion with discrete motion for pebble bed reactors
Authors: Robert Yves , Siaraferas Tatiana , Fratoni Massimiliano
Published: August 2023

Hyper-fidelity (HxF) depletion of pebble bed reactors (PBRs) is the capability to model depletion for every pebble while accounting for motion through the core. Previous HxF work demonstrated feasibility to deplete hundreds of thousands of stationary pebbles concurrently within reasonable timeframes. This work illustrates the second step towards HxF, coupling depletion with a discrete motion scheme. The model assumes an ordered bed with pebbles occupying fixed positions. Motion is simplified as discrete since pebbles move in straight lines from one set position to another. The methodology was implemented in Serpent 2, combined with its transport and depletion capabilities. Ad-hoc routines were developed ensuring compatibility with domain decomposition and pebble recirculation after each pass based on discharge criteria and fresh pebble insertion. Capabilities of HxF with discrete motion are demonstrated using a full-scale high-temperature gas-cooled reactor model. Specifically, an approach to equilibrium is performed, and example results are shown for in-core and discarded pebbles. The data illustrates how HxF provides unique insights into PBR fuel, producing information on statistical distributions rather than average values only, as obtained by traditional methods that rely on spectral zoning for depletion. Knowledge of these distributions can greatly improve analysis and assessment of PBRs.
Prevalence and risk factors of diabetes mellitus and hypertension in North East Tunisia calling for efficient and effective actions
Authors: Kheriji Nadia , Dakhlaoui Thouraya , Kamoun Rebai Wafa , Maatoug Sonia , Thabet Mohamed Taher , Mellah Thouraya , Mrad Mehdi , Trabelsi Hajer , Soltani Manel , Kabbage Maria , Hassine Hichem Ben , Hadj Salah Bahlous Afef , Mahjoub Faten , Jamoussi Henda , Abid Abdelmajid , Abdelhak Sonia , Kefi Rym
Published: August 2023

Diabetes and hypertension are a serious public health problem worldwide. In the last decades, prevalence of these two metabolic diseases has dramatically increased in the Middle East and North Africa region, especially in Tunisia. This study aimed to determine the prevalence of type 2 diabetes (T2D) and High Blood Pressure (HBP) in Zaghouan, a North-East region of Tunisia. To this end, an exploratory study with stratified random sampling of 420 participants has been carried out. Various data were collected. Blood samples and urine were drawn for biochemical assay. Then, all data were analyzed using the statistical R software. Results showed an alarming situation with an inter-regional difference in prevalence of obesity (50.0%, CI 95.0%), HBP (39.0%, CI 95.0%) and T2D (32.0%, CI 95.0%). This study allowed the discovery of 24, 17 and 2 new cases of T2D, HBP and T2D&HBP respectively. The association of some socio-economic factors and biochemical parameters with these chronic diseases has been highlighted. To conclude, the health situation in the governorate of Zaghouan requires urgent interventions to better manage the growing epidemic of non-communicable diseases (NCD) in the region. This study demonstrated the importance of engaging health policy makers in road mapping and implementing national NCD prevention programs.
Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease
Authors: Gálvez-Barrón César , Pérez-López Carlos , Villar-Álvarez Felipe , Ribas Jesús , Formiga Francesc , Chivite David , Boixeda Ramón , Iborra Cristian , Rodríguez-Molinero Alejandro
Published: August 2023

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.
Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images
Authors: Lee Joonsang , Warner Elisa , Shaikhouni Salma , Bitzer Markus , Kretzler Matthias , Gipson Debbie , Pennathur Subramaniam , Bellovich Keith , Bhat Zeenat , Gadegbeku Crystal , Massengill Susan , Perumal Kalyani , Saha Jharna , Yang Yingbao , Luo Jinghui , Zhang Xin , Mariani Laura , Hodgin Jeffrey B. , Rao Arvind
Published: August 2023

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.
Wavelet gated multiformer for groundwater time series forecasting
Authors: Serravalle Reis Rodrigues Vitor Hugo , de Melo Barros Junior Paulo Roberto , dos Santos Marinho Euler Bentes , Lima de Jesus Silva Jose Luis
Published: August 2023

Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.
Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material
Authors: Lee Jaehwan , Shin Seokwon , Lee Jaeho , Han Young-Kyu , Lee Woojin , Son Youngdoo
Published: August 2023

Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ($$\Delta G_H$$

Δ

G
H

) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications.
Plasmodium berghei oocysts possess fatty acid synthesis and scavenging routes
Authors: Saeed Sadia , Tremp Annie Z. , Dessens Johannes T.
Published: August 2023

Malaria parasites carry out fatty acid synthesis (FAS) in their apicoplast organelle via a bacterially related (type II) enzymatic pathway. In the vertebrate host, exoerythrocytic Plasmodium stages rely on FAS, whereas intraerythrocytic stages depend on scavenging FA from their environment. In the mosquito, P. falciparum oocysts express and rely on FAS enzymes for sporozoite formation, but P. yoelii oocysts do not express, nor depend on, FAS enzymes and thus rely on FA scavenging to support sporogony. In P. berghei, FAS enzymes are similarly expendable for sporogony, indicating it conforms to the P. yoelii scenario. We show here that P. berghei, unexpectedly, expresses FAS enzymes throughout oocyst development. These findings indicate that P. berghei can employ FAS alongside FA scavenging to maximise sporogony and transmission, and is more similar to P. falciparum than previously assumed with respect to FA acquisition by the oocyst. The ability of oocysts to switch between FAS and scavenging could be an important factor in the non-competitive relationship of resource exploitation between Plasmodium parasites and their mosquito vectors, which shapes parasite virulence both in the insect and vertebrate.
Effect of struvite (Crystal Green) fertilization on soil element content determined by different methods under soybean cultivation
Authors: Jama-Rodzeńska Anna , Gałka Bernard , Szuba-Trznadel Anna , Jandy Anita , Kamińska Joanna A.
Published: August 2023

Struvite is regarded as a promising phosphorus fertilizer alternative to mineral fertilizers; however before fertilizing, soil tests should be undertaken to determine fertilizer recommendations. In May 2022, soil was sampled from a pot experiment with the application of phosphorus set up at the Wroclaw University and Environmental and Life Sciences. Chemical analysis of the soil included total and available phosphorus, potassium, magnesium determined by the Egner–Riehm, Mehlich 3 and Yanai methods. The purpose of the article is to compare soil element extraction by three different methods under struvite fertilization and its use in soybean cultivation. The application of these methods indicated an unambiguous increase in soil Mg content after struvite application. Broadcast soybean fertilization affected the phosphorus content of the soil. The results of the study indicated that different extraction methods presented different contents of P from soil. The content of available phosphorus was circa 122–156 mg kg−1 dm, 35.4–67.5 mg kg−1 dm and 100–159 mg kg−1 dm according to the Mehlich, Yanai and Egner–Riehm methods, respectively. A positive correlation was found between the content of Mg and K in soil determined by the Mehlich 3 and Yanai methods, which may suggest that the Yanai method could be introduced into standard soil chemical analysis in Poland. Such a correlation was not found for phosphorus, which is a difficult element to determine due to the multitude of factors affecting its availability.
Variability of blue carbon storage in arid evaporitic environment of two coastal Sabkhas or mudflats
Authors: Al Disi Zulfa Ali , Naja Khaled , Rajendran Sankaran , Elsayed Hadil , Strakhov Ivan , Al-Kuwari Hamad Al Saad , Sadooni Fadhil , Dittrich Maria , Al-Khayat Jassim Abdulla A.
Published: August 2023

Coastal Sabkhas are mudflats found in arid coastal regions that are located within the supratidal zone when high rates of evaporation lead to high salinity. While evaporitic minerals often accumulate underneath the surface, the microbial mats are present on the surface of Sabkhas. Coastal Sabkha, an under-studied ecosystem in Qatar, has the potential to store blue carbon. In the present study, we investigated the carbon storage capacity of two Sabkhas from contrasting geological backgrounds. The spatial and temporal variabilities of the carbon stocks were examined. The results showed that both studied Sabkhas exhibit a considerable potential for soil carbon storage with carbon stocks of 109.11 ± 7.07 Mg C ha−1 and 67.77 ± 18.10 Mg C ha−1 in Dohat Faishakh and Khor al Adaid Sabkha respectively. These values fall within the reported range for carbon stocks in coastal Sabkhas in the region (51–194 Mg C ha−1). Interestingly, the carbon stocks in the sediments of the Sabkhas were higher than those in the sediments of Qatari mangroves (50.17 ± 6.27 Mg C ha−1). These finding suggest that coastal Sabkhas can serve as blue carbon ecosystems in arid environments.
A spatio-temporal analysis of fire occurrence patterns in the Brazilian Amazon
Authors: Valente Fernanda , Laurini Márcio
Published: August 2023

Wildfires in the Amazon significantly impact the forest structure and carbon cycle. Understanding the patterns of fire occurrence is crucial for effective management. A novel spatio-temporal point process framework was used to analyze changes in fire occurrence patterns in the Brazilian Amazon. A dynamic representation of a Log Gaussian Cox process was used to model the intensity function, which was decomposed into trend, seasonality, cycles, covariates, and spatial effects. The results show a marked decrease in long-term fire occurrence movements between the start of the sample and 2012, followed by an increase until the end of the sample, attributed to governance measures and market mechanisms. Spatial variability of fire occurrence rates in the Brazilian Amazon was successfully captured, with regions having more dry seasons experiencing higher fire occurrence rates. This analysis provides valuable insights into fire occurrence patterns in the Amazon region and the factors driving them.
IL-1β transgenic mouse model of inflammation driven esophageal and oral squamous cell carcinoma
Authors: Muthupalani Sureshkumar , Annamalai Damodaran , Feng Yan , Ganesan Suresh M. , Ge Zhongming , Whary Mark T. , Nakagawa Hiroshi , Rustgi Anil K. , Wang Timothy C. , Fox James G.
Published: August 2023

Chronic inflammation is integral to the development of esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), although the latter has not been associated with reflux esophagitis. The L2-IL-1β transgenic mice, expressing human interleukin (IL)-1β in the oral, esophageal and forestomach squamous epithelia feature chronic inflammation and a stepwise development of Barrett’s esophagus-like metaplasia, dysplasia and adenocarcinoma at the squamo-columnar junction. However, the functional consequences of IL-1β-mediated chronic inflammation in the oral and esophageal squamous epithelia remain elusive. We report for the first time that in addition to the previously described Barrett’s esophagus-like metaplasia, the L2-IL-1β mice also develop squamous epithelial dysplasia with progression to squamous cell carcinoma (SCC) in the esophagus and the tongue. L2-IL-1β showed age-dependent progression of squamous dysplasia to SCC with approximately 40% (n = 49) and 23.5% (n = 17) incidence rates for esophageal and tongue invasive SCC respectively, by 12–15 months of age. Interestingly, SCC development and progression in L2-IL-1β was similar in both Germ Free (GF) and Specific Pathogen Free (SPF) conditions. Immunohistochemistry revealed a T cell predominant inflammatory profile with enhanced expression of Ki67, Sox2 and the DNA double-strand break marker, γ-H2AX, in the dysplastic squamous epithelia of L2-IL-1β mice. Pro-inflammatory cytokines, immunomodulatory players, chemoattractants for inflammatory cells (T cells, neutrophils, eosinophils, and macrophages) and oxidative damage marker, iNOS, were significantly increased in the esophageal and tongue tissues of L2-IL-1β mice. Our recent findings have expanded the translational utility of the IL-1β mouse model to aid in further characterization of the key pathways of inflammation driven BE and EAC as well as ESCC and Oral SCC.
Thai and American mothers socialize preschoolers’ emotional development differently
Authors: Rochanavibhata Sirada , Marian Viorica
Published: August 2023

Cultures vary in beliefs about appropriate display of emotion. Children rely on adults to help them understand emotional experiences and display emotions in a culturally appropriate manner. The present study compared how emotion display differs between Thai and American mother–child interactions during preschool. Language samples from 21 Thai and 21 American mother–child dyads were elicited using prompted reminiscing, book reading, toy play, and child personal narrative tasks. Results revealed group differences in emotion talk and behavior. American dyads expressed more intense emotions during interactions compared to Thai dyads. American dyads also displayed more emotion behaviors than Thai dyads, whereas Thai dyads used more emotion words compared to American dyads. Additionally, there were gender differences in the expression of emotion, with boy dyads more emotionally intense than girl dyads in both groups. Boys displayed more negative emotion behaviors compared to girls during prompted reminiscing, whereas girls used more negative emotion words than boys during the personal narrative task. These findings demonstrate cultural and gender differences in socialization goals and practices regarding emotion display and underscore the influence of mothers’ scaffolding on children’s emotional development. This research reveals the variability in beliefs and values that underlie emotional development across sociocultural contexts.
An investigation on the cyclic temperature-dependent performance behaviors of ultrabright air-stable QLEDs
Authors: Mokarian Zanjani Saeedeh , Sadeghi Sadra , Shahalizad Afshin , Pahlevani Majid
Published: August 2023

The aerobic and thermal stability of quantum-dot light-emitting diodes (QLEDs) is an important factor for the practical applications of these devices under harsh environmental conditions. We demonstrate all-solution-processed amber QLEDs with an external quantum efficiency (EQE) of > 14% with almost negligible efficiency roll-off (droop) and a peak brightness of > 600,000 cd/m2, unprecedented for QLEDs fabricated under ambient air conditions. We investigate the device efficiency and brightness level at a temperature range between − 10 and 85 °C in a 5-step cooling/heating cycle. We conducted the experiments at brightness levels higher than 10,000 cd/m2, required for outdoor lighting applications. Our device performance proves thermal stability, with minimal standard deviation in the performance parameters. Interestingly, the device efficiency parameters recover to the initial values upon returning to room temperature. The variations in the performance are correlated with the modification of charge transport characteristics and induced radiative/non-radiative exciton relaxation dynamics at different temperatures. Being complementary to previous studies on the subject, the present work is expected to shed light on the potential feasibility of realizing aerobic-stable ultrabright droop-free QLEDs and encourage further research for solid-state lighting applications.
Transcranial alternating current stimulation over multiple brain areas with non-zero phase delays other than 180 degrees modulates visuospatial working memory performance
Authors: Park Jimin , Lee Sangjun , Park Seonghun , Lee Chany , Kim Sungshin , Im Chang-Hwan
Published: August 2023

While zero-phase lag synchronization between multiple brain regions has been widely observed, relatively recent reports indicate that systematic phase delays between cortical regions reflect the direction of communications between cortical regions. For example, it has been suggested that a non-zero phase delay of electroencephalography (EEG) signals at the gamma frequency band between the bilateral parietal areas may reflect the direction of communication between these areas. We hypothesized that the direction of communication between distant brain areas might be modulated by multi-site transcranial alternating current stimulation (tACS) with specific phase delays other than 0° and 180°. In this study, a new noninvasive brain stimulation (NIBS) method called multi-site multi-phase tACS (msmp-tACS) was proposed. The efficacy of the proposed method was tested in a case study using a visuospatial working memory (VWM) paradigm in which the optimal stimulation conditions including amplitudes and phases of multiple scalp electrodes were determined using finite element analysis adopting phasor representation. msmp-tACS was applied over the bilateral intraparietal sulci (IPS) and showed that 80 Hz tACS with the phase for the right IPS leading that for the left IPS by 90° (= 3.125 ms) partialized VWM performance toward the right visual hemifield. The three stimulation conditions were synchronized, RL, and LR, which refers to stimulation condition with no phase lag, stimulation phase of right IPS (rIPS) leading left IPS (lIPS) by 90° and the stimulation of lIPS leading rIPS by 90°, respectively. The lateralization of VWM significantly shifted towards right visual hemifield under the RL condition compared to the synchronized and LR conditions. The shift in VWM was the result of the stimulation affecting both left and right visual hemifield trials to certain degrees, rather than significantly increasing or decreasing VWM capacity of a specific visual hemifield. Altered brain dynamics caused by msmp-tACS partialized VWM performance, likely due to modulation of effective connectivity between the rIPS and lIPS. Our results suggest that msmp-tACS is a promising NBS method that can effectively modulate cortical networks that cannot be readily modulated with conventional multi-site stimulation methods.
Microbial community composition of food waste before anaerobic digestion
Authors: Tang Linjie , O’Dwyer Jack , Kimyon Önder , Manefield Michael J.
Published: August 2023

Anaerobic digestion is widely used to process and recover value from food waste. Commercial food waste anaerobic digestion facilities seek improvements in process efficiency to enable higher throughput. There is limited information on the composition of microbial communities in food waste prior to digestion, limiting rational exploitation of the catalytic potential of microorganisms in pretreatment processes. To address this knowledge gap, bacterial and fungal communities in food waste samples from a commercial anaerobic digestion facility were characterised over 3 months. The abundance of 16S rRNA bacterial genes was approximately five orders of magnitude higher than the abundance of the fungal intergenic spacer (ITS) sequence, suggesting the numerical dominance of bacteria over fungi in food waste before anaerobic digestion. Evidence for the mass proliferation of bacteria in food waste during storage prior to anaerobic digestion is presented. The composition of the bacterial community shows variation over time, but lineages within the Lactobacillaceae family are consistently dominant. Nitrogen content and pH are correlated to community variation. These findings form a foundation for understanding the microbial ecology of food waste and provide opportunities to further improve the throughput of anaerobic digestion.
Hyperfructosemia in sleep disordered breathing: metabolome analysis of Nagahama study
Authors: Nakatsuka Yoshinari , Murase Kimihiko , Sonomura Kazuhiro , Tabara Yasuharu , Nagasaki Tadao , Hamada Satoshi , Matsumoto Takeshi , Minami Takuma , Kanai Osamu , Takeyama Hirofumi , Sunadome Hironobu , Takahashi Naomi , Nakamoto Isuzu , Tanizawa Kiminobu , Handa Tomohiro , Sato Taka-Aki , Komenami Naoko , Wakamura Tomoko , Morita Satoshi , Takeuchi Osamu , Nakayama Takeo , Hirai Toyohiro , Kamatani Yoichiro , Matsuda Fumihiko , Chin Kazuo
Published: August 2023

Sleep disordered breathing (SDB), mainly obstructive sleep apnea (OSA), constitutes a major health problem due to the large number of patients. Intermittent hypoxia caused by SDB induces alterations in metabolic function. Nevertheless, metabolites characteristic for SDB are largely unknown. In this study, we performed gas chromatography-mass spectrometry-based targeted metabolome analysis using data from The Nagahama Study (n = 6373). SDB-related metabolites were defined based on their variable importance score in orthogonal partial least squares discriminant analysis and fold changes in normalized peak-intensity levels between moderate-severe SDB patients and participants without SDB. We identified 20 metabolites as SDB-related, and interestingly, these metabolites were frequently included in pathways related to fructose. Multivariate analysis revealed that moderate-severe SDB was a significant factor for increased plasma fructose levels (β = 0.210, P = 0.006, generalized linear model) even after the adjustment of confounding factors. We further investigated changes in plasma fructose levels after continuous positive airway pressure (CPAP) treatment using samples from patients with OSA (n = 60) diagnosed by polysomnography at Kyoto University Hospital, and found that patients with marked hypoxemia exhibited prominent hyperfructosemia and their plasma fructose levels lowered after CPAP treatment. These data suggest that hyperfructosemia is the abnormality characteristic to SDB, which can be reduced by CPAP treatment.
Comparative proteomics in captive giant pandas to identify proteins involved in age-related cataract formation
Authors: You Yuyan , Bai Chao , Wang Wei , Zhan Tongtong , Hu Xin , Hao Feier , Xia Maohua , Liu Yan , Ma Tao , Liu Yanhui , Zheng Changming , Pu Tianchun , Zhang Yizhuo , Lu Yanping , Ding Nan , Li Jing , Yin Yanqiang , Chen Yucun , Wang Liqin , Zhou Jun , Niu Lili , Xiu Yunfang , Lu Yan , Jia Ting , Liu Xuefeng , Zhang Chenglin
Published: August 2023

Approximately 20% of aged captive giant pandas (Ailuropoda melanoleuca) have cataracts that impair their quality of life. To identify potential biomarkers of cataract formation, we carried out a quantitative proteomics analysis of 10 giant pandas to find proteins differing in abundance between healthy and cataract-bearing animals. We identified almost 150 proteins exceeding our threshold for differential abundance, most of which were associated with GO categories related to extracellular localization. The most significant differential abundance was associated with components of the proteasome and other proteins with a role in proteolysis or its regulation, most of which were depleted in pandas with cataracts. Other modulated proteins included components of the extracellular matrix or cytoskeleton, as well as associated signaling proteins and regulators, but we did not find any differentially expressed transcription factors. These results indicate that the formation of cataracts involves a complex post-transcriptional network of signaling inside and outside lens cells to drive stress responses as a means to address the accumulation of protein aggregates triggered by oxidative damage. The modulated proteins also indicate that it should be possible to predict the onset of cataracts in captive pandas by taking blood samples and testing them for the presence or absence of specific protein markers.
Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta
Authors: Wang Xuchun , Ren Jiahui , Ren Hao , Song Wenzhu , Qiao Yuchao , Zhao Ying , Linghu Liqin , Cui Yu , Zhao Zhiyang , Chen Limin , Qiu Lixia
Published: August 2023

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.
Application of photocatalytic proxone process for petrochemical wastewater treatment
Authors: Aghazadeh Mehrab , Hassani Amir Hessam , Borghei Mehdi
Published: August 2023

Industrial wastewaters are different from sanitary wastewaters, and treatment complications due to their unique characteristics, so biological processes are typically disrupted. High chemical oxygen demand, dye, heavy metals, toxic organic and non-biodegradable compounds present in petroleum industry wastewater. This study intends to optimize the photocatalytic proxone process, utilizing a synthesized ZnO–Fe3O4 nanocatalyst, for petroleum wastewater treatment. The synthesis of ZnO–Fe3O4 was done by air oxidation and layer-by-layer self-assembly method and XRD, SEM, EDAX, FT-IR, BET, DRS, and VSM techniques were used to characterize the catalyst. Central composite design (CCD) method applied to investigated the effect of pH (4–8), reaction time (30–60 min), ozone gas concentration (1–2 mg/L-min), hydrogen peroxide concentration (2–3 mL/L) and the amount of catalyst (1–0.5 g/L) on the process. In the optimal conditions, biological oxygen demand (BOD5) and total petroleum hydrocarbon (TPH) removal, reaction kinetic, and synergistic effect mechanisms on the process were studied. Based on the ANOVA, a quadratic model with R2 = 0.99, P-Value = 0.0001, and F-Value = 906.87 was proposed to model the process. Based on the model pH = 5.7, ozone concentration = 1.8 mg/L-min, hydrogen peroxide concentration = 2.5 mL/L, reaction time = 56 min, and the catalyst dose = 0.7 g/L were proposed as the optimum condition. According to the model prediction, an efficiency of 85.3% was predicted for the removal of COD. To evaluate the accuracy of the prediction, an experiment was carried out in optimal conditions, and experimentally, a 52% removal efficiency was obtained. Also, at the optimum condition, BOD5 and TPH removal were 91.1% and 89.7% respectively. The reaction kinetic follows the pseudo-first-order kinetic model (R2 = 0.98). Also, the results showed that there is a synergistic effect in this process. As an advanced hybrid oxidation process, the photocatalytic proxone process has the capacity to treat petroleum wastewater to an acceptable standard.
Epidemiology and treatment of proximal femoral fractures in the elderly U.S. population
Authors: Walter Nike , Szymski Dominik , Kurtz Steven M. , Lowenberg David W. , Alt Volker , Lau Edmund C. , Rupp Markus
Published: August 2023

Proximal femoral fractures are a serious complication, especially for elderly patients. Detailed epidemiological analyzes provide a valuable resource for stakeholders in the health care system in order to foresee future development possibly influenceable by adaption of therapeutic procedures and prevention strategies. This work aimed at answering the following research questions: (1) What are the incidence rates of proximal femoral fractures in the elderly U.S. population? (2) What is the preferred treatment procedure for these fractures? Proximal femoral fractures occurred between January 1, 2009 and December 31, 2019 in patients ≥ 65 years were identified from the Medicare Physician Service Records Data Base. The 5% sample of Medicare beneficiaries, equivalent to the records from approximately 2.5 million enrollees formed the basis of this study. Fractures were grouped into head/neck, intertrochanteric, and subtrochanteric fractures. The overall incidence rate, age and sex specific incidence rates as well as incidence rate ratios were calculated. Common Procedural Terminology (CPT) codes were used to identify procedures and operations. In 2019, a total number of 7982 femoral head/neck fractures was recorded. In comparison to 9588 cases in 2009, the incidence substantially decreased by 26.6% from 666.7/100,000 inhabitants to 489.3/100,000 inhabitants (z =  − 5.197, p < 0.001). Also, in intertrochanteric fractures, a significant decline in the incidence by 17.3% was evident over the years from 367.7/100,000 inhabitants in 2009 to 304.0 cases per 100,000 inhabitants in 2019 (z =  − 2.454, p = 0.014). A similar picture was observable for subtrochanteric fractures, which decreased by 29.6% (51.0 cases per 100,000 to 35.9 cases per 100,000) over the time period (z =  − 1.612, p = 0.107). Head/neck fractures were mainly treated with an arthroplasty (n = 36,301, 40.0%). The majority of intertrochanteric fractures and subtrochanteric fractures received treatment with an intramedullary device (n = 34,630, 65.5% and n = 5870, 77.1%, respectively). The analysis indicated that the incidence of all types of proximal femoral neck fractures decreased for the population of elderly patients in the U.S. within the last decade. Treatment of head and neck fractures was mainly conducted through arthroplasty, while intertrochanteric and subtrochanteric fractures predominantly received an intramedullary nailing.