bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetics and immunity of Anopheles response to the entomopathogenic fungus Metarhizium anisopliae overlap with immunity to Plasmodium
Authors: Bukhari Tullu , Aimanianda Vishukumar , Bischoff Emmanuel , Brito-Fravallo Emma , Eiglmeier Karin , Riehle Michelle M. , Vernick Kenneth D. , Mitri Christian
Published: April 2022

Entomopathogenic fungi have been explored as a potential biopesticide to counteract the insecticide resistance issue in mosquitoes. However, little is known about the possibility that genetic resistance to fungal biopesticides could evolve in mosquito populations. Here, we detected an important genetic component underlyingAnopheles coluzziisurvival after exposure to the entomopathogenic fungusMetarhizium anisopliae. A familiality study detected variation for survival among wild mosquito isofemale pedigrees, and genetic mapping identified two loci that significantly influence mosquito survival after fungus exposure. One locus overlaps with a previously reported locus forAnophelessusceptibility to the human malaria parasitePlasmodium falciparum. Candidate gene studies revealed that two LRR proteins encoded by APL1C and LRIM1 genes in this newly mapped locus are required for protection of femaleA. coluzziifromM. anisopliae, as is the complement-like factor Tep1. These results indicate that naturalAnophelespopulations already segregate frequent genetic variation for differential mosquito survival after fungal challenge and suggest a similarity inAnophelesprotective responses against fungus andPlasmodium. However, this immune similarity raises the possibility that fungus-resistant mosquitoes could also display enhanced resistance toPlasmodium, suggesting an advantage of selecting for fungus resistance in vector populations to promote naturally diminished malaria vector competence.
Seasonal and sexual variation in mRNA expression of selected adipokine genes affecting fat deposition and metabolism of the emu (Dromaius novaehollandiae)
Authors: Kim Ji Eun , Bennett Darin C. , Wright Kristina , Cheng Kimberly M.
Published: April 2022

Emus are farmed for fat production. Oil rendered from their back and abdominal fat pads has good anti-oxidant and anti-inflammatory properties and has ingredients that promote cell growth. Our objective is to examine the mRNA expression of 7 emu adipokine genes (eFABP4,eSCD1,eAdipoQ,eAdipoR1,eAdipoR2,eLEPandeLepR) to identify gene markers that may help improve emu fat production. Back and abdominal fat tissues from 11 adult emus were biopsied at four time points (April, June, August and November). Total RNA was isolated and cDNA was synthesized. Gene specific primers were designed for partial cloning fragments to amplify the open reading frame of the 7 genes. eLEPwas not expressed in emu fat tissue. Nucleotides and amino acids sequences of the 6 expressed gene were compared with homologs from other species and phylogenetic relationships established. Seasonal mRNA expression of each gene was assessed by quantitative RT-PCR and differential expression analysed by the 2−ΔΔCTmethod. The 6 expressed genes showed seasonal variation in expression and showed association of expression level with back fat adiposity. More whole-genome scanning studies are needed to develop novel molecular markers that can be applied to improve fat production in emus.
Population expansion, divergence, and persistence in Western Fence Lizards (Sceloporus occidentalis) at the northern extreme of their distributional range
Authors: Davis Hayden R. , Des Roches Simone , Anderson Roger A. , Leaché Adam D.
Published: April 2022

Population dynamics within species at the edge of their distributional range, including the formation of genetic structure during range expansion, are difficult to study when they have had limited time to evolve. Western Fence Lizards (Sceloporus occidentalis) have a patchy distribution at the northern edge of their range around the Puget Sound, Washington, where they almost exclusively occur on imperiled coastal habitats. The entire region was covered by Pleistocene glaciation as recently as 16,000 years ago, suggesting that populations must have colonized these habitats relatively recently. We tested for population differentiation across this landscape using genome-wide SNPs and morphological data. A time-calibrated species tree supports the hypothesis of a post-glacial establishment and subsequent population expansion into the region. Despite a strong signal for fine-scale population genetic structure across the Puget Sound with as many as 8–10 distinct subpopulations supported by the SNP data, there is minimal evidence for morphological differentiation at this same spatiotemporal scale. Historical demographic analyses suggest that populations expanded and diverged across the region as the Cordilleran Ice Sheet receded. Population isolation, lack of dispersal corridors, and strict habitat requirements are the key drivers of population divergence in this system. These same factors may prove detrimental to the future persistence of populations as they cope with increasing shoreline development associated with urbanization.
Habitat preferences, estimated abundance and behavior of tree hyrax (Dendrohyrax sp.) in fragmented montane forests of Taita Hills, Kenya
Authors: Rosti Hanna , Heiskanen Janne , Loehr John , Pihlström Henry , Bearder Simon , Mwangala Lucas , Maghenda Marianne , Pellikka Petri , Rikkinen Jouko
Published: April 2022

We studied a previously almost unknown nocturnal mammal, an apparently undescribed species of tree hyrax (Dendrohyrax sp.) in the moist montane forests of Taita Hills, Kenya. We used thermal imaging to locate tree hyraxes, observe their behavior, and to identify woody plants most frequently visited by the selective browsers. We also documented acoustic behavior in forest fragments of different sizes. Data on calling type and frequency were analyzed together with lidar data to estimate population densities and to identify forest stand characteristics associated with large populations. Viable populations were found only in the largest forest fragments (> 90 ha), where tree hyraxes preferred most pristine forest stands with high, multilayered canopies. The estimated population sizes in smaller forest fragments were very limited, and hyraxes were heard to call only during late night and early morning hours, presumably in order to avoid detection. While we frequently recorded tree hyrax songs in the largest forest fragments, we almost never heard songs in the small ones. All remaining subpopulations of the Taita tree hyrax are under threat of human disturbance and further habitat deterioration. Conservation efforts should include protection of all remaining habitat patches, but also reforestation of former habitat is urgently needed.
Aberrant functional connectivity and temporal variability of the dynamic pain connectome in patients with low back related leg pain
Authors: Pei Yixiu , Peng Jidong , Zhang Yong , Huang Muhua , Zhou Fuqing
Published: April 2022

Neuroimaging studies have suggested a link between the intensity of chronic low back pain intensity and structural and functional brain alterations. However, chronic pain results from the coordination and dynamics among several brain networks that comprise the dynamic pain connectome. Here, we use resting-state functional magnetic resonance imaging and measures of static (sFC) and dynamic functional connectivity (dFC) variability in the typical (0.01–0.1 Hz) and five specific (slow-6 to slow-2) frequency bands to test hypotheses regarding disruption in this variability in low back-related leg pain (LBLP) patients who experience chronic pain and numbness. Twenty-four LBLP patients and 23 healthy controls completed clinical assessments, and partial correlational analyses between altered sFC and dFC variability and clinical measures were conducted. We found a lower within-network sFC in the ascending nociceptive pathway (Asc) and a lower cross-network sFC between nodes of the salience network and the Asc in the typical frequency band. In the slow-5 frequency band, a lower within-network sFC was found in the Asc. Abnormal cross-network sFC was found between nodes of the salience network-Asc (slow-5 and slow-6) and the default mode network-Asc (slow-4 and slow-6). Furthermore, cross-network abnormalities in the typical and certain specific frequency bands were linked to clinical assessments. These findings indicate that frequency-related within- and cross-network communication among the nodes in the dynamic pain connectome is dysfunctional in LBLP patients and that selecting specific frequencies may be potentially useful for detecting LBLP-related brain activity.
Decoding personalized motor cortical excitability states from human electroencephalography
Authors: Hussain Sara J. , Quentin Romain
Published: April 2022

Brain state-dependent transcranial magnetic stimulation (TMS) requires real-time identification of cortical excitability states. Current approaches deliver TMS during brain states that correlate with motor cortex (M1) excitability at the group level. Here, we hypothesized that machine learning classifiers could successfully discriminate between high and low M1 excitability states in individual participants using information obtained from low-density electroencephalography (EEG) signals. To test this, we analyzed a publicly available dataset that delivered 600 single TMS pulses to the right M1 during EEG and electromyography (EMG) recordings in 20 healthy adults. Multivariate pattern classification was used to discriminate between brain states during which TMS evoked small and large motor-evoked potentials (MEPs). Results show that personalized classifiers successfully discriminated between low and high M1 excitability states in 80% of tested participants. MEPs elicited during classifier-predicted high excitability states were significantly larger than those elicited during classifier-predicted low excitability states in 90% of tested participants. Personalized classifiers did not generalize across participants. Overall, results show that individual participants exhibit unique brain activity patterns which predict low and high M1 excitability states and that these patterns can be efficiently captured using low-density EEG signals. Our findings suggest that deploying individualized classifiers during brain state-dependent TMS may enable fully personalized neuromodulation in the future.
Non-invasive sampling of water-borne hormones demonstrates individual consistency of the cortisol response to stress in laboratory zebrafish (Danio rerio)
Authors: Midttun H. L. E. , Øverli Ø. , Tudorache C. , Mayer I , Johansen I. B.
Published: April 2022

Glucocorticoid (GC) stress hormones are well-known for their impact on phenotypic traits ranging from immune function to behaviour and cognition. For that reason, consistent aspects of an individual’s physiological stress response (i.e. GC responsiveness) can predict major elements of life-history trajectory. Zebrafish (Danio rerio) emerge as a promising model to study such consistent trait correlations, including the development of individual stress coping styles, i.e. consistent associations between physiological and behavioral traits. However, consistency in GC responsiveness of this popular animal model remains to be confirmed. Such a study has so far been hampered by the small-bodied nature and insufficient blood volume of this species to provide repeated measurements of circulating GCs. Here, we adopted a technique that allows for repeated, non-invasive sampling of individual zebrafish by quantifying GCs from holding water. Our findings indicate consistency of the magnitude of post-stress GC production over several consecutive stress events in zebrafish. Moreover, water-borne GCs reflect individual variation in GC responsiveness with the strongest consistency seen in males.
Cross subkey side channel analysis based on small samples
Authors: Hu Fanliang , Wang Huanyu , Wang Junnian
Published: April 2022

The majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in this paper with only 5K power traces, the deep learning (DL) model cannot effectively learn the internal features of the data due to insufficient training data. In this paper, we propose a cross-subkey training approach that acts as a trace augmentation. We train deep-learning models not only on a segment of traces containing the SBox operation of the target subkey of AES-128 but also on segments for other 15 subkeys. Experimental results show that the accuracy of the subkey combination training model is $$28.20\%$$

28.20
%

higher than that of the individual subkey training model on traces captured in the microcontroller implementation of the STM32F3 with AES-128. And validation is performed on two additional publicly available datasets. At the same time, the number of traces that need to be captured when the model is trained is greatly reduced, demonstrating the effectiveness and practicality of the method.
Loss of microbiota-derived protective metabolites after neutropenic fever
Authors: Rashidi Armin , Ebadi Maryam , Rehman Tauseef Ur , Elhusseini Heba , Halaweish Hossam , Holtan Shernan G. , Ramamoorthy Sivapriya , Weisdorf Daniel J. , Khoruts Alexander , Staley Christopher
Published: April 2022

Neutropenic fever (NF) is a common complication of chemotherapy in patients with cancer which often prolongs hospitalization and worsens the quality of life. Although an empiric antimicrobial approach is used to prevent and treat NF, a clear etiology cannot be found in most cases. Emerging data suggest an altered microbiota-host crosstalk leading to NF. We profiled the serum metabolome and gut microbiome in longitudinal samples before and after NF in patients with acute myeloid leukemia, a prototype setting with a high incidence of NF. We identified a circulating metabolomic shift after NF, with a minimal signature containing 18 metabolites, 13 of which were associated with the gut microbiota. Among these metabolites were markers of intestinal epithelial health and bacterial metabolites of dietary tryptophan with known anti-inflammatory and gut-protective effects. The level of these metabolites decreased after NF, in parallel with biologically consistent changes in the abundance of mucolytic and butyrogenic bacteria with known effects on the intestinal epithelium. Together, our findings indicate a metabolomic shift with NF which is primarily characterized by a loss of microbiota-derived protective metabolites rather than an increase in detrimental metabolites. This analysis suggests that the current antimicrobial approach to NF may need a revision to protect the commensal microbiota.
Deep learning model for tongue cancer diagnosis using endoscopic images
Authors: Heo Jaesung , Lim June Hyuck , Lee Hye Ran , Jang Jeon Yeob , Shin Yoo Seob , Kim Dahee , Lim Jae Yol , Park Young Min , Koh Yoon Woo , Ahn Soon-Hyun , Chung Eun-Jae , Lee Doh Young , Seok Jungirl , Kim Chul-Ho
Published: April 2022

In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five university hospitals in South Korea, collected between 2010 and 2020 with the participation of otolaryngologists. To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed. Of the 12,400 total images, 5576 images related to the tongue were extracted. The CNN models showed a mean area under the receiver operating characteristic curve (AUROC) of 0.845 and a mean area under the precision-recall curve (AUPRC) of 0.892. The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively. Meanwhile, fair agreement between the oncologist and the developed model was shown for cancer diagnosis (kappa value = 0.685). The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.
The effect of preoperative patient-reported anxiety on morbidity and mortality outcomes in patients undergoing major general surgery
Authors: Kassahun Woubet Tefera , Mehdorn Matthias , Wagner Tristan Cedric , Babel Jonas , Danker Helge , Gockel Ines
Published: April 2022

Excessive levels of anxiety may negatively influence treatment outcomes and likely increase patient suffering. We designed a prospective observational study to assess whether preoperative patient-reported anxiety affects major general surgery outcomes. We prospectively administered the State-Trait Anxiety Inventory (STAI) to measure preoperative anxiety in patients awaiting major general surgical procedures. Patients were grouped by STAI scores according to established cutoffs: no anxiety (STAI < 40) and anxiety (STAI ≥ 40). Four hundred patients completed the questionnaires and underwent surgery, with an average interval from questionnaire completion to surgery of 4 days. Applying a state anxiety (STAI-S) score ≥ 40 as a reference point, the prevalence of patient-reported anxiety was 60.5% (241 of 400). The mean STAI-S score for these patients was 50.48 ± 7.77. The mean age of the entire cohort was 58.5 ± 14.12 years. The majority of participants were male (53.8%). The distribution of sex by anxiety status showed that 53.5% of women and 46.5% of men had anxiety (p = 0.003). In the entire cohort, postoperative complications occurred in 23.9% and 28.6% of the no anxiety and anxiety groups, respectively. The difference was nonsignificant. In a subgroup of patients who underwent high-risk complex procedures (N = 221), however, postoperative complications occurred in 31.4% and 45.2% of the no anxiety and anxiety groups, respectively. This difference was significant atp = 0.004. Of the patients who were anxious, 3.3% (8 of 241) died during hospitalization following surgery, compared with 4.4% of the patients (7 of 159) who were not anxious (p = 0.577). In the multivariable analysis adjusted for covariates and based on the results of subgroup analysis, preoperative anxiety assessed by the STAIS score was associated with morbidity (OR 2.12, CI 1.14–3.96;p = 0.018) but not mortality. The majority of enrolled patients in this study were classified as having high- to very high-level preoperative clinical anxiety, and we found a significant quantitative effect of patient-reported anxiety on morbidity but not mortality after surgery.
Culture and multiomic analysis of lung cancer patient-derived pleural effusions revealed distinct druggable molecular types
Authors: Seo Ha-Young , Kim Soon-Chan , Roh Woo-lee , Shin Young-Kyoung , Kim Soyeon , Kim Dong-Wan , Kim Tae Min , Ku Ja-Lok
Published: April 2022

Malignant pleural effusion (MPE) is an independent determinant of poor prognostic factor of non-small cell lung cancer (NSCLC). The course of anchorage independent growth within the pleural cavity likely reforms the innate molecular characteristics of malignant cells, which largely accounts for resistance to chemotherapy and poor prognosis after the surgical resection. Nevertheless, the genetic and transcriptomic features with respect to various drug responses of MPE-complicated NSCLC remain poorly understood. To obtain a clearer overview of the MPE-complicated NSCLC, we established 28 MPE-derived lung cancer cell lines which were subjected to genomic, transcriptomic and pharmacological analysis. Our results demonstrated MPE-derived NSCLC cell lines recapitulated representative driver mutations generally found in the primary NSCLC. It also exhibited the presence of distinct translational subtypes in accordance with the mutational profiles. The drug responses of several targeted chemotherapies accords with both genomic and transcriptomic characteristics of MPE-derived NSCLC cell lines. Our data also suggest that the impending drawback of mutation-based clinical diagnosis in evaluating MPE-complicated NSCLS patient responses. As a potential solution, our work showed the importance of comprehending transcriptomic characteristics in order to defy potential drug resistance caused by MPE.
Bursting of excitatory cells is linked to interictal epileptic discharge generation in humans
Authors: Hofer Katharina T. , Kandrács Ágnes , Tóth Kinga , Hajnal Boglárka , Bokodi Virág , Tóth Estilla Zsófia , Erőss Loránd , Entz László , Bagó Attila G. , Fabó Dániel , Ulbert István , Wittner Lucia
Published: April 2022

Knowledge about the activity of single neurons is essential in understanding the mechanisms of synchrony generation, and particularly interesting if related to pathological conditions. The generation of interictal spikes—the hypersynchronous events between seizures—is linked to hyperexcitability and to bursting behaviour of neurons in animal models. To explore its cellular mechanisms in humans we investigated the activity of clustered single neurons in a human in vitro model generating both physiological and epileptiform synchronous events. We show that non-epileptic synchronous events resulted from the finely balanced firing of excitatory and inhibitory cells, which was shifted towards an enhanced excitability in epileptic tissue. In contrast, interictal-like spikes were characterised by an asymmetric overall neuronal discharge initiated by excitatory neurons with the presumptive leading role of bursting pyramidal cells, and possibly terminated by inhibitory interneurons. We found that the overall burstiness of human neocortical neurons is not necessarily related to epilepsy, but the bursting behaviour of excitatory cells comprising both intrinsic and synaptically driven bursting is clearly linked to the generation of epileptiform synchrony.
Breath chemical markers of sexual arousal in humans
Authors: Wang N. , Pugliese G. , Carrito M. , Moura C. , Vasconcelos P. , Cera N. , Li M. , Nobre P. , Georgiadis J. R. , Schubert J. K. , Williams J.
Published: April 2022

The chemical composition of exhaled breath was examined for volatile organic compound (VOC) indicators of sexual arousal in human beings. Participants (12-male, 12-female) were shown a randomized series of three emotion-inducing 10-min film clips interspersed with 3-min neutral film clips. The films caused different arousals: sports film (positive-nonsexual); horror film (negative-nonsexual); and erotic (sexual) that were monitored with physiological measurements including genital response and temperature. Simultaneously the breath was monitored for VOC and CO2. While some breath compounds (methanol and acetone) changed uniformly irrespective of the film order, several compounds did show significant arousal associated changes. For both genders CO2 and isoprene decreased in the sex clip. Some male individuals showed particularly strong increases of indole, phenol and cresol coincident with sexual arousal that decreased rapidly afterwards. These VOCs are degradation products of tyrosine and tryptophan, precursors for dopamine, noradrenalin, and serotonin, and therefore represent potential breath markers of sexual arousal.
Experimental study on the deformation localisation and acoustic emission characteristics of coal in Brazilian splitting tests
Authors: Guo Wei-yao , Zhang Wei , Zhang Cheng-guo , Chen Yang
Published: April 2022

This paper presents an experimental study to assess the behaviour of coal samples under tensile loadings to better understand the failure mechanisms and the interactions with the coal characteristics. A set of Brazilian splitting tests were carried out using disk specimens obtained from Tashan Coal Mine in China. The digital speckle correlation method and acoustic emission (AE) were used to capture the deformation localisation and AE characteristics of each specimen during the loading process. The precursor characteristics of AE and the failure mechanism are discussed. It was found that the entire loading process mainly consists of compaction, elastic and post-peak dropping stage without an obvious yielding stage. Two kinds of deformation localisation were observed: central symmetry and axis symmetry. The corresponding AE evolution patterns have different phases, including gradual rise, step rise, transient rise and steady rise. During the subcritical failure stage, AE counts demonstrate a “rapidly increasing + flatten” intermittent feature. The results provide a reference for a better understanding of the damage process of the brittle coal material and its application in ground control design.
CRISPRi for specific inhibition of miRNA clusters and miRNAs with high sequence homology
Authors: Drobna-Śledzińska Monika , Maćkowska-Maślak Natalia , Jaksik Roman , Dąbek Paulina , Witt Michał , Dawidowska Małgorzata
Published: April 2022

miRNAs form a class of noncoding RNAs, involved in post-transcriptional regulation of gene expression, broadly studied for their involvement in physiological and pathological context. Inhibition of mature miRNA transcripts, commonly used in miRNA loss-of-function experiments, may not be specific in case of miRNAs with high sequence homology, e.g. miRNAs from the same seed family. Phenotypic effects of miRNA repression might be biased by the repression of highly similar miRNAs. Another challenge is simultaneous inhibition of multiple miRNAs encoded within policistronic clusters, potentially co-regulating common biological processes. To elucidate roles of miRNA clusters and miRNAs with high sequence homology, it is of key importance to selectively repress only the miRNAs of interest. Targeting miRNAs on genomic level with CRISPR/dCas9-based methods is an attractive alternative to blocking mature miRNAs. Yet, so far no clear guidelines on the design of CRISPR inhibition (CRISPRi) experiments, specifically for miRNA repression, have been proposed. To address this need, here we propose a strategy for effective inhibition of miRNAs and miRNA clusters using CRISPRi. We provide clues on how to approach the challenges in using CRISPR/dCas in miRNA studies, which include prediction of miRNA transcription start sites (TSSs) and the design of single guide RNAs (sgRNAs). The strategy implements three TSS prediction online tools, dedicated specifically for miRNAs: miRStart, FANTOM 5 miRNA atlas, DIANA-miRGen, and CRISPOR tool for sgRNAs design; it includes testing and selection of optimal sgRNAs. We demonstrate that compared to siRNA/shRNA-based miRNA silencing, CRISPRi improves the repression specificity for miRNAs with highly similar sequence and contribute to higher uniformity of the effects of silencing the whole miRNA clusters. This strategy may be adapted for CRISPR-mediated activation (CRISPRa) of miRNA expression.
High-cytocompatible semi-IPN bio-ink with wide molecular weight distribution for extrusion 3D bioprinting
Authors: Li Meiqi , Shi Tingchun , Yao Danyu , Yue Xiuyan , Wang Haoxuan , Liu Kezhou
Published: April 2022

The development of 3D printing has recently attracted significant attention on constructing complex three-dimensional physiological microenvironments. However, it is very challenging to provide a bio-ink with cell-harmless and high mold accuracy during extrusion in 3D printing. To overcome this issue, a technique improving the shear-thinning performance of semi-IPN bio-ink, which is universally applicable to all alginate/gelatin-based materials, was developed. Semi-IPN bio-ink prepared by cyclic heating–cooling treatment in this study can reduce the cell damage without sacrificing the accuracy of the scaffolds for its excellent shear-thinning performance. A more than 15% increase in post-printing Cell viability verified the feasibility of the strategy. Moreover, the bio-ink with low molecular weight and wide molecular weight distribution also promoted a uniform cell distribution and cell proliferation in clusters. Overall, this strategy revealed the effects of molecular parameters of semi-IPN bio-inks on printing performance, and the cell activity was studied and it could be widely applicable to construct the simulated extracellular matrix with various bio-inks.
Machine learning models for prediction of adverse events after percutaneous coronary intervention
Authors: Niimi Nozomi , Shiraishi Yasuyuki , Sawano Mitsuaki , Ikemura Nobuhiro , Inohara Taku , Ueda Ikuko , Fukuda Keiichi , Kohsaka Shun
Published: April 2022

An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events [acute kidney injury (AKI), bleeding, and in-hospital mortality] with better discrimination or calibration than the National Cardiovascular Data Registry (NCDR-CathPCI) risk scores, we developed logistic regression and gradient descent boosting (XGBoost) models for each outcome using data from a prospective, all-comer, multicenter registry that enrolled consecutive coronary artery disease patients undergoing PCI in Japan between 2008 and 2020. The NCDR-CathPCI risk scores demonstrated good discrimination for each outcome (C-statistics of 0.82, 0.76, and 0.95 for AKI, bleeding, and in-hospital mortality) with considerable calibration. Compared with the NCDR-CathPCI risk scores, the XGBoost models modestly improved discrimination for AKI and bleeding (C-statistics of 0.84 in AKI, and 0.79 in bleeding) but not for in-hospital mortality (C-statistics of 0.96). The calibration plot demonstrated that the XGBoost model overestimated the risk for in-hospital mortality in low-risk patients. All of the original NCDR-CathPCI risk scores for adverse periprocedural events showed adequate discrimination and calibration within our cohort. When using the ML-based technique, however, the improvement in the overall risk prediction was minimal.
Self-perceived empathic abilities of people with autism towards living beings mostly differs for humans
Authors: Miralles Aurélien , Grandgeorge Marine , Raymond Michel
Published: April 2022

Being phylogenetically close involves greater empathic perceptions towards other species. To explore this phenomenon, this study investigates the influence of neurocognitive predispositions to empathy on our perceptions of other organisms. Autistic spectrum disorders (ASD) are characterized, among others, by weakened empathic skills. Our online survey involved a group of 202 raters with ASD and a control group of 1100 raters, who had to make choices to assess their empathic perceptions toward an extended photographic sampling of organisms. Results highlight that both groups present overall similar trends in their empathic preferences, with empathy scores significantly decreasing with the phylogenetic distance relatively to humans. However, the empathy score attributed to Homo sapiens in the ASD group represents a striking outlier in the yet very sharp overall correlation between empathy scores and divergence time, scoring our species as low as cold-blooded vertebrates. These results are consistent with previous studies, which emphasized that (1) understanding human beings would be more difficult for people with ASD than decoding “animals” and (2) that Theory of Mind impairment would not represent a global deficit in people with ASD but may relate to the mindreading of specifically human agents.
Facile microfabrication of three dimensional-patterned micromixers using additive manufacturing technology
Authors: Koo Doheon , So Hongyun
Published: April 2022

This study investigates the manufacturing method of oblique patterns in microchannels and the effect of these patterns on mixing performance in microchannels. To fabricate three-dimensional (3D) and oblique patterns in microchannels, 3D printing and replica methods were utilized to mold patterns and microchannels, respectively. The angle and size of the patterns were controlled by the printing angle and resolution, respectively. The mixing efficiency was experimentally characterized, and the mixing principle was analyzed using computational fluid dynamics simulation. The analysis showed that the mixing channel cast from the mold printed with a printing angle of 30° and resolution of 300 μm exhibited the best mixing efficiency with a segregation index of approximately 0.05 at a Reynolds number of 5.4. This was because, as the patterns inside the microchannel were more oblique, “split” and “recombine” behaviors between two fluids were enhanced owing to the geometrical effect. This study supports the use of the 3D printing method to create unique patterns inside microchannels and improve the mixing performance of two laminar flows for various applications such as point-of-care diagnostics, lab-on-a-chip, and chemical synthesis.
Observation of five types of leaders contained in a negative triggered lightning
Authors: Wang Jianguo , Su Rui , Wang Junlin , Wang Fukun , Cai Li , Zhao Yating , Huang Yijun
Published: April 2022

A negative triggered lightning involving five types of leaders was recorded by high-speed camera using frame rate of 20,000 fps and fast antennas at different distances. Five types of leaders contained one upward positive stepped leader, one upward positive dart leader, ten downward negative dart leader, one bidirectional leader and three downward negative dart-stepped leaders were propagated successively in the same channel. The upward positive dart leader occurred after initial continuous current pulse with average 2-D speed of 1.40 × 106 m/s and started second continuous current process. The bidirectional leader was transformed from decaying unidirectional leader and showed the unique electric field changes. Faster return strokes are found to be induced by downward leaders propagating evenly and deposit more positive charge in the following residual channel. The positive charge can inhibit the potential initiation of an upward positive leader and boost the propagation of the next downward negative leader.
Optimization of intraperitoneal aerosolized drug delivery using computational fluid dynamics (CFD) modeling
Authors: Rahimi-Gorji Mohammad , Debbaut Charlotte , Ghorbaniasl Ghader , Cosyns Sarah , Willaert Wouter , Ceelen Wim
Published: April 2022

Intraperitoneal (IP) aerosolized anticancer drug delivery was recently introduced in the treatment of patients with peritoneal metastases. However, little is known on the effect of treatment parameters on the spatial distribution of the aerosol droplets in the peritoneal cavity. Here, computational fluid dynamics (CFD) modeling was used in conjunction with experimental validation in order to investigate the effect of droplet size, liquid flow rate and viscosity, and the addition of an electrostatic field on the homogeneity of IP aerosol. We found that spatial distribution is optimal with small droplet sizes (1–5 µm). Using the current clinically used technology (droplet size of 30 µm), the optimal spatial distribution of aerosol is obtained with a liquid flow rate of 0.6 mL s−1. Compared to saline, nebulization of higher viscosity liquids results in less homogeneous aerosol distribution. The addition of electrostatic precipitation significantly improves homogeneity of aerosol distribution, but no further improvement is obtained with voltages higher than 6.5 kV. The results of the current study will allow to choose treatment parameters and settings in order to optimize spatial distribution of IP aerosolized drug, with a potential to enhance its anticancer effect.
Study of wave propagation in discontinuous and heterogeneous media with the dynamic lattice method
Authors: Sattari Amir S. , Rizvi Zarghaam H. , Aji Hendrawan D. B. , Wuttke Frank
Published: April 2022

The development of a new dynamic lattice element method (dynamicLEM) as well as its application in the simulation of the propagation of body waves in discontinuous and heterogeneous media is the focus of this research paper. The conventional static lattice models are efficient numerical methods to simulate crack initiation and propagation in cemented geomaterials. The advantages of the LEM and the developed dynamic solution, such as simulation of arbitrary crack initiation and propagation, illustration and simulation of existing inherent material heterogeneity as well as stress redistribution upon crack opening, opens a new engineering field and tool for material analysis. To realize the time dependency of the dynamic LEM, the equation of motion of forced vibration is solved while using the Newmark-$$\beta$$
β
method and implementing the non-linear Newton–Raphson Jacobian method. The method validation is done according to the results of a boundary element method (BEM) in the plane P-SV-wave propagation within a plane strain domain. Further tests comparing the generated wave types, simulation and study of crack discontinuities as well as inherent heterogeneities in the geomaterials are conducted to illustrate the accurate applicability of the new dynamic lattice method. The results indicate that with increasing heterogeneity within the material, the wave field becomes significantly scattered and further analysis of wave fields according to the wavelength/heterogeneity ratio become indispensable. Therefore, in a heterogeneous medium, the application of continuum methods in relation to structural health monitoring should be precisely investigated and improved. The developed dynamic lattice element method is an ideal simulation tool to consider particle scale irregularities, crack distributions and inherent material heterogeneities and can be easily implemented in various engineering applications.
Blindfolded adults use mental transformation strategies for spatial scaling of tactile maps
Authors: Szubielska Magdalena , Möhring Wenke
Published: April 2022

The current study tested strategies of spatial scaling in the haptic domain. Blindfolded adults (N = 31, aged 20–24 years) were presented with an embossed graphic including a target and asked to encode a target location on this map, imagine this map at a given scale, and to localize a target at the same spot on an empty referent space. Maps varied in three different sizes whereas the referent space had a constant size, resulting in three different scaling factors (1:1, 1:2, 1:4). Participants’ response times and localization errors were measured. Analyses indicated that both response times and errors increased with higher scaling factors, suggesting the usage of mental transformation stratergies for spatial scaling. Overall, the present study provides a suitable, novel methodology to assess spatial scaling in the haptic domain.
Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model
Authors: Amor Nesrine , Noman Muhammad Tayyab , Petru Michal , Sebastian Neethu
Published: April 2022

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.
Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations
Authors: Moadab Amirhossein , Farajzadeh Fatemeh , Fatahi Valilai Omid
Published: April 2022

The fast and cost-efficient delivery of goods ordered online is logistically a challenging problem.
Many firms are looking for ways to cut delivery times and costs by exploring opportunities to take advantage of drone technology. Deploying drones as a promising technology is more efficient from both environmental and economic perspectives in last-mile delivery. This paper considers a last-mile delivery system in which a set of drones are operated in coordination with public transportation system to deliver a set of orders to customer locations. A mathematical model based on Vehicle routing Problem (VRP) is extended to solve this problem. A real-world case inspired by Bremen 2025 transportation paradigm is also developed to validate the developed mathematical formulation. Results show that the sequence of visiting customers and public transport stations highly impacts the remaining charge and efficiency of drone tour planning. Also, using public transport vehicles, which enables drones to charge their battery or to approach customers, can reduce the number of drones required for satisfying the demands in a service area. The results show that there are high potentials to save energy for drone-enabled last-mile delivery by using the public transportation network.
Tmem174, a regulator of phosphate transporter prevents hyperphosphatemia
Authors: Sasaki Sumire , Shiozaki Yuji , Hanazaki Ai , Koike Megumi , Tanifuji Kazuya , Uga Minori , Kawahara Kota , Kaneko Ichiro , Kawamoto Yasuharu , Wiriyasermkul Pattama , Hasegawa Tomoka , Amizuka Norio , Miyamoto Ken-ichi , Nagamori Shushi , Kanai Yoshikatsu , Segawa Hiroko
Published: April 2022

Renal type II sodium-dependent inorganic phosphate (Pi) transporters NaPi2a and NaPi2c cooperate with other organs to strictly regulate the plasma Pi concentration. A high Pi load induces expression and secretion of the phosphaturic hormones parathyroid hormone (PTH) and fibroblast growth factor 23 (FGF23) that enhance urinary Pi excretion and prevent the onset of hyperphosphatemia. How FGF23 secretion from bone is increased by a high Pi load and the setpoint of the plasma Pi concentration, however, are unclear. Here, we investigated the role of Transmembrane protein 174 (Tmem174) and observed evidence for gene co-expression networks in NaPi2a and NaPi2c function. Tmem174 is localized in the renal proximal tubules and interacts with NaPi2a, but not NaPi2c. In Tmem174-knockout (KO) mice, the serum FGF23 concentration was markedly increased but increased Pi excretion and hypophosphatemia were not observed. In addition, Tmem174-KO mice exhibit reduced NaPi2a responsiveness to FGF23 and PTH administration. Furthermore, a dietary Pi load causes marked hyperphosphatemia and abnormal NaPi2a regulation in Tmem174-KO mice. Thus, Tmem174 is thought to be associated with FGF23 induction in bones and the regulation of NaPi2a to prevent an increase in the plasma Pi concentration due to a high Pi load and kidney injury.
Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia
Authors: Lu Yan , Zhang Qiaohong , Jiang Jinwen
Published: April 2022

Risk stratification and prognosis evaluation of severe thrombocytopenia are essential for clinical treatment and management. Currently, there is currently no reliable predictive model to identify patients at high risk of severe thrombocytopenia. This study aimed to develop and validate a prognostic nomogram model to predict in-hospital mortality in patients with severe thrombocytopenia in the intensive care unit. Patients diagnosed with severe thrombocytopenia (N = 1561) in the Medical Information Mart for Intensive Care IV database were randomly divided into training (70%) and validation (30%) cohorts. In the training cohort, univariate and multivariate logistic regression analyses with positive stepwise selection were performed to screen the candidate variables, and variables with p < 0.05 were included in the nomogram model. The nomogram model was compared with traditional severity assessment tools and included the following 13 variables: age, cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, mean arterial pressure, respiration rate, mechanical ventilation, vasopressor, continuous renal replacement therapy, prothrombin time, partial thromboplastin time, and blood urea nitrogen. The nomogram was well-calibrated. According to the area under the receiver operating characteristics, reclassification improvement, and integrated discrimination improvement, the nomogram model performed better than the traditional sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II). Additionally, according to decision curve analysis, a threshold probability between 0.1 and 0.75 indicated that our constructed nomogram model showed more net benefits than the SOFA score and SAPS II. The nomogram model we established showed superior predictive performance and can assist in the quantitative assessment of the prognostic risk in patients with severe thrombocytopenia.
Microbial changes in stool, saliva, serum, and urine before and after anti-TNF-α therapy in patients with inflammatory bowel diseases
Authors: Park Yong Eun , Moon Hye Su , Yong Dongeun , Seo Hochan , Yang Jinho , Shin Tae-Seop , Kim Yoon-Keun , Kim Jin Ran , Lee Yoo Na , Kim Young-Ho , Kim Joo Sung , Cheon Jae Hee
Published: April 2022

Inflammatory bowel diseases (IBD), including Crohn’s disease and ulcerative colitis, are chronic immune-mediated intestinal inflammatory disorders associated with microbial dysbiosis at multiple sites, particularly the gut. Anti-tumor necrosis factor-α (TNF-α) agents are important treatments for IBD. We investigated whether microbiome changes at multiple sites can predict the effectiveness of such treatment in IBD. Stool, saliva, serum, and urine biosamples were collected from 19 IBD patients before (V1) and 3 months after (V2) anti-TNF-α treatment, and 19 healthy subjects (control). Microbiota analysis was performed using extracellular vesicles (EVs; all four sample types) and next-generation sequencing (NGS; stool and saliva). The stool, using NGS analysis, was the only sample type in which α-diversity differed significantly between the IBD and control groups at V1 and V2. Relative to non-responders, responders to anti-TNF-α treatment had significantly higher levels of Firmicutes (phylum), Clostridia (class), and Ruminococcaceae (family) in V1 stool, and Prevotella in V1 saliva. Non-responders had significantly higher V2 serum and urine levels of Lachnospiraceae than responders. Finally, Acidovorax caeni was detected in all V1 sample types in responders, but was not detected in non-responders. Microbiome changes at multiple sites may predict the effectiveness of anti-TNF-α treatment in IBD, warranting further research.