Chikungunya virus nonstructural protein 1 is a versatile RNA capping and decapping enzyme.

Chikungunya virus (CHIKV) nonstructural protein 1 (nsP1) contains both the N7-guanine methyltransferase and guanylyltransferase activities and catalyzes the 5' end cap formation of viral RNAs. To further understand its catalytic activity and role in virus-host interaction, we demonstrate that purified recombinant CHIKV nsP1 can reverse the guanylyl transfer reaction and remove the m7GMP from a variety of capped RNA substrates including host mRNAs. We then provide the structural basis of this function with a high-resolution cryo-EM structure of nsP1 in complex with the unconventional cap-1 substrate RNA m7GpppAmU. We show that the 5'ppRNA species generated by decapping can trigger retinoic acid-inducible gene I-mediated interferon response. We further demonstrate that the decapping activity is conserved among the alphaviral nsP1s. To our knowledge, this is a new mechanism through which alphaviruses activate the antiviral immune response. This decapping activity could promote cellular mRNA degradation and facilitate viral gene expression, which is functionally analogous to the cap-snatching mechanism by influenza virus.

Oxygen saturation thresholds in managing sickle cell disease at US children's hospitals.

BACKGROUND: Adequate oxygen saturation (SpO2 ) is crucial for managing sickle cell disease (SCD). Children with SCD are at increased risk for occult hypoxemia; therefore, understanding SpO2 threshold practices would help identify barriers to oxygen optimization in a population sensitive to oxyhemoglobin imbalances. We investigated SpO2 cutoff levels used in clinical algorithms for management of acute SCD events at children's hospitals across the United States, and determined their consistency with recommended national guidelines (SpO2  > 95%). METHODS: Clinical pathways and algorithms used for the management of vaso-occlusive crisis (VOC) and acute chest syndrome (ACS) in SCD were obtained and reviewed from large children's hospitals in the United States. RESULTS: Responses were obtained from 94% (140/149) of eligible children's hospitals. Of these, 63 (45%) had available clinical algorithms to manage VOC and ACS. SpO2 cutoff was provided in 71.4% (45/63) of clinical algorithms. Substantial variation in SpO2 cutoff levels was noted, ranging from ≥90% to more than 95%. Only seven hospitals (5% of total hospitals and 15.6% of hospitals with clinical algorithms available) specified oxygen cutoffs that were consistent with national guidelines. Hospitals geographically located in the South (46.8%; n = 29/62) and Midwest (54.8%; n = 17/31) were more likely to have VOC and ACS clinical algorithms, compared to the Northeast (26.5%; n = 9/34) and West (36.4%; n = 8/22). CONCLUSION: There is inconsistency in the use of clinical algorithms and oxygen thresholds for VOC and ACS across US children's hospitals. Children with SCD could be at risk for insufficient oxygen therapy during adverse acute events.

Synthesis of a 3D Flower-Like BiOCl/Bi-MOF Heterostructure for High-Performance Removal of Rhodamine B and Tetracycline Hydrochloride.

Semiconductor materials based on bismuth metal have been extensively explored for their potential in photocatalytic applications owing to their distinctive crystal structure. Herein, we present the development of a hybrid photocatalyst, CAU-17/BiOCl, featuring a flower-like nanosheet morphology tailored for the photocatalytic degradation of organic contaminants such as rhodamine B (RhB) and tetracycline hydrochloride (TCH). The composite material is obtained by growing thin CAU-17 layers directly onto the host flower-like BiOCl nanosheets under solvothermal conditions. The optimized CAU-17/BiOCl composite possesses excellent photocatalytic performance, achieving a notable 96.0% removal rate for RhB and 78.4% for TCH after 60 and 90 min of LED light irradiation, respectively. This boosted activity is attributed to the heightened absorption of visible light caused by BiOCl and the provision of additional reaction sites due to the thin CAU-17 layers. Furthermore, the establishment of an S-scheme heterojunction mechanism enables efficient charge separation between CAU-17 and BiOCl, facilitating the separation of photoinduced electrons (e-) and holes (h+). Analysis of the degradation mechanism of RhB and TCH reveals the predominant role of superoxide radicals (•O2-), e-, and h+ in the photocatalytic degradation process. Moreover, the removal efficiency of TCH can reach approximately 64.5% after four cycles of recycling of CAU-17/BiOCl. Our work provides a facile, effective solution and a theoretically explained approach for the effective degradation of pollutants using heterojunction photocatalysts.

Visualizing the invisible tie: Linking parent-child neural synchrony to parents' and children's attachment representations.

It is a central tenet of attachment theory that individual differences in attachment representations organize behavior during social interactions. Secure attachment representations also facilitate behavioral synchrony, a key component of adaptive parent-child interactions. Yet, the dynamic neural processes underlying these interactions and the potential role of attachment representations remain largely unknown. A growing body of research indicates that interpersonal neural synchrony (INS) could be a potential neurobiological correlate of high interaction and relationship quality. In this study, we examined whether interpersonal neural and behavioral synchrony during parent-child interaction is associated with parent and child attachment representations. In total, 140 parents (74 mothers and 66 fathers) and their children (age 5-6 years; 60 girls and 80 boys) engaged in cooperative versus individual problem-solving. INS in frontal and temporal regions was assessed with functional near-infrared spectroscopy hyperscanning. Attachment representations were ascertained by means of the Adult Attachment Interview in parents and a story-completion task in children, alongside video-coded behavioral synchrony. Findings revealed increased INS during cooperative versus individual problem solving across all dyads (𝛸2(2) = 9.37, p = 0.009). Remarkably, individual differences in attachment representations were associated with INS but not behavioral synchrony (p > 0.159) during cooperation. More specifically, insecure maternal attachment representations were related to higher mother-child INS in frontal regions (𝛸2(3) = 9.18, p = 0.027). Conversely, secure daughter attachment representations were related to higher daughter-parent INS within temporal regions (𝛸2(3) = 12.58, p = 0.006). Our data thus provide further indication for INS as a promising correlate to probe the neurobiological underpinnings of attachment representations in the context of early parent-child interactions. RESEARCH HIGHLIGHTS: We assessed attachment representations using narrative measures and interpersonal neural synchrony (INS) during parent-child problem-solving. Dyads including mothers with insecure attachment representations showed higher INS in left prefrontal regions. Dyads including daughters with secure attachment representations showed higher INS in right temporo-parietal regions. INS is a promising correlate to probe the neurobiological underpinnings of attachment representations in the context of parent-child interactions, especially within the mutual prediction framework.

Variability in Liver Size Measurements Using Different View Angles in Ultrasound Imaging.

PURPOSE: The aim of this study was to compare liver size measurements in different conventional B-mode ultrasound image (US) field views using magnetic resonance imaging (MRI) measurement as a reference. METHODS: After receiving Institutional Review Board approval and informed consent, three operators measured the largest sagittal and transverse dimensions of adult livers on three US image field views (90°, 120°, and 140°) with a single curvilinear transducer. We analyzed the differences in liver size across three image field views using one-way analysis of variance (ANOVA) and examined the correlations between MRI and ultrasound measurements using Spearman regression. We used 95% Bland-Altman limits of agreement (95% LOA) to analyze the confidence interval for liver size measurements between MRI and US. Intra-observer and inter-observer reliability in measuring liver size were assessed using intraclass correlation coefficient (ICC). RESULTS: Based on sagittal liver length, 28 adult participants (7 men and 21 women, mean age 43 years) were divided into Group 1 (<17 cm, n = 10) or Group 2 (≥17 cm, n = 18). There was a significant difference in the liver size measurements across the three image field views (P < .001) in both groups. The highest correlation in liver size measurements between MRI and US was with ultra-wide-view (R2 = .87 in sagittal; R2 = .79 in transverse). Bland-Altman LOA also indicated good agreement between MRI and ultra-wide-view measurements. Intra-observer and inter-observer reliability in measuring liver size were good (ICC = 0.82-0.98). CONCLUSION: The study suggests that ultrasound ultra-wide-view provides the most accurate liver size measurement and good intra- and inter-operator reliability.

Malignancy in pregnancy: Multimodality imaging and treatment.

The diagnosis of cancer is increasingly made in the pregnant population, thought to be from the increasing average age of pregnancy and the use of prenatal fetal noninvasive screening techniques, leading to incidental detection of cancer in the mother. Complex challenges are associated with imaging, diagnosis, staging, and treatment of cancers in this patient population, which require highly specialized interdisciplinary management. This report discusses the use of multimodality imaging and safety considerations in pregnant patients, reviews the current guidelines for ionizing radiation imaging techniques, and presents a series of commonly and uncommonly encountered cancers in pregnancy with current diagnostic imaging guidelines. The authors also discuss the role of multidisciplinary management and treatment options and provide an overview of therapy-related considerations in the age of novel anticancer therapies. PLAIN LANGUAGE SUMMARY: The diagnosis and management of pregnant patients who have cancer are actively evolving as novel imaging techniques and anticancer therapies are being developed. Radiologically, there are inherent difficulties in balancing the minimization of fetal ionization while acquiring diagnostic quality imaging necessary for the diagnosis, staging, and treatment of maternal disease. Standardized imaging protocols are still being developed, with evolving imaging guidelines coupled with rapidly expanding research and development of novel anticancer therapies, which come with their side effects and complications. Caring for this patient population is especially challenging and requires specialized multidisciplinary attention.

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site prediction problems. Traditional data-based approaches for handling data imbalance employ linear interpolation of minority class samples. Such approaches may not be fully exploited by deep neural networks on downstream tasks. We present a novel technique for balancing input classes by developing a deep neural network-based variational autoencoder (VAE) that aims to learn important attributes of the minority classes concerning nonlinear combinations. After learning, the trained VAE was used to generate new minority class samples that were later added to the original data to create a balanced dataset. Finally, a convolutional neural network was used for classification, for which we assumed that the nonlinearity could be fully integrated. As a case study, we applied our method to the identification of FAD- and FMN-binding sites of electron transport proteins. Compared with the best classifiers that use traditional machine learning algorithms, our models obtained a great improvement on sensitivity while maintaining similar or higher levels of accuracy and specificity. We also demonstrate that our method is better than other data imbalance handling techniques, such as SMOTE, ADASYN, and class weight adjustment. Additionally, our models also outperform existing predictors in predicting the same binding types. Our method is general and can be applied to other data types for prediction problems with moderate-to-heavy data imbalances.

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.

Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet powerful language model that achieved novel state-of-the-art performance. BERT adopted the concept of contextualized word embedding to capture the semantics and context of the words in which they appeared. In this study, we present a novel technique by incorporating BERT-based multilingual model in bioinformatics to represent the information of DNA sequences. We treated DNA sequences as natural sentences and then used BERT models to transform them into fixed-length numerical matrices. As a case study, we applied our method to DNA enhancer prediction, which is a well-known and challenging problem in this field. We then observed that our BERT-based features improved more than 5-10% in terms of sensitivity, specificity, accuracy and Matthews correlation coefficient compared to the current state-of-the-art features in bioinformatics. Moreover, advanced experiments show that deep learning (as represented by 2D convolutional neural networks; CNN) holds potential in learning BERT features better than other traditional machine learning techniques. In conclusion, we suggest that BERT and 2D CNNs could open a new avenue in biological modeling using sequence information.

Pulmonary hypertension screening in children with sickle cell disease.

BACKGROUND: Screening for pulmonary hypertension (PHT) is recommended in children with sickle cell disease (SCD). However, best approaches are poorly described. We examined the utility of PHT symptoms, echocardiogram (ECHO), N-terminal-pro hormone brain natriuretic peptide (NT-proBNP), and BNP to screen for PHT in the SCD pediatric population. METHODS: Children (8-18 years old) with SCD-HbSS and HbSthal° were prospectively included and underwent PHT screening. The screening consisted of a comprehensive PHT symptoms evaluation, ECHO measurement, and NT-proBNP and BNP levels. RESULTS: A total of 73 patients were included (mean age 12 ± 5.7 years; >80% on hydroxyurea), of which 37% had a symptom consistent with PHT, including exertional dyspnea (26.5%), fatigue (17.6%), palpitation (14.7%), and chest pain (10.3%). ECHO was obtained in 53 (72.6%) patients, with only ECHO of 48 patients included in the final analysis. Elevated ECHO peak tricuspid regurgitant jet velocity (TRV) >2.5 m/s or indirect findings to suggest PHT were seen in only two of 48 (4.2%). No significant differences were seen between those with and without PHT symptoms when compared for NT-proBNP, BNP, hemoglobin, pulmonary function testing, fractional exhaled nitric oxide, asthma, oxygen saturation, and sleep apnea. CONCLUSION: PHT symptoms are not consistent with ECHO, NT-proBNP nor BNP findings in children with SCD. PHT prevalence based on TRV was low in children on hydroxyurea, therefore screening may not be warranted for this group.

An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex techniques, which were then advanced by machine learning and computational approaches to facilitate the APs recognition task. However, most studies focused on recognizing separate ones in the APs family or the APs in general with non-APs, lacking one comprehensive strategy to distinguish the complexes of AP subtypes. Herein, we proposed a novel method to implement one novel task as discriminating the AP complexes in the APs family, utilizing an interpretable deep neural network architecture on sequence-based encoding features. This work also introduced a benchmark data set of AP complexes originating from the UniProt and GeneOntology databases. To assess the robustness of our proposed method, we compared our performance to various machine learning algorithms and feature extraction strategies. Furthermore, the interpretation of the model's prediction performance was implemented using t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and SHapley Additive exPlanations (SHAP) analysis to show the distribution of AP complexes on optimal features. The promising performance of our architecture can assist scientists not only in AP complexes distinction but also in general protein sequences. Moreover, we have also made our work publicly on GitHub https://github.com/khanhlee/adaptor-dnn.

Screening for asthma in preschool children with sickle cell disease.

BACKGROUND: Asthma in preschool children is poorly defined, proving to be a challenge for early detection. The Breathmobile Case Identification Survey (BCIS) has been shown to be a feasible screening tool in older SCD children and could be effective in younger children. We attempted to validate the BCIS as an asthma screening tool in preschool children with SCD. METHODS: This is a prospective, single-center study of 50 children aged 2-5 years with SCD. BCIS was administered to all patients and a pulmonologist blinded to the results evaluated patients for asthma. Demographic, clinical, and laboratory data were obtained to assess risk factors for asthma and acute chest syndrome in this population. RESULTS: Asthma prevalence (n = 3/50; 6%) was lower than atopic dermatitis (20%) and allergic rhinitis (32%). Sensitivity (100%), specificity (85%), positive predictive value (30%), and negative predictive value (100%) of the BCIS were high. Clinical demographics, atopic dermatitis, allergic rhinitis, asthma, viral respiratory infection, hematology parameters, sickle hemoglobin subtype, tobacco smoke exposure, and hydroxyurea were not different between patients with or without history of ACS, although eosinophil was significantly lower in the ACS group (p = 0.0093). All those with asthma had ACS, known viral respiratory infection resulting in hospitalization (3 RSV and 1 influenza), and HbSS (homozygous Hemoglobin SS) subtype. CONCLUSION: The BCIS is an effective asthma screening tool in preschool children with SCD. Asthma prevalence in young children with SCD is low. Previously known ACS risk factors were not seen, possibly from the beneficial effects of early life initiation of hydroxyurea.

Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models.

One of the major crucial issues that need worldwide attention is open stubble burning, which imposes a variety of adverse impacts on nature and human society, destroying the world's biodiversity. Many earth observation satellites render information to monitor and assess agricultural burning activities. In this study, different remotely sensed data (Sentinel-2A, VIIRS) has been employed to estimate the quantitative measurements of agricultural burned areas of the Purba Bardhaman district from October-December 2018. The multi-temporal image differencing techniques and indices (NDVI, NBR, and dNBR) and VIIRS active fires data (VNP14IMGT) have been utilized to spot agricultural burned areas. In the case of the NDVI technique, a prominent area, 184.82 km2 of agricultural burned area (7.85% of the total agriculture), was observed. The highest (23.04 km2) burned area was observed in the Bhatar block, located in the middle part of the district, and the lowest (0.11 km2) burned area was observed in the Purbasthali-II block, which is located in the eastern part of the district. On the other hand, the dNBR technique revealed that the agricultural burned areas enwrap 8.18% of the total agricultural area, which is 192.45 km2. As per the earlier NDVI technique, the highest agricultural burned areas (24.82 km2) were observed in the Bhatar block, and the lowest (0.13 km2) burn area occurred in the Purbashthali-II block. In both cases, it is observed that agricultural residue burning is high in the western part of the Satgachia block and the adjacent areas of the Bhatar block, which is in the middle part of Purba Bardhaman. The agricultural burned area was extracted using different spectral separability analyses, and the performance of dNBR was the most effective in spectral discrimination of burned and unburned surfaces. This study manifested that agricultural residue burning started in the central part of Purba Bardhaman. Later it spread all over the district due to the trend of early harvesting rice crops in this region. The performance of different indices for mapping the burned areas was evaluated and compared, revealing a strong correlation (R2) = 0.98. To estimate the campaign's effectiveness against the dangerous practice and plan the control of the menace, regular monitoring of crop stubble burning using satellite data is required.

Using multiple convolutional window scanning of convolutional neural network for an efficient prediction of ATP-binding sites in transport proteins.

Protein multiple sequence alignment information has long been important features to know about functions of proteins inferred from related sequences with known functions. It is therefore one of the underlying ideas of Alpha fold 2, a breakthrough study and model for the prediction of three-dimensional structures of proteins from their primary sequence. Our study used protein multiple sequence alignment information in the form of position-specific scoring matrices as input. We also refined the use of a convolutional neural network, a well-known deep-learning architecture with impressive achievement on image and image-like data. Specifically, we revisited the study of prediction of adenosine triphosphate (ATP)-binding sites with more efficient convolutional neural networks. We applied multiple convolutional window scanning filters of a convolutional neural network on position-specific scoring matrices for as much as useful information as possible. Furthermore, only the most specific motifs are retained at each feature map output through the one-max pooling layer before going to the next layer. We assumed that this way could help us retain the most conserved motifs which are discriminative information for prediction. Our experiment results show that a convolutional neural network with not too many convolutional layers can be enough to extract the conserved information of proteins, which leads to higher performance. Our best prediction models were obtained after examining them with different hyper-parameters. Our experiment results showed that our models were superior to traditional use of convolutional neural networks on the same datasets as well as other machine-learning classification algorithms.

Sleep Apnea Screening in Children With Sickle Cell Anemia.

BACKGROUND: Screening for obstructive sleep apnea (OSA) is recommended by current guidelines in children with sickle cell anemia (SCA), but no specific approach is described. The Pediatric Sleep Questionnaire (PSQ) is a validated detection tool for OSA in children. We assessed the utility of PSQ to screen for OSA in children with concomitant SCA and snoring. MATERIALS AND METHODS: A prospective study, in children 4 to 18 years old with SCA. Subjects were assessed for snoring and PSQ administered at the same visit. All children with snoring were then referred for polysomnography. RESULTS: A total of 106 subjects were screened. Habitual snoring prevalence was 51/106 (48.1%). In the snoring group, OSA was detected in 83.9% (apnea-hypopnea index [AHI] ≥1.0/h) and 22.6% (AHI ≥5.0/h), respectively. Sensitivity and specificity of PSQ in children with snoring was 46.2% and 20.0% (AHI ≥1.0/h), and 57.1% and 50.0% (AHI ≥5.0/h), respectively. Physician assessment for snoring had a high sensitivity of 70.3% but low specificity of 58.4% (AHI ≥1.0/h), and 87.5% and 41.5% (AHI ≥5.0/h), respectively. CONCLUSION: PSQ is a poor screening tool for detection of OSA in those children with SCA who snore. Physician assessment for snoring could however be an initial approach before polysomnography.

Does financial literacy influence preventive health check-up behavior in Japan? a cross-sectional study.

BACKGROUND: General health check-ups are an important element of healthcare, as they are designed to detect diseases, thereby reducing morbidity and mortality. Recent studies have found that financial literacy promotes preventive healthcare usage and reduces risky health behaviors such as smoking, lack of exercise, and gambling. Based on this evidence, we hypothesize that financial literacy, as a rational decision-making tool, is positively associated with health check-up behavior in Japan. METHODS: We extracted data on financial literacy, the main explanatory variable of this study, from the 2010 wave of the Preference Parameter Study (PPS) of Osaka University. Data on health check-up behavior as a dependent variable, along with control variables, were obtained from the 2011 PPS wave. Our sample focused on Japan's middle-aged working population (40-64 years), and we applied probit regressions to test our hypothesis. RESULTS: Our final sample size was 2,208 participants after merging the two datasets. Descriptive statistics show that respondents had moderate financial literacy (mean = 0.62, SD = 0.33), low financial education (mean = 0.17, SD = 0.38), and low participation (mean = 31.75%, SD = 46.56%) in the health check-up. The probit regression analysis showed that financial literacy is insignificantly associated with health check-up behavior in Japan (coefficient = -0.0229; 95% CI: -0.2011-0.1551; p-value = 0.801). However, demographic factors such as being male (coefficient = -0.2299; 95% CI: -0.3649--0.0950; p-value = 0.001), older (coefficient = 0.0280; 95% CI: 0.0188 - 0.0371; p-value = 0.000), and married (coefficient = 0.3217; 95% CI: 0.0728 - 0.5705; p-value = 0.011), as well as risky health behavior such as smoking (coefficient = -0.2784; 95% CI: -0.4262--0.1305; p-value = 0.000) are significantly related to health check-up behavior. CONCLUSIONS: Our results suggest that financial literacy insignificantly motivates people to behave rationally and understand the value of health check-ups as a tool for sustainable health.

An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm.

Node coverage is one of the crucial metrics for wireless sensor networks' (WSNs') quality of service, directly affecting the target monitoring area's monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the scale of the network, and the operating environment's complexity and constant change. This paper proposes a solution to the optimal node coverage of unbalanced WSN distribution during random deployment based on an enhanced Archimedes optimization algorithm (EAOA). The best findings for network coverage from several sub-areas are combined using the EAOA. In order to address the shortcomings of the original Archimedes optimization algorithm (AOA) in handling complicated scenarios, we suggest an EAOA based on the AOA by adapting its equations with reverse learning and multidirection techniques. The obtained results from testing the benchmark function and the optimal WSN node coverage of the EAOA are compared with the other algorithms in the literature. The results show that the EAOA algorithm performs effectively, increasing the feasible range and convergence speed.

Proximity and touch are associated with neural but not physiological synchrony in naturalistic mother-infant interactions.

Caregiver touch plays a vital role in infants' growth and development, but its role as a communicative signal in human parent-infant interactions is surprisingly poorly understood. Here, we assessed whether touch and proximity in caregiver-infant dyads are related to neural and physiological synchrony. We simultaneously measured brain activity and respiratory sinus arrhythmia of 4-6-month-old infants and their mothers (N=69 dyads) in distal and proximal joint watching conditions as well as in an interactive face-to-face condition. Neural synchrony was higher during the proximal than during the distal joint watching conditions, and even higher during the face-to-face interaction. Physiological synchrony was highest during the face-to-face interaction and lower in both joint watching conditions, irrespective of proximity. Maternal affectionate touch during the face-to-face interaction was positively related to neural but not physiological synchrony. This is the first evidence that touch mediates mutual attunement of brain activities, but not cardio-respiratory rhythms in caregiver-infant dyads during naturalistic interactions. Our results also suggest that neural synchrony serves as a biological pathway of how social touch plays into infant development and how this pathway could be utilized to support infant learning and social bonding.

Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.

KEY MESSAGE: This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-methyladenine sites has been a very active topic of computational biology due to the unavailability of suitable methods to identify them accurately, especially in plants. Substantial results were obtained with a great effort put in extracting, heuristic searching, or fusing a diverse types of features, not to mention a feature selection step. In this study, we regarded DNA sequences as textual information and employed natural language processing techniques to decipher hidden biological meanings from those sequences. In other words, we considered DNA, the human life book, as a book corpus for training DNA language models. K-mer embeddings then were generated from these language models to be used in machine learning prediction models. Skip-gram neural networks were the base of the language models and ensemble tree-based algorithms were the machine learning algorithms for prediction models. We trained the prediction model on Rosaceae genome dataset and performed a comprehensive test on 3 plant genome datasets. Our proposed method shows promising performance with AUC performance approaching an ideal value on Rosaceae dataset (0.99), a high score on Rice dataset (0.95) and improved performance on Rice dataset while enjoying an elegant, yet efficient feature extraction process.

Validation of the breathmobile case identification survey for asthma screening in children with sickle cell disease.

Background: Asthma is a chronic airway disorder with variable/recurring symptoms, airflow obstruction, bronchial hyperresponsiveness, and an inflammation. The expert panel report of the National Heart Lung and Blood Institute recommends asthma screening in sickle cell disease (SCD); however, specific approach is not mentioned. We hypothesize that the breathmobile case identification survey (BCIS) is a valid asthma screening tool in children with SCD.Methods: This prospective, single-center study enrolled 129 SCD patients aged 5 to 18 years from March 2016 to March 2018. All patients completed BCIS, spirometry, and fractional exhaled nitric oxide (FeNO). A single pulmonologist blinded to the BCIS results evaluated patients for asthma.Results: Asthma prevalence was 41%. Male gender (60.4%; p = 0.041), allergic rhinitis (86.8%; p < 0.01), hydroxyurea usage (73.6%; p < 0.01), and family history of asthma (34%; p < 0.01) were higher but not self-reported parental asthma history, eczema, and tobacco smoke exposure in the asthma group compared to the nonasthma group. FEV1 (p = 0.003), FVC (p = 0.02), FEV1/FVC (p = 0.053), and FEF25-75% (p = 0.02) were lower in asthma. FeNO levels were comparable in both groups. The sensitivity, specificity, positive predictive value, and negative predictive value of the abbreviated BCIS were 67.3%, 90.8%, 83.3%, and 80.2% for asthma; and 82.1%, 90.8%, 76.7%, and 93.2% for persistent asthma, respectively. Persistent asthma patients had a trend of higher hydroxyurea use (82.8% vs. 58.3%; p = 0.049) and tobacco smoke exposure (55.2% vs. 29.2%; p = 0.057) compared to intermittent asthma.Conclusion: We have validated the BCIS to screen for asthma in SCD. Spirometry but not FeNO may support an asthma diagnosis.

Interpersonal Neural Synchrony During Father-Child Problem Solving: An fNIRS Hyperscanning Study.

Interpersonal neural synchrony (INS) has been previously evidenced in mother-child interactions, yet findings concerning father-child interaction are wanting. The current experiment examined whether fathers and their 5- to 6-year-old children (N = 66) synchronize their brain activity during a naturalistic interaction, and addressed paternal and child factors related to INS. Compared to individual problem solving and rest, father-child dyads showed increased INS in bilateral dorsolateral prefrontal cortex and left temporo-parietal junction during cooperative problem solving. Furthermore, the father's attitude toward his role as a parent was positively related to INS during the cooperation condition. These results highlight the implication of the father's attitude to parenting in INS processes for the first time.