Transcendent thinking counteracts longitudinal effects of mid-adolescent exposure to community violence in the anterior cingulate cortex.

Adolescence involves extensive brain maturation, characterized by social sensitivity and emotional lability, that co-occurs with increased independence. Mid-adolescence is also a hallmark developmental stage when youths become motivated to reflect on the broader personal, ethical, and systems-level implications of happenings, a process we term transcendent thinking. Here, we examine the confluence of these developmental processes to ask, from a transdisciplinary perspective, how might community violence exposure (CVE) impact brain development during mid-adolescence, and how might youths' dispositions for transcendent thinking be protective? Fifty-five low-SES urban youth with no history of delinquency (32 female; 27 Latinx, 28 East Asian) reported their CVE and underwent structural MRI first at age 14-18, and again 2 years later. At the study's start, participants also discussed their feelings about 40 minidocumentaries featuring other teens' compelling situations in a 2-h private interview that was transcribed and coded for transcendent thinking. Controlling for CVE and brain structure at the start: (1) New CVE during the 2-year inter-scan interval was associated with greater gray matter volume (GMV) reduction over that interval in the anterior cingulate cortex (ACC), a central network hub whose reduced volume has been associated with posttraumatic stress disorder, and across multiple additional cortical and subcortical regions; (2) participants' transcendent thinking in the interview independently predicted greater GMV increase during the 2-year inter-scan interval in the ACC. Findings highlight the continued vulnerability of mid-adolescents to community violence and the importance of supporting teens' dispositions to reflect on the complex personal and systems-level implications and affordances of their civic landscape.

Diverse adolescents' transcendent thinking predicts young adult psychosocial outcomes via brain network development.

Developmental scientists have long described mid-adolescents' emerging capacities to make deep meaning about the social world and self, here called transcendent thinking, as a hallmark developmental stage. In this 5-years longitudinal study, sixty-five 14-18 years-old youths' proclivities to grapple psychologically with the ethical, systems-level and personal implications of social stories, predicted future increases in the coordination of two key brain networks: the default-mode network, involved in reflective, autobiographical and free-form thinking, and the executive control network, involved in effortful, focused thinking; findings were independent of IQ, ethnicity, and socioeconomic background. This neural development predicted late-adolescent identity development, which predicted young-adult self-liking and relationship satisfaction, in a developmental cascade. The findings reveal a novel predictor of mid-adolescents' neural development, and suggest the importance of attending to adolescents' proclivities to engage agentically with complex perspectives and emotions on the social and personal relevance of issues, such as through civically minded educational approaches.

cnnImpute: missing value recovery for single cell RNA sequencing data.

The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis.

Rhabdomyosarcoma With Epithelioid Features And NSD3::FOXO1 Fusion: Evidence For Reconsideration Of Previously Reported FOXO1::FGFR1 Fusion.

Epithelioid rhabdomyosarcoma is a rare rhabdomyosarcoma variant for which no diagnostic recurrent driver genetic events have been identified. Here we report a rapidly progressive and widely metastatic rhabdomyosarcoma with epithelioid features that arose in the thigh of a male infant. Conventional cytogenetics revealed a t(8;13)(p11.2;q14) translocation. Fluorescence in situ hybridization studies showed rearrangement of FOXO1 and amplification of its 3" end, and rearrangement of NSD3 and amplification of its 5` end. Next generation sequencing identified a NSD3::FOXO1 fusion, which is a previously unreported gene fusion. We also review the historic report of a FOXO1::FGFR1 fusion in a solid variant of alveolar rhabdomyosarcoma and propose that NSD3::FOXO1 fusion may have been the more appropriate interpretation of the data presented in that report.

Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia.

Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by a unique BCR-ABL fusion gene. Tyrosine kinase inhibitors (TKIs) were developed to target the BCR-ABL oncoprotein, inhibiting its abnormal kinase activity. TKI treatments have significantly improved CML patient outcomes. However, the patients can develop drug resistance and relapse after therapy discontinues largely due to intratumor heterogeneity. It is critical to understand the differences in therapeutic responses among subpopulations of cells. Single-cell RNA sequencing measures the transcriptome of individual cells, allowing us to differentiate and analyze individual cell populations. Here, we integrated a single-cell RNA sequencing profile of CML stem cells and network analysis to decipher the mechanisms of distinct TKI responses. Compared to normal hematopoietic stem cells, a set of genes that were concordantly differentially expressed in various types of stem cells of CML patients was revealed. Further transcription regulatory network analysis found that most of these genes were directly controlled by one or more transcript factors and the genes have more regulators in the cells of the patients who responded to the treatment. The molecular markers including a known drug-resistance gene and novel gene signatures for treatment response were also identified. Moreover, we combined protein-protein interaction network construction with a cancer drug database and uncovered the drugs that target the marker genes directly or indirectly via the protein interactions. The gene signatures and their interacted proteins identified by this work can be used for treatment response prediction and lead to new strategies for drug resistance monitoring and prevention. Our single-cell-based findings offered novel insights into the mechanisms underlying the therapeutic response of CML.

Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples.

BACKGROUND: Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. RESULTS: Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, while the inner samples display no quality degradation related to fixation time. CONCLUSIONS: To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

Default and executive networks' roles in diverse adolescents' emotionally engaged construals of complex social issues.

Across adolescence, individuals enrich their concrete, empathic, context-specific interpretations of social-world happenings with abstract, situation-transcending, system-level considerations-invoking values, bigger implications and broader emotional perspectives. To investigate neural mechanisms involved in abstract construals vs concrete construals and the effects of emotional engagement on these mechanisms, 65 mid-adolescents aged 14-18 years reacted to compelling video mini-documentaries during private, open-ended interviews and again during functional magnetic resonance imaging. Following calls to diversify samples, participants were ethnically diverse low-socioeconomic status (SES) urban adolescents performing well in school. Participants spontaneously produced both concrete and abstract construals in the interview, and tendencies to produce each varied independently. As hypothesized, participants who made more abstract construals showed a greater subsequent default mode network (DMN) activity; those who made more concrete construals showed greater executive control network (ECN) activity. Findings were independent of IQ, SES, age and gender. Within individuals, DMN activation, especially when individuals were reporting strong emotional engagement, and ECN deactivation together predicted an abstract construal to a trial. Additionally, brief ECN activation early in the trial strengthened the DMN-abstraction relationship. Findings suggest a neural mechanism for abstract social thought in adolescence. They also link adolescents' natural construals of social situations to distinct networks' activity and suggest separable sociocognitive traits that may vary across youths.

Artificial Intelligence and Cancer Drug Development.

BACKGROUND: The development of cancer drugs is among the most focused "bench to bedside activities" to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and Artificial Intelligence (AI). In the meantime, challenges, like curating the data of low quality, remain to be resolved. OBJECTIVES: This review focused on the recent advancements and challenges of AI in developing cancer drugs. METHODS: We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies. RESULTS AND CONCLUSION: AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

Gene Regulation Analysis Reveals Perturbations of Autism Spectrum Disorder during Neural System Development.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impedes patients' cognition, social, speech and communication skills. ASD is highly heterogeneous with a variety of etiologies and clinical manifestations. The prevalence rate of ASD increased steadily in recent years. Presently, molecular mechanisms underlying ASD occurrence and development remain to be elucidated. Here, we integrated multi-layer genomics data to investigate the transcriptome and pathway dysregulations in ASD development. The RNA sequencing (RNA-seq) expression profiles of induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs) and neuron cells from ASD and normal samples were compared in our study. We found that substantially more genes were differentially expressed in the NPCs than the iPSCs. Consistently, gene set variation analysis revealed that the activity of the known ASD pathways in NPCs and neural cells were significantly different from the iPSCs, suggesting that ASD occurred at the early stage of neural system development. We further constructed comprehensive brain- and neural-specific regulatory networks by incorporating transcription factor (TF) and gene interactions with long 5 non-coding RNA(lncRNA) and protein interactions. We then overlaid the transcriptomes of different cell types on the regulatory networks to infer the regulatory cascades. The variations of the regulatory cascades between ASD and normal samples uncovered a set of novel disease-associated genes and gene interactions, particularly highlighting the functional roles of ELF3 and the interaction between STAT1 and lncRNA ELF3-AS 1 in the disease development. These new findings extend our understanding of ASD and offer putative new therapeutic targets for further studies.

Technological properties of chickpea (Cicer arietinum): Production of snacks and health benefits related to type-2 diabetes.

Chickpea (Cicer arietinum) is one of the most consumed pulses worldwide (over 2.3 million tons enter the world market annually). Some chickpea components have shown, in preclinical and clinical studies, several health benefits, including antioxidant capacity, and antifungal, antibacterial, analgesic, anticancer, antiinflammatory, and hypocholesterolemic properties, as well as angiotensin I-converting enzyme inhibition. In the United States, chickpea is consumed mostly in the form of hummus. However, the development of new products with value-added bioactivity is creating new opportunities for research and food applications. Information about bioactive compounds and functional properties of chickpea ingredients in the development of new products is needed. The objective of this review was to summarize available scientific information, from the last 15 years, on chickpea production, consumption trends, applications in the food industry in the elaboration of plant-based snacks, and on its bioactive compounds related to type 2 diabetes (T2D). Areas of opportunity for future research and new applications of specific bioactive compounds as novel food ingredients are highlighted. Research is key to overcome the main processing obstacles and sensory challenges for the application of chickpea as ingredient in snack preparations. The use of chickpea bioactive compounds as ingredient in food products is also a promising area for accessibility of their health benefits, such as the management of T2D.

Comparison of machine learning approaches for enhancing Alzheimer's disease classification.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Alcohol Recidivism Following Transjugular Intrahepatic Portosystemic Shunt Placement: Frequency and Predictive Factors.

PURPOSE: To determine the frequency and predictive factors for alcohol recidivism following transjugular intrahepatic portosystemic shunts (TIPS) placed in patients with alcoholic cirrhosis. METHODS: One hundred ninety-nine patients who had a TIPS placed at a single institution for different indications in the setting of alcoholic cirrhosis were reviewed. Length of sobriety prior to TIPS placement and maintained sobriety at 1, 3 and 6-12 months after TIPS placement were recorded. Smoking history, substance abuse and psychiatric comorbidities were also recorded as was ascitic response to TIPS at 1, 3 and 6-12 months. RESULTS: At 1 month 11/199 (5.5%) patients had experienced a relapse while, 20/199 (10.1%) had at 3 months, and 44/199 (22.1%) had at 12 months. There was no difference in ascitic response in those who did and did not relapse at 1 month (p = 0.57), 3 months (p = 1.00) or 1 year (p = 0.44). The mean time of sobriety at the time of TIPS placement for those who relapsed by 12 months was significantly less than those who did not relapse (5.11 (1.10-7.90) months vs 18.32 (8.63-48.12) months, p < 0.001). Concurrent psychiatric comorbidity (p < 0.001), substance abuse (p < 0.001), age less than 40 (p = 0.004) and smoking history at the time of procedure (p < 0.001) were also associated with alcohol relapse. CONCLUSION: Recidivism is a frequent issue for patients following TIPS placement; those who have concurrent psychiatric comorbidity, substance abuse, smoking history are younger than 40 and shorter sobriety duration prior to TIPS may be at increased risk.

An fMRI study of error monitoring in Montessori and traditionally-schooled children.

The development of error monitoring is central to learning and academic achievement. However, few studies exist on the neural correlates of children's error monitoring, and no studies have examined its susceptibility to educational influences. Pedagogical methods differ on how they teach children to learn from errors. Here, 32 students (aged 8-12 years) from high-quality Swiss traditional or Montessori schools performed a math task with feedback during fMRI. Although the groups' accuracies were similar, Montessori students skipped fewer trials, responded faster and showed more neural activity in right parietal and frontal regions involved in math processing. While traditionally-schooled students showed greater functional connectivity between the ACC, involved in error monitoring, and hippocampus following correct trials, Montessori students showed greater functional connectivity between the ACC and frontal regions following incorrect trials. The findings suggest that pedagogical experience influences the development of error monitoring and its neural correlates, with implications for neurodevelopment and education.

Pathologic arterial changes in neurovascularly intact Gartland III supracondylar humerus fractures: a pilot study.

This pilot study was performed to describe changes in arterial flow in completely displaced neurovascularly intact Gartland III pediatric supracondylar humerus fractures using Duplex ultrasonography. This is a prospective study of 11 Gartland type III supracondylar humerus fractures with no cortical continuity but with palpable radial pulse and normal neurologic examination. Duplex ultrasonography was performed on injured and uninjured arms, both preoperatively and postpinning, and interpreted by a board-certified pediatric radiologist. Degree of artery stenosis and peak systolic velocity (PSV) of arterial flow were recorded from the duplex. Ultrasound wrist/brachial indexes (WBI) were calculated using the higher value of the radial/brachial or the ulnar/brachial index. Only three patients had normal Duplexes without stenosis and with flow comparable in the brachial, radial, and ulnar arteries of the affected arm, compared to the unaffected arm, both preoperatively and postpinning. One group of six patients had brachial artery stenosis at the fracture site when compared to the artery proximal to the fracture site, increased PSV at the fracture site compared to proximal to the fracture site, and the WBI was variable when compared to the contralateral side. A third group of two patients also had brachial artery stenosis at the fracture site but had decreased PSV and decreased WBI compared to the contralateral side. Type III supracondylar humerus patients with a normal neurovascular examination may have abnormal Duplex ultrasonography with brachial artery stenosis and elevated peak systolic velocity preoperatively although distal flow remains comparable to the contralateral side. Level of evidence: prognostic - Level II.

Canal of Nuck hernias.

The canal of Nuck, caused by the failed closure of the processus vaginalis in the female, is the continued outpouching of parietal peritoneum through the inguinal canal to the labia majora. Disorders of the canal of Nuck include hernia and hydrocele. Some canal of Nuck hernias, especially of the ovary, may result in emergent complications such as incarceration, strangulation, and ovarian torsion. Knowledge of canal of Nuck disorders and prompt diagnosis are important to avoid serious complications. Imaging, especially ultrasound, is essential for timely diagnosis, leading to appropriate management and better patient care.

Feature space learning model.

With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces.

Correction to: MISC: missing imputation for single-cell RNA sequencing data.

It was highlighted that the original article [1] contained a typesetting error in the last name of Allon Canaan. This was incorrectly captured as Allon Canaann in the original article which has since been updated.

Image Captioning with Bidirectional Semantic Attention-Based Guiding of Long Short-Term Memory.

Automatically describing contents of an image using natural language has drawn much attention because it not only integrates computer vision and natural language processing but also has practical applications. Using an end-to-end approach, we propose a bidirectional semantic attention-based guiding of long short-term memory (Bag-LSTM) model for image captioning. The proposed model consciously refines image features from previously generated text. By fine-tuning the parameters of convolution neural networks, Bag-LSTM obtains more text-related image features via feedback propagation than other models. As opposed to existing guidance-LSTM methods which directly add image features into each unit of an LSTM block, our fine-tuned model dynamically leverages more text-conditional image features, acquired by the semantic attention mechanism, as guidance information. Moreover, we exploit bidirectional gLSTM as the caption generator, which is capable of learning long term relations between visual features and semantic information by making use of both historical and future contextual information. In addition, variations of the Bag-LSTM model are proposed in an effort to sufficiently describe high-level visual-language interactions. Experiments on the Flickr8k and MSCOCO benchmark datasets demonstrate the effectiveness of the model, as compared with the baseline algorithms, such as it is 51.2% higher than BRNN on CIDEr metric.

The emerging role of microRNA-4487/6845-3p in Alzheimer's disease pathologies is induced by Aß25-35 triggered in SH-SY5Y cell.

BACKGROUND: Accumulation of amyloid ß-peptide (Aß) is implicated in the pathogenesis and development of Alzheimer's disease (AD). Neuron-enriched miRNA was aberrantly regulated and may be associated with the pathogenesis of AD. However, regarding whether miRNA is involved in the accumulation of Aß in AD, the underlying molecule mechanism remains unclear. Therefore, we conduct a systematic identification of the promising role of miRNAs in Aß deposition, and shed light on the molecular mechanism of target miRNAs underlying SH-SY5Y cells treated with Aß-induced cytotoxicity. RESULTS: Statistical analyses of microarray data revealed that 155 significantly upregulated and 50 significantly downregulated miRNAs were found on the basis of log2 | Fold Change | ≥ 0.585 and P < 0.05 filter condition through 2588 kinds of mature miRNA probe examined. PCR results show that the expression change trend of the selected six miRNAs (miR-6845-3p, miR-4487, miR-4534, miR-3622-3p, miR-1233-3p, miR-6760-5p) was consistent with the results of the gene chip. Notably, Aß25-35 downregulated hsa-miR-4487 and upregulated hsa-miR-6845-3p in SH-SY5Y cell lines associated with Aß-mediated pathophysiology. Increase of hsa-miR-4487 could inhibit cells apoptosis, and diminution of hsa-miR-6845-3p could attenuate axon damage mediated by Aß25-35 in SH-SY5Y. CONCLUSIONS: Together, these findings suggest that dysregulation of hsa-miR-4487 and hsa-miR-6845-3p contributed to the pathogenesis of AD associated with Aß25-35 mediated by triggering cell apoptosis and synaptic dysfunction. It might be beneficial to understand the pathogenesis and development of clinical diagnosis and treatment of AD. Further, our well-designed validation studies will test the miRNAs signature as a prognostication tool associated with clinical outcomes in AD.

Increased AURKA promotes cell proliferation and predicts poor prognosis in bladder cancer.

BACKGROUND: Bladder cancer (BC) is the most common cancer of the urinary bladder and upper tract, in which the clinical management is limited. AURKA (aurora kinase A) has been identified as an oncogene in cancer development; however, its potential role and underlying mechanisms in the progression of BC remain unknown. RESULTS: In this study, we evaluated Aurora kinase A (AURKA) expression in patient samples by performing gene expression profiling, and found that AURKA expression levels were significantly higher in BC tissues than in normal tissues. Increased AURKA in BC was strongly associated with stage and grade. Moreover, BC patients with elevated AURKA achieved poor overall survival rates. The experiments in vitro comprehensively validated the critical role of AURKA in promoting BC cell proliferation using the methods of gene overexpression and gene silencing. Furthermore, we proved that AURKA inhibitor MLN8237 arrested BC cell growth and induced apoptosis. CONCLUSIONS: These findings implicate AURKA acting as an effective biomarker for BC detection and prognosis, as well as therapeutic target.