Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Density SNP Genetic Map Construction Using ddRAD-Seq and Mapping of Capsule Shattering Trait in Sesame

The seed-bearing capsule of sesame shatters at harvest. This wildish trait makes the crop unsuitable for mechanized harvesting and also restricts its commercial potential by limiting the cultivation for countries that have no access to low-cost labor. Therefore, the underlying genetic basis of the capsule shattering trait is highly important in order to develop mechanization-ready varieties for sustainable sesame farming. In the present study, we generated a sesame F2 population derived from a cross between a capsule shattering cultivar (Muganli-57) and a non-shattering mutant (PI 599446), which was used to construct a genetic map based on double-digest restriction-site-associated DNA sequencing. The resulting high-density genetic map contained 782 single-nucleotide polymorphisms (SNPs) and spanned a length of 697.3 cM, with an average marker interval of 0.89 cM. Based on the reference genome, the capsule shattering trait was mapped onto SNP marker S8_5062843 (78.9 cM) near the distal end of LG8 (chromosome 8). In order to reveal genes potentially controlling the shattering trait, the marker region (S8_5062843) was examined, and a candidate gene including six CDSs was identified. Annotation showed that the gene encodes a protein with 440 amino acids, sharing ∼99% homology with transcription repressor KAN1. Compared with the capsule shattering allele, the SNP change and altered splicing in the flanking region of S8_5062843 caused a frameshift mutation in the mRNA, resulting in the loss of function of this gene in the mutant parent and thus in non-shattering capsules and leaf curling. With the use of genomic data, InDel and CAPS markers were developed to differentiate shattering and non-shattering capsule genotypes in marker-assisted selection studies. The obtained results in the study can be beneficial in breeding programs to improve the shattering trait and enhance sesame productivity.
Added Value of Systemic Inflammation Markers in Predicting Clinical Stage T1 Renal Cell Carcinoma Pathologically Upstaged to T3a

Objectives We aimed to determine preoperative risk factors associated with pathologic T3a (pT3a) upstaging of clinical T1 (cT1) renal cell carcinomas (RCCs) and develop a novel model capable of accurately identifying those patients at high risk of harboring occult pT3a characteristics. Methods A retrospective analysis of 1324 cT1 RCC patients who underwent partial nephrectomy (PN) or radical nephrectomy (RN) was performed. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with cT1 to pT3a upstaging and subsequently, those significant risk factors were used to construct models. We used the area under the curve (AUC) to determine the model with the highest discrimination power. Decision curve analyses (DCAs) were applied to evaluate clinical net benefit associated with using the predictive models. Results The rates of upstaging were 6.1% (n = 81), 5.8% (n = 54) and 6.8% (n = 27) in the total population, training cohort and validation cohort, respectively. Tumor size, clinical T stage, R.E. N.A. L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior) nephrometry score, lymphocyte to monocyte ratio (LMR), prognostic nutrition index (PNI) and albumin to globulin ratio (AGR) were significantly associated with pT3a upstaging. The model that consisted of R.E. N.A. L. score, LMR, AGR and PNI achieved the highest AUC of 0.70 in the validation cohort and yielded the highest net benefit. In the subpopulation with complete serum lipid profile, the inclusion of low-density lipoprotein cholesterol (LDL-C) and Castelli risk index-I (CRI-I) significantly improved the discrimination of model (AUC = 0.86). Conclusions Our finding highlights the importance of systemic inflammation response markers and serum lipid parameters in predicting pT3a upstaging. Our model had relatively good discrimination in predicting occult pT3a disease among patients with cT1 renal lesions, and the use of the model may be greatly beneficial to urologists in risk stratification and management decisions.
BEAN: Interpretable and Efficient Learning With Biologically-Enhanced Artificial Neuronal Assembly Regularization

Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, especially in dense layers. One crucial issue of the classical DNN model such as multilayer perceptron (MLP) is that neurons in the same layer of DNNs are conditionally independent of each other, which makes co-training and emergence of higher modularity difficult. In contrast to DNNs, biological neurons in mammalian brains display substantial dependency patterns. Specifically, biological neural networks encode representations by so-called neuronal assemblies: groups of neurons interconnected by strong synaptic interactions and sharing joint semantic content. The resulting population coding is essential for human cognitive and mnemonic processes. Here, we propose a novel Biologically Enhanced Artificial Neuronal assembly (BEAN) regularization1 to model neuronal correlations and dependencies, inspired by cell assembly theory from neuroscience. Experimental results show that BEAN enables the formation of interpretable neuronal functional clusters and consequently promotes a sparse, memory/computation-efficient network without loss of model performance. Moreover, our few-shot learning experiments demonstrate that BEAN could also enhance the generalizability of the model when training samples are extremely limited.
Disruption of Neural Activity and Functional Connectivity in Adolescents With Major Depressive Disorder Who Engage in Non-suicidal Self-Injury: A Resting-State fMRI Study

Background: Non-suicidal self-injury (NSSI), which commonly occurs during adolescence, often co-occurs with major depressive disorder (MDD). However, the underlying neurobiological mechanisms in adolescents with MDD who engage in NSSI remain unclear. The current study examined the aberrant local neural activity in certain areas of the visual regions and the default mode network (DMN) and the resting-state functional connectivity (rs-FC) in changed brain regions in adolescents with MDD who engage in NSSI and adolescents with MDD only. Methods: A total of 67 adolescents with MDD were divided into two groups based on their NSSI behavior: the NSSI group (n = 31) and an age-, gender-, and education-matched MDD group (n = 36). The Hamilton Depression Rating Scale (HAMD) was used to assess the severity of MDD. Amplitude of low-frequency fluctuation (ALFF) analysis was used to detect alterations in local neural activity. Brain regions with aberrant neural activity were considered regions of interest (ROI). ALFF-based rs-FC analysis was used to further explore the underlying changes in connectivity between ROI and other areas in the NSSI group. Correlation analyses were performed to examine the relationship between neural changes and clinical characteristics. Results: There was no significant difference in HAMD scores between the two groups. ALFF analysis revealed that, compared to adolescents with MDD only, adolescents with MDD who engaged in NSSI displayed significantly enhanced neural activity in the right fusiform gyrus (FFG. R) and the right median cingulate and paracingulate gyri (DCG. R). Significantly reduced rs-FC of the FFG. R-bilateral medial orbital of the superior frontal gyrus (ORBsupmed. L/R)/bilateral medial superior frontal gyrus (SFGmed. L/R), FFG. R-bilateral posterior cingulate gyrus (PCG. L/R), DCG. R-left pallidum (PAL. L), DCG. R-right superior temporal gyrus (STG. R), and DCG. R-right postcentral gyrus (PoCG. R)/right inferior parietal lobule (IPL. R) was found in adolescents with MDD who were engaged in NSSI. Additionally, no significant correlations were observed between ALFF or rs-FC values and the HAMD scores between the two groups. Limitations: Owing to the cross-sectional design, the alterations in ALFF and rs-FC values in the FFG. R and DCG. R could not demonstrate that it was a state or feature in adolescents with MDD who engaged in NSSI. Additionally, the sample size was relatively small. Conclusions: This study highlights changes in regional brain activity and remote connectivity in the FFG. R and DCG. R in adolescents with MDD who engage in NSSI. This could provide a new perspective for further studies on the neurobiological mechanism of NSSI behavior in adolescents with MDD.
An Updated Organ-Based Multi-Level Model for Glucose Homeostasis: Organ Distributions, Timing, and Impact of Blood Flow

Glucose homeostasis is the tight control of glucose in the blood. This complex control is important, due to its malfunction in serious diseases like diabetes, and not yet sufficiently understood. Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. Over the last 10 years, this model has been used to insert new insights from the intra-cellular level into the larger whole-body perspective. However, the original cell-organ-body translation has during these years never been updated, despite several critical shortcomings, which also have not been resolved by other modeling efforts. For this reason, we here present an updated multi-level model. This model provides a more accurate sub-division of how much glucose is being taken up by the different organs. Unlike the original model, we now also account for the different dynamics seen in the different organs. The new model also incorporates the central impact of blood flow on insulin-stimulated glucose uptake. Each new improvement is clear upon visual inspection, and they are also supported by statistical tests. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. We hope that this model will serve as an improved basis for future data integration, useful for research and drug developments within diabetes.
Prevalence of TP-53/Rb-1 Co-Mutation in Large Cell Neuroendocrine Carcinoma

Introduction Large cell neuroendocrine carcinoma (LCNEC) is a rare and highly aggressive high-grade neuroendocrine neoplasm, which can arise from anywhere in the body. Due to its rarity there is a lacuna in our understanding of LCNEC’s molecular biology. In 2016, Rekhtman and colleagues presented one of the largest molecular sequencing series of pulmonary LCNEC. They differentiated genomic profiles of LCNEC into two major subsets: small cell lung cancer (SCLC)-like, characterized by TP53 + RB1 co-mutation/loss, and non-small cell lung cancer (NSCLC)-like, characterized by the lack of co-altered TP53 + RB1. This finding is of significance because at present LCNEC patients are often treated like SCLC. However, the universal genomic SCLC biomarker of TP53 and RB1 co-mutation was only found in 40% of their cohort. Since then various other scientists have looked into molecular profiling of LCNEC with markedly discordant results. The objective of this study was to conduct a systematic review of publicly available next generation sequencing (NGS) data to evaluate the prevalence of TP53 + RB1 co-mutation in LCNEC. Method We conducted a literature search using PubMed. Seven studies including 302 patients with pulmonary LCNEC and four studies including 20 patients with extra-pulmonary LCNEC underwent final analysis. Results The prevalence of TP53 + RB1 co-mutation was 36% (109/302) among pulmonary LCNEC patients and 35% (7/20) among the extra-thoracic LCNEC cohort. This finding is in stark contrast to >90% TP53 + RB1 co-mutation in SCLC. Conclusion It is now well established that LCNEC is molecularly distinct from SCLC. LCNEC seems to have two molecularly defined sub-cohort based on TP53 + RB1 co-mutation status. Future studies should look into prognostic and predictive implication of TP53 + RB1 co-mutation status in LCNEC. Prospective studies should be designed to characterize molecular subtypes and direct treatment accordingly. We are currently conducting a prospective pilot clinical trial wherein LCNEC patients are treated based on TP53 + RB1 co-mutation status. The study is currently enrolling. “Next Generation Sequencing-Based Stratification of Front Line Treatment of Neuroendocrine Carcinoma (PRECISION-NEC). Systematic Review ClinicalTrials.gov, identifier NCT04452292.
Gene Expression Profile in Immortalized Human Periodontal Ligament Fibroblasts Through hTERT Ectopic Expression: Transcriptome and Bioinformatic Analysis

Human periodontal ligament fibroblast (hPLF) cells play an important role in maintaining oral cavity homeostasis with special function in tissue regeneration and maintenance of dental alveoli. Although their primary cell cultures are considered a good experimental model with no genetic changes, the finite life span may limit some experimental designs. The immortalization process increases cell life span but may cause genetic changes and chromosomal instability, resulting in direct effects on physiological cell responses. In this way, we aimed to investigate the global gene expression of hPLFs after the immortalization process by the ectopic expression of the catalytic subunit of the enzyme telomerase reverse transcriptase (hTERT) through transcriptome analysis. The embryonic origin of the primary culture of hPLF cells and immortalized hPLF-hTERT was also tested by vimentin staining, hTERT synthesis evaluated by indirect immunocytochemistry, analysis of cell proliferation, and morphology. The results indicated that hPLFs and hPLF-hTERT were positive for vimentin. On the 20th cell passage, hPLFs were in senescence, while hPLF-hTERT maintained their proliferation and morphology characteristics. At the same passage, hPLF-hTERT presented a significant increase in hTERT synthesis, but transcriptome did not reveal overexpression of the hTERT gene. Fifty-eight genes had their expression altered (11 upregulated and 47 downregulated) with the absence of changes in the key genes related to these cell types and in the main cancer-associated genes. In addition, the increase in hTERT protein expression without the overexpression of its gene indicates posttranscriptional level regulation. Successful immortalization of hPLFs through the ectopic expression of hTERT encourages further studies to design experimental protocols to investigate clinical questions from a translational perspective.
Clinical and Genetic Characteristics of 153 Chinese Patients With X-Linked Hypophosphatemia

X-linked hypophosphatemia (XLH) is caused by inactivating mutations in the phosphate-regulating endopeptidase homolog, X-linked (PHEX) gene, resulting in an excess of circulating intact fibroblast growth factor-23 (iFGF-23) and a waste of renal phosphate. In the present study, we retrospectively reviewed the clinical and molecular features of 153 Chinese patients, representing 87 familial and 66 sporadic cases with XLH. A total of 153 patients with XLH presented with signs or symptoms at a median age of 18.0 months (range, 9.0 months–26.0 years). Lower-limb deformity was the most frequent clinical manifestation, accounting for 79.1% (121/153). Biochemical screening showed increased serum levels of iFGF23 in patients with XLH, with a wide variation ranging from 14.39 to 730.70 pg/ml. Median values of serum iFGF23 in pediatric and adult patients were 94.87 pg/ml (interquartile range: 74.27–151.86 pg/ml) and 72.82 pg/ml (interquartile range: 39.42–136.00 pg/ml), respectively. Although no difference in circulating iFGF23 levels between these two groups was observed (P = 0.062), the proportion of patients with high levels of circulating iFGF23 (>42.2 pg/ml) was greater in the pediatric group than in the adult group (P = 0.026). Eighty-eight different mutations in 153 patients were identified, with 27 (30.7%) being novel. iFGF23 levels and severity of the disease did not correlate significantly with truncating and non-truncating mutations or N-terminal and C-terminal PHEX mutations. This study provides a comprehensive description of the clinical profiles, circulating levels of iFGF23 and gene mutation features of patients with XLH, further enriching the genotypic spectrum of the diseases. The findings show no evident correlation of circulating iFGF23 levels with the age or disease severity in patients with XLH.
Experimental Resonances in Viscoelastic Microfluidics

Pulsatile flows of viscoelastic fluids are very important for lab-on-a-chip devices, because most biofluids have viscoelastic character and respond distinctively to different periodic forcing. They are also very important for organ-on-a-chip devices, where the natural mechanical conditions of cells are emulated. The resonance frequency of a fluid refers to a particular pulsatile periodicity of the pressure gradient that maximizes the amplitude of flow velocity. For viscoelastic fluids, this one has been measured experimentally only at macroscales, since fine tuning of rheological properties and system size is needed to observe it at microscales. We study the dynamics of a pulsatile (zero-mean flow) fluid slug formed by a viscoelastic fluid bounded by two air-fluid interfaces, in a microchannel of polymethyl methacrylate. We drive the fluid slug by a single-mode periodic pressure drop, imposed by a piezoactuator. We use three biocompatible polymer solutions of polyethylene oxide as model viscoelastic fluids, and find resonances. We propose a model accounting for surface tension and fluid viscoelasticity that has an excellent agreement with our experimental findings. It also provides an alternative way of measuring relaxation times. We validate the method with parameters reported in the literature for two of the solutions, and estimate the relaxation time for the third one.
Two-Year Follow-Up Study of the Relationship Between Brain Structure and Cognitive Control Function Across the Adult Lifespan

Age-related decline in cognitive control and general slowing are prominent phenomena in aging research. These declines in cognitive functions have been shown to also involve age-related decline in brain structure. However, most evidence in support of these associations is based on cross-sectional data. Therefore, the aim of this study is to contrast cross-sectional and longitudinal analyses to re-examine if the relationship between age-related brain structure and cognitive function are similar between the two approaches. One hundred and two participants completed two sessions with an average interval of 2 years. All participants were assessed by questionnaires, a series of cognitive tasks, and they all underwent neuroimaging acquisition. The main results of this study show that the majority of the conclusions regarding age effect in cognitive control function and processing speed in the literature can be replicated based on the cross-sectional data. Conversely, when we followed up individuals over an average interval of 2 years, then we found much fewer significant relationships between age-related change in gray matter structure of the cognitive control network and age-related change in cognitive control function. Furthermore, there was no “initial age” effect in the relationships between age-related changes in brain structure and cognitive function. This finding suggests that the “aging” relationship between brain structure and cognitive function over a short period of time are independent of “initial age” difference at time point 1. The result of this study warrants the importance of longitudinal research for aging studies to elucidate actual aging processes on cognitive control function.
Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.
Synaptopathy Mechanisms in ALS Caused by C9orf72 Repeat Expansion

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disease caused by degeneration of motor neurons (MNs). ALS pathogenic features include accumulation of misfolded proteins, glutamate excitotoxicity, mitochondrial dysfunction at distal axon terminals, and neuronal cytoskeleton changes. Synergies between loss of C9orf72 functions and gain of function by toxic effects of repeat expansions also contribute to C9orf72-mediated pathogenesis. However, the impact of haploinsufficiency of C9orf72 on neurons and in synaptic functions requires further examination. As the motor neurons degenerate, the disease symptoms will lead to neurotransmission deficiencies in the brain, spinal cord, and neuromuscular junction. Altered neuronal excitability, synaptic morphological changes, and C9orf72 protein and DPR localization at the synapses, suggest a potential involvement of C9orf72 at synapses. In this review article, we provide a conceptual framework for assessing the putative involvement of C9orf72 as a synaptopathy, and we explore the underlying and common disease mechanisms with other neurodegenerative diseases. Finally, we reflect on the major challenges of understanding C9orf72-ALS as a synaptopathy focusing on integrating mitochondrial and neuronal cytoskeleton degeneration as biomarkers and potential targets to treat ALS neurodegeneration.