Growth and development symposium: Stem and progenitor cells in animal growth: The regulation of beef quality by resident progenitor cells

© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. The intramuscular adipose tissue deposition in the skeletal muscle of beef cattle is a highly desired trait essential for high-quality beef. In contrast, the excessive accumulation of crosslinked collagen in intramuscular connective tissue contributes to beef toughness. Recent studies revealed that adipose tissue and connective tissue share an embryonic origin in mice and may be derived from a common immediate bipotent precursor in mice and humans. Having the same linkages in the development of adipose tissue and connective tissue in beef, the lineage commitment and differentiation of progenitor cells giving rise to these tissues may directly affect beef quality. It has been shown that these processes are regulated by some key transcription regulators and are subjective to epigenetic modifications such as DNA methylation, histone modifications, and microRNAs. Continued exploration of relevant regulatory pathways is very important for the identification of mechanisms influencing meat quality and the development of proper management strategies for beef quality improvement

Comparing 2-nt 3' overhangs against blunt-ended siRNAs: a systems biology based study

In this study, we formulate a computational reaction model following a chemical kinetic theory approach to predict the binding rate constant for the siRNA-RISC complex formation reaction. The model allowed us to study the potency difference between 2-nt 3' overhangs against blunt-ended siRNA molecules in an RNA interference (RNAi) system. The rate constant predicted by this model was fed into a stochastic simulation of the RNAi system (using the Gillespie stochastic simulator) to study the overall potency effect. We observed that the stochasticity in the transcription/translation machinery has no observable effects in the RNAi pathway. Sustained gene silencing using siRNAs can be achieved only if there is a way to replenish the dsRNA molecules in the cell. Initial findings show about 1.5 times more blunt-ended molecules will be required to keep the mRNA at the same reduced level compared to the 2-nt overhang siRNAs. However, the mRNA levels jump back to saturation after a longer time when blunt-ended siRNAs are used. We found that the siRNA-RISC complex formation reaction rate was 2 times slower when blunt-ended molecules were used pointing to the fact that the presence of the 2-nt overhangs has a greater effect on the reaction in which the bound RISC complex cleaves the mRNA

Comparing 2-nt 3\u27 Overhangs Against Blunt-Ended siRNAs: A Systems Biology Based Study

In this study, we formulate a computational reaction model following a chemical kinetic theory approach to predict the binding rate constant for the siRNA-RISC complex formation reaction. The model allowed us to study the potency difference between 2-nt 3\u27 overhangs against blunt-ended siRNA molecules in an RNA interference (RNAi) system. The rate constant predicted by this model was fed into a stochastic simulation of the RNAi system (using the Gillespie stochastic simulator) to study the overall potency effect. We observed that the stochasticity in the transcription/translation machinery has no observable effects in the RNAi pathway. Sustained gene silencing using siRNAs can be achieved only if there is a way to replenish the dsRNA molecules in the cell. Initial findings show about 1.5 times more blunt-ended molecules will be required to keep the mRNA at the same reduced level compared to the 2-nt overhang siRNAs. However, the mRNA levels jump back to saturation after a longer time when blunt-ended siRNAs are used. We found that the siRNA-RISC complex formation reaction rate was 2 times slower when blunt-ended molecules were used pointing to the fact that the presence of the 2-nt overhangs has a greater effect on the reaction in which the bound RISC complex cleaves the mRNA

Does the built environment make a difference? An investigation of household vehicle use in Zhongshan Metropolitan Area, China

To address worsening urban traffic and environmental issues, planners and policy makers in China have begun to recognize the importance of shaping vehicle use through the built environment. However, very few studies can be found that examine the relationship between the built environment and vehicle u

Acute versus delayed reverse total shoulder arthroplasty for proximal humerus fractures in the elderly: Mid-term outcomes

Background: Treatment of proximal humerus fractures (PHFs) via reverse total shoulder arthroplasty (RTSA) has shown early promise when compared to historical treatment modalities. Ideal surgical timing remains unclear. The purpose of this study was to compare the outcomes of early versus delayed RTSA for PHF. We hypothesized that acute RTSA would display superior outcomes compared to those receiving delayed surgical intervention. Methods: This multicenter study retrospectively analyzed 142 patients who underwent RTSA for fracture. Patients treated within 4 weeks of injury were placed in the acute group (n = 102), and patients treated longer than 4 weeks after injury were placed in the chronic group (n = 38). A comprehensive panel of patient reported outcome measures, VAS pain scores, range of motion, and patient satisfaction were evaluated. Results: The acute group had significantly better final follow-up SPADI scores (20.8 ± 23.9 vs. 30.7 ± 31.7) (p\u3c0.05). No further differences were detected in other postoperative range of motion measurements, subjective outcomes, or VAS scores. Conclusions: Our results suggest that patients treated acutely display similar mid-term outcomes to those who receive delayed treatment. With this in mind, surgeons may first give consideration to a period of nonoperative treatment. Level of evidence: Level II

Opposing Tumor-Promoting and -Suppressive Functions of Rictor/mTORC2 Signaling in Adult Glioma and Pediatric SHH Medulloblastoma.

Most human cancers arise from stem and progenitor cells by the sequential accumulation of genetic and epigenetic alterations, while cancer modeling typically requires simultaneous multiple oncogenic events. Here, we show that a single p53 mutation, despite causing no defect in the mouse brain, promoted neural stem and progenitor cells to spontaneously accumulate oncogenic alterations, including loss of multiple chromosomal (chr) regions syntenic to human chr10 containing Pten, forming malignant gliomas with PI3K/Akt activation. Rictor/mTORC2 loss inhibited Akt signaling, greatly delaying and reducing glioma formation by suppressing glioma precursors within the subventricular zone stem cell niche. Rictor/mTORC2 loss delayed timely differentiation of granule cell precursors (GCPs) during cerebellar development, promoting sustained GCP proliferation and medulloblastoma formation, which recapitulated critical features of TP53 mutant sonic hedgehog (SHH) medulloblastomas with GLI2 and/or N-MYC amplification. Our study demonstrates that Rictor/mTORC2 has opposing functions in neural stem cells and GCPs in the adult and the developing brain, promoting malignant gliomas and suppressing SHH-medulloblastoma formation, respectively

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Background: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. Results: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. Conclusions: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes

Subsequent monitoring of ferric ion and ascorbic acid using graphdiyne quantum dots-based optical sensors

Graphdiyne (GDY) as an emerging carbon nanomaterial has attracted increasing attention because of its uniformly distributed pores, highly π-conjugated, and tunable electronic properties. These excellent characteristics have been widely explored in the fields of energy storage and catalysts, yet there is no report on the development of sensors based on the outstanding optical property of GDY. In this paper, a new sensing mechanism is reported built upon the synergistic effect between inner filter effect and photoinduced electron transfer. We constructed a novel nanosensor based upon the newly-synthesized nanomaterial and demonstrated a sensitive and selective detection for both Fe3+ ion and ascorbic acid, enabling the measurements in real clinical samples. For the first time fluorescent graphdiyne oxide quantum dots (GDYO-QDs) were prepared using a facile ultrasonic protocol and they were characterized with a range of techniques, showing a strong blue-green emission with 14.6% quantum yield. The emission is quenched efficiently by Fe3+ and recovered by ascorbic acid (AA). We have fabricated an off/on fluorescent nanosensors based on this unique property. The nanosensors are able to detect Fe3+ as low as 95 nmol L−1 with a promising dynamic range from 0.25 to 200 μmol L−1. The LOD of AA was 2.5 μmol L−1, with range of 10–500 μmol L−1. It showed a promising capability to detect Fe3+ and AA in serum samples

A Built-In Mechanism to Mitigate the Spread of Insect-Resistance and Herbicide-Tolerance Transgenes into Weedy Rice Populations

BACKGROUND: The major challenge of cultivating genetically modified (GM) rice (Oryza sativa) at the commercial scale is to prevent the spread of transgenes from GM cultivated rice to its coexisting weedy rice (O. sativa f. spontanea). The strategic development of GM rice with a built-in control mechanism can mitigate transgene spread in weedy rice populations. METHODOLOGY/PRINCIPAL FINDINGS: An RNAi cassette suppressing the expression of the bentazon detoxifying enzyme CYP81A6 was constructed into the T-DNA which contained two tightly linked transgenes expressing the Bt insecticidal protein Cry1Ab and the glyphosate tolerant 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), respectively. GM rice plants developed from this T-DNA were resistant to lepidopteran pests and tolerant to glyphosate, but sensitive to bentazon. The application of bentazon of 2000 mg/L at the rate of 40 mL/m(2), which is approximately the recommended dose for the field application to control common rice weeds, killed all F(2) plants containing the transgenes generated from the Crop-weed hybrids between a GM rice line (CGH-13) and two weedy rice strains (PI-63 and PI-1401). CONCLUSIONS/SIGNIFICANCE: Weedy rice plants containing transgenes from GM rice through gene flow can be selectively killed by the spray of bentazon when a non-GM rice variety is cultivated alternately in a few-year interval. The built-in control mechanism in combination of cropping management is likely to mitigate the spread of transgenes into weedy rice populations

Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition