Plant somatic cells can be reprogrammed into totipotent embryonic cells that are able to form differentiated embryos in a process called somatic embryogenesis (SE), by hormone treatment or through ...overexpression of certain transcription factor genes, such as BABY BOOM (BBM). Here we show that overexpression of the AT-HOOK MOTIF CONTAINING NUCLEAR LOCALIZED 15 (AHL15) gene induces formation of somatic embryos on Arabidopsis thaliana seedlings in the absence of hormone treatment. During zygotic embryogenesis, AHL15 expression starts early in embryo development, and AH15 and other AHL genes are required for proper embryo patterning and development beyond the globular stage. Moreover, AHL15 and several of its homologs are upregulated and required for SE induction upon hormone treatment, and they are required for efficient BBM-induced SE as downstream targets of BBM. A significant number of plants derived from AHL15 overexpression-induced somatic embryos are polyploid. Polyploidisation occurs by endomitosis specifically during the initiation of SE, and is caused by strong heterochromatin decondensation induced by AHL15 overexpression.
Post-embryonic development and longevity of flowering plants are, for a large part, determined by the activity and maturation state of stem cell niches formed in the axils of leaves, the so-called ...axillary meristems (AMs)
. The genes that are associated with AM maturation and underlie the differences between monocarpic (reproduce once and die) annual and the longer-lived polycarpic (reproduce more than once) perennial plants are still largely unknown. Here we identify a new role for the Arabidopsis AT-HOOK MOTIF NUCLEAR LOCALIZED 15 (AHL15) gene as a suppressor of AM maturation. Loss of AHL15 function accelerates AM maturation, whereas ectopic expression of AHL15 suppresses AM maturation and promotes longevity in monocarpic Arabidopsis and tobacco. Accordingly, in Arabidopsis grown under longevity-promoting short-day conditions, or in polycarpic Arabidopsis lyrata, expression of AHL15 is upregulated in AMs. Together, our results indicate that AHL15 and other AHL clade-A genes play an important role, directly downstream of flowering genes (SOC1, FUL) and upstream of the flowering-promoting hormone gibberellic acid, in suppressing AM maturation and extending the plant's lifespan.
The Lielmezs-Merriman equation of state has been modified in such a way that it can be applied over the entire PVT surface except along the critical isotherm. The dimensionless T* coordinate has been ...defined according to the two regions on the PVT surface as:;
Formula Omitted;
The two substance-dependent constants p and q are generated from the vapor pressure data.;
The applicability of the proposed modification has been tested by comparing its predictions of various pure compound physical and thermodynamic properties with known experimental data and with predictions from the Soave-Redlich-Kwong and Peng-Robinson equations of state. The proposed equation is the most accurate equation of state for calculating vapor pressure, and saturated vapor and liquid volumes. The Peng-Robinson equation is the best for enthalpy and entropy of vaporization estimations. The Soave-Redlich-Kwong equation is the least accurate equation for pressure and volume predictions in the single phase regions. For temperature prediction, all three equations of state give similar results in the subcritical and supercritical regions. None of the three equations is capable of representing all departure functions accurately. The Peng-Robinson equation and the proposed equation are very similar in accuracy except in the region where the temperature is near the critical. That is, between 0.95 ≤ Tr ≤ 1.05, the proposed equation gives rather poor results. For isobaric heat capacity calculation, both Soave-Redlich-Kwong and Peng-Robinson equations are adequate. The Soave-Redlich-Kwong equation gives the lowest overall average RMS % error for Joule-Thomson coefficient estimation. The Soave-Redlich-Kwong equation also provides the most reliable prediction for the Joule-Thomson inversion curve right up to the maximum inversion pressure.;
None of the cubic equations of state studied in this work is recommended;
for second virial coefficient calculation below Tr = 0.8. An α-function specifically;
designed for the calculation of second virial coefficient has been included in this;
work. The estimation from the proposed function gives equal, if not better, accuracy than the Tsonopoulos correlation.
Applied Science, Faculty of
Chemical and Biological Engineering, Department of
Graduate
Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical ...imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.
We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years range 32.5-93.3, survival median = 1.7 years range 0.0-11.7). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years range 32.5-93.3, survival median = 1.3 years range 0.0-11.7). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years range 37.2-88.0, survival median = 3.1 years range 0.0-8.8). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve AUC = 0.70 95% CI 0.63-0.78, p < 0.001) and surgery (AUC = 0.71 95% CI 0.60-0.82, p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks.
Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.
In this paper, we review and provide additional details about our progress on multilayer silicon nitride (SiN)-on-silicon (Si) integrated photonic platforms. In these platforms, one or more SiN ...waveguide layers are monolithically integrated onto a Si photonic layer. This paper focuses on the development of three-layer platforms for the O- and SCL-bands for very large-scale photonic integrated circuits requiring hundreds or thousands of waveguide crossings. Low-loss interlayer transitions and ultralow-loss waveguide crossings have been demonstrated, along with bilevel and trilevel grating couplers for fiber-to-chip coupling. The SiN and Si passive devices have been monolithically integrated with high-efficiency optical modulators, photodetectors, and thermal tuners in a single photonic platform.
Abstract
Visible and near-infrared spectrum photonic integrated circuits are quickly becoming a key technology to address the scaling challenges in quantum information and biosensing. Thus far, ...integrated photonic platforms in this spectral range have lacked integrated photodetectors. Here, we report silicon nitride-on-silicon waveguide photodetectors that are monolithically integrated in a visible light photonic platform on silicon. Owing to a leaky-wave silicon nitride-on-silicon design, the devices achieved a high external quantum efficiency of >60% across a record wavelength span from
λ
~ 400 nm to ~640 nm, an opto-electronic bandwidth up to 9 GHz, and an avalanche gain-bandwidth product up to 173 ± 30 GHz. As an example, a photodetector was integrated with a wavelength-tunable microring in a single chip for on-chip power monitoring.
Transforming growth factor‐β (TGF‐β) is the key player in tissue fibrosis. However, antifibrotic therapy targeting this multifunctional protein may interfere with other physiological processes to ...cause side effects. Thus, precise therapeutic targets need to be identified by further understanding the underlying mechanisms of TGF‐β1 signalling during fibrogenesis. Equilibrium of Smad signalling is crucial for TGF‐β‐mediated renal fibrosis, where Smad3 is pathogenic but Smad2 and Smad7 are protective. The activation of TGF‐β1/Smad signalling triggers extracellular matrix deposition, and local myofibroblast generation and activation. Mechanistic studies have shown that TGF‐β/Smad3 transits the microRNA profile from antifibrotic to profibrotic and therefore promotes renal fibrosis via regulating non‐coding RNAs at transcriptional levels. More importantly, disease‐specific Smad3‐dependent long non‐coding RNAs have been recently uncovered from mouse kidney disease models and may represent novel precision therapeutic targets for chronic kidney disease. In this review, mechanisms of TGF‐β‐driven renal fibrosis via non‐coding RNAs and their translational capacities will be discussed in detail.
TGF‐β1/Smad3 signaling promotes renal fibrosis via transcriptional regulation. TGF‐β1 triggers the development and progression of renal fibrosis in the injured kidney by regulating the expression levels of both pro‐fibrotic and anti‐fibrotic genes at transcriptional level via Smad3 activation.
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