Collective cell migration: Implications for wound healing and cancer invasion.

During embryonic morphogenesis, wound repair and cancer invasion, cells often migrate collectively via tight cell-cell junctions, a process named collective migration. During such migration, cells move as coherent groups, large cell sheets, strands or tubes rather than individually. One unexpected finding regarding collective cell migration is that being a "multicellular structure" enables cells to better respond to chemical and physical cues, when compared with isolated cells. This is important because epithelial cells heal wounds via the migration of large sheets of cells with tight intercellular connections. Recent studies have gained some mechanistic insights that will benefit the clinical understanding of wound healing in general. In this review, we will briefly introduce the role of collective cell migration in wound healing, regeneration and cancer invasion and discuss its underlying mechanisms as well as implications for wound healing

Genetic Polymorphisms and Posttraumatic Complications

Major trauma is the leading cause of death in young adults. Despite advances in prehospital system and treatment in hospital, mortality rates have not improved significantly over the past decades. Victims of severe injuries who survive the initial hours have great risk for additional life-threatening complicaitons, including uncontrollable infection (sepsis) and multiple organ dysfunction syndrome (MODS). Single nucleotide polymorphisms (SNPs) have been shown to affect susceptibility to the course of numerous diseases. Accumulating evidence suggests that genetic backgrounds also play important roles in posttraumatic complications. Genetic polymorphisms may become powerful biomarkers for diagnosis and prognosis of trauma-induced complications. Recent advances in studies on associations between genetic polymorphisms and sepsis or MODS have led to better understanding of posttraumatic complications. Here we summarise recent findings on genetic variations in molecules of the innate immune system and other systems as well as their connection with susceptibility to posttraumatic complications

Tars: Timeliness-aware Adaptive Replica Selection for Key-Value Stores

In current large-scale distributed key-value stores, a single end-user request may lead to key-value access across tens or hundreds of servers. The tail latency of these key-value accesses is crucial to the user experience and greatly impacts the revenue. To cut the tail latency, it is crucial for clients to choose the fastest replica server as much as possible for the service of each key-value access. Aware of the challenges on the time varying performance across servers and the herd behaviors, an adaptive replica selection scheme C3 is proposed recently. In C3, feedback from individual servers is brought into replica ranking to reflect the time-varying performance of servers, and the distributed rate control and backpressure mechanism is invented. Despite of C3's good performance, we reveal the timeliness issue of C3, which has large impacts on both the replica ranking and the rate control, and propose the Tars (timeliness-aware adaptive replica selection) scheme. Following the same framework as C3, Tars improves the replica ranking by taking the timeliness of the feedback information into consideration, as well as revises the rate control of C3. Simulation results confirm that Tars outperforms C3.Comment: 10pages,submitted to ICDCS 201

Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement

Soil moisture is a crucial hydrological state variable that has significant importance to the global environment and agriculture. Precise monitoring of soil moisture in crop fields is critical to reducing agricultural drought and improving crop yield. In-situ soil moisture sensors, which are buried at pre-determined depths and distributed across the field, are promising solutions for monitoring soil moisture. However, high-density sensor deployment is neither economically feasible nor practical. Thus, to achieve a higher spatial resolution of soil moisture dynamics using a limited number of sensors, we integrate a physics-based agro-hydrological model based on Richards' equation in a physics-constrained deep learning framework to accurately predict soil moisture dynamics in the soil's root zone. This approach ensures that soil moisture estimates align well with sensor observations while obeying physical laws at the same time. Furthermore, to strategically identify the locations for sensor placement, we introduce a novel active learning framework that combines space-filling design and physics residual-based sampling to maximize data acquisition potential with limited sensors. Our numerical results demonstrate that integrating Physics-constrained Deep Learning (P-DL) with an active learning strategy within a unified framework--named the Physics-constrained Active Learning (P-DAL) framework--significantly improves the predictive accuracy and effectiveness of field-scale soil moisture monitoring using in-situ sensors

The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation

Soil moisture is a key hydrological parameter that has significant importance to human society and the environment. Accurate modeling and monitoring of soil moisture in crop fields, especially in the root zone (top 100 cm of soil), is essential for improving agricultural production and crop yield with the help of precision irrigation and farming tools. Realizing the full sensor data potential depends greatly on advanced analytical and predictive domain-aware models. In this work, we propose a physics-constrained deep learning (P-DL) framework to integrate physics-based principles on water transport and water sensing signals for effective reconstruction of the soil moisture dynamics. We adopt three different optimizers, namely Adam, RMSprop, and GD, to minimize the loss function of P-DL during the training process. In the illustrative case study, we demonstrate the empirical convergence of Adam optimizers outperforms the other optimization methods in both mini-batch and full-batch training

miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

Simultaneous saccharification and cofermentation of lignocellulosic residues from commercial furfural production and corn kernels using different nutrient media

<p>Abstract</p> <p>Background</p> <p>As the supply of starch grain and sugar cane, currently the main feedstocks for bioethanol production, become limited, lignocelluloses will be sought as alternative materials for bioethanol production. Production of cellulosic ethanol is still cost-inefficient because of the low final ethanol concentration and the addition of nutrients. We report the use of simultaneous saccharification and cofermentation (SSCF) of lignocellulosic residues from commercial furfural production (furfural residue, FR) and corn kernels to compare different nutritional media. The final ethanol concentration, yield, number of live yeast cells, and yeast-cell death ratio were investigated to evaluate the effectiveness of integrating cellulosic and starch ethanol.</p> <p>Results</p> <p>Both the ethanol yield and number of live yeast cells increased with increasing corn-kernel concentration, whereas the yeast-cell death ratio decreased in SSCF of FR and corn kernels. An ethanol concentration of 73.1 g/L at 120 h, which corresponded to a 101.1% ethanol yield based on FR cellulose and corn starch, was obtained in SSCF of 7.5% FR and 14.5% corn kernels with mineral-salt medium. SSCF could simultaneously convert cellulose into ethanol from both corn kernels and FR, and SSCF ethanol yield was similar between the organic and mineral-salt media.</p> <p>Conclusions</p> <p>Starch ethanol promotes cellulosic ethanol by providing important nutrients for fermentative organisms, and in turn cellulosic ethanol promotes starch ethanol by providing cellulosic enzymes that convert the cellulosic polysaccharides in starch materials into additional ethanol. It is feasible to produce ethanol in SSCF of FR and corn kernels with mineral-salt medium. It would be cost-efficient to produce ethanol in SSCF of high concentrations of water-insoluble solids of lignocellulosic materials and corn kernels. Compared with prehydrolysis and fed-batch strategy using lignocellulosic materials, addition of starch hydrolysates to cellulosic ethanol production is a more suitable method to improve the final ethanol concentration.</p

Novel, rosin‐based, hydrophobically modified cationic polyacrylamide for kaolin suspension flocculation

A novel, hydrophobically modified cationic polyacrylamide (HMPAM) was synthesized via the copolymerization of acrylamide, diallyl dimethyl ammonium chloride (DMDAAC), and diallylmethyl dehydroabietic acid propyl ester ammonium bromide. Optimum conditions for preparing HMPAM were such that the amount of initiator was 0.075 wt % of the total monomer mass, the monomer concentration was 20 wt %, and the amount of DMDAAC was 18 mol % of the total monomer molar mass. HMPAM was characterized with an UV–visible spectrometer, 1H‐NMR, Ubbelohde viscometer, rotational viscometer, and rotational rheometer. HMPAM solutions exhibited strong hydrophobic associations, and the critical association concentration of the HMPAM aqueous solution was about 0.7 wt %; the HMPAM solutions also showed salt thickening and shear resistance. The surface morphologies of the freeze‐dried HMPAM samples (1 wt %) were also observed via scanning electron microscopy. Compared with unmodified cationic polyacrylamide, Synthesis of HMPAM‐0.5 exhibited a stronger flocculation capacity, and the optimal transmittance of the supernatants was above 95%. HMPAM‐0.5 showed significant flocculation performances for 3–4 and 3–5 wt % kaolin suspensions at 40 and 50 mg/L, respectively. Moreover, the flocculation performance was enhanced with the addition of NaCl and CaCl2. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46637.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144283/1/app46637.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144283/2/app46637_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144283/3/app46637-sup-0001-suppinfo1.pd

Is exponential gravity a viable description for the whole cosmological history?

Here we analysed a particular type of $F(R)$ gravity, the so-called exponential gravity which includes an exponential function of the Ricci scalar in the action. Such term represents a correction to the usual Hilbert-Einstein action. By using Supernovae Ia, Barionic Acoustic Oscillations, Cosmic Microwave Background and $H(z)$ data, the free parameters of the model are well constrained. The results show that such corrections to General Relativity become important at cosmological scales and at late-times, providing an alternative to the dark energy problem. In addition, the fits do not determine any significant difference statistically with respect to the $\Lambda$CDM model. Finally, such model is extended to include the inflationary epoch in the same gravitational Lagrangian. As shown in the paper, the additional terms can reproduce the inflationary epoch and satisfy the constraints from Planck data.Comment: 20 pages, 6 figures, analysis extended, version published in EPJ