COVID-19 vaccines for low- and middle-income countries.

The COVID-19 pandemic is the biggest threat to public health in a century. Through hard work and ingenuity, scientists have developed a number of safe and effective vaccines against COVID-19 disease. However, demand far outstrips supply and countries around the world are competing for available vaccines. This review describes how low- and middle-income countries access COVID-19 vaccines, what is being done to distribute vaccines fairly, as well as the challenges ahead

Cyclin D1 fine-tunes the neurogenic output of embryonic retinal progenitor cells

<p>Abstract</p> <p>Background</p> <p>Maintaining the correct balance of proliferation versus differentiation in retinal progenitor cells (RPCs) is essential for proper development of the retina. The cell cycle regulator cyclin D1 is expressed in RPCs, and mice with a targeted null allele at the cyclin D1 locus (<it>Ccnd1</it>^-/-) have microphthalmia and hypocellular retinas, the latter phenotype attributed to reduced RPC proliferation and increased photoreceptor cell death during the postnatal period. How cyclin D1 influences RPC behavior, especially during the embryonic period, is unclear.</p> <p>Results</p> <p>In this study, we show that embryonic RPCs lacking cyclin D1 progress through the cell cycle at a slower rate and exit the cell cycle at a faster rate. Consistent with enhanced cell cycle exit, the relative proportions of cell types born in the embryonic period, such as retinal ganglion cells and photoreceptor cells, are increased. Unexpectedly, cyclin D1 deficiency decreases the proportions of other early born retinal neurons, namely horizontal cells and specific amacrine cell types. We also found that the laminar positioning of horizontal cells and other cell types is altered in the absence of cyclin D1. Genetically replacing cyclin D1 with cyclin D2 is not efficient at correcting the phenotypes due to the cyclin D1 deficiency, which suggests the D-cyclins are not fully redundant. Replacement with cyclin E or inactivation of cyclin-dependent kinase inhibitor p27Kip1 restores the balance of RPCs and retinal cell types to more normal distributions, which suggests that regulation of the retinoblastoma pathway is an important function for cyclin D1 during embryonic retinal development.</p> <p>Conclusion</p> <p>Our findings show that cyclin D1 has important roles in RPC cell cycle regulation and retinal histogenesis. The reduction in the RPC population due to a longer cell cycle time and to an enhanced rate of cell cycle exit are likely to be the primary factors driving retinal hypocellularity and altered output of precursor populations in the embryonic <it>Ccnd1</it>^-/-retina.</p

Drosophila Smad2 Opposes Mad Signaling during Wing Vein Development

In the vertebrates, the BMP/Smad1 and TGF-β/Smad2 signaling pathways execute antagonistic functions in different contexts of development. The differentiation of specific structures results from the balance between these two pathways. For example, the gastrula organizer/node of the vertebrates requires a region of low Smad1 and high Smad2 signaling. In Drosophila, Mad regulates tissue determination and growth in the wing, but the function of dSmad2 in wing patterning is largely unknown. In this study, we used an RNAi loss-of-function approach to investigate dSmad2 signaling during wing development. RNAi-mediated knockdown of dSmad2 caused formation of extra vein tissue, with phenotypes similar to those seen in Dpp/Mad gain-of-function. Clonal analyses revealed that the normal function of dSmad2 is to inhibit the response of wing intervein cells to the extracellular Dpp morphogen gradient that specifies vein formation, as measured by expression of the activated phospho-Mad protein. The effect of dSmad2 depletion in promoting vein differentiation was dependent on Medea, the co-factor shared by Mad and dSmad2. Furthermore, double RNAi experiments showed that Mad is epistatic to dSmad2. In other words, depletion of Smad2 had no effect in Mad-deficient wings. Our results demonstrate a novel role for dSmad2 in opposing Mad-mediated vein formation in the wing. We propose that the main function of dActivin/dSmad2 in Drosophila wing development is to antagonize Dpp/Mad signaling. Possible molecular mechanisms for the opposition between dSmad2 and Mad signaling are discussed

The Intrinsically X-ray Weak Quasar PHL 1811. I. X-ray Observations and Spectral Energy Distribution

This is the first of two papers reporting observations and analysis of the unusually bright (m_b=14.4), luminous (M_B=-25.5), nearby (z=0.192) narrow-line quasar PHL 1811, focusing on the X-ray properties and the spectral energy distribution. Two Chandra observations reveal a weak X-ray source with a steep spectrum. Variability by a factor of 4 between the two observations separated by 12 days suggest that the X-rays are not scattered emission. The XMM-Newton spectra are modelled in the 0.3--5 keV band by a steep power law with \Gamma = 2.3\pm 0.1, and the upper limit on intrinsic absorption is 8.7 x 10^{20} cm^{-2}. The spectral slopes are consistent with power law indices commonly observed in NLS1s, and it appears that we observe the central engine X-rays directly. Including two recent Swift ToO snapshots, a factor of ~5 variability was observed among the five X-ray observations reported here. In contrast, the UV photometry obtained by the XMM-Newton OM and Swift UVOT, and the HST spectrum reveal no significant UV variability. The \alpha_{ox} inferred from the Chandra and contemporaneous HST spectrum is -2.3 \pm 0.1, significantly steeper than observed from other quasars of the same optical luminosity. The steep, canonical X-ray spectra, lack of absorption, and significant X-ray variability lead us to conclude that PHL 1811 is intrinsically X-ray weak. We also discuss an accretion disk model, and the host galaxy of PHL 1811.Comment: 45 pages, 9 figures, accepted for publication in Ap

Estimating Soil Moisture Under Low Frequency Surface Irrigation Using Crop Water Stress Index

The present study investigated the relationship between the crop water stress index (CWSI) and soil moisture for surface irrigated cotton (Gossypium hirsutum, Delta Pine 90b) at Maricopa, Arizona during the 1998 season. The CWSI was linked to soil moisture through the water stress coefficient Ks that accounts for reduced crop evapotranspiration when there is a shortage of soil water. A stress recovery coefficient Krec was introduced to account for reduced crop evapotranspiration as the crop recovered from water stress after irrigation events. A soil water stress index (SWSI) was derived in terms of Ks and Krec . The SWSI compared reasonably well to the CWSI, but atmospheric stability correction for the CWSI did not improve comparisons. When the CWSI was substituted into the SWSI formulation, it gave good prediction of soil moisture depletion (fDEP; when to irrigate) and depth of root zone depletion (Dr ; how much to irrigate). Disagreement was greatest for fDEP\u3c0.6 because cotton is less sensitive to water stress in this range

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. The proposed model consistently outperformed previous approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.Comment: To be presented at AAAI 202

Water Stress Detection Under High Frequency Sprinkler Irrigation with Water Deficit Index

A remote sensing package called the agricultural irrigation imaging system (AgIIS) aboard a linear move irrigation system was developed to simultaneously monitor water status, nitrogen status, and canopy density at one-meter spatial resolution. The present study investigated the relationship between water status detected by AgIIS and soil moisture for the 1999 cotton (Gossypium hirsutum, Delta Pine 90b) season in Maricopa, Ariz. Water status was quantified by the water deficit index (WDI), an expansion of the crop water stress index where the influence of soil temperature is accounted for through a linear mixing model of soil and vegetation temperature. The WDI was best correlated to soil moisture through the FAO 56 water stress coefficient Ks model; stability correction of aerodynamic resistance did not improve correlation. The AgIIS did provide field images of the WDI that might aid irrigation scheduling and increase water use efficiency

Analyzing the Role of Model Uncertainty for Electronic Health Records

In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise well-tuned deep neural networks to vary in their individual predicted probabilities. In light of this, we investigate the role of model uncertainty methods in the medical domain. Using RNN ensembles and various Bayesian RNNs, we show that population-level metrics, such as AUC-PR, AUC-ROC, log-likelihood, and calibration error, do not capture model uncertainty. Meanwhile, the presence of significant variability in patient-specific predictions and optimal decisions motivates the need for capturing model uncertainty. Understanding the uncertainty for individual patients is an area with clear clinical impact, such as determining when a model decision is likely to be brittle. We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.Comment: Published in the ACM Conference on Health, Inference, and Learning (CHIL) 2020. Code available at https://github.com/Google-Health/records-researc

The Use of Global System of Mobile Communication (GSM) Among University Students in Malaysia

This study examines the use of the Global System of Mobile Communication (GSM) among University students in Malaysia. The survey approach was used in data collection from a sample population from University Teknologi Malaysia. Fifty undergraduate and post graduate students were sampled at the university library. Results from a multiple regression analysis shows that there is a significant relationship between age, monthly income/allowance of respondents, marital status and rate of calls made and received per day (P<0.05). Gender and mode of study were found to be insignificant (P>0.05).Results from t test equally indicate that the respondents do not vary in their perception on benefits derivable from the use of GSM (t = -.483, P >0.05).Majority of the respondents also agreed that they use GSM to contact their lecturers, course mates, parents, siblings and sending of short message services (SMS).The study conclude with discussions on findings which would be relevant to education policy makers and other interest partie