Building block libraries and structural considerations in the self-assembly of polyoxometalate and polyoxothiometalate systems

Inorganic metal-oxide clusters form a class of compounds that are unique in their topological and electronic versatility and are becoming increasingly more important in a variety of applications. Namely, Polyoxometalates (POMs) have shown an unmatched range of physical properties and the ability to form structures that can bridge several length scales. The formation of these molecular clusters is often ambiguous and is governed by self-assembly processes that limit our ability to rationally design such molecules. However, recent years have shown that by considering new building block principles the design and discovery of novel complex clusters is aiding our understanding of this process. Now with current progress in thiometalate chemistry, specifically polyoxothiometalates (POTM), the field of inorganic molecular clusters has further diversified allowing for the targeted development of molecules with specific functionality. This chapter discusses the main differences between POM and POTM systems and how this affects synthetic methodologies and reactivities. We will illustrate how careful structural considerations can lead to the generation of novel building blocks and further deepen our understanding of complex systems

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

[1] Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote‐sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote‐sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote‐sensing data

Lubiprostone Stimulates Duodenal Bicarbonate Secretion in Rats

Lubiprostone, a bicyclic fatty acid, is used for the treatment of chronic constipation. No published study has addressed the effect of lubiprostone on intestinal ion secretion in vivo. The aim of this study was to test the hypothesis that lubiprostone augments duodenal HCO3 − secretion (DBS). Rat proximal duodenal loops were perfused with pH 7.0 Krebs, control vehicle (medium-chain triglycerides), or lubiprostone (0.1–10 μM). We measured DBS with flow-through pH and CO2 electrodes, perfusate [Cl−] with a Cl− electrode, and water flux using a non-absorbable ferrocyanide marker. Some rats were pretreated with a potent, selective CFTR antagonist, CFTRinh-172 (1 mg/kg, ip), 1 h before experiments. Perfusion of lubiprostone concentration dependently increased DBS, whereas net Cl− output and net water output were only increased at 0.1 μM, compared with vehicle. CFTRinh-172 reduced lubiprostone (10 μM)-induced DBS increase, whereas net Cl− output was also unchanged. Nevertheless, CFTRinh-172 reduced basal net water output, which was reversed by lubiprostone. Furthermore, lubiprostone-induced DBS was inhibited by EP4 receptor antagonist, not by an EP1/2 receptor antagonist or by indomethacin pretreatment. In this first study of the effect of lubiprostone on intestinal ion secretion in vivo, lubiprostone stimulated CFTR-dependent DBS without changing net Cl− secretion. This effect supports the hypothesis that Cl− secreted by CFTR is recycled across the apical membrane by anion exchangers. Recovery of water output during CFTR inhibition suggests that lubiprostone may improve the intestinal phenotype in CF patients. Furthermore, increased DBS suggests that lubiprostone may protect the duodenum from acid-induced injury via EP4 receptor activation

The correlation between endometrial thickness and outcome of in vitro fertilization and embryo transfer (IVF-ET) outcome

<p>Abstract</p> <p>Background</p> <p>To evaluate the relationship between endometrial thickness on day of human chorionic gonadotrophin administration (hCG) and pregnancy outcome in a large number of consecutive in vitro fertilization and embryo transfer (IVF-ET) cycles.</p> <p>Methods</p> <p>A retrospective cohort study including all patients who had IVF-ET from January 2003–December 2005 conducted at a tertiary center.</p> <p>Results</p> <p>A total of 2464 cycles were analysed. Pregnancy rate (PR) was 35.8%. PR increased linearly (r = 0.864) from 29.4% among patients with a lining of less than or equal to 6 mm, to 44.4% among patients with a lining of greater than or equal to 17 mm. ROC showed that endometrial thickness is not a good predictor of PR, so a definite cut-off value could not be established (AUC = 0.55).</p> <p>Conclusion</p> <p>There is a positive linear relationship between the endometrial thickness measured on the day of hCG injection and PR, and is independent of other variables. Hence aiming for a thicker endometrium should be considered.</p

Assessing Syndromic Surveillance of Cardiovascular Outcomes from Emergency Department Chief Complaint Data in New York City

Prospective syndromic surveillance of emergency department visits has been used for near-real time tracking of communicable diseases to detect outbreaks or other unexpected disease clusters. The utility of syndromic surveillance for tracking cardiovascular events, which may be influenced by environmental factors and influenza, has not been evaluated. We developed and evaluated a method for tracking cardiovascular events using emergency department free-text chief complaints.There were three phases to our analysis. First we applied text processing algorithms based on sensitivity, specificity, and positive predictive value to chief complaint data reported by 11 New York City emergency departments for which ICD-9 discharge diagnosis codes were available. Second, the same algorithms were applied to data reported by a larger sample of 50 New York City emergency departments for which discharge diagnosis was unavailable. From this more complete data, we evaluated the consistency of temporal variation of cardiovascular syndromic events and hospitalizations from 76 New York City hospitals. Finally, we examined associations between particulate matter ≤2.5 µm (PM(2.5)), syndromic events, and hospitalizations. Sensitivity and positive predictive value were low for syndromic events, while specificity was high. Utilizing the larger sample of emergency departments, a strong day of week pattern and weak seasonal trend were observed for syndromic events and hospitalizations. These time-series were highly correlated after removing the day-of-week, holiday, and seasonal trends. The estimated percent excess risks in the cold season (October to March) were 1.9% (95% confidence interval (CI): 0.6, 3.2), 2.1% (95% CI: 0.9, 3.3), and 1.8% (95%CI: 0.5, 3.0) per same-day 10 µg/m(3) increase in PM(2.5) for cardiac-only syndromic data, cardiovascular syndromic data, and hospitalizations, respectively.Near real-time emergency department chief complaint data may be useful for timely surveillance of cardiovascular morbidity related to ambient air pollution and other environmental events

Probing Mechanical Properties of Graphene with Raman Spectroscopy

The use of Raman scattering techniques to study the mechanical properties of graphene films is reviewed here. The determination of Gruneisen parameters of suspended graphene sheets under uni- and bi-axial strain is discussed and the values are compared to theoretical predictions. The effects of the graphene-substrate interaction on strain and to the temperature evolution of the graphene Raman spectra are discussed. Finally, the relation between mechanical and thermal properties is presented along with the characterization of thermal properties of graphene with Raman spectroscopy.Comment: To appear in the Journal of Materials Scienc

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

Sherpa Romeo green journal. Permission to archive final published versionCarbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote-sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote-sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote-sensing dataYe