Effects of dopants with various valences on densification behavior and phase composition of a ZrO2–SiO2 nanocrystalline glass-ceramic

Effects of dopants with different valences on the densification behavior and phase composition of a ZrO₂–SiO₂ nanocrystalline glass-ceramic (NCGC) during pressureless sintering were investigated in this study. The raw powder of Ca²⁺, La³⁺, Ce⁴⁺ and Ta⁵⁺ ions doped ZrO₂–SiO₂ (referred to as Ca-ZS, La-ZS, Ce-ZS, Ta-ZS, respectively) and pure ZrO₂–SiO₂ (PZS) sample were synthesized by sol-gel method, followed by pressureless sintering. Compared with the PZS sample, doping of Ca²⁺ and La³⁺ ions significantly promoted the densification of the NCGCs. The “densification promotion” effect was attributed to the formation of oxygen vacancies and the decrease of SiO₂ viscosity due to doping of aliovalent cations. The dopants with various valences showed significant effects on the phase compositions of the NCGCs during sintering. Doping of Ca²⁺ ion accelerated the reaction kinetics between ZrO₂ nanocrystallites and amorphous SiO₂ to yield ZrSiO₄. The La³⁺ ion acted as destabilizer of t-ZrO₂, which resulted in a rapid tetragonal (t) to monoclinic (m) ZrO₂ phase transformation during sintering, while in the Ta⁵⁺ and Ce⁴⁺ ions doped sample, the phase transformation occurred gradually. All the doping ions increased the lattice parameters and the volume of t-ZrO₂ unit cell, while the effects of the doping ions on the lattice parameters of m-ZrO₂ unit cell were more complex.     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Leucokinin and Associated Neuropeptides Regulate Multiple Aspects of Physiology and Behavior in Drosophila

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. LKs act on G-protein-coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly’s behavior and physiology, including feeding, sleep–metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (abdominal LK neurons, ABLKs) of the abdominal neuromeres co-express LK and a diuretic hormone (DH44), and together, these regulate water and ion homeostasis and associated stress as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs (anterior LK neurons), in the brain express LK in larvae, but inconsistently so in adults. These ALKs co-express three other neuropeptides and regulate water and ion homeostasis, feeding, and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.     

Comparing open data benchmarks: Which metrics and methodologies determine countries’ positions in the ranking lists?

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.     

Electrochemical Quantification of Neurotransmitters in Single Live Cell Vesicles Shows Exocytosis is Predominantly Partial

Exocytosis plays an essential role in the communication between cells in the nervous system. Understanding the regulation of neurotransmitter release during exocytosis and the amount of neurotransmitter content that is stored in vesicles is of importance, as it provides fundamental insights to understand how the brain works and how neurons elicit a certain behavior. In this minireview, we summarize recent progress in amperometric measurements for monitoring exocytosis in single cells and electrochemical cytometry measurements of vesicular neurotransmitter content in individual vesicles. Important steps have increased our understanding of the different mechanisms of exocytosis. Increasing evidence is firmly establishing that partial release is the primary mechanism of release in multiple cell types.     

Efficient Visible-to-UV Photon Upconversion Systems Based on CdS Nanocrystals Modified with Triplet Energy Mediators

Developing high-performance visible-to-UV photon upconversion systems based on triplet–triplet annihilation photon upconversion (TTA-UC) is highly desired, as it provides a potential approach for UV light-induced photosynthesis and photocatalysis. However, the quantum yield and spectral range of visible-to-UV TTA-UC based on nanocrystals (NCs) are still far from satisfactory. Here, three different sized CdS NCs are systematically investigated with triplet energy transfer to four mediators and four annihilators, thus substantially expanding the available materials for visible-to-UV TTA-UC. By improving the quality of CdS NCs, introducing the mediator via a direct mixing fashion, and matching the energy levels, a high TTA-UC quantum yield of 10.4% (out of a 50% maximum) is achieved in one case, which represents a record performance in TTA-UC based on NCs without doping. In another case, TTA-UC photons approaching 4 eV are observed, which is on par with the highest energies observed in optimized organic systems. Importantly, the in-depth investigation reveals that the direct mixing approach to introduce the mediator is a key factor that leads to close to unity efficiencies of triplet energy transfer, which ultimately governs the performance of NC-based TTA-UC systems. These findings provide guidelines for the design of high-performance TTA-UC systems toward solar energy harvesting.     

Improving the fill factor of N2200-based all polymer solar cells by introducing EPPDI as a solid additive

A cheap and commercially available small molecule (namely EPPDI) is introduced to the active layer of N2200-based all polymer solar cells as a solid additive. EPPDI at the optimal ratio can improve the D-A nano-scale morphology and reduce trap density of the active layer by filling morphological spaces. As a result, the photovoltaic performance of the resulting devices based on PF2:N2200 are increased from 6.28% to 7.03% with significantly enhanced fill factor. This work demonstrates a facile approach for improving the performance of all polymer solar cells.     

A semantic-level component-based scheduling method for customized manufacturing

To meet the increasingly complex needs of customers, scheduling faces challenges of the high uncertainty of product arrival in customized manufacturing (CM). This paper proposes a semantic-level component-based scheduling method to solve the uncertainty via the integration of the information model and the computation model. In our proposal, we first construct a component-based framework to illustrate the composition and execution mechanism of a component. Then we present a semantic-enriched information model to obtain the state of the shop floor through automatic semantic reasoning. Additionally, we build a computation model to abstract the stochastic scheduling process of CM. Finally, we design an iteration algorithm to solve the computation model through the interaction between the information model and computation model. In experiments, we show that for random arrivals of products, our proposal can ensure the timeliness of the learning and decision-making, and the task assignment performance is the best compared with the other two methods.     

Lactones in the Synthesis of Prostaglandins and Prostaglandin Analogs

In the total stereo-controlled synthesis of natural prostaglandins (PGs) and their structural analogs, a vast class of compounds and drugs, known as the lactones, are encountered in a few key steps to build the final molecule, as: δ-lactones, γ-lactones, and 1,9-, 1,11-, and 1,15-macrolactones. After the synthesis of 1,9-PGF_2α and 1,15-PGF_2α lactones, many 1,15-lactones of E₂, E₃, F₂, F₃, A₂, and A₃ were found in the marine mollusc Tethys fimbria and the quest for understanding their biological role stimulated the research on their synthesis. Then 1,9-, 1,11-, and 1,15-PG lactones of the drugs were synthesized as an alternative to the corresponding esters, and the first part of the paper describes the methods used for their synthesis. The efficient Corey procedure for the synthesis of prostaglandins uses the key δ-lactone and γ-lactone intermediates with three or four stereocenters on the cyclopentane fragment to link the PG side chains. The paper describes the most used procedures for the synthesis of the milestone δ-Corey-lactones and γ-Corey-lactones, their improvements, and some new promising methods, such as interesting, new stereo-controlled and catalyzed enantioselective reactions, and methods based on the chemical/enzymatic resolution of the compounds in different steps of the sequences. The many uses of δ-lactones not only for the synthesis of γ-lactones, but also for obtaining 9β-halogen-PGs and halogen-substituted cyclopentane intermediates, as synthons for new 9β-PG analogs and future applications, are also discussed.