Tectonic Proteins Are Important Players in Non-Motile Ciliopathies

Primary cilium is a ubiquitous, tiny organelle on the apex of the mammalian cells. Non-motile (primary) ciliopathies are diseases caused by the dysfunction of the primary cilium and they are characterized by diverse clinical and genetic heterogeneity. To date, nearly 200 genes have been shown to be associated with primary ciliopathies. Among them, tectonic genes are the important causative genes of ciliopathies. Tectonic proteins including TCTN1, TCTN2, and TCTN3 are important component proteins residing at the transition zone of cilia. Indeed, many ciliopathies have been reported to involve tectonics mutations, highlighting a pivotal role for tectonic proteins in ciliary functions. However, the specific functions of tectonic proteins remain largely enigmatic. Herein, we discuss the recent advances on the localization and structure of tectonic proteins and the functions of tectonic proteins. The increasing line of evidences demonstrates that tectonic proteins are required for ciliogenesis and regulate ciliary membrane composition. More importantly, Tectonic proteins play a vital role in the regulation of the Sonic Hedgehog (Shh) pathway; Tectonic deficient mice show the Shh pathway-related developmental defects. Tectonic proteins share similar functions including neural patterning and Gli3 processing but also each has a unique and indispensable role in the ciliogenesis and signaling pathways. At the same time, the mutations of tectonic genes are the causes of a serial of primary ciliopathies including Meckel-Gruber syndrome, Oral-facial-digital syndrome, and Joubert syndrome. Therefore, full understanding of functions of tectonic proteins will help to crack ciliopathies and improve life quality of patients by future gene therapy

Unsymmetrical Diboron Reagents: Application in Borylation Reactions of Unsaturated Bonds

In the past decades, borylation reactions have received extensive research interest and have developed into effective tools in the synthesis of versatile organoboron compounds. Boranes and symmetrical diboron compounds are commonly utilized as borylating reagents in these transformations, especially in the borylation reactions of unsaturated bonds. More recently, several types of unsymmetrical diboron reagents have been synthesized and applied in these borylation reactions, allowing for complementary chemo- and regioselectivity. This review aimed to highlight the recent development in this rising research field, focusing on new reactivity and selectivity that originates from the use of these unsymmetrical diboron reagents

Crystal structure of 8a,8a′′-oxybis(8aH-8,9-dioxa-3a1λ4-aza-8aλ4-borabenzo[fg]tetracene), C34H22B2N2O5

C34H22B2N2O5, monoclinic, P21/c (no. 14), a = 9.9886(3) Å, b = 17.8995(6) Å, c = 14.3437(5) Å, β = 98.141(3)°, V = 2538.68(15) Å3, Z = 4, Rgt(F) = 0.0391, wRref(F2) = 0.1022, T = 100 K

Correction: Ding, S., et al. NNB-Type Tridentate Boryl Ligands Enabling a Highly Active Iridium Catalyst for C–H Borylation. <i>Molecules</i> 2019, <i>24</i>, 1434

The authors wish to make the following corrections to this paper [...

NNB-Type Tridentate Boryl Ligands Enabling a Highly Active Iridium Catalyst for C–H Borylation

Boryl ligands play a very important role in catalysis because of their very high electron-donating property. In this paper, NNB-type boryl anions were designed as tridentate ligands to promote aryl C&#8211;H borylation. In combination with [IrCl(COD)]2, they generate a highly active catalyst for a broad range of (hetero)arene substrates, including highly electron-rich and/or sterically hindered ones. This work provides a new NNB-type tridentate boryl ligand to support homogeneous organometallic catalysis

Identification of Three Novel Conidiogenesis-Related Genes in the Nematode-Trapping Fungus <i>Arthrobotrys oligospora</i>

For filamentous fungi, conidiogenesis is the most common reproductive strategy for environmental dispersal, invasion, and proliferation. Understanding the molecular mechanisms controlling conidiation and increasing conidium yield may provide promising applications in commercial development in the future for nematode-trapping fungi. However, the molecular mechanism for regulating conidium production of filamentous fungi is not fully understood. In this study, we characterized three novel conidiogenesis-related genes via gene knockout in A. oligospora. The absence of the genes AoCorA and AoRgsD caused significant increases in conidia production, while the absence of AoXlnR resulted in a decrease in conidiogenesis. Moreover, we characterized the ortholog of AbaA, a well-known conidiogenesis-related gene in Aspergillus nidulans. The deletion of AoAbaA not only completely abolished conidium production but also affected the production of nematode-trapping traps

Rheological, sensory, and microstructural properties of fresh and frozen/thawed mashed potatoes enriched with different proteins

In this study, the effect of the addition of soybean protein isolate (SPI), whey protein isolate (WPI), whole milk powder (WMP), and sodium caseinate (SC) on the rheological, physical, sensory, and structural properties of fresh and frozen/thawed mashed potatoes formulated with added cryoprotectants [kappa-carrageenan (κ-C) and xanthan gum (XG)] was compared. Steady shear flow rate curves indicated a non-Newtonian fluid and exhibited typical shear thinning (pseudoplastic) behavior (n &lt; 1) with all added proteins. The results showed that there is no significant change in the apparent viscosity (ηapp) of fresh mashed potatoes (FMP) and frozen/thawed mashed potatoes (F/TMP) with added WMP and SC at a concentration of 5 g/kg. While the addition of SPI at a concentration of 10 g/kg increased the ηapp and pseudoplasticity, which indicates that the SPI behaves as harder fillers. Based on the sensory evaluation results, WPI and WMP could incorporate to FMP without losing the sensory quality of the product. F/TMP samples with addition of SPI were judged more acceptable than other processed samples, evidencing the ability of this protein to reduce the influence of freeze/thaw process.El objetivo del presente estudio consistió en comparar el efecto que provoca la adición de aislado de proteína de soya [soja] (SPI), proteína de suero de leche aislada (WPI), leche entera en polvo (WMP) y caseinato de sodio (SC) en las propiedades reológicas, físicas, sensoriales y estructurales de puré de papas (MP) fresco (F) y congelado/descongelado (F/T), formulado con la agregación de crioprotectores, kappa-carragenina y goma xantana (XG). Las curvas de flujos con constantes esfuerzos cortantes [steady shear flow rate curves] indican que se trata de un fluído no newtoniano, que exhibe un comportamiento pseudoplástico típico (n&lt;1) en presencia de todas las proteínas adicionadas. Los resultados obtenidos revelan que no existen cambios significativos en la viscosidad aparente (ηapp) del FMP y el F/TMP cuando se adicionan WMP y SC en una concentración de 5 g/kg. Se constató que la adición de SPI en una concentración de 10 g/kg aumentó tanto la ηapp como la pseudoplasticidad, lo cual muestra que el SPI se comporta como un relleno duro [harder fillers] . A partir de los resultados de la evaluación sensorial se verifica que es posible incorporar WPI y WMP al puré de papas fresco (FMP) sin que se produzca pérdida de la calidad sensorial del producto. Las muestras de F/TMP adicionadas con SPI fueron consideradas más aceptables que otras muestras procesadas, lo que evidencia la capacidad de esta proteína de reducir el efecto provocado por el proceso de congelamiento/descongelamiento

Prediction of Ammonia Concentration in a Pig House Based on Machine Learning Models and Environmental Parameters

Accurately predicting the air quality in a piggery and taking control measures in advance are important issues for pig farm production and local environmental management. In this experiment, the NH3 concentration in a semi-automatic piggery was studied. First, the random forest algorithm (RF) and Pearson correlation analysis were combined to analyze the environmental parameters, and nine input schemes for the model feature parameters were identified. Three kinds of deep learning and three kinds of conventional machine learning algorithms were applied to the prediction of NH3 in the piggery. Through comparative experiments, appropriate environmental parameters (CO2, H2O, P, and outdoor temperature) and superior algorithms (LSTM and RNN) were selected. On this basis, the PSO algorithm was used to optimize the hyperparameters of the algorithms, and their prediction performance was also evaluated. The results showed that the R2 values of PSO-LSTM and PSO-RNN were 0.9487 and 0.9458, respectively. These models had good accuracy when predicting NH3 concentration in the piggery 0.5 h, 1 h, 1.5 h, and 2 h in advance. This study can provide a reference for the prediction of air concentrations in pig house environments