2,3,4-Tri-O-acetyl-β-d-xylosyl 2,4-dichloro­phenoxy­acetate

In the title compound, C19H20Cl2O10, the hexopyranosyl ring adopts a chair conformation. The four substituents are in equatorial positions. The mol­ecules arelinked via C—H⋯O contacts along the a axis

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X

Transgenic studies reveal the positive role of LeEIL-1 in regulating shikonin biosynthesis in Lithospermum erythrorhizon hairy roots

Time-course accumulation of shikonin in four typical hairy root lines. Value of Ei-19 or EO-13 is significantly different from that of the control line WT-1 or EV-9 at each time point from 3 to 12 days, respectively (Student’s t-test, P < 0.05). (TIF 125 kb

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Influence of Vacancy on Structural Stability, Mechanical Properties and Electronic Structures of a Ti₅Sn₃ Compound from First-Principles Calculations

Titanium alloy is widely used in biomedical materials. Ti-Sn alloy is a new type β titanium alloy with no toxicity. In this paper, the mechanical and electronic properties of Ti5Sn3 with vacancy defects have been studied by using first-principles method. The vacancy formation energy, vacancy formation enthalpy, elastic constant, elastic modulus, hardness and electronic structure of perfect Ti5Sn3 and Ti5Sn3 with different vacancies were also calculated and discussed. The results show that Ti5Sn3 is more likely to form vacancies at VTi2. In addition, the bulk deformation resistance of Ti5Sn3 is weakened by the vacancy, and the shear resistance, stiffness and hardness of Ti5Sn3 are increased by the Ti vacancy, but the brittleness of Ti5Sn3 is increased. On the contrary, the presence of Sn vacancy decreases the shear resistance, stiffness and hardness of Ti5Sn3, and increases the toughness of Ti5Sn3. By analyzing the change of electronic structure, it is found that removing the Ti atom at the VTi2 position can improve the interaction between atoms, while Sn vacancy can weaken the interaction

Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network

In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire detection in a limited range of engine running speed, running load or misfire types. In this work, the misfire patterns consist of normal condition, six types of one-cylinder misfire faults and fifteen types of two-cylinder misfire faults. All the misfire patterns are tested under wide range of running conditions of the tested engine. The traditional misfire detection method is tested on the datasets first, and the result show its limitation on high-speed low-load conditions. The LSTM RNN is a type of artificial neural network which has the ability of considering both the current input in-formation and the previous input information; hence it is helpful in extracting features of crank speed in which the misfire-induced speed fluctuation will last one or a few cycles. In order to select the engine operating conditions for network training properly, five data division strategies are attempted. For the sake of acquiring high performance of designed network, four types of network structure are tested. The results show that, utilizing the datasets in this work, the LSTM RNN based algorithm can overcome the limitation at high-speed low-load conditions of traditional misfire detection method. Moreover, the network which takes fixed segment of raw speed signal as input and takes misfire or fault-free labels as output achieves the best performance with the misfire diagnosis accuracy not less than 99.90%