Several haloarchaea are reported to be pleomorphic, while others exhibit remarkable shapes, such as squares. Recently, Haloferax volcanii was found to alter its morphology during growth. Cells are motile rods in early exponential phase, and immotile plates in stationary phase. It is unknown if this growth phase dependent cell shape alteration is a specific feature of Hfx. volcanii, or conserved amongst haloarchaea. Here, we studied the cell shape and motility of two haloarchaea species Haloarcula hispanica and Haloarcula californiae. With a combination of light and electron microscopy, we observed that both strains undergo a growth phase dependent morphological development, albeit in a slightly different fashion as Hfx. volcanii. For both Haloarcula strains, the cell size is changing throughout growth. Cell shape seems to be related with motility, as highly motile cells on semi-solid agar plates are predominantly rod-shaped. We conclude that the growth phase dependent cell morphology alteration might be a common feature amongst haloarchaea, and that cell shape is generally linked with a motile life style. The conservation of this phenomenon underscores the importance of studies of the molecular mechanisms regulating cell shape in archaea.A three-dimensional (3D) image sensor based on Single-Photon Avalanche Diode (SPAD) requires a time-to-digital converter (TDC) with a wide dynamic range and fine resolution for precise depth calculation. In this paper, we propose a novel high-performance TDC for a SPAD image sensor. In our design, we first present a pulse-width self-restricted (PWSR) delay element that is capable of providing a steady delay to improve the time precision. Meanwhile, we employ the proposed PWSR delay element to construct a pair of 16-stages vernier delay-rings to effectively enlarge the dynamic range. Moreover, we propose a compact and fast arbiter using a fully symmetric topology to enhance the robustness of the TDC. To validate the performance of the proposed TDC, a prototype 13-bit TDC has been fabricated in the standard 0.18-µm complementary metal-oxide-semiconductor (CMOS) process. The core area is about 200 µm × 180 µm and the total power consumption is nearly 1.6 mW. The proposed TDC achieves a dynamic range of 92.1 ns and a time precision of 11.25 ps. The measured worst integral nonlinearity (INL) and differential nonlinearity (DNL) are respectively 0.65 least-significant-bit (LSB) and 0.38 LSB, and both of them are less than 1 LSB. The experimental results indicate that the proposed TDC is suitable for SPAD-based 3D imaging applications.During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc.  https://www.selleckchem.com/products/ak-7.html  In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.Surgical treatment is the most important part of therapy for endometrial cancer. The aim of the study was to define factors having the most significant impact on surgical treatment of endometrial cancer when using traditional and laparoscopic methods. In the study, we evaluated 75 females who were treated for endometrial cancer via laparoscopic surgery in 2019 and used a historical control of 70 patients treated by laparotomy in 2011. The evaluated risk factors included the method of surgery, type of lymphadenectomy, patient's age, various obesity parameters, histological grading, cancer clinical staging, pelvic dimensions, previous abdominal surgeries, comorbidities, and number of deliveries. The duration of hospitalization, operation time, loss of hemoglobin, and procedure-related complications were used as parameters of perioperative outcomes. Multivariable linear regression analysis confirmed the following factors as being predictors of worse perioperative outcomes laparotomy, abdominal obesity (waist circumstance and waist-to-hip ratio), range of lymphadenectomy, prior abdominal surgeries, and larger pelvic dimensions. Abdominal obesity is a significant risk factor in the treatment of endometrial cancer. Laparotomy continues to be utilized frequently in the management of endometrial cancer in Poland as well as elsewhere, and adopting a minimally invasive approach is likely to be beneficial for patient outcome.Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, -92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400-1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.