Many small nucleolar RNAs and many of the hairpin precursors of miRNAs are processed from long non-protein-coding host genes. In contrast to their highly conserved and heavily structured payload, the host genes feature poorly conserved sequences. Nevertheless, there is mounting evidence that the host genes have biological functions beyond their primary task of carrying a ncRNA as payload. So far, no connections between the function of the host genes and the function of their payloads have been reported. Here we investigate whether there is evidence for an association of host gene function or mechanisms with the type of payload. To assess this hypothesis we test whether the miRNA host genes (MIRHGs), snoRNA host genes (SNHGs), and other lncRNA host genes can be distinguished based on sequence and/or structure features unrelated to their payload. A positive answer would imply a functional and mechanistic correlation between host genes and their payload, provided the classification does not depend on the presence and type of the payload. A negative answer would indicate that to the extent that secondary functions are acquired, they are not strongly constrained by the prior, primary function of the payload. We find that the three classes can be distinguished reliably when the classifier is allowed to extract features from the payloads. They become virtually indistinguishable, however, as soon as only sequence and structure of parts of the host gene distal from the snoRNAs or miRNA payload is used for classification. This indicates that the functions of MIRHGs and SNHGs are largely independent of the functions of their payloads. Furthermore, there is no evidence that the MIRHGs and SNHGs form coherent classes of long non-coding RNAs distinguished by features other than their payloads. Spinal and bulbar muscular atrophy (SBMA) is a progressive, X-linked lower motor neuron disorder exclusively affecting men. Since knowledge on sleep disorders in SBMA is scarce compared to other motoneuron diseases, this retrospective case-control study aimed to investigate sleep and sleep-related breathing in patients with SBMA. In 23 non-ventilated patients with SBMA (median age 52 years), clinical disease characteristics, forced vital capacity and diagnostic polysomnographies were retrospectively evaluated. In 16 patients, overnight transcutaneous capnometry was available. Twenty-three male control subjects with chronic insomnia were matched for age and body mass index. In patients with SBMA obstructive sleep apnoea (OSA, apnoea-hypopnoea index/AHI > 5/h) was more frequent than in control subjects (14/23 or 61% vs. 6/23 or 26%, p = 0.02), and median AHI was significantly higher in patients (9.0/h vs. 3.4/h, p < 0.01). Among SBMA patients, the AHI was not related to age or body mass index. Alveonscutaneous capnometry are recommended. To assess automatic sleep staging of three ear-EEG setups with different electrode configurations and compare performance with concurrent polysomnography and wrist-worn actigraphy recordings. Automatic sleep staging was performed for single-ear, single-ear with ipsilateral mastoid, and cross-ear electrode configurations, and for actigraphy data. The polysomnography data were manually scored and used as the gold standard. The automatic sleep staging was tested on 80 full-night recordings from 20 healthy subjects. The scoring performance and sleep metrics were determined for all ear-EEG setups and theactigraphy device. The single-ear, the single-ear with ipsilateral mastoid setup, and the cross-ear setup performed five class sleep staging with kappa values 0.36, 0.63, and 0.72, respectively. For the single-ear with mastoid electrode and the cross-ear setup, the performance of the sleep metrics, in terms of mean absolute error, was better than the sleep metrics estimated from the actigraphy device in the c ipsilateral mastoid configuration were better than the single-ear configuration; and the cross-ear configuration was consistently better than both the actigraphy device and the single-ear configuration.Application of biochar-based metal oxide nanocomposites can acquire new composites and combine the benefits of biochar with nanomaterials. For the first time, this research was conducted to evaluate the possible effects of solid biochar (25 g biochar kg-1 soil) and biochar-based nanocomposites (BNCs) of magnesium oxide (25 g BNC-MgO kg-1 soil), manganese oxide (25 g BNC-MnO biochar kg-1 soil) and combined use of these nanocomposites (12.5 g BNC-MgO + 12.5 g BNC-MnO kg-1 soil) on salt (non-saline, 6 and 12 dSm-1 NaCl salinities) tolerance of safflower plants (Carthamus tinctorius L.). https://www.selleckchem.com/products/valemetostat-ds-3201.html Salinity reduced potassium, magnesium and manganese contents in root and leaf tissues, chlorophyll content index, photosynthetic pigments, maximum quantum yield of photosystem II (Fv/Fm) and relative photosynthetic electron transport rate (RETR), leaf water content and plant biomass, but increased the sodium content, reactive oxygen species generation (ROS), oxidative stress and antioxidants and ROS detoxification potential of safflower roots and leaves. Application of biochar and BNCs increased the contents of potassium, manganese and magnesium in plant tissues, photosynthetic pigments, Fv/Fm and RETR, leaf water content and reduced sodium accumulation, ROS generation and oxidative stress under saline conditions, leading to a higher plant biomass in comparison with control. The BNC-MgO + BNC-MnO was the superior treatment on reducing salt toxicity. This treatment reduced oxidative stress by enhancing photosynthetic pigments, Fv/Fm and RETR of safflower under salt stress. These results revealed that BNCs have a great potential for improving salt tolerance of plants through increasing RETR and decreasing sodium accumulation and ROS generation. We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have a deeper and wider network architecture while still accommodates the 3D information. However, there has been no detailed investigation of the parameter configurations on this type of network model. Some parameters, such as the number of stacked layers, image contrast, and the number of network layers, were studied and implemented on neural networks based on 2.5D model. Networks are trained and tested by utilizing the dataset from liver and tumor segmentation challenge (LiTS). The network performance was further evaluated by comparing the network segmentation with manual segmentation from nine technical physicians and an experienced radiologist. Slice arrangement testing shows that multiple stacked layers have better performance than a single-layer network. However, the dice scores start decreasing when the number of stacked layers is more than three layers.