Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

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Dynamic contrast-enhanced magnetic resonance imaging (MRI) for tracking glymphatic system transport with paramagnetic contrast such as gadoteric acid (Gd-DOTA) administration into cerebrospinal fluid (CSF) requires pre-contrast data for proper quantification. Here we introduce an alternative approach for glymphatic system quantification in the mouse brain via T1 mapping which also captures drainage of Gd-DOTA to the cervical lymph nodes. The Gd-DOTA injection into CSF was performed on the bench after which the mice underwent T1 mapping using a 3D spoiled gradient echo sequence on a 9.4 T MRI. In Ketamine/Xylazine (KX) anesthetized mice, glymphatic transport and drainage of Gd-DOTA to submandibular and deep cervical lymph nodes was demonstrated as 25-50% T1 reductions in comparison to control mice receiving CSF saline. To further validate the T1 mapping approach we also verified increased glymphatic transport of Gd-DOTA transport in mice anesthetized with KX in comparison with ISO. The novel T1 mapping method allows for quantification of glymphatic transport as well as drainage to the deep and superficial cervical lymph nodes. The ability to measure glymphatic transport and cervical lymph node drainage in the same animal longitudinally is advantageous and time efficient and the coupling between the two systems can be studied and translated to human studies. The glymphatic system (GS) in rodents was first described by Iliff and Nedergaard in 2012 as a brain-wide perivascular transit passageway for cerebrospinal fluid (CSF) facilitating waste clearance in an aquaporin 4 (AQP4) water channel dependent manner 1. Visualization and quantification of GS transport in whole mouse brain was performed ex vivo by first allowing tracers administered into CSF to circulate for a prefixed time interval (30 min-2 h) after which the rodents were euthanized and their brains examined for tracer uptake using fluorescence microscopy 1. In 2013, we introduced dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) as an in vivo method for tracking GS transport in the whole rodent brain 2 and later refined gadolinium based contrast administration into CSF and GS quantification 3,4. The DCE-MRI approach for brain-wide GS transport assessment has been widely used and adapted to different species including mice 5-7 , non-human primates 8 , and humans 9,10. Recently, the DCE-MRI approach for GS transport assessment was extended to studies in awake rats. The DCE-MRI GS method involves administering small molecular weight (MW) (e.g. gadopentetic acid MW 547 Da, gadoteric acid (Gd-DOTA), MW 559 Da) or large MW (e.g. GadoSpin P, MW 200 kDa) paramagnetic tracers into CSF via the cisterna magna (CM) 2,3 , cerebral lateral ventricles 11 or lumbar intrathecal route 9. Transport of the paramagnetic contrast molecule (a.k.a. 'solute') from CSF into brain parenchyma is tracked dynamically by a series of 3D T1-weighted MRI scans 1. The time series of 3D T1-weighted spoiled gradient echo (SPGR) brain images acquired before, during and after solute...

This paper explores the association between the driver’s starting intentions and control of dry clutch engagement during the vehicle launch in order to develop an intelligent automated mechanical transmission system. The optimal engagement laws are deduced by finding a compromise between the friction loss, the shock intensity, the engine torque and the engine angular acceleration, based upon the extremum value theorem. The optimal parameters of clutch engagement are adjusted for the value of the shock intensity. The driver’s starting intentions, which can be divided into three patterns, namely fast starting, medium-speed starting and slow starting, are associated with optimal control of clutch engagement according to fuzzy control theory. It is demonstrated that optimal control of dry clutch engagement can reflect the driver’s starting intentions correctly. The obtained optimal accelerator opening has more advantages than the actual accelerator opening, which can be adopted as the ideal input of the vehicle with an intelligent automated mechanical transmission system.

Background Large differences in glymphatic system transport—similar in magnitude to those of the sleep/wake cycle—have been observed during anesthesia with dexmedetomidine supplemented with low dose isoflurane (DEXM-I) in comparison to isoflurane (ISO). However, the biophysical and bioenergetic tissue status underlying glymphatic transport differences between anesthetics remains undefined. To further understand biophysical characteristics underlying these differences we investigated volume status across cerebral tissue compartments, water diffusivity, and T2* values in rats anesthetized with DEXM-I in comparison to ISO. Methods Using a crossover study design, a group of 12 Sprague Dawley female rats underwent repetitive magnetic resonance imaging (MRI) under ISO and DEXM-I. Physiological parameters were continuously measured. MRI included a proton density weighted (PDW) scan to investigate cerebrospinal fluid (CSF) and parenchymal volumetric changes, a multigradient echo scan (MGE) to calculate T2* maps as a measure of ‘bioenergetics’, and a diffusion scan to quantify the apparent diffusion coefficient (ADC). Results The heart rate was lower with DEXM-I in comparison to ISO, but all other physiological variables were similar across scans and groups. The PDW images revealed a 1% parenchymal volume increase with ISO compared to DEXM-I comprising multiple focal tissue areas scattered across the forebrain. In contrast, with DEXM-I the CSF compartment was enlarged by ~ 6% in comparison to ISO at the level of the basal cisterns and peri-arterial conduits which are main CSF influx routes for glymphatic transport. The T2* maps showed brain-wide increases in T2* in ISO compared to DEXM-I rats. Diffusion-weighted images yielded no significant differences in ADCs across the two anesthesia groups. Conclusions We demonstrated CSF volume expansion with DEXM-I (in comparison to ISO) and parenchymal (GM) expansion with ISO (in comparison to DEXM-I), which may explain the differences in glymphatic transport. The T2* changes in ISO are suggestive of an increased bioenergetic state associated with excess cellular firing/bursting when compared to DEXM-I.