Value-Based Search Efficiency Is Encoded in the Substantia Nigra Reticulata Firing Rate, Spiking Irregularity and Local Field Potential.

Recent results show that valuable objects can pop out in visual search, yet its neural mechanisms remain unexplored. Given the role of substantia nigra reticulata (SNr) in object value memory and control of gaze, we recorded its single-unit activity while male macaque monkeys engaged in efficient or inefficient search for a valuable target object among low-value objects. The results showed that efficient search was concurrent with stronger inhibition and higher spiking irregularity in the target-present (TP) compared with the target-absent (TA) trials in SNr. Importantly, the firing rate differentiation of TP and TA trials happened within ∼100 ms of display onset, and its magnitude was significantly correlated with the search times and slopes (search efficiency). Time-frequency analyses of local field potential (LFP) after display onset revealed significant modulations of the gamma band power with search efficiency. The greater reduction of SNr firing in TP trials in efficient search can create a stronger disinhibition of downstream superior colliculus, which in turn can facilitate saccade to obtain valuable targets in competitive environments.

A novel methodology for exact targeting of human and non-human primate brain structures and skull implants using atlas-based 3D reconstruction.

BACKGROUND: Accurate targeting of brain areas for stimulation and/or electrophysiological recording is key in many therapeutic applications and basic neuroscience research. Nevertheless, there are currently no end-to-end packages that accommodate all steps required for exact localization, visualization, and targeting regions of interest (ROIs) using standard atlases and for designing skull implants. NEW METHOD: We have implemented a new processing pipeline that addresses this issue in macaques and humans including various preprocessing, registration, warping procedures, and 3D reconstructions, and provide a noncommercial open-source graphical software which we refer to as the MATLAB-based reconstruction for recording and stimulation (MATres). RESULTS: The results of skull stripping were shown to work seamlessly in humans and monkeys. Linear and nonlinear warping of the standard atlas to the native space outperformed state-of-the-art using AFNI with improvements being more prominent in humans which had a more complex gyration geometry. The skull surface extracted by MATres using MRI images had more than 90% match with CT ground truth and could be used to design skull implants that conformed well to the skull's local curvature. COMPARISON WITH EXISTING METHOD(S): The accuracy of the various steps including skull stripping, standard atlas registration, and skull reconstruction in MATres was compared with and shown to outperform the AFNI. The localization accuracy of the recording chambers designed with MATres and implanted in two macaque monkeys was further confirmed using MRI imaging. CONCLUSIONS: Precise localization of ROIs offered by MATres can be used to plan electrode penetrations for recording and shallow or deep brain stimulation (DBS).

Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel.

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/ diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 µM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE < 5.70 × 10-3, RMSE < 7.55 × 10-2, and MAPE < 3.86 × 10-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 µM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations were found to be 7.26 vol% , 0.46 vol% , 95.7  ppm, and 36.2 ppm, respectively, under the selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level ( 34.0 %). In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Optimal pretreatment determination of kiwifruit drying via online monitoring.

BACKGROUND: Pre-treating is a crucial stage of drying process. The best pretreatment for hot air drying of kiwifruit was investigated using a computer vision system (CVS), for online monitoring of drying attributes including drying time, colour changes and shrinkage, as decision criteria and using clustering method. Slices were dried at 70 °C with hot water blanching (HWB), steam blanching (SB), infrared blanching (IR) and acid ascorbic 1% w/w (AA) as pretreatments each with three durations of 5, 10 and 15 min. RESULTS: The results showed that the cells in HWB-pretreated samples stretched without any cell wall rupture, while the highest damage was observed in AA-pretreated kiwifruit microstructure. Increasing duration of AA and HWB significantly lengthened the drying time while SB showed opposite results. The drying rate had a profound effect on the progression of the shrinkage. The total colour change of pretreated samples was higher than those with no pretreatment except for AA and HWB. The AA could well prevent colour change during the initial stage of drying. Among all pretreatments, SB and IR had the highest colour changes. CONCLUSION: HWB with a duration of 5 min is the optimum pretreatment method for kiwifruit drying. © 2016 Society of Chemical Industry.