Prepulse Inhibition of Auditory Cortical Responses in the Caudolateral Superior Temporal Gyrus in Macaca mulatta

Prepulse inhibition (PPI) refers to a decreased response to a startling stimulus when another weaker stimulus precedes it. Most PPI studies have focused on the physiological startle reflex and fewer have reported the PPI of cortical responses. We recorded local field potentials (LFPs) in four monkeys and investigated whether the PPI of auditory cortical responses (alpha, beta, and gamma oscillations and evoked potentials) can be demonstrated in the caudolateral belt of the superior temporal gyrus (STGcb). We also investigated whether the presence of a conspecific, which draws attention away from the auditory stimuli, affects the PPI of auditory cortical responses. The PPI paradigm consisted of Pulse-only and Prepulse + Pulse trials that were presented randomly while the monkey was alone (ALONE) and while another monkey was present in the same room (ACCOMP). The LFPs to the Pulse were significantly suppressed by the Prepulse thus, demonstrating PPI of cortical responses in the STGcb. The PPI-related inhibition of the N1 amplitude of the evoked responses and cortical oscillations to the Pulse were not affected by the presence of a conspecific. In contrast, gamma oscillations and the amplitude of the N1 response to Pulse-only were suppressed in the ACCOMP condition compared to the ALONE condition. These findings demonstrate PPI in the monkey STGcb and suggest that the PPI of auditory cortical responses in the monkey STGcb is a pre-attentive inhibitory process that is independent of attentional modulation.Peer reviewe

Forward and Backward Information Retention for Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods

RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments

Intention-oriented object detection aims to detect desired objects based on specific intentions or requirements. For instance, when we desire to "lie down and rest", we instinctively seek out a suitable option such as a "bed" or a "sofa" that can fulfill our needs. Previous work in this area is limited either by the number of intention descriptions or by the affordance vocabulary available for intention objects. These limitations make it challenging to handle intentions in open environments effectively. To facilitate this research, we construct a comprehensive dataset called Reasoning Intention-Oriented Objects (RIO). In particular, RIO is specifically designed to incorporate diverse real-world scenarios and a wide range of object categories. It offers the following key features: 1) intention descriptions in RIO are represented as natural sentences rather than a mere word or verb phrase, making them more practical and meaningful; 2) the intention descriptions are contextually relevant to the scene, enabling a broader range of potential functionalities associated with the objects; 3) the dataset comprises a total of 40,214 images and 130,585 intention-object pairs. With the proposed RIO, we evaluate the ability of some existing models to reason intention-oriented objects in open environments.Comment: NeurIPS 2023 D&B accepted. See our project page for more details: https://reasonio.github.io

Modeling Rett Syndrome Using TALEN-Edited MECP2 Mutant Cynomolgus Monkeys

Gene-editing technologies have made it feasible to create nonhuman primate models for human genetic disorders. Here, we report detailed genotypes and phenotypes of TALEN-edited MECP2 mutant cynomolgus monkeys serving as a model for a neurodevelopmental disorder, Rett syndrome (RTT), which is caused by loss-of-function mutations in the human MECP2 gene. Male mutant monkeys were embryonic lethal, reiterating that RTT is a disease of females. Through a battery of behavioral analyses, including primate-unique eye-tracking tests, in combination with brain imaging via MRI, we found a series of physiological, behavioral, and structural abnormalities resembling clinical manifestations of RTT. Moreover, blood transcriptome profiling revealed that mutant monkeys resembled RTT patients in immune gene dysregulation. Taken together, the stark similarity in phenotype and/or endophenotype between monkeys and patients suggested that gene-edited RTT founder monkeys would be of value for disease mechanistic studies as well as development of potential therapeutic interventions for RTT

Effects of long-term fertilization on soil humic acid composition and structure in Black Soil.

The composition and structure of humic acid (HA) can be affected by fertilization, but the short-term effects are difficult to detect using traditional analysis methods. Using a 35-year long-term experiment in Black Soil, the molecular structure of HA was analyzed with Fourier transform infrared spectroscopy (FTIR), 13C nuclear magnetic resonance spectroscopy (NMR), and fluorescence spectroscopy. Variation in HA was analyzed after long-term fertilization, including fertilization with manure (M), inorganic N, P and K fertilizer (NPK), manure combined with inorganic N, P, and K fertilizer (MNPK), and a no-fertilizer control (CK). The application of each fertilizer treatment increased crop yields compared with the CK treatment, and the MNPK treatment increased crop yield the most. The ratio of main IR absorption peak of HA at 2,920 cm-1 compared with the peak at 2,850 cm-1 (2920/2850) was higher in the NPK and MNPK treatments compared with the CK treatment. The application of manure (MNPK and M treatments) increased the ratio of hydrogen to carbon (H/C) in HA, and raised the ratio of the main IR absorption peak of HA at 2920 cm-1 to that at 1720 cm-1 (2920/1720). Manure treatments also raised the ratio of aliphatic carbon (C) to aromatic C, alkyl C to alkoxy C and hydrophobic C to hydrophilic C and the fluorescence index (f 450/500), but decreased the degree of aromatization of HA, when compared with the CK treatment. The ratio between each type of C in HA was similar among all the fertilizer treatments, but NPK had a lower ratio of H/C and a lower content of aliphatic C compared with the CK treatment. These results indicated that the molecular structure of HA in Black Soil tends to be aliphatic, simpler, and younger after the application of manure. While the application of inorganic fertilizers increased in the degree of condensation of HA and made HA structure complicated. The application of manure alone or combined with inorganic fertilizers may be an effective way to increase crop yield and improve the structure of soil organic matter

The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data

Infrared spectra of humic acid in 2012 (2012-HA) in different fertilization treatments of Black Soil.

<p>CK, no-fertilizer control treatment; NPK, inorganic fertilizers of nitrogen (N), phosphate (P), and potassium (K) fertilizer treatment; M, manure treatment; MNPK, treatment of manure combined with inorganic fertilizers of N, P, and K fertilizers.</p

Application amount of different fertilizers at wheat-soybean-maize rotation season.

<p>Application amount of different fertilizers at wheat-soybean-maize rotation season.</p

C distributions of different types of humic acid in solid-state polarization magic-spin ¹³C nuclear magnetic resonance spectroscopic analysis in Black Soil under different fertilization treatments.

<p>C distributions of different types of humic acid in solid-state polarization magic-spin ¹³C nuclear magnetic resonance spectroscopic analysis in Black Soil under different fertilization treatments.</p

Relative intensities of the main absorption peak of humic acid infrared spectra in different fertilization treatments of Black Soil (semi-quantity, cm⁻¹).

<p>Relative intensities of the main absorption peak of humic acid infrared spectra in different fertilization treatments of Black Soil (semi-quantity, cm⁻¹).</p