Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural deviations of the posterior fossa and the cerebellum and their cognitive links in a neurodevelopmental deletion syndrome

High-impact genetic variants associated with neurodevelopmental disorders provide biologically defined entry points for etiological discovery. The 3q29 deletion (3q29Del) is one such variant that confers a ~40-fold increased risk for schizophrenia, and a ~30-fold increased risk for autism. However, the specific neural mechanisms underlying this link remain largely unknown. Here, we report the first in vivo quantitative neuroimaging study in 3q29Del subjects (N = 24) and healthy controls (N = 1,608) using structural MRI. Given prior reports of posterior fossa abnormalities in 3q29Del, we focus our investigation on the cerebellum and its primary tissue-types. Additionally, we compare the prevalence of cystic/cyst-like malformations of the posterior fossa between 3q29Del subjects and controls, and examine the association between neuroanatomical findings and standardized behavioral measures to probe gene-brain-behavior relationships. 3q29Del subjects had smaller cerebellar cortex volumes than controls, both before and after correction for intracranial volume (ICV). 3q29Del subjects also had larger cerebellar white matter volumes than controls following ICV-correction. The 3q29Del group displayed an elevated rate of posterior fossa arachnoid cysts and mega cisterna magna findings independent of cerebellar volume. Sex played a moderating role in a subset of findings. Cerebellar white matter volume was positively associated with visual-motor integration skills and cognitive ability, while cystic/cyst-like malformations yielded no behavioral link. Abnormal development of posterior fossa structures may represent neuroimaging-based biomarkers in 3q29Del. Results reveal cerebellar associations with sensorimotor and cognitive deficits in 3q29Del and present a novel point of genetic convergence with cerebellar pathology reported in idiopathic forms of neurodevelopmental disease.
PD Control of Swiveling Jib System of Concrete Delivery Truck

Based on the multi-body dynamic theory,the movement of every member of the swiveling jib and relevant hydraulic cylinders on a concrete delivery truck are both analyzed.With the Lagrange equation,the multi-body dynamic equation of the swiveling jib system is derived.A control model is then developed for the swiveling jib system via applying the PD control rate to every member's drive torque so as to enable it to move in accordance to the expected rotating angles.Further,based on the multi-body dynamic equation of swiveling jib system as above,a dynamic equation of the integrated jib truck system is built with a numerical solution given to it,thus analyzing comparatively the calculated and expected values of both rotating angle of each member of swiveling jib and the kinematic trajectory of its end.It is proved that the integrated dynamic equation thus built can exactly describe all the dynamic characteristics of the truck.
The walking and running control of a human musculoskeletal model using a low-power consumption hardware central pattern generator model

Applying a control system with low energy consumption and low load of motion control to robots, similar to living organisms, is considered to be one of the most important issues in robot development. We have been studying systems that use pulse-type hardware neural networks to control robotic motion with a small number of control signals, as is the case in living organisms. In particular, it has been mimicking the function of the central pattern generator localized in the spinal cord of living organisms to generate motion patterns. In the present article, a new biomimetic control system using pulse-type hardware neural networks for biped gait control is reported.
Historic changes in the fire-rainfall relationship at a woodland-savanna transition zone in southern Africa

Southern Africa is dominated by fire-prone arid and semi-arid landscapes that are expected to experience increased maximum temperatures, rainfall variation and frequency of extreme rainfall events in the future. These conditions will affect fire and vegetation dynamics, but feedback and interactions among fire, rainfall and woody cover limit our ability to predict future ecosystem changes. Moreover, human activities can also drive changes in these components and their interactions. There are few long-term datasets available to monitor these changes over ecologically relevant time-scales. Here the combined analysis of a rainfall proxy, developed from a baobab tree core (Adansonia digitata) and fire proxy, from a sediment core, in the savanna-woodland transition zone in South Central Africa elucidates the history of two major drivers of savanna structure for the past 600 years. They show a system that oscillates between wooded and grassy vegetation states over time, as well as a change in the spatial scale of fires, which could be linked to human activities and recent fire management legislation.
geoTangle: Interactive Design of Geodesic Tangle Patterns on Surfaces

Tangles are complex patterns, which are often used to decorate the surface of real-world artisanal objects. They consist of arrangements of simple shapes organized into nested hierarchies, obtained by recursively splitting regions to add progressively finer details. In this article, we show that 3D digital shapes can be decorated with tangles by working interactively in the intrinsic metric of the surface. Our tangles are generated by the recursive application of only four operators, which are derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface without relying on parametrization. We reach interactive speed on meshes of a few million triangles by relying on an efficient approximate graph-based geodesic solver.
Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification

Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are more time-consuming and expensive than computational approaches. Several tools are available for identifying long non-coding RNAs. These tools, however, did not explain the features in their tools that contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that Xlnc1DCNN outperformed other state-of-the-art tools in terms of accuracy and F1-score. The explanation results revealed that lncRNA transcripts were mainly identified as sequences with no conserved regions, short patterns with unknown functions, or only regions of transmembrane helices while protein-coding transcripts were mostly classified by conserved protein domains or families. The explanation results also conveyed the probably inconsistent annotations among the public databases, lncRNA transcripts which contain protein domains, protein families, or intrinsically disordered regions (IDRs). Xlnc1DCNN is freely available at https://github.com/cucpbioinfo/Xlnc1DCNN.
Multi-threshold Wavelet Packet De-noising Method and Its Application in Deformation Analysis

In the field of deformation monitoring,the traditional wavelet de-noising method retains only the low frequency of useful information.It is easy to get rid of intermediate frequency and high frequency useful information.The wavelet packet analysis is a new kind of wavelet analysis method developed in recent years,which is a more subtle de-noising method for considering useful information of the various bands.The key of the wavelet packet de-noising is to select the appropriate threshold criteria and to process the wavelet packet decomposition coefficients by the threshold,but the researches using traditional wavelet packet de-noising method are not sufficient.This article is for the lack of traditional wavelet and wavelet packet analysis.According to the distribution of different signals and their noise,wavelet packet decomposition coefficients are arranged by the frequency order,and segmented in accordance with information type,to select the appropriate threshold criteria for each band and to perform threshold processing.It is the method of wavelet packet de-noising with multi-threshold criteria based on frequency order.The results show that this method can effectively remove the noise of each band through theoretical analysis and practical applications.The de-noising ability of this method is better than the other methods such as traditional wavelet de-noising or wavelet packet denoising.Studies have shown that this method can preserve the useful information from the de-noising signal after de-noising when the sampling frequency is low.Therefore,it can be widely used in the field of high-precision deformation monitoring.
Dual species transcriptomics reveals core metabolic and immunologic processes in the interaction between primary human neutrophils and Neisseria gonorrhoeae strains

Neisseria gonorrhoeae (the gonococcus, Gc) is the causative agent of the sexually transmitted infection gonorrhea. Gc is a prominent threat to human health by causing severe and lifelong clinical sequelae, including infertility and chronic pelvic pain, which is amplified by the emergence of “superbug” strains that are resistant to all current antibiotics. Gc is highly adapted to colonize human mucosal surfaces, where it survives despite initiating a robust inflammatory response and influx of polymorphonuclear leukocytes (PMNs or neutrophils) that typically clear bacteria. Here, dual-species RNA-sequencing (RNA-seq) was used to define Gc and PMN transcriptional profiles alone and after infection. Three strains of Gc and three human donors’ transcriptional responses were assessed to characterize core host and bacterial responses. Comparative analysis of Gc transcripts revealed major overlap between the Gc response to PMNs, iron, and hydrogen peroxide; specifically, the TonB system and TonB dependent transporters (TDT) were upregulated in response to PMNs. We experimentally confirmed that induction of the iron-dependent TDT TbpB is responsive to the presence of PMNs and that tonB is required for Gc survival from PMNs. Pathway analysis of PMN transcripts induced by Gc infection revealed differential expression of genes driving pathways involved in cell adhesion and migration, inflammatory responses, and inflammation resolution. Production of pro-inflammatory cytokines, including IL1B and IL8, the adhesion factor ICAM1, and the anti-inflammatory prostaglandin PGE2 was confirmed to be induced in PMNs in response to Gc. Together, this study represents a comprehensive and experimentally validated dual-species transcriptomic analysis of three isolates of Gc and primary human PMNs that gives insight into how this bacterium survives innate immune onslaught to cause disease in humans.
A Bayesian approach to single-particle electron cryo-tomography in RELION-4.0

We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and contrast transfer function (CTF) refinement in single-particle analysis, we describe approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt series alignments, beam-induced motions of the particles throughout the tilt series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, in particular for those researchers who are already familiar with its single-particle analysis tools. We illustrate for three applications that our approaches allow structure determination from cryo-ET data to resolutions sufficient for de novo atomic modelling.
Fast and functionally specific cortical thickness changes induced by visual stimulation

Structural characteristics of the human brain serve as important markers of brain development, aging, disease progression and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with MRI may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared the them to BOLD fMRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray-white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3 % or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.
Station-keeping of L2 halo orbits under sampled-data model predictive control

The paper deals with the design of an improved model predictive control scheme for achieving station-keeping in a quasi Halo orbit around the $L_2$ point in the Earth-Moon system. The improvement is obtained thanks to a multi-rate sampled-data trajectory planner that allows for simplifying the optimization problem of the model predictive controller while guaranteeing feasibility and convergence to the desired orbit. The multi-rate planner is designed based on a simplified model of the dynamics under a preliminary nonlinear regulation feedback. The proposed control scheme is shown to outperform recent station-keeping nonlinear model predictive control designs both in terms of tracking error and energy expenditures in different situations. Finally, a brief study of aspects pertaining to computational time are carried out so highlighting the possibility for real time implementation on modern hardware.
Key technologies for lunar rover locomotive system

For the purpose of studying the lunar rover locomotive system, the main functions of the lunar rover locomotive system based on the analysis of the features of complex lunar environment, are discussed by analyzing the characteristics of some kinds of the locomotion, include legged, wheeled and tracked modes, a 6 -wheeled rocker - bogie mode locomotive system is designed to provide good performance and strong self - adapting characteristic. By studying the fundamental configuration of the locomotive system, its main specifications are established. The key technologies related to the structure of the wheel train, the steering mechanism and the power system on the 6 - wheeled rocker - bogie mode locomotive system are discussed in depth.
Efficient Low-Space Simulations From the Failure of the Weak Pigeonhole Principle

A recurring challenge in the theory of pseudorandomness and circuit complexity is the explicit construction of “incompressible strings,” i.e. finite objects which lack a specific type of structure or simplicity. In most cases, there is an associated NP search problem which we call the “compression problem,” where we are given a candidate object and must either find a compressed/structured representation of it or determine that none exist. For a particular notion of compressibility, a natural question is whether an efficient algorithm for the compression problem would aide us in the construction of incompressible objects. Consider the following two instances of this question:
Preemptive Identification of End-of-Life for Stationary Battery Systems

Stationary Battery Systems (SBS) are becoming a critical component in power distribution networks across the world. Penetration of intermittent renewable energy sources is a huge influence on the requirement of SBS. SBSs are also used in other applications such as peak load management, load-shifting, voltage regulation, and power quality improvement. With the increase in penetration of SBS, the requirement for predicting the End-of-Life (EOL) of these batteries is also increasing drastically. Machine-Learning (ML) based models are widely used for such predictions in various applications. With minimal resource requirements and the capability to leverage smart meter/sensor data, ML approaches provide flexibility in predicting EOL. In this paper, the study on the preemptive identification of EOL for lithium-ion batteries utilizing historical data is presented. Various ML methods are used to predict the EOL of the batteries; with a goal to predict the EOL at least 30 cycles in advance. The prediction is aimed to enable timely maintenance and replacement of the batteries effectively. The performance of the EOL prediction with the different methods that can improve the accuracy is presented as an analysis. Correlation to the data from a practical Battery Management System is also presented to demonstrate the practical applicability.
Computer Code Representation through Natural Language Processing for fMRI Data Analysis

There are many attempts to analyze the relationship between functional magnetic resonance imaging (fMRI) data and text stimuli representation in cognitive neuroscience research. Because programming codes are exemplary text stimuli, appropriate code representation for neuroscience research has been actively studied. In this paper, we focus on representing python code for fMRI research through natural language processing (NLP) techniques. We collect 7,893 python codes of 23 question types from a code competition website and build three different models based on sequence-to-sequence, bag-of-words, and bigram representation. The model is evaluated to classify the types of questions. Finally, the model is applied to classify 108 python codes which were used for a cognitive neuroscience study of fMRI. We are looking forward to analyzing fMRI data with the proposed code representation for understanding how the human brain is active.
Smart grid big data processing technology and cloud computing application status quo and challenges

With the rapid development of China’s economy, people’s demand for electricity is increasing, so that the power grid will generate a large amount of data information in the actual operation, and the smart grid big data processing technology is to store and use the data information generated by the operation of the power grid.The ultimate purpose of applying big data processing technology is to make efficient use of the data information generated by the power grid, improve the rate of data access and processing, so as to provide necessary support and help for the normal operation of the power grid, and ensure that the national power grid can provide more stable power for people and enterprises.This paper focuses on the smart grid big data processing technology and opportunities and challenges.
Multi-Loss Function for Distance-to-collision Estimation

Estimation of the drone’s distance-to-collision is the key to the indoor autonomous obstacle avoidance and navigation of monocular UAVs. At present, the distance-to-collision model mainly uses regression loss or ordinal regression for training Regression loss utilizes the continuity of distance, and ordinal regression loss utilizes the order of distance. To improve the prediction performance of the model, this paper proposes a multi-loss function trained deep learning model based on the linear combination of ordinal regression loss and regression loss. The regression loss can be obtained by adding a distance decoder after the ordinal regression estimation, without changing the original structure of the model. Finally, we test the model performance in public datasets and obtain good results.
Comparative Performance Analysis of Code-Domain NOMA and Power-Domain NOMA

Orthogonal Multiple Access (OMA) schemes provide limit on the number of users and do not meet 5th generation requirements. Non-Orthogonal Multiple Access (NOMA) improves spectral efficiency and user capacity without sacrificing system performance. In order to develop link adaptation mechanism for 5G communication technology based on NOMA, detailed parametric analysis and computation of bit error rate (BER) bounds is an essential requirement. Major focus of this paper to evaluate performance analysis, based on the BER curve of CD-NOMA and PD NOMA using different parameters. The chosen schemes for Code Domain NOMA are LDS-CDMA, LDS-OFDM and SCMA. Power Domain NOMA is implemented for two user model. CD NOMA schemes are compared with PD NOMA and their fair comparison is evaluated. All schemes are run in MATLAB under AWGN Channel. There system model, key performance parameters and achieved results are discussed. From analysis, we obtained better results for LDS-OFDM amongst LDS-CDMA and SCMA for six users. LDS-OFDM model results, which is run for six users is closed to Power Domain NOMA run for two users.
Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review

Pedestrian detection is an important branch of computer vision, and has important applications in the fields of autonomous driving, artificial intelligence and video surveillance. With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and has achieved better performance. However, the performance of state-of-the-art methods is far behind expectations, especially when occlusion and scale variance exist. Therefore, many works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. First, a brief progress of pedestrian detection in the past two decades is summarized. Second, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trends in pedestrian detection are discussed.