Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of superstition of cognitive control during neurofeedback training

Background Real-time fMRI neurofeedback is growing in reputation as a means to alter brain activity patterns and alleviate psychiatric symptoms. Activity in ventral striatum structures is considered an index of training efficacy. fMRI response in these brain regions indicates neurofeedback-driven associative learning. Here we investigated the impact of mere superstition of control as observed during neurofeedback training on patterns of fMRI activation. Methods We examined the brain activations of a large sample of young participants (n = 97, 50 female, age range 18-54yrs) in a simple fMRI task. Participants saw a display similar to that typically used for real-time fMRI. They were instructed to watch the bars’ movements or to control them with their own brain activity. Bar movements were not connected with brain activity of participants in any way and perceptions of control were superstitious. After the pretended control condition, they rated how well they were able to control the bars’ movements. Results Strong activation in the basal ganglia and ventral striatum as well as in large portions of the anterior insula, supplementary motor area, and the middle frontal gyrus due to the superstition of brain control. Conclusions The superstition of control over one’s own brain activity in a pretended neurofeedback training session activates the same neural networks as neurofeedback-driven learning. Therefore, activity in the basal ganglia and ventral striatum cannot be taken as evidence for neurofeedback-driven associative learning unless its effects are proven to supersede those elicited by appropriate sham conditions.
Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution: relevance to diffusion tractography

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic and subcortical areas, relatively weak projections to parietal and insular cortices but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative enhanced green fluorescent protein-labelled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.
Functional group-dependent responses of forest bird communities to invasive predator control and habitat fragmentation

Aim Mounting global pressure on bird populations from invasive predators and habitat loss has driven a rapid growth in restorative and protective conservation action around the world, yet the efficacy of such actions is still not well understood. We investigated the relative effects of invasive predator control and habitat fragmentation on the abundance of native birds and invasive mammalian predators in native forest fragments. Location Waikato region, New Zealand Methods We sampled invasive mammalian predator and native bird abundances using camera traps and bird counts at 26 sites in 15 forest fragments across New Zealand’s Waikato region. Fragment area, shape complexity, and surrounding land cover of exotic and native forest were determined in ArcMap. We further created two composite gradients reflecting predator control intensity and temporal distribution of control based on seven quantitative variables recorded in each of the five years preceding native bird data collection. Finally, we estimated the relative influence of these drivers on invasive mammals and functional groups of native birds using model averaging. Results Of the two invasive predator control variables, only control intensity significantly affected invasive predator abundance and was also a more important driver than landscape or fragment structure, but responses varied among invasive mammal species. In contrast, both invasive predator control intensity and fragment structure were similarly important drivers of native bird abundance, though bird community responses varied markedly between functional groups. Main conclusions Our findings suggest that spatial extent of invasive mammal control is important for controlling mammal numbers and enhancing bird abundance, especially for small insectivorous species, and that habitat fragmentation is less important for invasive mammals but at least as important for native bird communities. Consequently, both drivers should be given strong consideration when undertaking landscape-scale conservation and restoration of bird communities in human-altered landscapes threatened by invasive predators.
Epitope scaffolding using α-synuclein cyclic peptides to generate oligomer-selective antibodies for Parkinson’s disease

Effectively scaffolding epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases, particularly neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s disease. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing the healthy forms of the protein that are often more abundant. To this end, we scaffolded epitopes in cyclic peptides by
Dissecting the allosteric networks governing agonist efficacy and potency in G protein-coupled receptors

G protein-coupled receptors (GPCRs) translate binding of extracellular ligands into intracellular responses through conformational changes. Ligand properties are described by the maximum response (efficacy) and the agonist concentration at half-maximal response (potency). Integrating structural changes with pharmacological properties remains challenging and has not yet been performed at the resolution of individual amino acids. We use epinephrine and β2-adrenergic receptor as a model to integrate residue-level pharmacology data with intramolecular residue contact data describing receptor activation. This unveils the allosteric networks driving ligand efficacy and potency. We provide detailed insights into how structural rearrangements are linked to fundamental pharmacological properties at single-residue level in a receptor-ligand system. Our approach can be used to determine such pharmacological networks for any receptor-ligand complex.
The genetic basis of tail-loss evolution in humans and apes

The loss of the tail is one of the main anatomical evolutionary changes to have occurred along the lineage leading to humans and to the “anthropomorphous apes”1,2. This morphological reprogramming in the ancestral hominoids has been long considered to have accommodated a characteristic style of locomotion and contributed to the evolution of bipedalism in humans3–5. Yet, the precise genetic mechanism that facilitated tail-loss evolution in hominoids remains unknown. Primate genome sequencing projects have made possible the identification of causal links between genotypic and phenotypic changes6–8, and enable the search for hominoid-specific genetic elements controlling tail development9. Here, we present evidence that tail-loss evolution was mediated by the insertion of an individual Alu element into the genome of the hominoid ancestor. We demonstrate that this Alu element – inserted into an intron of the TBXT gene (also called T or Brachyury10–12) – pairs with a neighboring ancestral Alu element encoded in the reverse genomic orientation and leads to a hominoid-specific alternative splicing event. To study the effect of this splicing event, we generated a mouse model that mimics the expression of human TBXT products by expressing both full-length and exon-skipped isoforms of the mouse TBXT ortholog. We found that mice with this genotype exhibit the complete absence of a tail or a shortened tail, supporting the notion that the exon-skipped transcript is sufficient to induce a tail-loss phenotype, albeit with incomplete penetrance. We further noted that mice homozygous for the exon-skipped isoforms exhibited embryonic spinal cord malformations, resembling a neural tube defect condition, which affects ∼1/1000 human neonates13. We propose that selection for the loss of the tail along the hominoid lineage was associated with an adaptive cost of potential neural tube defects and that this ancient evolutionary trade-off may thus continue to affect human health today.
Integrated gene analyses of de novo mutations from 46,612 trios with autism and developmental disorders

Most genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here we analyzed de novo mutations (DNMs) from 15,560 ASD (6,557 are new) and 31,052 DD trios independently and combined as broader neurodevelopmental disorders (NDD) using three models. We identify 615 candidate genes (FDR 5%, 189 potentially novel) by one or more models, including 138 reaching exome-wide significance (p < 3.64e-07) in all models. We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes show particular mutational-bias including enrichments for missense (n=41) or truncating DNM (n=12). We find 22 genes with evidence of sex-bias including five X chromosome genes also with significant female burden (DDX3X, MECP2, SMC1A, WDR45, and HDAC8). NDD risk genes group into five functional networks associating with different brain developmental lineages based on single-cell nuclei transcriptomic data, which provides important insights into disease subtypes and future functional studies.
Multiplicity, Parity and Angular Momentum of a Cooper Pair in Unconventional Superconductors of D4h Symmetry: Sr2RuO4 and Fe-Pnictide Materials

Sr2RuO4 and Fe-pnictide superconductors belong to the same point group symmetry D4h. Many experimental data confirm odd pairs in Sr2RuO4 and even pairs in Fe-pnictides, but opposite conclusions also exist. Recent NMR results of Pustogow et al., which revealed even Cooper pairs in Sr2RuO4, require reconsideration of symmetry treatment of its SOP (superconducting order parameter). In the present work making use of the Mackey–Bradley theorem on symmetrized squares, a group theoretical investigation of possible pairing states in D4h symmetry is performed. It is obtained for I4/mmm , i.e., space group of Sr2RuO4, that triplet pairs with even spatial parts are possible in kz direction and in points M and Y. For the two latter cases pairing of equivalent electrons with nonzero total momentum is proposed. In P4/nmm space group of Fe- pnictides in point M, even and odd pairs are possible for singlet and triplet cases. It it shown that even and odd chiral states with angular momentum projection m=±1 have nodes in vertical planes, but Eg is nodal , whereas Eu is nodeless in the basal plane. It is also shown that the widely accepted assertion that the parity of angular momentum value is directly connected with the spatial parity of a pair is not valid in a space-group approach to the wavefunction of a Cooper pair.
A Preconditioned Variant of the Refined Arnoldi Method for Computing PageRank Eigenvectors

The PageRank model computes the stationary distribution of a Markov random walk on the linking structure of a network, and it uses the values within to represent the importance or centrality of each node. This model is first proposed by Google for ranking web pages, then it is widely applied as a centrality measure for networks arising in various fields such as in chemistry, bioinformatics, neuroscience and social networks. For example, it can measure the node centralities of the gene-gene annotation network to evaluate the relevance of each gene with a certain disease. The networks in some fields including bioinformatics are undirected, thus the corresponding adjacency matrices are symmetry. Mathematically, the PageRank model can be stated as finding the unit positive eigenvector corresponding to the largest eigenvalue of a transition matrix built upon the linking structure. With rapid development of science and technology, the networks in real applications become larger and larger, thus the PageRank model always desires numerical algorithms with reduced algorithmic or memory complexity. In this paper, we propose a novel preconditioning approach for solving the PageRank model. This approach transforms the original PageRank eigen-problem into a new one that is more amenable to solve. We then present a preconditioned version of the refined Arnoldi method for solving this model. We demonstrate theoretically that the preconditioned Arnoldi method has higher execution efficiency and parallelism than the refined Arnoldi method. In plenty of numerical experiments, this preconditioned method exhibits noticeably faster convergence speed over its standard counterpart, especially for difficult cases with large damping factors. Besides, this superiority maintains when this technique is applied to other variants of the refined Arnoldi method. Overall, the proposed technique can give the PageRank model a faster solving process, and this will possibly improve the efficiency of researches, engineering projects and services where this model is applied.
Poisson-Lie Groups and Gauge Theory

We review Poisson–Lie groups and their applications in gauge theory and integrable systems from a mathematical physics perspective. We also comment on recent results and developments and their applications. In particular, we discuss the role of quasitriangular Poisson–Lie groups and dynamical r-matrices in the description of moduli spaces of flat connections and the Chern–Simons gauge theory.
A Runtime Reconfigurable Design of Compute-in-Memory-Based Hardware Accelerator for Deep Learning Inference

Compute-in-memory (CIM) is an attractive solution to address the “memory wall” challenges for the extensive computation in deep learning hardware accelerators. For custom ASIC design, a specific chip instance is restricted to a specific network during runtime. However, the development cycle of the hardware is normally far behind the emergence of new algorithms. Although some of the reported CIM-based architectures can adapt to different deep neural network (DNN) models, few details about the dataflow or control were disclosed to enable such an assumption. Instruction set architecture (ISA) could support high flexibility, but its complexity would be an obstacle to efficiency. In this article, a runtime reconfigurable design methodology of CIM-based accelerators is proposed to support a class of convolutional neural networks running on one prefabricated chip instance with ASIC-like efficiency. First, several design aspects are investigated: (1) the reconfigurable weight mapping method; (2) the input side of data transmission, mainly about the weight reloading; and (3) the output side of data processing, mainly about the reconfigurable accumulation. Then, a system-level performance benchmark is performed for the inference of different DNN models, such as VGG-8 on a CIFAR-10 dataset and AlexNet GoogLeNet, ResNet-18, and DenseNet-121 on an ImageNet dataset to measure the trade-offs between runtime reconfigurability, chip area, memory utilization, throughput, and energy efficiency.
Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.
Schrödinger Equations in Electromagnetic Fields: Symmetries and Noncommutative Integration

We study symmetry properties and the possibility of exact integration of the time-independent Schrödinger equation in an external electromagnetic field. We present an algorithm for constructing the first-order symmetry algebra and describe its structure in terms of Lie algebra central extensions. Based on the well-known classification of the subalgebras of the algebra e(3), we classify all electromagnetic fields for which the corresponding time-independent Schrödinger equations admit first-order symmetry algebras. Moreover, we select the integrable cases, and for physically interesting electromagnetic fields, we reduced the original Schrödinger equation to an ordinary differential equation using the noncommutative integration method developed by Shapovalov and Shirokov.
Linearizability of 2: -3 Resonant Systems with Quadratic Nonlinearities

In this paper, the linearizability of a 2:−3 resonant system with quadratic nonlinearities is studied. We provide a list of the conditions for this family of systems having a linearizable center. The conditions for linearizablity are obtained by computing the ideal generated by the linearizability quantities and its decomposition into associate primes. To successfully perform the calculations, we use an approach based on modular computations. The sufficiency of the obtained conditions is proven by several methods, mainly by the method of Darboux linearization.
One-sentence summary: Advanced microscopy and quantitative analysis of vesicular TGN

24 Arabidopsis root hairs develop as long tubular extensions from the rootward pole of 25 trichoblasts and exert polarized tip growth. The establishment and maintenance of root hair 26 polarity is a complex process involving the local apical production of reactive oxygen species 27 (ROS) generated by NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOG 28 PROTEIN C/ROOT HAIR DEFECTIVE 2 (AtRBOHC/RHD2). It has been shown that loss29
Characterizing a Mini Gamma Detector

Received: October 10, 2019 Accepted: January 31, 2020 Published online: February 28, 2020 There are several types of gamma radiation detectors, which have different characteristics depending on its use. We designed and instrumented a gamma detector for low energies of a small and portable size to obtain spectrum from radioactive sources and from that analyze each spectrum. This instrument basically consists of a scintillator crystal coupled to a SiPM this in turn coupled to a PCB card designed with capacitors and resistors for a better signal, a voltage source of 29 volts. For signal acquisition the system must be connected to an oscilloscope this in turn is controlled by a script developed in Python. For the calibration radioactive isotopes with the same dimensions were used, caesium-137 (Cs-137), cobalto-60 (Co-60), sodium-22 (Na-22) and manganese-54 (Mn-54) as gamma ray emission.
Green Vehicle Routing Under Customer Demand Uncertainty

customer satisfaction is the main focus area in supply chain management and the distribution of goods plays a vital role in customer satisfaction. cost optimization and in time delivery leads to customer satisfaction. Optimization of Vehicle route plan is the method generally applied to deal it. such plans shall consider the minimization of pollution emissions. This paper proposes a optimization method to handle the vehicle routing problem(vrp). Genetic algorithm and fuzzy clustering algorithm are applied in the method.
A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification

The automatic classification of bird sounds is an ongoing research topic and several results have been reported for the classification of selected bird species. In this contribution we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate in detail if and how classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300, randomly drawing sounds of species from a dataset of 659 bird species taken from Xeno-Canto database. We observe that the shallow artificial neural network trained on pre-computed sound features is able to classify the bird sounds relatively well. The classification performance is evaluated using several common measures such as precision, recall, accuracy, mean average precision and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depends on the individual composition of the subset and varies across 20 randomly drawn subsets.