Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the Neuroevolution of Low-level artificial general intelligence

In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.
Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications

Applications with low data reuse and frequent irregular memory accesses, such as graph or sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core utilization. While prior work with prefetching, decoupling, or pipelining can mitigate memory latency and improve core utilization, memory bottlenecks persist due to limited off-chip bandwidth. Approaches doing processing in-memory (PIM) with Hybrid Memory Cube (HMC) overcome bandwidth limitations but fail to achieve high core utilization due to poor task scheduling and synchronization overheads. Moreover, the high memory-per-core ratio available with HMC limits strong scaling.We introduce Dalorex, a hardware-software co-design that achieves high parallelism and energy efficiency, demonstrating strong scaling with >16,000 cores when processing graph and sparse linear algebra workloads. Over the prior work in PIM, both using 256 cores, Dalorex improves performance and energy consumption by two orders of magnitude through (1) a tile-based distributed-memory architecture where each processing tile holds an equal amount of data, and all memory operations are local; (2) a task-based parallel programming model where tasks are executed by the processing unit that is co-located with the target data; (3) a network design optimized for irregular traffic, where all communication is one-way, and messages do not contain routing metadata; (4) novel traffic-aware task scheduling hardware that maintains high core utilization; and (5) a data-placement strategy that improves work balance.This work proposes architectural and software innovations to provide the greatest scalability to date for running graph algorithms while still being programmable for other domains.
Validation of a 16th Century Traditional Chinese Medicine Use of Ginkgo biloba as a Topical Antimicrobial

In the search for new therapeutic solutions to address an increasing number of multidrug-resistant bacterial pathogens, secondary metabolites from plants have proven to be a rich source of antimicrobial compounds. Ginkgo biloba, a tree native to China, has been spread around the world as an ornamental tree. Its seeds have been used as snacks and medical materials in Traditional Chinese Medicine (TCM), while over the last century its leaf extracts emerged as a source of rising pharmaceutical commerce related to brain health in Western medicine. Besides studies on the neuro-protective effects of Ginkgo, its antibacterial activities have gained more attention from researchers in the past decades, though its leaves were the main focus. We reviewed a 16th-century Chinese text, the Ben Cao Gang Mu by Li Shi-Zhen, to investigate the ancient prescription of Ginkgo seeds for skin infections. We performed antibacterial assays on various Ginkgo seed extracts against pathogens (Staphylococcus aureus, Cutibacterium acnes, Klebsiella pneumoniae, Acinetobacter baumannii, Streptococcus pyogenes) relevant to skin and soft tissue infections (SSTIs). We demonstrate here that Ginkgo seed coats and immature seeds exhibit antibacterial activity against Gram-positive skin pathogens (C. acnes, S. aureus, and S. pyogenes), and thus validated its use in TCM. We also identified one compound tied to the antibacterial activity observed, ginkgolic acid C15:1, and examine its toxicity to human keratinocytes. These results highlight the relevance of ancient medical texts as leads for the discovery of natural products with antimicrobial activities.
Evolutionary Multiparty Distance Minimization

In the ﬁeld of evolutionary multiobjective optimization, the decision maker (DM) concerns conﬂicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these objectives. Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP with multiple decision makers involved, where each party concerns about certain some objectives of all. However, in the evolutionary computation ﬁeld, there is not much attention paid on MPMOPs. This paper constructs a series of MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal solutions can be vividly visualized. To address MPMOPs, the new proposed algorithm OptMPNDS3 uses the multiparty initializing method to initialize the population and takes JADE2 operator to generate the oﬀsprings. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result shows that OptMPNDS3 is strongly comparable to other algorithms.
Storm Surge Modeling as an Application of Local Time‐Stepping in MPAS‐Ocean

This paper presents the first practical application of local time‐stepping (LTS) schemes in the Model for Prediction Across Scales‐Ocean (MPAS‐O). We use LTS schemes in a single‐layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable‐resolution meshes used are of unprecedentedly high resolution in MPAS‐O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third‐order LTS scheme (LTS3) produces sea‐surface height solutions that are of comparable quality to solutions produced by the classical four‐stage, fourth‐order Runge‐Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time‐step relative to those using the fine time‐step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS‐O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time‐step relative to those using the fine time‐step, typically at least 1:5 to see an increase in performance.
Statistical-physics approaches to RNA molecules, families and networks

This contribution focuses on the fascinating RNA molecule, its sequence-dependent folding driven by base-pairing interactions, the interplay between these interactions and natural evolution, and its multiple regulatory roles. The four of us have dug into these topics using the tools and the spirit of the statistical physics of disordered systems, and in particular the concept of a disordered (energy/fitness) landscape. After an introduction to RNA molecules and the perspectives they open not only in evolutionary and synthetic biology but also in medicine, we will introduce the important notions of energy and fitness landscapes for these molecules. In Section III we will review some models and algorithms for RNA sequence-to-secondary-structure mapping. Section IV discusses how the secondary-structure energy landscape can be derived from unzipping data. Section V deals with the inference of RNA structure from evolutionary sequence data sampled in different organisms. This will shift the focus from the `sequence-to-structure' mapping described in Section III to a `sequence-to-function' landscape that can be inferred from laboratory evolutionary data on DNA aptamers. Finally, in Section VI, we shall discuss the rich theoretical picture linking networks of interacting RNA molecules to the organization of robust, systemic regulatory programs. Along this path, we will therefore explore phenomena across multiple scales in space, number of molecules and time, showing how the biological complexity of the RNA world can be captured by the unifying concepts of statistical physics.
Representing Paths in Graph Database Pattern Matching

Modern graph database query languages such as GQL, SQL/PGQ, and their academic predecessor G-Core promote paths to first-class citizens in the sense that their pattern matching facility can return paths , as opposed to only nodes and edges. This is challenging for database engines, since graphs can have a large number of paths between a given node pair, which can cause huge intermediate results in query evaluation. We introduce the concept of path multiset representations (PMRs) , which can represent multisets of paths exponentially succinctly and therefore bring significant advantages for representing intermediate results. We give a detailed theoretical analysis that shows that they are especially well-suited for representing results of regular path queries and extensions thereof involving counting, random sampling, and unions. Our experiments show that they drastically improve scalability for regular path query evaluation, with speedups of several orders of magnitude.
Technology Asymmetries, Group Algebra and Multi-Plant Cost Minimization

For a homogeneous good, this article studies firm-level production activities when the firm controls a plural number of plants. Through careful construction of cost function technology asymmetries, we inquire into the ordinal structure of the production vector for multiplant cost minimizers. When Schur-convexity of a reference pseudo-cost technology is the principal functional assumption, then ordinal concepts of technical asymmetries map into rank order on the equilibrium production vector. Under a more generalized characterization of the technical asymmetries a multiplant firm encounters, group theory is used to map a cardinal concept of asymmetries into cardinal bounds on the optimum allocation.
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.
A study of liquid argon detector's $n$/$\gamma$ discrimination capability with PMT or SiPM readout

Liquid Argon (LAr) is used as a target material in several current and planned experiments related to dark matter direct searching and neutrino detection. Argon provides excellent Pulse Shape Discrimination (PSD) capability which could separate the electron recoil backgrounds from the expected nuclear recoil signals. This essay simulated the PSD capability of an LAr detector when PMTs or three kinds of SiPMs are used as photosensors based on the experimental data. The results show that the J-60035 SiPM could help the LAr detector achieve the highest PSD capability event though SiPM's After-Pulse (AP) and Cross-Talk (CT) deteriorate its PSD capability. In addition, the results also show that the effect from AP is greater than CT. This is instructive for selecting photosensors for LAr detectors.
Staphylococcus aureus induces a muted host response in human blood that blunts the recruitment of neutrophils

Significance We show here that infection of human blood with Staphylococcus aureus triggers a suppressing transcriptional signature. The signature was conserved across S. aureus lineages and present in bacteremic patients infected with S. aureus. In contrast to S. aureus, we show that Staphylococcus epidermidis did not elicit the suppressing signature, implicating S. aureus virulence factors as drivers of the suppression. Indeed, the master regulator of virulence, S. aureus exoprotein expression (SaeRS), and the SaeRS-regulated toxins were found to be responsible for the “muted” transcriptional response. The S. aureus–elicited suppressing response resulted in impaired production of chemokines and decreased recruitment of neutrophils. Thus, we describe a previously unappreciated immunosuppressive strategy employed by S. aureus to interfere with the host antimicrobial response.
Immunoenhancement activity of Bletilla striata polysaccharide through MAPK and NF-κB signalling pathways in vivo and in vitro

Abstract Bletilla striata (Thunb.) Reichb.f. , is a traditional Chinese medicine, and the Bletilla striata polysaccharide (BSP) is one of the principal components extracted from Bletilla striata with various biological activities. Previous studies have shown that many natural polysaccharides have significant immunomodulatory activities. However, as a plant polysaccharide, the research of BSP on immunomodulatory activities is limited. In this study, we aim to investigate the immunomodulatory effect of BSP in vivo and further explore its underlying mechanism in vitro. In vivo, a cyclophosphamide (CTX)-induced immunosuppression mice mode was established by intraperitoneal injection of CTX, and the immune-enhancing effect of BSP (25, 50 and 100 mg/kg) on immunosuppressed mice were evaluated. The result indicated that BSP could significantly improve the immune organ index and the content of immunoglobulin, TNF-α and IL-4 in serum. It was also found that BSP could clearly ameliorate the spleen damage induced by CTX. Meanwhile, the result showed that BSP could not only improve the proliferation of splenocytes, but also activate the lactate dehydrogenase (LDH) and acid phosphatase (ACP) in mouse spleen tissue. In vitro, potential mechanism was further revealed in macrophages. The result supported that BSP could activate macrophages with high phagocytic ability, and induce macrophages to secrete cytokines. Finally, it revealed that activation of NF-κB and MAPK signalling pathway should be the underlying mechanism of the immunoenhancment of BSP.
Sodium Selenite Promotes Osteoblast Differentiation via The WNT/ß-Catenin Signaling Pathway

Objectives Osteoporosis is regarded as a silent disorder affecting bone slowly, leading to an increased risk of fractures. Lately, selenium has been found to be associated with the acquisition and maintenance of bone health by affecting the bone remodeling process. However, the mechanism of action of selenium on bone is poorly understood. Here, the objective of this study is to examine the protective effects and mechanism of sodium selenite on the differentiation process of osteoblasts as well as under oxidative stress-induced conditions by evaluating the expression of osteoblast differentiation markers in the sodium selenite and/or hydrogen peroxide (H2O2)-treated MC3T3-E1 cell line. Materials and Methods In this experimental study, we confirmed the inducible osteogenic effect of sodium selenite on MC3T3-E1 cells. Moreover, we investigated the recovery of expression levels of osteogenic markers of sodium selenite in H2O2-treated MC3T3-E1 cells. Results It was observed that sodium selenite could promote alkaline phosphatase (ALP) activity and collagen synthesis in pre-osteoblasts. Also, sodium selenite enhanced the mRNA expression levels of osteogenic transcriptional factors, like osterix (OSX) and runt-related transcription factor 2 (Runx2). In addition, the terminal differentiation markers, such as osteocalcin (OCN) and collagen 1α (Col1α) were also increased after the treatment of sodium selenite. Also treatment of sodium selenite recused the H2O2-induced inhibition of osteoblastic differentiation of pre-osteoblasts cells via the WNT signaling pathway, implicating its antioxidant activity. Furthermore, sodium selenite restored the H2O2 repressed β-catenin stability and axin-2 reporter activity in MC3T3-E1 cells. Conclusion It may be concluded that sodium selenite can stimulate bone formation and rescue the oxidative repression of osteogenesis by activating WNT signaling pathways. Further detailed studies on the role of selenium and its ability to stimulate bone formation via the WNT signaling pathway may project it as a potential therapeutic intervention for osteoporosis.
Noise-robust phase-space deconvolution for light-field microscopy

Abstract. Significance: Light-field microscopy has achieved success in various applications of life sciences that require high-speed volumetric imaging. However, existing light-field reconstruction algorithms degrade severely in low-light conditions, and the deconvolution process is time-consuming. Aim: This study aims to develop a noise robustness phase-space deconvolution method with low computational costs. Approach: We reformulate the light-field phase-space deconvolution model into the Fourier domain with random-subset ordering and total-variation (TV) regularization. Additionally, we build a time-division-based multicolor light-field microscopy and conduct the three-dimensional (3D) imaging of the heart beating in zebrafish larva at over 95 Hz with a low light dose. Results: We demonstrate that this approach reduces computational resources, brings a tenfold speedup, and achieves a tenfold improvement for the noise robustness in terms of SSIM over the state-of-the-art approach. Conclusions: We proposed a phase-space deconvolution algorithm for 3D reconstructions in fluorescence imaging. Compared with the state-of-the-art method, we show significant improvement in both computational effectiveness and noise robustness; we further demonstrated practical application on zebrafish larva with low exposure and low light dose.
Multi-scale discriminative regions analysis in FDG-PET imaging for early diagnosis of Alzheimer’s disease

Objective. Alzheimer’s disease (AD) is a degenerative brain disorder, one of the main causes of death in elderly people, so early diagnosis of AD is vital to prompt access to medication and medical care. Fluorodeoxyglucose positron emission tomography (FDG-PET) proves to be effective to help understand neurological changes via measuring glucose uptake. Our aim is to explore information-rich regions of FDG-PET imaging, which enhance the accuracy and interpretability of AD-related diagnosis. Approach. We develop a novel method for early diagnosis of AD based on multi-scale discriminative regions in FDG-PET imaging, which considers the diagnosis interpretability. Specifically, a multi-scale region localization module is discussed to automatically identify disease-related discriminative regions in full-volume FDG-PET images in an unsupervised manner, upon which a confidence score is designed to evaluate the prioritization of regions according to the density distribution of anomalies. Then, the proposed multi-scale region classification module adaptively fuses multi-scale region representations and makes decision fusion, which not only reduces useless information but also offers complementary information. Most of previous methods concentrate on discriminating AD from cognitively normal (CN), while mild cognitive impairment, a transitional state, facilitates early diagnosis. Therefore, our method is further applied to multiple AD-related diagnosis tasks, not limited to AD vs. CN. Main results. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative dataset show that the proposed method achieves superior performance over state-of-the-art FDG-PET-based approaches. Besides, some cerebral cortices highlighted by extracted regions cohere with medical research, further demonstrating the superiority. Significance. This work offers an effective method to achieve AD diagnosis and detect disease-affected regions in FDG-PET imaging. Our results could be beneficial for providing an additional opinion on the clinical diagnosis.
Stability of African swine fever virus in feed during environmental storage.

African swine fever virus (ASFV) causes high case fatality in pigs and a trade-limiting disease resulting in significant economic losses to pork production. ASFV is resistant to environmental degradation and maintains infectivity in feed ingredients exposed to transoceanic shipment conditions. As ASFV is transmissible through consumption of contaminated feed, the objective of this study was to evaluate the stability of ASFV Georgia 2007 in three feed matrices (complete feed, soybean meal, ground corncobs) exposed to three environmental storage temperatures (40°F, 68°F, 95°F) for up to 365 days. ASFV DNA was highly stable and detectable by qPCR in almost all feed matrices through the conclusion of each study. Infectious ASFV was most stable in soybean meal, maintaining infectivity for at least 112 days at 40°F, at least 21 days at 68°F, and at least 7 days at 95°F. This data helps define risk of ASFV introduction and transmission through feed ingredients. This article is protected by copyright. All rights reserved.