Nervonic Acid Inhibits Replicative Senescence of Human Wharton's Jelly-Derived Mesenchymal Stem Cells.

Cellular senescence causes cell cycle arrest and promotes permanent cessation of proliferation. Since the senescence of mesenchymal stem cells (MSCs) reduces proliferation and multipotency and increases immunogenicity, aged MSCs are not suitable for cell therapy. Therefore, it is important to inhibit cellular senescence in MSCs. It has recently been reported that metabolites can control aging diseases. Therefore, we aimed to identify novel metabolites that regulate the replicative senescence in MSCs. Using a fecal metabolites library, we identified nervonic acid (NA) as a candidate metabolite for replicative senescence regulation. In replicative senescent MSCs, NA reduced senescence-associated ß-galactosidase positive cells, the expression of senescence-related genes, as well as increased stemness and adipogenesis. Moreover, in non-senescent MSCs, NA treatment delayed senescence caused by sequential subculture and promoted proliferation. We confirmed, for the first time, that NA delayed and inhibited cellular senescence. Considering optimal concentration, duration, and timing of drug treatment, NA is a novel potential metabolite that can be used in the development of technologies that regulate cellular senescence.

Cognotyping by What-Where-When Retrieval Reveals the Potential Role of Temporal Memory and Its Neural Correlates in Understanding Individual Differences across Aging and Alzheimer Disease.

Despite distinct neural representation of what, where, and when information, studies of individual differences in episodic memory have neglected to test the three components separately. Here, we used a componential episodic memory task to measure cognitive profiles across a wide age range and in Alzheimer disease (AD) and to examine the role of theta oscillations in explaining performance. In Experiment 1, we tested a group of 47 young adults (age 21-30 years, 21 women) while recording their scalp EEG. A separate behavioral experiment (Experiment 2) was performed in 42 older adults (age 66-85 years, 29 women) and in a group of 16 AD patients (age 80-90 years, 12 women). In Experiment 1, K-means clustering based on behavioral data resulted in three "cognotypes" whose memory profiles showed corresponding differences in their EEG markers: What and where memory depended on frontal theta power and when memory depended on theta modulation by temporal distance between retrieved items. In Experiment 2, healthy older adults showed three cognotypes similar to those found in younger adults; moreover, AD patients had an overlapping profile with one specific cognotype, characterized by marked difficulties in when memory. These findings highlight the utility of componential episodic memory tests and cognotyping in understanding individual strengths and vulnerabilities in age-related neurocognitive decline.

Domain-General and Domain-Specific Electrophysiological Markers of Cognitive Distance Coding for What, Where, and When Memory Retrieval.

The what, where, and when components of episodic memory can be differentiated based on their distinctive domain-specific underlying neural correlates. However, recent studies have proposed that a common neural mechanism of conceptual mapping may be involved in the coding of cognitive distance across all domains. In this study, we provide evidence that both domain-specific and domain-general processes occur simultaneously during memory retrieval by identifying distinctive and common neural representations for mapping what (i.e., semantic distance), where (i.e., spatial distance), and when (i.e., temporal distance) using scalp EEG from 47 healthy participants (age 21-30, 26 male and 21 female). First, we found that all three components commonly showed a positive correlation between cognitive distance and slow theta power (2.5-5 Hz) in parietal channels. Meanwhile, fast theta power (5-8.5 Hz) specifically represented spatial and temporal distance in occipital and parietal channels, respectively. Additionally, we identified a unique correlate of temporal distance coding in frontal/parietal slow theta power during the early phase of retrieval. All of the above neural markers of cognitive mapping, both domain-general and specific, were associated with individual differences in what, where, and when memory accuracy.SIGNIFICANCE STATEMENT The Cognitive Map Theory was originally founded to explain how we remember and organize the immense amount of spatial information that we face when we navigate. However, memory research has recently trended in the direction of emphasizing the generalizability of cognitive mapping mechanisms to information in any domain, represented as distances in an abstract conceptual space. In a single study, we show that both common and unique neural coding of semantic distance (i.e., what), spatial distance (i.e., where), and temporal distance (i.e., when) simultaneously support episodic memory retrieval. Our results suggest that our ability to accurately distinguish between memories is achieved through an integration of domain-specific and domain-general neurocognitive mechanisms that work in parallel.

Laser frequency stabilization in the 10 -14 range via optimized modulation transfer spectroscopy on the 87Rb D2 line.

We present a high-performance laser frequency stabilization method using modulation transfer spectroscopy (MTS) on the rubidium 87D2 transition line. A substantial improvement of the laser frequency stability was achieved by searching for the optimal diameter and intensity settings of the probe and pump beam. The frequency instability measured from the beat frequency of two locked external cavity diode lasers (ECDLs) reached a short-term stability of 4.5×10-14/τ and did not exceed 2 × 10-12 until 105 s, which is the best performance reported thus far with a D2 transition. The long-term stability is limited by the offset fluctuations of the baseline induced by the residual amplitude modulation (RAM), which can be further improved by reducing the current temperature variation of about 0.2 K by means of temperature stabilization or through a further reduction of the RAM.

Coherent consolidation of trillions of nucleations for mono-atom step-level flat surfaces.

Constructing a mono-atom step-level ultra-flat material surface is challenging, especially for thin films, because it is prohibitively difficult for trillions of clusters to coherently merge. Even though a rough metal surface, as well as the scattering of carriers at grain boundaries, limits electron transport and obscures their intrinsic properties, the importance of the flat surface has not been emphasised sufficiently. In this study, we describe in detail the initial growth of copper thin films required for mono-atom step-level flat surfaces (MSFSs). Deposition using atomic sputtering epitaxy leads to the coherent merging of trillions of islands into a coplanar layer, eventually forming an MSFS, for which the key factor is suggested to be the individual deposition of single atoms. Theoretical calculations support that single sputtered atoms ensure the formation of highly aligned nanodroplets and help them to merge into a coplanar layer. The realisation of the ultra-flat surfaces is expected to greatly assist efforts to improve quantum behaviour by increasing the coherency of electrons.

Moving-frame imaging of transiting cold atoms for precise long-range transport.

Transporting cold atoms between interconnected vacuum chambers is an important technique for increasing the versatility of cold atom setups, particularly for those that couple atoms to photonic devices. In this report, we introduce a method where we are able to image the atoms at all points during transport via moving optical dipole trap. Cooled 87Rb atoms are transported ∼50 cm into an auxiliary vacuum chamber while being monitored with a moving-frame imaging system for which in-situ characterization of the atom transport is demonstrated. Precise positioning of the atoms near photonic devices is also tested across several tapered fibers showing an axial positioning resolution of ∼450 µm.

A New Method of Myostatin Inhibition in Mice via Oral Administration of Lactobacillus casei Expressing Modified Myostatin Protein, BLS-M22.

Myostatin is a member of the transforming growth factor-beta superfamily and is an endogenous negative regulator of muscle growth. This study aimed to determine whether an oral administration of Lactobacillus casei expressing modified human myostatin (BLS-M22) could elicit sufficient levels of myostatin-specific antibody and improve the dystrophic features of an animal model of Duchenne muscular dystrophy (DMD; mdx mouse). BLS-M22 is a recombinant L. casei engineered to harbor the pKV vector and poly-gamma-glutamic acid gene linked to a modified human myostatin gene. Serological analysis showed that anti-myostatin IgG titers were significantly increased, and serum creatine kinase was significantly reduced in the BLS-M22-treated mdx mice compared to the control mice. In addition, treatment of BLS-M22 resulted in a significant increase in body weight and motor function (Rotarod behavior test). Histological analysis showed an improvement in the dystrophic features (fibrosis and muscle hypertrophy) of the mdx mice with the administration of BLS-M22. The circulating antibodies generated after BLS-M22 oral administration successfully lowered serum myostatin concentration. Myostatin blockade resulted in serological, histological, and functional improvements in mdx mice. Overall, the findings suggest the potential of BLS-M22 to treat DMD; however, further clinical trials are essential to ascertain its efficacy and safety in humans.

Wharton's Jelly-Derived Mesenchymal Stem Cells with High Aurora Kinase A Expression Show Improved Proliferation, Migration, and Therapeutic Potential.

Mesenchymal stem cells (MSCs) are effective therapeutic agents that contribute to tissue repair and regeneration by secreting various factors. However, donor-dependent variations in MSC proliferation and therapeutic potentials result in variable production yields and clinical outcomes, thereby impeding MSC-based therapies. Hence, selection of MSCs with high proliferation and therapeutic potentials would be important for effective clinical application of MSCs. This study is aimed at identifying the upregulated genes in human Wharton's jelly-derived MSCs (WJ-MSCs) with high proliferation potential using mRNA sequencing. Aurora kinase A (AURKA) and dedicator of cytokinesis 2 (DOCK2) were selected as the upregulated genes, and their effects on proliferation, migration, and colony formation of the WJ-MSCs were verified using small interfering RNA (siRNA) techniques. mRNA expression levels of both the genes were positively correlated with the proliferation capacity of WJ-MSCs. Moreover, AURKA from human WJ-MSCs regulated the antiapoptotic effect of skeletal muscle cells by upregulating the chemokine (C motif) ligand (XCL1); this was further confirmed in the mdx mouse model. Taken together, the results indicated that AURKA and DOCK2 can be used as potential biomarkers for proliferation and migration of human WJ-MSCs. In particular, human WJ-MSCs with high expression of AURKA might have therapeutic efficacy against muscle diseases, such as Duchenne muscular dystrophy (DMD).

A FAIR and AI-ready Higgs boson decay dataset.

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization.

We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian optimization with the evaluation of the trap performance given by a Monte-Carlo simulation. Modeling gMOTs for six representative atomic species - 7Li, 23Na, 87Rb, 88Sr, 133Cs, 174Yb - allows us to discover that the optimal grating reflectivity is consistently higher than a simple estimation based on balanced optical molasses. Our algorithm also yields the optimal diffraction angle which is independent of the beam waist. The validity of the optimal parameter set for the case of 87Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Wharton's Jelly-Derived Mesenchymal Stem Cells Reduce Fibrosis in a Mouse Model of Duchenne Muscular Dystrophy by Upregulating microRNA 499.

The aim of this study was to evaluate the therapeutic effects and mechanisms of Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) in an animal model of Duchenne muscular dystrophy (DMD). Mdx mice (3-5 months old) were administered five different doses of WJ-MSCs through their tail veins. A week after injection, grip strength measurements, creatine kinase (CK) assays, immunohistochemistry, and western blots were performed for comparison between healthy mice, mdx control mice, and WJ-MSC-injected mdx mice. WJ-MSCs exerted dose-dependent multisystem therapeutic effects in mdx mice, by decreasing CK, recovering normal behavior, regenerating muscle, and reducing apoptosis and fibrosis in skeletal muscle. We also confirmed that miR-499-5p is significantly downregulated in mdx mice, and that intravenous injection of WJ-MSCs enhanced its expression, leading to anti-fibrotic effects via targeting TGFßR 1 and 3. Thus, WJ-MSCs may represent novel allogeneic "off-the-shelf" cellular products for the treatment of DMD and possibly other muscle disorders.

Exposure of Mesenchymal Stem Cells to an Alzheimer's Disease Environment Enhances Therapeutic Effects.

Mesenchymal stem cells (MSCs) have emerged as a promising tool for the treatment of Alzheimer's disease (AD). Previous studies suggested that the coculture of human MSCs with AD in an in vitro model reduced the expression of amyloid-beta 42 (Aß42) in the medium as well as the overexpression of amyloid-beta- (Aß-) degrading enzymes such as neprilysin (NEP). We focused on the role of primed MSCs (human Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) exposed to an AD cell line via a coculture system) in reducing the levels of Aß and inhibiting cell death. We demonstrated that mouse groups treated with naïve MSCs and primed MSCs showed significant reductions in cell death, ubiquitin conjugate levels, and Aß levels, but the effects were greater in primed MSCs. Also, mRNA sequencing data analysis indicated that high levels of TGF-ß induced primed-MSCs. Furthermore, treatment with TGF-ß reduced Aß expression in an AD transgenic mouse model. These results highlighted AD environmental preconditioning is a promising strategy to reduce cell death and ubiquitin conjugate levels and maintain the stemness of MSCs. Further, these data suggest that human WJ-MSCs exposed to an AD environment may represent a promising and novel therapy for AD.

Chip-Scale Ultra-Low Field Atomic Magnetometer Based on Coherent Population Trapping.

We report a chip-scale atomic magnetometer based on coherent population trapping, which can operate near zero magnetic field. By exploiting the asymmetric population among magnetic sublevels in the hyperfine ground state of cesium, we observe that the resonance signal acquires sensitivity to magnetic field in spite of degeneracy. A dispersive signal for magnetic field discrimination is obtained near-zero-field as well as for finite fields (tens of micro-tesla) in a chip-scale device of 0.94 cm3 volume. This shows that it can be readily used in low magnetic field environments, which have been inaccessible so far in miniaturized atomic magnetometers based on coherent population trapping. The measured noise floor of 300 pT/Hz1/2 at the zero-field condition is comparable to that of the conventional finite-field measurement obtained under the same conditions. This work suggests a way to implement integrated atomic magnetometers with a wide operating range.

Dynamic adsorption/desorption of p-xylene on nanomorphic MFI zeolites: Effect of zeolite crystal thickness and mesopore architecture.

Zeolites have attracted great interest as an adsorbent for the removal of volatile organic compounds. However, they suffer from low adsorption capacities due to severe diffusion limitations. Here, the effects of zeolite thickness and mesopore architecture on dynamic adsorption of p-xylene have been examined with a number of MFI-type zeolites with different crystal thicknesses and mesopore openings (i.e. open mesopore, constricted mesopore), which were prepared via hydrothermal synthesis with various organic structure-directing agents and post-synthetic desilication. The results showed that the breakthrough time of MFI zeolite could be improved by more than 2.3 times by reducing the crystal thickness of zeolite to a single-unit-cell dimension (∼2 nm). The time improvement can be attributed to the short diffusion path length that results in easy access of p-xylene to intracrystalline micropores and a large external crystal surface area. In the case of mesopore openings, the presence of constricted mesopores caused the mass transfer of p-xylene into zeolite adsorbents to slow down while open mesopores did not. Furthermore, mesopore opening is an important factor for the desorption behavior of p-xylene. Adsorbed p-xylene by mesoporous zeolites could be desorbed at lower temperatures only when facile diffusion to the exterior through mesoporous channels was possible.

A framework for designing data-driven optimization systems for neural modulation.

Objective.Neural modulation is a fundamental tool for understanding and treating neurological and psychiatric diseases. However, due to the high-dimensional space, subject-specific responses, and variability within each subject, it is a major challenge to select the stimulation parameters that have the desired effect. Data-driven optimization provides a range of different algorithms and tools for addressing this challenge, but each of these algorithms has specific strengths and limitations, and therefore must be carefully designed for a given neural modulation problem. Here we present a framework for designing data-driven optimization algorithms for neural modulation.Approach.We develop this framework using an optogenetic medial septum stimulation model, where the goal is to find the stimulation parameters that modulate hippocampal gamma power to a desired value. This framework proceeds in four steps: (a) collecting stimulation data, (b) creating high-throughput simulation models, (c) prototyping a range of different data-driven optimization algorithms and evaluating their performance, and (d) deploying the best performing algorithmin vivo. Main results.Following this framework, we prototype and design an algorithm specifically for finding the medial septum optogenetic stimulation parameters that maximize hippocampal gamma power. Building on this, we then change our objective function to find the stimulation parameters that modulate gamma to a specific setpoint, use the framework to understand and anticipate the results before deployingin vivo. Significance.We show that this framework can be used to design an effective optimization solution for a specific neural modulation problem, and discuss how it can potentially be applied beyond the optogenetic medial septum stimulation model.

Industrial carbon dioxide capture and utilization: state of the art and future challenges.

Dramatically increased CO2 concentration from several point sources is perceived to cause severe greenhouse effect towards the serious ongoing global warming with associated climate destabilization, inducing undesirable natural calamities, melting of glaciers, and extreme weather patterns. CO2 capture and utilization (CCU) has received tremendous attention due to its significant role in intensifying global warming. Considering the lack of a timely review on the state-of-the-art progress of promising CCU techniques, developing an appropriate and prompt summary of such advanced techniques with a comprehensive understanding is necessary. Thus, it is imperative to provide a timely review, given the fast growth of sophisticated CO2 capture and utilization materials and their implementation. In this work, we critically summarized and comprehensively reviewed the characteristics and performance of both liquid and solid CO2 adsorbents with possible schemes for the improvement of their CO2 capture ability and advances in CO2 utilization. Their industrial applications in pre- and post-combustion CO2 capture as well as utilization were systematically discussed and compared. With our great effort, this review would be of significant importance for academic researchers for obtaining an overall understanding of the current developments and future trends of CCU. This work is bound to benefit researchers in fields relating to CCU and facilitate the progress of significant breakthroughs in both fundamental research and commercial applications to deliver perspective views for future scientific and industrial advances in CCU.

Pressure Stimuli Improve the Proliferation of Wharton's Jelly-Derived Mesenchymal Stem Cells under Hypoxic Culture Conditions.

Mesenchymal stem cells (MSCs) are safe, and they have good therapeutic efficacy through their paracrine action. However, long-term culture to produce sufficient MSCs for clinical use can result in side-effects, such as an inevitable senescence and the reduction of the therapeutic efficacy of the MSCs. In order to overcome this, the primary culture conditions of the MSCs can be modified to simulate the stem cells' niche environment, resulting in accelerated proliferation, the achievement of the target production yield at earlier passages, and the improvement of the therapeutic efficacy. We exposed Wharton's jelly-derived MSCs (WJ-MSCs) to pressure stimuli during the primary culture step. In order to evaluate the proliferation, stemness, and therapeutic efficacy of WJ-MSCs, image, genetic, and Western blot analyses were carried out. Compared with standard incubation culture conditions, the cell proliferation was significantly improved when the WJ-MSCs were exposed to pressure stimuli. However, the therapeutic efficacy (the promotion of cell proliferation and anti-apoptotic effects) and the stemness of the WJ-MSCs was maintained, regardless of the culture conditions. Exposure to pressure stimuli is a simple and efficient way to improve WJ-MSC proliferation without causing changes in stemness and therapeutic efficacy. In this way, clinical-grade WJ-MSCs can be produced rapidly and used for therapeutic applications.

Anti-Fibrotic Effect of Human Wharton's Jelly-Derived Mesenchymal Stem Cells on Skeletal Muscle Cells, Mediated by Secretion of MMP-1.

Extracellular matrix (ECM) components play an important role in maintaining skeletal muscle function, but excessive accumulation of ECM components interferes with skeletal muscle regeneration after injury, eventually inducing fibrosis. Increased oxidative stress level caused by dystrophin deficiency is a key factor in fibrosis in Duchenne muscular dystrophy (DMD) patients. Mesenchymal stem cells (MSCs) are considered a promising therapeutic agent for various diseases involving fibrosis. In particular, the paracrine factors secreted by MSCs play an important role in the therapeutic effects of MSCs. In this study, we investigated the effects of MSCs on skeletal muscle fibrosis. In 2-5-month-old mdx mice intravenously injected with 1 × 105 Wharton's jelly (WJ)-derived MSCs (WJ-MSCs), fibrosis intensity and accumulation of calcium/necrotic fibers were significantly decreased. To elucidate the mechanism of this effect, we verified the effect of WJ-MSCs in a hydrogen peroxide-induced fibrosis myotubes model. In addition, we demonstrated that matrix metalloproteinase-1 (MMP-1), a paracrine factor, is critical for this anti-fibrotic effect of WJ-MSCs. These findings demonstrate that WJ-MSCs exert anti-fibrotic effects against skeletal muscle fibrosis, primarily via MMP-1, indicating a novel target for the treatment of muscle diseases, such as DMD.

Optimizing neuromodulation based on surrogate neural states for seizure suppression in a rat temporal lobe epilepsy model.

OBJECTIVE: Developing a new neuromodulation method for epilepsy treatment requires a large amount of time and resources to find effective stimulation parameters and often fails due to inter-subject variability in stimulation effect. As an alternative, we present a novel data-driven surrogate approach which can optimize the neuromodulation efficiently by investigating the stimulation effect on surrogate neural states. APPROACH: Medial septum (MS) optogenetic stimulation was applied for modulating electrophysiological activities of the hippocampus in a rat temporal lobe epilepsy model. For the new approach, we implemented machine learning techniques to describe the pathological neural states and to optimize the stimulation parameters. Specifically, first, we found neural state surrogates to estimate a seizure susceptibility based on hippocampal local field potentials. Second, we modulated the neural state surrogates in a desired way with the subject-specific optimal stimulation parameters found by in vivo Bayesian optimization. Finally, we tested whether modulating the neural state surrogates affected seizure frequency. MAIN RESULTS: We found two neural state surrogates: The first was hippocampal theta power by considering its well-known relationship with epilepsy, and the second was the output of pre-ictal state model (PriSM) which was built by characterizing the hippocampal activity during the pre-ictal period. The optimal stimulation parameters found by Bayesian optimization outperformed the other parameters in terms of modulating the surrogates toward anti-seizure neural state. When treatment efficacy was tested, the subject-specific optimal parameters for increasing theta power were more effective to suppress seizures than fixed stimulation parameter (7 Hz). However, modulation of the other neural state surrogate, PriSM, did not suppress seizures. SIGNIFICANCE: The surrogate approach can save enormous time and resources to find subject-specific optimal stimulation parameters which can effectively modulate neural states and further improve therapeutic effectiveness. This approach can also be used for improving neuromodulation treatment of other neurological or psychiatric diseases.