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Moiré magnetism and moiré excitons in twisted CrSBr bilayers

Moiré excitons and moiré magnetism are essential to semiconducting van der Waals magnets. In this work, we perform a comprehensive first-principles study to elucidate the interplay of electronic excitation and magnetism in twisted magnetic CrSBr bilayers. We predict a twist-induced quantum phase transition for interlayer magnetic coupling and estimate the critical twist angle below which moiré magnetism with mixed ferromagnetic and antiferromagnetic domains could emerge. Localized one-dimensional moiré excitons are stable if the interlayer coupling is ferromagnetic and become unstable if the coupling turns to antiferromagnetic. Exciton energy modulation by magnons is estimated and dependence of exciton oscillator strength on the twist angle and interlayer coupling is analyzed. An orthogonally twisted bilayer is revealed to exhibit layer-dependent, anisotropic optical transitions. Electric field is shown to induce net magnetic moments in moiré excitons, endowing them with exceedingly long lifetimes. Our work lays the foundation for using magnetic moiré bilayers in spintronic, optoelectronic, and quantum information applications.

Mobile Sleep Lab: Comparison of polysomnographic parameters with a conventional sleep laboratory

In remote areas, visiting a laboratory for sleep testing is inconvenient. We, therefore, developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as feasible as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (the first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the discrepancy of different places or different nights. No significant differences were found between the laboratories other than the percentage of total sleep time in stage N3. Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements: the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as total sleep time, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R latency, showed poor reliabilities (<0.5) based on Koo and Li’s criteria. In contrast, the percentage of stage N3 showed moderate (0.5–0.75) or good (0.75–0.9) reliabilities. As almost all sleep variables showed no difference and same level of test-retest reliability between the Mobile Sleep Lab and Human Sleep Lab, the Mobile Sleep Lab might be suitable for conducting polysomnography as a conventional sleep laboratory. The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders. Practical application of the Mobile Sleep Lab can transform sleep medicine in remote areas.

Numerical simulation of coal particle motion characteristics in the envelope region of spiral drum based on discrete element method

AbstractIn order to study the movement characteristics of coal particles in the coal loading process of spiral drums, the spiral drum of a certain type of shearer was taken as the research object, and the intrinsic parameters of the materials were calibrated through the determination results of coal sample properties, the relevant parameters of coal particle adhesion were determined, and a discrete element model of spiral drum coal loading was established. The distribution of coal particle movement subsequent to the fracture of the coal wall was derived through simulation. By spatially dividing the envelope region of the spiral drum along the radial and axial directions, the number and velocity distribution of coal particles in different envelope regions were obtained. The study revealed that the number of coal particles in radial regions III and IV was significantly higher than that in regions I and II. Most of the coal particles in regions III and IV moved outward along the drum axis under the action of the spiral blades, while a small portion moved from regions III and IV towards regions I and II. The coal particles in the axial region near the outer side of the coal wall have a strong ability to flow towards the scraper conveyor, and the probability of coal particles being thrown towards the rear of the spiral drum is higher in the region near the end plate. The increase in traction speed has little effect on the velocity of coal particles in all directions within the envelope region, while the increase of drum rotation speed can significantly improve the velocity of coal particles in each region. Through statistical analysis, it was found that the coal loading rate decreases with the increasing of traction speed. As the drum speed increases, the coal loading rate first increases and then decreases. By comparing the results of industrial experiments and numerical simulations underground, the accuracy of the discrete element method used in this paper to analyze the particle motion in the envelope region of the spiral drum has been confirmed. The research results provide reference for the selection of motion parameters of coal shearer and the improving of coal loading efficiency of spiral drums.

Molecular basis of Spns1-mediated lysophospholipid transport from the lysosome

Spns1 mediates the rate-limiting efflux of lysophospholipids from the lysosome to the cytosol. Deficiency of Spns1 is associated with embryonic senescence, as well as liver and skeletal muscle atrophy in animal models. However, the mechanisms by which Spns1 transports lysophospholipid and proton sensing remain unclear. Here, we present a cryogenic electron microscopy structure of human Spns1 in lysophosphatidylcholine (LPC)-bound lumen-facing conformation. Notably, LPC snugly binds within the luminal-open cavity, where the molecular dynamics simulations reveal that LPC presents a propensity to enter between transmembrane-helices (TM) 5 and 8. Structural comparisons and cell-based transport assays uncover several pivotal residues at TM 5/8 that orchestrate the transport cycle, which are unique to Spns1. Furthermore, we identify a five-residue network that is crucial for proton-sensing by Spns1. Transference of these network residues to Spns2, a sphingosine-1-phosphate uniporter, causes the chimeric Spns2 to be low pH dependent. Our results reveal molecular insights into lysosomal LPC transport and the proton-sensing mechanism by Spns1.

Fault tolerance in distributed systems using deep learning approaches

Recently, distributed systems have become the backbone of technological development. It serves as the foundation for new trends technologies such as blockchain, the internet of things and others. A distributed system provides fault tolerance and decentralization, where a fault in any component does not result in a whole system failure. In addition, deep learning model enables processing data to find patterns, which helps in classification, regression, prediction, and clustering. This work employs deep learning to handle faults within distributed systems in three scenarios. Firstly, a faulty processor may not be able to produce the right output. Therefore, deep learning model uses the inputs and outputs of other processors to find patterns and produces the proper output of the faulty processor. Secondly, if a faulty possessor corrupts its inputs as well, then the deep learning model learns from the inputs and the outputs of successful processors and produces the proper output of the faulty processor, even with corrupted inputs. Thirdly, for unrelated data, in which the patterns of the input of the faulty processors differ from the patterns of the inputs of successful ones. In this case, the model is able to discover the new pattern and to be labeled as unknown. In the experiments, we use deep learning models like VGG16, VGG19, AlexNet LSTM and ResNet34, to investigate the performance of the deep learning in the three mentioned scenarios. For unstructured datasets, the accuracy of the models is affected by the size of the faulty data. The accuracy of all models lies between 60% when the size of the faulty data is 90%, and 96%, when the size of the faulty data is 90%. The structured datasets are not significantly affected by the portion of the faulty data and the accuracy reaches 99%.

Identification of new reference genes with stable expression patterns for cell cycle experiments in human leukemia cell lines

AbstractCell cycle-dependent gene expression analysis is particularly important as numerous genes show tightly regulated expression patterns at different phases of the cell cycle. For cancer cells, analysis of cell cycle-related events is of paramount significance since tumorigenesis is characteristically coupled to cell cycle perturbations. RT-qPCR is a highly sensitive technique to investigate cell cycle-dependent transcriptional regulation. However, for reliable evaluation of qPCR data reference genes with stable mRNA expression are required. Although several studies investigating cell cycle-dependent gene expression employ frequently used reference genes, the suitability of these reference genes has not been thoroughly investigated so far. Moreover, such potential reference genes for cell cycle analysis have not been described in the literature. Therefore, we aimed to identify reference genes characterized with stable expression throughout the cell cycle in MOLT4 and U937 human leukemia cell lines synchronized with RO-3306 CDK1 inhibitor using RT-qPCR. Here we show that for cell cycle-dependent gene expression analysis the commonly used TBP is suitable, while the recently recognized reference genes SNW1 and CNOT4 are applicable in a cell line-dependent manner. We also suggest that proper selection of reference genes for each experimental condition is crucial for reliable normalization as these aspects can severely compromise conclusions.

Carbon gain in upper but loss in deeper cropland soils across China over the last four decades

Increasing soil organic carbon (SOC) in agricultural systems is a primary nature-based option for mitigating climate change, improving soil fertility, and ensuring food security. However, the consequences of global warming and increases in carbon inputs on cropland SOC stocks over the last few decades remain largely unknown, particularly in deeper soil layers. Here, by using repeated measurements, we reassess variations in SOC stocks across a 0 to 100 cm soil profile at the same locations in China’s upland croplands in 1980 and 2023. We observe an overall net accumulation of 0.74 Pg SOC (7%) with a mean sequestration rate of 13.7 g SOC m −2 y −1 . This accumulation results from 0.86 Pg SOC gains at upper soil depths (0 to 60 cm) induced by carbon inputs, alongside 0.12 Pg SOC losses at deeper soil layers (60 to 100 cm) prompted by warming-enhanced decomposition. While our findings indicate a successful, though lower than expected, increase in overall net SOC stocks, the loss of more recalcitrant deeper SOC warrants further consideration of its effects on enhancing cropland SOC accumulation to achieve carbon neutrality over the long term.

Accurate multi-behavior sequence-aware recommendation via graph convolution networks

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim to provide personalized recommendations, thereby improving user experience, engagement, and satisfaction across different applications such as e-commerce platforms, streaming services, news websites, and content platforms. While previous approaches in multi-behavior recommendation have focused on incorporating behavioral order and dependencies into embedding learning, they often overlook the nuanced importance of individual behaviors in shaping user preferences during model training. We propose MBA (Multi-Behavior sequence-Aware recommendation via graph convolution networks), an accurate framework for multi-behavior recommendations. MBA adopts a novel approach by learning embeddings that capture both the dependencies between behaviors and their relative importance in influencing user preferences. Additionally, MBA employs sophisticated sampling strategies that consider the sequential nature of behaviors during model training, ensuring that the model effectively learns from the entire behavioral sequence. Through extensive experiments on real-world datasets, we demonstrate the superior performance of MBA compared to existing methods. MBA outperforms the best competitor, achieving improvements of up to 11.2% and 11.4% in terms of HR@10 and nDCG@10, respectively. These findings underscore the effectiveness of MBA in providing accurate and personalized recommendations tailored to individual user preferences.

Environment friendly pesticide formulation by adding certain adjuvants and their biological performance against Sitophilus oryzae (L.)

Abstract Formulation and adjuvant technologies can facilitate the use of insecticides that have higher biological efficiency application features. Safety, physicochemical properties by increasing consumer demand for safe food and enhancing operator safety. The aim of this current work was to develop a green efficient, and stable pesticide formulation. Therefore, certain nano emulsions with and without Adjuvants Calcium Alkyl Benzene Sulphonate (Atlox 4838B), and non-ionic surfactant based on trisiloxane ethoxylate (ARGAL), were testing against Sitophilus oryzae (Coleoptera: Curculionidae). Certain analytical techniques were used for determining the characterization of the nano emulsions (Sesame, Clove, and Cinnamon). Results showed that all formulations were penal, achieving nanometric size for all compounds. Scanning Electron Microscopy (SEM) micrographs revealed spherical or quasi-spherical morphologies for the tested nanoemulsion formulation nanodroplets. Furthermore, dynamic light scattering (DLS) showed that the particle size of the formulation with the adjuvants showed a slight increase in the droplet size compared to the formulations without adjuvants. In comparison to the tested nanoemulsions with adjuvants, the viscosity of the nanoemulsions without adjuvants was lower. All studied formulations, both with and without adjuvant, showed an acidic to slightly acidic pH, except for sesame (NE) with AtloxTM 4838B, which showed a neutral pH, and they were kinetically stable with no phase separation, creaming, or crystallization. Furthermore, supporting the stability of these nanoemulsion particles was the absence of a separation phase following centrifugation, freeze–thaw cycles, and heating–cooling cycles. Findings proved that ARGAL and Atlox 4838B adjuvant stabilized NE by increasing Brownian motion, weakening the attractive forces with smaller droplets, increasing the value of zeta potential and polydispersive index (< 0.6), and decreasing surface tension. The bioassay technique using film residue to estimate LC50 values on S. oryzae adults indicate that Clove, Sesame, and Cinnamon nano emulsions with Atolx adjuvants were the most effective against S. oryzae adults under laboratory conditions, where the LC50 Values are 0.022, 0.032 and 0.035 µL/cm2 respectively after 27 h, or exposure time. Clove, Cinnamon, and Sesame nanoemulsion (NE) with 0.01% (w/w) adjuvant exhibited remarkable insecticidal activity against S. oryzae L., of 100, 100 and 97.5% respectively by in vitro assay.

Memory control deficits in the sleep-deprived human brain

Sleep disturbances are associated with intrusive memories, but the neurocognitive mechanisms underpinning this relationship are poorly understood. Here, we show that sleep deprivation disrupts prefrontal inhibition of memory retrieval, and that the overnight restoration of this inhibitory mechanism is associated with time spent in rapid eye movement (REM) sleep. The functional impairments arising from sleep deprivation are linked to a behavioral deficit in the ability to downregulate unwanted memories, and coincide with a deterioration of deliberate patterns of self-generated thought. We conclude that sleep deprivation gives rise to intrusive memories via the disruption of neural circuits governing mnemonic inhibitory control, which may rely on REM sleep.

Label-free determination of diffusion coefficients at the nanoscale through modelling of the Surface Plasmon Resonance signal

Surface plasmon resonance (SPR) is normally used to measure the kinetic parameters of biomolecular interactions between a molecule immobilized on a gold surface and another one flowing in a microfluidic channel above the surface. During the SPR measurements, convection-diffusion phenomena occur inside the microfluidic channels, but they are generally minimized by appropriate experimental setup in order to obtain diffusion free kinetic parameters of the molecular interactions. In this work, for the first time, a commercial SPR apparatus has been used to obtain non canonical scientific parameters. Indeed, a specifically designed SPR experimental setup is described for carrying out measurements of the diffusion coefficient (D) of molecules in solutions. The high precision and reproducibility of the approach, as well as the wide applicability of the newly proposed SPR based method for the measurement of D of many different molecules and biomolecules, are here demonstrated and illustrated in detail.

Patchiness of plankton ecosystem structure due to nutrient mixing along the shelf edge in the North Sea

Abstract Mid-water column turbulence has been shown to cause elevated vertical nutrient flux at the shelf edge in the northeastern North Sea. Here, we demonstrate that phytoplankton communities in this region tend to be dominated by larger cells (estimated from percentage of chlorophyll captured on a 10 μm filter) than beyond the shelf edge. F v /F m (PSII electron transport capacity) corrected for photoinhibition in the surface layer correlated in this study with the percentage of chlorophyll captured on a 10 µm filter (assumed to be large cells), suggesting that the phytoplankton community was responding to increased nutrients in the euphotic zone by increasing photosynthetic efficiency and altering community composition. The greatest abundances of larger copepods and the highest rates of Centropages typicus egg production were also generally found at the shelf edge. These results suggested that impact from increased nutrient fluxes cascaded up the planktonic food web. As these regions of nutrient flux were very localised, this led to sub-mesoscale heterogeneity in plankton ecosystem structure. Reports of higher abundances of fish and mammals at the shelf edge are common and we hypothesise that their distributions are a response to the impact of mid-water column nutrient upwelling on the plankton food web in the region.

The unexplored potential of exosomes in the muscle–brain axis

Mobile clinics routing and scheduling in the Witzenberg region of South Africa

Despite much literature on operations research applied to various healthcare problems, impactful implementation in public healthcare is limited, which often results in allocative inefficiency. This article uses a mobile clinic routing and scheduling problem in the Witzenberg region of South Africa as a case study to demonstrate the improvement of implementation success through cross-disciplinary collaboration, and also to propose a new three-stage approach for modelling a mobile clinic problem that incorporates continuity of care, fairness, and minimisation of distance travelled. Mobile clinics are used in many countries to improve access to healthcare for rural communities. Decision makers must assign farms or villages to mobile clinics, and determine their monthly visit schedules. To improve implementation success, we follow a collaborative three-phased mixed-methods approach with healthcare professionals to improve workload balance, fairness, and transportation cost. During phase 1, qualitative and quantitative data are gathered through qualitative research methods. In phase 2, fairly distributed optimal routes and schedules are designed using a three-stage model that incorporates a multi-vehicle routing problem to determine daily routes, a knapsack problem to establish a fair allocation of these daily routes between different clinics, and another variation on the vehicle routing problem to determine the monthly visit schedule that minimises the distance between the last farm visited on each consecutive day in the case of having to return to a farm the next day. Different input parameter estimations result in different routes and schedules. In phase 3, AHP is performed with main decision makers to determine their preferred solution. Final routes and schedules are designed based on model results, AHP results, and contextual input from decision makers. In our case study, an improved workload balance, a 23% reduction in total distance travelled, and buy-in to implement the changes, were obtained.

Comparison of Gensini score and SYNTAX score for predicting in-stent restenosis in patients with coronary artery disease and drug-eluting stent implantation

Molecular mechanism governing the plasticity of use-dependent spike broadening in dorsal root ganglion neurons

Use-dependent spike broadening (UDSB) results from inactivation of the voltage-gated K + (Kv) channels that regulate the repolarization of the action potential. However, the specific signaling and molecular processes that modulate UDSB have remained elusive. Here, we applied an adeno-associated viral vector approach and dynamic clamping to conclusively demonstrate how multisite phosphorylation of the N-terminal inactivation domain (NTID) of the Kv3.4 channel modulates UDSB in rat dorsal root ganglion (DRG) neurons. The Kv3.4 phosphonull variant promotes slow recovery from inactivation, cumulative inactivation, and UDSB. In contrast, the Kv3.4 phosphomimic variant promotes fast recovery from inactivation and robust resistance to cumulative inactivation and UDSB. Furthermore, knocking down Kv3.4 maximizes AP width and eliminates UDSB modulation. Together with the evidence from previous work, the results concretely suggest how dynamic UDSB modulation governed by multisite phosphorylation of the NTID of Kv3.4 in DRG neurons may play a significant role in mechanosensory transduction and pain modulation.

ChatGPT-4o can serve as the second rater for data extraction in systematic reviews

Background Systematic reviews provide clarity of a bulk of evidence and support the transfer of knowledge from clinical trials to guidelines. Yet, they are time-consuming. Artificial intelligence (AI), like ChatGPT-4o, may streamline processes of data extraction, but its efficacy requires validation. Objective This study aims to (1) evaluate the validity of ChatGPT-4o for data extraction compared to human reviewers, and (2) test the reproducibility of ChatGPT-4o’s data extraction. Methods We conducted a comparative study using papers from an ongoing systematic review on exercise to reduce fall risk. Data extracted by ChatGPT-4o were compared to a reference standard: data extracted by two independent human reviewers. The validity was assessed by categorizing the extracted data into five categories ranging from completely correct to false data. Reproducibility was evaluated by comparing data extracted in two separate sessions using different ChatGPT-4o accounts. Results ChatGPT-4o extracted a total of 484 data points across 11 papers. The AI’s data extraction was 92.4% accurate (95% CI: 89.5% to 94.5%) and produced false data in 5.2% of cases (95% CI: 3.4% to 7.4%). The reproducibility between the two sessions was high, with an overall agreement of 94.1%. Reproducibility decreased when information was not reported in the papers, with an agreement of 77.2%. Conclusion Validity and reproducibility of ChatGPT-4o was high for data extraction for systematic reviews. ChatGPT-4o was qualified as a second reviewer for systematic reviews and showed potential for future advancements when summarizing data.

Characterization and in vitro anticancer study of PEGylated liposome dually loaded with ferulic acid and doxorubicin

A mechanosensitive circuit of FAK, ROCK, and ERK controls biomineral growth and morphology in the sea urchin embryo

Biomineralization is the utilization of different minerals by a vast array of organisms to form hard tissues and shape them in various forms. Within this diversity, a common feature of all mineralized tissues is their high stiffness, implying that mechanosensing could be commonly used in biomineralization. Yet, the role of mechanosensing in biomineralization is far from clear. Here, we use the sea urchin larval skeletogenesis to investigate the role of substrate stiffness and focal adhesion kinase (FAK) in biomineralization. We demonstrate that substrate stiffness alters spicule morphology and growth, indicating a mechanosensitive response during skeletogenesis. We show that active FAK, F-actin, and vinculin are enriched around the spicules, indicating the formation of focal adhesion complexes and suggesting that the cells sense the mechanical properties of the biomineral. Furthermore, we find that FAK activity is regulated by Rho-associated protein kinase (ROCK) and is crucial for skeletal growth and normal branching. FAK and ROCK activate extracellular signal-regulated kinase (ERK), which regulates skeletogenic gene expression at the tips of the spicules. Thus, the FAK-ROCK-ERK circuit seems to provide essential mechanical feedback on spicule elongation to the skeletogenic gene regulatory network, enabling skeletal growth. Remarkably, the same factors govern mammalian osteoblast differentiation in vitro and pathological calcification in vivo. Thus, this study highlights a common mechanotransduction pathway in biomineralization that was probably independently co-opted across different organisms to shape mineralized structures in metazoans.