Spin-flip reflection at the normal metal-spin superconductor interface

We study spin transport through a normal metal-spin superconductor junction. A spin-flip reflection is demonstrated at the interface, where a spin-up electron incident from the normal metal can be reflected as a spin-down electron and the spin $2\times \hbar/2$ will be injected into the spin superconductor. When the (spin) voltage is smaller than the gap of the spin superconductor, the spin-flip reflection determines the transport properties of the junction. We consider both graphene-based (linear-dispersion-relation) and quadratic-dispersion-relation normal metal-spin superconductor junctions in detail. For the two-dimensional graphene-based junction, the spin-flip reflected electron can be along the specular direction (retro-direction) when the incident and reflected electron locates in the same band (different bands). A perfect spin-flip reflection can occur when the incident electron is normal to the interface, and the reflection coefficient is slightly suppressed for the oblique incident case. As a comparison, for the one-dimensional quadratic-dispersion-relation junction, the spin-flip reflection coefficient can reach 1 at certain incident energies. In addition, both the charge current and the spin current under a charge (spin) voltage are studied. The spin conductance is proportional to the spin-flip reflection coefficient when the spin voltage is less than the gap of the spin superconductor. These results will help us get a better understanding of spin transport through the normal metal-spin superconductor junction.Comment: 11 pages, 9 figure

PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways

PANTHER is a freely available, comprehensive software system for relating protein sequence evolution to the evolution of specific protein functions and biological roles. Since 2005, there have been three main improvements to PANTHER. First, the sequences used to create evolutionary trees are carefully selected to provide coverage of phylogenetic as well as functional information. Second, PANTHER is now a member of the InterPro Consortium, and the PANTHER hidden markov Models (HMMs) are distributed as part of InterProScan. Third, we have dramatically expanded the number of pathways associated with subfamilies in PANTHER. Pathways provide a detailed, structured representation of protein function in the context of biological reaction networks. PANTHER pathways were generated using the emerging Systems Biology Markup Language (SBML) standard using pathway network editing software called CellDesigner. The pathway collection currently contains ∼1500 reactions in 130 pathways, curated by expert biologists with authorship attribution. The curation environment is designed to be easy to use, and the number of pathways is growing steadily. Because the reaction participants are linked to subfamilies and corresponding HMMs, reactions can be inferred across numerous different organisms. The HMMs can be downloaded by FTP, and tools for analyzing data in the context of pathways and function ontologies are available at

The nestin-expressing and non-expressing neurons in rat basal forebrain display different electrophysiological properties and project to hippocampus

<p>Abstract</p> <p>Background</p> <p>Nestin-immunoreactive (nestin-ir) neurons have been identified in the medial septal/diagonal band complex (MS/DBB) of adult rat and human, but the significance of nestin expression in functional neurons is not clear. This study investigated electrophysiological properties and neurochemical phenotypes of nestin-expressing (nestin+) neurons using whole-cell recording combined with single-cell RT-PCR to explore the significance of nestin expression in functional MS/DBB neurons. The retrograde labelling and immunofluorescence were used to investigate the nestin+ neuron related circuit in the septo-hippocampal pathway.</p> <p>Results</p> <p>The results of single-cell RT-PCR showed that 87.5% (35/40) of nestin+ cells expressed choline acetyltransferase mRNA (ChAT+), only 44.3% (35/79) of ChAT+ cells expressed nestin mRNA. Furthermore, none of the nestin+ cells expressed glutamic acid decarboxylases 67 (GAD₆₇) or vesicular glutamate transporters (VGLUT) mRNA. All of the recorded nestin+ cells were excitable and demonstrated slow-firing properties, which were distinctive from those of GAD₆₇or VGLUT mRNA-positive neurons. These results show that the MS/DBB cholinergic neurons could be divided into nestin-expressing cholinergic neurons (NEChs) and nestin non-expressing cholinergic neurons (NNChs). Interestingly, NEChs had higher excitability and received stronger spontaneous excitatory synaptic inputs than NNChs. Retrograde labelling combined with choline acetyltransferase and nestin immunofluorescence showed that both of the NEChs and NNChs projected to hippocampus.</p> <p>Conclusions</p> <p>These results suggest that there are two parallel cholinergic septo-hippocampal pathways that may have different functions. The significance of nestin expressing in functional neurons has been discussed.</p

DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research

PULP AND FIBER CHARACTERIZATION OF WHEAT STRAW AND EUCALUPTUS PULPS - A COMPARISON

The response to refining of wheat straw and eucalyptus pulps as well as the relationships between refining, fiber properties, and paper properties are described in this paper. Pulps were bleached applying different bleaching sequences and thereafter refined to varying degrees. Pulp and fiber properties were investigated and set into relation to the final sheet properties. The results show that wheat straw pulps respond to refining more easily than eucalyptus pulps and that the differences are due mainly to morphological and ultrastructural differences as well as fines content and xylan content. The development of strength properties of the different pulps was found to be strongly correlated to the number of dislocations, i.e. weak points in the fiber wall, as well as to the morphological appearance of the pulp fibers after refining. A higher initial number and a faster development of dislocations together with the creation of large amounts of fines explain the slower and lower development of strength properties of wheat straw pulps than of eucalyptus pulps. Removal of fines from wheat straw pulps improved not only the drainability of the pulp suspension but also the mechanical and optical sheet properties. This indicates that the fines in the wheat straw pulps act mainly as filler with low bonding properties. The fact that fractionated D(EOP)D wheat straw pulps can deliver good mechanical sheet properties at very good drainability with no or only minor refining is very interesting when evaluating the potential of replacing or partially replacing eucalyptus with domestic Chinese raw materials in furnishes for production of different paper products

Origin-Destination Travel Time Oracle for Map-based Services

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.Comment: 15 pages, 12 figures, accepted by SIGMOD International Conference on Management of Data 202

Applications for protein sequence–function evolution data: mRNA/protein expression analysis and coding SNP scoring tools

The vast amount of protein sequence data now available, together with accumulating experimental knowledge of protein function, enables modeling of protein sequence and function evolution. The PANTHER database was designed to model evolutionary sequence–function relationships on a large scale. There are a number of applications for these data, and we have implemented web services that address three of them. The first is a protein classification service. Proteins can be classified, using only their amino acid sequences, to evolutionary groups at both the family and subfamily levels. Specific subfamilies, and often families, are further classified when possible according to their functions, including molecular function and the biological processes and pathways they participate in. The second application, then, is an expression data analysis service, where functional classification information can help find biological patterns in the data obtained from genome-wide experiments. The third application is a coding single-nucleotide polymorphism scoring service. In this case, information about evolutionarily related proteins is used to assess the likelihood of a deleterious effect on protein function arising from a single substitution at a specific amino acid position in the protein. All three web services are available at

Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Inflammatory cytokines and stroke and its subtypes: a genetic correlation and two-sample Mendelian randomization study

IntroductionThe causal relationship between inflammatory factors and stroke subtypes remains unclear. This study aimed to analyze the causal relationship between 41 inflammatory factors and these two factors using Mendelian randomization (MR).MethodsWe performed a two-sample MR analysis to assess the causal effects of 41 inflammatory cytokines on stroke and its subtypes and conducted a genome-wide association study (GWAS) data. The inverse-variance weighted (IVW) method was adopted as the main MR method, and we performed a series of two-sample Mendelian randomizations and related sensitivity analyses.ResultsThe study indicated some suggestive evidences: using the IVW approach, we found that lower possible levels of IL-4 were positively associated with the occurrence of stroke (odds ratio [OR] = 0.93, 95% confidence interval [CI]: 0.88–0.99, p = 0.014), higher interleukin (IL)-1β, IL-12p70 levels may be positively correlated with the occurrence of stroke (OR = 1.09, 95% CI: 1.01–1.18, p = 0.027; OR = 1.08, 95% CI: 1.02–1.15, p = 0.015). For IS, results showed that lower levels of IL-4, tumor necrosis factor-related apoptosis-inducing ligand were positively associated with the occurrence of possible ischemic stroke (IS) (OR = 0.92, 95% CI: 0.87–0.98, p = 0.006; OR = 0.95, 95% CI: 0.91–1.00, p = 0.031), higher levels of IL-1β, IL-12p70 and vascular endothelial growth factor (VEGF) may be positively correlated with the occurrence of IS (OR = 1.09, 95% CI: 1.00–1.19, p = 0.042; OR = 1.07, 95% CI: 1.01–1.15, p = 0.035; OR = 1.06, 95% CI: 1.00–1.12, p = 0.034). Our findings suggest that decreased IL-17 levels could potentially be linked to a higher likelihood of intracerebral hemorrhage (ICH) (OR = 0.51, 95% CI: 0.28–0.93, p = 0.028). For subtypes of stroke, IS and ICH, higher levels of growth regulated oncogene-α, beta nerve growth factor, IL-18, macrophage colony-stimulating factor, and induced protein 10 upregulated the risk factors while lower levels of IL-2ra and IL-17 upregulated the risk factors.ConclusionIn summary, our research validated that inflammatory markers have a pivotal impact on the development of stroke and could potentially offer a fresh approach to treating this condition

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route\&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field