Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability

Background The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM. Methods A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient’ preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785). Conclusion The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.
Male Pedigree Toolbox: A Versatile Software for Y-STR Data Analyses

Y-chromosomal short tandem repeats (Y-STRs) are widely used in forensic, genealogical, and population genetics. With the recent increase in the number of rapidly mutating (RM) Y-STRs, an unprecedented level of male differentiation can be achieved, widening and improving the applications of Y-STRs in various fields, including forensics. The growing complexity of Y-STR data increases the need for automated data analyses, but dedicated software tools are scarce. To address this, we present the Male Pedigree Toolbox (MPT), a software tool for the automated analysis of Y-STR data in the context of patrilineal genealogical relationships. The MPT can estimate mutation rates and male relative differentiation rates from input Y-STR pedigree data. It can aid in determining ancestral haplotypes within a pedigree and visualize the genetic variation within pedigrees in all branches of family trees. Additionally, it can provide probabilistic classifications using machine learning, helping to establish or prove the structure of the pedigree and the level of relatedness between males, even for closely related individuals with highly similar haplotypes. The tool is flexible and easy to use and can be adjusted to any set of Y-STR markers by modifying the intuitive input file formats. We introduce the MPT software tool v1.0 and make it publicly available with the goal of encouraging and supporting forensic, genealogical, and other geneticists in utilizing the full potential of Y-STRs for both research purposes and practical applications, including criminal casework.
Computer-Aided Strategy on 5-(Substituted benzylidene) Thiazolidine-2,4-Diones to Develop New and Potent PTP1B Inhibitors: QSAR Modeling, Molecular Docking, Molecular Dynamics, PASS Predictions, and DFT Investigations

A set of 5-(substituted benzylidene) thiazolidine-2,4-dione derivatives was explored to study the main structural requirement for the design of protein tyrosine phosphatase 1B (PTP1B) inhibitors. Utilizing multiple linear regression (MLR) analysis, we constructed a robust quantitative structure–activity relationship (QSAR) model to predict inhibitory activity, resulting in a noteworthy correlation coefficient (R2) of 0.942. Rigorous cross-validation using the leave-one-out (LOO) technique and statistical parameter calculations affirmed the model’s reliability, with the QSAR analysis revealing 10 distinct structural patterns influencing PTP1B inhibitory activity. Compound 7e(ref) emerged as the optimal scaffold for drug design. Seven new PTP1B inhibitors were designed based on the QSAR model, followed by molecular docking studies to predict interactions and identify structural features. Pharmacokinetics properties were assessed through drug-likeness and ADMET studies. After that density functional theory (DFT) was conducted to assess the stability and reactivity of potential diabetes mellitus drug candidates. The subsequent dynamic simulation phase provided additional insights into stability and interactions dynamics of the top-ranked compound 11c. This comprehensive approach enhances our understanding of potential drug candidates for treating diabetes mellitus.
Efficient Vehicular Content Distribution for B5G C-V2X Networks: A Matching Theory Approach

With the rapid development of cellular vehicle-to-everything (C-V2X) networks, a great number of vehicular users (VUEs) will be connected for content distribution, which is with the challenges of high mobility of VUEs and frequent network handovers of 5G networks. Recently, a network architecture called 6G fully-decoupled radio access network (FD-RAN) was proposed, which has the potential to solve these challenges. This paper investigates the efficient vehicular edge content distribution in the fully-decoupled downlink beyond 5G (B5G) C-V2X networks, in which multiple base stations (BSs) can be collaborated to increase the data download volume of VUEs and realize the efficiency of vehicular content distribution. Considering the high mobility of vehicles, user fairness, and system capacity, we propose an effective vehicular content distribution method by leveraging the many-to-many matching theory. We transform the content distribution problem into the matching game between BSs and VUEs. Furthermore, we propose a vehicular content distribution algorithm (VCDA) to achieve a stable solution for efficient vehicular content distribution. Extensive simulation results demonstrate the efficiency and superiority of the VCDA, which is compared with the random multiple connection algorithm in which each VUE can select BSs within the limited range.
Overlapping community detection in dynamic networks

In complex networks, the notion of community structure refers to the presence of groups of nodes in a network. These groups are more densely connected internally than with the rest of the network. The presence of communities inside a network gives an insight on network structural properties. For example, in social networks, communities are based on common interests, location, hobbies. Generally, a community structure is described by a partition of the network nodes, where each node belongs to a unique community. A more reasonable description seems to be overlapping community structure, where nodes are allowed to be shared by several communities. Moreover, when considering dynamic networks whose interactions between nodes evolve in time, it appears crucial to consider also the evolution of the intrinsic community structure. This thesis focus on mining dynamic community evolution and overlapping community detection. We have proposed two distinct methods for overlapping community detection. The first one named clique optimization and the second one called fuzzy detection. Our clique optimization aims to identify granular overlaps and it is a fine grain scale approach. Our fuzzy detection is at a coarser grain scale with the strategy of identifying modular overlaps. Their applications in synthetic and real networks indicate that both methods can be used for characterizing overlapping nodes but in distinct and complementary views. We also propose the definition of predecessor and successor in mining community evolution. Such definition describes the relationship between communities at different time steps. We use it to detect community evolution in dynamic networks and show how modular overlaps evolve over time. A visualization tool called lineage diagrams is used to show community evolution by connecting communities in relationship of predecessor and successor. Several cases are studied.
Effects of different gonadotropin preparations in GnRH antagonist protocol for patients with polycystic ovary syndrome during IVF/ICSI: a retrospective cohort study

Purpose To compare the effects of recombinant FSH alfa (rFSH-alfa), rFSH-beta, highly purified human menopausal gonadotropin (HP-hMG) and urinary FSH (uFSH) in women with polycystic ovarian syndrome who have undertaken the GnRH antagonist protocol during IVF/ICSI treatment. Method A single-center retrospective cohort study including women with PCOS who received the GnRH antagonist protocol from January 2019 to July 2022 was conducted. Patients were divided into rFSH-alfa group, HP-hMG group, uFSH group, and rFSH-beta group, and the number of oocytes retrieved, clinical pregnancy rate of the fresh cycle (primary outcomes), embryo quality, and severe OHSS rate (secondary outcomes) were compared. Results No statistical differences were found among the four groups in fresh cycle clinical pregnancy rate (p=0.426), nor in the subgroup analyses. The HP-hMG group had a smaller number of oocytes retrieved and a higher high-quality D3 embryo rate than the three FSH groups (p<0.05). No statistical differences were found among the four groups in the severe OHSS rate (p=0.083). Conclusion For women with PCOS undergoing the GnRH antagonist protocol, the clinical pregnancy rates of fresh IVF/ICSI-ET cycle are similar for all four types of Gn. With a lower risk of OHSS and a similar number of high-quality and available embryos, HP-hMG may have an advantage in the PCOS population.
Replication and Injury Associated With SARS-CoV-2 in Cultured Hepatocytes

Background: Liver dysfunction is one of the hallmarks of SARS-CoV-2 infection. The mechanism(s) of hepatic injury in SARS-CoV-2 infection remains controversial with some reporting viral replication and cellular injury and others suggesting lack of replication and injury due to non-cytopathogenic etiologies. To investigate this further, we evaluated SARS-CoV-2 replication in immortalized hepatic cell lines and primary hepatocytes, examined whether cell injury was associated with apoptotic pathways, and also determined the effect of the antiviral remdesivir on these processes. Methods: Immortalized hepatocyte cell lines (HepG2 and Huh7.5), as well as primary human hepatocytes, were exposed to SARS-CoV-2 at a multiplicity of infection of 0.1 PFU/mL. Viral replication was evaluated by plaque assays, immunohistochemical staining for the viral spike protein, and caspase-3 expression evaluated with and without exposure to remdesivir. Results: All hepatocyte cell lines and primary hepatocytes supported active replication of SARS-CoV-2. Significant cytopathic effect was observed by light microscopy, and caspase-3 staining supported activation of apoptotic pathways. Remdesivir abrogated infection in a dose-dependent fashion and was not independently associated with hepatocyte injury. Conclusion: Hepatocytes appear to be highly permissive of SARS-CoV-2 replication which leads to rapid cell death associated with activation of apoptotic pathways. Viral replication and hepatocytes injury are abrogated with remdesivir. We conclude that active viral replication is most likely a key contributor to liver enzyme abnormalities observed in the setting of acute SARS-CoV-2 infection.
Estimation Method of Armor Stability Against Tsunami Overtopping Caisson Breakwater Based on Overflow Depth

This paper presents a practical design method for armor units to cover a rubble mound at the rear side of a caisson breakwater against tsunami overflow. In this method, the overflow depth of tsunami is used to represent the external force. This enables the estimation of the required mass of the armor units to be done more robustly and easily than in the conventional method based on the flow velocity. Hydraulic model experiments were conducted to investigate the armor stability. We found two important factors for armor stability. These were the impingement position of the overflow jet and the harbor-side water level. These effects were taken into account in the method. Numerical analysis on the fluid forces acting on the armor blocks was also conducted to explore the fundamental expression of the new design formula for armor stability. Empirical formulae for the stability estimation were then derived based on the findings from experiments and numerical analysis. The overflow depths of the stability limit corresponding to two failure modes, overturning and sliding, were obtained by two formulae. The stability numbers for each armor unit were determined through the experiments. The estimated results by this method agreed well with the experimental ones.
Impacts of Habit Formation Effect on Food Consumption and Nutrient Intake in Rural China

This study employs panel data and a dynamic Almost Ideal Demand System (AIDS) model to investigate the habit formation effect of food consumption among Chinese rural residents and its consequential impact on nutritional intake. The dataset, spanning from 2012 to 2018, encompasses nine provinces in China and involves 5390 rural households. The findings reveal that, excluding beef, mutton, and poultry, there are significant habit formation effect on the consumption of food categories, notably grains, vegetables, and edible oils. Lower-income and younger demographics demonstrate a more pronounced reliance on established dietary habits. Influenced by the habit formation effect, there is a substantial reduction in the income elasticity differences across various food types. Overlooking the habit formation effect in food consumption would lead to an underestimation of the income elasticity of energy, fat, and carbohydrates. This suggests that, over the long term, food consumption habit formation is a pivotal factor in enabling the enhancement of residents’ dietary structures, amplifying the incremental energy intake associated with income increases, and accelerating the transition towards nutritional surplus. The conclusions drawn from this study offer valuable insights for ensuring food security and nutritional balance. Policy-makers of food and nutrition strategies should duly consider the habit formation effect on residents’ food consumption, and seek to optimize dietary patterns and promote nutritional transformation by food consumption habit intervention.
Blockchain Civic Tech for Smarter Cities: Governance Challenges from a Multistakeholder Ecosystem Perspective

Recently, many governments have attempted to develop more inclusive and participatory urban development processes through civic technology. CivicTech platforms support urban development by engaging citizens in urban matters decisions in more meaningful ways. Blockchain has been used as a digital technology for CivicTech platform as its unique features of decentralization, traceability and immutability ensure data integrity and reliability. Currently, there is a lack of a systematic guidance on the governance of blockchain-based platforms that accompanies their design and implementation. The lack of a clear governance design causes strategic uncertainty and hinder cooperation among the involved stakeholders. Therefore, the scope of the paper is to identify a number of governance trade-offs and related solutions in order to overcome the problems which could emerge during the design and implementation of a blockchain-based civic tech platform for citizens engagement. Through the analysis of the case study of BBBlockchain, a blockchain-based CivicTech platform used by urban planners to improve the transparency of urban development and better involve citizens in decision making, the paper contributes to the literature of blockchain governance in the public sector.
Knowledge-Driven Deep Learning Based Channel Estimation for Terahertz Extra-Large MIMO Systems

Accurate channel state information (CSI) is critical in wideband terahertz (THz) extra-large multiple-input multiple-output (XL-MIMO) systems. However, the existing channel estimation methods suffer from the beam squint effect. In this paper, we propose a knowledge-driven channel estimation network called generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) to jointly estimate the channels, whereby the approximately same physical angles shared by different subcarriers can be exploited. In particular, we propose a frequency-dependent discrete Fourier transform (DFT) wideband redundant dictionary (WRD), so that the support shift caused by the beam squint effect is eliminated. Simulation results demonstrate that the proposed approach can effectively achieve channel estimation with only several layers at the cost of limited pilots.
Recently discovered populations of the Honduran endemic salamanders Oedipina kasios and Oedipina quadra (Caudata: Plethodontidae)

1) 10770 SW 164th Street, Miami, Florida 33157–2933, USA 2) Museum fur Naturkunde, Leibniz Institute for Research on Evolution and Biodiversity at the Humboldt University Berlin, Invalidenstr, 43, D-10115 Berlin, Germany 3) Australian Museum, 6 College Street Sydney NSW 2010 Australia 4) Gerente General de “Hondufauna,” Investigador Privado, Colonia America, Bloque 9, Casa 1806, Comayaguela, MDC, Honduras
On-demand Routing Protocol Based on Prediction Model in Dynamic Scene

Mobility is a pervasive phenomenon in wireless sensor networks (WSNs), which can significantly impact network routing performance. By combining trajectory prediction with appropriate routing protocols, the problem of high network latency caused by node mobility can be effectively solved. This paper proposes a real-time routing protocol for WSNs that leverages an intelligent trajectory prediction method based on Long Short-Term Memory (LSTM) algorithm. Our model uses the node's historical trajectory and the memory of neighboring nodes to predict the network topology to recalculate the route in dynamic scenes. Simulation and experimental results demonstrate that our proposed protocol outperforms existing protocols in terms of packet delivery ratio and end-to-end delay.
Intelligent Preamble Allocation for 5G RAN Slicing: A Hierarchical Reinforcement Learning Based Approach

The development of 5G has greatly enriched people's lives, and the three network slicing types it supports (eMBB, URLLC, and mMTC) provide diverse services for vertical industries. The random access congestion problem in 5G radio access network slicing is a significant challenge that must be solved to fully realize the potential of 5G networks, where the slice preamble resource isolation at the base station and the cooperative preamble selection at the devices are both very important. However, few works consider these two aspects simultaneously. Meanwhile, reinforcement learning (RL)-based algorithms have shown promising results in addressing resource allocation problems, by allowing the network to learn how to make real-time resource management and allocation decisions based on the state of the network and the actions of devices. In view of this, we propose a hierarchical preamble resource allocation and selection mechanism, which allocates isolated preamble pool for each slice through deep Q-learning at the base station and assigns unique access preambles for each device through lightweight Q-learning. Performance evaluation shows that the proposed intelligent preamble allocation mechanism can significantly improve the slice access rate of the device.
Secure Communications in IRS-Assisted NOMA Networks with Location Optimization

This paper proposes a secrecy transmission for intelligent reflecting surface (IRS) assisted non-orthogonal mul-tiple access (NOMA) network. Different from existing schemes with fixed IRS location, we optimize the IRS location and active/passive beamforming to maximize the sum secrecy rate, considering channel spatial correlation and only eavesdropper's statistical channel state information (CSI). We propose an alter-nating optimization (AO) scheme that optimizes beamforming, phase shifts, and IRS location iteratively. Simulation results demonstrate the significance of IRS location optimization in secure transmission.
Combining a Drug and a Nutraceutical: A New Cocrystal of Praziquantel and Curcumin

This study explores the co-crystallization between the drug praziquantel (PZQ) and the nutraceutical curcumin (CU). The investigation revealed two novel solid forms: a cocrystal solvate with ethyl acetate and a non-solvated cocrystal. This novel drug–nutraceutical cocrystal is a praziquantel–curcumin (2:1) cocrystal. The cocrystal solvate has ethyl acetate molecules occupying the voids with minimal interactions within the crystal lattice. The application of heat treatment induces solvent removal and prompts the transition to the non-solvated cocrystal, as highlighted by variable-temperature X-ray powder diffraction (VT-XRPD). Thermal analyses demonstrate the stability of the cocrystal solvate up to approximately 100 °C, beyond which it transforms into the non-solvated phase, which eventually melts at 130 °C.
RubberMate - Integrated Platform for Rubber Crop Harvest and Production

Rubber cultivation plays a vital role in the global economy as a primary natural rubber source necessitates efficient plantation management for sustainability and latex quality. This paper introduces an integrated mobile app that enhances rubber plantation management through real-time monitoring of tapping, disease identification via image processing, weed detection, and latex quality assessment. Addressing these challenges becomes pivotal to improving latex quality, minimizing waste, and promoting sustainable practices in the rubber industry. In conclusion, the proposed mobile application serves as a prospective solution for estate owners, utilizing selected algorithms to tackle these challenges effectively.
Visualizing Comparisons of Bill of Materials

The complexity of distributed manufacturing and software development coupled with the increasing prevalence of cyber and supply chain attacks necessitates a greater understanding of the hardware and software components that comprise equipment in critical infrastructure. When a vulnerability in a single software library can have disastrous consequences, being able to identify where that library may exist in equipment or software becomes a prerequisite for protecting the overall infrastructure. This need has sparked a large effort around the development and incorporation of bill-of-materials (BOM) into security, asset management, and procurement practices to aid in mitigating, and responding to future attacks. While much of the current research is devoted to creating BOMs, it is equally important to develop methods for comparing them to answer questions, such as: How has my software changed? Are two pieces of equipment equivalent? Does this piece of equipment that just arrived match my historical information? In this work, we demonstrate how BOMs can be represented by graph structures. We then describe how these structures can be fed into a graph comparison algorithm to produce a novel interactive visualization that allows us to not only identify differences in BOMs but show exactly where they are in the product.