Miracle-dz/UniFAR-FTUs huggingface dataset:
Population Genetics of Manila Clam (Ruditapes philippinarum) in China Inferred from Microsatellite Markers

Simple Summary The Manila clam (Ruditapes philippinarum) is one of the most commercially important bivalves along the coast of China. The increasing expanding of clam culture may result in some serious problems. In this paper, we investigated the genetic diversity and differentiation of R. philippinarum populations and tested the hypothesis that clam population differentiation is influenced by the southern breeding and northern culture. The present findings will provide useful information for natural resource conservation and genetic breeding of the Manila clam in China. Abstract The Manila clam (Ruditapes philippinarum) is one of the most commercially important bivalves along the coast of China. With the continuous expansion of clam farming scale, it may lead to some serious problems, including loss of genetic variation, inbreeding depression, and reduced effective population size (Ne). In the present study, eleven microsatellite markers were used to investigate the genetic diversity and differentiation among 13 clam populations along the coast of China. As a result, 150 alleles were detected according to the genotyping results of eleven microsatellite loci. The observed heterozygosity (Ho) was estimated to be ranging from 0.437 to 0.678, while the expected heterozygosity (He) was calculated to be varying from 0.587 to 0.700. Fst values between populations ranged from 0.0046-0.1983. In particular, the Laizhou population had the highest genetic variability, which was significantly different from the others (all Fst values > 0.1). For all the clam populations, there was no significant linear regression between genetic and geographic distance, indicating that these populations do not follow a pattern of isolation by distance (IBD). Genetic structure was estimated according to NJ, principal coordinates (PCoA), and structure-based clustering. Estimates of effective population size range from dozens to thousands among different populations, based on linkage-disequilibrium and molecular coancestry methods. The results reveal the genetic diversity of clams and verify the hypothesis that clam population differentiation may be influenced by the mode of southern breeding and northern culture, providing guiding information for natural resource conservation and genetic breeding of clams.
Empirical Parameterization of Broadband VLF Attenuation in the Earth‐Ionosphere Waveguide

The Earth‐ionosphere waveguide (EIW) determines the propagation of Very Low Frequency (VLF; ∼3−30 kHz) waves. Characterizing the waveguide is a longstanding challenge due to its large spatial scale and the complex variability of the lower ionosphere. Here we apply a novel linear basis function regression technique to characterize attenuation in the EIW using broadband measurements of lightning‐generated radio waves. The process begins with defining a basis function set, which ideally encompasses a feature set that can predict the variability seen in VLF attenuation properties. With this basis set defined, a system of linear equations is then constructed using sensor pair observations to eliminate the dependency on source amplitude in each observation. Using this formalism, an empirical attenuation model for broadband signals from lightning is constructed and the dependence on attenuation properties with the boundary conditions is explored. The empirically derived results show attenuation rates over ice that are 12 dB/Mm higher compared to paths over saltwater. Well‐known east/west attenuation rate asymmetry stemming from anisotropic reflection coefficients of the ionosphere is also demonstrated and investigated. For example, under a daytime ionosphere, westward‐propagating waves suffer up to 2.8 dB/Mm greater attenuation compared to eastward‐propagating waves. The trained model is used for propagation corrections in a global lightning locating system (LLS), but this technique can be expanded to further study VLF attenuation rates by employing different sets of basis functions.
Improving the TCP Newreno Congestion Avoidance Algorithm on 5G Networks

Enhancing Transmission Control Protocol (TCP) performance is one of the key solutions for improving the performance of modern wireless communication networks. It is a highly dependable protocol for communication between network hosts based on an internet protocol (IP). It uses packet sequence numbering and acknowledgment (ACK) packets to ensure that data is recoverable in the event of problems like data loss, and corruption. Loss-based Congestion Control (CC) algorithms overreact and underperform in the presence of rapid channel oscillations, resulting in buffer bloat and significant delays. The goal of this paper is to develop an efficient congestion control mechanism for the new TCP agent Newreno over the fifth generation (5G) network, as well as to improve the increment and decrement strategy for adjusting the congestion window (cwnd) during the congestion avoidance phase, It also fast adapts to the current network requirements and adjusts the transmission rate accordingly. TCP enhancements can improve performance under dynamic traffic loads and over long product channels with high bandwidth. As a result, when such a TCP congestion control mechanism is improved, the network’s throughput increases, and low packet losses, and delay are reduced.
The Theory of Lubrication

When we deal theoretically with the subject of the lubrication, we treat it on the supposition that the viscosity of the lubricant is constant for the convenience of the calculation. But we can't regard it as constant, owing to the fact that the lubricant in bearing is affected by the pressure and temperature. I analysed theoretically and investigated the conditions of the pressure of the oil film, on the occasion of the viscosity of the lubricant is changed by the pressure as well as the temperature.
A tuning scheme for generalized predictive controller based on fuzzy criterion function

The fuzzy decision-making function is investigated and M control actions are obtained by unconstrained predictive control algorithm. Fuzzy goals and fuzzy constraints can then be calculated and the global satisfactory degree is obtained by fuzzy inference. Moreover, the weighting coefficient in the cost function is tuned by using simulation-optimization according to the fuzzy criteria.
Efficient Multimodal Sampling via Tempered Distribution Flow

Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice. However, great challenges emerge when the target density function is unnormalized and contains isolated modes. We tackle this difficulty by fitting an invertible transformation mapping, called a transport map, between a reference probability measure and the target distribution, so that sampling from the target distribution can be achieved by pushing forward a reference sample through the transport map. We theoretically analyze the limitations of existing transport-based sampling methods using the Wasserstein gradient flow theory, and propose a new method called TemperFlow that addresses the multimodality issue. TemperFlow adaptively learns a sequence of tempered distributions to progressively approach the target distribution, and we prove that it overcomes the limitations of existing methods. Various experiments demonstrate the superior performance of this novel sampler compared to traditional methods, and we show its applications in modern deep learning tasks such as image generation. The programming code for the numerical experiments is available at https://github.com/yixuan/temperflow.
Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis

Abstract Aims Life-threatening ventricular arrhythmias (LTVAs) are common manifestations of sepsis. The majority of sepsis patients with LTVA are unresponsive to initial standard treatment and thus have a poor prognosis. There are very limited studies focusing on the early identification of patients at high risk of LTVA in sepsis to perform optimal preventive treatment interventions. We aimed to develop a prediction model to predict LTVA in sepsis using machine learning (ML) approaches. Methods and results Six ML algorithms including CatBoost, LightGBM, and XGBoost were employed to perform the model fitting. The least absolute shrinkage and selection operator (LASSO) regression was used to identify key features. Methods of model evaluation involved in this study included area under the receiver operating characteristic curve (AUROC), for model discrimination, calibration curve, and Brier score, for model calibration. Finally, we validated the prediction model both internally and externally. A total of 27 139 patients with sepsis were identified in this study, 1136 (4.2%) suffered from LTVA during hospitalization. We screened out 10 key features from the initial 54 variables via LASSO regression to improve the practicability of the model. CatBoost showed the best prediction performance among the six ML algorithms, with excellent discrimination (AUROC = 0.874) and calibration (Brier score = 0.157). The remarkable performance of the model was presented in the external validation cohort (n = 9492), with an AUROC of 0.836, suggesting certain generalizability of the model. Finally, a nomogram with risk classification of LTVA was shown in this study. Conclusion We established and validated a machine leaning-based prediction model, which was conducive to early identification of high-risk LTVA patients in sepsis, thus appropriate methods could be conducted to improve outcomes.
Estimation of Collapse Load for Thin-Walled Rectangular Tubes Under Bending

In two recent papers, the authors investigated the bending collapse load of rectangular tubes consisting of a perfectly elastoplastic material. From these investigations, it is found that the collapse may also occur due to the buckling of web in a rectangular tube under bending, when the tube has a cross section with a large aspect ratio of web to flange b/a. In order to evaluate the collapse load of such tubes under bending, the effective width concept given by Rusch and Lindner for a plate compressed with stress gradient was used to calculate the postbuckling strength of the tube web. However, in the solution of Rusch and Lindner, the plate is supported at only one longitudinal edge with the other longitudinal edge being free. This boundary condition is obviously different from that of the tube web. This paper complements the previous work by addressing the postbuckling strength of the web under stress gradients. The postbuckling strength of the web under stress gradients is also calculated using the effective width concept given in AS/NZS 4600 standard and North American specification (NAS) for a plate compressed with stress gradient and supported at both two longitudinal edges. Moreover, the web slenderness also affects the condition for reaching cross-sectional fully plastic yielding when the web is wider. A new method is proposed to predict the maximum moment considering the effect of web slenderness on the cross-sectional fully plastic yielding. The validity of the collapse load estimation is checked by the results of FEM (finite element method) numerical analysis.
On a three-state sojourn time problem

Consider the real-valued stochastic process {S(t), 0 ≦ t < ∞} which assumes values in an arbitrary space X. For a given subset T ⊂ X we define which represents the length in time of a visit to state T. We shall restrict ourselves to processes such that τT is a random variable having a differentiable distribution function which is independent of the time t 0 at which the visit to state T begins.
Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms

Source acquisition device identification from recorded audio aims to identify the source recording device by analyzing the intrinsic characteristics of audio, which is a challenging problem in audio forensics. In this paper, we propose a spatiotemporal representation learning framework with multi-attention mechanisms to tackle this problem. In the deep feature extraction stage of recording devices, a two-branch network based on residual dense temporal convolution networks (RD-TCNs) and convolutional neural networks (CNNs) is constructed. The spatial probability distribution features of audio signals are employed as inputs to the branch of the CNN for spatial representation learning, and the temporal spectral features of audio signals are fed into the branch of the RD-TCN network for temporal representation learning. This achieves simultaneous learning of long-term and short-term features to obtain an accurate representation of device-related information. In the spatiotemporal feature fusion stage, three attention mechanisms—temporal, spatial, and branch attention mechanisms—are designed to capture spatiotemporal weights and achieve effective deep feature fusion. The proposed framework achieves state-of-the-art performance on the benchmark CCNU_Mobile dataset, reaching an accuracy of 97.6% for the identification of 45 recording devices, with a significant reduction in training time compared to other models.
Statistical Treatment of Experimental Data

1. Introduction. 2. Probability. 3. Random Variables and Sampling Distributions. 4. Some Important Probability Distributions. 5. Estimation. 6. Confidence Intervals. 7. Hypothesis Testing. 8. Tests on Means. 9. Tests on Variances. 10. Goodness of Fit Tests. 11. Correlation. 12. The Straight Line Through the Original or Through Some Other Fixed Point. 13. The Polynomial Through the Origin or Through Some Other Fixed Point. 14. The General Straight Line. 15. The General Polynomial. 16. A Brief Look at Multiple Regression. Appendices: 1. Drawing a Random Sample Using a Table of Random Numbers. 2. Orthogonal Polynomials in x. References. Index.
Multi-Targeting Anticancer Activity of a New 4-Thiazolidinone Derivative with Anti-HER2 Antibodies in Human AGS Gastric Cancer Cells

Combining chemotherapy with immunotherapy still remains a regimen in anticancer therapy. Novel 4-thiazolidinone-bearing hybrid molecules possess well-documented anticancer activity, and together with anti-HER2 antibodies, may represent a promising strategy in treating patients with gastric cancer with confirmed human epidermal growth factor receptor 2 (HER2) expression. The aim of the study was to synthesize a new 4-thiazolidinone derivative (Les-4367) and investigate its molecular mechanism of action in combination with trastuzumab or pertuzumab in human AGS gastric cancer cells. AGS cell viability and antiproliferative potential were examined. The effect of the tested combinations as well as monotherapy on apoptosis and autophagy was also determined. Metalloproteinase-2 (MMP-2), intercellular adhesion molecule 1 (ICAM-1), pro-inflammatory and anti-inflammatory cytokine concentrations were also demonstrated by the ELISA technique. We proved that pertuzumab and trastuzumab were very effective in increasing the sensitivity of AGS gastric cancer cells to novel Les-4367. The molecular mechanism of action of the tested combination is connected with the induction of apoptosis. Additionally, the anticancer activity is not associated with the autophagy process. Decreased concentrations of pro-inflammatory cytokines, MMP-2 and ICAM-1—were observed. The novel combination of drugs based on anti-HER2 antibodies with Les-4367 is a promising strategy against AGS gastric cancer cells.
Gait Recognition Based on Sparse Representation and Segmented Frame Difference Energy Image

In order to deal with the problems that the gait recognition algorithms are complicated,cost a lot of time and can’t be used in real-time condition in extracting the feature of the gait silhouette,a method based on sparse representation and segmented frame difference energy image is proposed.Firstly,the improved segmented frame difference energy image is established as a feature image of gait.Then a dictionary is established based on it and an improved orthogonal matching pursuit algorithm is used for the fast coefficient decomposition.Finally,hidden Markov model is applied to establishing the gait recognition model based on the improved partial frame difference energy image.Selecting 90 views,an experient is done on CASIA B database.Experimental results show that this methods get better recognition rate and can be used in real-time system.
A 6-in Integrated Emitter Turn-OFF Thyristor With 17.5 kA Turn-OFF Current

The demand and requirement for high-capacity power electronic devices increase rapidly, and integrated emitter turn-off thyristor (IETO) has excellent performance in both on-state voltage drop and turn-off capability. An advanced 6-in 4.5 kV IETO device is proposed and developed, including optimized package and driver unit. A low-thickness package with compact disc spring assemblies is put forward, which achieved excellent pressure distribution with the same thickness as integrated gate commutated thyristor for complex IETO structure. A composite signal extraction structure without hindrance for ceramic seal is also proposed. Several electromagnetic transient problems during large current turn-off are analyzed, such as the emitter voltage oscillation during commutation, the damping characteristics of gate control signals, and the induced voltage on signal extraction structure. Based on the analysis, a new external driver unit with higher reliability for large current is designed. After optimization, a prototype was developed with all problems solved. According to the experiments, the 6-in 4.5 kV IETO achieved 17.5 kA turn-off current, and the transient characteristics were verified. The commutation time is less than 300 ns, which has enormous potential for larger turn-off current with better gate commutated thyristor (GCT) wafers. Compared with the other GCT-based devices, the IETO in this article reaches the maximum turn-off current.
An analysis of intensity harmonization techniques for Parkinson’s multi-site MRI datasets

Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting 2-3% of the population over 65 years of age. Considerable research has investigated the benefit of using neuroimaging to improve PD diagnosis. However, it is challenging for medical experts to manually identify the subtle differences associated with PD in such complex data. It has been shown that machine learning models can achieve human-like accuracies for many computer-aided diagnosis applications. However, model performance usually depends on the amount and diversity of training data available, whereas most Parkinson’s disease classification models were trained on rather small datasets. Training data size and diversity can be increased by curating multi-site datasets. However, this may also increase biological and non-biological variances due to differences in participant cohorts, scanners, and data acquisition protocols. Thus, data harmonization is important to reduce those variances and enable the models to focus primarily on the patterns associated with PD. This work compares intensity harmonization techniques on 1796 MRI scans from twelve studies. Our results show that a histogram matching approach does not improve classification accuracy (78%) compared to the model trained on unharmonized data (baseline). However, it reduces the disparity between sensitivity and specificity from 81% and 73% to 77% and 79%, respectively. Moreover, combining histogram matching and least squares mean tissue intensity harmonization methods outperform the baseline model (accuracy of 74% compared to 67%) for an independent test set. Finally, our analysis considering sex (male, female) and groups (PD, healthy) shows that models trained on harmonized data exhibited reduced performance disparities between groups, which may be interpreted as a form of bias mitigation.
Two Cases of Myofibrillar Myopathies: Genetic and Quality of Life Study

We describe two cases of myofibrillar myopathies, due to different gene mutations. The first was a girl with cardiomyopathy and sensory axonal neuropathy that underwent cardiac transplantation at 15 years and suffers from rotatory scoliosis due to BAG3 mutation. The second is a male patient, with evident limb-girdle weakness since age 3. Two muscle biopsies were performed at ages 3 and 15, with muscle MRI, and LDB3 gene sequence analysis also carried out. Muscle biopsies revealed the presence of dystrophic changes in the first biopsy and myopathic abnormalities in the second, and the MRI images of the lower limbs showed an asymmetrical involvement in the thigh of quadriceps muscles and in the calf of gastrocnemius muscles. The patient was responsive to treatment with an intermittent steroid regimen and muscle-strengthening exercises. Considerations on both muscle–bone interaction and psychological and socioeconomic conditions are carried out for both cases.
Evaluation of Transmembrane Protein Structural Models Using HPMScore

Transmembrane proteins (TMPs) are a class of essential proteins for biological and therapeutic purposes. Despite an increasing number of structures, the gap with the number of available sequences remains impressive. The choice of a dedicated function to select the most probable/relevant model among hundreds is a specific problem of TMPs. Indeed, the majority of approaches are mostly focused on globular proteins. We developed an alternative methodology to evaluate the quality of TMP structural models. HPMScore took into account sequence and local structural information using the unsupervised learning approach called hybrid protein model. The methodology was extensively evaluated on very different TMP all-α proteins. Structural models with different qualities were generated, from good to bad quality. HPMScore performed better than DOPE in recognizing good comparative models over more degenerated models, with a Top 1 of 46.9% against DOPE 40.1%, both giving the same result in 13.0%. When the alignments used are higher than 35%, HPM is the best for 52%, against 36% for DOPE (12% for both). These encouraging results need further improvement particularly when the sequence identity falls below 35%. An area of enhancement would be to train on a larger training set. A dedicated web server has been implemented and provided to the scientific community. It can be used with structural models generated from comparative modeling to deep learning approaches.
JAK1 Pseudokinase V666G Mutant Dominantly Impairs JAK3 Phosphorylation and IL-2 Signaling

Overactive Janus kinases (JAKs) are known to drive leukemia, making them well-suited targets for treatment. We sought to identify new JAK-activating mutations and instead found a JAK1-inactivating pseudokinase mutation, V666G. In contrast to other pseudokinase mutations that canonically lead to an active kinase, the JAK1 V666G mutation led to under-activation seen by reduced phosphorylation. To understand the functional role of JAK1 V666G in modifying kinase activity we investigated its influence on other JAK kinases and within the Interleukin-2 pathway. JAK1 V666G not only inhibited its own activity, but its presence could inhibit other JAK kinases. These findings provide new insights into the potential of JAK1 pseudokinase to modulate its own activity, as well as of other JAK kinases. Thus, the features of the JAK1 V666 region in modifying JAK kinases can be exploited to allosterically inhibit overactive JAKs.