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Background The genus Takifugu includes highly valued fish species known for their delicate flavor, making them popular in multiple countries. However, many species from this genus face significant threats. In order to better understand the genetic diversity and evolutionary dynamics of Takifugu , a syntelog-based pan-genome and graph genome were constructed using the data of seven Takifugu species. Results The analysis of 28,085 syntelog groups (SGs) composed of protein-coding genes revealed that only 57.3% of the SGs were shared among all individuals, whereas the remaining genes presented presence-absence variation (PAV) across the seven genomes. Using the graph genome as a reference, a population of 160 Takifugu individuals was analyzed, from which 20,133,471 SNPs, 4,606,141 Indels, and 152,200 SVs were identified. The gene flow analysis revealed directional gene flow from Takifugu bimaculatus and Takifugu flavidus to Takifugu oblongus . Notably, a 51-bp insertion in the ABCB9 gene differed significantly in frequency between the two migratory populations, suggesting the potential role of this gene in the migratory behavior of these species. Additionally, the expression profiles from 13 tissues or organs (brain, gallbladder, gill, gonad, heart, kidney, liver, muscle, pituitary, skin, spleen, stomach, and swim bladder) revealed a unique expression pattern in the liver, with the tissue-specific genes exhibiting evolutionary conservation to varying degrees. The highest proportion of core genes was found in the pituitary, whereas the lowest was found in the spleen. Conclusions This study provides comprehensive genomic resources that enhance the understanding of the genetic diversity and evolutionary dynamics of Takifugu species. The findings offer insights for research on both breeding and conservation of Takifugu .

Background： Spinocerebellar ataxia type 3 （SCA3） is the most common subtype of SCA worldwide, and runs a slowly progressive and unremitting disease course. There is currently no curable treatment available. Growing evidence has suggested that nerve growth factor （NGF） may have therapeutic effects in neurodegenerative diseases, and possibly also in SCA3. The objective of this study was to test the efficacy of NGF in SCA3 patients. Methods： We performed an open-label prospective study in genetically confirmed adult （〉18 years old） SCA3 patients. NGF was administered by intramuscular injection （18 μg once daily） fbr 28 days consecutively. All the patients were evaluated at baseline and 2 and 4 weeks after treatment using the Chinese version of the scale for assessment and rating of ataxia （SARA）. Results： Twenty-one SCA3 patients （ 10 men and 11 women, mean age 39.14 ± 7.81 years, mean disease duration 4.14 ± 1.90 years, mean CAG repeats number 77,57 ± 2.27） were enrolled. After 28 days of NGF treatment, the mean total SARA score decreased significantly from a baseline of 8.48± 2.40 to 6.30 ± 1.87 （P 〈 0.001 ）. Subsections SARA scores also showed significant improvements in stance （P = 0.003）, speech （P = 0.023）, finger chase （P = 0.015）, fast alternating hand movements （P = 0.009）, and heel-shin slide （P = 0.001）. Conclusions： Our preliminary data suggest that NGF may be effective in treating patients with SCA3.

Acorus calamus Linn, sweet flag (Araceae family), is commonly used as a traditional medicine as well as against a number of invertebrate pests, including stored grain insects. Essential oil, an aromatic and volatile liquid, can be extracted from sweet flag. The chemicals in sweet flag oils are secondary metabolites, which play an important role in food preservation as they possess insecticidal and antimicrobial properties. Furthermore, food-borne diseases are a growing public health problem worldwide, calling for more effective preservation strategies. The antibacterial antifungal properties of sweet flag oil and their constituents have been documented extensively. Sweet flag oils have potential for developing into natural insecticide for food preservation. The purpose of this chapter is to provide an overview of current knowledge about the insecticidal and antimicrobial properties and the mode of action of sweet flag oils, to identify research avenues that can facilitate implementation of sweet flag oils as a natural preservative in food.

The origin of water’s anomalies has been a matter of long-standing debate. A two-state model, dating back to Röntgen, relies on the dynamical coexistence of two types of local structures—locally favored tetrahedral structure (LFTS) and disordered normal-liquid structure (DNLS)—in liquid water. Phenomenologically, this model not only explains water’s thermodynamic anomalies but also can rationalize the existence of a liquid–liquid critical point (LLCP) if there is a cooperative formation of LFTS. We recently found direct evidence for the coexistence of LFTS and DNLS in the experimental structure factor of liquid water. However, the existence of the LLCP and its impact on water’s properties has remained elusive, leaving the origin of water’s anomalies unclear. Here we propose a unique strategy to locate the LLCP of liquid water. First, we make a comprehensive analysis of a large set of experimental structural, thermodynamic, and dynamic data based on our hierarchical two-state model. This model predicts that the two thermodynamic and dynamical fluctuation maxima lines should cross at the LLCP if it exists, which we confirm by hundred-microsecond simulations for model waters. Based on recent experimental results of the compressibility and diffusivity measurements in the no man’s land, we reveal that the two lines cross around 184 K and 173 MPa for real water, suggesting the presence of the LLCP around there. Nevertheless, we find that the criticality is almost negligible in the experimentally accessible region of liquid water because it is too far from the LLCP. Our findings would provide a clue to settle the long-standing debate.

Mesoporous silica nanoparticles (MSNs) are recognized as a prime example of nanotechnology applied in the biomedical field, due to their easily tunable structure and composition, diverse surface functionalization properties, and excellent biocompatibility. Over the past two decades, researchers have developed a wide variety of MSNs-based nanoplatforms through careful design and controlled preparation techniques, demonstrating their adaptability to various biomedical application scenarios. With the continuous breakthroughs of MSNs in the fields of biosensing, disease diagnosis and treatment, tissue engineering, etc., MSNs are gradually moving from basic research to clinical trials. In this review, we provide a detailed summary of MSNs in the biomedical field, beginning with a comprehensive overview of their development history. We then discuss the types of MSNs-based nanostructured architectures, as well as the classification of MSNs-based nanocomposites according to the elements existed in various inorganic functional components. Subsequently, we summarize the primary purposes of surface-functionalized modifications of MSNs. In the following, we discuss the biomedical applications of MSNs, and highlight the MSNs-based targeted therapeutic modalities currently developed. Given the importance of clinical translation, we also summarize the progress of MSNs in clinical trials. Finally, we take a perspective on the future direction and remaining challenges of MSNs in the biomedical field.

Liquids can be broadly classified into two categories, fragile and strong ones, depending on how their dynamical properties change with temperature. The dynamics of a strong liquid obey the Arrhenius law, whereas the fragile one displays a super-Arrhenius law, with a much steeper slowing down upon cooling. Recently, however, it was discovered that many materials such as water, oxides, and metals do not obey this simple classification, apparently exhibiting a fragile-to-strong transition far above T g. Such a transition is particularly well known for water, and it is now regarded as one of water’s most important anomalies. This phenomenon has been attributed to either an unusual glass transition behavior or the crossing of a Widom line emanating from a liquid–liquid critical point. Here by computer simulations of two popular water models and through analyses of experimental data, we show that the emergent fragile-to-strong transition is actually a crossover between two Arrhenius regimes with different activation energies, which can be naturally explained by a two-state description of the dynamics. Our finding provides insight into the fragile-to-strong transition observed in a wide class of materials.

A bstract We revisit the status of the new-physics interpretations of the anomalies in semileptonic B decays in light of the new data reported by Belle on the lepton-universality ratios R D (*) using the semileptonic tag and on the longitudinal polarization of the D * in B → D * τν , F L D ∗ . The preferred solutions involve new left-handed currents or tensor contributions. Interpretations with pure right-handed currents are disfavored by the LHC data, while pure scalar models are disfavored by the upper limits derived either from the LHC or from the B c lifetime. The observable F L D ∗ also gives an important constraint leading to the exclusion of large regions of parameter space. Finally, we investigate the sensitivity of different observables to the various scenarios and conclude that a measurement of the tau polarization in the decay mode B → Dτν would effectively discriminate among them.

Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory (LSTM, a variant of RNN), our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing.

Inherited the advantages of inorganic salts and organic solvents, ionic liquids (ILs) exhibit many superior properties allowing them promising green solvents for the future. Although it has been widely acknowledged that the unique features of ILs originate from their dual ionic and organic nature, its microscopic physical origin still remains blurry. In this work, by comparing the ion/molecule cage structures obtained from molecular dynamics simulations for seven prototypic liquids—a molten inorganic salt, four ILs, a strongly polar organic solvent and a weakly polar organic solvent, we have revealed that the depth of the cage energy landscape characterizes the ionic nature of ILs, whereas the slope and curvature of its mimimum determine the organic nature of ILs. This finding advances our understanding of ILs and thus will help their efficient utilization as well as the systematic design of novel functionalized ILs.

We introduce MedMNIST v2 , a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into a small size of 28 × 28 (2D) or 28 × 28 × 28 (3D) with the corresponding classification labels so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label). The resulting dataset, consisting of 708,069 2D images and 9,998 3D images in total, could support numerous research/educational purposes in biomedical image analysis, computer vision, and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D/3D neural networks and open-source/commercial AutoML tools. The data and code are publicly available at https://medmnist.com/ . Measurement(s) supervised machine learning Technology Type(s) machine learning