Chromosome-level genome assembly of Scathophaga stercoraria provides new insights into the evolutionary adaptations of dung flies.

The yellow dung fly Scathophaga stercoraria is a widely distributed species in high-altitude regions of the Northern Hemisphere. It plays important roles as a decomposer, predator, and pollinator in the ecosystem. As a staple model organism, S. stercoraria serves as a standard test species for assessing the toxicity of drug residues in livestock dung and has been the focus of numerous studies. The genetic mechanisms underlying the ecological adaptability of S. stercoraria remain poorly understood. To fill the gap, we first assembled a high-quality chromosome-level genome of S. stercoraria, resulting in a final assembly size of 549.64 Mb, with a contig N50 of 4.06 Mb, and 92.53 % of the sequence anchored to six chromosomes. Gene family analysis revealed an expansion of Toll (Toll1), GNBP3, Cyp303a1, Cyp4d14, Cyp6g1, OR67d, and yolk protein genes in the S. stercoraria genome. Transcriptome analysis indicated that most genes in the trypsin and carboxypeptidase gene families are predominantly expressed during the larval stage, whereas the α-Amylase gene family is mainly expressed during the adult stage. Additionally, PGRP-SC is highly expressed during the larval stage, OBPs are primarily expressed during the adult stage, and yolk protein genes exhibit female-biased expression. Our study not only provides a new resource for the dung flies genomic pool, but also identifies the expression patterns of key ecologically adaptative genes and gene families at the developmental stages, which provides new insights into the ecological adaptive evolution of dung flies.

Foundation model for efficient biological discovery in single-molecule data.

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

A new Tseng method for supply chain network equilibrium model.

To solve the equilibrium problem of the supply chain network, a new subgradient extragradient method is introduced. The proposal achieves adaptive parameter selection, and supports a one-step subgradient projection operator, which can theoretically reduce the computational complexity of the algorithm. The introduction of subgradient projection operators makes the calculation of algorithms easier, and transforms the projection difficulty problem into how to find suitable sub-differential function problems. The given convergence proof further shows the advantages of the proposed algorithm. Finally, the presented algorithm is operated to a concrete supply chain network model. The comparisons show the proposed algorithm is better than other methods in term of CPU running time and iteration steps.

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan.

The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks. We present an international, prospective evaluation of our models' performance across all states and counties in the USA and prefectures in Japan. Nationally, incident mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths during prospective deployment remained consistently <8% (US) and <29% (Japan), while cumulative MAPE remained <2% (US) and <10% (Japan). We show that our models perform well even during periods of considerable change in population behavior, and are robust to demographic differences across different geographic locations. We further demonstrate that our framework provides meaningful explanatory insights with the models accurately adapting to local and national policy interventions. Our framework enables counterfactual simulations, which indicate continuing Non-Pharmaceutical Interventions alongside vaccinations is essential for faster recovery from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions.

Topological floppy modes in models of epithelial tissues.

Recent advances in topological mechanics have revealed unusual phenomena such as topologically protected floppy modes and states of self-stress that are exponentially localized at boundaries and interfaces of mechanical networks. In this paper, we explore the topological mechanics of epithelial tissues, where the appearance of these boundary and interface modes could lead to localized soft or stressed spots and play a role in morphogenesis. We consider both a simple vertex model (VM) governed by an effective elastic energy and its generalization to an active tension network (ATN) which incorporates active adaptation of the cytoskeleton. By analyzing spatially periodic lattices at the Maxwell point of mechanical instability, we find topologically polarized phases with exponential localization of floppy modes and states of self-stress in the ATN when cells are allowed to become concave, but not in the VM.

Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.

Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.

Correlated Rigidity Percolation and Colloidal Gels.

Rigidity percolation (RP) occurs when mechanical stability emerges in disordered networks as constraints or components are added. Here we discuss RP with structural correlations, an effect ignored in classical theories albeit relevant to many liquid-to-amorphous-solid transitions, such as colloidal gelation, which are due to attractive interactions and aggregation. Using a lattice model, we show that structural correlations shift RP to lower volume fractions. Through molecular dynamics simulations, we show that increasing attraction in colloidal gelation increases structural correlation and thus lowers the RP transition, agreeing with experiments. Hence, the emergence of rigidity at colloidal gelation can be understood as a RP transition, but occurs at volume fractions far below values predicted by the classical RP, due to attractive interactions which induce structural correlation.

Topological Edge Floppy Modes in Disordered Fiber Networks.

Disordered fiber networks are ubiquitous in a broad range of natural (e.g., cytoskeleton) and manmade (e.g., aerogels) materials. In this Letter, we discuss the emergence of topological floppy edge modes in two-dimensional fiber networks as a result of deformation or active driving. It is known that a network of straight fibers exhibits bulk floppy modes which only bend the fibers without stretching them. We find that, interestingly, with a perturbation in geometry, these bulk modes evolve into edge modes. We introduce a topological index for these edge modes and discuss their implications in biology.

Privacy-Preserving and Secure Sharing of PHR in the Cloud.

As a new summarized record of an individual's medical data and information, Personal Health Record (PHR) can be accessible online. The owner can control fully his/her PHR files to be shared with different users such as doctors, clinic agents, and friends. However, in an open network environment like in the Cloud, these sensitive privacy information may be gotten by those unauthorized parties and users. In this paper, we consider how to achieve PHR data confidentiality and provide fine-grained access control of PHR files in the public Cloud based on Attribute Based Encryption(ABE). Differing from previous works, we also consider the privacy preserving of the receivers since the attributes of the receivers relate to their identity or medical information, which would make some sensitive data exposed to third services. Anonymous ABE(AABE) not only enforces the security of PHR of the owners but also preserves the privacy of the receivers. But a normal AABE with a single private key generation(PKG) center may not match a PHR system in the hierarchical architecture. Therefore, we discuss not only the construction of the PHR sharing system base on AABE but also how to construct the PHR sharing system based on the hierarchical AABE. The proposed schemes(especially based on hierarchical AABE) have many advantages over the available such as short public keys, constant-size private keys, which overcome the weaknesses in the existing works. In the standard model, the introduced schemes achieve compact security in the prime order groups.

The catalytic activity of Ag2S-montmorillonites as peroxidase mimetic toward colorimetric detection of H2O2.

Nanocomposites based on silver sulfide (Ag2S) and Ca-montmorillonite (Ca(2+)-MMT) were synthesized by a simple hydrothermal method. The nanocomposites were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infrared spectra (FTIR). The as-prepared Ag2S-MMT nanocomposites were firstly demonstrated to possess intrinsic peroxidase-like activity and could rapidly catalytically oxidize the substrate 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of H2O2 to produce a blue product which can be seen by the naked eye in only one minute. The experimental results revealed that the Ag2S-MMT nanocomposites exhibit higher thermal durance. Based on the TMB-H2O2 catalyzed color reaction, the Ag2S-MMT nanocomposites were exploited as a new type of biosensor for detection and estimation of H2O2 through a simple, cheap and selective colorimetric method.

Finite-temperature mechanical instability in disordered lattices.

Mechanical instability takes different forms in various ordered and disordered systems and little is known about how thermal fluctuations affect different classes of mechanical instabilities. We develop an analytic theory involving renormalization of rigidity and coherent potential approximation that can be used to understand finite-temperature mechanical stabilities in various disordered systems. We use this theory to study two disordered lattices: a randomly diluted triangular lattice and a randomly braced square lattice. These two lattices belong to two different universality classes as they approach mechanical instability at T=0. We show that thermal fluctuations stabilize both lattices. In particular, the triangular lattice displays a critical regime in which the shear modulus scales as G∼T(1/2), whereas the square lattice shows G∼T(2/3). We discuss generic scaling laws for finite-T mechanical instabilities and relate them to experimental systems.

Enhanced peroxidase-like activity of porphyrin functionalized ceria nanorods for sensitive and selective colorimetric detection of glucose.

Ceria nanorods modified with 5,10,15,20-tetrakis(4-carboxyl phenyl)-porphyrin (H2TCPP) were prepared. These nanocomposites (H2TCPP-CeO2) exhibited the intrinsic peroxidase-like activity and could catalyze the oxidation of classical peroxidase substrate 3,3',5,5'-tetramethylbiphenyl dihydrochloride (TMB·2HCl) in the presence of H2O2 to produce a typical color reaction from colorless to blue. Our results demonstrated that both the H2TCPP-CeO2 nanocomposites and CeO2 nanorods exhibited higher thermal durance than that of HRP. The affinity of The H2TCPP-CeO2 nanocomposites toward H2O2 and TMB is similar to that of HRP. Fluorescent results indicated that the catalytic mechanism of the H2TCPP-CeO2 nanocomposites were from the decomposition of H2O2 into hydroxyl radicals. Based on these studies, a simple, sensitive, and selective visual and colorimetric method using TMB as the substrate was designed to detect glucose when combined with glucose oxidase. The proposed colorimetric method can detect H2O2 at a low detection limit of 6.1×10(-6)M and a dynamic range of 10(-5)-10(-4)mol·L(-1). This method can also detect glucose at a low detection limit of 3.3×10(-5)mol·L(-1) and a dynamic range of 5.0×10(-5)-1.0×10(-4)mol·L(-1).

One-pot synthesis of porphyrin functionalized γ-Fe2O3 nanocomposites as peroxidase mimics for H2O2 and glucose detection.

Meso-tetrakis(4-carboxyphenyl)-porphyrin-functionalized γ-Fe2O3 nanoparticles (H2TCPP-γ-Fe2O3) were successfully prepared by one-pot method under hydrothermal conditions and were found to possess intrinsic peroxidase-like activity. The H2TCPP-γ-Fe2O3 nanocomposites can catalytically oxidize peroxidase substrate 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of H2O2 to produce a blue color reaction, which can be easily observed by the naked eye. Furthermore, kinetic studies indicate that the H2TCPP-γ-Fe2O3 nanocomposites have an even higher affinity to TMB than that of the natural enzyme, horseradish peroxidase (HRP). On the basis of the high activity, the reaction provides a simple, sensitive and selective method for colorimetric detection of H2O2 over a range of 10-100 µM with a minimum detection limit of 1.73 µM. Moreover, H2TCPP-γ-Fe2O3/glucose oxidase (GOx)/TMB system provides a novel colorimetric sensor for glucose and shows good response toward glucose detection over a range of 5-25 µM with a minimum detection limit of 2.54 µM. The results indicated that it is a simple, cheap, convenient, highly selective, sensitive and easy handling colorimetric assay. Results of a fluorescent probe suggest that the catalase-mimic activity of the H2TCPP-γ-Fe2O3 nanocomposites effectively catalyze the decomposition of H2O2 into H2O and O2.

Rigidity percolation by next-nearest-neighbor bonds on generic and regular isostatic lattices.

We study rigidity percolation transitions in two-dimensional central-force isostatic lattices, including the square and the kagome lattices, as next-nearest-neighbor bonds ("braces") are randomly added to the system. In particular, we focus on the differences between regular lattices, which are perfectly periodic, and generic lattices with the same topology of bonds but whose sites are at random positions in space. We find that the regular square and kagome lattices exhibit a rigidity percolation transition when the number of braces is ∼LlnL, where L is the linear size of the lattice. This transition exhibits features of both first-order and second-order transitions: The whole lattice becomes rigid at the transition, and a diverging length scale also exists. In contrast, we find that the rigidity percolation transition in the generic lattices occur when the number of braces is very close to the number obtained from Maxwell's law for floppy modes, which is ∼L. The transition in generic lattices is a very sharp first-order-like transition, at which the addition of one brace connects all small rigid regions in the bulk of the lattice, leaving only floppy modes on the edge. We characterize these transitions using numerical simulations and develop analytic theories capturing each transition. Our results relate to other interesting problems, including jamming and bootstrap percolation.