A large population-based and validated study on the follow-up management and supportive strategy of locally advanced rectal cancer patients.

OBJECTIVE: Our objective was to evaluate the predictive factors and metastatic time for liver and lung metastasis in locally advanced rectal cancer (RC) patients. METHODS: Univariate and multivariate analysis were performed to identify risk factors and prognostic factors for liver metastasis and lung metastasis in RC. Survival probabilities were calculated using the Kaplan-Meier model and compared using the log-rank test between groups. The probability of time-to-event occurrence was calculated using the random survival forest model. Finally, the SEER database was used to verify our findings. RESULTS: Our results indicated that pathological T stage and pathological N stage were independent predictive factors for liver metastasis. Furthermore, CEA level, pathological T stage, and tumor deposit were independent predictive factors for lung metastasis. Based on the results of a multivariate Cox analysis, we categorized patients with liver and lung metastasis into three groups based on their scores. The results revealed that patients with higher scores had a higher probability of experiencing metastasis. For liver metastasis, Groups 1, 2, and 3 all exhibited higher occurrence rates within the first 24 months. However, for lung metastasis, Group 4 showed the highest occurrence rate at the 12th month, while Groups 5 and 6 exhibited the highest occurrence rates at the 15th month. CONCLUSIONS: In summary, we developed predictive models to determine the likelihood of liver and lung metastasis in RC patients. It is crucial to implement a more intensive surveillance program for patients with unfavorable risk profiles in order to facilitate early detection of metastasis.

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis. AVAILABILITY AND IMPLEMENTATION: The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.

Ontology of clinical practice guidelines for Integrated Traditional Chinese and Western Medicine.

OBJECTIVE: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision-making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed. Therefore, we are proposing an ontology of CPGs for Integrated TCM and WM. METHODS: We first initialized domain concepts and relationships to ensure the accuracy of the ontology knowledge structure. We then screened CPGs that meet the standards for Integrated TCM and WM, analyzed and classified the contents, and extracted the common structures. Based on the seven-step ontology construction method combined with inference-complement, referring to the representation methods and hierarchical relationships of terms and concepts in MeSH, ICD-10, SNOMED-CT, and other ontologies and terminology sets, we formed the concept structure and semantic relationship tables for the ontology. We also achieved the matching and mapping between the ontology and reference ontologies and term sets. Next, we defined the aspects and constraints of properties, selected multiple Integrated TCM and WM CPGs as instances to populate, and used ontology reasoning tools and formulated defined inference rules to reason and extend the ontology. Finally, we evaluated the performance of the ontology. RESULTS: The content of the Integrated TCM and WM CPGs is divided into nine parts: basic information, background, development method, clinical question, recommendation, evidence, conclusion, result, and reason for recommendations. The Integrated TCM and WM CPG ontology has 152 classes and defines 90 object properties and 114 data properties, with a maximum classification depth of 4 layers. The terms of disease, drug and examination item names in the ontology have been standardized. CONCLUSIONS: This study proposes an Integrated TCM and WM CPG ontology. The ontology adopts a modular design, which has both sharing and scaling ability, and can express rich guideline knowledge. It provides important support for the semantic processing and computational application of guideline documents.

A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity.

Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion's remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.

Frequency-dependent dielectric spectroscopic analysis on phytochemical and antioxidant activities in Radix Glycyrrhizae extract.

Radix Glycyrrhizae, the dried roots of the Glycyrrhiza glabra plant, is a popular Chinese herbal medicine known for its various health benefits. It is particularly effective in relieving respiratory problems like coughs, sore throats, bronchitis, and asthma. However, there is limited research on the electrical properties of Radix Glycyrrhizae, likely due to its complex composition of phytochemical and antioxidant activities. This research aims to investigate the potential of these active biological compounds and understand their electrochemical properties. In this study, High-Performance Liquid Chromatography (HPLC) analysis revealed that Radix Glycyrrhizae decoction contains significant amounts of flavonoids and saponins, compounds known for their health benefits and therapeutic effects. Further analysis using Fourier Transform Infrared Spectroscopy (FTIR) identified several functional groups, including phenols, alcohols, alkynes, alkenes, ethers, and glycosides, which contribute to the plant's medicinal potential and affect the impedance and dielectric properties of the extract. The antioxidant activity of Radix Glycyrrhizae decoction was also evaluated using DPPH assays, showing similar radical scavenging activity to gallic acid. Dielectric and impedance measurement of Radix Glycyrrhizae extract were performed using an Agilent vector network analyzer and a Hioki impedance analyzer. The dielectric constant measured was consistent across both analyzers. However, the loss factor showed different trends: the vector network analyzer indicated a decrease in the loss factor with increasing frequency in the range of 5 MHz-20 GHz, while the impedance analyzer showed the opposite trend in the frequency range of 4 Hz-5 MHz.

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning.

Background: Radiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs and TiME. Methods: Predictive models for preoperative radiotherapy (preRT) response were developed using machine learning, identifying EIF5A as a key gene associated with radioresistance. EIF5A expression was analyzed via bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Functional assays and in vivo experiments validated EIF5A's role in radioresistance and TiME modulation. Results: EIF5A was significantly upregulated in radioresistant colorectal cancer (CRC) tissues. EIF5A knockdown in CRC cell lines reduced cell viability, migration, and invasion after radiation, and increased radiation-induced apoptosis. Mechanistically, EIF5A promoted cancer stem cell (CSC) characteristics through the Hedgehog signaling pathway. Analysis of the TiME revealed that the radiation-resistant group had an immune-desert phenotype, characterized by low immune cell infiltration. In vivo experiments showed that EIF5A knockdown led to increased infiltration of CD8+ T cells and M1 macrophages, and decreased M2 macrophages and Tregs following radiation therapy, thereby enhancing the radiotherapy response. Conclusion: EIF5A contributes to CRC radioresistance by promoting CSC traits via the Hedgehog pathway and modulating the TiME to an immune-suppressive state. Targeting EIF5A could enhance radiation sensitivity and improve immune responses, offering a potential therapeutic strategy to optimize radiotherapy outcomes in CRC patients.

Silencing siRNA microneedle patches versus silicone sheets in reducing post-surgical scars: a randomized single-blinded intra-individually controlled clinical trial.

BACKGROUND: A common complication of wounds is exuberant growth of fibrotic scar tissue, which can lead to hypertrophic scars or keloids. There are currently no treatments with good evidence for preventing excessive scar tissue formation. In this study, we explored the use of microneedle patches containing siRNA inhibiting SPARC mRNA in reducing the volume of post-surgical scars. OBJECTIVE: We aim to compare the differences in the volume of post-surgical scars between daily application of siRNA-embedded dissolving microneedle patches and silicone sheets. The primary study outcome measure was the 3D volume of scar elevation.Our hypothesis was that scar formation in the half of the wound treated with siRNA microneedle patches will be lesser, as reflected by a smaller 3D volume, as compared to the half treated with silicone sheets. METHODS: This was an 8-week, single-blinded intra-individually controlled randomised trial in a tertiary dermatological centre. Patients with two-week-old post-operative wounds were recruited. Each half of the scar was randomly assigned to the microneedle patch or silicone sheet. Three-dimensional (3D) volumes were obtained from the scars via a high-resolution scanner at day 0, 30 and 60. RESULTS: At day 30, scars treated with the microneedle patches had a lower geometric mean volume of 0.79mm3 when compared to scars treated with silicone sheets, with a difference in mean percentage volume reduction of 10.70%.At day 60, scars treated with the microneedle patches had a statistically significant lower volume (8.88mm3) when compared to the side treated with silicone sheets (12.77mm3, p=0.005), with a difference in mean percentage reduction of 9.66%. Additionally, there was also a statistically significant difference between the percentage reduction in scar volume, compared to baseline, on the side treated with microneedle patches (mean=83.78%) compared to the side treated with silicone sheets (mean=74.11%). CONCLUSIONS: There was a significantly greater reduction in the volume of post-surgical scars on the side treated with microneedle patches compared to the side treated with silicone sheets. This demonstrates the use of transdermal gene silencing technology for scar inhibition and that siRNA microneedle patches can be an effective and safe modality in the reduction of scar tissue formation. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12622000558729, https://www.anzctr.org.au.

S100A8/A9-activated IFNγ+ NK cells trigger ß-cell necroptosis in hepatitis B virus-associated liver cirrhosis.

BACKGROUND AND AIMS: Hepatitis B virus (HBV)-associated liver cirrhosis (LC), a common condition with high incidence and mortality rates, is often associated with diabetes mellitus (DM). However, the molecular mechanisms underlying impaired glucose regulation during HBV-associated LC remain unclear. METHODS: Data from 63 patients with LC and 62 patients with LC-associated DM were analysed. Co-culture of NK cells and islet ß cell lines were used to study the glucose regulation mechanism. A mouse model of LC was used to verify the effect of S100A8/A9 on the glucose regulation. RESULTS: Higher levels of interferon (IFN)-γ derived from natural killer (NK) cells and lower levels of insulin emerged in the peripheral blood of patients with both LC and DM compared with those from patients with LC only. IFN-γ derived from NK cells facilitated ß cell necroptosis and impaired insulin production. Furthermore, S100A8/A9 elevation in patients with both LC and DM was found to upregulate IFN-γ production in NK cells. Consistently, in the mouse model for LC, mice treated with carbon tetrachloride (CCL4) and S100A8/A9 exhibited increased blood glucose, impaired insulin production, increased IFN-γ, and increased ß cells necroptosis compared with those treated with CCL4. Mechanistically, S100A8/A9 activated the p38 MAPK pathway to increase IFN-γ production in NK cells. These effects were diminished after blocking RAGE. CONCLUSION: Together, the data indicate that IFN-γ produced by NK cells induces ß cell necroptosis via the S100A8/A9-RAGE-p38 MAPK axis in patients with LC and DM. Reduced levels of S100A8/A9, NK cells, and IFN-γ could be valuable for the treatment of LC with DM. Accumulation of S100A8/A9 in patients with LC may indicate the emergence of DM.

Decoupling of the onset of anharmonicity between a protein and its surface water around 200 K.

The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Hepatic Metabolomic Responses to Low-Temperature Stress in the Invasive Turtle, Trachemys scripta elegans.

Investigating the physiological and biochemical changes of ectothermic species before entering hibernation would contribute to the understanding of how they adapt to low-temperature environments. Here, red-eared slider turtle (Trachemys scripta elegans) hatchlings were maintained under different thermal treatments (24 °C, slowly decreasing temperatures from 24 °C to 14 °C, and to 4 °C). Hepatic metabolite alterations were measured to assess the metabolic impacts of low-temperature stress in this species. Of these differentially changed metabolites, some (e.g., raffinose, spermidine, allocholic acid, taurohyocholate, 2-ketobutyric acid, acetylcysteine) were shown to decrease, while others (e.g., stearolic acid, D-mannose) increased in low-temperature treatments. Our results indicated that short-term low-temperature stress might have limited impacts on lipid and energy metabolism in this species. The changes in other metabolites (e.g., allocholic acid, taurohyocholate, spermine, acetylcysteine) might be associated with a low food intake (and thus reduced digestive performance) and weakened immune ability of low-temperature-exposed animals.

On the temperature dependence of the density of states of liquids at low energies.

We report neutron-scattering measurements of the density of states (DOS) of water and liquid Fomblin in a wide range of temperatures. In the liquid phase, we confirm the presence of a universal low-energy linear scaling of the experimental DOS as a function of the frequency, g ( ω ) = a ( T ) ω , which persists at all temperatures. The low-frequency scaling of the DOS exhibits a sharp jump at the melting point of water, below which the standard Debye's law, g ( ω ) ∝ ω 2 , is recovered. On the contrary, in Fomblin, we observe a continuous transition between the two exponents reflecting its glassy dynamics, which is confirmed by structure measurements. More importantly, in both systems, we find that the slope a(T) grows with temperature following an exponential Arrhenius-like form, a ( T ) ∝ exp ( - ⟨ E ⟩ / T ) . We confirm this experimental trend using molecular dynamics simulations and show that the prediction of instantaneous normal mode (INM) theory for a(T) is in qualitative agreement with the experimental data.

Unconventional Thermophotonic Charge Density Wave.

Charge-order states of broken symmetry, such as charge density wave (CDW), are able to induce exceptional physical properties, however, the precise understanding of the underlying physics is still elusive. Here, we combine fluctuational electrodynamics and density functional theory to reveal an unconventional thermophotonic effect in CDW-bearing TiSe_{2}, referred to as thermophotonic-CDW (tp-CDW). The interplay of plasmon polariton and CDW electron excitations give rise to an anomalous negative temperature dependency in thermal photons transport, offering an intuitive fingerprint for a transformation of the electron order. Additionally, the demonstrated nontrivial features of tp-CDW transition hold promise for a controllable manipulation of heat flow, which could be extensively utilized in various fields such as thermal science and electron dynamics, as well as in next-generation energy devices.

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Effects of potassium diformate on growth performance, apparent digestibility of nutrients, serum biochemical indices, and intestinal microflora in Cherry Valley ducks.

This study was performed to investigate the effects of potassium diformate (KDF) on growth performance, apparent digestibility of nutrients, serum biochemical indices, and intestinal microflora of Cherry Valley ducks. In total, 144 female healthy 1-day-old Cherry Valley ducks were divided into 3 groups with 6 replicates per group and 8 ducks per replicate according to the principle of similar body weight. The control group was fed a basic diet. In the 2 experimental groups, 0.8% and 1.2% KDF was added to the basic diet, respectively. The trial period was 6 wk and the pretrial period was 3 wk. The final weight and ADG were significantly higher in the 0.8% KDF group than in the control group (P < 0.05). The feed-to-gain ratio was significantly lower in both KDF groups than in the control group (P < 0.05). The apparent digestibility of CP was significantly higher in both KDF groups than in the control group (P < 0.05). The apparent digestibility of calcium was also significantly higher in the 0.8% KDF group (P < 0.05). The serum levels of alkaline phosphatase, cholesterol, and total protein were significantly lower in the 0.8% KDF group than in the control group (P < 0.05), the IgM content was significantly higher (P < 0.05), the low-density lipoprotein cholesterol, triglyceride, and urea levels were significantly lower (P < 0.01), and the glucose level was significantly higher (P < 0.01). The serum total protein level was significantly higher in the 1.2% KDF group than in the control group (P < 0.05). The relative abundance of Firmicutes and Patescibacteria in the gut of ducks was significantly higher in the 0.8% KDF group than in the control group (P < 0.05), the relative abundance of unclassified Erysipelotrichaceae and Lactobacillus was significantly higher (P < 0.01), and the relative abundance of Fusobacteriota was significantly lower (P < 0.05). However, the relative abundance of Firmicutes in the gut of ducks was significantly higher in the 1.2% KDF group than in the control group (P < 0.05). The relative abundance of unclassified Erysipelotrichaceae and Clostridium sensu stricto 1 was significantly higher (P < 0.01), as was the relative abundance of Fusobacteriota and Proteobacteria (P < 0.05). These findings indicate that the addition of 0.8% KDF to the diet can improve the growth performance of Cherry Valley ducks, promote the absorption of nutrients, change the structure of the microflora in the cecum, and increase the relative abundance of dominant bacteria. It was also shown that there was a significant difference between the 0.8% and 1.2% KDF levels which suggest that the safety margin for overdosing is quite low.

The SPARC-related modular calcium binding 1 (Smoc1) regulated by androgen is required for mouse gubernaculum development and testicular descent.

ABSTRACT: Testicular descent occurs in two consecutive stages: the transabdominal stage and the inguinoscrotal stage. Androgens play a crucial role in the second stage by influencing the development of the gubernaculum, a structure that pulls the testis into the scrotum. However, the mechanisms of androgen actions underlying many of the processes associated with gubernaculum development have not been fully elucidated. To identify the androgen-regulated genes, we conducted large-scale gene expression analyses on the gubernaculum harvested from luteinizing hormone/choriogonadotropin receptor knockout (Lhcgr KO) mice, an animal model of inguinoscrotal testis maldescent resulting from androgen deficiency. We found that the expression of secreted protein acidic and rich in cysteine (SPARC)-related modular calcium binding 1 (Smoc1) was the most severely suppressed at both the transcript and protein levels, while its expression was the most dramatically induced by testosterone administration in the gubernacula of Lhcgr KO mice. The upregulation of Smoc1 expression by testosterone was curtailed by the addition of an androgen receptor antagonist, flutamide. In addition, in vitro studies demonstrated that SMOC1 modestly but significantly promoted the proliferation of gubernacular cells. In the cultures of myogenic differentiation medium, both testosterone and SMOC1 enhanced the expression of myogenic regulatory factors such as paired box 7 (Pax7) and myogenic factor 5 (Myf5). After short-interfering RNA-mediated knocking down of Smoc1, the expression of Pax7 and Myf5 diminished, and testosterone alone did not recover, but additional SMOC1 did. These observations indicate that SMOC1 is pivotal in mediating androgen action to regulate gubernaculum development during inguinoscrotal testicular descent.

Preparation of Monodispersed Nanofibrous Gelatin Microspheres Using Homebuilt Microfluidics.

Gelatin, a protein derivative from collagen, is a versatile material with promising applications in tissue engineering. Among the various forms of gelatin scaffolds, nanofibrous gelatin microspheres (NFGMs) are attracting research efforts due to their fibrous nature and injectability. However, current methods for synthesizing nanofibrous gelatin microspheres (NFGMs) have limitations, such as wide size distributions and the use of toxic solvents. To address these challenges, the article introduces a novel approach. First, it describes the creation of a microfluidic device using readily available supplies. Subsequently, it outlines a unique process for producing monodispersed NFGMs through a combination of the microfluidic device and thermally induced phase separation (TIPS). This innovative method eliminates the need for sieving and the use of toxic solvents, making it a more ecofriendly and efficient alternative.

Fast, sensitive detection of protein homologs using deep dense retrieval.

The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.

Emergence of Debye Scaling in the Density of States of Liquids under Nanoconfinement.

In the realm of nanoscience, the dynamic behaviors of liquids at scales beyond the conventional structural relaxation time, τ, unfold a fascinating blend of solid-like characteristics, including the propagation of collective shear waves and the emergence of elasticity. However, in classical bulk liquids, where τ is typically of the order of 1 ps or less, this solid-like behavior remains elusive in the low-frequency region of the density of states (DOS). Here, we provide evidence for the emergent solid-like nature of liquids at short distances through inelastic neutron scattering measurements of the low-frequency DOS in liquid water and glycerol confined within graphene oxide membranes. In particular, upon increasing the strength of confinement, we observe a transition from a liquid-like DOS (linear in the frequency ω) to a solid-like behavior (Debye law, ∼ω2) in the range of 1-4 meV. Molecular dynamics simulations confirm these findings and reveal additional solid-like features, including propagating collective shear waves and a reduction in the self-diffusion constant. Finally, we show that the onset of solid-like dynamics is pushed toward low frequency along with the slowing-down of the relaxation processes upon confinement. This nanoconfinement-induced transition, aligning with k-gap theory, underscores the potential of leveraging liquid nanoconfinement in advancing nanoscale science and technology, building more connections between fluid dynamics and materials engineering.