A high-quality bonobo genome refines the analysis of hominid evolution.

The divergence of chimpanzee and bonobo provides one of the few examples of recent hominid speciation1,2. Here we describe a fully annotated, high-quality bonobo genome assembly, which was constructed without guidance from reference genomes by applying a multiplatform genomics approach. We generate a bonobo genome assembly in which more than 98% of genes are completely annotated and 99% of the gaps are closed, including the resolution of about half of the segmental duplications and almost all of the full-length mobile elements. We compare the bonobo genome to those of other great apes1,3-5 and identify more than 5,569 fixed structural variants that specifically distinguish the bonobo and chimpanzee lineages. We focus on genes that have been lost, changed in structure or expanded in the last few million years of bonobo evolution. We produce a high-resolution map of incomplete lineage sorting and estimate that around 5.1% of the human genome is genetically closer to chimpanzee or bonobo and that more than 36.5% of the genome shows incomplete lineage sorting if we consider a deeper phylogeny including gorilla and orangutan. We also show that 26% of the segments of incomplete lineage sorting between human and chimpanzee or human and bonobo are non-randomly distributed and that genes within these clustered segments show significant excess of amino acid replacement compared to the rest of the genome.

Familial long-read sequencing increases yield of de novo mutations.

Studies of de novo mutation (DNM) have typically excluded some of the most repetitive and complex regions of the genome because these regions cannot be unambiguously mapped with short-read sequencing data. To better understand the genome-wide pattern of DNM, we generated long-read sequence data from an autism parent-child quad with an affected female where no pathogenic variant had been discovered in short-read Illumina sequence data. We deeply sequenced all four individuals by using three sequencing platforms (Illumina, Oxford Nanopore, and Pacific Biosciences) and three complementary technologies (Strand-seq, optical mapping, and 10X Genomics). Using long-read sequencing, we initially discovered and validated 171 DNMs across two children-a 20% increase in the number of de novo single-nucleotide variants (SNVs) and indels when compared to short-read callsets. The number of DNMs further increased by 5% when considering a more complete human reference (T2T-CHM13) because of the recovery of events in regions absent from GRCh38 (e.g., three DNMs in heterochromatic satellites). In total, we validated 195 de novo germline mutations and 23 potential post-zygotic mosaic mutations across both children; the overall true substitution rate based on this integrated callset is at least 1.41 × 10-8 substitutions per nucleotide per generation. We also identified six de novo insertions and deletions in tandem repeats, two of which represent structural variants. We demonstrate that long-read sequencing and assembly, especially when combined with a more complete reference genome, increases the number of DNMs by >25% compared to previous studies, providing a more complete catalog of DNM compared to short-read data alone.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Tunable Tail Swing of Nanomillipedes.

The physical properties of graphene nanoribbons (GNRs) are closely related to their morphology; meanwhile GNRs can easily slide on surfaces (e.g., superlubricity), which may largely affect the configuration and hence the properties. However, the morphological evolution of GNRs during sliding remain elusive. We explore the intriguing tail swing behavior of GNRs under various sliding configurations on Au substrate. Two distinct modes of tail swing emerge, characterized by regular and irregular swings, depending on the GNR width and initial position relative to the substrate. The mechanism can be explained by the moiré effect, presenting both symmetric and asymmetric patterns, resembling a mesmerizing nanomillipede. We reveal a compelling correlation between the tail swing mode and the edge wrinkle patterns of GNRs induced by the moiré effect. These findings provide fundamental understanding of how edge effects influence the tribomorphological responses of GNRs, offering valuable insights for precise manipulation and operation of GNRs.

Crosstalk between peripheral immunity and central nervous system in Alzheimer's disease.

The significance of peripheral immunity in the pathogenesis and progression of Alzheimer's diseases (AD) has been recognized. Brain-infiltrated peripheral immune components transporting across the blood-brain barrier (BBB) may reshape the central immune environment. However, mechanisms of how these components open the BBB for AD occurrence and development and correlations between peripheral and central immunity have not been fully explored. Herein, we formulate a hypothesis whereby peripheral immunity as a critical factor allows AD to progress. Peripheral central immune cell crosstalk is associated with early AD pathology and related risk factors. The damaged BBB permits peripheral immune cells to enter the central immune system to deprive its immune privilege promoting the progression toward developing AD. This review summarizes the influences of risk factors on peripheral immunity, alongside their functions, highlighting the concept of peripheral and central immunity as an integrated system in AD pathogenesis, which has received scant attention before.

Loss of microglial EED impairs synapse density, learning, and memory.

The embryonic ectoderm development (EED) is a core component of the polycomb-repressive complex 2 (PRC2) whose mutations are linked to neurodevelopmental abnormalities, intellectual disability, and neurodegeneration. Although EED has been extensively studied in neural stem cells and oligodendrocytes, its role in microglia is incompletely understood. Here, we show that microglial EED is essential for synaptic pruning during the postnatal stage of brain development. The absence of microglial EED at early postnatal stages resulted in reduced spines and impaired synapse density in the hippocampus at adulthood, accompanied by upregulated expression of phagocytosis-related genes in microglia. As a result, deletion of microglial Eed impaired hippocampus-dependent learning and memory in mice. These results suggest that microglial EED is critical for normal synaptic and cognitive functions during postnatal development.

Viewpoint-dependent highlight depiction with microdisparity for autostereoscopic displays.

The rendering of specular highlights is a critical aspect of 3D rendering on autostereoscopic displays. However, the conventional highlight rendering techniques on autostereoscopic displays result in depth conflicts between highlights and diffuse surfaces. To address this issue, we propose a viewpoint-dependent highlight depiction method with head tracking, which incorporates microdisparity of highlights in binocular parallax and preserves the motion parallax of highlights. Our method was found to outperform physical highlight depiction and highlight depiction with microdisparity in terms of depth perception and realism, as demonstrated by experimental results. The proposed approach offers a promising alternative to traditional physical highlights on autostereoscopic displays, particularly in applications that require accurate depth perception.

HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using Computed Tomography Images and Text.

Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients' numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model's strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.

A nano-innate immune system activator for cancer therapy in a 4T1 tumor-bearing mouse model.

BACKGROUND: Harnessing the immune system to fight cancer has led to prominent clinical successes. Strategies to stimulate innate immune effectors are attracting considerable interest in cancer therapy. Here, through conjugating multivalent Fc fragments onto the surface of mesoporous silica nanoparticles (MSN), we developed a nanoparticle-based innate immune system activator (NISA) for breast cancer immunotherapy. METHODS: NISA was prepared through conjugating mouse IgG3 Fc to MSN surface. Then, long-chain PEG5000, which was used to shield Fc to confer nanoparticle colloidal stability, was linked to the MSN surface via matrix metalloprotease-2 (MMP-2)-cleavable peptide (GPLGIAGQC). The activation of multiple components of innate immune system, including complement and the innate cells (macrophages and dendritic cells) and the associated anticancer effect were investigated. RESULTS: Fc fragments of NISA can be exposed through hydrolysis of long-chain PEG5000 by highly expressed MMP-2 in tumor microenvironment. Then, effective stimulation and activation of multiple components of innate immune system, including complement, macrophages, and dendritic cells were obtained, leading to efficient antitumor effect in 4T1 breast cancer cells and orthotopic breast tumor model in mice. CONCLUSIONS: The antitumor potency conferred by NISA highlights the significance of stimulating multiple innate immune elements in cancer immunotherapy.

Human-specific tandem repeat expansion and differential gene expression during primate evolution.

Short tandem repeats (STRs) and variable number tandem repeats (VNTRs) are important sources of natural and disease-causing variation, yet they have been problematic to resolve in reference genomes and genotype with short-read technology. We created a framework to model the evolution and instability of STRs and VNTRs in apes. We phased and assembled 3 ape genomes (chimpanzee, gorilla, and orangutan) using long-read and 10x Genomics linked-read sequence data for 21,442 human tandem repeats discovered in 6 haplotype-resolved assemblies of Yoruban, Chinese, and Puerto Rican origin. We define a set of 1,584 STRs/VNTRs expanded specifically in humans, including large tandem repeats affecting coding and noncoding portions of genes (e.g., MUC3A, CACNA1C). We show that short interspersed nuclear element-VNTR-Alu (SVA) retrotransposition is the main mechanism for distributing GC-rich human-specific tandem repeat expansions throughout the genome but with a bias against genes. In contrast, we observe that VNTRs not originating from retrotransposons have a propensity to cluster near genes, especially in the subtelomere. Using tissue-specific expression from human and chimpanzee brains, we identify genes where transcript isoform usage differs significantly, likely caused by cryptic splicing variation within VNTRs. Using single-cell expression from cerebral organoids, we observe a strong effect for genes associated with transcription profiles analogous to intermediate progenitor cells. Finally, we compare the sequence composition of some of the largest human-specific repeat expansions and identify 52 STRs/VNTRs with at least 40 uninterrupted pure tracts as candidates for genetically unstable regions associated with disease.

Fracture line distribution in femoral head fractures: a complement to Pipkin, Brumback, and AO/OTA classifications.

PURPOSE: This study aimed to explore the fracture line distribution and validate fracture classifications of Femoral head fractures (FHFs). MATERIALS AND METHODS: A total of 209 FHFs were reviewed retrospectively. Subjects were classified by associated injuries and commonly used fracture classifications (Pipkin, Brumback, and AO/OTA), and the universality degree of classifications was evaluated. The fracture line directions were determined in the coronal and axial CT planes. 3D mapping analysis of fracture lines was performed separately by each group. 3D maps were employed to analyze the discrimination degree of inter-subtype classifications and create a new classification. The subjects were subsequently reclassified. Correlations between classifications were analyzed to determine the matching degree of the three classifications. RESULTS: The universality degrees were 98.6% (pipkin), 44.5% (Brumback), and 94.3% (AO/OTA). The cases of (100%) Brumback and (98.5%) AO/OTA can be classified by Pipkin. The mean angles of fracture lines to the coronal and axial axis of primary compressive trabeculae were 20.25° and 54.56°. The discrimination degrees of inter-subtype of classifications were 0 (Pipkin), 60% (Brumback), and 33% (AO/OTA). A new classification with three regions and five types was created on 3D maps. Pipkin and AO/OTA matched one region, while Brumback matched two regions. CONCLUSIONS: There were three distributed fracture regions in FHFs that mismatched Pipkin, Brumback, and AO/OTA classifications. The new classification, based on morphometric features of FHFs, could compensate for the shortcomings of commonly used classifications, improving their applicability in treating FHFs.

Optically Driven Gold Nanoparticles Seed Surface Bubble Nucleation in Plasmonic Suspension.

Photothermal surface bubbles play important roles in applications like microfluidics and biosensing, but their formation on transparent substrates immersed in a plasmonic nanoparticle (NP) suspension has an unknown origin. Here, we reveal NPs deposited on the transparent substrate by optical forces are responsible for the nucleation of such photothermal surface bubbles. We show the surface bubble formation is always preceded by the optically driven NPs moving toward and deposited to the surface. Interestingly, such optically driven motion can happen both along and against the photon stream. The laser power density thresholds to form a surface bubble drastically differ depending on if the surface is forward- or backward-facing the light propagation direction. We attributed this to different optical power densities needed to enable optical pulling and pushing of NPs in the suspension, as optical pulling requires higher light intensity to excite supercavitation around NPs to enable proper optical configuration.

Controlled Synthesis of Palladium Nanoparticles with Size-Dependent Catalytic Activities Enabled by Organic Molecular Cages.

Particle size plays a key role in the performance of metal nanoparticles (MNPs). However, the size-controlled synthesis of MNPs still represents a challenging task. In this work, we revealed a strong solvent effect on the growth of palladium nanoparticles (PdNPs), which was directed by a porous [2 + 3] organic molecular cage (OMC, Phos-cage) containing triphenylphosphine moieties. PdNPs with different average diameters of 0.8, 1.2, and 3.3 nm supported by Phos-cage were obtained by simply varying the reaction media. The catalytic performance of such ultrafine PdNPs in the reduction of p-nitrophenol and a Suzuki-Miyaura coupling reaction has been studied, which clearly shows size-dependent catalytic activity and stability. The knowledge gained in this study, controlling the size of PdNPs supported by the OMC template in different solvents, will open new possibilities for size-controlled synthesis of ultrafine MNPs with high catalytic activity and stability.

Electron-phonon coupling mechanisms of broadband near-infrared emissions from Cr3+ in the Ca3Sc2Si3O12 garnet.

Cr3+ in the Ca3Sc2Si3O12 garnet (CSSG) has the ability to convert blue light to broadband near-infrared (NIR) emissions, which is a promising strategy for next-generation smart NIR light sources based on blue LEDs. The Cr3+ luminescence strongly depends on temperature due to electron-phonon coupling (EPC). We reveal the EPC mechanism of Cr3+ in CSSG for the first time by temperature-dependent photoluminescence measurement from 77 to 573 K and cathodoluminescence using a scanning electron microscope. Cr3+ occupies the Sc3+ site and experiences a weak crystal field in CSSG, manifesting a broad NIR emission in the 700-900 nm range that originates from the 4T2g→4A2g transition. The zero phonon line (ZPL) of the 4T2 state is observed at ∼713 nm with a vibrational energy of ∼310 cm-1. A strong EPC leads to a large Stokes shift (∼2900 cm-1). The Huang-Rhys parameter (S = 4), crystal field strength (Dq/B), and Racah parameters (B and C) are estimated.

Estimating a Large Travel Time Matrix Between Zip Codes in the United States: A Differential Sampling Approach.

Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.-a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.

Effect of light atoms on thermal transport across solid-solid interfaces.

Thermal transport across solid interfaces is of great importance for applications like power electronics. In this work, we perform non-equilibrium molecular dynamics simulations to study the effect of light atoms on the thermal transport across SiC/GaN interfaces, where light atoms refer to substitutional or interstitial defect atoms lighter than those in the pristine lattice. Various light atom doping features, such as the light atom concentration, mass of the light atom, and skin depth of the doped region, have been investigated. It is found that substituting Ga atoms in the GaN lattice with lighter atoms (e.g. boron atoms) with 50% concentration near the interface can increase the thermal boundary conductance (TBC) by up to 50%. If light atoms are introduced interstitially, a similar increase in TBC is observed. Spectral analysis of interfacial heat transfer reveals that the enhanced TBC can be attributed to the stronger coupling of mid- and high-frequency phonons after introducing light atoms. We have also further included quantum correction, which reduces the amount of enhancement, but it still exists. These results may provide a route to improve TBC across solid interfaces as light atoms can be introduced during material growth.

The Rate Stabilizing Tool: Generating Stable Local-Level Measures of Chronic Disease.

Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. In response, we developed the Rate Stabilizing Tool (RST), an ArcGIS-based tool that enables users to input their own record-level data to generate more reliable age-standardized measures of chronic disease (eg, prevalence rates, mortality rates) or other population health outcomes at the county or census tract levels. The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. The RST also provides indicators of the reliability of point estimates. In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. We demonstrate the dramatic reduction in root mean-squared error (rMSE), indicating a better compromise between accuracy and stability for both smoothing approaches relative to the unsmoothed estimates. Finally, we provide an example of the RST's use. This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test.

Generalized Two-Temperature Model for Coupled Phonons in Nanosized Graphene.

The design of graphene-based composite with high thermal conductivity requires a comprehensive understanding of phonon coupling in nanosized graphene. We extended the two-temperature model to coupled groups of phonons. The study give new physical quantities, the phonon-phonon coupling factor and length, to characterize the couplings quantitatively. Besides, our proposed coupling length has an obvious dependence on system size. Our studies can not only observe the nonequilibrium between different groups of phonons but explain theoretically the thermal resistance inside nanosized graphene.

[The clinical value of pancreatic fistula risk predicting system after pancreaticoduodenectomy].

OBJECTIVE: To evaluate the clinical value of a preoperative predictive scoring system which was established by the National Cancer Center Hospital (NCCH) for the postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy. METHODS: The clinical data of 269 patients who underwent pancreaticoduodenectomy at the Affiliated Provincial Hospital of Anhui Medical University from February 2008 to February 2014 were studied retroprospectively. The five indexes which including gender, portal invasion, pancreatic cancer, main pancreatic duct index and intra abdominal fat thickness were calculated in the NCCH predictive score system. Patients with a score over 4 were defined as high risk of POPF, and those with score less than 4 were defined as low risk of POPF. Then the factors associated with POPF were analyzed by Logistic regression test. The enumeration data and measurement data were compared with χ2 test and t test. Risk factors for postoperative pancreatic fistula were analyzed through single factor and multiple factors Logistic regression analysis. The sensitivity and specificity of the predictive scoring system were determined by receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 33 patients were diagnosed as POPF, including 15 in grade A, 11 in grade B and 7 in grade C. The univariate analysis showed that the factors associated with POPF are gender, total serum bilirubin level, pancreatic cancer, portal invasion, the pancreatic texture, main pancreatic duct diameter and the pancreaticojejunostomy. The multivariate analysis showed that gender, pancreatic texture, portal invasion and main pancreatic duct diameter were the independent risk factor of POPF. The rate of pancreatic fistula of high risk group was 53.8% (14/26), and the rate of pancreatic fistula of the low risk group was 7.8% (19/243). There were significant differences in the pancreatic fistula rate between the high risk and low risk of POPF (χ2=46.231, P<0.01). The results of ROC curve analysis showed that the sensitivity and specificity of the predictive scoring system were 87.9% and 94.1%, respectively. The area under the curve was 0.946 (95% CI: 0.895-0.997). CONCLUSIONS: The NCCH preoperative predictive scoring system could accurately predict the occurrence of POPF. While large, multicenter prospective randomized controlled trials is still needed to further confirm it.

Model Selection for Exposure-Mediator Interaction.

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators M and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.