SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants.

Accurate variant pathogenicity predictions are important in genetic studies of human diseases. Inframe insertion and deletion variants (indels) alter protein sequence and length, but not as deleterious as frameshift indels. Inframe indel Interpretation is challenging due to limitations in the available number of known pathogenic variants for training. Existing prediction methods largely use manually encoded features including conservation, protein structure and function, and allele frequency to infer variant pathogenicity. Recent advances in deep learning modeling of protein sequences and structures provide an opportunity to improve the representation of salient features based on large numbers of protein sequences. We developed a new pathogenicity predictor for SHort Inframe iNsertion and dEletion (SHINE). SHINE uses pretrained protein language models to construct a latent representation of an indel and its protein context from protein sequences and multiple protein sequence alignments, and feeds the latent representation into supervised machine learning models for pathogenicity prediction. We curated training data from ClinVar and gnomAD, and created two test datasets from different sources. SHINE achieved better prediction performance than existing methods for both deletion and insertion variants in these two test datasets. Our work suggests that unsupervised protein language models can provide valuable information about proteins, and new methods based on these models can improve variant interpretation in genetic analyses.

Ordered structure constructed from C2-symmetric hexa-peri-hexabenzocoronene linked with oligo(dimethylsiloxane).

The preparation of sub-5-nm ordered structures is very important to the development of today's nanotechnology. Block molecules have the potential to form structures with significantly small characteristic dimensions. Herein two novel organic-inorganic block molecules composed of a hexa-peri-hexabenzocoronene (HBC) core and two oligo(dimethylsiloxane) (ODMS) tails with C2 symmetry are reported. A hierarchical lamello-columnar structure with a two-dimensional rectangular lattice where HBC cores adopt a tilted arrangement was obtained from their bulk self-assembly. The feature sizes are all below 5 nm and can be regulated via the number of ODMS chains. Sub-5-nm line structures were obtained through spin-coating of the block molecules onto silicon substrates modified with poly(dimethylsiloxane). As organic-inorganic hybrid materials, these block molecules may be further applied in sub-5-nm nanopatterning.

A New Design of a CMOS Vertical Hall Sensor with a Low Offset.

Vertical Hall sensors (VHSs), compatible with complementary metal oxide semiconductor (CMOS) technology, are used to detect magnetic fields in the plane of the sensor. In previous studies, their performance was limited by a large offset. This paper reports on a novel CMOS seven-contact VHS (7CVHS), which is formed by adding two additional contacts to a traditional five-contact VHS (5CVHS) to alleviate the offset. The offset voltage and offset magnetic field of the 7CVHS are reduced by 90.20% and 88.31% of those of the 5CVHS, respectively, with a 16.16% current-related sensitivity loss. Moreover, the size and positions of the contacts are optimized in standard GLOBALFOUNDRIES 0.18 µm BCDliteTM technology by scanning parameters using FEM simulations. The simulation data are analyzed in groups to study the influence of the size and contact positions on the current-related sensitivity, offset voltage, and offset magnetic field.

5 nm Ordered Structures Self-Assembled by C2 -Symmetric Hybrids with Polyhedral Oligomeric Silsesquioxane and Hexa-peri-Hexabenzocoronene.

Hybrids consisting of polyhedral oligomeric silsesquioxane (POSS) and hexa-peri-hexabenzocoronene (HBC) with a dumbbell topology and C2 symmetry were designed and synthesized. They self-assemble into 5 nm ordered structures. In particular, the increased steric effect with increasing POSS units stabilizes a square columnar phase (Colsqu ) which is important in nanotemplating. These hybrids containing discotic liquid crystal HBC and POSS units have an excellent etching contrast and present an approach to obtain 5 nm nanopatterns.

Hierarchically ordered structures of disk-cube triads containing hexa-peri-hexabenzocoronene and polyhedral oligomeric silsesquioxane.

Obtaining nanoscale-ordered structures is important for the development of nanotechnology. We designed and synthesized a series of disk-cube triads containing one hexa-peri-hexabenzocoronene (HBC) and two polyhedral oligomeric silsesquioxane (POSS) moieties, HBC-2POSS. The two POSS units were linked via ester or amide bonds. With the amide linkage used, the hydrogen bonding that was introduced affected the balance between the π-π interaction of HBC cores and crystallization interaction of POSS units. Hierarchically ordered structures were obtained from HBC-2POSS triads owing to the synergistic effect of multiple secondary interactions: π-π interaction, hydrogen bonding, and crystallization interaction. As organic-inorganic hybrid materials, these HBC-2POSS triads are promising candidates for templates <10 nm.

A New Design of a Single-Device 3D Hall Sensor: Cross-Shaped 3D Hall Sensor.

In this paper, a new single-device three-dimensional (3D) Hall sensor called a cross-shaped 3D Hall device is designed based on the five-contact vertical Hall device. Some of the device parameters are based on 0.18 µm BCDliteTM technology provided by GLOBALFOUNDRIES. Two-dimensional (2D) and 3D finite element models implemented in COMSOL are applied to understand the device behavior under a constant magnetic field. Besides this, the influence of the sensing contacts, active region's depth, and P-type layers are taken into account by analyzing the distribution of the voltage along the top edge and the current density inside the devices. Due to the short-circuiting effect, the sensing contacts lead to degradation in sensitivities. The P-type layers and a deeper active region in turn are responsible for the improvement of sensitivities. To distinguish the P-type layer from the active region which plays the dominant role in reducing the short-circuiting effect, the current-related sensitivity of the top edge (Stop) is defined. It is found that the short-circuiting effect fades as the depth of the active region grows. Despite the P-type layers, the behavior changes a little. When the depth of the active region is 7 µm and the thickness of the P-type layers is 3 µm, the sensitivities in the x, y, and z directions can reach 91.70 V/AT, 92.36 V/AT, and 87.10 V/AT, respectively.

Head-Tail Asymmetry as the Determining Factor in the Formation of Polymer Cubosomes or Hexasomes in a Rod-Coil Amphiphilic Block Copolymer.

The self-assembly of a rod-coil amphiphilic block copolymer (ABCP) led to Im3‾ m and Pn3‾ m polymer cubosomes and p6mm polymer hexasomes. This is the first time that these structures are observed in a rod-coil system. By varying the hydrophobic chain length, the initial concentration of the polymer solution, or the solubility parameter of the mixed solvent, head-tail asymmetry is adjusted to control the formation of polymer cubosomes or hexasomes. The formation mechanism of the polymer cubosomes was also studied. This research opens up a new way for further study of the bicontinuous and inverse phases in different ABCP systems.

Influences of an Aluminum Covering Layer on the Performance of Cross-Like Hall Devices.

This work studies the effects of an aluminum covering on the performance of cross-like Hall devices. Four different Hall sensor structures of various sizes were designed and fabricated. The sensitivity and offset of the Hall sensors, two key points impacting their performance, were characterized using a self-built measurement system. The work analyzes the influences of the aluminum covering on those two aspects of the performance. The aluminum layer covering mainly leads to an eddy-current effect in an unstable magnetic field and an additional depletion region above the active region. Those two points have influences on the sensitivity and the offset voltage, respectively. The analysis guides the designer whether to choose covering with an aluminum layer the active region of the Hall sensor as a method to reduce the flicker noise and to improve the stability of the Hall sensor. Because Hall devices, as a reference element, always suffer from a large dispersion, improving their stability is a crucial issue.

Performance comparison of cross-like Hall plates with different covering layers.

This paper studies the effects of the covering layers on the performance of a cross-like Hall plate. Three different structures of a cross-like Hall plate in various sizes are designed and analyzed. The Hall plate sensitivity and offset are characterized using a self-built measurement system. The effect of the P-type region over the active area on the current-related sensitivity is studied for different Hall plate designs. In addition, the correlation between the P-type covering layer and offset is analyzed. The best structure out of three designs is determined. Besides, a modified eight-resistor circuit model for the Hall plate is presented with improved accuracy by taking the offset into account.

Population structure and genetic diversity of mango (Mangifera indica L.) germplasm resources as revealed by single-nucleotide polymorphism markers.

Introduction: Mango is a vital horticultural fruit crop, and breeding is an essential strategy to enhance ongoing sustainability. Knowledge regarding population structure and genetic diversity in mango germplasm is essential for crop improvement. Methods: A set of 284 mango accessions from different regions of the world were subjected to high-throughput sequencing and specific-locus amplified fragment (SLAF) library construction to generate genomic single-nucleotide polymorphism (SNP). Results: After filtering, raw data containing 539.61 M reads were obtained. A total of 505,300 SLAFs were detected, of which, 205,299 were polymorphic. Finally, 29,136 SNPs were employed to dissect the population structure, genetic relationships, and genetic diversity. The 284 mango accessions were divided into two major groups: one group consisted mainly of mango accessions from Australia, the United States, Cuba, India, Caribbean, Israel, Pakistan, Guinea, Burma, China, and Sri Lanka, which belonged to the Indian type (P1); the other group contained mango accessions from the Philippines, Thailand, Indonesia, Vietnam, Cambodia, Malaysia, and Singapore, which belonged to Southeast Asian type (P2). Genetic diversity, principal component analysis (PCA), and population structure analyses revealed distinct accession clusters. Current results indicated that the proposed hybridization occurred widely between P1 and P2. Discussion: Most of the accessions (80.99%) were of mixed ancestry, perhaps including multiple hybridization events and regional selection, which merits further investigation.

Genomic diversity, population structure, and genome-wide association reveal genetic differentiation and trait improvements in mango.

Mango (Mangifera indica L.) has been widely cultivated as a culturally and economically significant fruit tree for roughly 4000 years. Despite its rich history, little is known about the crop's domestication, genomic variation, and the genetic loci underlying agronomic traits. This study employs the whole-genome re-sequencing of 224 mango accessions sourced from 22 countries, with an average sequencing depth of 16.37×, to explore their genomic variation and diversity. Through phylogenomic analysis, M. himalis J.Y. Liang, a species grown in China, was reclassified into the cultivated mango group known as M. indica. Moreover, our investigation of mango population structure and differentiation revealed that Chinese accessions could be divided into two distinct gene pools, indicating the presence of independent genetic diversity ecotypes. By coupling genome-wide association studies with analyses of genotype variation patterns and expression patterns, we identified several candidate loci and dominant genotypes associated with mango flowering capability, fruit weight, and volatile compound production. In conclusion, our study offers valuable insights into the genetic differentiation of mango populations, paving the way for future agronomic improvements through genomic-assisted breeding.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data.

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level (d) and population level (selection coefficient, s), assuming that in the same gene, missense variants with similar d have similar s. MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s. Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

Effective Plug-Ins for Reducing Inference-Latency of Spiking Convolutional Neural Networks During Inference Phase.

Convolutional Neural Networks (CNNs) are effective and mature in the field of classification, while Spiking Neural Networks (SNNs) are energy-saving for their sparsity of data flow and event-driven working mechanism. Previous work demonstrated that CNNs can be converted into equivalent Spiking Convolutional Neural Networks (SCNNs) without obvious accuracy loss, including different functional layers such as Convolutional (Conv), Fully Connected (FC), Avg-pooling, Max-pooling, and Batch-Normalization (BN) layers. To reduce inference-latency, existing researches mainly concentrated on the normalization of weights to increase the firing rate of neurons. There are also some approaches during training phase or altering the network architecture. However, little attention has been paid on the end of inference phase. From this new perspective, this paper presents 4 stopping criterions as low-cost plug-ins to reduce the inference-latency of SCNNs. The proposed methods are validated using MATLAB and PyTorch platforms with Spiking-AlexNet for CIFAR-10 dataset and Spiking-LeNet-5 for MNIST dataset. Simulation results reveal that, compared to the state-of-the-art methods, the proposed method can shorten the average inference-latency of Spiking-AlexNet from 892 to 267 time steps (almost 3.34 times faster) with the accuracy decline from 87.95 to 87.72%. With our methods, 4 types of Spiking-LeNet-5 only need 24-70 time steps per image with the accuracy decline not more than 0.1%, while models without our methods require 52-138 time steps, almost 1.92 to 3.21 times slower than us.

Structural complexity induced by topology change in hybrids consisting of hexa-peri-hexabenzocoronene and polyhedral oligomeric silsesquioxane.

Organic-inorganic hybrids with hexa-peri-hexabenzocoronene (HBC) and polyhedral oligomeric silsesquioxane (POSS) were designed and synthesized. The increase of the POSS content (or a change in topology) results in more complex self-assembled structures. This work provides a new approach for the design and synthesis of materials with sub-10 nm sizes.

Solid Polymer Electrolytes with Excellent High-Temperature Properties Based on Brush Block Copolymers Having Rigid Side Chains.

A series of brush block copolymers (BBCPs) with polynorbornene backbones containing poly{2,5-bis[(4-methoxyphenyl)oxycarbonyl]styrene} (PMPCS, which is a rigid chain) and poly(ethylene oxide) (PEO) side chains were synthesized by tandem ring-opening metathesis polymerizations. The weight fractions of PEO in BBCPs are similar, and the degrees of polymerization (DPs) of PEO side chains are the same while the DPs of PMPCS are different. The bulk self-assembling behaviors were studied by small-angle X-ray scattering (SAXS). The neat BBCPs cannot form ordered nanostructures. However, after the doping of lithium salt, the BBCPs self-assemble into lamellar (LAM) structures. When the DPs of the PEO and PMPCS side chains are similar, the LAM structure is more ordered, which is attributed to the more flat interface between PMPCS and PEO phases. The ionic conductivity (σ) values of the BBCP/lithium salt complex with the most ordered LAM structure at different temperatures were measured. The σ value increases with increasing temperature in the range of 40-200 °C, and the relationship between σ and T fits the Vogel-Tamman-Fulcher (VTF) equation. The σ value at 200 °C is 1.58 × 10-3 S/cm, which is one of the highest values for PEO-based polymer electrolytes. These materials with high σ values at high temperatures may be used in high-temperature lithium ion batteries.