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Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.

Can our brain perceive a sense of ownership towards an independent supernumerary limb; one that can be moved independently of any other limb and provides its own independent movement feedback? Following the rubber-hand illusion experiment, a plethora of studies have shown that the human representation of “self” is very plastic. But previous studies have almost exclusively investigated ownership towards “substitute” artificial limbs, which are controlled by the movements of a real limb and/or limbs from which non-visual sensory feedback is provided on an existing limb. Here, to investigate whether the human brain can own an independent artificial limb, we first developed a novel independent robotic “sixth finger.” We allowed participants to train using the finger and examined whether it induced changes in the body representation using behavioral as well as cognitive measures. Our results suggest that unlike a substitute artificial limb (like in the rubber hand experiment), it is more difficult for humans to perceive a sense of ownership towards an independent limb. However, ownership does seem possible, as we observed clear tendencies of changes in the body representation that correlated with the cognitive reports of the sense of ownership. Our results provide the first evidence to show that an independent supernumerary limb can be embodied by humans.

Genotype-specific incompatibility in legume–rhizobium symbiosis has been suggested to be controlled by effector-triggered immunity underlying pathogenic host-bacteria interactions. However, the rhizobial determinant interacting with the host resistance protein (e.g., Rj2) and the molecular mechanism of symbiotic incompatibility remain unclear. Using natural mutants of Bradyrhizobium diazoefficiens USDA 122, we identified a type III-secretory protein NopP as the determinant of symbiotic incompatibility with Rj2 -soybean. The analysis of nopP mutations and variants in a culture collection reveal that three amino acid residues (R60, R67, and H173) in NopP are required for Rj2 -mediated incompatibility. Complementation of rj2 -soybean by the Rj2 allele confers the incompatibility induced by USDA 122-type NopP. In response to incompatible strains, Rj2 -soybean plants activate defense marker gene PR-2 and suppress infection thread number at 2 days after inoculation. These results suggest that Rj2 -soybeans monitor the specific variants of NopP and reject bradyrhizobial infection via effector-triggered immunity mediated by Rj2 protein. The soybean Rj2 gene encodes a TIR-NBS-LRR protein that confers resistance to nodulation by certain rhizobial strains. Here, the authors show that T3SS effector NopP is an avirulence protein that is necessary for Bradyrhizobium diazoefficiens USDA 122 to trigger Rj2-dependent incompatibility.

Dysmenorrhea is a monthly menstrual pain that can limit a woman’s quality of life (QOL). The relationship between dysmenorrhea severity and QOL has been reported in several countries; however, the results cannot be generalized because lifestyle and cultural background affect menstrual pain. This study sought to uncover whether 1) different factors, such as emotions and ways of coping with symptoms, vary with the severity of dysmenorrhea and 2) the severity of dysmenorrhea ultimately affects QOL in Japan. A web-based cross-sectional survey was sent to 1000 Japanese females aged 16–30 years. The respondents were divided into two groups: those without dysmenorrhea (n = 24) and those with dysmenorrhea (n = 471). The severity of dysmenorrhea was classified using the Numerical Rating Scale as either mild (1–3), moderate (4–7), or severe (8–10). In total, 156 respondents reported mild dysmenorrhea, 249 reported moderate dysmenorrhea, and 66 reported severe dysmenorrhea. QOL was measured using the 26-item World Health Organization Quality of Life scale. One-way ANOVA and Kruskal-Wallis tests were used to compare QOL across different levels of dysmenorrhea severity, depending on normality. Ultimately, significant differences in QOL scores (p<0.001) were observed based on dysmenorrhea severity, with respondents with severe dysmenorrhea reporting the lowest QOL scores. Meanwhile, significant differences were observed in the physical, psychological, and environmental subscales (p<0.001, p<0.001, p = 0.019) across respondents with different levels of dysmenorrhea severity; notably, respondents with severe dysmenorrhea demonstrated a negative spiral of chronic pain, which may significantly impact QOL, and, relatedly, a relatively low psychological QOL. This study is the first to show the relationship between dysmenorrhea severity and QOL in Japanese females, who are more likely to experience negative feelings during menstruation.

Recent advances in protein design have ushered in an era of constructing intricate higher-order structures. Nonetheless, orchestrating the assembly of diverse protein units into cohesive artificial structures akin to biological assembly systems, especially in tubular forms, remains elusive. To this end, we develop a methodology inspired by nature, which utilises two distinct protein units to create unique tubular structures under carefully designed conditions. These structures demonstrate dynamic flexibility similar to that of actin filaments, with cryo electron microscopy revealing diverse morphologies, like microtubules. By mimicking actin filaments, helical conformations are incorporated into tubular assemblies, thereby enriching their structural diversity. Notably, these assemblies can be reversibly disassembled and reassembled in response to environmental stimuli, including changes in salt concentration and temperature, mirroring the dynamic behaviour of natural systems. This methodology combines rational protein design with biophysical insights, leading to the creation of biomimetic, adaptable, and reversible higher-order assemblies. This approach deepens our understanding of protein assembly design and complex biological structures. Concurrently, it broadens the horizons of synthetic biology and material science, holding significant implications for unravelling life’s fundamental processes and enabling future applications. Recent advances in protein design have enabled the construction of higher-order structures, however, the assembly of diverse protein units into cohesive artificial structures akin to biological assembly systems, especially in tubular forms, remains elusive. Here, the authors report a method employing two distinct protein units to create tubular structures which demonstrate dynamic flexibility and reversible assembly similar to that of the cytoskeleton.

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models. Polygenic scores aggregate the effects of multiple genetic variants and can be used to predict disease risk. Here, the authors present a polygenic score method that incorporates non-additive inheritance modes (recessive, dominant, over-recessive, and over-dominant) and show that this can improve risk prediction for certain polygenic diseases.

Mutants of the C 4 -symmetric protein RhuA were designed to self-assemble into two-dimensional crystalline lattices with precise spatial arrangements and patterns; the lattices of one of the variants are auxetic and deform perpendicularly to an applied force in a way that is contrary to what is generally expected in typical materials. Protein assemblies designed to surprise Auxetic materials are those that, because of their internal structure, deform perpendicularly to an applied force in a manner opposite to what is generally expected. So, when stretched, they get thicker across their width, and when compressed they get thinner. Akif Tezcan and colleagues have created a crystalline protein lattice that demonstrates such behaviour, dependent on the positioning and type of linkages between each individual protein unit. They designed mutants of the C 4 -symmetric protein RhuA to self-assemble into two-dimensional crystalline lattices with precise spatial arrangements and patterns. Disulfide bonds and metal-mediated coordination between units provide a balance between robustness and flexibility, such that large, low-defect lattices are formed that exhibit coherent rotational motion in response to an applied stress. Two-dimensional (2D) crystalline materials possess unique structural, mechanical and electronic properties 1 , 2 that make them highly attractive in many applications 3 , 4 , 5 . Although there have been advances in preparing 2D materials that consist of one or a few atomic or molecular layers 6 , 7 , bottom-up assembly of 2D crystalline materials remains a challenge and an active area of development 8 , 9 , 10 . More challenging is the design of dynamic 2D lattices that can undergo large-scale motions without loss of crystallinity. Dynamic behaviour in porous three-dimensional (3D) crystalline solids has been exploited for stimuli-responsive functions and adaptive behaviour 11 , 12 , 13 . As in such 3D materials, integrating flexibility and adaptiveness into crystalline 2D lattices would greatly broaden the functional scope of 2D materials. Here we report the self-assembly of unsupported, 2D protein lattices with precise spatial arrangements and patterns using a readily accessible design strategy. Three single- or double-point mutants of the C 4 -symmetric protein RhuA were designed to assemble via different modes of intermolecular interactions (single-disulfide, double-disulfide and metal-coordination) into crystalline 2D arrays. Owing to the flexibility of the single-disulfide interactions, the lattices of one of the variants ( C98 RhuA) are essentially defect-free and undergo substantial, but fully correlated, changes in molecular arrangement, yielding coherently dynamic 2D molecular lattices. C98 RhuA lattices display a Poisson’s ratio of −1—the lowest thermodynamically possible value for an isotropic material—making them auxetic.

Kerr parametric oscillators (KPOs) have attracted increasing attention in terms of their application to quantum information processing and quantum simulations. The state preparation and measurement of KPOs are typical requirements when used as qubits. The methods previously proposed for state preparations of KPOs utilize modulation of external fields such as a pump and drive fields. We study the stochastic state preparation of stable coherent states of a KPO with homodyne detection, which does not require modulation of external fields, and thus can reduce experimental efforts and exclude unwanted effects of possible imperfection in control of external fields. We quantitatively show that the detection data, if averaged over an optimal averaging time to decrease the effect of measurement noise, has a strong correlation with the state of the KPO, and therefore can be used to estimate the state (stochastic state preparation). We examine the success probability of the state estimation taking into account the measurement noise and bit flips. Moreover, the proper range of the averaging time to realize a high success probability is obtained by developing a binomial-coherent-state model, which describes the stochastic dynamics of the KPO under homodyne detection.

Photoacoustic microscopy (PAM) is uniquely positioned for biomedical applications because of its ability to visualize optical absorption contrast in vivo in three dimensions. Here we propose motionless volumetric spatially invariant resolution photoacoustic microscopy (SIR-PAM). To realize motionless volumetric imaging, SIR-PAM combines two-dimensional Fourier-spectrum optical excitation with single-element depth-resolved photoacoustic detection. To achieve spatially invariant lateral resolution, propagation-invariant sinusoidal fringes are generated by a digital micromirror device. Further, SIR-PAM achieves 1.5 times finer lateral resolution than conventional PAM. The superior performance was demonstrated in imaging both inanimate objects and animals in vivo with a resolution-invariant axial range of 1.8 mm, 33 times the depth of field of the conventional PAM counterpart. Our work opens new perspectives for PAM in biomedical sciences. Photoacoustic microscopy allows for label-free 3D in vivo imaging by detecting the acoustic response of a photoexcited material. Here, Yang et. al use a digital-micromirror-device based structured illumination scheme to both improve resolution and greatly increase the depth of field, enabling 3D volumetric imaging.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) are known to be sensitive to short-wavelength light (460–480 nm; blue or cyan light) and to play a role in regulating physiological responses such as circadian rhythms. Previous studies have shown that exposure to blue light improves performance on working memory tasks compared with exposure to amber light. However, it remains unclear whether these cognitive benefits via light are attributable to integrated signals across ipRGCs and rod/cone or ipRGC alone. To address this, the present study investigates the specific contribution of ipRGCs to working memory performance using a silent substitution method that selectively manipulates ipRGC activity while minimizing the influence of LMS cone responses. Participants engaged in 1- and 2-back tasks under low- or high-ipRGC activation light, a metameric color perceived as magenta. Results showed that hit rate in the 2-back task was significantly higher under exposure to high-ipRGC light than to low-ipRGC light. Our overall findings provide direct evidence that isolated ipRGC activation, independent of perceptual blue or cone involvement, can modulate cognitive task processing.