Novel chiral magnetic domain wall structure in Fe/Ni/Cu(001) films.

Using spin-polarized low energy electron microscopy, we discovered a new type of domain wall structure in perpendicularly magnetized Fe/Ni bilayers grown epitaxially on Cu(100). Specifically, we observed unexpected Néel-type walls with fixed chirality in the magnetic stripe phase. Furthermore, we find that the chirality of the domain walls is determined by the film growth order with the chirality being right handed in Fe/Ni bilayers and left handed in Ni/Fe bilayers, suggesting that the underlying mechanism is the Dzyaloshinskii-Moriya interaction at the film interfaces. Our observations may open a new route to control chiral spin structures using interfacial engineering in transition metal heterostructures.

The mouse Ames waltzer hearing-loss mutant is caused by mutation of Pcdh15, a novel protocadherin gene.

The neuroepithelia of the inner ear contain hair cells that function as mechanoreceptors to transduce sound and motion signals. Mutations affecting these neuroepithelia cause deafness and vestibular dysfuction in humans. Ames waltzer (av) is a recessive mutation found in mice that causes deafness and a balance disorder associated with the degeneration of inner ear neuroepithelia. Here we report that the gene that harbours the av mutation encodes a novel protocadherin. Cochlear hair cells in the av mutants show abnormal stereocilia by 10 days after birth (P10). This is the first evidence for the requirement of a protocadherin for normal function of the mammalian inner ear.

Super-resolution of magnetic systems using deep learning.

We construct a deep neural network to enhance the resolution of spin structure images formed by spontaneous symmetry breaking in the magnetic systems. Through the deep neural network, an image is expanded to a super-resolution image and reduced to the original image size to be fitted with the input feed image. The network does not require ground truth images in the training process. Therefore, it can be applied when low-resolution images are provided as training datasets, while high-resolution images are not obtainable due to the intrinsic limitation of microscope techniques. To show the usefulness of the network, we train the network with two types of simulated magnetic structure images; one is from self-organized maze patterns made of chiral magnetic structures, and the other is from magnetic domains separated by walls that are topological defects of the system. The network successfully generates high-resolution images highly correlated with the exact solutions in both cases. To investigate the effectiveness and the differences between datasets, we study the network's noise tolerance and compare the networks' reliabilities. The network is applied with experimental data obtained by magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy.

Topological magnetic structure generation using VAE-GAN hybrid model and discriminator-driven latent sampling.

Recently, deep generative models using machine intelligence are widely utilized to investigate scientific systems by generating scientific data. In this study, we experiment with a hybrid model of a variational autoencoder (VAE) and a generative adversarial network (GAN) to generate a variety of plausible two-dimensional magnetic topological structure data. Due to the topological properties in the system, numerous and diverse metastable magnetic structures exist, and energy and topological barriers separate them. Thus, generating a variety of plausible spin structures avoiding those barrier states is a challenging problem. The VAE-GAN hybrid model can present an effective approach to this problem because it brings the advantages of both VAE's diversity and GAN's fidelity. It allows one to perform various applications including searching a desired sample from a variety of valid samples. Additionally, we perform a discriminator-driven latent sampling (DDLS) using our hybrid model to improve the quality of generated samples. We confirm that DDLS generates various plausible data with large coverage, following the topological rules of the target system.

Optimization of physical quantities in the autoencoder latent space.

We propose a strategy for optimizing physical quantities based on exploring in the latent space of a variational autoencoder (VAE). We train a VAE model using various spin configurations formed on a two-dimensional chiral magnetic system. Three optimization algorithms are used to explore the latent space of the trained VAE. The first algorithm, the single-code modification algorithm, is designed for improving the local energetic stability of spin configurations to generate physically plausible spin states. The other two algorithms, the genetic algorithm and the stochastic algorithm, aim to optimize the global physical quantities, such as topological index, magnetization, energy, and directional correlation. The advantage of our method is that various optimization algorithms can be applied in the latent space containing the abstracted representation constructed by the trained VAE model. Our method based on latent space exploration is utilized for efficient physical quantity optimization.

Searching for the ground state of complex spin-ice systems using deep learning techniques.

Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization.

Estimating the effective fields of spin configurations using a deep learning technique.

The properties of complicated magnetic domain structures induced by various spin-spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.

Magnetic Hamiltonian parameter estimation using deep learning techniques.

Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.

Candidate gene associated with a mutation causing recessive polycystic kidney disease in mice.

A line of transgenic mice was generated that contains an insertional mutation causing a phenotype similar to human autosomal recessive polycystic kidney disease. Homozygotes displayed a complex phenotype that included bilateral polycystic kidneys and an unusual liver lesion. The mutant locus was cloned and characterized through use of the transgene as a molecular marker. Additionally, a candidate polycystic kidney disease (PKD) gene was identified whose structure and expression are directly associated with the mutant locus. A complementary DNA derived from this gene predicted a peptide containing a motif that was originally identified in several genes involved in cell cycle control.

Association of preoperative anemia and perioperative allogenic red blood cell transfusion with oncologic outcomes in patients with nonmetastatic colorectal cancer.

Background: We investigated whether preoperative anemia and perioperative blood transfusion (pbt) are associated with overall survival and recurrence-free survival in patients with nonmetastatic colorectal cancer. Methods: From 1 January 2009 to 31 December 2014, 1003 patients with primary colorectal cancer were enrolled in the study. Perioperative clinical and oncologic outcomes were analyzed based on the presence of preoperative anemia and pbt. Results: Preoperative anemia was found in 468 patients (46.7%). In the anemia and no-anemia groups, pbt was performed in 44% and 15% of patients respectively. Independent predictors for pbt were preoperative anemia, higher American Society of Anesthesiologists score, laparotomy, lengthy operative time, advanced TNM stage, T4 stage, and 30-day morbidity. The use of pbt, but not preoperative anemia, was found to be an independent adverse prognostic factor for overall survival. In terms of recurrence-free survival, the presence of preoperative anemia was similarly not a significant prognostic factor, but the use of pbt was an independent factor for an unfavourable prognosis. Conclusions: The use of pbt, but not preoperative anemia, was independently associated with worse overall and recurrence-free survival in nonmetastatic colorectal cancer. For better oncologic outcomes, our findings indicate a need to reduce the use of blood transfusion during the perioperative period.

An innovative magnetic state generator using machine learning techniques.

We propose a new efficient algorithm to simulate magnetic structures numerically. It contains a generative model using a complex-valued neural network to generate k-space information. The output information is hermitized and transformed into real-space spin configurations through an inverse fast Fourier transform. The Adam version of stochastic gradient descent is used to minimize the magnetic energy, which is the cost of our algorithm. The algorithm provides the proper ground spin configurations with outstanding performance. In model cases, the algorithm was successfully applied to solve the spin configurations of magnetic chiral structures. The results also showed that a magnetic long-range order could be obtained regardless of the total simulation system size.

The spin structures of interlayer coupled magnetic films with opposite chirality.

Using Monte-Carlo simulations and micromagnetic simulations, we reveal how the spin structural correlation and the skyrmion dynamics are affected by the interlayer coupling in a chiral magnetic bilayer system, in which the two layers have opposite chirality. The interaction through interlayer coupling between chiral magnetic structures influences the static and dynamics properties profoundly. The competition between the Dzyaloshinskii-Moriya interaction and the interlayer interaction allows multiple magnetic structures to be energetically stable, which includes sole skyrmion states (skyrmion appears in only one of the layers) and skyrmion pair states (coupled skyrmions in top and bottom layers). When current driven spin transfer torques are applied to each state, the sole skyrmion state is mainly propelled by a spin transfer torque causing the skyrmion hall effect, but the skyrmion pair state is propelled by a torque from skyrmion-skyrmion interaction and not influenced by the skyrmion hall effect. Also upon application of an external magnetic field, we found the skyrmions in a skyrmion pair state extinguish in an exclusive way, as the annihilation of a skyrmion in one of the layers stabilizes the once paired skyrmion in the other layer, i.e. the skyrmion lattice sites have only one skyrmion in either layer.

Role of HSPA1L as a cellular prion protein stabilizer in tumor progression via HIF-1α/GP78 axis.

The cellular prion protein (PrPC) is associated with metastasis, tumor progression and recurrence; however, the precise mechanisms underlying its action is not well understood. Our study found that PrPC degradation decreased tumor progression in colorectal cancer (CRC). In a CRC cell line and human CRC tissue exposed to hypoxia, induced heat-shock 70-kDa protein-1-like (HSPA1L) expression stabilized hypoxia-inducible factor-1α (HIF-1α) protein and promoted PrPC accumulation and tumorigenicity in vivo. PrPC was degraded via the proteasome pathway mediated by the ubiquitin-protein E3 ligase glycoprotein 78 (GP78), which interacts directly with PrPC. However, hypoxia-induced HSPA1L interacted with GP78 and inhibited its functions. HSPA1L knockdown facilitated the interaction of GP78 and PrPC, thereby increasing PrPC ubiquitination. Thus, GP78 was identified as the ubiquitinase for PrPC, thereby revealing an essential mechanism that controls PrPC levels in CRC. Our results suggest that the HSPA1L/HIF-1α/GP78 axis has a crucial role in PrPC accumulation during tumor progression.

Hydrogen peroxide-mediated Cu,Zn-superoxide dismutase fragmentation: protection by carnosine, homocarnosine and anserine.

The fragmentation of human Cu,Zn-superoxide dismutase (SOD) was observed during incubation with H(2)O(2). Hydroxyl radical scavengers such as sodium azide, formate and mannitol protected the fragmentation of Cu,Zn-SOD. These results suggested that *OH was implicated in the hydrogen peroxide-mediated Cu,Zn-SOD fragmentation. Carnosine, homocarnosine and anserine have been proposed to act as anti-oxidants in vivo. We investigated whether three compounds could protect the fragmentation of Cu,Zn-SOD induced by H(2)O(2). The results showed that carnosine, homocarnosine and anserine significantly protected the fragmentation of Cu,Zn-SOD. All three compounds also protected the loss of enzyme activity induced by H(2)O(2). Carnosine, homocarnosine and anserine effectively inhibited the formation of *OH by the Cu,Zn-SOD/H(2)O(2) system. These results suggest that carnosine and related compounds can protect the hydrogen peroxide-mediated Cu,Zn-SOD fragmentation through the scavenging of *OH.

Oxidative modification and inactivation of Cu,Zn-superoxide dismutase by 2,2'-azobis(2-amidinopropane) dihydrochloride.

We have investigated oxidative modification of human Cu, Zn-superoxide dismutase (SOD) by alkylperoxyl radicals and alkylperoxides. To generate free radicals, we used the hydrophilic azocompound, 2,2'-azobis(2-amidinopropane) dihydrochloride (AAPH). When Cu,Zn-SOD was incubated with AAPH, the enzyme activity was decreased gradually in a time-dependent manner. The oxidative damage to Cu,Zn-SOD by AAPH-derived radicals led to protein fragmentation which is associated with the inactivation of enzyme. Incubation with AAPH resulted in the release of copper ions from Cu,Zn-SOD and the generation of protein carbonyl derivatives. Catalase did not protect the fragmentation of Cu,Zn-SOD whereas azide, glutathione and a metal chelator, diethylenetriamine pentaacetic acid inhibited the protein fragmentation. When Cu,Zn-SOD that has been exposed to AAPH was subsequently analyzed by amino acid analysis, lysine, histidine, proline, and valine residues were particularly sensitive. It is suggested that oxidative damage of Cu,Zn-SOD by AAPH-derived radicals may induce the perturbation of cellular antioxidant defense systems and subsequently lead to the deleterious condition in cells.

Oxidative modification of human ceruloplasmin by peroxyl radicals.

Ceruloplasmin (CP), the blue oxidase present in all vertebrates, is the major copper-containing protein of plasma. We investigated oxidative modification of human CP by peroxyl radicals generated in a solution containing 2,2'-azobis(2-amidinopropane) dihydrochloride (AAPH). When CP was incubated with AAPH, the aggregation of proteins was increased in a time- and dose-dependent manner. Incubation of CP with AAPH resulted in a loss of ferroxidase activity. Superoxide dismutase and catalase did not protect the aggregation of CP, whereas hydroxyl radical scavengers such as ethanol and mannitol protected the protein aggregation. The aggregation of proteins was significantly inhibited by the copper chelators, diethyldithiocarbamate and penicillamine. Exposure of CP to AAPH led to the release of copper ions from the enzyme and the generation of protein carbonyl derivatives. Subsequently, when the amino acid composition of CP reacted with AAPH was analyzed, cysteine, tryptophan, methionine, histidine, tyrosine, and lysine residues were particularly sensitive.

Comparison of diameter and perimeter methods for tumor volume calculation.

PURPOSE: Lesion volume is often used as an end point in clinical trials of oncology therapy. We sought to compare the common method of using orthogonal diameters to estimate lesion volume (the diameter method) with a computer-assisted planimetric technique (the perimeter method). METHODS: Radiologists reviewed 825 magnetic resonance imaging studies from 219 patients with glioblastoma multiforme. Each study had lesion volume independently estimated via the diameter and perimeter methods. Cystic areas were subtracted out or excluded from the outlined lesion. Inter- and intrareader variability was measured by using multiple readings on 48 cases. Where serial studies were available in noncystic cases, a mock response analysis was used. RESULTS: The perimeter method had a reduced interreader and intrareader variability compared with the diameter method (using SD of differences): intrareader, 1.76 mL v 7.38 mL (P < .001); interreader, 2.51 mL v 9.07 mL (P < .001) for perimeter and diameter results, respectively. Of the 121 noncystic cases, 23 had serial data. In six (26.1%) of those 23, a classification difference occurred when the perimeter method was used versus the diameter method. CONCLUSION: Variability of measurements was reduced with the computer-assisted perimeter method compared with the diameter method, which suggests that changes in volume can be detected more accurately with the perimeter method. The differences between these techniques seem large enough to have an impact on grading the response to therapy.

A new mouse insertional mutation that causes sensorineural deafness and vestibular defects.

This article describes a new recessive insertional mutation in the transgenic line TgN2742Rpw that causes deafness and circling behavior in mice. Histologic analysis revealed virtually complete loss of the cochlear neuroepithelium (the organ of Corti) in adult mutant mice. In association with the neuroepithelial changes, there is a dramatic reduction of the cochlear nerve supply. Adult mutants also show morphological defects of the vestibular apparatus, including degeneration of the saccular neuroepithelium and occasional malformation of utricular otoconia. Audiometric evaluations demonstrated that the mice displaying the circling phenotype are completely deaf. Molecular analysis of this mutant line revealed that the transgenic insertion occurred without creating a large deletion of the host DNA sequences. The mutant locus was mapped to a region on mouse chromosome 10, where other spontaneous, recessive mutations causing deafness in mice have been mapped.