Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications

In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a method called the Generalized Low-Rank Update (GLRU) which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as SVM and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the amount of dataset changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods

Serum type IV collagen-degrading enzyme in hepatocellular carcinoma with metastasis.

The activity of serum type IV collagen-degrading enzyme was measured in 18 patients with hepatocellular carcinoma (HCC). The enzyme activity was significantly higher, in HCC patients with a tumor thrombus in the portal vein than in healthy controls, liver cirrhosis patients and HCC patients without a tumor thrombus. Moreover, the activity in HCC patients with lung metastasis tended to be higher than that in HCC patients without lung metastasis. The activity of serum type IV collagen-degrading enzyme did not correlate with tumor size, serum alpha-fetoprotein level, or macroscopic classification of tumor growth. These results suggest that the activity of serum type IV collagen-degrading enzyme represents the metastatic potential or the ongoing metastatic activity of HCC. The enzyme is a useful serum marker of metastasis from HCC.</p

Type IV collagen-degrading enzyme activity in human serum.

Type IV collagen-degrading enzyme activity was detected in human serum. Serum was preincubated with 4-aminophenylmercuric acetate and trypsin to activate the enzyme prior to assay. Type IV collagen, purified from human placentas and radiolabeled with [1-14C] acetic anhydride, was used as the substrate. The enzyme activity was measured at pH 7.5 and inhibited by treatment with ethylenediaminetetraacetic acid or heat. The assay of type IV collagen-degrading enzyme in human serum might be useful for estimating the degradation of type IV collagen.</p

Sclerite formation in the hydrothermal-vent “scaly-foot” gastropod — possible control of iron sulfide biomineralization by the animal

A gastropod from a deep-sea hydrothermal field at the Rodriguez triple junction, Indian Ocean, has scale-shaped structures, called sclerites, mineralized with iron sulfides on its foot. No other organisms are known to produce a skeleton consisting of iron sulfides. To investigate whether iron sulfide mineralization is mediated by the gastropod for the function of the sclerites, we performed a detailed physical and chemical characterization. Nanostructural characterization of the iron sulfide sclerites reveals that the iron sulfide minerals pyrite (FeS2) and greigite (Fe3S4) form with unique crystal habits inside and outside of the organic matrix, respectively. The magnetic properties of the sclerites, which are mostly consistent with those predicted from their nanostructual features, are not optimized for magnetoreception and instead support use of the magnetic minerals as structural elements. The mechanical performance of the sclerites is superior to that of other biominerals used in the vent environment for predation as well as protection from predation. These characteristics, as well as the co-occurrence of brachyuran crabs, support the inference that the mineralization of iron sulfides might be controlled by the gastropod to harden the sclerites for protection from predators. Sulfur and iron isotopic analyses indicate that sulfur and iron in the sclerites originate from hydrothermal fluids rather than from bacterial metabolites, and that iron supply is unlikely to be regulated by the gastropod for iron sulfide mineralization. We propose that the gastropod may control iron sulfide mineralization by modulating the internal concentrations of reduced sulfur compounds

Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., theranostic biomarker) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce recent approach for creating a theranostic biomarker, which consists mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (ii) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.Comment: 46 pages, 5 figure