Measurement Non-invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias

Algorithmic and machine learning bias have stirred concern in society as machine learning continues to channel into sensitive and high-stakes applications, including in healthcare, hiring, and criminal justice. While research surrounding machine learning bias may be relatively new, psychometricians have for decades researched a closely paralleled topic of test bias in psychological and educational testing. Leveraging the connection between these two fairness domains, this thesis studies the problem of machine learning bias from a measurement perspective, specifically focusing on measurement non-invariance in outcome variables as a source of machine learning bias. A framework is introduced, which conceptualizes machine learning bias in a psychometric sense and allows for tests of measurement invariance in machine learning. Using a Monte Carlo simulation study, the consequences of measurement bias on machine learning bias are demonstrated, as well as the effectiveness of a proposed bias mitigation technique to address these effects of measurement bias, which also follows from the proposed framework. The application of the proposed methods is illustrated with data from a large-scale health survey. Broader implications of the relevance of fairness in measurement for fairness in machine learning are discussed.Master of Art

In Vitro and In Vivo Cell Uptake of a Cell-Penetrating Peptide Conjugated with Fluorescent Dyes Having Different Chemical Properties

In molecular imaging, a targeting strategy with ligands is widely used because specificity can be significantly improved. In fluorescence imaging based on a targeting strategy, the fluorescent dyes conjugated with ligands may affect the targeting efficiency depending on the chemical properties. Herein, we used a cell-penetrating peptide (CPP) as a ligand with a variety of fluorescent cyanine dye. We investigated in vitro and in vivo cell uptake of the dye-CPP conjugates when cyanine dyes with differing charge and hydrophilicity/lipophilicity were used. The results showed that the conjugates with positively charged and lipophilic cyanine dyes accumulated in cancer cells in vitro, but there was almost no accumulation in tumors in vivo. On the other hand, the conjugates with negatively charged and hydrophilic cyanine dyes did not accumulate in cancer cells in vitro, but fluorescence was observed in tumors in vivo. These results show that there are some cases in which the cell uptake of the dye-peptide conjugates may differ significantly between in vitro and in vivo experiments due to the chemical properties of the fluorescent dyes. This suggests that attention should be paid to the chemical properties of fluorescent dyes in fluorescence imaging based on a targeting strategy

Special radionuclide production activities – recent developments at QST and throughout Japan

National Institutes for Quantum Science and Technology (QST), formerly known as the National Institute of Radiological Sciences (NIRS), has been engaged in work on radiopharmaceutical science using cyclotrons since 1974. Eight pioneering researchers founded the basis of this field of research at NIRS, and to the present, many researchers and technicians have accumulated both scientific and technical achievements, as well as inherited the spirit of research. Besides, in recent years, we have developed production systems with AVF-930 cyclotron for various ‘non-standard’ radioisotopes applied in both diagnosis and therapy. Here, we review the past 50 years of our activities on radioisotope and radiopharmaceutical development, as well as more recent activities

Acceptance of the Use of Artificial Intelligence in Medicine Among Japan's Doctors and the Public : A Questionnaire Survey

Background: The use of artificial intelligence (AI) in the medical industry promises many benefits, so AI has been introduced to medical practice primarily in developed countries. In Japan, the government is preparing for the rollout of AI in the medical industry. This rollout depends on doctors and the public accepting the technology. Therefore it is necessary to consider acceptance among doctors and among the public. However, little is known about the acceptance of AI in medicine in Japan. Objective: This study aimed to obtain detailed data on the acceptance of AI in medicine by comparing the acceptance among Japanese doctors with that among the Japanese public. Methods: We conducted an online survey, and the responses of doctors and members of the public were compared. AI in medicine was defined as the use of AI to determine diagnosis and treatment without requiring a doctor. A questionnaire was prepared referred to as the unified theory of acceptance and use of technology, a model of behavior toward new technologies. It comprises 20 items, and each item was rated on a five-point scale. Using this questionnaire, we conducted an online survey in 2018 among 399 doctors and 600 members of the public. The sample-wide responses were analyzed, and then the responses of the doctors were compared with those of the public using t tests. Results: Regarding the sample-wide responses (N=999), 653 (65.4%) of the respondents believed, in the future, AI in medicine would be necessary, whereas only 447 (44.7%) expressed an intention to use AI-driven medicine. Additionally, 730 (73.1%) believed that regulatory legislation was necessary, and 734 (73.5%) were concerned about where accountability lies. Regarding the comparison between doctors and the public, doctors (mean 3.43, SD 1.00) were more likely than members of the public (mean 3.23, SD 0.92) to express intention to use AI-driven medicine (P<.001), suggesting that optimism about AI in medicine is greater among doctors compared to the public. Conclusions: Many of the respondents were optimistic about the role of AI in medicine. However, when asked whether they would like to use AI-driven medicine, they tended to give a negative response. This trend suggests that concerns about the lack of regulation and about accountability hindered acceptance. Additionally, the results revealed that doctors were more enthusiastic than members of the public regarding AI-driven medicine. For the successful implementation of AI in medicine, it would be necessary to inform the public and doctors about the relevant laws and to take measures to remove their concerns about them

Development of a stable peptide-based PET tracer for detecting CD133-expressing cancer stem cells

CD133 has been recognized as a prominent biomarker for cancer stem cells (CSCs), which promote tumor relapse and metastasis. Herein, we developed a clinically relevant, stable, peptide-based positron emission tomography (PET) tracer, [64Cu]CM-2, for mapping CD133 protein in several kinds of cancers. Through the incorporation of a 6-aminohexanoic acid (Ahx) into the N-terminus of a CM peptide, we constructed a stable peptide tracer [64Cu]CM-2, which exhibited specific binding to CD133-positive CSCs in multiple preclinical tumor models. Both PET imaging and ex vivo biodistribution verified the superior performance of [64Cu]CM-2. Furthermore, the matched physical- and biological half-life of [64Cu]CM-2 makes it a state-of-art PET tracer for CD133. Therefore, [64Cu]CM-2 PET may not only enable the longitudinal tracking of CD133 dynamics in the cancer stem cell niche, but also provide a powerful and noninvasive imaging tool to track down CSCs in refractory cancers.第4回日本核医学会分科会放射線薬品科学研究会 第20回放射性医薬品・画像診断薬研究

Autism spectrum disorder-like behavior caused by reduced excitatory synaptic transmission in pyramidal neurons of mouse prefrontal cortex

CNTNAP2 or AHI1 are autism-associated genes. Here the authors show using knockdown of the genes that this results in reduced excitatory synaptic transmission in layer 2/3 pyramidal neurons in the prefrontal cortex and is associated with impaired social interaction in mice

Duplicated pollen–pistil recognition loci control intraspecific unilateral incompatibility in Brassica rapa

In plants, cell–cell recognition is a crucial step in the selection of optimal pairs of gametes to achieve successful propagation of progeny. Flowering plants have evolved various genetic mechanisms, mediated by cell–cell recognition, to enable their pistils to reject self-pollen, thus preventing inbreeding and the consequent reduced fitness of progeny (self-incompatibility, SI), and to reject foreign pollen from other species, thus maintaining species identity (interspecific incompatibility)1. In the genus Brassica, the SI system is regulated by an S-haplotype-specific interaction between a stigma-expressed female receptor (S receptor kinase, SRK) and a tapetum cell-expressed male ligand (S locus protein 11, SP11), encoded by their respective polymorphic genes at the S locus2,​3,​4,​5,​6. However, the molecular mechanism for recognition of foreign pollen, leading to reproductive incompatibility, has not yet been identified. Here, we show that recognition between a novel pair of proteins, a pistil receptor SUI1 (STIGMATIC UNILATERAL INCOMPATIBILITY 1) and a pollen ligand PUI1 (POLLEN UNILATERAL INCOMPATIBILITY 1), triggers unilateral reproductive incompatibility between plants of two geographically distant self-incompatible Brassica rapa lines, even though crosses would be predicted to be compatible based on the S haplotypes of pollen and stigma. Interestingly, SUI1 and PUI1 are similar to the SI genes, SRK and SP11, respectively, and are maintained as cryptic incompatibility genes in these two populations. The duplication of the SRK and SP11 followed by reciprocal loss in different populations would provide a molecular mechanism of the emergence of a reproductive barrier in allopatry