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Recently, business organizations have increasingly turned to a novel form of non-monetary incentives— that is, \"gamification,\" which refers to a motivation technique using video game elements, such as digital points, badges, and friendly competition in non-game contexts like workplaces. The introduction of gamification to the context of human resource management has immediately become embroiled in serious moral debates. Most notable is the accusation that using gamification as a motivation tool, employers exploit workers. This article offers an in-depth analysis of the moral charge of exploitation. This article maintains that there are no clear grounds for believing that gamification of labor is exploitative and that if gamification of labor involves a wrong or vice, it must be something other than exploitation.

Businesses increasingly rely on algorithms that are data-trained sets of decision rules (i.e., the output of the processes often called “machine learning”) and implement decisions with little or no human intermediation. In this article, we provide a philosophical foundation for the claim that algorithmic decision-making gives rise to a “right to explanation.” It is often said that, in the digital era, informed consent is dead. This negative view originates from a rigid understanding that presumes informed consent is a static and complete transaction. Such a view is insufficient, especially when data are used in a secondary, noncontextual, and unpredictable manner—which is the inescapable nature of advanced artificial intelligence systems. We submit that an alternative view of informed consent—as an assurance of trust for incomplete transactions—allows for an understanding of why the rationale of informed consent already entails a right to ex post explanation.

Alzheimer's disease (AD) is the most common cause of dementia and represents one of the highest unmet needs in medicine today. Drug development efforts for AD have been encumbered by largely unsuccessful clinical trials in the last decade. Drug repositioning, a process of discovering a new therapeutic use for existing drugs or drug candidates, is an attractive and timely drug development strategy especially for AD. Compared with traditional de novo drug development, time and cost are reduced as the safety and pharmacokinetic properties of most repositioning candidates have already been determined. A majority of drug repositioning efforts for AD have been based on positive clinical or epidemiological observations or in vivo efficacy found in mouse models of AD. More systematic, multidisciplinary approaches will further facilitate drug repositioning for AD. Some experimental approaches include unbiased phenotypic screening using the library of available drug collections in physiologically relevant model systems (e.g. stem cell-derived neurons or glial cells), computational prediction and selection approaches that leverage the accumulating data resulting from RNA expression profiles, and genome-wide association studies. This review will summarize several notable strategies and representative examples of drug repositioning for AD.

We offer a precautionary account of why business managers should proactively rethink about what kinds of automation firms ought to implement, by exploring two challenges that automation will potentially pose. We engage the current debate concerning whether life without work opportunities will incur a meaning crisis, offering an argument in favor of the position that if technological unemployment occurs, the machine age may be a structurally limited condition for many without work opportunities to have or add meaning to their lives. We term this the axiological challenge. This challenge, if it turns out to be persuasive, leads to a second challenge, to which managers should pay special attention: the teleological challenge, a topic especially relevant to the broad literature about corporate purpose and governance. We argue that both the shareholder profit-maximization model and its major alternative, stakeholder theory, are insufficient to address the meaning crisis. Unless rebutted, the two challenges compel business leaders to proactively rethink the purpose of business for future society. Otherwise, businesses will be contributors to a major ethical crisis and societal externality in the coming society.

Familial Alzheimer's disease (FAD)-associated presenilin 1 (PS1) serves as a catalytic subunit of γ-secretase complex, which mediates the proteolytic liberation of β-amyloid (Aβ) from β-amyloid precursor protein (APP). In addition to its proteolytic role, PS1 is involved in non-proteolytic functions such as protein trafficking and ion channel regulation. Furthermore, postmortem AD brains as well as AD patients showed dysregulation of cholesterol metabolism. Since cholesterol has been implicated in regulating Aβ production, we investigated whether the FAD PS1-associated cholesterol elevation could influence APP processing. We found that in CHO cells stably expressing FAD-associated PS1 ΔE9, total cholesterol levels are elevated compared to cells expressing wild-type PS1. We also found that localization of APP in cholesterol-enriched lipid rafts is substantially increased in the mutant cells. Reducing the cholesterol levels by either methyl-β-cyclodextrin or an inhibitor of CYP51, an enzyme mediating the elevated cholesterol in PS1 ΔE9-expressing cells, significantly reduced lipid raft-associated APP. In contrast, exogenous cholesterol increased lipid raft-associated APP. These data suggest that in the FAD PS1 ΔE9 cells, the elevated cellular cholesterol level contributes to the altered APP processing by increasing APP localized in lipid rafts.

The paucity of enzymes that efficiently deconstruct plant polysaccharides represents a major bottleneck for industrial-scale conversion of cellulosic biomass into biofuels. Cow rumen microbes specialize in degradation of cellulosic plant material, but most members of this complex community resist cultivation. To characterize biomass-degrading genes and genomes, we sequenced and analyzed 268 gigabases of metagenomic DNA from microbes adherent to plant fiber incubated in cow rumen. From these data, we identified 27,755 putative carbohydrate-active genes and expressed 90 candidate proteins, of which 57% were enzymatically active against cellulosic substrates. We also assembled 15 uncultured microbial genomes, which were validated by complementary methods including single-cell genome sequencing. These data sets provide a substantially expanded catalog of genes and genomes participating in the deconstruction of cellulosic biomass.

The exchange interaction governs static and dynamic magnetism. This fundamental interaction comes in two flavours—symmetric and antisymmetric. The symmetric interaction leads to ferro- and antiferromagnetism, and the antisymmetric interaction has attracted significant interest owing to its major role in promoting topologically non-trivial spin textures that promise fast, energy-efficient devices. So far, the antisymmetric exchange interaction has been found to be rather short ranged and limited to a single magnetic layer. Here we report a long-range antisymmetric interlayer exchange interaction in perpendicularly magnetized synthetic antiferromagnets with parallel and antiparallel magnetization alignments. Asymmetric hysteresis loops under an in-plane field reveal a unidirectional and chiral nature of this interaction, which results in canted magnetic structures. We explain our results by considering spin–orbit coupling combined with reduced symmetry in multilayers. Our discovery of a long-range chiral interaction provides an additional handle to engineer magnetic structures and could enable three-dimensional topological structures.An antisymmetric and chiral long range interlayer magnetic exchange interaction is measured, with implications for spintronics and chiral magnetic devices.

The inflow of warm and salty Circumpolar Deep Water affects the melting of the ice shelf on the Amundsen Sea, a significant contributor to global sea level rise. Multi‐year mooring data (2014–2016 and 2018–2020) from the front of the Dotson Ice Shelf show the modified Circumpolar Deep Water layer was thicker during 2018–2020 than during 2014–2016. During 2014–2016, Ocean surface stress curl influenced the barotropic process and strengthened southward velocity, while during 2018–2020, it caused lift and downwelling of thermocline depth, increasing the impact of the baroclinic process in ocean circulation. The heat transport to the ice shelf during 2018–2020 (57.42 MW m−1) was half as much as it was during 2014–2016 (111.06 MW m−1) due to a weaker lower layer current. The difference in ocean circulation between two periods, caused by a difference in warm layer thickness, ultimately impacts the heat transport entering the ice shelf cavity. Plain Language Summary Warm and salty water from the deep ocean flows into the ice shelf cavities in the West Antarctic, causing the ice to melt and contribute to global sea level rise. We measured the current and water properties in front of the Dotson Ice Shelf during 2014–2016 and 2018–2020 and found that the warm layer was thicker during the latter period. Unlike during 2014–2016 when ocean surface stress curl created a spatial imbalance in sea level and affected the southward current due to pressure gradients, during 2018–2020, ocean surface stress curl changed the density by causing upwelling and downwelling. This density change influenced the velocity variation toward the ice shelf. Although the mCDW layer was ticker, the heat influx to the ice shelf during 2018–2020 was half as much as it was during 2014–2016. The difference in ocean current during these two periods, due to differences in the warm layer thickness, ultimately affects how much heat is transported into the ice shelf cavity. Key Points The modified circumpolar deep water layer was thicker in 2018–2020 than that during 2014–2016 The baroclinic effect plays a more important role in the variability of the current entering the ice shelf during 2018–2020 Differences in the seasonal cycle of the ocean surface stress curl can affect ocean circulation by changing ocean conditions

This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The ability to interpret can be made more accurate by reducing uncertain learning data, applying data in different environments, and applying techniques that explain the reasoning behind the results. We designed a generalized model using three different datasets, and each speech was converted into a spectrogram image through STFT preprocessing. The spectrogram was divided into the time domain with overlapping to match the input size of the model. Each divided section is expressed as a Gaussian distribution, and the quality of the data is investigated by the correlation coefficient between distributions. As a result, the scale of the data is reduced, and uncertainty is minimized. VGGish and YAMNet are the most representative pretrained deep learning networks frequently used in conjunction with speech processing. In dealing with speech signal processing, it is frequently advantageous to use these pretrained models synergistically rather than exclusively, resulting in the construction of ensemble deep networks. And finally, various explainable models (Grad CAM, LIME, occlusion sensitivity) are used in analyzing classified results. The model exhibits adaptability to voices in various environments, yielding a classification accuracy of 87%, surpassing that of individual models. Additionally, output results are confirmed by an explainable model to extract essential emotional areas, converted into audio files for auditory analysis using Grad CAM in the time domain. Through this study, we enhance the uncertainty of activation areas that are generated by Grad CAM. We achieve this by applying the interpretable ability from previous studies, along with effective preprocessing and fusion models. We can analyze it from a more diverse perspective through other explainable techniques.

The Getz region of West Antarctica is losing ice at an increasing rate; however, the forcing mechanisms remain unclear. Here we use satellite observations and an ice sheet model to measure the change in ice speed and mass balance of the drainage basin over the last 25-years. Our results show a mean increase in speed of 23.8 % between 1994 and 2018, with three glaciers accelerating by over 44 %. Speedup across the Getz basin is linear, with speedup and thinning directly correlated confirming the presence of dynamic imbalance. Since 1994, 315 Gt of ice has been lost contributing 0.9 ± 0.6 mm global mean sea level, with increased loss since 2010 caused by a snowfall reduction. Overall, dynamic imbalance accounts for two thirds of the mass loss from this region of West Antarctica over the past 25-years, with a longer-term response to ocean forcing the likely driving mechanism. The Getz region of West Antarctica is losing ice at an increasing rate; however, the forcing mechanisms remain unclear. Here we show for the first time that since 1994, widespread speedup has occurred on the majority of glaciers in the Getz drainage basin, with some glaciers speeding up by over 44 %.