Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat wave–related mortality in Sweden: A case-crossover study investigating effect modification by neighbourhood deprivation

Aims: The present study aimed to investigate if set thresholds in the Swedish heat-wave warning system are valid for all parts of Sweden and if the heat-wave warning system captures a potential increase in all-cause mortality and coronary heart disease (CHD) mortality. An additional aim was to investigate whether neighbourhood deprivation modifies the relationship between heat waves and mortality. Methods: From 1990 until 2014, in 14 municipalities in Sweden, we collected data on daily maximum temperatures and mortality for the five warmest months. Heat waves were defined according to the categories used in the current Swedish heat-wave warning system. Using a case-crossover approach, we investigated the association between heat waves and mortality in Sweden, as well as a modifying effect of neighbourhood deprivation. Results: On a national as well as a regional level, heat waves significantly increased both all-cause mortality and CHD mortality by approximately 10% and 15%, respectively. While neighbourhood deprivation did not seem to modify heat wave–related all-cause mortality, CHD mortality did seem to modify the risk. Conclusions: It may not be appropriate to assume that heat waves in Sweden will have the same impact in a northern setting as in a southern, or that the impact of heat waves will be the same in affluent and deprived neighbourhoods. When designing and implementing heat-wave warning systems, neighbourhood, regional and national information should be incorporated.
On the control of the proton current in the voltage-gated proton channel Hv1

Significance Voltage activation of proton transport is an important mechanism that plays a key role in fundamental biological processes, yet the detailed control of such processes is far from clear. Here we investigate the voltage-activated proton channel Hv1 to explore the structure/function basis of its activation by combining empirical valence bond and electrostatic calculations. Our study provides a detailed description of the exquisite use of an ionizable aspartic acid residue and arginine residues in controlling the crucial selectivity of the proton transport through the channel. This type of mechanism is likely to have implications for the action of other related systems. The nature of the action of voltage-activated proton transport proteins is a conundrum of great current interest. Here we approach this issue by exploring the action of Hv1, a voltage-gated proton channel found in different cells in humans and other organisms. Our study focuses on evaluating the free energy of transporting a proton through the channel, as well as the effect of the proton transfer through D112, in both the closed and open channel conformations. It is found that D112 allows a transported proton to bypass the electrostatic barrier of the open channel, while not being able to help in passing the barrier in the closed form. This reflects the change in position of the gating arginine residues relative to D112, upon voltage activation. Significantly, the effect of D112 accounts for the observed trend in selectivity by overcoming the electrostatic barrier at its highest point. Thus, the calculations provide a structure/function correlation for the Hv1 system. The present work also clarifies that the action of Hv1 is not controlled by a Grotthuss mechanism but, as is always the case, by the protein electrostatic potential at the rate-limiting barriers.
Novel radiomics evaluation of bone formation utilizing multimodal (SPECT/X-ray CT) in vivo imaging

Although an extensive research is being undertaken, the ideal bone graft and evaluation method of the bone formation draw still a warranted attention. The purpose of this study was to develop a novel multimodal radiomics evaluation method, utilizing X-ray computed tomography (CT) and single photon emission computed tomography (SPECT) with Tc-99m-Methyl diphosphonate (Tc-99m-MDP) tracer. These modalities are intended to provide quantitative data concerning the mineral bone density (after evaluation it is referred to as opacity) and the osteoblast activity, at the same time. The properties of bone formation process within poly (methyl methacrylate)-based bone cement graft (PMMA) was compared to that of albumin coated, sterilized, antigen-extracted freeze-dried human bone grafts (HLBC), in caudal vertebrae (C5) of rats. The animals were scanned at 3 and 8 weeks after surgery. In both groups, the mean opacity increased, while the mean Tc-99m-MDP activity decreased. The later parameter was significant (n = 4, p = 0.002) only in HLBC group. The linear regression analysis of PMMA-treated group variables (mean opacity increase; mean Tc-99m-MDP activity decrease), revealed a negative correlation with the medium strength (r = 0.395, p = 0.605). Whereas, it showed strong positive correlation when HLBC group variables were analyzed (r = 0.772, p = 0.012). These results indicate that using HLBC grafts is advantageous in terms of the osteoblast activity and bone vascularization over PMMA cement. Using this regression analysis method, we were able to distinguish characteristics that otherwise could not be distinguished by a regular data analysis. Hence, we propose utilizing this novel method in preclinical tests, and in clinical monitoring of bone healing, in order to improve diagnosis of bone-related diseases.
Toward Smart Rings as Assistive Devices for People with Motor Impairments: A Position Paper

In this position paper we highlight the potential of finger augmentation devices, such as smart rings, for designing assistive technology for people with upper body motor impairments. We review the literature on assistive input for motor impairments and point out the fact that no prior work has examined the application opportunities of finger augmentation devices for people with motor impairments, despite vast attention dedicated to other input devices and modalities, such as touch, voice, eye gaze tracking, and direct brain-computer input. To foster explorations of smart rings as assistive devices for users with motor impairments, we recommend several directions of investigation, such as designing one-button or microswitch interactions, design of multi-device user interfaces that combine smart rings with touch input on mobile devices, recognition of mid-air gestures performed by movements of the upper arm and shoulder, and application of the principles of ability-based design for self-adapting smart ring gesture user interfaces to accommodate a wide range of motor impairments.
Improved Parallel Cache-Oblivious Algorithms for Dynamic Programming and Linear Algebra

For many cache-oblivious algorithms for dynamic programming and linear algebra, we observe that the key factor that affects the cache complexity is the number of input entries involved in each basic computation cell. In this paper, we propose a level of abstraction to capture this property, and refer to it as the $k$-d grid computation structure. We then show the computational lower bounds for this grid structure, and propose efficient and highly-parallel algorithms to compute such grid structure that optimize the number of arithmetic operations, parallel depth, and the cache complexity in both the classic setting when reads and writes have the same cost, and the asymmetric variant that considers writes to be more expensive than reads. Using the abstraction with the proposed algorithms as the implementation, we propose cache-oblivious algorithms for many fundamental problems with improved cache complexities in both the classic and asymmetric settings. The cache bounds are optimal in most applications we consider. Meanwhile, we also reduce the parallel depths of many problems. We believe that the novelty of our framework is of interests and leads to many new questions for future work.
Anderson Acceleration for Reinforcement Learning

Anderson acceleration is an old and simple method for accelerating the computation of a fixed point. However, as far as we know and quite surprisingly, it has never been applied to dynamic programming or reinforcement learning. In this paper, we explain briefly what Anderson acceleration is and how it can be applied to value iteration, this being supported by preliminary experiments showing a significant speed up of convergence, that we critically discuss. We also discuss how this idea could be applied more generally to (deep) reinforcement learning.
RETROSPECTIVE:PRIME: A Novel Processing-in-memory Architecture for Neural Network Computation in ReRAM-based Main Memory

This paper was originally published in ISCA 2016. One notable event during that ISCA was the inauguration of the Architecture 2030 Workshop [1], led by visionary leaders within our community. This workshop provided a platform for envisioning the trajectory of our field and discussing future challenges and opportunities. During this workshop, I had the privilege of presenting my vision on ”Technology-driven Architecture Innovation: Challenges and Opportunities. ” To exemplify the potential for novel architecture designs, I highlighted this ISCA 2016 paper, showcasing the possibilities that arise from combining emerging applications such as AI with cutting-edge technologies like ReRAM. The workshop resulted in a report that outlines the collective vision of computer architecture research for the next 15 years, highlighting five high-potential directions: specialized accelerator design, cloud computing, 3D stacked architecture, technology-driven architecture (e.g., new memory, quantum, etc.), and machine learning as a key workload. Remarkably, our ISCA 2016 paper serves as an excellent illustrative example of three out of these five directions: emerging memory technology, domain-specific accelerators, and machine learning workloads.
On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include huge state space, varying action space, long horizon, etc. In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II. We investigate a hierarchical approach, where the hierarchy involves two levels of abstraction. One is the macro-actions extracted from expert’s demonstration trajectories, which can reduce the action space in an order of magnitude yet remain effective. The other is a two-layer hierarchical architecture, which is modular and easy to scale. We also investigate a curriculum transfer learning approach that trains the agent from the simplest opponent to harder ones. On a 64×64 map and using restrictive units, we train the agent on a single machine with 4 GPUs and 48 CPU threads. We achieve a winning rate of more than 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93% winning rate against the most difficult noncheating built-in AI (level-7) within days. We hope this study could shed some light on the future research of large-scale reinforcement learning.
Impacts of prescribed fires and benefits from their reduction for air quality, health, and visibility in the Pacific Northwest of the United States

ABSTRACT Using a WRF-SMOKE-CMAQ modeling framework, we investigate the impacts of smoke from prescribed fires on model performance, regional and loc al air quality, health impacts, and visibility in protected natural environments using three different prescribed fire emission scenarios: 100% fire, no fire, and 30% fire. The 30% fire case reflects a 70% reduction in fire activities due to harvesting of logging residues for use as a feedstock for a potential aviation biofuel supply chain. Overall model performance improves for several performance metrics when fire emissions are included, especially for organic carbon, irrespective of the model goals and criteria used. This effect on model performance is more pronounced for the rural and remote IMPROVE sites for organic carbon and total PM2.5. A reduction in prescribed fire emissions (30% fire case) results in significant improvement in air quality in areas in western Oregon, northern Idaho, and western Montana, where most prescribed fires occur. Prescribed burning contributes to visibility impairment, and a relatively large portion of protected class I areas will benefit from a reduced emission scenario. For the haziest 20% days, prescribed burning is an important source of visibility impairment, and approximately 50% of IMPROVE sites in the model domain show a significant improvement in visibility for the reduced fire case. Using BenMAP, a health impact assessment tool, we show that several hundred additional deaths, several thousand upper and lower respiratory symptom cases, several hundred bronchitis cases, and more than 35,000 workday losses can be attributed to prescribed fires, and these health impacts decrease by 25–30% when a 30% fire emission scenario is considered. Implications: This study assesses the potential regional and local air quality, public health, and visibility impacts from prescribed burning activities, as well as benefits that can be achieved by a potential reduction in emissions for a scenario where biomass is harvested for conversion to biofuel. As prescribed burning activities become more frequent, they can be more detrimental for air quality and health. Forest residue-based biofuel industry can be source of cleaner fuel with co-benefits of improved air quality, reduction in health impacts, and improved visibility.
Exploring the Potential of Concept Associations for the Creative Generation of Linguistic Artifacts: A Case Study With Riddles and Rhetorical Figures

Automatic generation of linguistic artifacts is a problem that has been sporadically tackled over the years. The main goal of this paper is to explore how concept associations can be useful from a computational creativity point of view to generate some of these artifacts. We present an approach where finding associations between concepts that would not usually be considered as related (for example life and politics or diamond and concrete) could be the seed for the generation of creative and surprising linguistic artifacts such as rhetorical figures (life is like politics) and riddles (what is as hard as concrete? ). Human volunteers evaluated the quality and appropriateness of the generated figures and riddles, and the results show that the concept associations obtained are useful for producing these kinds of creative artifacts.
Security Aware Dual Authentication based Routing Scheme using Fuzzy Logic with Secure data Dissemination for Mobile Ad-hoc Networks

ABSTRACT The security of mobile ad-hoc networks (MANETs) has been getting a great deal of consideration in the field of wireless networking because MANETs are vulnerable to malicious attacks. Secure transmission without node failure in MANETs is a testing issue because of the nonappearance of centralized administration, openness in the network topology. In order to upgrade the security of network and shield the nodes from vulnerabilities, this article proposes a Security-Aware Dual Authentication based Routing (SDAR) which utilizes fuzzy logic control (FLC) based prediction rules to select the most reliable path. We have coordinated the proposed scheme into the cipher text policy-attribute based encryption (CP-ABE) for secure data dispersal among the source to destination with game theory based attack detection model. The performance of the proposed scheme is assessed with respect to different evaluation metrics in different node density levels. The results obtained clearly describes the proposed scheme provides a flexible and feasible decision to select the route that meets the security requirement of data packets transmission in MANETs environment.
What Influences Choice of Business-to-Business Connectivity Platforms?

ABSTRACT Business-to-Business platforms provide connectivity and data exchange services and compete in several dedicated services, such as electronic invoicing (e-invoicing). Thus far, little research has examined the factors that influence firms’ choice among competing platforms: Which platform features matter, and in what proportion in the decision maker’s choice? To address this gap, we conduct an empirical study and triangulate with past theoretical explanations that have sought to account for the firm’s choice. Based on the analysis, we formulate a platform selection model that includes nine features: (1) reach, (2) total cost, (3) usability, (4) ease of system integration, (5) implementation capability, (6) platform support for service improvement, (7) service customization, (8) platform reputation, and (9) long-term sustainability. We apply conjoint analysis using firms’ selection data, collected from 282 firms that have recently made a purchase decision among e-invoicing platforms. All features except the vendor’s implementation capability are found to significantly influence the platform choice. Two features—usability and reach—dominate the choice and account for nearly 50 percent of the likely outcome. We use cluster analysis to examine the effect of firm size on firms’ preferences in platform features. As hypothesized, larger companies prefer interoperability, scale, and network effects, while smaller companies value local use efficiency and ease of use because they are more concerned with usability and cost. Our theoretical and managerial claims concerning platform choice highlight the difficulty of bootstrapping, the role of pricing and cost, the minor importance of implementation-related features, and the impact of longevity during platform contracting.
Striatal dopamine 2 receptor upregulation during development predisposes to diet-induced obesity by reducing energy output in mice

Significance The need to understand the early determinants for obesity has never been greater with currently over 700 million obese people worldwide. Several decades of research have suggested that dopamine 2 receptors (D2R) in the striatum might be particularly important for obesity etiology, but no study has thus far outlined an unambiguous causal relationship. Here we show that striatal D2R upregulation induces obesity in mice fed a high-fat diet and this is due to reduced energy expenditure rather than increased eating. Importantly, we show that elevated D2R during development is sufficient to persistently increase obesity susceptibility. This work identifies excess D2Rs early in life as a potential key predisposition factor toward obesity and therefore may help uncover strategies for early interventions. Dopaminergic signaling in the striatum, particularly at dopamine 2 receptors (D2R), has been a topic of active investigation in obesity research in the past decades. However, it still remains unclear whether variations in striatal D2Rs modulate the risk for obesity and if so in which direction. Human studies have yielded contradictory findings that likely reflect a complex nonlinear relationship, possibly involving a combination of causal effects and compensatory changes. Animal work indicates that although chronic obesogenic diets reduce striatal D2R function, striatal D2R down-regulation does not lead to obesity. In this study, we evaluated the consequences of striatal D2R up-regulation on body-weight gain susceptibility and energy balance in mice. We used a mouse model of D2R overexpression (D2R-OE) in which D2Rs were selectively up-regulated in striatal medium spiny neurons. We uncover a pathological mechanism by which striatal D2R-OE leads to reduced brown adipose tissue thermogenesis, reduced energy expenditure, and accelerated obesity despite reduced eating. We also show that D2R-OE restricted to development is sufficient to promote obesity and to induce energy-balance deficits. Together, our findings indicate that striatal D2R-OE during development persistently increases the propensity for obesity by reducing energy output in mice. This suggests that early alterations in the striatal dopamine system could represent a key predisposition factor toward obesity.
Early developmental arrest and impaired gastrointestinal homeostasis in U12-dependent splicing-defective Rnpc3-deficient mice

Splicing is an essential step in eukaryotic gene expression. While the majority of introns is excised by the U2-dependent, or major class, spliceosome, the appropriate expression of a very small subset of genes depends on U12-dependent, or minor class, splicing. The U11/U12 65K protein (hereafter 65K), encoded by RNPC3, is one of seven proteins that are unique to the U12-dependent spliceosome, and previous studies including our own have established that it plays a role in plant and vertebrate development. To pinpoint the impact of 65K loss during mammalian development and in adulthood, we generated germline and conditional Rnpc3-deficient mice. Homozygous Rnpc3−/− embryos died prior to blastocyst implantation, whereas Rnpc3+/− mice were born at the expected frequency, achieved sexual maturity, and exhibited a completely normal lifespan. Systemic recombination of conditional Rnpc3 alleles in adult (Rnpc3lox/lox) mice caused rapid weight loss, leukopenia, and degeneration of the epithelial lining of the entire gastrointestinal tract, the latter due to increased cell death and a reduction in cell proliferation. Accompanying this, we observed a loss of both 65K and the pro-proliferative phospho-ERK1/2 proteins from the stem/progenitor cells at the base of intestinal crypts. RT-PCR analysis of RNA extracted from purified preparations of intestinal epithelial cells with recombined Rnpc3lox alleles revealed increased frequency of U12-type intron retention in all transcripts tested. Our study, using a novel conditional mouse model of Rnpc3 deficiency, establishes that U12-dependent splicing is not only important during development but is indispensable throughout life.
Plasma ghrelin O‐acyltransferase (GOAT) enzyme levels: A novel non‐invasive diagnosis tool for patients with significant prostate cancer

Early detection of PCa faces severe limitations as PSA displays poor‐specificity/sensitivity. As we recently demonstrated that plasma ghrelin O‐acyltransferase (GOAT)‐enzyme is significantly elevated in PCa‐patients compared with healthy‐controls, using a limited patients‐cohort, we aimed to further explore the potential of GOAT to improve PCa diagnosis using an ample patients‐cohort (n = 312) and defining subgroups (i.e. significant PCa/metastatic patients, etc.) that could benefit from this biomarker. Plasma GOAT‐levels were evaluated by ELISA in patients with (n = 183) and without (n = 129) PCa. Gleason Score ≥ 7 was considered clinically significant PCa. GOAT‐levels were higher in PCa patients vs control patients, and in those with significant PCa vs non‐significant PCa. GOAT‐levels association with the diagnoses of significant PCa was independent from traditional clinical variables (i.e. PSA/age/DRE). Remarkably, GOAT outperformed PSA in patients with PSA‐levels ranging 3‐20 ng/mL for the significant PCa diagnosis [GOAT‐AUC = 0.612 (0.531‐0.693) vs PSA‐AUC = 0.494 (0.407‐0.580)]. A panel of key variables including GOAT/age/DRE/testosterone also outperformed the same panel but with PSA [AUC = 0.720 (0.710‐0.730) vs AUC = 0.705 (0.695‐0.716), respectively]. Notably, GOAT‐levels could also represent a novel predictive biomarker of aggressiveness, as its levels are positively associated with Gleason Score and the presence of metastasis at the time of diagnoses. Altogether, our data reveal that GOAT‐levels can be used as a non‐invasive biomarker for significant PCa diagnosis in patients at risk of PCa (with PSA: 3‐20 ng/mL).