Miracle-dz/UniFAR-FTUs huggingface dataset:
FGF signaling promotes spreading of fat body precursors necessary for adult adipogenesis in Drosophila

Knowledge of adipogenetic mechanisms is essential to understand and treat conditions affecting organismal metabolism and adipose tissue health. In Drosophila, mature adipose tissue (fat body) exists in larvae and adults. In contrast to the well-known development of the larval fat body from the embryonic mesoderm, adult adipogenesis has remained mysterious. Furthermore, conclusive proof of its physiological significance is lacking. Here, we show that the adult fat body originates from a pool of undifferentiated mesodermal precursors that migrate from the thorax into the abdomen during metamorphosis. Through in vivo imaging, we found that these precursors spread from the ventral midline and cover the inner surface of the abdomen in a process strikingly reminiscent of embryonic mesoderm migration, requiring fibroblast growth factor (FGF) signaling as well. FGF signaling guides migration dorsally and regulates adhesion to the substrate. After spreading is complete, precursor differentiation involves fat accumulation and cell fusion that produces mature binucleate and tetranucleate adipocytes. Finally, we show that flies where adult adipogenesis is impaired by knock down of FGF receptor Heartless or transcription factor Serpent display ectopic fat accumulation in oenocytes and decreased resistance to starvation. Our results reveal that adult adipogenesis occurs de novo during metamorphosis and demonstrate its crucial physiological role.
Desmosomal protein degradation as an underlying cause of arrhythmogenic cardiomyopathy

Arrhythmogenic cardiomyopathy (ACM) is an inherited progressive cardiac disease. Many patients with ACM harbor mutations in desmosomal genes, predominantly in plakophilin-2 (PKP2). Although the genetic basis of ACM is well characterized, the underlying disease-driving mechanisms remain unresolved. Explanted hearts from patients with ACM had less PKP2 compared with healthy hearts, which correlated with reduced expression of desmosomal and adherens junction (AJ) proteins. These proteins were also disorganized in areas of fibrotic remodeling. In vitro data from human-induced pluripotent stem cell–derived cardiomyocytes and microtissues carrying the heterozygous PKP2 c.2013delC pathogenic mutation also displayed impaired contractility. Knockin mice carrying the equivalent heterozygous Pkp2 c.1755delA mutation recapitulated changes in desmosomal and AJ proteins and displayed cardiac dysfunction and fibrosis with age. Global proteomics analysis of 4-month-old heterozygous Pkp2 c.1755delA hearts indicated involvement of the ubiquitin-proteasome system (UPS) in ACM pathogenesis. Inhibition of the UPS in mutant mice increased area composita proteins and improved calcium dynamics in isolated cardiomyocytes. Additional proteomics analyses identified lysine ubiquitination sites on the desmosomal proteins, which were more ubiquitinated in mutant mice. In summary, we show that a plakophilin-2 mutation can lead to decreased desmosomal and AJ protein expression through a UPS-dependent mechanism, which preceded cardiac remodeling. These findings suggest that targeting protein degradation and improving desmosomal protein stability may be a potential therapeutic strategy for the treatment of ACM. Description Plakophilin-2 mutations lead to disorganized desmosomal proteins and their enhanced ubiquitination in a mouse model of arrhythmogenic cardiomyopathy. PKPing up the rhythm Arrhythmogenic cardiomyopathy (ACM) is a debilitating disease with clear genetic links but variable penetrance and poorly understood mechanisms. Here Tsui and colleagues analyzed heart tissue from patients with ACM along with inducible pluripotent stem cell–derived cardiomyocytes and mice carrying a plakophilin-2 (Pkp2) mutation to reveal that decreased expression of desmosomal and adherens junction proteins correlated with cardiac dysfunction and fibrosis. Proteomics data indicated involvement of the ubiquitin-proteasome system (UPS) in the degradation of these proteins, and inhibition of the UPS improved protein expression and calcium dynamics in isolated mutant cardiomyocytes. These results suggest that therapies aimed at increasing stability of desmosomal proteins may improve cardiac function in patients with ACM. --AW
Effect of Tinnitus on Sleep Quality and Insomnia

Abstract Introduction Tinnitus is a conscious perception of a sound resulting from abnormal activity within the nervous system. A relevant percentage of tinnitus patients report symptoms severe enough to significantly affect quality of life, including sleep disorders. Objective To analyze the sleep quality, insomnia, daytime sleepiness, and risk of obstructive sleep apnea (OSA) in participants with tinnitus. Methods The sample comprised 18 adults and older adults aged between 18 and 85 years old (mean age = 58.7 ± 17.5 years old), females and males, with complaint of continuous tinnitus for > 1 month. The instruments used were the Tinnitus Handicap Inventory (THI) questionnaire, the Insomnia Severity Index, the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, and the STOP-Bang questionnaire. Results By means of the THI questionnaire, the tinnitus severity degree reported by most participants was mild (27.8%) and moderate (27.8%), having a positive (r = 0.582) and significant (0.011) correlation to sleep quality, measured by means of the Pittsburgh questionnaire. There was a positive correlation between the Insomnia Severity Index and tinnitus handicap (r = 0.499; p = 0.035). A total of 72.2% of the participants self-assessed their sleep quality as poor, in addition to moderate insomnia (27.8%), although there is low risk of OSA (66.7%), without complaints of excessive daytime sleepiness (72.2%). Conclusion Subjects with tinnitus complaint self-rated their sleep quality as poor. Moreover, the higher the reported tinnitus handicap, the greater the symptoms of insomnia. There was no influence of tinnitus in relation to daytime sleepiness and no relationship between the severity of tinnitus and the risk of OSA.
ECG P,T wave complex detection algorithm based on lifting wavelet

In order to solve the problem that the ECG P,T-wave signal is complex,weak,difficult to identify as well as the problem of low efficiency and invalidity of the implementation of the identification algorithm,a combined algorithm was proposed using the fast speed characteristic of lifting wavelet analysis signal in time-domain and frequency domain based on the analysis to the principle of lifting wavelet algorithm.It combined the lifting wavelet transform with different OP,utilized the lifting wavelet to denoise,and indentified the P,T wave using composite algorithm in the reconstructed corresponding level low-frequency signal.A follower threshold function adapting to individual differences and abnormal ECG changes was proposed.Finally,the results of MIT-BIH ECG database experiments show that the proposed algorithm has high accuracy than traditional wavelet recognition method,at least doubling algorithm execution speed,and easier for hardware implementation.
Automated Design of Chiplets

Chiplet-based designs have gained recognition as a promising alternative to monolithic SoCs due to their lower manufacturing costs, improved re-usability, and optimized technology specialization. Despite progress made in various related domains, the design of chiplets remains largely reliant on manual processes. In this paper, we provide an examination of the historical evolution of chiplets, encompassing a review of crucial design considerations and a synopsis of recent advancements in relevant fields. Further, we identify and examine the opportunities and challenges in the automated design of chiplets. To further demonstrate the potential of this nascent area, we present a novel task that
Eliminating Space Scanning: Fast mmWave Beam Alignment with UWB Radios

Due to their large bandwidth and impressive data speed, millimeter-wave (mmWave) radios are expected to play a key role in the 5G and beyond (e.g., 6G) communication networks. Yet, to release mmWave’s true power, the highly directional mmWave beams need to be aligned perfectly. Most existing beam alignment methods adopt an exhaustive or semi-exhaustive space scanning, which introduces up to seconds of delays. To eliminate the need for complex space scanning, this article presents an Ultra-wideband (UWB)-assisted mmWave communication framework, which leverages the co-located UWB antennas to estimate the best angles for mmWave beam alignment. One major challenge of applying this idea in the real world is the barrier of limited antenna numbers. Commercial-Off-The-Shelf (COTS) devices are usually equipped with only a small number of UWB antennas, which are not enough for the existing algorithms to provide an accurate angle estimation. To solve this challenge, we design a novel Multi-Frequency MUltiple SIgnal Classification (MF-MUSIC) algorithm, which extends the classic MUltiple SIgnal Classification (MUSIC) algorithm to the frequency domain and overcomes the antenna limitation barrier in the spatial domain. Extensive real-world experiments and numerical simulations illustrate the advantage of the proposed MF-MUSIC algorithm. MF-MUSIC uses only three antennas to achieve an accurate angle estimation, which is a mere 0.15° (or a relative difference of 3.6%) different from the state-of-the-art 16-antenna-based angle estimation method.
Efficient Runtime Power Modeling with On-Chip Power Meters

Accurate and efficient power modeling techniques are crucial for both design-time power optimization and runtime on-chip IC management. In prior research, different types of power modeling solutions have been proposed, optimizing multiple objectives including accuracy, efficiency, temporal resolution, and automation level, targeting various power/voltage-related applications. Despite extensive prior explorations in this topic, new solutions still keep emerging and achieve state-of-the-art performance. This paper aims at providing a review of the recent progress in power modeling, with more focus on runtime on-chip power meter (OPM) development techniques. It also serves as a vehicle for discussing some general development techniques for the runtime on-chip power modeling task.
The ALIGN Automated Analog Layout Engine: Progress, Learnings, and Open Issues

The ALIGN (Analog Layout, Intelligently Generated from Netlists) project [1, 2] is a joint university-industry effort to push the envelope of automated analog layout through a systematic new approach, novel algorithms, and open-source software [3]. Analog automation research has been active for several decades, but has not found widespread acceptance due to its general inability to meet the needs of the design community. Therefore, unlike digital design, which has a rich history of automation and extensive deployment of design tools, analog design is largely unautomated. ALIGN attempts to overcome several of the major issues associated with this lack of success. First, to mimic the human designer's ability to recognize sub-blocks and specify constraints, ALIGN has used machine learning (ML) based methods to assist in these tasks. Second, to overcome the limitation of past automation approaches, which are largely specific to a class of designs, ALIGN attempts to create a truly general layout engine by decomposing the layout automation process into a set of steps, with specific constraints that are specific to the family of circuits, which are divided into four classes: low-frequency components (e.g., analog-to-digital converters (ADCs), amplifiers, and filters); wireline components for high-speed links (e.g., equalizers, clock/data recovery circuits, and phase interpolators); RF/Wireless components (e.g., components of RF transmitters and receivers), and power delivery components (e.g., capacitor- and inductor-based DC-DC converters and low dropout (LDO) regulators). For each class of circuits, different sets of constraints are important, depending on their frequency, parasitic sensitivity, need for matching, etc., and ALIGN creates a unified methodological framework that can address each class. Third, in each step, ALIGN has generated new algorithms and approaches to help improve the performance of analog layout. Fourth, given that experienced analog designers desire greater visibility into the process and input into the way that design is carried out, ALIGN is built modularly, providing multiple entry points at which a designer may intervene in the process.
Recent Progress in the Analysis of Electromigration and Stress Migration in Large Multisegment Interconnects

Traditional approaches to analyzing electromigration (EM) in on-chip interconnects are largely driven by semi-empirical models. However, such methods are inexact for the typical multisegment lines that are found in modern integrated circuits. This paper overviews recent advances in analyzing EM in on-chip interconnect structures based on physics-based models that use partial differential equations, with appropriate boundary conditions, to capture the impact of electron-wind and back-stress forces within an interconnect, across multiple wire segments. Methods for both steady-state and transient analysis are presented, highlighting approaches that can solve these problems with a computation time that is linear in the number of wire segments in the interconnect.
‘Below the surface’: power and professionalism in the further education sector

ABSTRACT Professionalism in the English Further Education (FE) system has been traditionally discussed in terms of superior teaching practices, attitudes and behaviours. The concept of ‘good work’ is therefore central to this paper’s analysis. Following a Bourdieusian tradition, professionalism is treated as a ‘site of struggle’ between FE teachers and managers, where managers hold the power to change the definition and substance of ‘good work’. This has implications for how teachers understand and speak about professionalism and highlights the discord that arises when teachers hold different viewpoints from managers. Drawing on a survey of 461 responses, the analysis makes three contributions: first, professionalism is theorised as a form of symbolic capital which actors attain by acquiring and deploying cultural, social and economic capital. Second, it shows how inequalities in capital can affect teachers’ perceived opportunities for professional recognition. Finally, it reveals that senior managers, with the greatest power, enforce a compliance model of professionalism based on the production of ‘good data’, which is the legitimate capital in the FE field, even when this is shown to be deprofessionalising to teachers.
Science mapping of water governance research in retrospect

ABSTRACT This study employs, for the first time, a science mapping approach for understanding how water governance science is socially, intellectually and conceptually structured. The research collaboration, co-citation and keyword co-occurrence networks were illustrated by these steps: data retrieval, preprocessing, network extraction, normalization, mapping, analysis and visualization. The Netherlands, the United States, Germany, the UK, Canada and Australia were found to be major countries collaborating on water governance. Three groups of water governance research were identified to be focused on governance, management and policy, respectively, and water governance research mainly studied issues such as poverty, privatization and institutions.
Mating systems and recombination landscape strongly shape genetic diversity and selection in wheat relatives

How and why genetic diversity varies among species is a long-standing question in evolutionary biology. Life history traits (especially life-span and reproductive mode) have been shown to be main determinants, but the underlying causes are debated. Demographic history and linked selection have been proposed as main mechanisms, but their relative impact is not well understood. Here, we used the Aegilops/Triticum grass species, a group characterized by contrasted mating systems (from obligate out-crossing to high selfing) and marked recombination rate variation across the genome, to quantify the interaction between demography and linked selection on neutral and adaptive diversity. By analysing phenotypic and transcriptomic data, we found that selfing strongly affects genetic diversity and the efficacy of selection by amplifying the intensity of linked selection genome wide. Differently from expectations, this effect was gradual, with mixed mating species showing intermediate patterns. These results bear implications for the evolution of mating systems and more generally for our understanding of the fundamental drivers of genetic diversity.
Accurate Prediction of Transcriptional Activity of Single Missense Variants in HIV Tat with Deep Learning

Tat is an essential gene for increasing the transcription of all HIV genes, and it affects HIV replication, HIV exit from latency, and AIDS progression. The Tat gene frequently mutates in vivo producing variants with diverse activities, contributing to HIV viral heterogeneity, as well as drug-resistant clones. Thus, identifying the transcriptional activities of Tat variants will help to better understand AIDS pathology and treatment. We recently reported the missense mutation landscape of all single amino acid Tat variants. In these experiments, a fraction of double missense alleles exhibited intragenic epistasis. It is too time-consuming and costly to determine a variants’ effect for all double mutant alleles with experiments. Therefore, we propose a combined GigaAssay/Deep learning approach. As a first step for determining activity landscapes for complex variants, we evaluated a deep learning framework using previously reported GigaAssay experiments to predict how transcription activity is affected by Tat variants with single missense substitutions. Our approach achieves a 0.94 Pearson correlation coefficient when comparing experimental to predicted activities. This hybrid approach should be extensible to more complex Tat alleles for better understanding the genetic control of HIV genome transcription.
Comparing Familiar with Inspiring Recommendations to Assist People in Moving More 8-16

Sufficient physical activity is crucial for people’s health and well-being. However, not enough people attain the weekly minimum of 150 minutes. Since current mobile health systems are not optimal to motivate and assist people to move more, this study investigates the effect of personalized suggestions generated by two types of recommender system algorithms: content-based (which provide more familiar recommendations, relevant to existing interests) and user-based collaborative filtering (which deliver more diverse recommendations, allowing inspiration for new interests). By conducting a longitudinal between-subject user study over eight weeks, we will investigate how the two algorithms separately affect motivation and behavior change. We developed two versions of an Android smartphone application to deliver the recommendations, with the only difference being the implemented recommender algorithm. In all other aspects, the apps are identical: Both systems use the same datasets of physical activities and tips to break sedentary behavior, apply the user profile and contextual filter, and integrate the combination of star rating and momentary motivation feedback to provide personalization on preferences and well-being. We will analyze the differences in people’s star rating feedback, motivation to move, physical activity, and sedentary behavior. The main hypothesis is that inspiring recommendations from the collaborative algorithm will motivate people more for more physical activity and less sedentary behavior. The results of this study will provide insights for future mobile health recommenders in what type of algorithm and recommendations are most effective in the domain of increasing physical activity and motivating people to move more.
The Impact of Salient Musical Features in a Hybrid Recommendation System for a Sound Library 156-162

EarSketch is an online learning environment that teaches coding and music concepts through the computational manipulation of sounds selected from a large sound library. It features sound recommendations based on acoustic similarity and co-usage with a user’s current sound selection in order to encourage exploration of the library. However, students have reported that the recommended sounds do not complement their current projects in terms of two areas: musical key and rhythm. We aim to improve the relevance of these recommendations through the inclusion of these two musically related features. This paper describes the addition of key signature and beat extraction to the EarSketch sound recommendation model in order to improve the musical compatibility of the recommendations with the sounds in a user’s project. Additionally, we present an analysis of the effects of these new recommendation strategies on user exploration and usage of the recommended sounds. The results of this analysis suggest that the addition of explicitly musically-relevant attributes increases the coverage of the sound library among sound recommendations as well as the sounds selected by users. It reflects the importance of including multiple musical attributes when building recommendation systems for creative and open-ended musical systems.
Towards an Explainable Machine Learning Framework for Sketched Diagram Recognition 190-197

In recent years, machine learning has made significant advancements in various fields, including image recognition. However, the complexity of these models often makes it difficult for users to understand the reasoning behind their predictions. This is especially true for sketch recognition, where the ability to understand and explain the model’s decision-making process is crucial. To address this issue, our research focuses on developing an explainable machine learning framework for sketch recognition. The framework incorporates techniques such as feature visualization and feature attribution methods which provide insights into the model’s decision-making process. The goal of this research is to not only improve the performance of sketch recognition models but also to increase their interpretability, making them more usable and trustworthy for users.
Norms and Causation in Artificial Morality 166-169

There’s been increasing interest into how to build Artificial Moral Agents (AMAs) that make moral decisions on the basis of causation rather than mere correction. One promising avenue for achieving this is to use a causal modelling approach. This paper explores an open and important problem with such an approach; namely, the problem of what makes a causal model an appropriate model. I explore why we need to establish criteria for what makes a model appropriate, and offer-up such criteria which appeals to normative considerations.
Uncertainties associated with noise dosimeters in mining

The accuracy of an occupational noise exposure measurement, obtained by the use of a personal noise dosimeter, is commonly specified as being within ±2.0 dB. Using data obtained from an acoustical instrument calibration program, a study on microphone placement effects, representative noise spectra found in the mining industry, and the statistical method of propagation of error, an analytical model is proposed and an estimate of the errors in a real‐world noise dosimeter measurement are presented. The findings of this investigation suggest that in a real‐world environment, the noise dosimeter can deviate from the true noise dose, however, this noise dose estimation will usually be well within the commonly specified limits of ±2.0 dB, provided the measurements are made in a diffuse field, or in the case of a free‐field measurement, the dosimeter microphone is placed on the shoulder of the most exposed ear.