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Patients with degenerative cervical myelopathy (DCM) experience structural and functional brain reorganization. However, few studies have investigated the influence of sex on cerebral alterations. The present study investigates the role of sex on brain functional connectivity (FC) and global network topology in DCM and healthy controls (HCs). The resting-state functional MRI data was acquired for 100 patients (58 males vs. 42 females). ROI-to-ROI FC and network topological features were characterized for each patient and HC. Group differences in FC and network topological features were examined. Compared to healthy counterparts, DCM males exhibited higher FC between vision-related brain regions, and cerebellum, brainstem, and thalamus, but lower FC between the intracalcarine cortex and frontal and somatosensory cortices, while DCM females demonstrated higher FC between the thalamus and cerebellar and sensorimotor regions, but lower FC between sensorimotor and visual regions. DCM males displayed higher FC within the cerebellum and between the posterior cingulate cortex (PCC) and vision-related regions, while DCM females displayed higher FC between frontal regions and the PCC, cerebellum, and visual regions. Additionally, DCM males displayed significantly greater intra-network connectivity and efficiency compared to healthy counterparts. Results from the present study imply sex-specific supraspinal functional alterations occur in patients with DCM.

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, application performance can be heavily affected by how data are partitioned, which in turn depends on the selected size for data blocks, i.e. the block size. Therefore, finding an effective partitioning, i.e. a suitable block size, is a key strategy to speed-up parallel data-intensive applications and increase scalability. This paper describes a methodology, namely BLEST-ML (BLock size ESTimation through Machine Learning), for block size estimation that relies on supervised machine learning techniques. The proposed methodology was evaluated by designing an implementation tailored to dislib, a distributed computing library highly focused on machine learning algorithms built on top of the PyCOMPSs framework. We assessed the effectiveness of the provided implementation through an extensive experimental evaluation considering different algorithms from dislib, datasets, and infrastructures, including the MareNostrum 4 supercomputer. The results we obtained show the ability of BLEST-ML to efficiently determine a suitable way to split a given dataset, thus providing a proof of its applicability to enable the efficient execution of data-parallel applications in high performance environments., This work has been partially supported by the European Commission through the Horizon 2020 Research and Innovation program and the EuroHPC JU under contract 955558 (eFlows4HPC project) and by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR (PCI2021‑121957 and CEX2021‑ 001148‑S) and by the Spanish Government (PID2019‑107255GB), and Generalitat de Catalunya (contract 2021‑SGR‑00412). We also acknowledge financial support from “National Centre for HPC, Big Data and Quantum Computing”, CN00000013 ‑ CUP H23C22000360005, and from “FAIR ‑ Future Artificial Intelligence Research” project ‑ CUP H23C22000860006., Peer Reviewed, Postprint (published version)

The future of agriculture depends on healthy soils, but soils are degrading within dominant agricultural systems, also in Denmark. Pioneer farmers seek a sustainable transition towards soil-centered management. However, reinforcing feedback loops in current agriculture is blocking such a transition. Founded in emancipatory social science and critical utopian action research, this thesis, in collaboration with eleven pioneer farmers across three production systems, envisions a real utopian transformation towards soil-health-centered agriculture, consisting of proposals regarding governance, on-farm practices, and consumer-farmer relations. The visions show promising, real utopian, transformative potential to break the current treadmills in agricultural management and can be used as a collective organizing tool toward social transformation. Transdisciplinary sustainability research must continue developing collaborative visioning methods to support the agricultural transition.

Patients with primary tumor progression after stereotactic body radiation therapy (SBRT) for stage I non-small cell lung cancer (NSCLC) have a second chance at complete tumor eradication with salvage local therapies, including lung resection, repeat course of SBRT, and percutaneous ablative therapies. In this paper, we presented our institutions initial experience with percutaneous high-dose-rate (HDR) brachyablation for a relapsed stage I NSCLC that had been treated with SBRT 4.3 years earlier. Lung tumor measuring approximately 5 cm in maximum tumor dimension at the time of relapse was histopathologically confirmed to be persistent squamous cell carcinoma, and successfully treated with a single fraction of 24 Gy with HDR brachyablation. Treatment was delivered via two percutaneous catheters inserted under CT-guidance, and treated in less than 20 minutes. The patient was discharged home later the same day without the need for a chest tube, and has been monitored with serial surveillance scans every 3 to 6 months without evidence of further lung cancer progression or complications at 2.8 years post-HDR brachyablation procedure and 7.8 years after initial SBRT.

Despite a substantial body of research, we lack fundamental understanding of the pathophysiology of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including pulmonary and cardiovascular outcomes, in part due to limitations of murine models. Most models use transgenic mice (K18) that express the human (h) angiotensin converting enzyme 2 (ACE2), ACE2 knock-in (KI) mice, or mouse-adapted strains of SARS-CoV-2. Further, many SARS-CoV-2 variants produce fatal neurologic disease in K18 mice and most murine studies focus only on acute disease in the first 14 days post inoculation (dpi). To better enable understanding of both acute (<14 dpi) and post-acute (>14 dpi) infection phases, we describe the development and characterization of a novel non-lethal KI mouse that expresses both the ACE2 and transmembrane serine protease 2 (TMPRSS2) genes (hACE2/hTMPRSS2). The human genes were engineered to replace the orthologous mouse gene loci but remain under control of their respective murine promoters, resulting in expression of ACE2 and TMPRSS2 instead of their murine counterparts. After intranasal inoculation with an omicron strain of SARS-CoV-2, hACE2/hTMPRSS2 KI mice transiently lost weight but recovered by 7 dpi. Infectious SARS-CoV-2 was detected in nasopharyngeal swabs 1-2 dpi and in lung tissues 2-6 dpi, peaking 4 dpi. These outcomes were similar to those in K18 mice that were inoculated in parallel. To determine the extent to which hACE2/hTMPRSS2 KI mice are suitable to model pulmonary and cardiovascular outcomes, physiological assessments measuring locomotion, behavior and reflexes, biomonitoring to measure cardiac activity and respiration, and micro computed tomography to assess lung function were conducted frequently to 6 months post inoculation. Male but not female SARS-CoV-2 inoculated hACE2/hTMPRSS2 KI mice showed a transient reduction in locomotion compared to control saline treated mice. No significant changes in respiration

Emergent literature suggests that micro- and macro-climates influence criminal behaviour; a complex phenomenon that is still incipient in theory development. This mixed-method research starts with a systematic review of the literature on the theoretical premises that urban heat islands amplify aggressive behaviour and crime. Further, it discusses the potential implications of the relationship between the environment and social outcomes on the design and planning of urban environments. A meta-synthesis was conducted to explain the correlations between patterns of criminal behaviour and thermal (dis)comfort. This correlation is to relate fundamental urban design principles to socially sustainable communities that dissuade violence and crime, and otherwise show poorly designed spaces do propagate criminal behaviour. Cross-validation was undertaken using a case study of Midland, a suburb of Perth, Western Australia. Data involves population demographics, temperature, and crime statistics relating to Offences against the Person. Analysis focused on homicide, assault, threatening behaviour, and robbery. The findings imply a positive correlation between long-term temperature, crime, population density, and green canopy cover. Variables of climate (e.g., short-to-long-term climate-related stressors) and crime types also show non-linear association. Nonetheless, forecasting the future of violence and trends of crime through attributes or potential impacts of heat and urban canopy cover on the built environment will inform sustainable social development policy, environmental planning, development strategy, designers, and planners. Recommendations are made around these in relation to making urban communities adaptive to the impacts of global warming and future densification.

Background: Microcephaly with early-onset seizures (MCSZ) is a neurodevelopmental disorder caused by pathogenic variants in the DNA strand break repair protein, polynucleotide kinase 3 '-phosphatase (PNKP). Methods: We have used whole genome sequencing and Sanger sequencing to identify disease-causing variants, followed by a minigene assay, Western blotting, alkaline comet assay, gamma H2AX, and ADP-ribose immunofluorescence. Results: Here, we describe a patient with compound heterozygous variants in PNKP, including a missense variant in the DNA phosphatase domain (T323M) and a novel splice acceptor site variant within the DNA kinase domain that we show leads to exon skipping. We show that primary fibroblasts derived from the patient exhibit greatly reduced levels of PNKP protein and reduced rates of DNA single-strand break repair, confirming that the mutated PNKP alleles are dysfunctional. Conclusion: The data presented show that the detected compound heterozygous variants result in reduced levels of PNKP protein, which affect the repair of both oxidative and TOP1-induced single-strand breaks, and most likely causes MCSZ in this patient.

This study focuses on the effects of greenwashing on financial performance in the fashion sector, which is one of the most polluting industries in the world, in an effort to further the continuing research on the environmental impact of companies. A proxy for greenwashing is produced by calculating the difference between a company's symbolic environmental initiatives and its real performance. The study's foundation is an in-depth review of the literature on the drivers and mechanisms of "greenwashing," with a focus on the impact of stakeholder pressure and disclosure risks on corporate green behaviour. The empirical research makes use of information from the Eikon Refinitiv database that spans five years and includes financial and ESG data for 83 fashion companies. In contrast to the expectations, the data suggest that greenwashing has no apparent impact on financial performance, with companies exhibiting varying degrees of success in meeting CO2 reduction goals and an unexpectedly significant correlation between carbon emissions and market value. The findings emphasise the importance of sincere environmental initiatives while highlighting the complexity of the financial consequences of greenwashing. The thesis concludes by offering suggestions for further study to improve the understanding and evaluation of greenwashing, with the goal of creating a more transparent and sustainable fashion sector.

Municipalities across the US are investing in smart technologies that rely on data collection tools and devices. Though proposals to procure these technologies often describe the benefits of optimization, privacy concerns and asymmetrical data access remain. Some municipalities are working to minimize such concerns by developing community working groups to evaluate the adoption of surveillance technologies. Many of these organizations have an explicit interest in geomedia technologies, yet their goals, composition, and technology review processes differ. We examined working groups from four US cities—Boston, Seattle, Syracuse, and Vallejo—to identify how group members articulate different sociotechnical imaginaries of geomedia. Through interviews with working group members and an analysis of public documents, we examine how working groups imagine the future use, and misuse, of these technologies in their communities. In turn, this project highlights how multi-stakeholder governance can shape decision-making about geomedia futures.

Tyree D Williams,1– 3 Talia Adler,2,4 Lindsey Smokoff,2,4 Anmoldeep Kaur,2,5 Benjamin Rodriguez,2,3,6 Kavita Jyoti Prakash,6 Edib Redzematovic,2 Turner S Baker,2,3,7 Benjamin I Rapoport,2,3 Edward S Yoon,8 Douglas P Beall,9 Jonathan S Dordick,1 Reade A De Leacy3 1Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; 2Sinai BioDesign, Mount Sinai Medical System, New York, NY, USA; 3Department of Neurosurgery, Mount Sinai Medical System, New York, NY, USA; 4Columbia University School of General Studies, New York, NY, USA; 5Department of Neuroscience, Smith College, Northampton, MA, USA; 6Icahn School of Medicine at Mount Sinai, New York, NY, USA; 7Department of Population Health Science & Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 8Hospital for Special Surgery, New York, NY, USA; 9Spine Fracture Institute, Oklahoma City, OK, USACorrespondence: Benjamin Rodriguez, Department of Neurosurgery, Mount Sinai Medical System, One Gustave L. Levy Place, Box 1136, New York, NY, USA, Tel +1 615 524-0211, Email tyree.williams@mssm.eduAbstract: Vertebral compression fractures (VCFs) are common in osteoporotic patients, with a frequency projected to increase alongside a growing geriatric population. VCFs often result in debilitating back pain and decreased mobility. Cement augmentation, a minimally invasive surgical technique, is widely used to stabilize fractures and restore vertebral height. Acrylic-based cements and calcium phosphate cements are currently the two primary fill materials utilized for these procedures. Despite their effectiveness, acrylic bone cements and calcium phosphate cements have been associated with various intraoperative and postoperative incidents impacting VCF treatment. Over the past decade, discoveries in the field of biomedical engineering and material science have shown advancements toward addressing these limitations. This narrative review aims to assess the potential pitfalls and barriers of the vari

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications., This work has been partially funded by the European Commission’s Horizon 2020 Framework program and the European High-Performance Computing Joint Undertaking (JU) under Grant agreement No 955558 and by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR (PCI2021-121957), project eFlows4HPC. It has also been supported by the Spanish Government (PID2019-107255GB) and by the Departament de Recerca i Universitats de la Generalitat de Catalunya to the Research Group MPiEDist (2021 SGR 00412) We also acknowledge financial support from “National Centre for HPC, Big Data and Quantum Computing," CN00000013 - CUP H23C22000360005, and from “FAIR - Future Artificial Intelligence Research" Project - CUP H23C22000860006., Peer Reviewed, Postprint (published version)

Penelitian ini membahas penerapan algoritma FP-Growth dalam analisis pola transaksi dengan tujuan untuk optimalisasi pengelolaan data transaksi di Toko Lia. Pendahuluan menjelaskan kompleksitas data transaksi dalam bisnis ritel dan kebutuhan akan metode analisis yang efektif. Masalah yang diidentifikasi meliputi ketidakmampuan sistem konvensional dalam menangani volume data transaksi yang besar serta kesulitan dalam mengidentifikasi pola transaksi yang signifikan. Tujuan penelitian adalah mengimplementasikan algoritma FP-Growth sebagai solusi untuk mengatasi masalah tersebut. Metode penelitian melibatkan pengumpulan data transaksi dari Toko Lia, penerapan algoritma FP-Growth, dan analisis hasil untuk mengidentifikasi pola transaksi yang relevan. Hasil penelitian menunjukkan bahwa penerapan algoritma FP-Growth berhasil mengoptimalkan pengelolaan data transaksi dengan efisien, memungkinkan identifikasi pola transaksi yang mendasar bagi keberhasilan strategi pemasaran dan pengelolaan stok di Toko Lia. Implikasi praktis dari penelitian ini adalah peningkatan kinerja operasional dan strategi bisnis Toko Lia melalui pemanfaatan algoritma FP-Growth sebagai alat analisis yang andal untuk menggali informasi berharga dari data transaksi.

The black oats (Avena strigosa S.) is an excellent forage plant, besides acting in the system of rotation and succession of cultures. In recent decades, oat genetic improvement programs have worked intensively to select materials that are more responsive to pests and diseases, in addition to seeking greater mass and grain productivity. However, these new materials need to be tested for agronomic performance in different seeding systems. Given the importance of culture, the objective of this work was to evaluate the agronomic performance of black oats conducted with two sowing methods. The treatments were arranged in blocks at random in a 2x2 factorial arrangement, that is, two sowing methods (at haul and row) and two cultivars (UPFA 21 - Moreninha and EMBRAPA 139), with five replications, in a useful area of ​​7.15 m2 per plot. Sixty days after sowing, the cultivars were evaluated for height, total fresh phytomass production (TFF) and total dry phytomass (TDP). The results indicated statistically significant differences in the production of TFF and TDP between cultivars and between sowing methods, being the cultivar UPFA 21 - Moreninha more productive, in addition to presenting better results in the sowing system in line. There were no significant differences in plant height between the treatments evaluated. However, sowing in line allowed greater production of TFF and TDP for the cultivar UPFA 21 - Moreninha., A aveia preta (Avena strigosa S.) é uma excelente planta forrageira, além de atuar no sistema de rotação e sucessão de culturas. Nas últimas décadas, os programas de melhoramento genético de aveia tem atuado de forma intensa na seleção de materiais mais responsivos a pragas e doenças, além de buscar maior produtividade de massa e grãos. Contudo, estes novos materiais precisam ser testados com relação ao desempenho agronômico em diferentes sistemas de semeadura. Diante da importância da cultura, o objetivo deste trabalho foi avaliar o desempenho agronômico da aveia preta conduzida com dois métodos de semeadura. Os tratamentos foram dispostos em blocos ao acaso em arranjo fatorial 2x2, com dois métodos de semeadura (a lanço e na linha) e duas cultivares (UPFA 21 Moreninha e EMBRAPA 139), com cinco repetições, em área útil de 7,15 m2 por parcela. Sessenta dias após a semeadura, as cultivares foram avaliadas quanto à altura de planta, produção de Fitomassa Fresca Total (FFT) e Fitomassa Seca Total (FST). Os resultados indicam diferenças estatísticas significativas na produção de FFT e FST entre as cultivares e entre os métodos de semeadura, sendo a cultivar UPFA 21 - Moreninha mais produtiva, além desta apresentar melhores resultados no sistema de semeadura em linha. Não foram observadas diferenças significativas na altura de planta entre os tratamentos avaliados. Porém, a semeadura em linha permitiu maior produção de FFT e FST para a cultivar UPFA 21 - Moreninha.

Measurements of the size of dust continuum emission are an important tool for constraining the spatial extent of star formation and hence the build-up of stellar mass. Compact dust emission has generally been observed at Cosmic Noon (z~2-3). However, at earlier epochs, toward the end of the Reionization (z~4-6), only the sizes of a handful of IR-bright galaxies have been measured. In this work, we derive the dust emission sizes of main-sequence galaxies at z~5 from the ALPINE survey. We measure the dust effective radius r_e,FIR in the uv-plane in Band 7 of ALMA for seven ALPINE galaxies with resolved emission and we compare it with rest-frame UV and [CII]158$\mu$m measurements. We study the r_e,FIR-L_IR scaling relation by considering our dust size measurements and all the data in literature at z~4-6. Finally, we compare our size measurements with predictions from simulations. The dust emission in the selected ALPINE galaxies is rather extended (r_e,FIR~1.5-3 kpc), similar to [CII]158 um but a factor of ~2 larger than the rest-frame UV emission. Putting together all the measurements at z~5, spanning 2 decades in luminosity from L_IR ~ 10^11 L_sun to L_IR ~ 10^13 L_sun, the data highlight a steeply increasing trend of the r_e,FIR-L_IR relation at L_IR< 10^12 L_sun, followed by a downturn and a decreasing trend at brighter luminosities. Finally, simulations that extend up to the stellar masses of the ALPINE galaxies considered in the present work predict a sub-set of galaxies (~25% at 10^10 M_sun < M_star < 10^11 M_sun) with sizes as large as those measured., Comment: 8 pages, Accepted by A&A

Fostered by upcoming data from new generation observational campaigns, we are about to enter a new era for the study of how galaxies form and evolve. The unprecedented quantity of data that will be collected, from distances only marginally grasped up to now, will require analysis tools designed to target the specific physical peculiarities of the observed sources and handle extremely large datasets. One powerful method to investigate the complex astrophysical processes that govern the properties of galaxies is to model their observed spectral energy distribution (SED) at different stages of evolution and times throughout the history of the Universe. To address these challenges, we have developed GalaPy, a new library for modelling and fitting SEDs of galaxies from the X-ray to the radio band, as well as the evolution of their components and dust attenuation/reradiation. GalaPy incorporates both empirical and physically-motivated star formation histories, state-of-the-art single stellar population synthesis libraries, a two-component dust model for attenuation, an age-dependent energy conservation algorithm to compute dust reradiation, and additional sources of stellar continuum such as synchrotron, nebular/free-free emission and X-ray radiation from low and high mass binary stars. GalaPy has a hybrid implementation that combines the high performance of compiled C++ with the flexibility of Python, and exploits an object-oriented design. It generates models on the fly without relying on templates, and exploits fully Bayesian parameter space sampling. In this first work, we introduce the project and showcase the photometric SED fitting tools already available to users. The library is available on the Python Package Index (PyPI) and comes with extensive online documentation and tutorials., Comment: 41 pages, 31 figures, 7 tables, to be published on A&A, links to documentation and PyPI available in the PDF, comments are very welcome

We present the first spectroscopic ALMA follow-up for a pilot sample of nine Radio-Selected NIRdark galaxies in the COSMOS field. These sources were initially selected as radio-detected sources (S(3GHz)>12.65 uJy), lacking an optical/NIR counterpart in the COSMOS2015 catalog (Ks>24.7 mag). Several studies highlighted how this selection could provide a population of highly dust-obscured, massive, and star-bursting galaxies. With these new ALMA observations, we assess the spectroscopic redshifts of this pilot sample of sources and improve the quality of the physical properties estimated through SED-fitting. Moreover, we measure the quantity of molecular gas present inside these galaxies and forecast their potential evolutionary path, finding that the RS-NIRdark galaxies could represent a likely population of high-z progenitors of the massive and passive galaxies discovered at z~3. Finally, we present some initial constraints on the kinematics of the ISM within the analyzed galaxies, reporting a high fraction (~55%) of double-peaked lines that can be interpreted as the signature of a rotating structure in our targets or with the presence of major mergers in our sample. Our results presented in this paper showcase the scientific potential of (sub)mm observations for this elusive population of galaxies and highlight the potential contribution of these sources in the evolution of the massive and passive galaxies at high-z., Comment: 16 pages, 9 figures, 5 tables. Submitted to A&A

In anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H$\alpha$, H$\beta$, [SII]$\lambda\lambda 6717, 6731$, [NII]$\lambda 6584$, [OI]$\lambda 6300$, [OIII]$\lambda 5007$, and [OII]$\lambda\lambda 3727, 3729$. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H$\alpha$-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H$\beta$-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H$\alpha$ and [OIII]+H$\beta$ to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H$\alpha$, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data., Comment: 32 pages, 15 figures, submitted to A&A

eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = −3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.

In response to a meal, insulin drives hepatic glycogen synthesis to help regulate systemic glucose homeostasis. The mechanistic target of rapamycin complex 1 (mTORC1) is a well-established insulin target and contributes to the postprandial control of liver lipid metabolism, autophagy, and protein synthesis. However, its role in hepatic glucose metabolism is less understood. Here, we used metabolomics, isotope tracing, and mouse genetics to define a role for liver mTORC1 signaling in the control of postprandial glycolytic intermediates and glycogen deposition. We show that mTORC1 is required for glycogen synthase activity and glycogenesis. Mechanistically, hepatic mTORC1 activity promotes the feeding-dependent induction of Ppp1r3b, a gene encoding a phosphatase important for glycogen synthase activity whose polymorphisms are linked to human diabetes. Reexpression of Ppp1r3b in livers lacking mTORC1 signaling enhances glycogen synthase activity and restores postprandial glycogen content. mTORC1-dependent transcriptional control of Ppp1r3b is facilitated by FOXO1, a well characterized transcriptional regulator involved in the hepatic response to nutrient intake. Collectively, we identify a role for mTORC1 signaling in the transcriptional regulation of Ppp1r3b and the subsequent induction of postprandial hepatic glycogen synthesis.

During human fetal development, sex differentiation occurs not only in the gonads but also in the adjacent developing reproductive tract. However, while the cellular composition of male and female human fetal gonads is well described, that of the adjacent developing reproductive tract remains poorly characterized. Here, we performed single-cell transcriptomics on male and female human fetal gonads together with the adjacent developing reproductive tract from first and second trimesters, highlighting the morphological and molecular changes during sex differentiation. We validated different cell populations of the developing reproductive tract and gonads and compared the molecular signatures between the first and second trimesters, as well as between sexes, to identify conserved and sex-specific features. Together, our study provides insights into human fetal sex-specific gonadogenesis and development of the reproductive tract beyond the gonads.

Efforts to decode neural network vision models necessitate a comprehensive grasp of both the spatial and semantic facets governing feature responses within images. Most research has primarily centered around attribution methods, which provide explanations in the form of heatmaps, showing where the model directs its attention for a given feature. However, grasping 'where' alone falls short, as numerous studies have highlighted the limitations of those methods and the necessity to understand 'what' the model has recognized at the focal point of its attention. In parallel, 'Feature visualization' offers another avenue for interpreting neural network features. This approach synthesizes an optimal image through gradient ascent, providing clearer insights into 'what' features respond to. However, feature visualizations only provide one global explanation per feature; they do not explain why features activate for particular images. In this work, we introduce a new method to the interpretability tool-kit, 'feature accentuation', which is capable of conveying both where and what in arbitrary input images induces a feature's response. At its core, feature accentuation is image-seeded (rather than noise-seeded) feature visualization. We find a particular combination of parameterization, augmentation, and regularization yields naturalistic visualizations that resemble the seed image and target feature simultaneously. Furthermore, we validate these accentuations are processed along a natural circuit by the model. We make our precise implementation of feature accentuation available to the community as the Faccent library, an extension of Lucent.

In both animals and robots, locomotion capabilities are determined by the physical structure of the system. The majority of legged animals and robots are bilaterally symmetric, which facilitates locomotion with consistent headings and obstacle traversal, but leads to constraints in their turning ability. On the other hand, radially symmetric animals have demonstrated rapid turning abilities enabled by their omni-directional body plans. Radially symmetric tripedal robots are able to turn instantaneously, but are commonly constrained by needing to change direction with every step, resulting in inefficient and less stable locomotion. We address these challenges by introducing a novel design for a tripedal robot that has both frictional and rolling contacts. Additionally, a freely rotating central sphere provides an added contact point so the robot can retain a stable tripod base of support while lifting and pushing with any one of its legs. The SKating, Omni-Oriented, Tripedal Robot (SKOOTR) is more versatile and stable than other existing tripedal robots. It is capable of multiple forward gaits, multiple turning maneuvers, obstacle traversal, and stair climbing. SKOOTR has been designed to facilitate customization for diverse applications: it is fully open-source, is constructed with 3D printed or off-the-shelf parts, and costs approximately $500 USD to build.

In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals.

This paper investigates the ability of transformer-based models to learn structural recursion from examples. Recursion is a universal concept in both natural and formal languages. Structural recursion is central to the programming language and formal mathematics tasks where symbolic tools currently excel beyond neural models, such as inferring semantic relations between datatypes and emulating program behavior. We introduce a general framework that nicely connects the abstract concepts of structural recursion in the programming language domain to concrete sequence modeling problems and learned models' behavior. The framework includes a representation that captures the general \textit{syntax} of structural recursion, coupled with two different frameworks for understanding their \textit{semantics} -- one that is more natural from a programming languages perspective and one that helps bridge that perspective with a mechanistic understanding of the underlying transformer architecture. With our framework as a powerful conceptual tool, we identify different issues under various set-ups. The models trained to emulate recursive computations cannot fully capture the recursion yet instead fit short-cut algorithms and thus cannot solve certain edge cases that are under-represented in the training distribution. In addition, it is difficult for state-of-the-art large language models (LLMs) to mine recursive rules from in-context demonstrations. Meanwhile, these LLMs fail in interesting ways when emulating reduction (step-wise computation) of the recursive function., Comment: arXiv admin note: text overlap with arXiv:2305.14699

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it. In this conception, vision inverts a generative model through an interrogation of the evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

Central America is consistently identified as a global hotspot for future anthropogenic climate change and is already exposed to considerable natural climate variability and frequent extreme events including tropical storms, heavy flooding, and drought. Pervasive inequalities and systemic vulnerabilities exacerbate the impacts of climate variability and change, especially for the millions of families who rely on rainfed agriculture throughout the region. This is particularly true for the Central American Dry Corridor, a region marked by less rainfall and more frequent dry spells in comparison to the rest of Central America. While projections for 21st century drying due to anthropogenic climate change are robust throughout Central America, internal climate variability is expected to dominate precipitation anomalies for the next several decades and very few locations have had significant or consistent changes in precipitation. However, smallholder farmers in the Guatemalan Dry Corridor have reported recent changes in the distribution and intensity of rainfall, making it harder to predict and contributing to crop losses and decreased production of food staples in recent years. Reducing uncertainty in near-term and future climate variability and change is therefore essential for mitigating climate risk and for improving hazard reduction, resource management, and agricultural decision-making throughout the region. This in part hinges on the production and dissemination of accurate and usable climate information. By combining instrumental climate data, smallholder farmers' observations of climate variability, and precipitation forecasts from climate models, my dissertation seeks to reduce some of these uncertainties. Expanding on this objective, I integrate these sources of information to (1) systematically assess how rainfall variability and extreme precipitation events in the Dry Corridor are changing, (2) determine the relevance of climate variability and change for ag

Current developments in autonomous off-road driving are steadily increasing performance through higher speeds and more challenging, unstructured environments. However, this operating regime subjects the vehicle to larger inertial effects, where consideration of higher-order states is necessary to avoid failures such as rollovers or excessive impact forces. Aggressive driving through Model Predictive Control (MPC) in these conditions requires dynamics models that accurately predict safety-critical information. This work aims to empirically quantify this aggressive operating regime and its effects on the performance of current models. We evaluate three dynamics models of varying complexity on two distinct off-road driving datasets: one simulated and the other real-world. By conditioning trajectory data on higher-order states, we show that model accuracy degrades with aggressiveness and simpler models degrade faster. These models are also validated across datasets, where accuracies over safety-critical states are reported and provide benchmarks for future work., Comment: Accepted to ICRA 2024 Workshop on Resilient Off-road Autonomy

How should zoomorphic, or bio-inspired, robots indicate to humans that interactions will be safe and fun? Here, a survey is used to measure how human willingness to interact with a simulated butterfly robot is affected by different flight patterns. Flapping frequency, flap to glide ratio, and flapping pattern were independently varied based on a literature review of butterfly and moth flight. Human willingness to interact with these simulations and demographic information were self-reported via an online survey. Low flapping frequency and greater proportion of gliding were preferred, and prior experience with butterflies strongly predicted greater interaction willingness. The preferred flight parameters correspond to migrating butterfly flight patterns that are rarely directly observed by humans and do not correspond to the species that inspired the wing shape of the robot model. The most realistic butterfly simulations were among the least preferred. An analysis of animated butterflies in popular media revealed a convergence on slower, less realistic flight parameters. This iterative and interactive artistic process provides a model for determining human preferences and identifying functional requirements of robots for human interaction. Thus, the robotic design process can be streamlined by leveraging animated models and surveys prior to construction.

Background: Hypertension management in older patients represents a challenge, particularly when hospitalized. Objective: The objective of this study is to investigate the determinants and related outcomes of antihypertensive drug prescription in a cohort of older hospitalized patients. Methods: A total of 5671 patients from REPOSI (a prospective multicentre observational register of older Italian in-patients from internal medicine or geriatric wards) were considered; 4377 (77.2%) were hypertensive. Minimum treatment (MT) for hypertension was defined according to the 2018 ESC guidelines [an angiotensin-converting-enzyme-inhibitor (ACE-I) or an angiotensin-receptor-blocker (ARB) with a calcium-channel-blocker (CCB) and/or a thiazide diuretic; if >80 years old, an ACE-I or ARB or CCB or thiazide diuretic]. Determinants of MT discontinuation at discharge were assessed. Study outcomes were any cause rehospitalization/all cause death, all-cause death, cardiovascular (CV) hospitalization/death, CV death, non-CV death, evaluated according to the presence of MT at discharge. Results: Hypertensive patients were older than normotensives, with a more impaired functional status, higher burden of comorbidity and polypharmacy. A total of 2233 patients were on MT at admission, 1766 were on MT at discharge. Discontinuation of MT was associated with the presence of comorbidities (lower odds for diabetes, higher odds for chronic kidney disease and dementia). An adjusted multivariable logistic regression analysis showed that MT for hypertension at discharge was associated with lower risk of all-cause death, all-cause death/hospitalization, CV death, CV death/hospitalization and non-CV death. Conclusions: Guidelines-suggested MT for hypertension at discharge is associated with a lower risk of adverse clinical outcomes. Nevertheless, changes in antihypertensive treatment still occur in a significant proportion of older hospitalized patients.

Degenerative cervical myelopathy (DCM) is a chronic, progressive disorder characterized by the age-related degeneration of osseocartilaginous structures within the cervical spine resulting in narrowing of the spinal canal and chronic compression of the spinal cord. Chronic spinal cord compression can result in persisting neck pain and neurological deficits including loss of fine motor skills, weakness or numbness in the upper limbs, and gait abnormalities and imbalance, ultimately requiring surgical intervention to relieve cord compression. DCM is the most common form of spinal cord injury in adults and as the elderly population continues to grow, incidence of DCM will rise alongside an increased demand on healthcare resources. Further investigation into the neural response to chronic spinal cord compression may not only inform disease progression and prognosis but may benefit patient monitoring and treatment planning.This dissertation aims to elucidate how symptom presentation, degree of spinal compression, microstructural and cellular integrity of the affected cord, and sex impact supraspinal structure and function in patients with DCM. To address the goals of the dissertation, we implemented a multimodal neuroimaging approach including anatomical, functional, and diffusion imaging of the brain and T2-weighted, diffusion, and metabolic imaging of the spine. First, we characterized and compared spinal cord compression induced alterations in cerebral morphometry and functional connectivity between symptomatic DCM and asymptomatic spinal cord compression (ASCC) patients to further uncover potential compensatory neural mechanisms driving symptom presentation and disease progression. Because the degree of cervical cord compression is not strongly linked to symptom severity, we investigated whether macrostructural, microstructural, and metabolic properties of the cervical spinal cord result in conventional anatomical and functional alterations within the brains of patie

Context. The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide valuable opportunities for forthcoming galaxy surveys through their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage. Aims. WEAVE is a new wide-field spectroscopic facility mounted on the 4.2 m William Herschel Telescope in La Palma. WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during its first five years of operations. It will observe galaxies using WEAVE MOS (∼950 fibres distributed across a field of view of ∼3 square degrees on the sky) in low-resolution mode (R ∼ 5000, spanning the wavelength range 3660-9590 Å). Methods. WEAVE-StePS will obtain high-quality spectra (S/N ∼ 10 Å -1 at R ∼ 5000) for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies, the majority selected at z ≥ 0.3. The survey goal is to provide precise spectral measurements in the crucial interval that bridges the gap between LEGA-C and SDSS data. The wide area coverage of ∼25 square degrees will enable us to observe galaxies in a variety of environments. The ancillary data available in each of the observed fields (including X-ray coverage, multi-narrow-band photometry and spectroscopic redshift information) will provide an environmental characterisation for each observed galaxy. Results. This paper presents the science case of WEAVE-StePS, the fields to be observed, the parent catalogues used to define the target sample, and the observing strategy that was chosen after a forecast of the expected performance of the instrument for our typical targets. Conclusions. WEAVE-StePS will go back further in cosmic time than SDSS, extending its reach to encompass more than ∼6 Gyr. This is nearly half of the age of the Universe. The spectral and redshift range covered by WEAVE-StePS will open a new observational window by continuously tracing the evolutionary path of galaxies in the largely unexplored intermediate

Objectives: We validated a screening protocol in which thoracic ultrasound (TUS) acts as a first-line complementary imaging technique in selecting patients which may deserve a second-line low-dose high resolution computed tomography (HRCT) scan among a population of asymptomatic high-risk subjects for interstitial lung abnormalities (ILA) and lung cancer. Due to heavy environmental pollution burden, the district Tamburi of Taranto has been chosen as “case study” for this purpose. Methods: From July 2018 to October 2020, 677 patients aged between 45 and 65 year and who had been living in the Tamburi district of Taranto for at least 10 years were included in the study. After demographic, clinical and risk factor exposition data were collected, each participant underwent a complete TUS examination. These subjects were then asked to know if they agreed to perform a second-level examination by low-dose HRCT scan. Results: On a total of 167 subjects (24.7%) who agreed to undergo a second-level HRCT, 85 patients (50.9%) actually showed pleuro-pulmonary abnormalities. Interstitial abnormalities were detected in a total of 36 patients on HRCT scan. In particular, 34 participants presented subpleural ILAs, that were classified in the fibrotic subtype in 7 cases. The remaining 2 patients showed non-subpleural interstitial abnormalities. Subpleural nodules were observed in 46 patients. TUS showed an overall diagnostic accuracy of 88.6% in detecting pleuro-pulmonary abnormalities in comparison with HRCT scan, with a sensitivity of 95.3%, a specificity of 81.7%, a positive predictive value of 84.4% and a negative predictive value of 94.4%. The matched evaluation of specific pulmonary abnormalities on HRTC scan (i.e., interstitial abnormalities or pulmonary nodules) with determinate sonographic findings revealed a reduction in both TUS sensibility and specificity. Focusing TUS evaluation on the assessment of interstitial abnormalities, a thickened pleural line showed a sensitivity

We report on our efforts building a new, large proof-repair dataset and benchmark suite for the Coq proof assistant. The dataset is made up of Git commits from open-source projects with old and new versions of definitions and proofs aligned across commits. Building this dataset has been a significant undertaking, highlighting a number of challenges and gaps in existing infrastructure. We discuss these challenges and gaps, and we provide recommendations for how the proof assistant community can address them. Our hope is to make it easier to build datasets and benchmark suites so that machine-learning tools for proofs will move to target the tasks that matter most and do so equitably across proof assistants.

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Why do readers typically fixate near the center of a word, with a bias towards word onset? Alhama, Siegelman, Frost, & Armstrong (2019) proposed an account based on (1) perceptual constraints that reduce the likelihood of perceiving a letter the further it is from the fixated location, and (2) the information available from the perceived letters for identifying the word. We expand this work to predict the fixation location distributions of 7-letter words for 43 languages from 9 language families. We found that in the majority of the languages, words were most likely to be correctly recognized when fixating near the center, slightly toward onset. However, there were deviations from this trend, such as predicting more accurate recognition when fixating slightly towards offset in Slavic languages. Our results provide novel predictions for experimental work that considers a flexible language system that optimizes initial fixations based on information theory constraints.