Miracle-dz/UniFAR-FTUs huggingface dataset:
The Velvet Assembler Using OpenACC Directives

There are several programs available in bioinformatics for DNA sequence assembly. This is typically an extremely time-consuming endeavor, as DNA sequences can be extensive and intricate. Velvet was created to combine short and long read sequencing data into larger genomic sequences. Using OpenMP parallel programming, the last version of Velvet was created to support multiple threads. Through OpenACC directives, we present a new version of Velvet that takes advantage of multiprocessing using graphical processing units (GPU). Our tests demonstrate that this extension of Velvet allows for faster performance and efficient memory use.
The Effect of Combined Respiratory Muscle Training on Dysarthric Speech Following Single Cerebrovascular Accident: A Retrospective Pilot Study

Background: Dysarthria frequently occurs as a result of stroke and adversely impacts speech sound production, making it more difficult for the listener to understand what the person with dysarthria is attempting to communicate. This in turn may lead to social isolation, depression, and increased cost of care. Some studies have underscored the importance of respiratory muscle strengthening as it relates to improvement of speech intelligibility. This retrospective investigation examined the effects of a combined Respiratory Muscle Training (cRMT) protocol upon speech intelligibility in persons post single cerebrovascular accident (CVA). Methods: The clinical data of 10 patients who requested pro bono speech therapy for the diagnosis and treatment of dysarthria following a single stroke was utilized for this study. The intervention group was treated with three 5-minute sessions with cRMT each day for 28 consecutive days. The control group received no cRMT and no other therapeutic exercise intervention during the time period. Respiratory and speech intelligibility were assessed pre- and post-intervention in terms of peak expiratory flow, subject self-perception of intelligibility, and word level intelligibility. Results: After 28 days of cRMT, the intervention group (IG) exhibited significant gains compared to the control group (CG) in peak expiratory flow (PEF) (IG: 73.12% vs CG: 4.66%), Self-Perception of Intelligibility (IG: 72.38% vs CG: 0.83%), and the word task of the Assessment of Intelligibility of Dysarthric Speech (AIDS) (IG: 43.92% vs. CG: 0%). Conclusion: These data suggest cRMT is a feasible and effective treatment for improving breath support and speech intelligibility in persons with dysarthric speech.
Harnessing Protein Language Models to improve classic Local Pairwise Alignments for more sensitive and scalable Deep Homology Detection

Figure 1 | Transformer model architecture of a Protein Language Model [1] Biological sequences are the foundation on which molecular biology research builds its comparative and analytical knowledge. As a result, high-throughput sequencing is becoming a bona fide technology that is employed across the life-science sector. This exponential growth of sequencing in the past decades dictates a new focus on organizing and classifying the immense amount of sequence data to capture the entire complexity of the biospheric sequence space. Classic global (e.g., Needleman-Wunsch) and local (Smith-Waterman) alignment approaches have been used to search and learn from this vast sequence space. However, newly emerging approaches incorporating machine learning are tested today with the ambition to build a fast classification procedure rather than a costly alignment process. Therefore, there are several models that capture the complexity of sequences and integrate the respective data into tractable probabilistic models [1, 2]. An important application of these efforts is exploring of the natural variation of the protein universe and the organization of species into a Tree of Life (ToL) based on protein sequence comparisons. In addition, recent milestones in structural biology (fold prediction based on AlphaFold2) are drawing new attention to biological sequences that are now proven predictive enough for structural explorations and functional annotation. This is relevant to various tasks, such as drug design and comparative genomics. Since it is complex to determine protein structures experimentally, several new approaches are emerging that claim to be able to infer biological information from sequences alone. Information can be transferred
Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules. There are two parts in the QGAN: a quantum generator that creates molecules based on the probability distributions of likely combinations and a classical discriminator that differentiates real molecules from the generated ones. We hypothesized that a quantum generator would be more impactful, offering a runtime speedup and increasing generated molecule possibilities, because we could use quantum mechanical phenomena superposition, entanglement, and interference to analyze more atom-bond combinations than a classical generator. The PyTorch-PennyLane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although the QGAN is a work in progress, it has demonstrated a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&D process.
Normal fault development in a sedimentary succession with multiple detachment levels: the Lower Cretaceous Oliete sub‐basin, Eastern Spain

Field exposures of Lower Cretaceous strata in the Oliete sub‐basin (eastern Spain) allow identification of syn‐rift features such as listric and planar normal faults, rotated fault blocks, fault‐related folds, sharp thickness variations and wedge‐shaped sedimentary geometries, as well as intra‐rift angular unconformities defined by the erosive truncation of rotated fault blocks and the onlap of upper units. The combined use of both stratigraphic and extensional tectonic features at the outcrop scale has allowed us to characterise different syn‐sedimentary tectonic events and their correlation between the footwall and the hangingwall block of the major extensional Gargallo fault. Such events have been interpreted as induced by the major Gargallo fault activity, and they are the basis for proposing a polyphase evolutionary model for this master fault. Data indicate that the deformation tends not to be concentrated on the major fault; instead, it is distributed over a wide area. We interpret that both the interlayered detachment levels in the pre‐rift (especially the Late Triassic Keuper Facies) and syn‐rift series, together with the rheology of the sedimentary pile, play an important role in transmitting deformation from master faults to hangingwall and footwall blocks.
Vertical profiles of volatile organic compounds and fine particles in 1 atmospheric air by using aerial drone with miniaturized samplers and 2 portable devices 3

The increase of volatile organic compounds (VOCs) emissions released into the atmosphere is one 17 of the main threats to human health and climate. VOCs can adversely affect human life through their 18 contribution to air pollution directly and indirectly by reacting via several mechanisms in the air to form 19 secondary organic aerosols. In this study, aerial drone equipped with miniaturized air sampling systems 20 including up to four solid-phase microextraction (SPME) Arrows and four in-tube extraction (ITEX) 21 samplers for the collection of VOCs, along with portable devices for the real-time measurement of black 22 carbon (BC) and total particle numbers at high altitudes was exploited. In total, 135 air samples were collected 23 under optimal sampling conditions in October 2021 at the boreal forest SMEAR II Station, Finland. A total 24 of 48 different VOCs, including nitrogen-containing compounds, alcohols, aldehydes, ketones
Boreal forest fires burn less intensely in Russia than in North America

Around 5–20 million hectares of boreal forest burns annually, mainly in Russia and North America. However, there are reports of significant differences in predominant fire type between these regions, which may have major implications for overall emissions of carbon, gases and aerosols. We examine boreal forest fire intensities via MODIS observations of fire radiative energy release rate. Results support the contention of a consistent difference in fire intensity and mean fuel consumption in Russia and North America, due to differences in dominant fire type. North American fires have higher mean intensities, increasing in proportion to percentage tree cover, characteristics indicating likely crown fire dominance. Russian fires have lower mean intensities, independent of percentage tree cover, characteristics more indicative of surface fire activity. Per unit area burnt, the results suggest Russian fires may burn less fuel and emit fewer products to the atmosphere than do those in North America.
Bayesian comparators: a probabilistic modeling tool for similarity evaluation between predicted and perceived patterns

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we deﬁne a probabilistic component, that we call a “Bayesian comparator”, that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, ﬁrst in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.
181st Meeting of the Acoustical Society of America

Measuring the listening effort exerted by an individual to understand speech under auditory masking conditions can provide vital information not available from speech intelligibility scores alone. The goal of this study was to compare the amount of listening effort elicited in young, normal-hearing subjects during an intelligible speech masking condition producing a high degree of informational masking (due to listener uncertainty, e.g. target-masker confusions) versus in two speech spectrum-shaped noise masking conditions producing primarily energetic masking (due to spectrotemporal overlap between target and maskers). One noise masking condition involved unmodulated noise, while the other involved speech-envelope-modulated noise. Listening effort was measured simultaneously using two physiological metrics: pupil dilation and alpha power (via electroencephalography). The target speech comprised 5-word matrix-style sentences in both conditions. In each condition, the target-to-masker ratio was set at each subject’s 75% correct point on the psychometric function. Target and maskers were spatially separated. Results indicated that the intelligible speech masking condition elicited a greater change in pupil size than the unmodulated noise masking condition. This finding is consistent with the view that greater effort is involved in ignoring acoustically and linguistically similar sources than highly dissimilar, low-information value sources. Work supported by: NIH K99DC018829
The Oxidoreductase DsbA1 negatively in�uences 2,4-diacetylphloroglucinol biosynthesis by interfering the function of Gcd in Pseudomonas �uorescens 2P24

The polyketide antibiotic 2,4-diacetylphloroglucinol (2,4-DAPG), produced by Pseudomonas �uorescens 2P24, is positively regulated by the GacS-GacA two-component system. Here we reported on the characterization of DsbA1 (disul�de oxidoreductase) as novel regulator of biocontrol activity in P. �uorescens . Our data showed that mutation of dsbA1 caused the accumulation of 2,4-DAPG in a GacA-independent manner. Further analysis indicated that DsbA1 interacts with membrane-bound glucose dehydrogenase Gcd, which positively regulates the production of 2,4-DAPG. Mutation of cysteine (C)-235, C275, and C578 of Gcd, signi�cantly reduced the interaction with DsbA1, enhanced the activity of Gcd and increased 2,4-DAPG production. Taken together, our results suggest that DsbA1 regulates the 2,4-DAPG concentration via ne-tuning the function of Gcd in P. �uorescens 2P24.
Cloud effects on radiative heating rate profiles over Darwin using ARM and A‐train radar/lidar observations

Observations of clouds from the ground‐based U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and satellite‐based A‐train are used to compute cloud radiative forcing profiles over the ARM Darwin, Australia site. Cloud properties are obtained from both radar (the ARM Millimeter Cloud Radar (MMCR) and the CloudSat satellite in the A‐train) and lidar (the ARM Micropulse lidar (MPL) and the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the A‐train) observations. Cloud microphysical properties are taken from combined radar and lidar retrievals for ice clouds and radar‐only or lidar‐only retrievals for liquid clouds. Large, statistically significant differences of up to 1.43 K/d exist between the mean ARM and A‐train net cloud radiative forcing profiles. The majority of the difference in cloud radiative forcing profiles is shown to be due to a large difference in the cloud fraction above 12 km. Above this altitude, the A‐train cloud fraction is significantly larger because many more clouds are detected by CALIPSO than by the ground‐based MPL. It is shown that the MPL is unable to observe as many high clouds as CALIPSO due to being more frequently attenuated and a poorer sensitivity. We also isolate the difference in cloud radiative forcing due to sampling and retrieval differences which are of comparable importance but are of smaller impact than cloud fraction differences. This study demonstrates that A‐train observations are better suited for the calculation of cloud radiative forcing profiles at Darwin. In addition, we find that it is necessary to supplement CloudSat with CALIPSO observations to obtain accurate cloud radiative forcing profiles.
A comparative patient-level prediction study in OMOP CDM demonstrates advantages for native and web-based implementations

Background: The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provides a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classi�cation, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specic patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Motivation: In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Methods: Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, availability of tools for handling imbalanced datasets, and ease of model implementation. Results: The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of speci�c ML classi�ers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. Conclusion: The ﬁ ndings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.
Interactive Canopies for a Climate Model

Abstract Climate models depend on evapotranspiration from models of plant stomatal resistance and leaf cover, and hence they depend on a description of the response of leaf cover to temperature and soil moisture. Such a description is derived as an addition to the Biosphere–Atmosphere Transfer Scheme and tested by simulations in a climate model. Rules for carbon uptake, allocation between leaves, fine roots, and wood, and loss terms from respiration, leaf, and root turnover and cold and drought stress, are used to infer the seasonal growth of leaf area as needed in a climate model, and to provide carbon fluxes (assuming also a simple soil carbon model) and net primary productivity. The scheme is tested in an 11-yr integration with the NCAR CCM3 climate model. After a spinup period of several years, the model equilibrates to a seasonal cycle plus some interannual variability. Effects of the latter are noticeable for the Amazon. Overall, drought stress has nearly as large an effect on leaf mortality as cold.
New Ways of Seeing through Qualitative Research

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Reconciling Bølling Warmth with peak deglacial meltwater discharge

Paleoclimatic data indicate warm conditions during the Bolling period, whereas climate modeling studies predict significant cooling, triggered by the concomitant deglacial meltwater flux into the Atlantic Ocean. We suggest that the lack of an adequate representation of deep water flow over the Greenland-Scotland Ridge in ocean circulation models can account for this discrepancy.
Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data

Next generation sequencing allows obtaining large amounts of gene expression data. Inferring regulatory relations between genes from such data has been a long standing challenge. Current algorithms are based on linear regression or a distance measures, like partial correlation or mutual information. The majority of algorithms are of very broad nature. Their aim is to infer a random normal network without using any additional assumptions. Regularized regression algorithms are an exception, since they assume sparsity. Additional truthful assumptions make inferences easier and more accurate. The proposed algorithm, Double Shrinking (DOSH), is based on regularized regression with assumptions about gene expression data and network structure. Reliability of gene expression values is assumed to depend upon their magnitude; larger values are more reliable. Each gene is assumed to be completely predictable from other genes. The effectiveness of the algorithm is demonstrated by identifying genetic markers of survival in lymphoid leukemia.
, innovation practice, and competitive advantage: Evidence from the FTSE MIB

Purpose The aim of this paper is to analyse companies listed on the FTSE MIB, the benchmark Stock Market index for the Borsa Italiana, in order to investigate the introduction of different types of open innovation practice as a key factor to develop a competitive advantage to pursue value creation for firms that for over 10 years have regularly paid dividends and beat the yield of the market.
Education and the Journey to the Core: Path Dependence or Leapfrogging?

We study changes in 130 countries’ indices of revealed comparative advantage for 1,240 products between 1995 and 2010, to answer: (i) whether export diversification is path-dependent, and whether it is more difficult to diversify into more sophisticated products; and (ii) whether education helps reduce path-dependence, or to develop comparative advantage in sophisticated products. We develop a regression framework that includes a measure of how distant a target product is from those products a country exports with comparative advantage, and measures of product sophistication. We combine these with estimates of the quality and quantity of education that a country’s workforce possesses. We find, first, that, over this period, the development of comparative advantage is a path-dependent process—stepping stones products must usually be developed to approach new products. Second, there is strong evidence consistent with the view that education helps reduce this path-dependence by facilitating more rapid incremental movement across the stepping stones of industrial development. Results also indicate that education quality matters more than education quantity, and that high-quality basic education matters for export diversification. Our framework also allows us to test whether leapfrogging into what we refer to as core products is possible and conclude that this is very unlikely.