Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexual conflict, heterochrony and tissue specificity as evolutionary problems of adaptive plasticity in development

Differential gene expression represents a fundamental cause and manifestation of phenotypic plasticity. Adaptive phenotypic plasticity in gene expression as a trait evolves when alleles that mediate gene regulation serve to increase organismal fitness by improving the alignment of variation in gene expression with variation in circumstances. Among the diverse circumstances that a gene encounters are distinct cell types, developmental stages and sexes, as well as an organism's extrinsic ecological environments. Consequently, adaptive phenotypic plasticity provides a common framework to consider diverse evolutionary problems by considering the shared implications of alleles that produce context-dependent gene expression. From this perspective, adaptive plasticity represents an evolutionary resolution to conflicts of interest that arise from any negatively pleiotropic effects of expression of a gene across ontogeny, among tissues, between the sexes, or across extrinsic environments. This view highlights shared properties within the general relation of fitness, trait expression and context that may nonetheless differ substantively in the grain of selection within and among generations to influence the likelihood of adaptive plasticity as an evolutionary response. Research programmes that historically have focused on these separate issues may use the insights from one another by recognizing their shared dependence on context-dependent gene regulatory evolution.
Oceanic crust in the mid-mantle beneath west-central Pacific subduction zones: evidence from S to P converted waveforms

SUMMARY The fate of subducted slabs is enigmatic, yet intriguing. We analyse seismic arrivals at ∼ 20– 50 s after the direct P wave in an array in northeast China (NECESSArray) recordings of four deep earthquakes occurring beneath the west-central Paciﬁc subduction zones (from the eastern Indonesia to Tonga region). We employ the array analysing techniques of fourth root vespagram and beam-forming analysis to constrain the slowness and backazimuth of later arrivals. Our analyses reveal that these arrivals have a slightly lower slowness value than the direct P wave and the backazimuth deviates slightly from the great circle direction. Along with calculation of 1-D synthetic seismograms, we conclude that the later arrival is corresponding to an energy of S -to- P converted at a scatterer below the sources. Total ﬁve scatterers are detected at depths varying from ∼ 700 to 1110 km in the study region. The past subducted oceanic crust most likely accounts for the seismic scatterers trapped in the mid-mantle beneath the west-central subduction zones. Our observation in turn reﬂects that oceanic crust at least partly separated from subducted oceanic lithosphere and may be trapped substantially in the mid-mantle surrounding subduction zones, in particular in the western Paciﬁc subduction zones.
Combining linear and nonlinear processes for multispectral material detection/identification

This paper describes a novel multi-spectral algorithm that combines linear and nonlinear processes to detect and identify materials with known spectral signatures. The nonlinear multi-spectral process is an anomaly detector that applies geometric whitening filters. It has demonstrated good detection and false alarm rejection performances without the knowledge of a prior target spectral information. In some instances, it achieved performance equivalent to material identification just by proper selection of spectral bands. This capability, i.e. material identification, was greatly enhanced by the incorporation of a priori target statistics.
The combined use of satellite data, air-mass trajectories and model applications for monitoring dust transport over Athens, Greece

This study focused on Saharan dust (SD) events over Athens, Greece, based on measurements of the daily aerosol optical depth at 550 nm (AOD550) and the fine-mode fraction (FM) derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Back-trajectories ending at Athens at altitudes of 500, 1000 and 4000 m were calculated by means of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Several criteria were taken into account to identify the SD events. According to these criteria and the altitudes of the air masses, three dust transport mechanisms were identified: (1) vertical transport (VT), including the whole atmospheric column, (2) upper atmosphere transport (UAT), above the atmospheric boundary layer, and (3) boundary layer transport (BLT), only within the lower atmospheric levels. The Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS) data was found to be adequate for characterization of the dust load over Athens, while higher values of the AI were found in cases where the dust was transported in the upper atmosphere. The predictions of the Dust Regional Atmospheric Model (DREAM) were consistent with the satellite observations and back trajectories, indicating the suitability of the model for monitoring dust transport over the Mediterranean. The analysis presents three case studies, one for each transport mechanism, where the aerosol field deduced from satellites and models is presented over the Eastern Mediterranean. In conclusion, this study shows that the combination of remote sensing measurements and back-trajectory calculations constitutes a powerful tool for the identification of SD events over Athens, while modelling can monitor the spread of the dust.
Multimodal prehabilitation to improve the clinical outcomes of frail elderly patients with gastric cancer: a study protocol for a multicentre randomised controlled trial (GISSG+2201)

Introduction Gastric cancer (GC) diagnosed in the elderly population has become a serious public health problem worldwide. Given the combined effects of frailty and the consequences of cancer treatment, older individuals with GC are more likely than young patients to suffer from postoperative complications and poor clinical outcomes. Nutrition, functional capacity and psychological state-based multimodal prehabilitation, which is dominated by Enhanced Recovery After Surgery (ERAS) pathway management, has been shown to reduce postoperative complications, promote functional recovery and decrease hospitalisation time in certain malignancies. However, no previous studies have investigated the clinical application of multimodal prehabilitation in frail older patients with GC. Methods and analysis The study is a prospective, multicentre randomised controlled trial in which a total of 368 participants who meet the inclusion criteria will be randomised into either a prehabilitation group or an ERAS group. The prehabilitation group will receive multimodal prehabilitation combined with ERAS at least 2 weeks before the gastrectomy is performed, including physical and respiratory training, nutritional support, and therapy and psychosocial treatment. The ERAS group patients will be treated according to the ERAS pathway. All interventions will be supervised by family members. The primary outcome measures are the incidence and severity of postoperative complications. Secondary outcomes include survival, functional capacity and other short-term postoperative outcomes. Overall, the multimodal prehabilitation protocol may improve functional capacity, reduce the surgical stress response and concomitant systemic inflammation, and potentially modulate the tumour microenvironment to improve short-term and long-term clinical outcomes and patients’ quality of life. Ethics and dissemination All procedures and participating centres of this study were approved by their respective ethics committees (QYFYKYLL 916111920). The final study results will be published separately in peer-reviewed journals. Trial registration number NCT05352802.
Proceedings of the Society for Computation in Linguistics Proceedings

This paper provides an algebraic characterization of the total input strictly local functions. Simultaneous, noniterative rules of the form A → B/C D, common in phonology, are definable as functions in this class whenever CAD represents a finite set of strings. The algebraic characterization highlights a fundamental connection between input strictly local functions and the simple class of definite string languages, as well as connections to string functions studied in the computer science literature, the definite functions and local functions. No effective decision procedure for the input strictly local maps was previously available, but one arises directly from this characterization. This work also shows that, unlike the full class, a restricted subclass is closed under composition.
A Full-System Perspective on UPMEM Performance

Recently, UPMEM has introduced the first commercially available processing in memory (PIM) platform. Its key feature are DRAM memory chips with built-in RISC CPUs for in-memory data processing. Naturally, this has sparked interest in the research community, which previously was limited to PIM simulators and custom FPGA prototypes. One result of this is the PrIM benchmark suite that combines an in-depth analysis of PIM performance with benchmarks that measure the speedup of PIM over processing on conventional CPUs and GPUs [10]. However, the current generation of UPMEM PIM faces limitations such as memory interleaving, and as such does not provide true in-memory computing. Applications must store data in DRAM and transfer it to/from UPMEM modules for processing, which behave just like computational offloading engines from this perspective. This paper examines the ramifications of treating them as such in comparative performance benchmarks. By extending the PrIM suite to address the challenges that computational offloading benchmarks face, we show that such a full-system perspective can drastically alter offloading recommendations, with 9 of 11 previously UPMEM-friendly benchmarks now performing best on a conventional server CPU.
WIND ENERGY RESOURCE ASSESSMENT OF WESTERN AND CENTRAL MASSACHUSETTS

A wind energy resource assessment of western and central Massachusetts has been performed using WindMap, a GIS-assisted software package. Data from five remote locations in western Massachusetts, from five airports, and upper-air data from Albany, NY were entered into WindMap to produce estimated wind speed maps at 50 meters. We find that the northwestern corner of the state has the most potential, as previously expected. The region around Worcester also has large winds, while the Connecticut River Valley near Springfield has Class 2 and 3 wind strengths. In contrast, the ridge tops of much of western Massachusetts have weaker wind speeds than predicted in the Wind Energy Resource Atlas.
Towards Efficient Hugepage-aware Memory Deduplication

Memory deduplication techniques aim to reduce memory consumption by sharing redundant memory pages. Additionally, hugepages are commonly employed to optimize performance by minimizing TLB misses. However, the simultaneous use of memory deduplication and hugepages presents a technical challenge. Existing approaches either split hugepages to achieve efficient memory deduplication or overlook redundant portions within hugepages to maintain high performance. As a result, they are compelled to compromise either performance or memory utilization. This paper identifies the fundamental issue causing the current dilemma as the absence of tight cooperation between the host and guest. Existing approaches manage all actions within the host OS without engaging the guest OS. In contrast, we propose a new memory deduplication framework, which adopts a host-guest co-design approach. We integrate host memory deduplication, shared data hot-plugging, and guest memory migration to simultaneously attain superior performance, a high deduplication rate, and reduced memory consumption.
Investigation on the Use of HDR Images for Cultural Heritage Documentation

High Dynamic Range Images have not yet been fully exploited for metric use. Their usefulness for photogrammetric applications in the field of Cultural Heritage Documentation is obvious. However it is not quite clear whether they constitute a solid metric base material for that purpose. For applications of Cultural Heritage Documentation, where during image acquisition lighting conditions might very often be difficult, HDR technology can positively contribute to the acquisition of images of better quality and, consequently, to the creation of orthophotos or other imagery products, including textured 3D models with no radiometric problems. In this paper, a detailed reference to HDRI technology is made and also the geometric reliability and photogrammetric applicability of HDR images is examined and investigated. In addition, an example of photogrammetric application in Cultural Heritage Documentation is presented and evaluated.
Apache flink : Stream and batch processing in a single engine

Modern enterprise applications are currently undergoing a complete paradigm shift away from traditional transactional processing to combined analytical and transactional processing. This challenge of combining two opposing query types in a single database management system results in additional requirements for transaction management as well. In this paper, we discuss our approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries. We present our approach to distributed snapshot isolation and optimized two-phase commit protocols.
Impact of the COVID-19 pandemic on the conduct of non-COVID-19 clinical trials: protocol for a scoping review

Introduction The COVID-19 pandemic posed a detrimental impact on the conduct of non-COVID-19 related clinical trials, raising concerns about the completeness of these studies and waste of resources. While several measures and strategies have been suggested to address these issues, a thorough and timely summarisation is still lacking. Therefore, our aim is to conduct a scoping review to summarise the negative effects of COVID-19 on non-COVID-19 clinical trials, outline the effective measures for mitigating these impacts, and provide insights for future pandemics. Methods and analysis This scoping review will be conducted in line with the Joanna Briggs Institute’s scoping review methodological framework, and the results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Relevant articles will be searched in PubMed, Embase and the Cochrane Library from 1 December 2019 to 1 July 2023. We will also screen the reference lists of the included studies manually to identify more potentially relevant articles. Articles focusing on the adverse impacts of COVID-19 on non-COVID-19 clinical trials and effective measures for mitigating them will be included. Two investigators will perform study selection and data extraction independently. A narrative summary as well as a descriptive analysis of the basic characteristics and key results of the included studies will be performed. Ethics and dissemination Ethical approval is not required, as this scoping review will be completed based only on published literature. The findings of this scoping review will be disseminated through a peer-reviewed publication and/or conference presentations.
Laminar architecture of visual responses in supplementary eye field of macaques

Previously, we have described the laminar organization of neurons in the supplementary eye field (SEF) that signal error, reward gain and loss, conflict, event timing, and goal maintenance. Here we describe the laminar organization of visually responsive neurons that were active during performance of a saccade stop-signal task. Nearly 40% of isolated neurons exhibited enhanced or suppressed responses to a visual target for a potential saccade, with the majority exhibiting enhanced activity and three-quarters with broad spikes. Visually responsive neurons were observed in all layers but were less common in layers 5 and 6. Response latencies were comparable to those reported previously, which are significantly later than those measured in occipital and temporal visual areas but overlapping those measured in cingulate cortex. Task-related visual response latency varied across cortical layers. Response latency was significantly earlier for neurons with narrow spikes. Neurons with task-related visual responses discharged until after saccade production. Around three-fifths of visually responsive neurons were most sensitive to the visual target appearing in one hemifield. Many neurons in layer 2 had ipsilateral receptive fields. Laminar current-source density aligned on visual target presentation revealed the earliest sink in layers 3 followed by a prolonged strong sink more superficially coupled with a weaker prolonged sink in layer 5 and a transient sink in layer 6. The current sink in layers 2 and 3 was stronger for ipsilateral stimuli. These findings reveal new details about visual processing in medial frontal cortex and complete the first catalogue of laminar organization of functional signals in a frontal lobe area.
Broad variation in response of individual introns to splicing inhibitors in a humanized yeast strain

Intron branch point (BP) recognition by the U2 snRNP is a critical step of splicing, vulnerable to recurrent cancer mutations and bacterial natural product inhibitors. The BP binds a conserved pocket in the SF3B1 (human) or Hsh155 (yeast) U2 snRNP protein. Amino acids that line this pocket affect binding of splicing inhibitors like Pladienolide-B (Plad-B), such that organisms differ in their sensitivity. To study the mechanism of splicing inhibitor action in a simplified system, we modified the naturally Plad-B resistant yeast Saccharomyces cerevisiae by changing 14 amino acids in the Hsh155 BP pocket to those from human. This humanized yeast grows normally, and splicing is largely unaffected by the mutation. Splicing is inhibited within minutes after addition of Plad-B, and different introns appear inhibited to different extents. Intron-specific inhibition differences are also observed during co-transcriptional splicing in Plad-B using single-molecule intron tracking (SMIT) to minimize gene-specific transcription and decay rates that cloud estimates of inhibition by standard RNA-seq. Comparison of Plad-B intron sensitivities to those of the structurally distinct inhibitor Thailanstatin-A reveals intron-specific differences in sensitivity to different compounds. This work exposes a complex relationship between binding of different members of this class of inhibitors to the spliceosome and intron-specific rates of BP recognition and catalysis. Introns with variant BP sequences seem particularly sensitive, echoing observations from mammalian cells, where monitoring individual introns is complicated by multi-intron gene architecture and alternative splicing. The compact yeast system may hasten characterization of splicing inhibitors, accelerating improvements in selectivity and therapeutic efficacy.
Estimating protein-ligand interactions with geometric deep learning and mixture density models

Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any in-silico drug-design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of protein was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design. Significance statement Current machine learning-based solutions to model protein-ligand interactions lack the level of interpretability that physics-based methods usually provide. Here, a workflow to embed protein binding surfaces as graphs was developed to serve as a viable data structure to be processed by geometric deep learning. The developed architecture based on mixture density models was employed to accurately estimate the position and conformation of the small molecule within the binding region. The likelihood-based scoring function was compared against existing physics-based alternatives, and significant performance improvements in terms of docking power, screening power and reverse screening power were observed. Taken together, the developed framework provides a platform for utilising geometric deep-learning models for interpretable prediction of protein-ligand interactions at a residue level.
How long is long? Word length effects in reading correspond to minimal graphemic units: An MEG study in Bangla

This paper presents a magnetoencephalography (MEG) study on reading in Bangla, an east Indo-Aryan language predominantly written in an abugida script. The study aims to uncover how visual stimuli are processed and mapped onto abstract linguistic representations in the brain. Specifically, we investigate the neural responses that correspond to word length in Bangla, a language with a unique orthography that introduces multiple ways to measure word length. Our results show that MEG signals localised in the anterior left fusiform gyrus, at around 130ms, are highly correlated with word length when measured in terms of the number of minimal graphemic units in the word rather than independent graphemic units (akśar) or phonemes. Our findings suggest that minimal graphemic units could serve as a suitable metric for measuring word length in non-alphabetic orthographies such as Bangla.
Accurate prediction of protein tertiary structural changes induced by single-site mutations with equivariant graph neural networks

Predicting the change of protein tertiary structure caused by singlesite mutations is important for studying protein structure, function, and interaction. Even though computational protein structure prediction methods such as AlphaFold can predict the overall tertiary structures of most proteins rather accurately, they are not sensitive enough to accurately predict the structural changes induced by single-site amino acid mutations on proteins. Specialized mutation prediction methods mostly focus on predicting the overall stability or function changes caused by mutations without attempting to predict the exact mutation-induced structural changes, limiting their use in protein mutation study. In this work, we develop the first deep learning method based on equivariant graph neural networks (EGNN) to directly predict the tertiary structural changes caused by single-site mutations and the tertiary structure of any protein mutant from the structure of its wild-type counterpart. The results show that it performs substantially better in predicting the tertiary structures of protein mutants than the widely used protein structure prediction method AlphaFold.