Miracle-dz/UniFAR-FTUs huggingface dataset:
Book Review: Porn Work: Sex, Labor, and Late Capitalism

gender, healthcare, and aging. The book is also unapologetic, engaging, and comprehensively clear; however, the facilitator must prepare the reader for a raw and explicitly detailed look at issues outside of the mainstream. The content is not for the faint of heart. Nonetheless, the facilitator would be naive based upon the content to think that the critical thinking analysis portion of the course will be an easy ride for all the participants. The book would be applicable for undergraduate and graduate students, health care professionals, and student services. Some universities student services adapt a book for the term/and or year, and this would be feasible due to the book length; it being a timely topic; and a page-turner. If used at the undergraduate level, the facilitator must take a moment to ensure that the reader grasp the numerous politically correct terms within the book. The terms are clearly articulated by the authors but may be uncharted territory for a few readers. Thus, creating a jam-packed short book which, if not addressed (terms), may turn the reader away from processing the material into only placating the task at hand (e.g. critical thinking). The incorporation of various ‘isms’ within chapter two showcases how brilliantly the book states and makes clear the objective of inclusivity within the narrative of the text and beyond. Limited research within this realm starts the reader on the initial pages of the book and the series; however, a closer look reveals barriers that demarcate between the route to true knowledge and truly addressing major issues to change the narrative and get something done.
On the structure of isometrically embeddable metric spaces

Since its popularization in the 1970s, the Fiedler vector of a graph has become a standard tool for clustering of the vertices of the graph. Recently, Mendel and Noar, Dumitriu and Radcliffe, and Radcliffe and Williamson have introduced geometric generalizations of the Fiedler vector. Motivated by questions stemming from their work, we provide structural characterizations for when a finite metric space can be isometrically embedded in a Hilbert space.
A BRIEF SURVEY OF HIGGS BUNDLES

Considering a compact Riemann surface of genus greater or equal than two, a Higgs bundle is a pair composed of a holomorphic bundle over the Riemann surface, joint with an auxiliar vector field, so-called Higgs field. This theory started around thirty years ago, with Hitchin’s work, when he reduced the self-duality equations from dimension four to dimension two, and so, studied those equations over Riemann surfaces. Hitchin baptized those fields as Higgs fields because in the context of physics and gauge theory, they describe similar particles to those described by the Higgs bosson. Later, Simpson used the name Higgs bundle for a holomorphic bundle together with a Higgs field. Today, Higgs bundles are the subject of research in several areas such as non-abelian Hodge theory, Langlands, mirror symmetry, integrable systems, quantum field theory (QFT), among others. The main purposes here are to introduce these objects, and to present a brief but complete construction of the moduli space of Higgs bundles.
Binary recovery via phase field regularization for first-arrival traveltime tomography

We propose a double obstacle phase field methodology for binary recovery of the slowness function of an Eikonal equation found in first-arrival traveltime tomography. We treat the inverse problem as an optimization problem with quadratic misfit functional added to a phase field relaxation of the perimeter penalization functional. Our approach yields solutions as we account for well posedness of the forward problem by choosing regular priors. We obtain a convergent finite difference and mixed finite element based discretization and a well defined descent scheme by accounting for the non-differentiability of the forward problem. We validate the phase field technique with a —convergence result and numerically by conducting parameter studies for the scheme, and by applying it to a variety of test problems with different geometries, boundary conditions, and source—receiver locations.
Accuracy and Completeness of Long Read Metagenomic Assemblies

Microbes, we can learn how microbes influence the surrounding environment, contribute to human health, and understand which pathogen interactions result in differences in disease severity. Metagenomics can be used as a tool to explore the interactions between microbes. Metagenomic assemblies built using long read nanopore data depend on the read level accuracy. The read level accuracy of nanopore sequencing has made dramatic improvements over the past several years. However, we do not know if the increased read level accuracy allows for faster assemblers to make as accurate metagenomic assemblies as slower assemblers. Here, we present the results of a benchmarking study comparing three commonly used long read assemblers, Flye, Raven, and Redbean. We used a prepared DNA standard of seven bacteria as our input community. We prepared a sequencing library on the VolTRAX V2 sequence using a MinION mk1b. We basecalled using the latest version of Guppy with the super-accuracy model. We found that increasing read depth benefited each of the assemblers, and nearly complete community member chromosomes were assembled with as little as 10x read depth. Polishing assemblies using Medaka had a predictable improvement in quality. Some assemblers struggled with particular members of the bacterial community, but we found Flye to be the most robust across taxa. We found Flye was the most effective assembler for recovering plasmids. Based on Flye’s consistency for chromosomes and increased effectiveness at assembling plasmids, we would recommend using Flye in future metagenomic studies.
Tau pathology in the dorsal raphe may be a prodromal indicator of Alzheimer’s disease

Protein aggregation in brainstem nuclei is thought to occur in the early stages of Alzheimer’s disease (AD), but its specific role in driving prodromal symptoms and disease progression is largely unknown. The dorsal raphe nucleus (DRN) contains a large population of serotonin (5-hydroxytryptamine; 5-HT) neurons that regulate mood, reward-related behavior, and sleep, which are all disrupted in AD. We report here that tau pathology is present in the DRN of individuals 54-80 years old without a known history of dementia and was found at higher frequency than α-synuclein and TDP-43. Most AD cases had tau pathology in the DRN (90%), whereas only a subset contained TDP-43 or α-synuclein, but not both (30%). To evaluate how early tau pathology impacts behavior, we overexpressed human P301L-tau in the DRN of mice and observed depressive-like behaviors and hyperactivity without any deficits in spatial memory. Tau pathology was predominantly found in neurons relative to glia and colocalized with a significant proportion of Tph2-expressing neurons in the DRN. 5-HT neurons were also hyperexcitable in P301L-tauDRN mice, and there was an increase in the amplitude of excitatory post-synaptic currents (EPSCs), suggestive of increased glutamatergic transmission. Moreover, astrocytic density was elevated in the DRN and accompanied by an increase in IL-1α and Frk expression, which is indicative of inflammation. Additionally, tau pathology was detected in axonal processes in the thalamus, hypothalamus, amygdala, and caudate putamen and a significant proportion of this tau pathology colocalized with the serotonin reuptake transporter (SERT), suggesting that tau may spread in an anterograde manner to regions outside the DRN. Together these results indicate that tau pathology accumulates in the DRN in a subset of individuals over 50 years and may lead to behavioral dysregulation, 5-HT neuronal dysfunction, and activation of local astrocytes which may be prodromal indicators of AD.
A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms. Author summary Predicting phenotypes from genotypes is a fundamental problem in quantitative genetics. Thanks to recent advances, it is increasingly feasible to collect data on many phenotypes and genome-wide genotypes in large samples. Here, we tackle the problem of predicting multiple phenotypes from genotypes using a new method based on a multivariate, multiple linear regression model. Although the use of a multivariate, multiple linear regression model is not new, in this paper we introduce a flexible and computationally efficient empirical Bayes approach based on this model. This approach uses a prior that captures how the effects of genotypes on phenotypes are shared across the different phenotypes, and then the prior is adapted to the data in order to capture the most prominent sharing patterns present in the data. We assess the benefits of this flexible Bayesian approach in simulated genetic data sets, and we illustrate its application in predicting gene expression measured in multiple human tissues. We show that our methods can outperform competing methods in terms of prediction accuracy, and the computations involved in fitting the model and making the predictions scale well to large data sets.
Prior Movement of One Arm Facilitates Motor Adaptation in the Other

Many movements in daily life are embedded in motion sequences that involve more than one limb, demanding the motor system to monitor and control different body parts in quick succession. During such movements, systematic changes in the environment or the body might require motor adaptation of specific segments. However, previous motor adaptation research has focused primarily on motion sequences produced by a single limb, or on simultaneous movements of several limbs. For example, adaptation to opposing force fields is possible in unimanual reaching tasks when the direction of a prior or subsequent movement is predictive of force field direction. It is unclear, however, whether multilimb sequences can support motor adaptation processes in a similar way. In the present study (38 females, 38 males), we investigated whether reaches can be adapted to different force fields in a bimanual motor sequence when the information about the perturbation is associated with the prior movement direction of the other arm. In addition, we examined whether prior perceptual (visual or proprioceptive) feedback of the opposite arm contributes to force field-specific motor adaptation. Our key finding is that only active participation in the bimanual sequential task supports pronounced adaptation. This result suggests that active segments in bimanual motion sequences are linked across limbs. If there is a consistent association between movement kinematics of the linked and goal movement, the learning process of the goal movement can be facilitated. More generally, if motion sequences are repeated often, prior segments can evoke specific adjustments of subsequent movements. SIGNIFICANCE STATEMENT Movements in a limb's motion sequence can be adjusted based on linked movements. A prerequisite is that kinematics of the linked movements correctly predict which adjustments are needed. We show that use of kinematic information to improve performance is even possible when a prior linked movement is performed with a different limb. For example, a skilled juggler might have learned how to correctly adjust his catching movement of the left hand when the right hand performed a throwing action in a specific way. Linkage is possibly a key mechanism of the human motor system for learning complex bimanual skills. Our study emphasizes that learning of specific movements should not be studied in isolation but within their motor sequence context.
Predicting Lifespan-Extending Chemical Compounds with Machine Learning and Biologically Interpretable Features

Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as on the use of machine learning for analysing ageing-related data. In this work we use machine learning methods to analyse data from DrugAge, a database of chemical compounds (including drugs) modulating lifespan in model organisms. To this end, we created four datasets for predicting whether or not a compound extends the lifespan of C. elegans (the most frequent model organism in DrugAge), using four different types of predictive biological features, based on compound-protein interactions, interactions between compounds and proteins encoded by ageing-related genes, and two types of terms annotated for proteins targeted by the compounds, namely Gene Ontology (GO) terms and physiology terms from the WormBase’s Phenotype Ontology. To analyse these datasets we used a combination of feature selection methods in a data pre-processing phase and the well-established random forest algorithm for learning predictive models from the selected features. The two best models were learned using GO terms and protein interactors as features, with predictive accuracies of about 82% and 80%, respectively. In addition, we interpreted the most important features in those two best models in light of the biology of ageing, and we also predicted the most promising novel compounds for extending lifespan from a list of previously unlabelled compounds.
Golgi-IP, a novel tool for multimodal analysis of Golgi molecular content

The Golgi is a membrane-bound organelle that is essential for protein and lipid biosynthesis. It represents a central trafficking hub that sorts proteins and lipids to various destinations or for secretion from the cell. The Golgi has emerged as a docking platform for cellular signalling pathways including LRRK2 kinase whose deregulation leads to Parkinson disease. Golgi dysfunction is associated with a broad spectrum of diseases including cancer, neurodegeneration, and cardiovascular diseases. To allow the study of the Golgi at high resolution, we report a rapid immunoprecipitation technique (Golgi-IP) to isolate intact Golgi mini-stacks for subsequent analysis of their content. By fusing the Golgi resident protein TMEM115 to three tandem HA epitopes (GolgiTAG), we purified the Golgi using Golgi-IP with minimal contamination from other compartments. We then established an analysis pipeline using liquid chromatography coupled with mass spectrometry to characterize the human Golgi proteome, metabolome and lipidome. Subcellular proteomics confirmed known Golgi proteins and identified novel ones. Metabolite profiling established the first known human Golgi metabolome and revealed the selective enrichment of uridine-diphosphate (UDP) sugars and their derivatives, which is consistent with their roles in protein and lipid glycosylation. Furthermore, targeted metabolomics validated SLC35A2 as the subcellular transporter for UDP-hexose. Finally, lipidomics analysis showed that phospholipids including phosphatidylcholine, phosphatidylinositol and phosphatidylserine are the most abundant Golgi lipids and that glycosphingolipids are enriched in this compartment. Altogether, our work establishes a comprehensive molecular map of the human Golgi and provides a powerful method to study the Golgi with high precision in health and disease states. Significance The Golgi is central to protein and lipid processing. It senses and responds to diverse cell states to allow trafficking of macromolecules based on cellular demands. Traditional techniques for purifying the Golgi shaped our understanding of its functions, however such methods are too slow to preserve the labile Golgi metabolome and transient protein interactions. Here, we overcome this issue through the development of a method for the rapid capture of intact Golgi from human cells using organelle-specific immunoprecipitation (Golgi-IP). Using high resolution mass spectrometry, we demonstrate that our approach allows the unbiased characterization of the Golgi proteome, metabolome and lipidome. Thus, we believe that the Golgi-IP will be useful for the study of the Golgi in health and disease states.
Single-sided focusing of the time-dependent Schrödinger equation

The problem of focusing is considered for the one-dimensional time-dependent Schr\"odinger equation with a local potential. Focusing is also considered for the closely related plasma wave equation. It is supposed that an experimenter can send in an incident wave from one side of the potential. Then to what degree can the wave be focused? It will be shown that an incident wave can be found so that the real part of the wave collapses to a delta function $\ensuremath{\delta}(x\ensuremath{-}{x}_{o})$ when $t=0. $ The equation that governs this previously unsolved ``single-sided'' focusing problem is derived and shown to be Marchenko's equation\char22{}the canonical equation of one-dimensional inverse-scattering theory.
Recording animal-view videos of the natural world using a novel camera system and software package

Plants, animals, and fungi display a rich tapestry of colors. Animals, in particular, use colors in dynamic displays performed in spatially complex environments. In such natural settings, light is reflected or refracted from objects with complex shapes that cast shadows and generate highlights. In addition, the illuminating light changes continuously as viewers and targets move through heterogeneous, continually fluctuating, light conditions. Although traditional spectrophotometric approaches for studying colors are objective and repeatable, they fail to document this complexity. Worse, they miss the temporal variation of color signals entirely. Here, we introduce hardware and software that provide ecologists and filmmakers the ability to accurately record animal-perceived colors in motion. Specifically, our Python codes transform photos or videos into perceivable units (quantum catches) for any animal of known photoreceptor sensitivity. We provide the plans, codes, and validation tests necessary for end-users to capture animal-view videos. This approach will allow ecologists to investigate how animals use colors in dynamic behavioral displays, the ways natural illumination alters perceived colors, and other questions that remained unaddressed until now due to a lack of suitable tools. Finally, our pipeline provides scientists and filmmakers with a new, empirically grounded approach for depicting the perceptual worlds of non-human animals.
EEG and pupillometric signatures of working memory overload

Understanding the physiological correlates of cognitive overload has implications for gauging the limits of human cognition, for developing novel methods to define cognitive overload, and for mitigating the negative outcomes associated with overload. Most previous psychophysiological studies manipulated verbal working memory load in a narrow range (an average load of 5 items). It is unclear, however, how the nervous system responds to working memory load exceeding typical capacity limits. The objective of the current study was to characterize the central and autonomic nervous system changes associated with memory overload, by means of combined recording of EEG and pupillometry. Eighty-six participants were presented with a digit span task involving the serial auditory presentation of items. Each trial consisted of sequences of either 5, 9, or 13 digits, each separated by 2 seconds. Both theta activity and pupil size, after the initial rise, expressed a pattern of a short plateau and a decrease with reaching the state of memory overload, indicating that pupil size and theta possibly have similar neural mechanisms. Based on the described above triphasic pattern of pupil size temporal dynamics, we concluded that cognitive overload causes physiological systems to reset, and to release effort. Although memory capacity limits were exceeded and effort was released (as indicated by pupil dilation), alpha continued to decrease with increasing memory load. These results suggest that associating alpha with the focus of attention and distractor suppression is not warranted.
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee

There is an ongoing discussion on what makes humans more engaged when interacting with conversational agents. However, in the area of language processing, there has been a paucity of studies on how people react to agents and share interactions with others. We attack this issue by investigating the user dialogues with human-like agents posted online and aim to analyze the dialogue patterns. We construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. We annotate the in-the-wild data, examine the reliability of the proposed scheme, and discuss how the categorization can be utilized for future research and industrial development.
The Effects of Surprisal across Languages: Results from Native and Non-native Reading

It is well known that the surprisal of an upcoming word, as estimated by language models, is a solid predictor of reading times (Smith and Levy, 2013). However, most of the studies that support this view are based on English and few other Germanic languages, leaving an open question as to the cross-lingual generalizability of such findings. Moreover, they tend to consider only the best-performing eye-tracking measure, which might conflate the effects of predictive and integrative processing. Furthermore, it is not clear whether prediction plays a role in non-native language processing in bilingual individuals (Grüter et al., 2014). We approach these problems at large scale, extracting surprisal estimates from mBERT, and assessing their psychometric predictive power on the MECO corpus, a cross-linguistic dataset of eye movement behavior in reading (Siegelman et al., 2022; Kuperman et al., 2020). We show that surprisal is a strong predictor of reading times across languages and fixation measurements, and that its effects in L2 are weaker with respect to L1.