Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning reveals relationships between song, climate, and migration in coastal Zonotrichia leucophrys

Vocalization behavior in birds, especially songs, strongly affects reproduction, but it is also highly impacted by geographic distance, climate, and time. For this reason, phenotypic differences in vocalizations between different bird populations are often interpreted as evidence of lineage divergence. Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. However, it is unknown whether the divergence in their songs correlates to environmental or geographical factors. Previous work has been hindered by time-consuming traditional methods to study bird songs that rely on the manual annotation of song spectrograms into individual syllables. Here we explore the performance of automated machine learning methods of song annotation, which can process large datasets more efficiently, paying attention to the question of subspecies differences. We utilize a recently published artificial neural network to automatically annotate hundreds of White-crowned Sparrow vocalizations across two subspecies. By analyzing differences in syllable usage and composition, we find that Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in syllables in the White-crowned Sparrow repertoire. However, the large sample size enabled by the AI approach allows us to demonstrate that divergence in song is correlated with environmental difference and migratory status, but not with geographical distance. Our findings support the hypothesis that the evolution of vocalization behavior is affected by environment, in addition to population structure. LAY SUMMARY Birdsong is an important behavior because it is important in bird communication and reproduction. White-crowned Sparrows in western North America are known to use different songs along their range, but it is unknown if those songs vary due to the environment. We used machine learning to analyze these songs and found that populations of White-crowned Sparrows can be differentiated based on their songs. Environmental factors during the breeding season exert a greater influence on song evolution in migratory subspecies.
XPA tumor variants lead to defects in NER that sensitize cells to cisplatin

Nucleotide excision repair (NER) neutralizes treatment with platinum (Pt)-based chemotherapy by removing Pt lesions from DNA. Previous study has identified that missense mutation or loss of either of the NER genes Excision Repair Cross Complementation Group 1 and 2 (ERCC1 and ERCC2) leads to improved patient outcomes after treatment with Pt-based chemotherapies. Although most NER gene alterations found in patient tumors are missense mutations, the impact of such mutations in the remaining nearly 20 NER genes is unknown. Towards this goal, we previously developed a machine learning strategy to predict genetic variants in an essential NER scaffold protein, Xeroderma Pigmentosum Complementation Group A (XPA), that disrupt repair activity on a UV-damaged substrate. In this study, we report in-depth analyses of a subset of the predicted NER-deficient XPA variants, including in vitro analyses of purified recombinant protein and cell-based assays to test Pt agent sensitivity in cells and determine mechanisms of NER dysfunction. The most NER deficient variant Y148D had reduced protein stability, weaker DNA binding, disrupted recruitment to damage, and degradation resulting from tumor missense mutation. Our findings demonstrate that tumor mutations in XPA impact cell survival after cisplatin treatment and provide valuable mechanistic insights to further improve variant effect prediction efforts. More broadly, these findings suggest XPA tumor variants should be considered when predicting patient response to Pt-based chemotherapy. Significance A destabilized, readily degraded tumor variant identified in the NER scaffold protein XPA sensitizes cells to cisplatin, suggesting that XPA variants can be used to predict response to chemotherapy.
Corticostriatal Responses to Social Reward are Linked to Trait Reward Sensitivity and Subclinical Substance Use in Young Adults

Aberrant levels of reward sensitivity have been linked to substance use disorder and are characterized by alterations in reward processing in the ventral striatum (VS). Less is known about how reward sensitivity and subclinical substance use relate to striatal function during social rewards (e.g., positive peer feedback). Testing this relation is critical for predicting risk for development of substance use disorder. In this pre-registered study, participants (N=44) underwent fMRI while completing well-matched tasks that assess neural response to reward in social and monetary domains. Contrary to our hypotheses, aberrant reward sensitivity blunted the relationship between substance use and striatal activation during receipt of rewards, regardless of domain. Moreover, exploratory whole-brain analyses showed unique relations between substance use and social rewards in temporoparietal junction. Psychophysiological interactions demonstrated that aberrant reward sensitivity is associated with increased connectivity between the VS and ventromedial prefrontal cortex during social rewards. Finally, we found that substance use was associated with decreased connectivity between the VS and dorsomedial prefrontal cortex for social rewards, independent of reward sensitivity. These findings demonstrate nuanced relations between reward sensitivity and substance use, even among those without substance use disorder, and suggest altered reward-related engagement of cortico-VS responses as potential predictors of developing disordered behavior.
Age-Related Differences in Alpha Power for Distractor Inhibition During Visual Working Memory

As visual working memory (WM) is limited in capacity, it is important to direct neural resources towards task-relevant information and away from task-irrelevant information. Neural oscillations in the alpha frequency band (8-12 Hz) have been suggested to play a role in the inhibition of distracting information during WM retention in younger adults, but it is unclear if alpha power modulation also supports distractor inhibition in older adults. Here, we recorded electroencephalography (EEG) while 24 younger (aged 18-35) and 24 older (aged 60-86) adults completed a modified delay match-to-sample task in which distractors of varying strength appeared during the retention period. We found: (1) strong distractors impaired WM performance compared with weak and no distractors in both age groups, but there were no age-differences in WM performance; (2) while younger adults demonstrated significant increases in alpha power prior to the onset of the distractor in all conditions, decreases in alpha power were seen in all distractor conditions in older adults; (3) there was no difference in alpha power between the strong and no distractor conditions; and (4) alpha power in anticipation of the distractor was only associated with task performance in younger adults. Our results suggest that younger adults, but not older adults, modulate alpha power in anticipation of distractors during the visual WM retention period.
Agricultural drivers of field margin plant communities are scale dependent

In recent decades, agricultural intensification has led to a strong decline in biodiversity. Field margins act as shelters and dispersal corridors for biodiversity in highly disturbed landscapes, and are critical to the maintenance of ecosystem services. However, they are also impacted by agricultural practices in neighbouring fields. Agricultural impacts are often studied at field to landscape scales, and rarely across biogeographic regions. One of the challenges in large-scale studies is the lack of standardized monitoring schemes including both biodiversity and accurate estimation of agricultural practices. Here, we take advantage of a national monitoring scheme in 462 sites in France, to assess the effects of agricultural practices on field margin flora at different extents and resolutions. We used spatial simultaneous autoregressive and generalized dissimilarity models to assess the response of plant richness and composition to climatic, soil and landscape conditions, and to agricultural (fertilization, herbicides) and margin management drivers. Analyses were repeated at the site-level, 40 and 75 km resolutions, and at regional and national extents. We found that the impact of agricultural practices on species richness was most important at the site-level, whereas climate and crop diversity became more important at the 75 km resolution. Compositional variations responded differently, with climate being more important at the site-level, and fertilization and crop diversity at the coarsest resolution. There was a strong variation in the variance explained by models among regions, but climate effects were weaker within biogeographic units compared to the national level, and different agricultural practices stood out as influential in different regions, suggesting that the regional context is fundamental in determining plant community structure. To efficiently conserve biodiversity, we therefore recommend the implementation of agricultural measures adapted to each region.
Cranes soar on thermal updrafts behind cold fronts as they migrate across the sea

Thermal soaring conditions above the sea have long been assumed absent or too weak for terrestrial migrating birds, forcing large obligate soarers to take long detours and avoid sea crossing, and facultative soarers to cross exclusively by costly flapping flight. Thus, while atmospheric convection does develop at sea and is utilized by some seabirds, it has been largely ignored in avian migration research. Here we provide direct evidence for routine thermal soaring over open sea in the common crane, the heaviest facultative soarer known among terrestrial migrating birds. Using high-resolution biologging from 44 cranes tracked across their transcontinental migration over 4 years, we show that soaring characteristics and performance were no different over sea than over land in mid-latitudes. Sea-soaring occurred predominantly in autumn when large water-air temperature difference followed mid-latitude cyclones. Our findings challenge a fundamental paradigm in avian migration research and suggest that large soaring migrants avoid sea crossing not due to absence or weakness of thermals but due to their uncertainty and the costs of prolonged flapping. Marine cold air outbreaks, imperative to the global energy budget and climate system, may also be important for bird migration, calling for more multidisciplinary research across biological and atmospheric sciences.
Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200 ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250 – 300 ms) and later (>400 ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition. Highlights Whether a face or scene is familiar can be decoded from the EEG signal with very similar temporal dynamics, starting at 200 ms and peaking around 400 ms after stimulus onset. The neural dynamics of this familiarity information generalizes across stimulus categories. Familiarity modulates stimulus category representations from 200 ms after stimulus onset, indicating deeper processing of familiar as compared to unfamiliar stimuli already during early processing stages.
Location Choices of Multi-plant Oligopolists: Theory and Evidence from the Cement Industry

This paper proposes new theory and estimation method to study the spatial interdependencies in multi-plant production. I develop a quantitative model of multi-plant oligopolists, wherein each firm decides the location of its set of plants and the variable markups charged in each market. Such location decision balances two competing forces. On one hand, coordination in pricing among a large set of plants enhances the firm’s competitive advantage against rivals. On the other hand, cannibalization between its own plants decreases the marginal benefit from plant expansion. Despite the high-dimensional discrete choice problem, the model can be estimated by leveraging the solution algorithm to a combinatorial problem when the location game is submodular and aggregative. I apply this framework to investigate the spatial organization of cement firms responding to environmental policy changes. I demonstrate that neglecting the interdependencies of plant locations owned by the same firm leads to significant biases that can misguide policy evaluation.
Poster: SecureCells: A Secure Compartmentalized Architecture

—Modern programs are monolithic, combining code of varied provenance without isolation, all the while running on network-connected devices. A vulnerability in any component may compromise code and data of all other components. Com-partmentalization separates programs into fault domains with limited policy-deﬁned permissions, following the Principle of Least Privilege, preventing arbitrary interactions between components. Unfortunately, existing compartmentalization mechanisms target weak attacker models, incur high overheads, or overﬁt to speciﬁc use cases, precluding their general adoption. The need of the hour is a secure, performant, and ﬂexible mechanism on which developers can reliably implement an arsenal of compartmentalized software. We present SecureCells, a novel architecture for intra-address space compartmentalization. SecureCells enforces per-Virtual Memory Area (VMA) permissions for secure and scalable access control, and introduces new userspace instructions for secure and fast compartment switching with hardware-enforced call gates and zero-copy permission transfers. Secure-Cells enables novel software mechanisms for call stack maintenance and register context isolation. In microbenchmarks, SecureCells switches compartments in only 8 cycles on a 5-stage in-order processor, reducing cost by an order of magnitude compared to state-of-the-art. Consequently, SecureCells helps secure high-performance software such as an in-memory key-value store with negligible overhead of less than 3%.
Improving Sound Wave Detection with Distributed Acoustic Sensing

Over the past decade, the burgeoning field of distributed acoustic sensing (DAS) has greatly improved the quality and interpretability of many monitoring efforts and en-abled new applications that were previously infeasible. One of the less-developed DAS research areas, especially for geophysical applications, is the recording of sound waves propagating in air. Here we detail our progressing work on quantifying and improving the ability of DAS systems to record sound waves, with the aim of deploying DAS acoustic sensors in heavy industrial settings. We record the DAS response to a suite of sources played by a small speaker for a fiber-optic cable deployed in several configurations: straight fiber laying on the ground, straight fiber suspended from the ceiling, loose fiber coils, a fiber-attached Plexiglas sheet, fiber wrapped around a tire, and fiber wrapped tightly around a PVC pipe. Although other studies have tested comparable configurations, we offer a relatively uniform comparison between each of the configurations. We find a straight cable (whether suspended or laying on the ground) does not record the source signal; however, the other configurations do with varying quality. The best overall recording system was produced by the cylinder with tightly wound fiber, though its sensitivity is suboptimal for frequencies less than 600 Hz. We conclude by discussing the next steps for this line of research.
Chemical Genetic Identi ﬁ cation of PKC Epsilon Substrates in Mouse Brain

PKC epsilon (PKC ε ) plays important roles in behavioral responses to alcohol and in anxiety-like behavior in rodents, making it a potential drug target for reducing alcohol consumption and anxiety. Identifying signals downstream of PKC ε could reveal additional targets and strategies for interfering with PKC ε signaling. We used a chemical genetic screen combined with mass spectrometry to identify direct substrates of PKC ε in mouse brain and validated ﬁ ndings for 39 of them using peptide arrays and in vitro kinase assays. Prioritizing substrates with several public databases such as LINCS-L1000, STRING, GeneFriends, and GeneMAINA predicted interactions between these putative substrates and PKC ε and identi ﬁ ed substrates associated with alcohol-related behaviors, actions of benzodiazepines, and chronic stress. The 39 substrates could be broadly classi ﬁ ed in three functional categories: cytoskeletal regulation, morphogenesis, and synaptic function. These results provide a list of brain PKC ε substrates, many of which are novel, for future investigation to determine the role of PKC ε signaling in alcohol responses, anxiety, responses to stress, and other related behaviors.
Co-optimization of Morphology and Behavior of Modular Robots via Hierarchical Deep Reinforcement Learning

—Modular robots hold the promise of changing their shape and even dimension to adapt to various tasks and environments. To realize this superiority, it is essential to find the appropriate morphology and its corresponding behavior simultaneously to ensure optimality of the reconfiguration. However, achieving co-optimization is challenging because robotic configuration and motion are interactive and coupled with each other, as well as their optimization processes. To this end, we proposed a co-optimization framework based on hierarchical Deep Reinforcement Learning (DRL), consisting of a configuration model and a motion model based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). The two network models update asynchronously with a shared reward to ensure co-optimality. We conduct simulations and experiments with the Webots platform to validate the proposed framework, and the preliminary results show that it yields high quality optimization schemes and thus allows modular robots to be more adaptive to dynamic and multi-task scenarios.
Contextualizing symbol, symbolizing context

When students learn algebra for the first time, inevitably they are experiencing transition from arithmetic to algebraic thinking. Once students could apprehend this essential mathematical knowledge, they are cultivating their ability in solving daily life problems by applying algebra. However, as we dig into this transitional stage, we identified possible students’ learning obstacles to be dealt with seriously in order to forestall subsequent hindrance in studying more advance algebra. We come to realize this recurring problem as we undertook the processes of re-personalization and re-contextualization in which we scrutinize the very basic questions: 1) what is variable, linear equation with one variable and their relationship with the arithmetic-algebraic thinking? 2) Why student should learn such concepts? 3) How to teach those concepts to students? By positioning ourselves as a seventh grade student, we address the possibility of children to think arithmetically when confronted with the problems of linear equation with one variable. To help them thinking algebraically, Bruner’s modes of representation developed contextually from concrete to abstract were delivered to enhance their interpretation toward the idea of variables. Hence, from the outset we designed the context for student to think symbolically initiated by exploring various symbols that could be contextualized in order to bridge student traversing the arithmetic-algebraic fruitfully.When students learn algebra for the first time, inevitably they are experiencing transition from arithmetic to algebraic thinking. Once students could apprehend this essential mathematical knowledge, they are cultivating their ability in solving daily life problems by applying algebra. However, as we dig into this transitional stage, we identified possible students’ learning obstacles to be dealt with seriously in order to forestall subsequent hindrance in studying more advance algebra. We come to realize this recurring problem as we undertook the processes of re-personalization and re-contextualization in which we scrutinize the very basic questions: 1) what is variable, linear equation with one variable and their relationship with the arithmetic-algebraic thinking? 2) Why student should learn such concepts? 3) How to teach those concepts to students? By positioning ourselves as a seventh grade student, we address the possibility of children to think arithmetically when confronted with the problems of li.
Preface to ‘ Surface movement and shallow processes : Stage 2 Report

In Chapter 2, InSAR data sourced from non-CSG producing regions were interrogated to try and identify baseline surface movement at locations throughout the Surat Cumulative Management Area (CMA). Note that ‘baseline’ in this context refers to analysis of areas away from CSG development to understand features, processes and environmental factors that cause fluctuations in surface elevation. This showed a background trend of subsidence in most locations during the period 2012 to 2016 as well as correlation between surface movement fluctuations and rainfall. Extension of this analysis resulted in the independently peer reviewed publication of Masoudian et al. (2019)1.
Electric Field Controlled Magnetic Properties in Diluted Magnetic Semiconductor

We report the effect of external electric field (EEF) on the magnetic properties of Mn x Ge 1-x , diluted magnetic semiconductor (DMS). We present a Kondo Lattice Model (KLM) type Hamiltonian with exchange coupling between localized spins, itinerant holes and the EEF. The magnetization, the dispersion and critical temperature (T c ) are calculated for different values of EEF parameters (α) using double time temperature dependent Green function formalism. The enhancement of the (T c ) with the EEF is shown to be a very distinct and is in agreement with recent experimental observation and much required for spintronics applications and devices.