Miracle-dz/UniFAR-FTUs huggingface dataset:
The architecture of phenotypic flexibility within a complex trait: an empirical case study using avian thermogenic performance

Reversible modifications to trait values can allow individuals to match their phenotypes to changing environmental conditions, a phenomenon known as phenotypic flexibility. A system’s capacity for flexibility may be determined by its underlying architecture, and these relationships can have important implications for both organismal adaptation and the evolvability of acclimatization responses. Theory provides two possible alternatives to explain the ways in which lower-level traits respond to environmental challenges and contribute to phenotypic flexibility in complex, whole-organism traits: symmorphosis predicts correspondence between structure and demand across all levels of a physiological system, while the alternative predicts that influence is concentrated in select elements of a physiological network. Here we provide a rich dataset — composed of 20 sub-organismal, physiological traits paired with whole-organism metabolic rates for 106 adult Dark-eyed Juncos (Junco hyemalis) — to explore the mechanistic basis of phenotypic flexibility in complex traits. When exposed to synthetic temperature cues, these individuals have previously been shown to increase their thermogenic capacity (Msum) and enhance their ability to maintain their body temperature in the cold. We show that the relationships among a number of the traits that contribute to Msum varied as the environmental context changed. Moreover, variation in Msum in response to temperature acclimation was correlated with only a handful of subordinate phenotypes. As a result, avian thermogenic flexibility does not appear to be a symmorphotic response. If this is generally true of complex traits, it suggests that simple and reversible modifications can significantly impact whole-organism performance, and thus that the evolution of phenotypic flexibility in a single component part could impart flexibility for the entire system.
Reevaluation of ethylene role in Arabidopsis cauline leaf abscission induced by water stress and rewatering

Patharkar and Walker (2016) reported that cauline leaf abscission in Arabidopsis is induced by a cycle of water stress and rewatering, which is regulated by the complex of INFLORESCENCE DEFICIENT IN ABSCISSION (IDA), HAESA (HAE), and HAESA-LIKE2 (HSL2) kinases. However, they stated without presenting experimental results that ethylene is not involved in this process. Since this statement contradicts the well-established role of ethylene in organ abscission induced by a cycle of water stress and rewatering, our present study was aimed to re-evaluate the possible involvement of ethylene in this process. For this purpose, we examined the endogenous ethylene production during water stress and following rewatering, as well as the effects of exogenous ethylene and 1-methylcyclopropene (1-MCP), on cauline leaf abscission of Arabidopsis wild type. Additionally, we examined whether this stress induces cauline leaf abscission in ethylene-insensitive Arabidopsis mutants. The results of the present study demonstrated that ethylene production rates increased significantly in cauline leaves at 4 h after rewatering of stressed plants, and remained high for at least 24 h in plants water-stressed to 40 and 30% of system weight. Ethylene treatment applied to well-watered plants induced cauline leaf abscission, which was inhibited by 1-MCP. Cauline leaf abscission was also inhibited by 1-MCP applied during a cycle of water stress and rewatering. Finally, no abscission occurred in two ethylene-insensitive mutants, ein2-1 and ein2-5, following a cycle of water stress and rewatering. Taken together, these results clearly indicate that ethylene is involved in Arabidopsis cauline leaf abscission induced by water stress. One sentence summary Unlike Patharker and Walker (2016), our results show that ethylene is involved in Arabidopsis cauline leaf abscission induced by water stress and rewatering, similar to leaf abscission in other plants.
Role of Solvent Compatibility in the Phase Behavior of Binary Solutions of Weakly Associating Multivalent Polymers

Condensate formation of biopolymer solutions, prominently those of various intrinsically disordered proteins (IDPs), is determined by “sticky” interactions between associating residues, multivalently present along the polymer backbone. Using a ternary mean field “stickers-and-spacers” model, we demonstrate that if sticker association is of the order of a few times the thermal energy, a delicate balance between specific binding and non-specific polymer-solvent interactions gives rise to a particularly rich ternary phase behavior under physiological circumstances. For a generic system represented by a solution comprising multi-associative scaffold and client polymers, the difference in solvent compatibility between the polymers modulates the nature of isothermal liquid-liquid phase separation (LLPS) between associative and segregative. The calculations reveal regimes of dualistic phase behavior, where both types of LLPS occur within the same phase diagram, either associated with the presence of multiple miscibility gaps, or a flip in the slope of the tie-lines belonging to a single coexistence region.
Temporal analysis of enhancers during mouse brain development reveals dynamic regulatory function and identifies novel regulators of cerebellar development

In this study, we identified active enhancers in the mouse cerebellum at embryonic and postnatal stages establishing the first catalog of enhancers active during embryonic cerebellum development. The majority of cerebellar enhancers have dynamic activity between embryonic and postnatal development. Cerebellar enhancers were enriched for neural transcription factor binding sites with temporally specific expression. Putative gene targets displayed spatially restricted expression patterns, indicating cell-type specific expression regulation. Functional analysis of target genes indicated that enhancers regulate processes spanning several developmental epochs such as specification, differentiation and maturation. We use these analyses to discover one novel regulator and one novel marker of cerebellar development: Bhlhe22 and Pax3, respectively. We identified an enrichment of de novo mutations and variants associated with autism spectrum disorder in cerebellar enhancers. Our study provides insight into the dynamics of gene expression regulation by enhancers in the developing brain and delivers a rich resource of novel gene-enhancer associations providing a basis for future in-depth studies in the cerebellum.
Lmo7 recruits myosin II heavy chain to induce apical constriction in Xenopus ectoderm

Apical constriction, or reduction of the apical domain, underlies many morphogenetic events during development, such as furrow or tube formation. Actomyosin complexes play an essential role in apical constriction, however the detailed analysis of molecular mechanisms is still pending. Here we show that Lim domain only protein 7 (Lmo7), a multidomain adaptor at apical junctions, promotes apical constriction in the Xenopus superficial ectoderm, whereas apical domain size increases in Lmo7-depleted cells. Lmo7 is localized to and promotes the formation of the circumferential actomyosin belt adjacent to apical junctions. We find that Lmo7-dependent apical constriction requires the RhoA-ROCK-Non-muscle myosin II (NMII) pathway. Strikingly, Lmo7 binds and recruits NMII heavy chains to apical junctions. Lmo7 overexpression altered the subcellular distribution of Wtip, a sensor of mechanical tension. Our findings suggest that Lmo7 serves as a scaffold organizing the actomyosin network to regulate contractility at apical junctions.
Assembly of graphene oxide‐formate dehydrogenase composites by nickel‐coordination with enhanced stability and reusability

Featuring unique planar structure, large surface area and biocompatibility, graphene oxide (GO) has been widely taken as an ideal scaffold for the immobilization of various enzymes. In this regard, nickel‐coordinated graphene oxide composites (GO‐Ni) were prepared as novel supporters for the immobilization of formate dehydrogenase. The catalytic activity, stability and morphology were studied. Compared with GO, the enzyme loading capacity of GO‐Ni was enhanced by 5.2‐fold, besides the immobilized enzyme GO‐Ni‐FDH exhibited better thermostability, storage stability and reuse stability than GO‐FDH. GO‐Ni‐FDH retained 40.9% of its initial activity after 3 h at 60°C, and retained 31.4% of its initial relative activity after 20 days’ storage at 4°C. After eight times usages, GO‐Ni‐FDH maintained 63.8% of its initial activity. Mechanism insights of the multiple interactions of enzyme with the GO‐Ni were studied, considering coordination bonds, hydrogen bonds, electrostatic forces, coordination bonds, and etc. A practical and simple immobilization strategy by metal ions coordination for multimeric dehydrogenase was developed.
Frailty trajectories in the oldest old: Is the level or the rate of change more predictive of mortality?

BACKGROUND: It is unclear whether frailty index (FI) change captures mortality risk better than and independently of the current FI level, i.e. whether a regular FI assessment among older adults provides additional insights for mortality risk stratification or not. METHODS: We used data from the LASA 75-PLUS-study, which monitored health among 508 older adults (75+) between 2016-2019 every 9 months. Joint models for longitudinal and time-to-event data were used to assess the impact of both current FI and within-person FI change during the last year on mortality risk. RESULTS: 20% of the participants died during 4.5 years of follow-up. Adding within-person FI change to the current FI model improved model fit and it showed that FI increases during the last year were associated with a strong increase in mortality risk. Consequently, the effect of the current FI decreased considerably and became statistically non-significant. CONCLUSIONS: The rate of FI change was more important than the current FI level for short-term mortality prediction among the oldest old, which highlights the benefits of regular frailty assessments.
Terahertz annular antenna driven with a short intense laser pulse

Generation of terahertz radiation by an oscillating discharge, excited with short laser pulses, may be controlled by geometry of the irradiated target. In this work, an annular target with a thin slit is considered as an efficient emitter of secondary radiation when driven by a short intense laser pulse. Under the irradiation, a slit works as a diode, which is quickly filled by dense plasma, closing the circuit for a travelling discharge pulse. Such a diode defines the discharge pulse propagation direction in a closed contour, enabling its multiple passes along the coil. The obtained oscillating charge efficiently generates terahertz waves with a maximum along the coil axis and controllable characteristics.
Spectrophotometric measurement of freshwater pH with purified meta‐cresol purple and phenol red

Impurities in indicator salts can significantly bias spectrophotometric pH determinations. In this work, two purified sulfonephthalein indicators, meta‐cresol purple (mCP) and phenol red (PR), were tested for analysis of freshwater pH on the free hydrogen ion concentration scale. These two purified indicators were characterized for the first time under low ionic strength conditions, providing their molar absorption coefficients and dissociation constants along with their temperature dependence from 8 °C to 30 °C. At 25 °C, the infinite dilution constants ( pKIo ) were determined to be 8.6606 and 8.0642 for mCP and PR, respectively. The accuracy and precision of the method, evaluated with a variety of buffers with known pH, were found to be +0.0014 pH units and ±0.0022 pH units, respectively (n = 30). The pH values of different freshwater samples were also determined using both indicators. The mCP and PR results were all within ± 0.01 pH units of each other with three out of seven pH differences within ± 0.001 pH units, indicating the high consistency between these two indicator methods. The work presented here is the first parallel comparison with two purified indicators used to determine pH of the same freshwater samples.
MATE: Multi-Attribute Table Extraction

A core operation in data discovery is to find joinable tables for a given table. Real-world tables include both unary and n-ary join keys. However, existing table discovery systems are optimized for unary joins and are ineffective and slow in the existence of n-ary keys. In this paper, we introduce Mate, a table discovery system that leverages a novel hash-based index that enables n-ary join discovery through a space-efficient super key. We design a filtering layer that uses a novel hash, Xash. This hash function encodes the syntactic features of all column values and aggregates them into a super key, which allows the system to efficiently prune tables with non-joinable rows. Our join discovery system is able to prune up to 1000 x more false positives and leads to over 60 x faster table discovery in comparison to state-of-the-art.
Optic Disc Segmentation using Disk-Centered Patch Augmentation

The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a color fundus image is straightforward, accurately segmenting its boundary at the pixel level is very challenging. In this work, we propose disc-centered patch augmentation (DCPA) -- a simple, yet novel training scheme for deep neural networks -- to address this problem. DCPA achieves state-of-the-art results on full-size images even when using small neural networks, specifically a U-Net with only 7 million parameters as opposed to the original 31 million. In DCPA, we restrict the training data to patches that fully contain the optic nerve. In addition, we also train the network using dynamic cost functions to increase its robustness. We tested DCPA-trained networks on five retinal datasets: DRISTI, DRIONS-DB, DRIVE, AV-WIDE, and CHASE-DB. The first two had available optic disc ground truth, and we manually estimated the ground truth for the latter three. Our approach achieved state-of-the-art F1 and IOU results on four datasets (95 % F1, 91 % IOU on DRISTI; 92 % F1, 84 % IOU on DRIVE; 83 % F1, 71 % IOU on AV-WIDE; 83 % F1, 71 % IOU on CHASEDB) and competitive results on the fifth (95 % F1, 91 % IOU on DRIONS-DB), confirming its generality. Our open-source code and ground-truth annotations are available at: https://github.com/saeidmotevali/fundusdisk
Multiphase Outflows in High-redshift Quasar Host Galaxies

We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of six radio-loud quasar host galaxies at z = 1.4–2.3. We combine the kiloparsec-scale resolution ALMA observations with high spatial resolution adaptive optics integral field spectrograph data of the ionized gas. We detect molecular gas emission in five quasar host galaxies and resolve the molecular interstellar medium using the CO (3–2) or CO (4–3) rotational transitions. Clumpy molecular outflows are detected in four quasar host galaxies and a merger system 21 kpc away from one quasar. Between the ionized and cold molecular gas phases, the majority of the outflowing mass is in a molecular phase, while for three out of four detected multiphase gas outflows, the majority of the kinetic luminosity and momentum flux is in the ionized phase. Combining the energetics of the multiphase outflows, we find that their driving mechanism is consistent with energy-conserving shocks produced by the impact of the quasar jets with the gas in the galaxy. By assessing the molecular gas mass to the dynamics of the outflows, we estimate a molecular gas depletion timescale of a few megayears. The gas outflow rates exceed the star formation rates, suggesting that quasar feedback is a major mechanism of gas depletion at the present time. The coupling efficiency between the kinetic luminosity of the outflows and the bolometric luminosity of the quasar of 0.1%–1% is consistent with theoretical predictions. Studying multiphase gas outflows at high redshift is important for quantifying the impact of negative feedback in shaping the evolution of massive galaxies.
Probabilistic Object Maps for Long-Term Robot Localization

Robots deployed in settings such as warehouses and parking lots must cope with frequent and substantial changes when localizing in their environments. While many previous localization and mapping algorithms have explored methods of identifying and focusing on long-term features to handle change in such environments, we propose a different approach - can a robot understand the distribution of movable objects and relate it to observations of such objects to reason about global localization? In this paper, we present probabilistic object maps (POMs), which represent the distributions of movable objects using pose-likelihood sample pairs derived from prior trajectories through the environment and use a Gaussian process classifier to generate the likelihood of an object at a query pose. We also introduce POM-Localization, which uses an observation model based on POMs to perform inference on a factor graph for globally consistent long-term localization. We present empirical results showing that POM-Localization is indeed effective at producing globally consistent localization estimates in challenging real-world environments and that POM-Localization improves trajectory estimates even when the POM is formed from partially incorrect data.
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting

We present the winning strategy of an electricity demand forecasting competition. This competition was organized to design new forecasting methods for unstable periods such as the one starting in Spring 2020. We rely on state-space models to adapt standard statistical and machine learning models. We claim that it achieves the right compromise between two extremes. On the one hand, purely time-series models such as autoregressives are adaptive in essence but fail to capture dependence to exogenous variables. On the other hand, machine learning methods allow to learn complex dependence to explanatory variables on a historical data set but fail to forecast non-stationary data accurately. The evaluation period of the competition was the occasion of trial and error and we put the focus on the final forecasting procedure. In particular, it was at the same time that a recent algorithm was designed to adapt the variances of a state-space model and we present the results of the final version only. We discuss day-today predictions nonetheless.
Multiscale Jump Testing and Estimation Under Complex Temporal Dynamics

We consider the problem of detecting jumps in an otherwise smoothly evolving trend whilst the covariance and higher-order structures of the system can experience both smooth and abrupt changes over time. The number of jump points is allowed to diverge to infinity with the jump sizes possibly shrinking to zero. The method is based on a multiscale application of an optimal jump-pass filter to the time series, where the scales are dense between admissible lower and upper bounds. For a wide class of non-stationary time series models and trend functions, the proposed method is shown to be able to detect all jump points within a nearly optimal range with a prescribed probability asymptotically under mild conditions. For a time series of length $n$, the computational complexity of the proposed method is $O(n)$ for each scale and $O(n\log^{1+\epsilon} n)$ overall, where $\epsilon$ is an arbitrarily small positive constant. Numerical studies show that the proposed jump testing and estimation method performs robustly and accurately under complex temporal dynamics.
Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation

Knowledge distillation usually transfers the knowledge from a pre-trained cumbersome teacher network to a compact student network, which follows the classical teacher-teaching-student paradigm. Based on this paradigm, previous methods mostly focus on how to efficiently train a better student network for deployment. Different from the existing practices, in this paper, we propose a novel student-helping-teacher formula, Teacher Evolution via Self-Knowledge Distillation (TESKD), where the target teacher (for deployment) is learned with the help of multiple hierarchical students by sharing the structural backbone. The diverse feedback from multiple students allows the teacher to improve itself through the shared feature representations. The effectiveness of our proposed framework is demonstrated by extensive experiments with various network settings on two standard benchmarks including CIFAR-100 and ImageNet. Notably, when trained together with our proposed method, ResNet-18 achieves 79.15% and 71.14% accuracy on CIFAR-100 and ImageNet, outperforming the baseline results by 4.74% and 1.43%, respectively. The code is available at: https://github.com/zhengli427/TESKD.
Electrochemical oxidation pathway of the anti-cancer agent dasatinib using disposable pencil graphite electrode and its adsorptive stripping voltammetric determination in biological samples

The present study describes the use of pencil graphite electrode (PGE) for the investigation of electro-oxidation mechanism and voltammetric stripping determination of dasatinib (DST) in Britton-Robinson buffer solution (BR). Relating to cyclic voltammetric studies, an irreversible oxidation signal was obtained at about 1.0 V. The oxidation electrode process is adsorption-controlled and pH-dependent. For quantitative determination of DST, square wave adsorptive stripping voltammetry (AdsSWV) was employed in BR of pH 3.0. The oxidation peak current varies linearly with the DST concentration in the range of 0.0092 – 1.0 µ M. Dedection (LOD) and quantification (LOQ) values are founded as 0.0028 µ M and 0.0092 µ M, respectively. The developed AdsSWV method based on disposible and cheap PGE was applied successfuly to the real urine samples and the recovery results are given in the range of 97.94% to 100.82%.
Propriedades estruturais, eletrônicas, ópticas e vibracionais do cristal ltreonina: simulações computacionais no formalismo DFT

Neste trabalho, estudamos as propriedades estruturais, eletronicas, opticas e vibracionais dos cristais ortorrombicos de treonina, obtidos atraves de simulacoes computacionais no formalismo DFT (Teoria Funcional da Densidade), nas aproximacoes de densidade local (LDA-CAPZ) e no gradiente generalizado GGA-PBE). L-treonina e um aminoacido essencial que desempenha um papel importante nos sistemas biologicos, sendo o ultimo dos 20 aminoacidos que compoem as proteinas, a serem identificados. Para o cristal l-treonina, a otimizacao da geometria cristalina, parâmetros de rede, ângulos e distâncias interatomicas, estrutura de banda, densidade de atomo a atomo, absorcao optica, funcao dieletrica, indice de refracao, condutividade otica, funcao de perda, espectro infravermelho e Raman. Os parâmetros de rede calculados estao proximos dos resultados experimentais, foram obtidos um gap de banda E = 5,06 eV e um gap de banda E (X → S) = 4,91 eV indiretos dentro do GGA e LDA, respectivamente. A analise da densidade de estados de eletrons permitiu identificar as contribuicoes por atomo para os estados das bandas de valencia e de conducao, nas propriedades opticas e possivel observar uma sensibilidade ao plano de polarizacao da luz incidente 001. Foi obtido um bom acordo entre Os espectros vibracionais IR e Raman dados teoricos e experimentais do cristal l-treonina.