Miracle-dz/UniFAR-FTUs huggingface dataset:
takeout gene expression is associated with temporal kin recognition

A key component of parental care is avoiding killing and eating your own offspring. Many organisms commit infanticide but switch to parental care at the time when their own offspring would be expected, known as temporal kin recognition. It is unclear how such indirect kin recognition is so common across taxa. One possibility is that temporal kin recognition can be achieved by altering a simple mechanism, such as co-opting the regulation of timing and feeding in other contexts. Here we determine whether takeout, a gene implicated in coordinating feeding, influences temporal kin recognition in the roundneck sexton beetle, Nicrophorus orbicollis. We find that takeout expression is not associated with non-parental feeding changes resulting from hunger, or in the general switch to the full parental care repertoire. However, beetles that accepted and provided care to their offspring had a higher takeout expression than beetles that committed infanticide. Together, these data support the idea that the evolution of temporal kin recognition may be enabled by co-option of mechanisms that integrate feeding behaviour in other contexts.
Quantitative comparison of a mobile, tablet-based and two stationary, video-based eye-trackers

The analysis of eye-movements is a non-invasive, reliable and fast method to detect and quantify brain (dys-)functions. Here, we investigated the performance of two novel eye-trackers: the Thomas-Oculus-Motus-research mobile (TOM-rm) and the TOM-research stationary (TOM-rs) and compared it with the performance of a well-established video-based eye-tracker, i.e., the EyeLink 1000 (EL). The TOM-rm is a fully integrated, tablet-based mobile device that presents visual stimuli and records head-unrestrained eye-movements at 30Hz without additional infrared (IR) illumination. The TOM-rs is a stationary, video-based eye-tracker that records eye-movements at either high spatial or high temporal resolution. We compared the performance of all three eye-trackers in two different behavioral tasks: pro– and anti-saccade and free viewing. We collected data from human subjects while running all three eye-tracking devices in parallel. Parameters requiring a high spatial or temporal resolution (e.g., saccade latency or gain), as derived from the data, differed significantly between the EL and the TOM-rm in the pro– and anti-saccade task. In the free viewing task, larger noise and the lower frame rate of the TOM-rm caused deviations of the results with respect to the EL. Differences between results derived from the TOM-rs and the EL were most likely due to experimental conditions, which could not be optimized for both systems simultaneously. We conclude that the TOM-rm can be used for measuring eye-movements reliably at comparably low spatial and temporal resolution. The TOM-rs, on the other hand, can provide high-resolution oculomotor data at least on a par with an established reference system.
Gene regulatory programs in the life history of Salpingoeca rosetta

The choanoflagellate Salpingoeca rosetta can differentiate into at least five morphologically and behaviorally distinct cell types during its lifetime, going from individual motile cells to linear and rosette-shaped colonies. Due to the capability to form colonies and its close relationship with Metazoa, this organism is considered a model for studying multicellular evolution. The gene regulatory programs underlying these transformations are, however, unknown. Here we analyze transcriptomic data obtained from Salpingoeca rosetta in different states to identify a core of genes associated with the formation of multicellular colonies. We then compare the results with other organisms which display simple forms of multicellularity, highlighting commonalities and differences.
Combination EZH2 inhibition and retinoic acid treatment promotes differentiation and apoptosis in rhabdomyosarcoma cells

Rhabdomyosarcomas (RMS) are predominantly pediatric sarcomas thought to originate from muscle precursor cells due to impaired myogenic differentiation. Despite intensive treatment, 5-year survival for patients with advanced disease remains low (<30%), highlighting a need for novel therapies to improve outcomes. Differentiation therapeutics are agents that induce differentiation of cancer cells from malignant to benign. The histone methyltransferase, Enhancer of Zeste Homolog 2 (EZH2) suppresses normal skeletal muscle differentiation and is highly expressed in RMS tumors. We demonstrate combining EZH2 inhibition with the differentiating agent retinoic acid (RA) is more effective at reducing cell proliferation in RMS cell lines than single agents alone. In PAX3 -FOXO1 positive RMS cells this is due to an RA-driven induction of the interferon pathway resulting in apoptosis. In fusion negative RMS, combination therapy led to an EZH2i-driven upregulation of myogenic signaling resulting in differentiation. These results provide insight into the mechanism that drives the anti-cancer effect of the EZH2/RA single agent and combination treatment and indicate that the reduction of EZH2 activity combined with the induction of RA signalling represents a potential novel therapeutic strategy to treat both subtypes of RMS. Highlights EZH2 expression is upregulated fusion positive (FPRMS) and fusion negative (FNRMS) rhabdomyosarcomas EZH2 inhibition combined with retinoic acid treatment was investigated RMS cell models. Combination treatment reduced cell proliferation and tumor spheroid volume. Combination treatment in FPRMS resulted in apoptosis in FPRMS via interferon signaling. Conversely, combination treatment in fusion negative RMS resulted in myogenic differentiation.
Mechanical properties of non-reconstructed defective single-wall carbon nanotubes

This paper describes the equivalent homogeneous uniaxial mechanical properties of defective single-wall carbon nanotubes. In particular, non-reconstructed defects that can be produced by ion or electronic irradiation have been considered. A discrete nonlinear finite-element approach based on the mechanical properties of individual carbon–carbon (C–C) bonds has been used. The individual C–C bonds in turn were simulated as beam structural elements. Extensive Monte Carlo based numerical simulation has been reported in the paper. The results show that the homogeneous elastic properties of the defective nanotubes can be qualitatively and quantitatively different from the pristine configurations. The defective nanotubes show a slight reduction in axial stiffness (Young's modulus), but large variations of Poisson's ratio outside the elastic bounds for isotropic materials, depending on the locations of the vacancies. The large fluctuations of Poisson's ratio can lead to extreme positive transversal contractions or to auxetic behaviour when the nanotubes are subjected to tensile loading.
Research on the Evolution of Logistics Identification Marking Technology Based on Mallet-LDA Model and Social Network

: The purpose of this paper is to propose a technology evolution analysis framework based on the Mallet-LDA models combined Social Network Analysis(SNA) method and to study the development trends of critical technologies in logistics identification marking technology. This paper establishes a complete and systematic framework for analyzing the technological evolution based on the logistics identification marking technology selected from the national Key industry patent information service platform in China as the data source database for the period 1993 to 2018 and uses topic evolution and SNA methods for evolution analysis to predict the development trend of the critical technologies in the future time. Results show that Mallet-LDA model combined SNA method is feasible for technology evolution analysis. In addition, the main development trend in logistics identification marking technology is concentrated in the direction of data collection and processing, drone logistics, cold chain logistics, intelligent terminals. And the technical topics have changed from the early days with the primary label, barcode technology, and other individual areas to the development state of multi-technology integration. The findings of this study are expected to enrich technology evolution research based on patent analysis and provide insights into the potential of using patent data for technology evolution development prediction in the context of smart logistics in China.
In vivo ultrasound biomicroscopy

A very high frequency ultrasonic biomicroscopy system (30-130 MHz) using a B-scan combined with a new Z-scan technique for in vivo imaging of the skin has been developed. To get high lateral resolution and a good signal to noise ratio, we utilized a very narrow focused single element ceramic transducer as used in conventional acoustic microscopy resulting in resolutions of 30 mm lateral and 6 mm axial. Due to the transducer beam shape corresponding to a very short usable focal zone, the B-scan technique has been modified by an additional Z-scan in order to get sufficient depth range suitable for dermatologic diagnostics. We proved the resolution capabilities of the system by in vivo imaging experiments of diseased and normal skin
The Influence of Students' Online Silent and Active Participation on Learning Performance and Experience

: The number of online learning users grows drastically and analyzing their learning behaviour has become a hot spot in recent years. However, there have been limited comparative studies on different learning processes, i.e. silent learning and active learning behaviour, and their influence on learning performance and learning experience. This study distinguishes between two types of learning behaviour, that is, silent learning which corresponds to content consumption, and active participation which corresponds to content contribution. By collecting and analyzing user log data from a Massive Online Open Course using OLS regression and sentiment analysis, we find that students’ reply, video watching, and test taking activities simultaneously enhance their learning performance and course learning experience. By contrast, comment behaviour does not indicate better student performance and learning experience. These findings provide novel insights in student silent and active participation, and point to future investigation of predictive studies based on real-time learning behaviour.
Accelerated Discovery of Advanced Thermoelectric Materials via Transfer Learning

Thermoelectric (TE) technology can realize direct conversion of widely distributed heat into useful electricity, which provides a promising route to solve the global energy crisis that is increasingly severe. However, it is extremely complex and time‐consuming to discover advanced TE materials via conventional trial‐and‐error approaches. In this work, using a pre‐trained neural network architecture for the electronic bandgap, a transfer learning (TL) strategy that allows ready and accurate prediction on the ZT values of any TE materials at arbitrary temperature is proposed. Compared with direct machine learning algorithms, the TL‐driven model exhibits significantly enhanced predictive power beyond the initial dataset, as characterized by improved Pearson correlation coefficient (reduced mean absolute error) from 23% to 95% (0.35 to 0.07) for the p‐type systems, and 46% to 94% (0.23 to 0.06) for the n‐type systems. By screening 6353 possible candidates in the AFLOW repository that having relatively smaller gaps, 925 p‐type and 788 n‐type systems are quickly identified to exhibit ZT exceeding 2.0. Equally importantly, the established TL model is highly adaptable to ZT prediction in an even larger search space, where the constituent atoms and/or stoichiometric compositions of the screened systems may be variously tuned to further optimize their TE performance.
Consciousness, Reduction, and Emergence

Abstract: Consciousness is often seen as requiring a special kind of explanation. But the various aspects of self‐awareness can presumably emerge when certain levels of complexity are reached in an organism: it is not necessary to assume additional mechanisms or hidden causes. Looking at the most fundamental level, that of elementary particle physics, three principles appear—the conformability of nature to herself, the applicability of the criterion of simplicity, and the utility of certain parts of mathematics in describing physical reality—which are in themselves emergent properties of the fundamental laws of physics. At successive levels, it is the availability of similar mathematical descriptions from related problems that makes the next step appear with simplicity and elegance. Thus, once the concept of emergence is properly established, a huge burden is lifted from the inquiring mind. The whole explanatory loop may be closed by looking at the ability of the human mind to figure out the laws of nature. All the other sciences emerge in principle from fundamental physics plus historical accidents, even though “reduction” is obviously inadequate as a strategy. While bridges or staircases are under construction connecting the various sciences, each science needs to be studied at its own level as well. Although the idea of “vital forces” in biology alien to physics and chemistry has largely disappeared, consciousness remains the last refuge of obscurantists. Finally, it is argued that appeals to the alleged weirdness of quantum mechanics are based on a misunderstanding and are unlikely to have any place in a discussion of consciousness.
Studies on Household Leverage Forecasting Using Multiple LSTMs

: LSTM has been proven to help understand multivariate time series since it can implement automatic feature learning and handle input sequences harnessed in an end-to-end model. Inspired by its success, this study proposed a new approach, named Multiple LSTMs, to settle the challenge of integrating multivariate LSTM and various characteristics of multiple time series to predict household leverage of China. The new method consists of multiple LSTM layers, where each LSTM is used to learn representations of multivariate time series automatically of various regions. To illustrate the performance, the new method is used to forecast household leverage in both the national level and regional levels based on multivariate time series which have 6 independent variables. The results show that the new approach provides more accurate predictions of household leverage than alternative methods. Overall, making use of multiple multivariate time series can gain a better result of the prediction of household leverage.
Coupled Systems of Differential Algebraic and Partial Differential Equations in Circuit and Device Simulation

Differential Algebraic Systems In order to treat coupled systems of partial differential equations (PDEs) and differential-algebraic equations (DAEs) in a systematic way we shall study abstract differential algebraic systems (ADASs) of the following form A d dt D(u(t), t) + B(u(t), t) = 0 for t ∈ [t0, T ]. (4.1) This equation is to be understood as an operator equation with operators A, D(·, t) and B(·, t) acting in real Hilbert spaces. More precisely, let X, Y , Z ⊆ Z̃ be Hilbert spaces and A : Z̃ → Y, D(·, t) : X → Z, B(·, t) : X → Y. (4.2) In [Mär02a, LMT01], the case Z = Z̃ has been considered. This is enough for the classical formulation of the coupled system (see Section 3.5). However, for the generalized formulation, we need the more general case Z ⊆ Z̃ (see Section 3.6). Such systems with A and D being invertible have already been studied in [GGZ74]. However, the classical formulation as well as the generalized formulation of the coupled system lead to abstract differential algebraic systems with operators A or D(·, t) that are not invertible on the whole time interval. Such systems are also called singular (see e.g. [Cam80, Cam82] for the finite dimensional case) or degenerate differential equations (see e.g. [Kur93, FY99]). 66 Abstract Differential Algebraic Systems In [FY99], systems of the form (4.1) with linear operators have been studied. More precisely, initial value systems of the form A d dt Du + Bu = Ag(t) for t ∈ (0, T ], (4.3) Du(0) = v0 ∈ imD, (4.4) with X = Y = Z, D = M and A = I are treated by a semigroup approach. I denotes the identity operator. The system (4.3)-(4.4) is reduced to multivalued differential equations of the form dv dt ∈ Av + f(t) for t ∈ (0, T ], (4.5) v(0) = v0, (4.6) where A := −BD. Here, the operator D is defined as (multi-valued) function satisfying Dv = {u ∈ DD : Du = v} for all v ∈ imD. with DD being the definition domain of D. The existence and uniqueness of classical solutions of (4.3)-(4.4) satisfying Du ∈ C([0, T ]; X) and Bu ∈ C([0, T ]; X) is shown provided that g ∈ C([0, T ]; X), Bu(0) ∈ imA, Re(−Bu|Du)X ≤ β‖Du‖ 2 X , for all u ∈ DB ⊆ DD (4.7) as well as the operator λ0AD + B : DB → X is bijective for some λ0 > β. (4.8) A similar result is obtained for systems of the form (4.3)-(4.4) with A = D or D = I. This approach via multi-valued differential equations concentrates on the dynamic part of the system. It is limited to systems with special constraints. So, for instance, condition (4.7) implies Re(Bw,Dv) = 0 for all w ∈ kerD ∩ DB, v ∈ DD. Together with condition (4.8), we obtain, for the finite dimensional case, a DAE of index 1 with the constraint (BQ)Bu = (BQ)g(t)
Antecedents of Consumers' Impulsive Buying Intention in Live Streaming Commerce - - Perspective of Live Streamer's Persuasive Ability

: As a new form of e-commerce, live streaming commerce can easily drive consumers to buy impulsively in a short period of time, which brings significant benefits to business. By applying Aristotle’s rhetoric appeals, we define the concept of e-commerce live streamer’s persuasive ability and distinguish its four dimensions, i.e., logic, expertise, morality and emotional contagion. Based on S-O-R paradigm, our study constructs a theoretical model to investigate how live streamer’s persuasive ability influences impulsive purchase behavior in live-steaming commerce. The results demonstrate that four dimensions of live streamer’s persuasive ability has varying degrees of impact on consumers’ affective reactions, i.e., affective trust and perceived pleasure, which in turn leads to their impulsive buying intention. This investigation provides both theoretical implications for the literature of live streaming commerce and practical implications for e-commerce live streamer.
A monomial ν-SV method for regression

In the present paper we describe a new formulation for Support Vector regression (SVR), namely monomial ν-SVR. Like the standard ν-SVR, the monomial ν-SVR method automatically adjusts the radius of insensitivity (the tube width, ǫ) to suit the training data. However, by replacing Vapnik’s ǫ-insensitive cost with a more general monomial ǫ-insensitive cost (and likewise replacing the linear tube shrinking term with a monomial tube shrinking term), the performance of the monomial ν-SVR is improved for data corrupted by a wider range of noise distributions. We focus on the quadric form of monomial ν-SVR and show that the dual form of this is simpler than the standard ν-SVR. We show that, like Suykens’ Least-Squares SVR (LS-SVR) method (and unlike standard ν-SVR), the quadric ν-SVR dual has a unique global solution. Comparisons are made between the asymptotic efficiency of our method and that of standard ν-SVR and LS-SVR which demonstrate the superiority of our method for the special case of higher order polynomial noise. These theoretic al predictions are validated using experimental comparisons with the alternative approaches of standard ν-SVR, LS-SVR and weighted LS-SVR.
Information Cascade and Spam E-commerce reviews filtering

: Rating and reviews provided by customers can send many signals to the market, especially in online marketplace. How to formulate an outstanding sales strategy and show users the most useful information of the product is particularly important. In this paper, we study the review information cascade and build a MLP model to distinguish the spam and informative reviews from the noise reviews. The information cascade phenomenon in E-commerce reviews is identified by Multinomial Logistic Regression. After seeing a series of one/five star ratings, consumers are more likely to write low/high score reviews and the effect caused by low star rating is more significant than that of high star rating. Therefore, we use the helprate of the review as the label to distinguish the informative reviews and spam reviews. The one-hot review vectors is reduce to 136 principal components components as the input variables to train the MLP Model. The accuracy of the model on the test set is 78.5%, and AUC is 0.713. The E-commerce companies can evaluate the informative review, improve online sales strategies, enhance the product desirability and identify customers’ preferences through the model proposed in this paper.
Deep Learning Restores Speech Intelligibility in Multi-Talker Interference for Cochlear Implant Users

Cochlear implants (CIs) fail to provide the same level of benefit in noisy settings as in quiet. Current noise reduction solutions in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) models have achieved noteworthy performance in speech enhancement and separation. However, little work has investigated the potential of DNN models in removing multi-talker interference. The research in this regard is even more scarce for CIs. Here, we implemented two DNN models that are well suited for applications in speech audio processing, including (1) recurrent neural network (RNN) and (2) SepFormer. The models were trained with a dataset developed in house (∼ 30 hours), and then tested with thirteen CI listeners. Both RNN and SepFormer models significantly improved CI listener’s speech intelligibility in noise without compromising the perceived quality of speech. These models not only increased the intelligibility in stationary non-speech noise, but also introduced substantially more improvements in non-stationary speech noise, where conventional signal processing strategies fall short with little benefits. These results show the promise of using DNN models as a solution for listening challenges in multi-talker noise interference.