Miracle-dz/UniFAR-FTUs huggingface dataset:
The Energetics of Fermentation in Natural Settings

Abstract Fermentation plays a fundamental role in organic carbon degradation on a global scale. However, little is known about how environmental variables influence this process. In a step towards quantifying how temperature and composition influence fermentation, we have calculated the Gibbs energies of 47 fermentation reaction, ΔGr, from 0–150 °C for a broad range of compositions representing microbial habitats as variable as sediments, estuaries, soils, and crustal rocks. The organic compounds in these reactions include amino acids, nucleic acid bases, monosaccharides, carboxylates, methanogenic compounds and more. The amount of energy available varies considerably, from +54 kJ (mol C)−1 for palmitate fermentation, to −184 kJ (mol C)−1 for methylamine disproportionation. For some reactions, there is little difference in ΔGr between low and high energy systems (e.g., the monosaccharide reactions) while others span a much broader range (e.g., the nucleic acid bases). There is no clear-cut trend between exergonicity and temperature, and the values of standard state Gibbs energies of reactions, , for nearly half of the reactions lie outside the range of ΔGr values. To carry out some of these calculations, the thermodynamic properties for six organic compounds were estimated: dimethylamine, trimethylamine, resorcinol, phloroglucinol and cyclohexane carboxylate and its conjugate acid.
Survival prediction for bladder cancer using machine learning: development of BlaCaSurv online survival prediction application

Background: Bladder cancer is the most common cancer of the urinary system among the American population and it is the fourth most common cause of cancer morbidity and the eight most common cause of cancer mortality among men. Using machine learning algorithms, we predict the five-year survival among bladder cancer patients and deploy the best performing algorithm as a web application for survival prediction. Methods: Microscopically confirmed adult bladder cancer patients were included from the Surveillance Epidemiology and End Results (SEER) database (2000-2017) and randomly split into training and test datasets (70/30 ratio). Five machine learning algorithms (logistic regression, support vector machine, gradient boosting, random forest, and K nearest neighbor) were trained on features to predict five-year survival. The algorithms were compared with performance metrics and the best performing algorithm was deployed as a web application. Results: A total of 52,529 patients were included in our study. The gradient boosting algorithm was the best performer in terms of predictive ability and discrimination. It was deployed as the survival prediction web application named BlaCaSurv (https://blacasurv.herokuapp.com/). Conclusions: We tested several machine learning algorithms and developed a web application for predicting five-year survival for bladder cancer patients. This application can be used as a supplementary prognostic tool to clinical decision making.
Direct interaction of PIWI and DEPS-1 is essential for piRNA function and condensate ultrastructure in Caenorhabditis elegans

Membraneless organelles are platforms for many aspects of RNA biology including small non-coding RNA (ncRNA) mediated gene silencing. How small ncRNAs utilise phase separated environments for their function is unclear. To address this question, we investigated how the PIWI-interacting RNA (piRNA) pathway engages with the membraneless organelle P granule in Caenorhabditis elegans. Proteomic analysis of the PIWI protein PRG-1 revealed an interaction with the constitutive P granule protein DEPS-1. Furthermore we identified a novel motif on DEPS-1, PBS, which interacts directly with the Piwi domain of PRG-1. This protein complex forms intertwining ultrastructures to build elongated condensates in vivo. These sub-organelle ultrastructures depend on the Piwi-interacting motif of DEPS-1 and mediate piRNA function. Additionally, we identify a novel interactor of DEPS-1, EDG-1, which is required for DEPS-1 condensates to form correctly. We show that DEPS-1 is not required for piRNA biogenesis but piRNA function: deps-1 mutants fail to produce the secondary endo-siRNAs required for the silencing of piRNA targets. Our study reveals how specific protein-protein interactions drive the spatial organisation and function of small RNA pathways within membraneless organelles.
Can Transcranial Direct Current Stimulation Over the Dorsolateral Prefrontal Cortex Enhance Proprioception?

Introduction Proprioception (perception of one’s limb position) is critical for accurate and consistent movement, and is processed by the sensorimotor cortex. Increased prefrontal activity is associated with improved proprioception and motor performance. Anodal transcranial direct current stimulation (tDCS) of the left dorsolateral prefrontal cortex (DLPFC) has been found to increase activity of the sensorimotor cortex. Thus, this study aimed to investigate whether anodal tDCS of the DLPFC may enhance proprioception measured with a target task. It was hypothesized that tDCS over the left DLPFC would improve motor performance (error and variability) on a target task completed without vision. Design Single blind, within-participant, sham-controlled trial. Methods Fifteen healthy young adults (M:F=6:9, age=23.3 years) completed 18 trials of a computerized target task (manipulating a mouse) with their non-dominant upper-limb, with and without vision, before and after (pre/post assessment) 20-minutes of stimulation (anodal tDCS of the left DLPFC) and sham conditions. Averages and coefficient of variation (CV, variability between trials) of spatio-temporal parameters associated with the movement were measured. Stimulation/ sham sessions were counterbalanced (stimulation first session, n=8), with each session separated by one week. Repeated-measures ANOVA and pairwise comparisons (95% confidence intervals [CI]) were conducted. Results Regarding distance travelled CV, a significant interaction between condition and assessment (F(1,14)=5.09, p=0.041) demonstrated that variability was significantly less post-stimulation compared to pre (p=0.003). A significant interaction between assessment and vision (F(1,14)=30.08, p<0.001) regarding distance travelled CV showed that without vision, variability was significantly less at post compared to pre (p<0.001), and this decrease was found after the stimulation condition only (95% CI = Δ 7.4 +/− 1.6 [4.0 to 10.9]). Conclusion Since variability of distance travelled during the target task without vision was lower post-stimulation compared to pre, consistency of movement without vision, and therefore proprioception, may have been enhanced by anodal tDCS of the DLPFC. This improvement could be due to modulation of fronto-striatal-thalamic circuits. These findings may be the first step in developing tDCS methods as an effective adjunct therapy for dysfunctional proprioception in various disorders, such as Parkinson’s disease.
Spectral properties for the Laplacian of a generalized Wigner matrix

In this paper, we consider the spectrum of a Laplacian matrix, also known as Markov matrices where the entries of the matrix are independent but have a variance profile. Motivated by recent works on generalized Wigner matrices we assume that the variance profile gives rise to a sequence of graphons. Under the assumption that these graphons converge, we show that the limiting spectral distribution converges. We give an expression for the moments of the limiting measure in terms of graph homomorphisms. In some special cases, we identify the limit explicitly. We also study the spectral norm and derive the order of the maximum eigenvalue. We show that our results cover Laplacians of various random graphs including inhomogeneous Erdős–Rényi random graphs, sparse W-random graphs, stochastic block matrices and constrained random graphs.
STRUCTURE INVESTIGATIONS OF XE-ADSORBATE LAYERS BY SPIN-POLARIZED LOW-ENERGY-ELECTRON DIFFRACTION .1. (ROOT-3X-ROOT-3)R30-DEGREES-XE PT(111)

The (root 3 x root 3)R30 degrees Xe layer on Pt(111) is studied by spin-polarized low-energy electron diffraction (SPLEED). In the experiment spin-polarized electrons from a GaAs photoemission source are scattered at the target, and the spin-dependent intensities are measured in different diffracted beams. Corresponding calculations are performed using a relativistic LEED program. Measurements and calculations show strong contributions from multiple scattering between the substrate and the adlayer. The scattering contributions from the substrate are found to dominate the general structure of asymmetry and intensity profiles of integer order beams. In adsorbate-induced beams the contributions from the substrate are significantly weaker but nevertheless clearly present. The integer-order beams as well as the adsorbate-induced beams are suitable for structure investigations. Comparing measurements and calculations by means of r-factor analysis, we determine the local adsorption geometry. The Xe atoms are adsorbed in domains of fee and hcp hollow sites on the Pt(111) surface with identical domain size probability and random distribution of the domains. The distance between the adsorbate layer and the topmost substrate layer is found to be 4.2+/-0.1 Angstrom. Measurements and calculations agree very well in general.
Long noncoding RNA lncGALM increases risk of liver metastasis in gallbladder cancer through facilitating N‐cadherin and IL‐1β‐dependent liver arrest and tumor extravasation

Abstract Background Long noncoding RNAs (lncRNA) represent significant factors of the mammalian transcriptome that mediates varied biological and pathological processes. The liver is the most common site for gallbladder cancer (GBC) distant metastasis and contributes to the majority of GBC‐related death. How lncRNA affects GBC metastasis is not completely understood. Results A novel lncRNA termed lncGALM (lncRNA in GBC associated with liver metastasis) was discovered to be highly expressed in cancer patients and xenografted tumors with liver metastasis. Elevated lncGALM in GBC patients also correlated to decreased survival. Invasion and migration of GBC cells were enhanced through lncGALM, both in vitro and in vivo. lncGALM functioned as sponges by competitively binding to and inactivating miR‐200 family members, which increase epithelial‐mesenchymal transition‐associated transcription factor ZEB1 and ZEB2, leading to a fibroblastic phenotype and increased expression of N‐cadherin. In addition, lncGALM bound to IL‐1β mRNA and stabilized the IL‐1β gene that mediates liver sinusoidal endothelial cell (LSECs) apoptosis. lncGALM‐expressing LiM2‐NOZ cells acquired a strong ability to migrate and adhere to LSECs, promoting LSECs apoptosis and therefore facilitating tumor cell extravasation and dissemination. Conclusions lncGALM promotes GBC liver metastasis by facilitating GBC cell migration, invasion, liver arrest, and extravasation via the invasion‐metastasis cascade. Targeting lncGALM may be protective against the development of liver metastasis in GBC patients.
Coming to Terms with Your Choices: An Existential Take on Dependent Types

Type-level programming is an increasingly popular way to obtain additional type safety. Unfortunately, it remains a second-class citizen in the majority of industrially-used programming languages. We propose a new dependently-typed system with subtyping and singleton types whose goal is to enable type-level programming in an accessible style. At the heart of our system lies a non-deterministic choice operator. We argue that embracing non-determinism is crucial for bringing dependent types to a broader audience of programmers, since real-world programs will inevitably interact with imprecisely-typed, or even impure code. Furthermore, we show that singleton types combined with the choice operator can serve as a replacement for many type functions of interest in practice. We establish the soundness of our approach using the Coq proof assistant. Our soundness approach models non-determinism using additional function arguments to represent choices. We represent type-level computation using singleton types and existential types that quantify over choice arguments. To demonstrate the practicality of our type system, we present an implementation as a modification of the Scala compiler. We provide a case study in which we develop a strongly-typed wrapper for Spark datasets.
First-principles based analysis of thermal transport in metallic nanostructures: Size effect and Wiedemann-Franz law

Metallic nanostructures (the nanofilms and nanowires) are widely used in electronic devices, and their thermal transport properties are crucial for heat dissipation. However, there are still gaps in understanding thermal transport in metallic nanostructures, especially regarding the size effect and validity of the Wiedemann-Franz law. In this work, we perform mode-by-mode first-principles calculations combining the Boltzmann transport equation to understand thermal transport in metallic nanostructures. We take the gold (Au) and tungsten (W) nanostructures as prototypes. It is found that when the size of nanostructures is on the order of several tens of nanometers, the electronic/phonon thermal conductivity is smaller than the bulk value and decreases with size. The phonon contribution increases in nanostructures for those metals with small bulk phonon thermal conductivity (like Au), while the phonon contribution may increase or be suppressed in nanostructures for those metals with large bulk phonon thermal conductivity (like W). By assuming that the grain boundary does not induce inelastic electron-phonon scattering, the Wiedemann-Franz law works well in both Au and W nanostructures if the Lorentz ratio is estimated using electronic thermal conductivity. The Wiedemann-Franz law also works well in Au nanostructures when the Lorentz ratio is estimated by total thermal conductivity.
A Study of Holographic Dark Energy Models with Configuration Entropy

The holographic dark energy models provide an alternative description of dark energy. These models are motivated by the possible application of the holographic principle to the dark energy problem. In this work, we present a theoretical study of the one parameter Li holographic dark energy and the two parameter Barrow holographic dark energy models using configuration entropy of the matter distribution in the universe. The configuration entropy rate exhibits a distinct minimum at a specific scale factor that corresponds to the epoch, beyond which dark energy takes a driving role in the accelerated expansion of the universe. We find that the location of the minimum and magnitude of the entropy rate at the minimum are sensitive to the parameters of the models. We find the best fit relations between these quantities and the parameters of each model. We propose that these relations can be used to constrain the parameters of the holographic dark energy models from future observations such as the SKA. Our study suggests that the signature of a large quantum gravitational effect on the future event horizon can be detected from measurements of the configuration entropy of the matter distribution at multiple redshifts.
Gambler’s Ruin and the ICM

Consider gambler's ruin with three players, 1, 2, and 3, having initial capitals $A$, $B$, and $C$. At each round a pair of players is chosen (uniformly at random) and a fair coin flip is made resulting in the transfer of one unit between these two players. Eventually, one of the players is eliminated and the game continues with the remaining two. Let $\sigma\in S_3$ be the elimination order (e.g., $\sigma=132$ means player 1 is eliminated first, player 3 is eliminated second, and player 2 is left with $A+B+C$). We seek approximations (and exact formulas) for the probabilities $P_{A,B,C}(\sigma)$. One frequently used approximation, the independent chip model (ICM), is shown to be inadequate. A regression adjustment is proposed, which seems to give good approximations to the players' elimination order probabilities.
SIR Model with Stochastic Transmission

The Susceptible-Infected-Recovered (SIR) model is the cornerstone of epidemiological models. However, this specification depends on two parameters only, which implies a lack of flexibility and the difficulty to replicate the volatile reproduction numbers observed in practice. We extend the classic SIR model by introducing nonlinear stochastic transmission, to get a stochastic SIR model. We derive its exact solution and discuss the condition for herd immunity. The stochastic SIR model corresponds to a population of infinite size. When the population size is finite, there is also sampling uncertainty. We propose a state-space framework under which we analyze the relative magnitudes of the observational and stochastic epidemiological uncertainties during the evolution of the epidemic. We also emphasize the lack of robustness of the notion of herd immunity when the SIR model is time discretized.
Online Monitoring of Object Detection Performance Post-Deployment

Post-deployment, an object detector is expected to operate at a similar level of performance that was reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector's performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.
Orbital gyrotropic magnetoelectric effect and its strain engineering in monolayer 
NbX2

Electrical control of the orbital degrees of freedom is an important area of research in the emerging field of "orbitronics. " Orbital {\it gyrotropic} magneto-electric effect (OGME) is the generation of an orbital magnetization in a nonmagnetic metal by an applied electric field. Here, we show that strain induces a large GME in the monolayer Nb$X_2$ ($X =$ S, Se) normal to the plane, primarily driven by the orbital moments of the Bloch bands as opposed to the conventional spin magnetization, without any need for spin-orbit coupling. The key physics is captured within an effective two-band valley-orbital model and it is shown to be driven by three key ingredients: the intrinsic valley orbital moment, broken $C_{3z}$ symmetry, and strain-induced Fermi surface changes. The effect can be furthermore switched by changing the strain condition, with potential for future device applications.
Predicting Disaggregated CPI Inflation Components via Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused mainly on predicting the inflation headline, many economic and financial entities are more interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model that utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Our evaluations, based on a large data-set from the US CPI-U index, indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines.
Pre-training Text-to-Text Transformers for Concept-centric Common Sense

Pre-trained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational commonsense knowledge about everyday concepts, which is crucial to many downstream tasks that need common sense to understand or generate. To augment PTLMs with concept-centric commonsense knowledge, in this paper, we propose both generative and contrastive objectives for learning common sense from the text, and use them as intermediate self-supervised learning tasks for incrementally pre-training PTLMs (before task-specific fine-tuning on downstream datasets). Furthermore, we develop a joint pre-training framework to unify generative and contrastive objectives so that they can mutually reinforce each other. Extensive experimental results show that our method, concept-aware language model (CALM), can pack more commonsense knowledge into the parameters of a pre-trained text-to-text transformer without relying on external knowledge graphs, yielding better performance on both NLU and NLG tasks. We show that while only incrementally pre-trained on a relatively small corpus for a few steps, CALM outperforms baseline methods by a consistent margin and even comparable with some larger PTLMs, which suggests that CALM can serve as a general, plug-and-play method for improving the commonsense reasoning ability of a PTLM.
Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

Deep reinforcement learning has been one of the fastest growing fields of machine learning over the past years and numerous libraries have been open sourced to support research. However, most codebases have a steep learning curve or limited flexibility that do not satisfy a need for fast prototyping in fundamental research. This paper introduces Tonic, a Python library allowing researchers to quickly implement new ideas and measure their importance by providing: 1) a collection of configurable modules such as exploration strategies, replays, neural networks, and updaters 2) a collection of baseline agents: A2C, TRPO, PPO, MPO, DDPG, D4PG, TD3 and SAC built with these modules 3) support for the two most popular deep learning frameworks: TensorFlow 2 and PyTorch 4) support for the three most popular sets of continuous-control environments: OpenAI Gym, DeepMind Control Suite and PyBullet 5) a large-scale benchmark of the baseline agents on 70 continuous-control tasks 6) scripts to experiment in a reproducible way, plot results, and play with trained agents.