Miracle-dz/UniFAR-FTUs huggingface dataset:
Quorum sensing‐based metabolic engineering of the precursor supply in Streptomyces coelicolor to improve heterologous production of neoaureothin

Streptomyces are important industrial bacteria that produce pharmaceutically valuable polyketides. However, mass production on an industrial scale is limited by low productivity, which can be overcome through metabolic engineering and the synthetic biology of the host strain. Recently, the introduction of an auto‐inducible expression system depending on microbial physiological state has been suggested as an important tool for the industrial‐scale production of polyketides. In this study, titer improvement by enhancing the pool of CoA‐derived precursors required for polyketide production was driven in a quorum sensing (QS)‐dependent manner. A self‐sustaining and inducer‐independent regulatory system, named the QS‐based metabolic engineering of precursor pool (QMP) system, was constructed, wherein the expression of genes involved in precursor biosynthesis was regulated by the QS‐responsive promoter, scbAp. The QMP system was applied for neoaureothin production in a heterologous host, Streptomyces coelicolor M1152, and productivity increased by up to 4‐fold. In particular, the engineered hyperproducers produced high levels of neoaureothin without adversely affecting cell growth. Overall, this study showed that self‐regulated metabolic engineering mediated by QS has the potential to engineer strains for polyketide titer improvement.
Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients' quality of life and prolong survival time. In this study, we explore the intelligibility of machine-learning and deep-learning models on end-stage renal disease (ESRD) prediction, based on readily-accessible clinical and laboratory features of patients suffering from CKD. Eight machine learning models were used to predict whether a patient suffering from CKD would progress to ESRD within three years based on demographics, clinical,and comorbidity information. LASSO, random forest, and XGBoost were used to identify the most significant markers. In addition, we introduced four advanced attribution methods to the deep learning model to enhance model intelligibility. The deep learning model achieved an AUC-ROC of 0.8991, which was significantly higher than that of baseline models. The interpretation generated by deep learning with attribution methods, random forest, and XGBoost was consistent with clinical knowledge, whereas LASSO-based interpretation was inconsistent. Hematuria, proteinuria, potassium, urine albumin to creatinine ratio were positively associated with the progression of CKD, while eGFR and urine creatinine were negatively associated. In conclusion, deep learning with attribution algorithms could identify intelligible features of CKD progression. Our model identified a number of critical, but under-reported features, which may be novel markers for CKD progression. This study provides physicians with solid data-driven evidence for using machine learning for CKD clinical management and treatment.
Spiking LCA in a Neural Circuit with Dictionary Learning and Synaptic Normalization

The Locally Competitive Algorithm (LCA) [17, 18] was put forward as a model of primary visual cortex [14, 17] and has been used extensively as a sparse coding algorithm for multivariate data. LCA has seen implementations on neuromorphic processors, including IBM’s TrueNorth processor [10], and Intel’s neuromorphic research processor, Loihi, which show that it can be very efficient with respect to the power resources it consumes [8]. When combined with dictionary learning [13], the LCA algorithm encounters synaptic instability [24], where, as a synapse’s strength grows, its activity increases, further enhancing synaptic strength, leading to a runaway condition, where synapses become saturated [3, 15]. A number of approaches have been suggested to stabilize this phenomenon [1, 2, 5, 7, 12]. Previous work demonstrated that, by extending the cost function used to generate LCA updates, synaptic normalization could be achieved, eliminating synaptic runaway [7]. It was also shown that the resulting algorithm could be implemented in a firing rate model [7]. Here, we implement a probabilistic approximation to this firing rate model as a spiking LCA algorithm that includes dictionary learning and synaptic normalization. The algorithm is based on a synfire-gated synfire chain-based information control network in concert with Hebbian synapses [16, 19]. We show that this algorithm results in correct classification on numeric data taken from the MNIST dataset. LA-UR-22-33004
Speech2Spikes: Efficient Audio Encoding Pipeline for Real-time Neuromorphic Systems

Despite the maturity and availability of speech recognition systems, there are few available spiking speech recognition tasks that can be implemented with current neuromorphic systems. The methods used previously to generate spiking speech data are not capable of encoding speech in real-time or encoding very large modern speech datasets efficiently for input to neuromorphic processors. The ability to efficiently encode audio data to spikes will enable a wider variety of spiking audio datasets to be available and can also enable algorithmic development of real-time neuromorphic automatic speech recognition systems. Therefore, we developed speech2spikes, a simple and efficient audio processing pipeline that encodes recorded audio into spikes and is suitable for real-time operation with low-power neuromorphic processors. To demonstrate the efficacy of our method for audio to spike encoding we show that a small feed-forward spiking neural network trained on data generated with the pipeline achieves accuracy on the Google Speech Commands recognition task, exceeding the state-of-the art set by Spiking Speech Commands, a prior spiking encoding of the Google Speech Commands dataset, by over 10%. We also demonstrate a proof-of-concept real-time neuromorphic automatic speech recognition system using audio encoded with speech2spikes streamed to an Intel Loihi neuromorphic research processor.
Sigma-Delta Networks for Robot Arm Control

Our autonomous robot, Bight, can be a reliable teammate that is capable of assisting in performing routine maintenance tasks on a Naval vessel. In this paper, we consider the task of maintaining the electrical panel. A vital first step is putting the robot into the correct position to view all of the parts of the electrical panel. The robot can get close, but the arm of the robot will need to move to where it can see everything. Here, we propose to solve this using a sigma delta spiking network that is trained using deep Q learning. Our approach is able to successfully solve this problem at varying distances. While we show how this works on this specific problem, we believe this approach to be general enough to be applied to any similar problem.
Additive manufacture of polymeric organometallic ferroelectric diodes (POMFeDs) for structural neuromorphic hardware

Hardware design and implementation for online machine learning applications is complicated by a number of facets of conventional artificial neural networks (ANN), e.g. deep neural networks (DNNs), such as reliance on atemporal locality, offline learning using large datasets, potential difficulties in transfer from model to substrates, and issues with processing of noisy sensory data using energy-efficient and asynchronous information processing modalities. Analog or mixed-signal spiking neural networks (SNNs) have promise for lower power, temporally localised, and stimuli selective sensing and inference but are difficult fabricate at low cost. Investigation of beyond-CMOS alternative organic substrates may be worthwhile for development of unconventional neuromorphic hardware with pseudo-spiking dynamics for structural electronics integration in bio-signal processing and robotics. Here, polymeric organometallic ferroelectric diodes (POMFeDs) are introduced for development of printable ferroelectric in-sensor SNNs.
Cognitive Psychology Based Text Analysis Using Feature Extraction and Classification by Deep Learning Architectures for English Language Analysis

Deep learning can provide English learners an edge and benefit teachers and students. Writing, translating, hearing, and speaking assist English language instruction change. This study proposes a unique deep learning architecture-based cognitive psychology-based educational data training method for English language learning. Student language learning and English teaching data has been collected here. Data is obtained from English-taught language students. Classification and feature extraction are done on this data. This data was extracted using probabilistic Laplacian Score-based Restricted Boltzmann machine. Rough Set Theory-based capsule Net Convolutional neural networks classified these collected features. Educational datasets from students were analysed experimentally for English teaching language learning. Parametric analysis showed Word Perplexity of 90.5%, Flesch-Kincaid (F-K) Grade Level for Readability of 55.6%, Cosine Similarity for Semantic Coherence of 85.4%, gradient change of NN of 46.5%, validation accuracy of 98%, and training accuracy of 96.8 by proposed Pr-Lap Sc RBM-RsT
Improved Wells Method using MRF Radom Field and Non-local Weight

In order to overcome the limitation of noise and intensity inhomogeneity during brain image segmentation, the MRF theory and the nonlocal information are used to construct a nonlocal Markov Random Field and to improve Wells et al. method. With our nonlocal MRF, the improved Wells method is robust to noise and intensity inhomogeneity. The experimental results demonstrate that our method is able to obtain better tissue segmentation results and simultaneously keep the image edge and slender topological structure very well.
COORDINATED PRICING AND INVENTORY CONTROL WITH BATCH PRODUCTION AND ERLANG LEADTIMES

This paper addresses a joint pricing and inventory control problem for a batch production system with random leadtimes. Assume that demand arrives according to a Poisson process with a price-dependent arrival rate. Each replenishment order contains a single batch of a fixed lot size. The replenishment leadtime follows an Erlang distribution, with the number of completed phases recording the delivery state of outstanding orders. The objective is to determine an optimal inventory-pricing policy that maximizes total expected discounted profit or long-run average profit. We first show that when there is at most one order outstanding at any point in time and that excess demand is lost, the optimal reorder policy can be characterized by a critical stock level and the optimal pricing decision is decreasing in the inventory level and delivery state. We then extend the analysis to mixed-Erlang leadtime distribution which can be used to approximate any random leadtime to any degree of accuracy. We further extend the analysis to allowing three outstanding orders where the optimal reorder point becomes state-dependent: the closer an outstanding order is to its arrival or the more orders are outstanding, the lower selling price is charged and the lower reorder point is chosen. Finally, we address the backlog case and show that the monotone pricing structure may not be true when the optimal reorder point is negative.
Hypnotizability in the Clinic, Viewed from the Laboratory

ABSTRACT A recent international survey discovered that clinicians who use hypnosis in their practice rarely assess the hypnotizability of their patients or clients. This contrasts sharply with the practice in laboratory research. One reason offered for this discrepancy is that hypnotizability does not strongly predict clinical outcome. But a comparison of this relationship with similar correlations in other domains shows that this criticism is misleading – especially when the treatment capitalizes on the alterations in perception, memory, and voluntary control that characterize the domain of hypnosis. Routine assessment of hypnotizability improves clinical practice by enabling clinicians to select patients for whom hypnosis is appropriate; and it improves clinical research by providing important information about the mechanisms underlying hypnotic effects.
Observations of type II plateau supernova: SNe 1988A, 1988H and 1989C

We present new photometric and spectroscopic observations of three type II supernovae with plateau light curves: SNe 1988A, 1988H and 1989C. From the new photometry of SN 1988A, we do not confirm the previous claim of a departure of the late-time light curve from 56 Co radioactive decay and thus have no indication of the emergence of an additional source of energy. The early spectral evolution of SN 1988A seems rather normal, but for the appearance of a blue emission-like feature on the Hα profile resembling a similar feature detected in SN 1987A. As in that supernova, this may be a manifestation of the appearance of radioactive 56 Co above the photosphere
Glial Draper signaling triggers cross-neuron plasticity in bystander neurons after neuronal cell death

Neuronal cell death and subsequent brain dysfunction are hallmarks of aging and neurodegeneration, but how the nearby healthy neurons (bystanders) respond to the cell death of their neighbors is not fully understood. In the Drosophila larval neuromuscular system, bystander motor neurons can structurally and functionally compensate for the loss of their neighbors by increasing their axon terminal size and activity. We termed this compensation as cross-neuron plasticity, and in this study, we demonstrated that the Drosophila engulfment receptor, Draper, and the associated kinase, Shark, are required in glial cells. Surprisingly, overexpression of the Draper-I isoform boosts cross-neuron plasticity, implying that the strength of plasticity correlates with Draper signaling. Synaptic plasticity normally declines as animals age, but in our system, functional cross-neuron plasticity can be induced at different time points, whereas structural cross-neuron plasticity can only be induced at early stages. Our work uncovers a novel role for glial Draper signaling in cross-neuron plasticity that may enhance nervous system function during neurodegeneration and provides insights into how healthy bystander neurons respond to the loss of their neighboring neurons.
Word embedding-based techniques for text clustering and topic modelling with application in the healthcare domain

In the field of text analytics, document clustering and topic modelling are two widelyused tools for many applications. Document clustering aims to automatically organize similar documents into groups, which is crucial for document organization, browsing, summarization, classification and retrieval. Topic modelling refers to unsupervised models that automatically discover the main topics of a collection of documents. In topic modelling, the topics are simply represented as probability distributions over the words in the collection (the different probabilities reveal what topic is at stake). In turn, each document is represented as a distribution over the topics. Such distributions can also be seen as low-dimensional representations of the documents that can be used for information retrieval, document summarization and classification. Document clustering and topic modelling are highly correlated and can mutually benefit from each other. Many document clustering algorithms exist, including the classic k-means. In this thesis, we have developed three new algorithms: 1) a maximum-margin clustering approach which was originally proposed for general data, but can also suit text clustering, 2) a modified global k-means algorithm for text clustering which is able to improve the local minima and find a deeper local solution for clustering document collections in a limited amount of time, and 3) a taxonomy-augmented algorithm which addresses two main drawbacks of the so-called “bag-of-words” (BoW) models, namely, the curse of dimensionality and the dismissal of word ordering. Our main emphasis is on high accuracy and effectiveness within the bounds of limited memory consumption. Although great effort has been devoted to topic modelling to date, a limitation of many topic models such as latent Dirichlet allocation is that they do not take the words’ relations explicitly into account. Our contribution has been two-fold. We have developed a topic
Title: Toleration of Frameshift Mutations in mRNA Sequences Encoding the N-terminal Peptides of Bacterial Type III Effectors

Gram-negative bacteria deliver effector proteins into eukaryotic host cells through type III and type IV secretion systems, causing infections and diseases. It remains unclear about the signals guiding the specific secretion of the effectors. Here, we adopted an in silico approach to analyze the mRNA sequences encoding the putative peptides essential for effective secretion and translocation of type III and IV effectors. A surprisingly high proportion of type III effectors showed tolerance on frameshift mutations in signal-encoding mRNA sequences, and in contrast, very low percentage of type IV effectors showed the similar frameshift tolerance. The type III effectors with frameshift tolerance of secretion signals were widely distributed in effector or signal families and bacterial species. Natural frameshifts could be identified in type III effector genes, which were often remedied in time by nearby paired insertions or deletions. Frameshift-derived peptide sequences also retained the common properties present in the signal peptides of raw type III effectors. Natural language processing models were adopted to represent the common features in the mRNA sequences encoding N-terminal peptides of type III effectors or C-terminal peptides of type IV effectors, with which transfer learning models could well predict the effectors, especially type IV effectors. The observations in the study would facilitate us understand the nature and evolution of secretion signals of type III and IV effectors. Significance It has been a debate on the nature of signals for translocation of type III secreted effectors for a long time. Meanwhile, there has been no examination on the possibility of mRNA being as translocation signals for type IV or other types of secreted effectors. By computational simulation, the study demonstrated the protein nature of translocation signals for both type IV effectors and most type III effectors. Despite wide frameshift tolerance and atypical common features in mRNA sequences encoding the putative N-terminal signal sequences of type III effectors, more typical common physicochemical and amino acid composition properties between the mutation-derived and raw peptides, and the frequent self-correction phenomenon for naturally happening frameshifts supported the translocation signals at protein level of type III effectors. The common features in mRNA sequences encoding the translocation signal peptides of type III and IV effectors could also be combined in models for better prediction of the effectors respectively.
Rewiring of RNA methylation by the oncometabolite fumarate in renal cell carcinoma

Metabolic reprogramming is a hallmark of cancer that facilitates changes in many adaptive biological processes. Mutations in the tricarboxylic acid (TCA) cycle enzyme fumarate hydratase (FH) lead to fumarate accumulation and cause hereditary leiomyomatosis and renal cell cancer (HLRCC). HLRCC is a rare, inherited disease characterized by the development of non-cancerous smooth muscle tumors of the uterus and skin, and an increased risk of a highly metastatic and aggressive form of kidney cancer. Fumarate has been shown to inhibit 2-oxyglutarate-dependent dioxygenases (2OGDDs) involved in the hydroxylation of HIF1α, as well as in DNA and histone demethylation. However, the link between fumarate accumulation and changes in RNA post-transcriptional modifications has not been defined. Here, we determine the consequences of fumarate accumulation on the activity of different members of the 2OGDD family targeting RNA modifications. By evaluating multiple RNA modifications in patient-derived HLRCC cell lines, we show that mutation of FH selectively alters the activity of demethylases acting upon N6-methyladenosine (m6A), while the demethylase acting upon N1-methyladenosine (m1A) and 5-formylcytosine (f5C) in mitochondrial RNA are unaffected. The observation that metabolites modulate specific subsets of RNA-modifying enzymes offers new insights into the intersection between metabolism and the epitranscriptome.
Penetration response of ultra-high performance concrete (UHPC) under projectile impact

This paper presents a numerical study in evaluating impact response of cylindrical ultrahigh performance concrete (UHPC) targets under projectile impact with broad striking velocities from 300 m/s to 1000 m/s. Steel ogive-nosed projectiles with a mass of 360 g are launched to penetrate UHPC targets with 750 mm diameter and 1000 mm length. The Karagozian & Case (K&C) cementitious concrete model, namely, *MAT_CONCRETE_DAMAGE_REL3 (Mat_72R3), is implemented into finite element package LS-DYNA to build UHPC targets. In order to accurately predict the depth of penetration (DOP) and cratering damage of UHPC targets, uniaxial compressive and four-point bending testing results are used to validate 3D finite element material model. With the validated numerical model incorporating dynamic increase factors (DIF) of UHPC, parametric studies are conducted to investigate effects of UHPC compressive strength, projectile striking velocity and projectile Calibre Radius Head (CRH) ratio on both DOP and cratering damage of UHPC targets. Moreover, an empirical formula to predict DOP is derived according to the simulated data.
Clathrin Light Chains are essential in negative regulation of cell death and immunity in Arabidopsis through interacting with autophagy pathway

Clathrin plays a critical role in clathrin-mediated endocytosis (CME) in plants, and it is required for autophagy in mammals. However, the functional interconnection of clathrin with autophagy has not been firmly established in plants. Here, we demonstrate that loss of function of clathrin light chain (CLC) subunit 2 and 3 results in salicylic acid (SA)- and H2O2-dependent accelerated senescence and activated defense responses in Arabidopsis, which are hallmarks of the autophagy-related gene (ATG) mutants. Similar to atg mutants, the clc2-1clc3-1 double mutant has enhanced sensitivity to both carbon and nitrogen starvation and enhanced resistance to biotrophic bacterial and fungal pathogens. In addition, the autophagy flux was significantly reduced in the roots of clc2-1clc3-1 mutant plants relative to Col-0 plants under carbon starvation conditions. Furthermore, our Yeast-2-hybrid (Y2H) and Luciferase complementation assays showed that CLC2 directly interacted with ATG8h and ATG8i. Mutations within the unique ATG8-interacting motif (AIM) of CLC2 as well as at the LIR/AIM-docking site (LDS) of ATG8h abolished the interaction between CLC2 and ATG8h. As anticipated, both GFP-ATG8h/GFP-ATG8i and CLC2 were subjected to autophagic degradation in the vacuoles. Together, our data revealed that the accelerated senescence and activated immune responses observed in Arabidopsis clc2-1clc3-1 mutant plants result from impaired autophagy, and CLC2 participates in autophagy through direct interactions with ATG8h and ATG8i in an AIM1- and LDS-dependent manner. Our results unveil a previously unidentified link between the function of CLCs and autophagy.