Miracle-dz/UniFAR-FTUs huggingface dataset:
The critical‐item, upper bounds, and a branch‐and‐bound algorithm for the tree knapsack problem

The tree knapsack problem (TKP) is a generalized 0–1 knapsack problem where all the items (nodes) are subjected to a partial ordering represented by a rooted tree. If a node is selected to be packed into the knapsack, then all the items on the path from the selected node to the root must also be packed. In this paper, we first define the so-called critical-item for the TKP and present an algorithm to find it. Then, we develop several upper bounds for the TKP. Finally, we present a branch-and-bound procedure for solving the TKP. Our computational results might suggest that our code is currently the most efficient procedure available in the literature. © 1998 John Wiley & Sons, Inc. Networks 31: 205–216, 1998
Classification of cardiac abnormality based on BCG signal using 1-D convolutional neural network

This paper presents a ballistocardiogram (BCG) classification method based on 1-D convolution neural networks (CNNs), which provides an auxiliary basis for clinical diagnosis, especially in the monitoring of cardiac function in the elderly. Four categories of BCG signals are used as input of 1-D CNN. By moving the convolution kernel on the time axis, the temporal variability of BCG signals can be better satisfied while retaining the frequency band correlation. Then they were sent to the multi-layer sensor, after that the features processed by softmax classifier were classified. We obtained an accuracy of 93.39%, of which the H class is 95.36%, the C class is 86.19%, the D class is 95.31%, and the Y class is 96.57%. Compared with the existing research results, the proposed method achieves a superior classification performance. This method is simple, fast and highly generic, which can serve as a reliable adjunct tool for the daily diagnosis of heart disease in the elderly.
Price movement prediction using deep learning: a case study of the China futures market

Financial time series prediction has always been a tricky problem due to the uncertainty in the market. It has attracted attention from industry to academia. In recent years, deep learning has shown excellent performance in many different fields. More and more researchers try to apply deep learning on financial markets. In this paper, the complete modeling process of price movement prediction is introduced. Based on high frequency data Limit Order Books, an improved deep learning model combining the local feature extraction ability of Convolutional Neural Network (CNN) with the sequential feature extraction ability of Long Short-Term Memory (LSTM) is proposed and evaluated on RB dominant contracts in the China futures market. Based on the experimental results, it is concluded that our model’s performance on prediction is better than that of single CNN and LSTM models. Through back testing, trading based on the predicted results of the proposed model can yield significantly more returns than other models.
Research on stock prediction based on simulated annealing algorithm and ensemble neural learning

By combining the relevant theoretical knowledge of simulated annealing algorithm, the parameters of the XGBoost model are optimized, and the LSTM-SA-XGBoost stock prediction model is designed. In order to verify the prediction performance of the combined model, the combined model and the single model are respectively evaluated by the prediction model performance evaluation indicators: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Accuracy rate (Accuracy) and the f1-score is analyzed and evaluated to verify that the two combined models proposed in this paper have good approximation ability and generalization ability in the prediction of stock fluctuations, and can improve the prediction performance of a single LSTM model or a single RNN model. The prediction performance of stock price rise and fall is verified, and the hybrid model based on LSTM neural network proposed in this paper has certain feasibility and stability in the prediction of stock price rise and fall.
Studying the Communication Possibilities Between a Collaborative Robot and an Industrial Computer

Abstract This project is based on the study of collaborative robots (hereafter: Cobot) and industrial PCs, including the communication possibilities between them. In the course of the project, a number of possibilities were studied in order to solve the problem and then summarised to select a collaborative robot and an industrial PC. After that, the focus of the project was on the communication options between the two systems: communication via I/O ports and net-work communication (TCP/IP, Modbus/TCP). Finally, the possible solutions were analysed from different points of view (complexity, flexibility, technical aspects, etc.) and communication between the chosen devices was implemented. The implementation was mainly carried out in a simulation environment, but as the project progressed a communication solution was tested on real industrial devices.
Latent representation of single-cell transcriptomes enables algebraic operations on cellular phenotypes

Robust characterization of cellular phenotypes from single-cell gene expression data is of paramount importance in understanding complex biological systems and diseases. Single-cell RNA-seq (scRNA-seq) datasets are inherently noisy due to small amounts of starting RNA. Over the last few years, several methods have been developed to make single-cell analysis fast and efficient. Most of these methods are based on statistical and machine learning principles. In the current work, we describe SCellBOW, which encodes single-cell expression vectors as documents, thereby enabling the application of powerful language models. Beyond the identification of robust cell type clusters, our algorithm provides a latent representation of single-cells in a manner that captures the 'semantics' associated with cellular phenotypes. These representations, aka embeddings, allow algebraic operations such as '+' and '-'. We use this hitherto unexplored utility to stratify cancer clones in terms of their aggressiveness and contribution to disease prognosis. Further, the application of SCellBOW to a scRNA-seq dataset comprising human splenocytes and matched peripheral blood mononuclear cells (~5000 cells) identifies unknown cell states that bear significance in advancing our understanding of spleen biology.
Desmosomal cell cohesion and epidermal differentiation are modulated by dolichol phosphate mannosyltransferase 1 (DPM1) through SERPINB5-dependent mechanisms

: Glycosylation is an essential mediator of cell-cell adhesion and epidermal differentiation. We used CRISPR/Cas9-based gene editing to determine the role of dolichol phosphate mannosyltransferase 1 (DPM1), a key enzyme for N- and O-glycosylation. DPM1 loss resulted in weakening of cell-cell adhesion, impaired localization of the desmosome components desmoplakin and desmoglein 2, and cytoskeletal organization defects in human keratinocytes. In a 3D organotypic human epidermis model, loss of DPM1 resulted in impaired differentiation with abnormally increased cornification, reduced thickness of non-corneal layers, and the formation of intercellular gaps in the epidermis. Using proteomic approaches, SERPINB5 was identified as novel interaction partner of desmoplakin, ameliorating the effects of DPM1 loss on cell-cell adhesion and epidermal differentiation. Further analysis showed that the changes induced by DPM1 and SERPINB5 loss were at least in part dependent on elevated TGF- β signalling. Together , we identify DPM1 through SERPINB5 as a novel regulator of cell-cell adhesion and differentiation.
Homotopy type theory as internal languages of diagrams of ∞-logoses

We show that certain diagrams of $\infty$-logoses are reconstructed in internal languages of their oplax limits via lex, accessible modalities, which enables us to use plain homotopy type theory to reason about not only a single $\infty$-logos but also a diagram of $\infty$-logoses. This also provides a higher dimensional version of Sterling's synthetic Tait computability -- a type theory for higher dimensional logical relations. To prove the main result, we establish a precise correspondence between the lex, accessible localizations of an $\infty$-logos and the lex, accessible modalities in the internal language of the $\infty$-logos. To do this, we also partly develop the Kripke-Joyal semantics of homotopy type theory in $\infty$-logoses.
Social context modulates multibrain broadband dynamics and functional brain-to-brain coupling in the group of mice

Although mice are social animals, studies that explore the simultaneously recorded neural activities of multiple mice, especially in a social setting, are still lacking. In this study, we simultaneously recorded local field potential (LFP) signals in the dorsomedial prefrontal cortex (dmPFC) from up to four mice. The brain activities of the mice were measured in two contradicting conditions - freely interacting in a group or being individually separated. We found that social context and the locomotive states predominately modulate the entire LFP structure. Power spectral density (PSD) estimate and spectrogram of LFP signals showed a broadband modulation; lower frequency bands—delta (<4Hz), theta (4-7Hz), and alpha (8-12Hz) power were highly correlated to each other and anti-correlated with gamma and high gamma (>30Hz) power. We calculated the high-to-low-power ratio (HLR) and found that HLR was higher when the mice were in a group than were separated. The HLR was also higher when they were active—whether or not they were moving. The mice in the group showed higher HLR in any locomotive states. We then analyzed whether social context can be divided into sub-contexts. Notably, the aggregation of animals, called huddling, decreased social context-induced increase in HLR. Multibrain analyses of HLR indicated that the mice in the group displayed high cross-correlation to each other, indicating interbrain synchrony. Then we examined whether there is any directional relationship between HLR from pairs of mice. A majority of dyad selected within the group of mice showed unilateral precedence of HLR by Granger causality analysis, comprising a hierarchical social structure based on a directionality of influence. Overall, this study shows the importance of the social environment in brain dynamics and emphasizes the value of simultaneous multibrain recording for researching social behaviors and their neural correlates. One-sentence summary Coexistence modulates overall brain activities with unilateral causal relationship.
The deubiquitinase Ubp3/Usp10 constrains glucose-mediated mitochondrial repression via phosphate budgeting

Many cells in high glucose repress mitochondrial respiration, as observed in the Crabtree and Warburg effects. Our understanding of biochemical constraints for mitochondrial activation is limited. Using a Saccharomyces cerevisiae screen, we identified the conserved deubiquitinase Ubp3 (Usp10), as necessary for mitochondrial repression. Ubp3 mutants have increased mitochondrial activity despite abundant glucose, along with decreased glycolytic enzymes, and a rewired glucose metabolic network with increased trehalose production. Utilizing Δubp3 cells, along with orthogonal approaches, we establish that the high glycolytic flux in glucose continuously consumes free Pi. This restricts mitochondrial access to inorganic phosphate (Pi), and prevents mitochondrial activation. Contrastingly, rewired glucose metabolism with enhanced trehalose production and reduced GAPDH (as in Δubp3 cells) restores Pi. This collectively results in increased mitochondrial Pi and derepression, while restricting mitochondrial Pi transport prevents activation. We therefore suggest that glycolytic-flux dependent intracellular Pi budgeting is a key constraint for mitochondrial repression.
Metabolic Engineering Of Lactococcus Lactis For The Production Of Heparosan

Heparosan is a precursor molecule for the widely used anticoagulant heparin, which also has other uses such as certain drug delivery applications and as a scaffold for tissue engineering in biomaterials. Traditionally, pathogenic bacteria such as E.Coli have been used as a host to produce heparosan as an alternative to animal and chemoenzymatic synthesis. Using GRAS status organisms like Lactococcus Lactis as the host for production of heparosan provides a safe alternative as well as being a well-established organism for genetic manipulation and reengineering. In this study, a functional heparosan synthesis pathway was successfully expressed in Lactococcus Lactis by the expression of E.coli K5 genes KfiA and KfiC, along with the overexpression of ugd, glmu and pgma genes present natively in the host organism. The genes were activated using the tightly controlled NICE expression system. The genes were cloned into plasmid p8148 and transformed into two strains, Lactococcus Lactis NZ9000 and Lactococcus Lactis NZ9020, totaling six different recombinant strains were created using these two hosts and various combinations of the heterologous genes. The recombinant Lactococcus Lactis SH6 strain, expressing the genes ugd-KfiA-KfiC-pgma yielded a maximum concentration of 754 mg/l in batch bioreactor experiments and the titer was increased to 1263 mg/l in fed-batch fermentation. NMR imaging successfully determined that the structure of the product derived from Lactococcus Lactis was indeed similar to E.coli heparosan. The molecular weight of heparosan varied from 10-20 KDa, indicating its potential use for chemoenzymatic heparin biosynthesis.
Unitary response regression models

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with this constant in place of a numerical or binary response. In a linear model with a positive response, dividing by its values yields a regression of constant output by the relative shares of individual predictors into the total response. Chemical reaction models use the agents' concentration, summing to a constant 100%. Another example can be found in priority modelling by Thurstone scaling for ranked or paired comparison data. The Thurstone scale can be estimated by probit or logit models with identical output across all the responses. Models with a unitary output can be constructed by software for regular regressions, but they give a different interpretation of results. For instance, the coefficient of multiple determination is not an estimate of the explained variance in the total response variance (which is zero), but a measure of the fitting quality of the constant approximated by an aggregate of predictors.
Evolutionary divergence of basal and activity-dependent exon splicing in cortical neurons

Alternative splicing of mRNA exons in mammalian neurons increases diversity of the proteome and is regulatable by signaling pathways. However, the degree of conservation of basal and signal-dependent exon usage between human neurons and those from experimental models such as mice is incompletely understood. We previously showed that cortical neuronal activity-dependent gene transcription exhibits human/mouse differences, driven by evolutionary divergence of cis-acting promoter elements (Qiu et al. 2016). Since alternative exon usage influences brain development and cognition, is controlled by neuronal activity, and is disturbed in brain disorders, we investigated human/mouse differences in exon usage in cortical neurons. Comparing orthologous exons, basal exon inclusion levels showed human-mouse conservation, but also significant differences determined by cis-acting sequences: human-mouse conservation and divergence in exon usage was recapitulated in neurons from Tc1 mice carrying human chromosome-21 (hCh21). Activity-dependent changes in exon usage also exhibited significant conservation: gene structure was more likely to be conserved in activity-regulated exons, and exons regulated in both human and mouse neurons were enriched in RBFOX and SAM68 targets, and genes were centred on cytoskeletal organisation, mRNA transcription/processing, and synaptic signaling. However, divergence was also evident, and human-specific activity-dependent exon usage was dominated by genes involved in lipid biosynthesis, signaling and trafficking. Notably, the pattern of activity-dependent usage of hCh21 exons in human neurons was not recapitulated in mouse Tc1 neurons. Thus, unlike species-specific differences in activity-dependent gene transcription, cis-acting DNA sequence divergence is insufficient to explain inter-species differences in activity-regulated exon usage. Trans-acting factors involved in activity-responsive splicing have likely also diverged.
Immunogenicity of COVID-eVax Is Moderately Impacted by Temperature and Molecular Isoforms

DNA integrity is a key issue in gene therapy and genetic vaccine approaches based on plasmid DNA. In contrast to messenger RNA that requires a controlled cold chain for efficacy, DNA molecules are considered to be more stable. In this study, we challenged this concept by characterizing the immunological response induced by a plasmid DNA vaccine delivered using electroporation. As a model, we used COVID-eVax, which is a plasmid DNA vaccine that targets the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Increased nicked DNA was produced by using either an accelerated stability protocol or a lyophilization protocol. Surprisingly, the immune response induced in vivo was only minimally affected by the percentage of open circular DNA. This result suggests that plasmid DNA vaccines, such as COVID-eVax that has completed a phase I clinical trial, retain their efficacy upon storage at higher temperatures and this feature may facilitate their use in low-/middle-income countries.
Electrophysiological Activity of Primary Cortical Neuron-Glia Mixed Cultures

Background We have previously described a tri-culture of neurons, astrocytes, and microglia that accurately mimics the in vivo neuroinflammatory response (both neurotoxic and neuroprotective) to a wide range of neuroinflammatory stimuli. Electrophysiological activity is an essential measure of neuronal function, which is influenced by neuroinflammation. Microelectrode array (MEA) technology is a versatile tool to non-invasively study in vitro neural networks by simultaneously recording electrophysiological activity from multiple sites. In this study, we used extracellular recordings to determine the effect of microglia on neural network formation and stability in primary cortical cultures and monitor the changes in neural activity in response to neuroinflammatory stimuli. Methods Primary neonatal rat cortical tri-cultures of neurons, astrocytes, and microglia or co-cultures of neurons and astrocytes were cultured on custom MEAs and the neural activity was monitored for 21 days in vitro to assess culture maturation and network formation. Quantification of synaptic puncta and averaged spike waveforms were used to determine the difference in excitatory to inhibitory neuron ratio (E/I ratio) of the neurons in tri- and co-cultures. The electrophysiological response to lipopolysaccharide (LPS) treatment of both culture types were compared. Results The tri- and co-culture showed minimal difference in electrophysiological markers of neural network formation and stability with the exception of a significant increase in spike frequency in the tri-culture at later timepoints (DIV 17 and 21). Additionally, there was no significant difference in the density of either post-synaptic or excitatory pre-synaptic puncta between the culture types. However, characterization of the average spike waveforms revealed that the tri-culture had an E/I ratio much closer to that found in the rat cortex. Finally, only the tri-culture displayed a significant decrease in both the number of active channels and spike frequency following LPS exposure. Conclusions This study demonstrates that the microglia in the tri-culture do not disrupt neural network formation and stability as quantified using extracellular recordings and may be a better representation of the in vivo cortex due to the closer E/I ratio than more traditional isolated neuron and neuron-astrocyte co-cultures. Additionally, the tri-culture is better able to mimic the neuroinflammatory response to LPS, which was quantified via changes in neural electrophysiological activity.
SARS-CoV-2 accessory proteins ORF3a and ORF7a modulate autophagic flux and Ca2+ homeostasis in yeast

Virus infection involves the manipulation of key host cell functions by specialized virulence proteins. The SARS-CoV-2 small accessory proteins ORF3a and ORF7a have been implicated in favoring virus replication and spreading by inhibiting the autophagic flux within the host cell. Here, we apply yeast models to gain insights into the physiological functions of both SARS-CoV-2 small ORFs. ORF3a and ORF7a can be stably overexpressed in yeast cells, producing a decrease in cellular fitness. Both proteins show a distinguishable intracellular localization. ORF3a specifically localizes to the vacuolar membrane, whereas ORF7a targets the endoplasmic reticulum. Overexpression of ORF3a and ORF7a leads to the accumulation of Atg8 specific autophagosomes. However, the underlying mechanism is different for each viral protein as assessed by the quantification of the autophagic degradation of Atg8-GFP fusion proteins, which is inhibited by ORF3a and stimulated by ORF7a. Overexpression of both SARS-CoV-2 ORFs decreases cellular fitness upon starvation conditions, where autophagic processes become essential. These data are in agreement with a model where both small ORFs have synergistic functions in stimulating intracellular autophagosome accumulation, ORF3a by inhibiting autophagosome processing at the vacuole and ORF7a by promoting autophagosome formation at the ER. ORF3a has an additional function in Ca2+ homeostasis. The overexpression of ORF3a confers calcineurin-dependent Ca2+ tolerance and activates a Ca2+ sensitive FKS2-luciferase reporter, suggesting a possible ORF3a-mediated Ca2+ efflux from the vacuole. Taken together, we show that viral accessory proteins can be functionally investigated in yeast cells and that SARS-CoV-2 ORF3a and ORF7a proteins interfere with autophagosome formation and processing as well as with Ca2+ homeostasis from distinct cellular targets.
Chromatin organization drives the exploration strategy of nuclear factors

Nuclear Factors (NFs) rapidly scan the genome for their targets, but the role of nuclear organization in such search is uncharted. Here we analyzed how multiple NFs explore chromatin, by combining live-cell single-molecule tracking with multifocal structured illumination of DNA density. We find that NFs displaying higher bound fractions sample DNA dense regions more exhaustively. Focusing on the tumor-suppressor p53, we demonstrated that this NF search for its targets by alternating between rapid diffusion in the interchromatin compartment and compact sampling of chromatin dense regions. Efficient p53 targeting requires balanced IDR/chromatin interactions: adding an exogenous IDR potentiates p53-mediated target gene activation, but excessive IDR/IDR interactions lead to p53 condensates, derailing its search and downregulating transcription. Our findings highlight the role of NF IDRs on their search and showcase a powerful method to generate traffic maps of the eukaryotic nucleus and dissect how nuclear organization guides NFs action.