Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Differentially Expressed Gene Modules in Heterogeneous Diseases

Motivation Identification of differentially expressed genes is necessary for unraveling disease pathogenesis. This task is complicated by the fact that many diseases are heterogeneous at the molecular level and samples representing distinct disease subtypes may demonstrate different patterns of dysregulation. Biclustering methods are capable of identifying genes that follow a similar expression pattern only in a subset of samples and hence can consider disease heterogeneity. However, identifying biologically significant and reproducible sets of genes and samples remains challenging for the existing tools. Many recent studies have shown that the integration of gene expression and protein interaction data improves the robustness of prediction and classification and advances biomarker discovery. Results Here we present DESMOND, a new method for identification of Differentially ExpreSsed gene MOdules iN Diseases. DESMOND performs network-constrained biclustering on gene expression data and identifies gene modules — connected sets of genes up- or down-regulated in subsets of samples. We applied DESMOND on expression profiles of samples from two large breast cancer cohorts and have shown that the capability of DESMOND to incorporate protein interactions allows identifying the biologically meaningful gene and sample subsets and improves the reproducibility of the results. Availability https://github.com/ozolotareva/DESMOND Contact ozolotareva@techfak.uni-bielefeld.de Supplementary information Supplementary data are available at Bioinformatics online.
Non-Isolated Dual Switch Based High Gain DC-DC Converter

This paper proposes a novel non-isolated dual switch based high gain dc-dc converter. This converter achieves high gain at a low duty cycle which results in low conduction loss and low voltage stress across diodes and switches. Transportation, photovoltaic and medical appliances are few prospective applications of high gain converter. The steady state analysis and passive elements design of this converter are included in this paper. A comparative evaluation of the converter is performed to explore its potential over other counterparts. To verify the theoretical concept, the simulation of proposed converter is performed.
CNN With Large Data Achieves True Zero-Training in Online P300 Brain-Computer Interface

Each person has his or her own distinct event-related potential (ERP) signals. Thus, traditional brain-computer interface (BCI) systems require a calibration process in which the subject’s data are extracted in order to train machine-learning classifiers. Despite past efforts to eliminate this process, often referred to as “zero-training,” BCI systems’ best performance is achievable only with some level of calibration. This tedious process is one of the factors that have limited the use of BCI systems in the real world. Meanwhile, convolutional neural networks (CNN) have been proven to be useful in distinguishing neurophysiological features. In this study, we investigated whether an existing convolutional neural network (CNN) combined with large ERP samples (n = 99,000) can achieve zero-training in a P300 BCI speller system. As a result, the zero-trained CNN achieved comparable performance (89%, p < 0.05) when compared to traditional approaches (94%) in an offline study. We also demonstrate that the constructed CNN works successfully in an online experiment in which twelve BCI subjects achieved 85% mean accuracy without calibration, compared to 82% (but the difference was not significant, p > 0.05) with calibration. Additionally, we illustrate a hybrid approach in order to further enhance performance, which adaptively updates a linear classifier using label information generated from a zero-trained CNN. With this technique, the hybrid approach achieved reasonable performance (92%), showing no statistical difference (p > 0.05) when compared to the traditional approach in the same offline data.
Survey of estrogenic activity in United Kingdom estuarine and coastal waters and its effects on gonadal development of the flounder Platichthys flesus

Estrogenic discharges are known or suspected to be present in many United Kingdom estuaries and are also occurring from offshore oil and gas installations. The aim of this study was to establish whether estrogens and their mimics are present in marine waters at concentrations that can produce biological responses in fish and, if so, to discover whether the effects are likely to be harmful to populations and communities through changes in reproductive and other physiological processes. Laboratory studies and extensive field surveys were carried out with the euryhaline flounder (Platichthys flesus), using the amount of yolk protein vitellogenin in the blood of free‐living male fish as an indicator of exposure to estrogens. Exposure to the synthetic estrogen ethynylestradiol for 3 weeks confirmed that the vitellogenin response was made and showed that this species is about one order of magnitude less sensitive than the freshwater species rainbow trout. The fish did not respond to the estrogen mimic nonylphenol at concentrations of 30 μg/L. Wild male flounder were sampled from four contaminated United Kingdom estuaries: the Tyne, Crouch, Thames, and Mersey. Fish from one or more sites in each estuary had significantly elevated concentrations of vitellogenin in their blood when compared to concentrations in fish from the clean control estuary (Alde River). Vitellogenin concentrations in the Tyne and Mersey fish were four and six orders of magnitude greater, respectively, than control concentrations. Elevated vitellogenin concentrations were also seen in some male flounder caught in coastal areas. Approximately 20% of male fish from the Mersey estuary contained oocytes in their testes, but this intersex condition was not seen elsewhere. The implications of these findings for fish populations are discussed.
Phylogenetic analysis of bovine pestiviruses: testing the evolution of clinical symptoms

This study presents a phylogenetic analysis of 115 bovine pestiviruses. A sequence data set from the 5′ untranslated genomic region was analyzed with maximum parsimony, bootstrapping and parsimony jackknifing. We tested for the proposed classifications of the group and analyzed the evolution of the symptoms associated with Pestivirus infections in bovines. Based on the historical framework provided by our phylogenetic trees, we also characterized the extent and importance of contamination caused in biologicals by the virus. Our phylogenetic analyses showed that the previously defined genotypes are monophyletic, except for genotype 1a. Based on our cladograms, we propose the existence of more than 12 monophyletic groups within the species BVDV 1. The mapping of clinical symptoms suggests that the emergence of some genotypes could have been driven by a change in the pathogenic process. Enteric Problems appear to be ancestral, while Reproductive and Respiratory Problems arise with the emergence of genotypes 1b, 1d and the herein‐proposed genotype Arg 1. The distribution of contaminant strains on the cladograms shows that pestiviral contamination is a common process, and also suggests that a contaminated product might be a vehicle for virus dispersion. Implications for virus evolution, virus taxonomy, veterinary medicine and biotechnology are discussed.
An Integrated 16-Element Phased-Array Transmitter Front-End for Wireless Communication at 185 GHz

This paper presents a phased-array transmitter front-end for beamsteering wireless communication at 185 GHz with 16 elements in a 4×4 arrangement on a single chip. Integrated in a 130 nm SiGe BiCMOS process, the circuit includes a times-six frequency multiplier providing the LO signal for the mixer. The up-converted signal is divided and distributed to the 16 outputs with Wilkinson dividers, followed by 360-degree phase shifters at each output to provide beamsteering capability. With a total dc power consumption of 661 mW, the transmitter reaches −13 dBm of saturated output power for each element, or −1 dBm of combined power. Centered at 185 GHz, the −3 dB RF bandwidth is 30 GHz. The power-efficient design is consuming a dc power of only 41.3 mW per element. As a result, the presented circuit can be useful for low-power short-range wireless communication links with spatial diversity, for instance in board-to-board data transmission. To the authors' knowledge, this is the first demonstration of a 16-element transmitter front-end at carrier frequencies above 120 GHz.
Ultrastructure of glandular hairs of Sigesbeckia jorullensis Kunth (Asteraceae)

Multicellular long-stalked glandular hairs of the outer involucral bracts of Sigesbeckia jorullensis Kunth were investigated with transmission electron micros-copy. The apical layer of head cells was characterized by many irregular leucoplasts, a well developed and in the secretory phase mostly agranular endoplasmic reticu-lum, few dictyosomes, and one calcium oxalate crystal per cell. The prevalent part of the secretion seems to originate from leucoplasts of apical head cells and is secreted into a subcuticular space after crossing cytoplasm and the plasma membrane. The sheath of this sub-cuticular space was formed exclusively by a thin cuticle, which was detached from the cell wall in the secretory phase. Some small wall protuberances were found between the sub-apical and apical cell layer. Whether the three to six layers of sub-apical head cells, containing many chloroplasts, contribute to the biogenesis of the lipophilic secretion or not, remains unclear. Light microscopy demonstrated that there was a.
A Regulated Electric Vehicle Charging Scheme in Coordination with Utility Pricing and Transformer Loading

Electric Vehicles (EVs) are becoming an integral part of the transportation fleet in many countries. They have substantially progressed to become a mainstream component of the smart electricity grid and are catalyzing changes in policies and regulations relating to power distribution systems. EVs represent distributed storage units whose load depends on the driving patterns and the battery charging characteristics. Random and uncontrolled EV charging has implications on both the network loading and the charging cost since time-based pricing is in effect for many utility companies. A well-managed charging system is thus important and must consider asset loading as well as time-varying tariff. This paper presents a regulated charging scheme that takes into account the charging cost which is reflective of both the transformer loading and the utility-level Time of Day (ToD) tariff. The task of finding a charging schedule that gives the least overall charging cost for the day has been formulated as an optimization problem for an actual distribution utility in India. The driving pattern was estimated based on a survey of EV owners plying in the utility license area. The results demonstrate how such a controlled charging scheme can save on the customers’ money and help the utility manage its load curve by ensuring that the distribution transformers serving the EVs are not overloaded.
An introduction to a model of abstract computation: the BSS-RAM model

We give a detailed deﬁnition of BSS RAM’s for the sequential computation over ﬁrst-order structures. These random access machines are abstract machines with an input procedure and an output procedure. For describing algorithms, all operations and relations of the underlying structure are available at unit cost. Each program of a machine is an element of a formal language whose syntax is deﬁned by syntactic rules. The semantics of any program is dependent on a transition system that results from the interpretation of the symbols in the program by means of the underlying structure. Since any machine has its own program, it is not necessary to have universal machines in this framework, but it can be useful to have them. Therefore, we also discuss the possibility to deﬁne universal programs for a large number of mathematical structures and some consequences
An Efficient Algorithmto Minimize Makespan on Parallel Batch Processing Machines

This paper proposes an efficient optimization approach to minimize makespan on identical parallel batch processing machines where the ready times and sizes of n jobs are arbitrary. The proposed algorithm initially employs a deterministic batch algorithm (DBA) for formation of the batches in order to minimize the makespan. Then, the batches formed by the DBA are further improved by a modified simulated annealing (MSA). We show through computational experimentation, that the proposed MSA outperforms three state-of-the-art methods for non-sparse problems, while the proposed DBA alone outperforms the compared algorithms for sparse problems.
Bidirectional Converter Using Fuzzy for Battery Charging of Electric Vehicle

Electric vehicles have been increasingly occupying space over traditional IC engine-based vehicles for addressing the aspects of GHG emission, fossil fuel depletion and climate change. In electric transportation, the charging and recharging of batteries are significant in the sense, they affect the dynamic performance of the vehicle. This paper discusses the characteristics and modes of working of a bidirectional converter with soft switching that can be used in electric vehicle battery charging. Closed loop is simulated with fuzzy logic and PI controllers and the comparative analysis clearly highlights the fact that fuzzy base controllers are more efficient than PI controllers. The circuit under discussion is a bidirectional converter which can be utilized for both EV battery charging and discharging.
Effect of Shenfu Injection on Isolated Empty Beating Hearts from Miniature Pigs

Objective To investigate the effect of Shenfu (SF) injection on donor heart preservation. Methods Twelve pigs were randomly divided into SF group (n=6) and control group (n=6). After eight hours of perfusion, the differences in hemoglobin, the expression of Bcl-2 and BAX, and changes in the myocardial ultrastructure were compared to illustrate the effects of SF injection in heart preservation. Results The differences in free hemoglobin between the SF group and the control group were statistically significant (P=0.001), and there was significant interaction of groups with times (P=0.019), but the perfusion time may not be associated with the hemoglobin concentration (P=0.616). According to Western blotting analysis, the expression of Bcl-2 was higher in the SF group than in the control group, while the expression of BAX was not different between the two groups. As to ultrastructural changes, both groups exhibited mitochondrial swelling and myofilament lysis, but the degree of damage in the SF group was smaller. Conclusion Our study suggests that the application of SF injection for heart preservation may protect against cardiomyocytes and erythrocytes apoptosis, and Bcl-2 protein may play a role in these physiological processes.
Effects of Water-Extracts of Bambusae concretio silicae(BCS, 天竺黃) on the Expression of Neurotransmitter Receptors

Objectives : BCS(Bambusae concretio silicae) is used as a traditional medicine in Korea for the incipient stroke. Recent reports indicated that BCS has a neuroprotective effect by anti-convulsion effect. However, it`s mechanism is not well studied. The purpose of this study was to investigate into the molecular mechanism of BCS for neuroprotection in normoxia of cultured rat hippocampal neurons. Methods : BCS (1.0, 2.5, 5.0, and ) was added to culture media (Neurobasal supplemented with B27) on DIV 0, given a normoxia, and the cell viability was measured by typical phase-contrast images of the cultures with 1.0, 2.5, 5.0, and on DIV 21. Effects of BCS on the expression of various synaptic proteins ( R1, R2, GlyR, PSD95) were observed by immunocytochemistry showing on DIV 3, 7 and 21. Results : Typical phase-contrast images of the cultures indicated that BCS has a protective effect of rat hippocampal cells in normoxia. The BCS upregulated R1 after normoxia on DIV 7, on DIV 21 and R2 on DIV 21. And the BCS downregulated PSD95 after normoxia on DIV 7. Conclusions : The present study showed evidence for the efficacy of BCS in Typical phase-contrast images, upregulation of inhibitory neurotransmitter receptors( R1) and downregulation of PSD95 which eventually protected neuronal cell death in normoxia.
Extinction risk in cloud forest fragments under climate change and habitat loss

To quantify the consequences of major threats to biodiversity, such as climate and land‐use change, it is important to use explicit measures of species persistence, such as extinction risk. The extinction risk of metapopulations can be approximated through simple models, providing a regional snapshot of the extinction probability of a species. We evaluated the extinction risk of three species under different climate change scenarios in three different regions of the Mexican cloud forest, a highly fragmented habitat that is particularly vulnerable to climate change.
Bandwidth Management in Avionic Networks based on SDN Paradigm and ML Techniques

The management of the Quality of Service (QoS) to meet key constraints has never been more crucial on board the aircraft as today, due to high-demand of bandwidth and low loss requirements for the running applications such as control, sensors, and communications. Although traditional airborne networks provide through manual control differentiated QoS levels for each type of traffic and resource allocation. However, application traffic has a dynamic nature and it is impacted by the mobility of the aircraft. An adaptive Bandwidth manager based on real-time traffic is necessary to enhance the performance of the aircraft network and improve the efficiency of the end-system applications. This paper provides a bandwidth management application running on top of the SDN controller and ensuring the QoS policy fulfillment for the aircraft critical and non-critical services. Any violation of the initial Bandwidth policy will trigger a reaction from the Bandwidth manager application. As a proof of concept, simulation and discussion of different scenarios will be also provided.
Evaluating the Optimized Mutation Analysis Approach in Context of Model-Based Testing

Data flow analysis rules help ensuring the correct flow of data and identifying the related state issues within the model-based testing practices. Though, studies have produced encouraging results for detecting data flow and states-oriented faults and enormous research work has been carried out in this direction, still optimal results from multiple criteria have not been achieved simultaneously. We are aiming to come up with a comprehensive approach that is able to (1): find the define-usage errors, (2): automatically generate the test data for UML state machines (3): mutate the states and flow with different level of complexity achieving efficient mutation score (4): provide the optimal def-use path complete coverage (5): applicable to all UML diagrams. This work in progress is a first step in this direction and we have validated our approach through an implementation which can run against state diagrams. Results have been tested based on a case study with one of the latest approaches and have proven to be promising and effective.
An Explainable Machine Learning Framework for Intrusion Detection Systems

In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs’ judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.
An Energy-Efficient In-Memory BNN Architecture With Time-Domain Analog and Digital Mixed-Signal Processing

Neural networks (NN) have been widely used in various applications. However, the high computational complexity and energy consumption of NNs impede their deployment on embedded and mobile platforms, where computational resources and energy are limited. Most of the existing accelerators are based on traditional Von Neumann(VN) architecture, which separates computing from storage, and the massive data movement between the processor and off-chip memory causes great power consumption. In-memory computing (IMC) architecture integrates computing and storage together to eliminate explicit memory access, reducing energy-hungry data transmission. On the other hand, binary neural network (BNN) restricts the weight and activation value to either -1 or +1, converting a large number of multiplications into simple bit-wise XNOR logical operations, and significantly reduces the complexity of computation and amount of memory access. We propose an energy efficient in-memory BNN architecture that performs matrix-vector multiplication (M×V) of FC layers. The bit-wise XNOR operation is realized on the bit line of SRAM, making the data fetching is part of the computation. The accumulation and binarization are performed in time domain with analog and digital mixed-signal processing, which can be blended with SRAM to minimize the data transmission overhead. In the operating condition of TT 0.5V 25 ć 50MHZ, the energy efficiency is 96.249 TOPS/W, surpassing the conventional digital implementation with similar performance by 2.09×.