Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Dimensional-PAGE Coupled with nLC-MS/MS-Based Identification of Differentially Expressed Proteins and Tumorigenic Pathways in MCF7 Breast Cancer Cells Transfected for JTB Protein Silencing

The identification of new cancer-associated genes/proteins, the characterization of their expression variation, the interactomics-based assessment of differentially expressed genes/proteins (DEGs/DEPs), and understanding the tumorigenic pathways and biological processes involved in BC genesis and progression are necessary and possible by the rapid and recent advances in bioinformatics and molecular profiling strategies. Taking into account the opinion of other authors, as well as based on our own team’s in vitro studies, we suggest that the human jumping translocation breakpoint (hJTB) protein might be considered as a tumor biomarker for BC and should be studied as a target for BC therapy. In this study, we identify DEPs, carcinogenic pathways, and biological processes associated with JTB silencing, using 2D-PAGE coupled with nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) proteomics applied to a MCF7 breast cancer cell line, for complementing and completing our previous results based on SDS-PAGE, as well as in-solution proteomics of MCF7 cells transfected for JTB downregulation. The functions of significant DEPs are analyzed using GSEA and KEGG analyses. Almost all DEPs exert pro-tumorigenic effects in the JTBlow condition, sustaining the tumor suppressive function of JTB. Thus, the identified DEPs are involved in several signaling and metabolic pathways that play pro-tumorigenic roles: EMT, ERK/MAPK, PI3K/AKT, Wnt/β-catenin, mTOR, C-MYC, NF-κB, IFN-γ and IFN-α responses, UPR, and glycolysis/gluconeogenesis. These pathways sustain cancer cell growth, adhesion, survival, proliferation, invasion, metastasis, resistance to apoptosis, tight junctions and cytoskeleton reorganization, the maintenance of stemness, metabolic reprogramming, survival in a hostile environment, and sustain a poor clinical outcome. In conclusion, JTB silencing might increase the neoplastic phenotype and behavior of the MCF7 BC cell line. The data is available via ProteomeXchange with the identifier PXD046265.
Polysubstance use in inflammatory bowel disease is associated with increased risk of emergency department visits: a longitudinal study

Background Polysubstance use (PSU), the simultaneous use of 2 or more substances of abuse, is common in inflammatory bowel disease (IBD). Preliminary studies suggest it may be associated with poor outcomes. This prospective study evaluated the impact of PSU on disease activity and healthcare resource utilization in IBD. Methods This study was conducted in a tertiary IBD center between October 29, 2015, and December 31, 2019. Participants were assessed over 2 time points (index and follow-up outpatient appointments) separated by a minimum of 6 months. Demographics, endoscopic disease activity, and surveys assessing symptoms, healthcare resource utilization and substance use (tobacco, alcohol, marijuana, cocaine, methamphetamine, heroin, opioid, or benzodiazepine) were abstracted. We identified PSU during the index appointment and computed descriptive statistics and contingency table analyses, and multivariate logistic regression models at follow up to evaluate outcomes. Results 162 consecutively enrolled IBD patients were included. Seventy-five patients (46%) were polysubstance users at the index appointment. The most common cohorts were utilizing tobacco and alcohol (n=40) or tobacco and opioids (n=13). On bivariate and multivariate analyses, PSU during the index visit was positively associated with emergency department (ED) visits (odds ratio [OR] 2.51, 95% confidence interval [CI] 1.24-5.07; P=0.01) and negatively associated with extraintestinal manifestations (OR 0.37, 95%CI 0.18-0.74; P=0.005). Age, sex, disease activity, disease subtype and IBD-related symptoms were not associated with PSU. Conclusions IBD patients exhibiting PSU had increased risk of future ED visits. This study highlights the risks of PSU and reinforces the importance of appropriate substance use screening.
Effect of Soybean (Glicyne max (L.) Merill) with Lamtoro Gung (Leucaena leucocephala) Proportion on the Characteristics of Soy Milk

The utilization of soy milk is an alternative for people with lactose intolerance. Soy milk can be made with lamtoro gung substitution because the nutritional content of lamtoro gung is close to soybeans. This research aimed to determine the effect of soybean/lamtoro gung proportion on the characteristics of soy milk. This research was conducted from May until July 2022. The experimental study used a randomized block design with the treatment were soybean (K) with lamtoro gung (L) proportion S1 (K 100%: L 0%), S2 (K 95%: L 5%), S3 (K 90%: L 10%), S4 (K 85%: L 15%), S5 (K 80: L 20%), S6 (K 75%: L25), S7 (K 70%: L 30%) S8 (K 65%: L 35%), and S9 (K 60%: L 40%). The data obtained were then analyzed using analysis of variance and further test BNJ 5%, the organoleptic test used Friedman test, and determine the best treatment used effectiveness index. The results showed a significant effect of soybean with lamtoro gung proportion on protein content, vitamin C content, vitamin A content, pH, viscosity, TDS, and hedonic test of aroma and taste. The best treatment was S3 (soybean 90%: lamtoro 10% with characteristics of protein content 3.304%, vitamin C content 0.291%, vitamin A content 2.239 mg/g, pH 5.917, viscosity 3.533×103 mPa.s, TDS 7.833 °brix, and hedonic test of color 3.40, aroma 3.30, taste 3.40, and texture 3.20. This study contributes to evaluate the soy milk characteristics from soybean and lamtoro gung.
Biological and molecular structure analysis of the controls on soil organic matter dynamics

Eldor A. Paul K. Magrini, R. Conant, R.F. Follett, S.J. Morris Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA 805231499, eldor@nrel.colostate. edu National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USDA-ARS, 2150 Centre Avenue, Bldg D, Ste 100, Fort Collins, CO 80525 Dept. of Biology, Bradley University, 1501 W. Bradley Ave., Peoria, IL 61625-0313
The EU ETS: Eight Years and Counting

This paper provides an introduction to the EU’s Emissions Trading System. As such it provides a discussion of the historical and legal context in which the EU ETS developed and now operates, a presentation of the key performance indicators for the first eight years through the end of the second phase in 2012, and some concluding observations on the system’s future. The paper is purposively descriptive to provide background for more analytically oriented articles, as well as to provide a matter-of-fact presentation for readers who wish to learn about or be updated on the progress of the EU ETS.
Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study

Abstract Aims Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated. Methods and results Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95–0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%). Conclusion This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
Transcriptome-Wide Analysis of Core Transcription Factors Associated with Defense Responses in Autotetraploid versus Diploid Rice under Saline Stress and Recovery

Saline stress is a major abiotic stress that inhibits plant growth and yields worldwide. The plant transcription factor (TF) family plays an important role in converting abiotic stress signals into gene expression changes. In this study, a transcriptome-based comparative analysis was performed to investigate the global gene expression of all the TFs in diploid and autotetraploid rice during the early stage of NaCl stress and recovery period. The phenotypic data indicated that the tetraploid rice exhibited a superior salt-tolerant ability compared to the diploid rice. A total of 55 TF families were co-expressed in the tetraploid and diploid rice, and the cumulative number of TF-expressed genes was relatively higher in the diploid rice than in the tetraploid rice at all time points. Unlike the diploid rice, the overall gene expression levels of the tetraploid rice were comparable to the control during recovery. The number of differentially expressed TFs (DE-TFs) in the tetraploid rice decreased after recovery, whereas it increased to a large extent in the diploid rice. GO and KEGG pathway enrichment analysis of the DE-TFs discovered the early switching of the ABA-activated signaling pathway and specific circadian rhythm in the tetraploid rice. Combining the PPI network and heatmap analysis, some core DE-TFs were found that may have potential roles to play in tetraploid salt tolerance. This study will pave the way for elucidating the complex network regulatory mechanisms of salt tolerance in tetraploid rice.
Submarine landslide-induced tsunami generation in Makassar strait, Indonesia using Non-hydrostatic wave model

Tsunami modelling of potential landslide-induced tsunami in Makassar Strait is carried out to quantify possible damage to the nearby cities. The numerical model is used to represent the wave generation by using NHWAVE model. The simulations consist of a series of scenarios based on distinct size of the landslide volume. Four landslides with volume 5, 8, 70, and 200 km3 are used as tsunami sources in the initiation stage. The sources are evenly distributed in the strait addressing different landslide location. Maximum wave heights of 1.5 m are found in the area between Palu and Bangkir from case 1 and around Talok from case 2 simulations. The empirical run-up calculation of 7.5 m is estimated at the land for the presented wave height. The value significantly elevates the case 3 and 4 proportional to the volume values. The waves impact more than half of coastline with maximum value found in the Sulawesi side. Interestingly, wide and narrow shelf next to Kalimantan Island plays an important role in reducing the tsunami hazard level.
Main Copper Deposit types and Copper Deposits Merging Overseas of China

In the 21st century,the Chinese mining enterprises have been taking in various forms,including merge,to take part in the acquisitions and development of mineral resources overseas in the process of "going out". Based on the copper deposit types,the geological character,ore forming age,structural setting,genesis,tonnage and grade distribution of porphyry copper deposits and sediment hosted copper deposits,the focus of this paper is briefly introduced in this paper.The copper deposits merging cases in overseas of Chinese mining enterprises,especially after the financial crisis,were reviewed.The super large copper deposits merged are mostly porphyry copper deposits and sediment hosted copper deposits.Important cases were introduced in the view of geology.The ideas and other methods for super large copper deposits merging were discussed.
1 Integrating ) and ) mining ) the ) chromatin ) landscape ) of ) cell ) type ) specificity ) using ) Self 6 Organizing ) Maps )

We tested whether Self-Organizing Maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq datasets from six biologically diverse cell lines studied by the ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional datasets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.
Archipelago: The Islands of Indonesia. From the Nineteenth-Century Discoveries of Alfred Russel Wallace to the Fate of Forests and Reefs in the Twenty-First Century

The Indonesian archipelago is a land of timeless natural beauty that, in the 21st century, faces unprecedented environmental degradation. It was also the biological laboratory of Alfred Russel Wallace, who, working independently of Charles Darwin, discovered the theory of evolution by natural selection. Wallace, who travelled for eight years in the archipelago, was one of the greatest field naturalists and nature writers of his century. A prodigious collector, he was the first to bring living birds of paradise to the West. This account of a true explorer sweeps from the time of Wallace's 19th-century discoveries in biogeography to the looming biodiversity crisis of the 21st century - from the exploration of natural wonders to the exploitation of natural resources. The result is a history that portrays the intricate connections of human life and natural life.
Outage Capacity of the Fading Relay Channel in the Low SNR Regime ∗

In this paper we look at the outage capacity of the fading relay channel with half-duplex constraint in the low SNR regime. First we consider the scenario that the channel state information (CSI) is available only at the receiver. In this case we show that a Bursty Amplify-Forward (BAF) protocol is optimal and achieves the outage capacity of this network. But as the channel estimation is quite challenging in the low SNR regime we investigate two extreme scenarios to understand the effect of the channel knowledge on the outage capacity of this network. One extreme is the scenario that neither the transmitter nor the receiver knows the channel state information (non coherent scenario), the other scenario is when this information is available at both the transmitter and the receiver (full CSI). Finally we consider two important extensions of our result. This work is supported by the National Science Foundation under grant CCR-0118784 and by a fellowship from the Vodafone Foundation.
Learning to Weight Samples for Dynamic Early-exiting Networks:

CIFAR-10 and CIFAR-100 [6] contain 10 and 100 classes of natural scene objects, respectively. Both datasets contain 50,000 training images and 10,000 test images. Each image has a resolution of 32× 32 pixels. ImageNet [2] contains 1.2 million training images and 50,000 validation images in 1000 classes. Following [4], we use the basic data augmentation for CIFAR-10, CIFAR-100 and ImageNet. The long-tailed CIFAR dataset [1], unlike CIFAR and ImageNet, does not have a well-balance class distribution. It reduces the number of training samples in each class based on an exponential function N ′ c = Ncμ , μ ∈ (0, 1), where c is the class index, and Nc is the original training sample number.
Discovering Structure in High-Dimensional Data Through Correlation Explanation Report

Discovering Structure in High-Dimensional Data Through Correlation Explanation Report Title We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual information. The method is unsupervised, requires no model assumptions, and scales linearly with the number of variables which makes it an attractive approach for very high dimensional systems. We demonstrate that Correlation Explanation (CorEx) automatically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language. Conference Name: Advances in Neural Information Processing Systems Conference Date: December 08, 2014 Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg Information Sciences Institute University of Southern California Marina del Rey, CA 90292 gregv@isi.edu Aram Galstyan Information Sciences Institute University of Southern California Marina del Rey, CA 90292
Achieving the Gaussian Rate–Distortion Function by Prediction

The ldquowater-fillingrdquo solution for the quadratic rate-distortion function of a stationary Gaussian source is given in terms of its power spectrum. This formula naturally lends itself to a frequency domain ldquotest-channelrdquo realization. We provide an alternative time-domain realization for the rate-distortion function, based on linear prediction. The predictive test channel has some interesting implications, including the optimality at all distortion levels of pre/post filtered vector-quantized differential pulse-code modulation (DPCM), and a duality relationship with decision-feedback equalization (DFE) for intersymbol interference (ISI) channels.
New characterisations of the Nordstrom-Robinson codes

In his doctoral thesis, Snover proved that any binary $(m,256,\delta)$ code is equivalent to the Nordstrom-Robinson code or the punctured Nordstrom-Robinson code for $(m,\delta)=(16,6)$ or $(15,5)$ respectively. We prove that these codes are also characterised as \emph{completely regular} binary codes with $(m,\delta)=(16,6)$ or $(15,5)$, and moreover, that they are \emph{completely transitive}. Also, it is known that completely transitive codes are necessarily completely regular, but whether the converse holds has up to now been an open question. We answer this by proving that certain completely regular codes are not completely transitive, namely, the (Punctured) Preparata codes other than the (Punctured) Nordstrom-Robinson code.
Analytical results for the distribution of first hitting times of random walks on random regular graphs

We present analytical results for the distribution of first hitting (FH) times of random walks (RWs) on random regular graphs (RRGs) of degree c ⩾ 3 and a finite size N. Starting from a random initial node at time t = 1, at each time step t ⩾ 2 an RW hops randomly into one of the c neighbors of its previous node. In some of the time steps the RW may hop into a yet-unvisited node while in other time steps it may revisit a node that has already been visited before. The first time at which the RW enters a node that has already been visited before is called the FH time or the first intersection length. The FH event may take place either by backtracking (BT) to the previous node or by retracing (RET), namely stepping into a node which has been visited two or more time steps earlier. We calculate the tail distribution P(T FH > t) of FH times as well as its mean ⟨T FH⟩ and variance Var(T FH). We also calculate the probabilities P BT and P RET that the FH event will occur via the BT scenario or via the RET scenario, respectively. We show that in dilute networks the dominant FH scenario is BT while in dense networks the dominant scenario is RET and calculate the conditional distributions P(T FH = t|BT) and P(T FH = t|RET), for the two scenarios. The analytical results are in excellent agreement with the results obtained from computer simulations. Considering the FH event as a termination mechanism of the RW trajectories, these results provide useful insight into the general problem of survival analysis and the statistics of mortality rates when two or more termination scenarios coexist.
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis

Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.
Deep Learning for micro-Electrocorticographic ({\mu}ECoG) Data

Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many research and clinical applications. Deep learning with artificial neural networks has recently seen increasing attention as a new approach in brain signal decoding. Here, we apply a deep learning approach using convolutional neural networks to {\mu}ECoG data obtained with a wireless, chronically implanted system in an ovine animal model. Regularized linear discriminant analysis (rLDA), a filter bank component spatial pattern (FBCSP) algorithm and convolutional neural networks (ConvNets) were applied to auditory evoked responses captured by {\mu}ECoG. We show that compared with rLDA and FBCSP, significantly higher decoding accuracy can be obtained by ConvNets trained in an end-to-end manner, i.e., without any predefined signal features. Deep learning thus proves a promising technique for {\mu}ECoG-based brain-machine interfacing applications.