ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass.

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.

An enhanced approach for predicting air pollution using quantum support vector machine.

The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.

Feasibility and Potential of Transcriptomic Analysis Using the NanoString nCounter Technology to Aid the Classification of Rejection in Kidney Transplant Biopsies.

BACKGROUND: Transcriptome analysis could be an additional diagnostic parameter in diagnosing kidney transplant (KTx) rejection. Here, we assessed feasibility and potential of NanoString nCounter analysis of KTx biopsies to aid the classification of rejection in clinical practice using both the Banff-Human Organ Transplant (B-HOT) panel and a customized antibody-mediated rejection (AMR)-specific NanoString nCounter Elements (Elements) panel. Additionally, we explored the potential for the classification of KTx rejection building and testing a classifier within our dataset. METHODS: Ninety-six formalin-fixed paraffin-embedded KTx biopsies were retrieved from the archives of the ErasmusMC Rotterdam and the University Hospital Cologne. Biopsies with AMR, borderline or T cell-mediated rejections (BLorTCMR), and no rejection were compared using the B-HOT and Elements panels. RESULTS: High correlation between gene expression levels was found when comparing the 2 chemistries pairwise (r = 0.76-0.88). Differential gene expression (false discovery rate; P < 0.05) was identified in biopsies diagnosed with AMR (B-HOT: 294; Elements: 76) and BLorTCMR (B-HOT: 353; Elements: 57) compared with no rejection. Using the most predictive genes from the B-HOT analysis and the Element analysis, 2 least absolute shrinkage and selection operators-based regression models to classify biopsies as AMR versus no AMR (BLorTCMR or no rejection) were developed achieving an receiver-operating-characteristic curve of 0.994 and 0.894, sensitivity of 0.821 and 0.480, and specificity of 1.00 and 0.979, respectively, during cross-validation. CONCLUSIONS: Transcriptomic analysis is feasible on KTx biopsies previously used for diagnostic purposes. The B-HOT panel has the potential to differentiate AMR from BLorTCMR or no rejection and could prove valuable in aiding kidney transplant rejection classification.

Correction of murine SCID-X1 by lentiviral gene therapy using a codon-optimized IL2RG gene and minimal pretransplant conditioning.

Clinical trials have demonstrated the potential of ex vivo hematopoietic stem cell gene therapy to treat X-linked severe combined immunodeficiency (SCID-X1) using γ-retroviral vectors, leading to immune system functionality in the majority of treated patients without pretransplant conditioning. The success was tempered by insertional oncogenesis in a proportion of the patients. To reduce the genotoxicity risk, a self-inactivating (SIN) lentiviral vector (LV) with improved expression of a codon optimized human interleukin-2 receptor γ gene (IL2RG) cDNA (coγc), regulated by its 1.1 kb promoter region (γcPr), was compared in efficacy to the viral spleen focus forming virus (SF) and the cellular phosphoglycerate kinase (PGK) promoters. Pretransplant conditioning of Il2rg(-/-) mice resulted in long-term reconstitution of T and B lymphocytes, normalized natural antibody titers, humoral immune responses, ConA/IL-2 stimulated spleen cell proliferation, and polyclonal T-cell receptor gene rearrangements with a clear integration preference of the SF vector for proto-oncogenes, contrary to the PGK and γcPr vectors. We conclude that SIN lentiviral gene therapy using coγc driven by the γcPr or PGK promoter corrects the SCID phenotype, potentially with an improved safety profile, and that low-dose conditioning proved essential for immune competence, allowing for a reduced threshold of cell numbers required.

Cryo-Gel embedding compound for renal biopsy biobanking.

Optimal preservation and biobanking of renal tissue is vital for good diagnostics and subsequent research. Optimal cutting temperature (OCT) compound is a commonly used embedding medium for freezing tissue samples. However, due to interfering polymers in OCT, analysis as mass spectrometry (MS) is difficult. We investigated if the replacement of OCT with Cryo-Gel as embedding compound for renal biopsies would enable proteomics and not disturb other common techniques used in tissue diagnostics and research. For the present study, fresh renal samples were snap-frozen using Cryo-Gel, OCT and without embedding compound and evaluated using different techniques. In addition, tissue samples from normal spleen, skin, liver and colon were analyzed. Cryo-Gel embedded tissues showed good morphological preservation and no interference in immunohistochemical or immunofluorescent investigations. The quality of extracted RNA and DNA was good. The number of proteins identified using MS was similar between Cryo-Gel embedded samples, samples without embedding compound and OCT embedded samples. However, polymers in the OCT disturbed the signal in the MS, while this was not observed in the Cryo-Gel embedded samples. We conclude that embedding of renal biopsies in Cryo-Gel is an excellent and preferable alternative for OCT compound for both diagnostic and research purposes, especially in those cases where proteomic analysis might be necessary.

The influence of tissue procurement procedures on RNA integrity, gene expression, and morphology in porcine and human liver tissue.

The advent of molecular characterization of tissues has brought an increasing emphasis on the quality of biospecimens, starting with the tissue procurement process. RNA levels are particularly affected by factors in the collection process, but the influence of different pre-analytical factors is not well understood. Here we present the influence of tissue specimen size, as well as the transport and freezing protocols, on RNA quality. Large, medium, and smaller porcine liver samples were stored either dry, on moist gauze, or in salt solution for various times, and then frozen in either liquid nitrogen or in pre-cooled isopentane. Large and small human liver samples were frozen in pre-cooled isopentane either immediately or after one hour at room temperature. The small samples were stored dry, on moist gauze, or in salt solution. RNA was isolated and RIN values were measured. The RNA for six standard reference genes from human liver was analyzed by RT-qPCR, and tissue morphology was assessed for artifacts of freezing. Experiments using porcine liver samples showed that RNA derived from smaller samples was more degraded after one hour of cold ischemia, and that cooled transport is preferable. Human liver samples showed significant RNA degradation after 1 h of cold ischemia, which was more pronounced in smaller samples. RNA integrity was not significantly influenced by the transport or freezing method, but changes in gene expression were observed in samples either transported on gauze or in salt solution. Based on observations in liver samples, smaller samples are more subject to gene expression variability introduced by post-excision sample handling than are larger samples. Small biopsies should be transported on ice and snap frozen as soon as possible after acquisition from the patient.

Fit for purpose frozen tissue collections by RNA integrity number-based quality control assurance at the Erasmus MC tissue bank.

About 5000 frozen tissue samples are collected each year by the Erasmus Medical Center tissue bank. Two percent of these samples are randomly selected annually for RNA isolation and RNA Integrity Number (RIN) measurement. A similar quality assessment was conducted during centralization of a 20-year-old tissue collection from the cancer institute, a 15-year-old liver sample archive (-80°C), and a 13-year-old clinical pathology frozen biopsy archive (Liquid Nitrogen). Samples were divided into either high-quality (RIN ≥6.5) or low-quality overall categories, or into four "fit-for-purpose" quality groups: RIN <5: not reliable for demanding downstream analysis; 5 ≤RIN <6: suitable for RT-qPCR; 6 ≤RIN <8: suitable for gene array analysis; and RIN ≥8: suitable for all downstream techniques. In general, low RIN values were correlated with fatty, fibrous, pancreatic, or necrotic tissue. When the percentage of samples with RIN ≥6.5 is higher than 90%, the tissue bank performance is adequate. The annual 2011 quality control assessment showed that 90.3% (n=93) of all samples had acceptable RIN values; 97.4% (n=39) of the cancer institute collection had RIN values above 6.5; and 88.6% (n=123) of samples from the liver sample archive collection had RIN values higher than 6.5. As the clinical pathology biopsy collection contained only 58.8% (n=24) acceptable samples, the procurement protocols used for these samples needed immediate evaluation. When the distribution of RIN values of the different collections were compared, no significant differences were found, despite differences in average storage time and temperature. According to the principle of "fit-for-purpose" distribution, the vast majority of samples are considered good enough for most downstream techniques. In conclusion, an annual tissue bank quality control procedure provides useful information on tissue sample quality and sheds light on where and if improvements need to be made.

Angiopoietin-like protein 3 promotes preservation of stemness during ex vivo expansion of murine hematopoietic stem cells.

Allogeneic hematopoietic stem cell (HSC) transplantations from umbilical cord blood or autologous HSCs for gene therapy purposes are hampered by limited number of stem cells. To test the ability to expand HSCs in vitro prior to transplantation, two growth factor cocktails containing stem cell factor, thrombopoietin, fms-related tyrosine kinase-3 ligand (STF) or stem cell factor, thrombopoietin, insulin-like growth factor-2, fibroblast growth factor-1 (STIF) either with or without the addition of angiopoietin-like protein-3 (Angptl3) were used. Culturing HSCs in STF and STIF media for 7 days expanded long-term repopulating stem cells content in vivo by ∼6-fold and ∼10-fold compared to freshly isolated stem cells. Addition of Angptl3 resulted in increased expansion of these populations by ∼17-fold and ∼32-fold, respectively, and was further supported by enforced expression of Angptl3 in HSCs through lentiviral transduction that also promoted HSC expansion. As expansion of highly purified lineage-negative, Sca-1+, c-Kit+ HSCs was less efficient than less pure lineage-negative HSCs, Angptl3 may have a direct effect on HCS but also an indirect effect on accessory cells that support HSC expansion. No evidence for leukemia or toxicity was found during long-term follow up of mice transplanted with ex vivo expanded HSCs or manipulated HSC populations that expressed Angptl3. We conclude that the cytokine combinations used in this study to expand HSCs ex vivo enhances the engraftment in vivo. This has important implications for allogeneic umbilical cord-blood derived HSC transplantations and autologous HSC applications including gene therapy.

5-Androstene-3ß,17ß-diol promotes recovery of immature hematopoietic cells following myelosuppressive radiation and synergizes with thrombopoietin.

PURPOSE: 5-Androstene-3ß,17ß-diol (5-AED) stimulates recovery of hematopoiesis after exposure to radiation. To elucidate its cellular targets, the effects of 5-AED alone and in combination with (pegylated) granulocyte colony-stimulating factor and thrombopoietin (TPO) on immature hematopoietic progenitor cells were evaluated following total body irradiation. METHODS AND MATERIALS: BALB/c mice were exposed to radiation delivered as a single or as a fractionated dose, and recovery of bone marrow progenitors and peripheral blood parameters was assessed. RESULTS: BALB/c mice treated with 5-AED displayed accelerated multilineage blood cell recovery and elevated bone marrow (BM) cellularity and numbers of progenitor cells. The spleen colony-forming unit (CFU-S) assay, representing the life-saving short-term repopulating cells in BM of irradiated donor mice revealed that combined treatment with 5-AED plus TPO resulted in a 20.1-fold increase in CFU-S relative to that of placebo controls, and a 3.7 and 3.1-fold increase in comparison to 5-AED and TPO, whereas no effect was seen of Peg-G-CSF with or without 5-AED. Contrary to TPO, 5-AED also stimulated reconstitution of the more immature marrow repopulating (MRA) cells. CONCLUSIONS: 5-AED potently counteracts the hematopoietic effects of radiation-induced myelosuppression and promotes multilineage reconstitution by stimulating immature bone marrow cells in a pattern distinct from, but synergistic with TPO.

Compliance, safety, and effectiveness of fixed-dose combinations of antihypertensive agents: a meta-analysis.

Two or more antihypertensive agents are increasingly used to control blood pressure (BP) in hypertensive patients. However, it is unclear whether fixed-dose combinations (FDCs) of 2 antihypertensive agents in a single tablet provide greater benefits than the corresponding free-drug components given separately. A meta-analysis was performed to assess compliance, persistence, BP control, and safety associated with FDCs in comparison with their free-drug components. Fifteen included studies (n=32331) reported on >or=1 of the evaluated outcomes. In 3 cohort studies and 2 trials reporting on drug compliance (n=17 999), the use of FDCs was associated with significantly better compliance (odds ratio: 1.21 [95% CI: 1.03 to 1.43]; P=0.02) compared with its corresponding free-drug combinations. In 3 cohort studies (n=12 653), there was a nonsignificant improvement in persistence with therapy (odds ratio: 1.54 [95% CI: 0.95 to 2.49]; P=0.08), and in 5 trials (n=1775) the odds ratio for adverse effects for FDC use compared with free-drug combination use was 0.80 (95% CI: 0.58 to 1.11; P=0.19). In 9 trials (n=1671) with BP data, use of an FDC was associated with nonsignificant changes in systolic and diastolic BPs of 4.1 mm Hg (95% CI: -9.8 to 1.5; P=0.15) and 3.1 mm Hg (95% CI: -7.1 to 0.9; P=0.13), respectively. In these BP-lowering comparisons, there was heterogeneity associated with differences in study design but no publication bias. In conclusion, compared with free-drug combinations, FDCs of antihypertensive agents are associated with a significant improvement in compliance and with nonsignificant beneficial trends in BP and adverse effects.