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Psychosis in Parkinson's disease is a common phenomenon associated with poor outcomes. To clarify the pathophysiology of this condition and the mechanisms of antipsychotic treatments, we have here characterized the neurophysiological brain states induced by clozapine, pimavanserin, and the novel prospective antipsychotic mesdopetam in a rodent model of Parkinson's disease psychosis, based on chronic dopaminergic denervation by 6-OHDA lesions, levodopa priming, and the acute administration of an NMDA antagonist. Parallel recordings of local field potentials from eleven cortical and sub-cortical regions revealed shared neurophysiological treatment effects for the three compounds, despite their different pharmacological profiles, involving reversal of features associated with the psychotomimetic state, such as a reduction of aberrant high-frequency oscillations in prefrontal structures together with a decrease of abnormal synchronization between different brain regions. Other drug-induced neurophysiological features were more specific to each treatment, affecting network oscillation frequencies and entropy, pointing to discrete differences in mechanisms of action. These findings indicate that neurophysiological characterization of brain states is particularly informative when evaluating therapeutic mechanisms in conditions involving symptoms that are difficult to assess in rodents such as psychosis, and that mesdopetam should be further explored as a potential novel antipsychotic treatment option for Parkinson psychosis.

Psychosis in Parkinson's disease is a common phenomenon associated with poor outcomes. To clarify the pathophysiology of this condition and the mechanisms of antipsychotic treatments, we have here characterized the neurophysiological brain states induced by clozapine, pimavanserin, and the novel prospective antipsychotic mesdopetam in a rodent model of Parkinson's disease psychosis, based on chronic dopaminergic denervation by 6-OHDA lesions, levodopa priming, and the acute administration of an NMDA antagonist. Parallel recordings of local field potentials from eleven cortical and sub-cortical regions revealed shared neurophysiological treatment effects for the three compounds, despite their different pharmacological profiles, involving reversal of features associated with the psychotomimetic state, such as a reduction of aberrant high-frequency oscillations in prefrontal structures together with a decrease of abnormal synchronization between different brain regions. Other drug-induced neurophysiological features were more specific to each treatment, affecting network oscillation frequencies and entropy, pointing to discrete differences in mechanisms of action. These findings indicate that neurophysiological characterization of brain states is particularly informative when evaluating therapeutic mechanisms in conditions involving symptoms that are difficult to assess in rodents such as psychosis, and that mesdopetam should be further explored as a potential novel antipsychotic treatment option for Parkinson psychosis.

Psychosis in Parkinson's disease is a common phenomenon associated with poor outcomes. To clarify the pathophysiology of this condition and the mechanisms of antipsychotic treatments, we have here characterized the neurophysiological brain states induced by clozapine, pimavanserin, and the novel prospective antipsychotic mesdopetam in a rodent model of Parkinson's disease psychosis, based on chronic dopaminergic denervation by 6-OHDA lesions, levodopa priming, and the acute administration of an NMDA antagonist. Parallel recordings of local field potentials from eleven cortical and sub-cortical regions revealed shared neurophysiological treatment effects for the three compounds, despite their different pharmacological profiles, involving reversal of features associated with the psychotomimetic state, such as a reduction of aberrant high-frequency oscillations in prefrontal structures together with a decrease of abnormal synchronization between different brain regions. Other drug-induced neurophysiological features were more specific to each treatment, affecting network oscillation frequencies and entropy, pointing to discrete differences in mechanisms of action. These findings indicate that neurophysiological characterization of brain states is particularly informative when evaluating therapeutic mechanisms in conditions involving symptoms that are difficult to assess in rodents such as psychosis, and that mesdopetam should be further explored as a potential novel antipsychotic treatment option for Parkinson psychosis.

Psychosis in Parkinson's disease is a common phenomenon associated with poor outcomes. To clarify the pathophysiology of this condition and the mechanisms of antipsychotic treatments, we have here characterized the neurophysiological brain states induced by clozapine, pimavanserin, and the novel prospective antipsychotic mesdopetam in a rodent model of Parkinson's disease psychosis, based on chronic dopaminergic denervation by 6-OHDA lesions, levodopa priming, and the acute administration of an NMDA antagonist. Parallel recordings of local field potentials from eleven cortical and sub-cortical regions revealed shared neurophysiological treatment effects for the three compounds, despite their different pharmacological profiles, involving reversal of features associated with the psychotomimetic state, such as a reduction of aberrant high-frequency oscillations in prefrontal structures together with a decrease of abnormal synchronization between different brain regions. Other drug-induced neurophysiological features were more specific to each treatment, affecting network oscillation frequencies and entropy, pointing to discrete differences in mechanisms of action. These findings indicate that neurophysiological characterization of brain states is particularly informative when evaluating therapeutic mechanisms in conditions involving symptoms that are difficult to assess in rodents such as psychosis, and that mesdopetam should be further explored as a potential novel antipsychotic treatment option for Parkinson psychosis.

We study the effect of the Large Magellanic Cloud (LMC) on the dark matter (DM) distribution in the Solar neighborhood, utilizing the Auriga magneto-hydrodynamical simulations of Milky Way (MW) analogues that have an LMC-like system. We extract the local DM velocity distribution at different times during the orbit of the LMC around the MW in the simulations. As found in previous idealized simulations of the MW-LMC system, we find that the DM particles in the Solar neighborhood originating from the LMC analogue dominate the high speed tail of the local DM speed distribution. Furthermore, the native DM particles of the MW in the Solar region are boosted to higher speeds as a result of a response to the LMC's motion. We simulate the signals expected in near future xenon, germanium, and silicon direct detection experiments, considering DM interactions with target nuclei or electrons. We find that the presence of the LMC causes a considerable shift in the expected direct detection exclusion limits towards smaller cross sections and DM masses, with the effect being more prominent for low mass DM. Hence, our study shows, for the first time, that the LMC's influence on the local DM distribution is significant even in fully cosmological MW analogues., Comment: 31 pages, 13 figures, 3 tables

Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.

We study the effect of the Large Magellanic Cloud (LMC) on the dark matter (DM) distribution in the Solar neighborhood, utilizing the Auriga magneto-hydrodynamical simulations of Milky Way (MW) analogues that have an LMC-like system. We extract the local DM velocity distribution at different times during the orbit of the LMC around the MW in the simulations. As found in previous idealized simulations of the MW-LMC system, we find that the DM particles in the Solar neighborhood originating from the LMC analogue dominate the high speed tail of the local DM speed distribution. Furthermore, the native DM particles of the MW in the Solar region are boosted to higher speeds as a result of a response to the LMC's motion. We simulate the signals expected in near future xenon, germanium, and silicon direct detection experiments, considering DM interactions with target nuclei or electrons. We find that the presence of the LMC causes a considerable shift in the expected direct detection exclusion limits towards smaller cross sections and DM masses, with the effect being more prominent for low mass DM. Hence, our study shows, for the first time, that the LMC's influence on the local DM distribution is significant even in fully cosmological MW analogues., Comment: 31 pages, 13 figures, 3 tables

Lung adenocarcinoma with lepidic growth pattern (LPA) is characterized by tumor cell proliferation along intact alveolar walls, and further classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive lepidic predominant adenocarcinoma (iLPA). Accurate diagnosis of lepidic lesions is critical for appropriate prognostication and management as five-year survival in patients with iLPA is lower than in those with AIS and MIA. We aimed to evaluate the accuracy of CT-guided core needle lung biopsy classifying LPA lesions and identify clinical and radiologic predictors of invasive disease in biopsied lesions. Thirty-four cases of adenocarcinoma with non-invasive lepidic growth pattern on core biopsy pathology that subsequently were resected between 2011 and 2018 were identified. Invasive LPA vs. non-invasive LPA (AIS or MIA) was defined based on explant pathology. Histopathology of core biopsy and resected tumor specimens was compared for concordance, and clinical, radiologic and pathologic variables were analyzed to assess for correlation with invasive disease. The majority of explanted tumors (70.6%) revealed invasive disease. Asian race (p = 0.03), history of extrathoracic malignancy (p = 0.02) and absence of smoking history (p = 0.03) were associated with invasive disease. CT-measured tumor size was not associated with invasiveness (p = 0.15). CT appearance of density (p = 0.61), shape (p = 0.78), and margin (p = 0.24) did not demonstrate a significant difference between the two subgroups. Invasiveness of tumors with lepidic growth patterns can be underestimated on transthoracic core needle biopsies. Asian race, absence of smoking, and history of extrathoracic malignancy were associated with invasive disease.

PurposeTissue acquisition in lung cancer is vital for multiple reasons. Primary reasons reported for molecular testing failure in lung cancer biopsy specimens include insufficient amount of tumor cells provided and inadequate tissue quality. Robotic bronchoscopy is a new tool enabling peripheral pulmonary lesion sampling; however, diagnostic yield remains imperfect possibly due to the location of nodules adjacent to or outside of the airway. The 1.1-mm cryoprobe is a novel diagnostic tool and accesses tissue in a 360-degree manner, thus potentially sampling eccentric/adjacent lesions. This study examines the diagnostic yield of the cryoprobe compared to standard needle aspiration and forceps biopsy. It additionally evaluates yield for molecular markers in cases of lung cancer.MethodsThis is a retrospective analysis of 112 patients with 120 peripheral pulmonary lesions biopsied via robotic bronchoscopy using needle aspirate, forceps, and cryobiopsy.ResultsThe overall diagnostic yield was 90%. Nearly 18% of diagnoses were made exclusively from the cryobiopsy sample. Molecular analysis was adequate on all cryobiopsy samples sent. Digital imaging software confirmed an increase in quantity and quality of samples taken via cryobiopsy compared to needle aspirate and traditional forceps biopsy.ConclusionUsing the 1.1-mm cryoprobe to biopsy PPN combined with the Ion robotic bronchoscopy system is safe, feasible, and provides more diagnostic tissue than needle aspirates or traditional forceps biopsies. The combination of cryobiopsy with robotic-assisted bronchoscopy increased diagnostic yield, likely due to its 360-degree tissue acquisition which is beneficial when targeting extraluminal lesions adjacent to the airway.

IntroductionTransbronchial lung cryobiopsy (TBLC) has emerged as a promising alternative to surgical lung biopsy for the diagnosis of interstitial lung disease. However, uncertainty remains regarding its overall complications due to a lack of procedural standardization including the size of cryoprobe utilized.MethodsThis is a prospective cohort study of a protocolized transbronchial cryobiopsy program utilizing a 2.4 mm cryoprobe. 201 consecutive subjects were enrolled at a single academic center.ResultsThe average biopsy size was 106.2 ± 39.3 mm2. Complications included a total pneumothorax rate of 4.9% with 3.5% undergoing chest tube placement. Severe bleeding defined by the Nashville Working Group occurred in 0.5% of cases. There were no deaths at 30-days.DiscussionA protocolized transbronchial cryobiopsy program utilizing a 2.4 mm cryoprobe in can achieve a high diagnostic yield with a favorable safety profile.

The compositions, essays, videos and architectural projects in this collection explore strategies and technologies of investigating beyond the predominantly Western modernist architectural format and the main framework for today’s uncontested architectural sites, trying to obscure, contradict or amplify on the notions of modernity. Echoing processual music terminologies, the dissonant practices and structures transform energy, twist and interfere with the virtual and physical context around, in a macro form on the territory of the complexity drive to change the ideologies of the fixed urban form. Through the approach of decolonial thinking being and doing one question that emerges is how to ..., https://www.librarystack.org/architectural-dissonances/?ref=unknown

The compositions, essays, videos and architectural projects in this collection explore strategies and technologies of investigating beyond the predominantly Western modernist architectural format and the main framework for today’s uncontested architectural sites, trying to obscure, contradict or amplify on the notions of modernity. Echoing processual music terminologies, the dissonant practices and structures transform energy, twist and interfere with the virtual and physical context around, in a macro form on the territory of the complexity drive to change the ideologies of the fixed urban form. Through the approach of decolonial thinking being and doing one question that emerges is how to fundamentally rethink and offer ways to reimagine society through spatial practice – beyond the utopian universalist constraints conceived within modern architecture. Recognizing the limits and slips of academic disciplines such as architecture, art history, museology and curating, and encouraging practices of unlearning, our energy is therefore to situate a critical conversation around decolonization in Europe but through challenging Western epistemologies in relation to architecture, living and working spaces, territories of care, urban and rural planning., https://www.librarystack.org/architectural-dissonances/?ref=unknown

The compositions, essays, videos and architectural projects in this collection explore strategies and technologies of investigating beyond the predominantly Western modernist architectural format and the main framework for today’s uncontested architectural sites, trying to obscure, contradict or amplify on the notions of modernity. Echoing processual music terminologies, the dissonant practices and structures transform energy, twist and interfere with the virtual and physical context around, in a macro form on the territory of the complexity drive to change the ideologies of the fixed urban form. Through the approach of decolonial thinking being and doing one question that emerges is how to fundamentally rethink and offer ways to reimagine society through spatial practice – beyond the utopian universalist constraints conceived within modern architecture. Recognizing the limits and slips of academic disciplines such as architecture, art history, museology and curating, and encouraging practices of unlearning, our energy is therefore to situate a critical conversation around decolonization in Europe but through challenging Western epistemologies in relation to architecture, living and working spaces, territories of care, urban and rural planning., https://www.librarystack.org/architectural-dissonances/?ref=unknown

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion centre that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times., Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times., Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia

Patient X: A 67-year-old Caucasian man slips on a patch of ice. He has abrasions to his hands and has sustained significant damage to his hip. At the emergency room, he informs clinicians he takes atorvastatin, metformin, and glimepiride to treat hypertension and Type 2 Diabetes Mellitus (T2DM). X-rays reveal a fractured hip, which will require total hip replacement surgery.

The agenda of the UN's Sustainable Development Goals (SDGs) 1 challenges the synthetic biology community—and the life sciences as a whole—to develop transformative technologies that help to protect, even expand our planet's habitability. While modern tools for genome editing already benefit applications in health and agriculture, sustainability also asks for a dramatic transformation of our use of natural resources. The challenge is not just to limit and, wherever possible revert emissions of pollutants and greenhouse gases, but also to replace environmentally costly processes based on fossil fuels with bio‐based sustainable alternatives. This task is not exclusively a scientific and technical one but will also require guidelines and regulations for the development and large‐scale deployment of this new type of bio‐based production. Some recent advances that can (or soon could) enable us to make progress in these areas—and several possible governance principles—need to be addressed.

Alternative splicing (AS) is a major mechanism of post-transcriptional gene regulation that contributes to transcriptome diversity in mammals. Alternatively spliced events are a hallmark of neuronal differentiation and it has been revealed that AS events in the brain are more highly conserved in comparison to other tissues. The advent of powerful RNA-seq technologies has transformed our ability to profile AS on a transcriptome-wide scale, and in this project we carried out an evolutionary comparison of changes in the mRNA processing of Homo sapiens and Rhesus macaque. We leveraged RNA-seq to study in vitro neuronal differentiation over the course of 5 weeks in human and rhesus. Upon mapping the RNAseq data, we assembled the RNA-seq alignments into potential transcripts. We further identified, quantified, and tested different types of alternative splicing events, and focused on skipped exon events that were differentially spliced between different time points. We next narrowed down our analysis to orthologous skipped exon events to identify temporally regulated species-specific and conserved alternative splicing patterns. Together, our data reveal mRNA isoforms that are biologically relevant during the early stages of neuronal differentiation.

Alternative splicing (AS) is a major mechanism of post-transcriptional gene regulation that contributes to transcriptome diversity in mammals. Alternatively spliced events are a hallmark of neuronal differentiation and it has been revealed that AS events in the brain are more highly conserved in comparison to other tissues. The advent of powerful RNA-seq technologies has transformed our ability to profile AS on a transcriptome-wide scale, and in this project we carried out an evolutionary comparison of changes in the mRNA processing of Homo sapiens and Rhesus macaque. We leveraged RNA-seq to study in vitro neuronal differentiation over the course of 5 weeks in human and rhesus. Upon mapping the RNAseq data, we assembled the RNA-seq alignments into potential transcripts. We further identified, quantified, and tested different types of alternative splicing events, and focused on skipped exon events that were differentially spliced between different time points. We next narrowed down our analysis to orthologous skipped exon events to identify temporally regulated species-specific and conserved alternative splicing patterns. Together, our data reveal mRNA isoforms that are biologically relevant during the early stages of neuronal differentiation.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcriptcoupled spatial barcodes at 2-mu m resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues., QC 20191022

Patient X: A 67-year-old Caucasian man slips on a patch of ice. He has abrasions to his hands and has sustained significant damage to his hip. At the emergency room, he informs clinicians he takes atorvastatin, metformin and glimepiride to treat hypertension and Type 2 Diabetes Mellitus (T2DM). X-rays reveal a fractured hip, which will require total hip replacement surgery. Biotechnology is a major force poised to help us to live longer and healthier lives. In 2015, the United Nations defined 17 Sustainable Development Goals aimed at providing an all-encompassing framework for improving the state of the world (**). Promoting healthy living for all at all ages, is one of the principal objectives. Using the commonplace example described above, we examine how some of the most well-known biotechnologies (genome editing, stem cell therapy, tissue engineering and precision medicine) are able to benefit Patient X today, in 2018 and we also provide perspective on the additional value these same technologies could offer only ∼20 years into the future (Fig. 1). During this ∼20 year gap, both technical challenges as well as ethical, legal and socio-economic questions must be addressed before these technologies can achieve broader impact. Further, as both the successes and the failures will likely have enduring effects on society, responsible oversight of these and other biotechnologies are necessary as science and applications move out of the lab into clinical practice. In the next 20 years biotechnology will undoubtedly transform healthcare but how, when and, in some cases, whether it should, require careful consideration.

Patient X: A 67-year-old Caucasian man slips on a patch of ice. He has abrasions to his hands and has sustained significant damage to his hip. At the emergency room, he informs clinicians he takes atorvastatin, metformin, and glimepiride to treat hypertension and Type 2 Diabetes Mellitus (T2DM). X-rays reveal a fractured hip, which will require total hip replacement surgery.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

The agenda of the UN's Sustainable Development Goals (SDGs) [1] challenges the synthetic biology community—and the life sciences as a whole—to develop transformative technologies that help to protect, even expand our planet's habitability. While modern tools for genome editing already benefit applications in health and agriculture, sustainability also asks for a dramatic transformation of our use of natural resources. The challenge is not just to limit and, wherever possible revert emissions of pollutants and greenhouse gases, but also to replace environmentally costly processes based on fossil fuels with bio‐based sustainable alternatives. This task is not exclusively a scientific and technical one but will also require guidelines and regulations for the development and large‐scale deployment of this new type of bio‐based production. Some recent advances that can (or soon could) enable us to make progress in these areas—and several possible governance principles—need to be addressed.

BACKGROUND: Over the past decades, the focus on the regenerative properties of platelets (PLTs) has intensified and many PLT-derived growth factors are readily used in medical settings. A general lack of standardization in the preparation of these growth factors remains, however, and this study therefore examines the dynamics of growth factors throughout the freeze-thaw procedure. STUDY DESIGN AND METHODS: Plateletpheresis (PA) and PLT-poor plasma (PPP) samples were collected from 10 healthy donors. PA was lysed to produce PLT lysate (PL) for 1, 3, 5, 10, and 30 freeze-thaw cycles. The resulting growth factor and cytokine concentrations from PPP, PA, and PL of different cycles were analyzed and compared using enzyme-linked immunosorbent assay and multiplex bead assays. RESULTS: PL produced by the freeze-thaw procedure resulted in approximately four-to 10-fold enrichment of transforming growth factor-b1, epidermal growth factor, PLT-derived growth factor (PDGF)-AB/BB, PLT factor-4, and fibroblast growth factor-2. The increase in concentrations plateaued at Cycles 3 and 5 and in some cases declined with further cycles. The concentrations of insulin-like growth factor-1, hepatocyte growth factor, vascular endothelial growth factor, and bone morphogenetic protein-2 in PL were essentially comparable to those in PPP. CONCLUSION: Using the freeze-thaw method, optimal preparation of PL with regard to the concentration of growth factors was achieved at Cycles 3 to 5. Based on our findings, the clinical significance of using a greater number of cycles is likely limited.

The most polymorphic part of the human genome, the MHC, encodes over 160 proteins of diverse function. Half of them, including the HLA class I and II genes, are directly involved in immune responses. Consequently, the MHC region strongly associates with numerous diseases and clinical therapies. Notoriously, the MHC region has been intractable to high-throughput analysis at complete sequence resolution, and current reference haplotypes are inadequate for large-scale studies. To address these challenges, we developed a method that specifically captures and sequences the 4.8-Mbp MHC region from genomic DNA. For 95 MHC homozygous cell lines we assembled, de novo, a set of high-fidelity contigs and a sequence scaffold, representing a mean 98% of the target region. Included are six alternative MHC reference sequences of the human genome that we completed and refined. Characterization of the sequence and structural diversity of the MHC region shows the approach accurately determines the sequences of the highly polymorphic HLA class I and HLA class II genes and the complex structural diversity of complement factor C4A/C4B It has also uncovered extensive and unexpected diversity in other MHC genes; an example is MUC22, which encodes a lung mucin and exhibits more coding sequence alleles than any HLA class I or II gene studied here. More than 60% of the coding sequence alleles analyzed were previously uncharacterized. We have created a substantial database of robust reference MHC haplotype sequences that will enable future population scale studies of this complicated and clinically important region of the human genome.

Heavy metal removal by plants bears a great potential to decontaminate soils. A major challenge remains to find plant species that accumulate heavy metal, harbor a sufficient biomass and grow in the desired environmental conditions. Here we present candidate plants for phytoremediation in arid climates. We sampled sixteen dominant plants from mining area naturally polluted with high Pb-Zn and Cd concentration. Plants were assessed for their ability to accumulate Zn, Pb and Cd and six species were selected on the base of their heavy metal concentration in shoots and leaves, enrichment coefficient and translocation factor. Out of all the tested species in field study, Alcea aucheri was the most promising one which accumulated over than 460 and 4089 ??g/g Pb in the roots and shoots, respectively. We confirmed this ability with a greenhouse experiment on soil spiked with different Pb and Cd concentrations. Concentration of Pb and Cd in aerial parts of A. aucheri were more than 1700 and 345 ??g/g in 2400 and 200 mg/kg Pb and Cd soil treatment respectively. We propose that A. aucheri as model hyperaccumulator able to live in adverse condition, producing high biomass, and supersede heavy metal accumulation reported to other plants, making of this species one of the best Pb hyperaccumulator reported to date.

Background: Platelet lysate is a readily available source of growth factors, and other mediators, which has been used in a variety of clinical applications. However, the product remains poorly standardized and the present investigation evaluates the composition of platelet lysate obtained from either fresh or stored pathogen-inactivated platelet units. Materials and Methods: Platelet pooled units (n = 10) were obtained from healthy blood donors and tested according to standard procedures. All units were pathogen inactivated using amotosalen hydrochloride and UVA exposure. Platelet lysate was subsequently produced at two separate time-points, either from fresh platelet units or after 5 days of storage, by repeated freeze-thaw cycles. The following mediators were determined at each time point: EGF, FGF-2, VEGF, IGF-1, PDGF-AB/BB, BMP-2, PF4, TGF-beta isoform 1, IL-1(i, IL-2, IL-6, IL-10, IL-12p70, 1L-17A, TNF-alpha, and IFN-gamma. Results: The concentration of growth factors and cytokines was affected by time in storage. Notably, TGF-beta, PDGF-AB/BB, and PF4 showed an increase of 27.2% (p < 0.0001), 29.5% (p = 0.04) and 8.2% (p = 0.0004), respectively. A decrease was seen in the levels of IGF-1 and FGF-2 with 22% (p = 0.041) and 11% (p = 0.01), respectively. Cytokines were present only in very low concentrations and all other growth factors remained stable with time in storage. Conclusion: The composition of mediators in platelet lysate obtained from pathogen inactivated platelet units differs when produced from fresh and stored platelet units, respectively. This underscores the need for further standardization and optimization of this important product, which potentially may influence the clinical effects.

The physiological functions of natural killer (NK) cells in human immunity and reproduction depend upon diverse interactions between killer cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands: HLA-A, HLA-B, and HLA-C. The genomic regions containing the KIR and HLA class I genes are unlinked, structurally complex, and highly polymorphic. They are also strongly associated with a wide spectrum of diseases, including infections, autoimmune disorders, cancers, and pregnancy disorders, as well as the efficacy of transplantation and other immunotherapies. To facilitate study of these extraordinary genes, we developed a method that captures, sequences, and analyzes the 13 KIR genes and HLA-A, HLA-B, and HLA-C from genomic DNA. We also devised a bioinformatics pipeline that attributes sequencing reads to specific KIR genes, determines copy number by read depth, and calls high-resolution genotypes for each KIR gene. We validated this method by using DNA from well-characterized cell lines, comparing it to established methods of HLA and KIR genotyping, and determining KIR genotypes from 1000 Genomes sequence data. This identified 116 previously uncharacterized KIR alleles, which were all demonstrated to be authentic by sequencing from source DNA via standard methods. Analysis of just two KIR genes showed that 22% of the 1000 Genomes individuals have a previously uncharacterized allele or a structural variant. The method we describe is suited to the large-scale analyses that are needed for characterizing human populations and defining the precise HLA and KIR factors associated with disease. The methods are applicable to other highly polymorphic genes.

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

The physiological functions of natural killer (NK) cells in human immunity and reproduction depend upon diverse interactions between killer cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands: HLA-A, HLA-B, and HLA-C. The genomic regions containing the KIR and HLA class I genes are unlinked, structurally complex, and highly polymorphic. They are also strongly associated with a wide spectrum of diseases, including infections, autoimmune disorders, cancers, and pregnancy disorders, as well as the efficacy of transplantation and other immunotherapies. To facilitate study of these extraordinary genes, we developed a method that captures, sequences, and analyzes the 13 KIR genes and HLA-A, HLA-B, and HLA-C from genomic DNA. We also devised a bioinformatics pipeline that attributes sequencing reads to specific KIR genes, determines copy number by read depth, and calls high-resolution genotypes for each KIR gene. We validated this method by using DNA from well-characterized cell lines, comparing it to established methods of HLA and KIR genotyping, and determining KIR genotypes from 1000 Genomes sequence data. This identified 116 previously uncharacterized KIR alleles, which were all demonstrated to be authentic by sequencing from source DNA via standard methods. Analysis of just two KIR genes showed that 22% of the 1000 Genomes individuals have a previously uncharacterized allele or a structural variant. The method we describe is suited to the large-scale analyses that are needed for characterizing human populations and defining the precise HLA and KIR factors associated with disease. The methods are applicable to other highly polymorphic genes.

Chronic hepatitis B virus (HBV) infection is epidemiologically associated with hepatocellular carcinoma (HCC), but its role in HCC remains poorly understood due to technological limitations. In this study, we systematically characterize HBV in HCC patients. HBV sequences were enriched from 48 HCC patients using an oligo-bead-based strategy, pooled together and sequenced using the FLX-Genome-Sequencer. In the tumors, preferential integration of HBV into promoters of genes (P < 0.001) and significant enrichment of integration into chromosome 10 (P < 0.01) were observed. Integration into chromosome 10 was significantly associated with poorly differentiated tumors (P < 0.05). Notably, in the tumors, recurrent integration into the promoter of the human telomerase reverse transcriptase (TERT) gene was found to correlate with increased TERT expression. The preferred region within the HBV genome involved in integration and viral structural alteration is at the 3'-end of hepatitis B virus X protein (HBx), where viral replication/transcription initiates. Upon integration, the 3'-end of the HBx is often deleted. HBxhuman chimeric transcripts, the most common type of chimeric transcripts, can be expressed as chimeric proteins. Sequence variation resulting in non-conservative amino acid substitutions are commonly observed in HBV genome. This study highlights HBV as highly mutable in HCC patients with preferential regions within the host and virus genome for HBV integration/structural alterations., QC 20140616

The Saccharomyces cerevisiae strains widely used for industrial fuel-ethanol production have been developed by selection, but their underlying beneficial genetic polymorphisms remain unknown. Here, we report the draft whole-genome sequence of the S. cerevisiae strain CAT-1, which is a dominant fuel-ethanol fermentative strain from the sugarcane industry in Brazil. Our results indicate that strain CAT-1 is a highly heterozygous diploid yeast strain, and the similar to 12-Mb genome of CAT-1, when compared with the reference S228c genome, contains similar to 36,000 homozygous and similar to 30,000 heterozygous single nucleotide polymorphisms, exhibiting an uneven distribution among chromosomes due to large genomic regions of loss of heterozygosity (LOH). In total, 58 % of the 6,652 predicted protein-coding genes of the CAT-1 genome constitute different alleles when compared with the genes present in the reference S288c genome. The CAT-1 genome contains a reduced number of transposable elements, as well as several gene deletions and duplications, especially at telomeric regions, some correlated with several of the physiological characteristics of this industrial fuel-ethanol strain. Phylogenetic analyses revealed that some genes were likely associated with traits important for bioethanol production. Identifying and characterizing the allelic variations controlling traits relevant to industrial fermentation should provide the basis for a forward genetics approach for developing better fermenting yeast strains., QC 20120625

Background: The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. Methodology/Results: We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Conclusions: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis., QC 20101203

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from "vanishingly rare'' (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs "miRNAs''). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 39 overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts., QC 20150211

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from "vanishingly rare" (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs "miRNAs"). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 3' overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts.

We have used multiplexed high-throughput sequencing to characterize changes in small RNA populations that occur during viral infection in animal cells. Small RNA-based mechanisms such as RNA interference (RNAi) have been shown in plant and invertebrate systems to play a key role in host responses to viral infection. Although homologs of the key RNAi effector pathways are present in mammalian cells, and can launch an RNAi-mediated degradation of experimentally targeted mRNAs, any role for such responses in mammalian host-virus interactions remains to be characterized. Six different viruses were examined in 41 experimentally susceptible and resistant host systems. We identified virus-derived small RNAs (vsRNAs) from all six viruses, with total abundance varying from "vanishingly rare" (less than 0.1% of cellular small RNA) to highly abundant (comparable to abundant micro-RNAs "miRNAs"). In addition to the appearance of vsRNAs during infection, we saw a number of specific changes in host miRNA profiles. For several infection models investigated in more detail, the RNAi and Interferon pathways modulated the abundance of vsRNAs. We also found evidence for populations of vsRNAs that exist as duplexed siRNAs with zero to three nucleotide 3' overhangs. Using populations of cells carrying a Hepatitis C replicon, we observed strand-selective loading of siRNAs onto Argonaute complexes. These experiments define vsRNAs as one possible component of the interplay between animal viruses and their hosts.

Glaucoma is a heterogeneous group of optic neuropathies that manifests by optic nerve head cupping or degeneration of the optic nerve, resulting in a specific pattern of visual field loss. Glaucoma leads to blindness if left untreated, and is considered the second leading cause of blindness worldwide. The subgroup primary congenital glaucoma (PCG) is characterized by an anatomical defect in the trabecular meshwork, and age at onset in the neonatal or infantile period. It is the most severe form of glaucoma. CYP1B1 was the first gene genetically linked to PCG, and CYP1B1 mutations are the cause of disease in 20-100% of patients in different populations. Here, we report that LTBP2 encoding latent transforming growth factor beta binding protein 2 is a PCG causing gene, confirming results recently reported. A disease-associated locus on chromosome 14 was identified by performing whole genome autozygosity mapping in Iranian PCG families using high density single nucleotide polymorphism chips, and two disease-segregating loss of function mutations in LTBP2, p.Ser472fsX3 and p.Tyr1793fsX55, were observed in two families while sequencing candidate genes in the locus. The p.Tyr1793fsX55 mutation affects an amino acid close to the C-terminal of the encoded protein. Subsequently, LTBP2 expression was shown in human eyes, including the trabecular meshwork and ciliary processes that are thought to be relevant to the etiology of PCG., QC 20150211

Spinal cord injury (SCI) is a major cause of paralysis. Currently, there are no effective therapies to reverse this disabling condition. The presence of ependymal stem/progenitor cells (epSPCs) in the adult spinal cord suggests that endogenous stem cell-associated mechanisms might be exploited to repair spinal cord lesions. epSPC cells that proliferate after SCI are recruited by the injured zone, and can be modulated by innate and adaptive immune responses. Here we demonstrate that when epSPCs are cultured from rats with a SCI (ependymal stem/progenitor cells injury [epSPCi]), these cells proliferate 10 times faster in vitro than epSPC derived from control animals and display enhanced self renewal. Genetic profile analysis revealed an important influence of inflammation on signaling pathways in epSPCi after injury, including the upregulation of Jak/Stat and mitogen activated protein kinase pathways. Although neurospheres derived from either epSPCs or epSPCi differentiated efficiently to oligodendrocites and functional spinal motoneurons, a better yield of differentiated cells was consistently obtained from epSPCi cultures. Acute transplantation of undifferentiated epSPCi or the resulting oligodendrocyte precursor cells into a rat model of severe spinal cord contusion produced a significant recovery of motor activity 1 week after injury. These transplanted cells migrated long distances from the rostral and caudal regions of the transplant to the neurofilament-labeled axons in and around the lesion zone. Our findings demonstrate that modulation of endogenous epSPCs represents a viable cell-based strategy for restoring neuronal dysfunction in patients with spinal cord damage. STEM CELLS2009;27:733–743