Recipient donor sex combinations in solid organ transplantation and impact on clinical outcome: A scoping review

INTRODUCTION

Solid organ transplantation (SOT) is a lifesaving treatment for end-stage organ failure. Although many factors affect the success of organ transplantation, recipient and donor sex are important biological factors influencing transplant outcome. However, the impact of the four possible recipient and donor sex combinations (RDSC) on transplant outcome remains largely unclear.

METHODS

A scoping review was carried out focusing on studies examining the association between RDSC and outcomes (mortality, graft rejection, and infection) after heart, lung, liver, and kidney transplantation. All studies up to February 2023 were included.

RESULTS

Multiple studies published between 1998 and 2022 show that RDSC is an important factor affecting the outcome after organ transplantation. Male recipients of SOT have a higher risk of mortality and graft failure than female recipients. Differences regarding the causes of death are observed. Female recipients on the other hand are more susceptible to infections after SOT.

CONCLUSION

Differences in underlying illnesses as well as age, immunosuppressive therapy and underlying biological mechanisms among male and female SOT recipients affect the post-transplant outcome. However, the precise mechanisms influencing the interaction between RDSC and post-transplant outcome remain largely unclear. A better understanding of how to identify and modulate these factors may improve outcome, which is particularly important in light of the worldwide organ shortage. An analysis for differences of etiology and causes of graft loss or mortality, respectively, is warranted across the RDSC groups.

PRACTITIONER POINTS

Recipient and donor sex combinations affect outcome after solid organ transplantation. While female recipients are more susceptible to infections after solid organ transplantation, they have higher overall survival following SOT, with causes of death differing from male recipients. Sex-differences should be taken into account in the post-transplant management.

Intrinsic polarity inversion in III-nitride waveguides for efficient nonlinear interactions

III-nitrides provide a versatile platform for nonlinear photonics. In this work, we explore a new promising configuration - composite waveguides containing GaN and AlN layers with inverted polarity, i.e., having opposite signs of the χ(2) nonlinear coefficient. This configuration allows us to address the limiting problem of the mode overlap for nonlinear interactions. Our modelling predicts a significant improvement in the conversion efficiency. We confirm our theoretical prediction with the experimental demonstration of second harmonic generation with an efficiency of 4%W-1cm-2 using a simple ridge waveguide. This efficiency is an order of magnitude higher compared to the previously reported results for III-nitride waveguides. Further improvement, reaching a theoretical efficiency of 30%W-1cm-2, can be achieved by reducing propagation losses.

Measurement of the ν_{e}-Nucleus Charged-Current Double-Differential Cross Section at ⟨E_{ν}⟩=2.4 GeV Using NOvA

The inclusive electron neutrino charged-current cross section is measured in the NOvA near detector using 8.02×10^{20} protons-on-target in the NuMI beam. The sample of GeV electron neutrino interactions is the largest analyzed to date and is limited by ≃17% systematic rather than the ≃7.4% statistical uncertainties. The double-differential cross section in final-state electron energy and angle is presented for the first time, together with the single-differential dependence on Q^{2} (squared four-momentum transfer) and energy, in the range 1  GeV≤E_{ν}<6  GeV. Detailed comparisons are made to the predictions of the GENIE, GiBUU, NEUT, and NuWro neutrino event generators. The data do not strongly favor a model over the others consistently across all three cross sections measured, though some models have especially good or poor agreement in the single differential cross section vs Q^{2}.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

Search for Active-Sterile Antineutrino Mixing Using Neutral-Current Interactions with the NOvA Experiment

This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 and 810 km from the beam source, is analyzed using an exposure of 12.51×10^{20} protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of 121 of neutral-current candidates are observed at the far detector, compared to a prediction of 122±11(stat.)±15(syst.) assuming mixing only between three active flavors. No evidence for ν[over ¯]_{μ}→ν[over ¯]_{s} oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles θ_{24}<25° and θ_{34}<32° at the 90% C.L. for 0.05  eV^{2}≤Δm_{41}^{2}≤0.5  eV^{2}, the range of mass splittings that produces no significant oscillations at the near detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration

The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE's sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

The circadian dynamics of the hippocampal transcriptome and proteome is altered in experimental temporal lobe epilepsy

Gene and protein expressions display circadian oscillations, which can be disrupted in diseases in most body organs. Whether these oscillations occur in the healthy hippocampus and whether they are altered in epilepsy are not known. We identified more than 1200 daily oscillating transcripts in the hippocampus of control mice and 1600 in experimental epilepsy, with only one-fourth oscillating in both conditions. Comparison of gene oscillations in control and epilepsy predicted time-dependent alterations in energy metabolism, which were verified experimentally. Although aerobic glycolysis remained constant from morning to afternoon in controls, it increased in epilepsy. In contrast, oxidative phosphorylation increased in control and decreased in epilepsy. Thus, the control hippocampus shows circadian molecular remapping, which is altered in epilepsy. We suggest that the hippocampus operates in a different functioning mode in epilepsy. These alterations need to be considered when studying epilepsy mechanisms, designing drug treatments, and timing their delivery.

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA

The NOvA experiment has seen a 4.4σ signal of ν[over ¯]_{e} appearance in a 2 GeV ν[over ¯]_{μ} beam at a distance of 810 km. Using 12.33×10^{20} protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ν[over ¯]_{μ}→ν[over ¯]_{e} candidates with a background of 10.3 and 102 ν[over ¯]_{μ}→ν[over ¯]_{μ} candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm_{32}^{2}|=2.48_{-0.06}^{+0.11}×10^{-3}  eV^{2}/c^{4} and sin^{2}θ_{23} in the ranges from (0.53-0.60) and (0.45-0.48) in the normal neutrino mass hierarchy. The data exclude most values near δ_{CP}=π/2 for the inverted mass hierarchy by more than 3σ and favor the normal neutrino mass hierarchy by 1.9σ and θ_{23} values in the upper octant by 1.6σ.

Learning in the machine: Recirculation is random backpropagation

Learning in physical neural systems must rely on learning rules that are local in both space and time. Optimal learning in deep neural architectures requires that non-local information be available to the deep synapses. Thus, in general, optimal learning in physical neural systems requires the presence of a deep learning channel to communicate non-local information to deep synapses, in a direction opposite to the forward propagation of the activities. Theoretical arguments suggest that for circular autoencoders, an important class of neural architectures where the output layer is identical to the input layer, alternative algorithms may exist that enable local learning without the need for additional learning channels, by using the forward activation channel as the deep learning channel. Here we systematically identify, classify, and study several such local learning algorithms, based on the general idea of recirculating information from the output layer to the hidden layers. We show through simulations and mathematical derivations that these algorithms are robust and converge to critical points of the global error function. In most cases, we show that these recirculation algorithms are very similar to an adaptive form of random backpropagation, where each hidden layer receives a linearly transformed, slowly-varying, version of the output error.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

BACKGROUND AND PURPOSE

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

MATERIALS AND METHODS

MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 (IDH1) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification.

RESULTS

Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features.

CONCLUSIONS

Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Enhanced Higgs boson to τ(+)τ(-) search with deep learning

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

How pervasive are circadian oscillations?

Circadian oscillations play a critical role in coordinating the physiology, homeostasis, and behavior of biological systems. Once thought to only be controlled by a master clock, recent high-throughput experiments suggest many genes and metabolites in a cell are potentially capable of circadian oscillations. Each cell can reprogram itself and select a relatively small fraction of this broad repertoire for circadian oscillations, as a result of genetic, environmental, and even diet changes.

Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis in irritable bowel syndrome

Enhanced stress responsiveness has been implicated as a potential mechanism contributing to the pathophysiology of irritable bowel syndrome (IBS), and should be reflected in altered function of the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system. Both of these systems can modulate mucosal immune function. The aims of this study were: (i) to characterize the basal circadian rhythm of adrenocorticotropin hormone (ACTH) and cortisol in IBS vs healthy controls; (ii) to compare stimulated ACTH, cortisol and noradrenaline responses to a pelvic visceral stressor (sigmoidoscopy) in IBS and controls; and (iii) to correlate neuroendocrine responses with colonic mucosal cytokine expression and symptoms in IBS. Two separate studies were conducted in women. In Study 1, basal cortisol levels were analysed in 41 IBS and 25 controls using 24-h collections of plasma ACTH and cortisol (q10 min sampling). In Study 2, 10 IBS patients with diarrhoea (IBS-D) and 10 controls underwent sigmoidoscopy with measurements of stimulated neuroendocrine responses and cytokine mRNA expression in colonic tissue. Basal ACTH levels were significantly blunted (P < 0.05), while basal and stimulated plasma cortisol levels were higher in patients. Basal cortisol levels prior to an experimental visceral stressor positively correlated with anxiety symptoms (P < 0.004), but not IBS symptoms. Irritable bowel syndrome patients with diarrhoea had significantly decreased mRNA expression of mucosal cytokines [interleukin (IL)-2, IL-6] in the sigmoid colon vs controls (P < 0.05). Although dysregulations in stress-responsive systems such as the HPA axis and mucosal immune function are demonstrated in IBS, they do not appear to have a primary role in modulating IBS severity and abdominal pain.

Mitochondrial mutations in cancer

The metabolism of solid tumors is associated with high lactate production while growing in oxygen (aerobic glycolysis) suggesting that tumors may have defects in mitochondrial function. The mitochondria produce cellular energy by oxidative phosphorylation (OXPHOS), generate reactive oxygen species (ROS) as a by-product, and regulate apoptosis via the mitochondrial permeability transition pore (mtPTP). The mitochondria are assembled from both nuclear DNA (nDNA) and mitochondrial DNA (mtDNA) genes. The mtDNA codes for 37 genes essential of OXPHOS, is present in thousands of copies per cell, and has a very high mutations rate. In humans, severe mtDNA mutations result in multisystem disease, while some functional population-specific polymorphisms appear to have permitted humans to adapt to new environments. Mutations in the nDNA-encoded mitochondrial genes for fumarate hydratase and succinate dehydrogenase have been linked to uterine leiomyomas and paragangliomas, and cancer cells have been shown to induce hexokinase II which harnesses OXPHOS adenosine triphosphate (ATP) production to drive glycolysis. Germline mtDNA mutations at nucleotides 10398 and 16189 have been associated with breast cancer and endometrial cancer. Tumor mtDNA somatic mutations range from severe insertion-deletion and chain termination mutations to mild missense mutations. Surprisingly, of the 190 tumor-specific somatic mtDNA mutations reported, 72% are also mtDNA sequence variants found in the general population. These include 52% of the tumor somatic mRNA missense mutations, 83% of the tRNA mutations, 38% of the rRNA mutations, and 85% of the control region mutations. Some associations might reflect mtDNA sequencing errors, but analysis of several of the tumor-specific somatic missense mutations with population counterparts appear legitimate. Therefore, mtDNA mutations in tumors may fall into two main classes: (1) severe mutations that inhibit OXPHOS, increase ROS production and promote tumor cell proliferation and (2) milder mutations that may permit tumors to adapt to new environments. The former may be lost during subsequent tumor oxygenation while the latter may become fixed. Hence, mitochondrial dysfunction does appear to be a factor in cancer etiology, an insight that may suggest new approaches for diagnosis and treatment.

A photonic quantum information interface

Quantum communication requires the transfer of quantum states, or quantum bits of information (qubits), from one place to another. From a fundamental perspective, this allows the distribution of entanglement and the demonstration of quantum non-locality over significant distances. Within the context of applications, quantum cryptography offers a provably secure way to establish a confidential key between distant partners. Photons represent the natural flying qubit carriers for quantum communication, and the presence of telecommunications optical fibres makes the wavelengths of 1,310 nm and 1,550 nm particularly suitable for distribution over long distances. However, qubits encoded into alkaline atoms that absorb and emit at wavelengths around 800 nm have been considered for the storage and processing of quantum information. Hence, future quantum information networks made of telecommunications channels and alkaline memories will require interfaces that enable qubit transfers between these useful wavelengths, while preserving quantum coherence and entanglement. Here we report a demonstration of qubit transfer between photons of wavelength 1,310 nm and 710 nm. The mechanism is a nonlinear up-conversion process, with a success probability of greater than 5 per cent. In the event of a successful qubit transfer, we observe strong two-photon interference between the 710 nm photon and a third photon at 1,550 nm, initially entangled with the 1,310 nm photon, although they never directly interacted. The corresponding fidelity is higher than 98 per cent.

SCRATCH: a protein structure and structural feature prediction server

SCRATCH is a server for predicting protein tertiary structure and structural features. The SCRATCH software suite includes predictors for secondary structure, relative solvent accessibility, disordered regions, domains, disulfide bridges, single mutation stability, residue contacts versus average, individual residue contacts and tertiary structure. The user simply provides an amino acid sequence and selects the desired predictions, then submits to the server. Results are emailed to the user. The server is available at .

High-performance guided-wave asynchronous heralded single-photon source

We report on a guided-wave asynchronous heralded single-photon source based on the creation of nondegenerate photon pairs by spontaneous parametric downconversion in a periodically poled lithium niobate wave-guide. We show that, by use of the signal photon at 1310 nm as a trigger, a gated detection process permits announcement of the arrival of single photons at 1550 nm at the output of a single-mode optical fiber with a high probability of 0.37. At the same time the multiphoton emission probability is reduced by a factor of 10 compared with Poissonian light sources. Furthermore, the model we have developed to calculate those figures of merit is shown to be accurate. This study can therefore serve as a paradigm for the conception of new quantum communication and computation networks.

Statistical detection of chromosomal homology using shared-gene density alone

MOTIVATION

Over evolutionary time, various processes including point mutations and insertions, deletions and inversions of variable sized segments progressively degrade the homology of duplicated chromosomal regions making identification of the homologous regions correspondingly difficult. Existing algorithms that attempt to detect homology are based on shared-gene density and colinearity and possibly also strand information.

RESULTS

Here, we develop a new algorithm for the statistical detection of chromosomal homology, CloseUp, which uses shared-gene density alone to fully exploit the observation that relaxing colinearity requirements in general is beneficial for homology detection and at the same time optimizes computation time. CloseUp has two components: the identification of candidate homologous regions followed by their statistical evaluation using Monte Carlo methods and data randomization. Using both artificial and real data, we compared CloseUp with two existing programs (ADHoRe and LineUp) for chromosomal homology detection and found that in general CloseUp compares favorably.

AVAILABILITY

CloseUp and supplementary information are available at http://www.igb.uci.edu/servers/cgss.html

CONTACT

pfbaldi@ics.uci.edu.

Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners

MOTIVATION

Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.

RESULTS

We develop a new set of flexible machine learning architectures for the prediction of contact maps, as well as other information processing and pattern recognition tasks. The architectures can be viewed as recurrent neural network implemantations of a class of Bayesian networks we call generalized input-output HMMs (GIOHMMs). For the specific case of contact maps, contextual information is propagated laterally through four hidden planes, one for each cardinal corner. We show that these architectures can be trained from examples and yield contact map predictors that outperform previously reported methods. While several extensions and improvements are in progress, the current version can accurately predict 60.5% of contacts at a distance cutoff of 8 A and 45% of distant contacts at 10 A, for proteins of length up to 300.

Cloning and linkage mapping of resistance gene homologues in apple

Apple ( Malus x domestica Borkh.) sequences sharing homology with known resistance genes were cloned using a PCR-based approach with degenerate oligonucleotide primers designed on conserved regions of the nucleotide-binding site (NBS). Sequence analysis of the amplified fragments indicated the presence of at least 27 families of NBS-containing genes in apple, each composed of several very similar or nearly identical sequences. The NBS-leucine-rich repeat homologues appeared to include members of the two major groups that have been described in dicot plants: one possessing a toll-interleukin receptor element and one lacking such a domain. Genetic mapping of the cloned sequences was achieved through the development of CAPS and SSCP markers using a segregating population of a cross between the two apple cultivars Fiesta and Discovery. Several of the apple resistance gene homologues mapped in the vicinity, or at least on the same linkage group, of known loci controlling resistance to various pathogens. The utility of resistance gene-homologue sequences as molecular markers for breeding purposes and for gene cloning is discussed.

[Mycobacteria in swimming pool water and the meaning of microbiological conventional indicators]

Monitoring program of hygienic quality water in twelve public swimming pools was performed. Legally required microbiological indicator parameters of safety for gastrointestinal illness were measured besides the analyses of Pseudomonas spp. and Staphylococcus spp. prevalence, frequency of recovery and number of nontuberculous mycobacteria. We detected positive samples for coliforms at lower rate (29.3%) than Pseudomonas (75.5%), Staphylococcus spp. (46%) and Mycobacteria (59.4%). We pointed out statistically significant correlation (r=0.67 p=0.0001) between Mycobacteria and Pseudomonas so we think that the latter might be a good predictive marker. As 82% of samples had free chlorine residual within the limits stated by Italian Laws, the efficacy of chlorination to prevent risk of infectious diseases transmission by route other than gastroenteric was discussed. A revision of both the sanitary significance of conventional microbial parameters and the related regulations appears necessary.

Improved prediction of the number of residue contacts in proteins by recurrent neural networks

Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.

Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12

We describe statistical methods based on the t test that can be conveniently used on high density array data to test for statistically significant differences between treatments. These t tests employ either the observed variance among replicates within treatments or a Bayesian estimate of the variance among replicates within treatments based on a prior estimate obtained from a local estimate of the standard deviation. The Bayesian prior allows statistical inference to be made from microarray data even when experiments are only replicated at nominal levels. We apply these new statistical tests to a data set that examined differential gene expression patterns in IHF(+) and IHF(-) Escherichia coli cells (Arfin, S. M., Long, A. D., Ito, E. T., Tolleri, L., Riehle, M. M., Paegle, E. S., and Hatfield, G. W. (2000) J. Biol. Chem. 275, 29672-29684). These analyses identify a more biologically reasonable set of candidate genes than those identified using statistical tests not incorporating a Bayesian prior. We also show that statistical tests based on analysis of variance and a Bayesian prior identify genes that are up- or down-regulated following an experimental manipulation more reliably than approaches based only on a t test or fold change. All the described tests are implemented in a simple-to-use web interface called Cyber-T that is located on the University of California at Irvine genomics web site.

A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes

MOTIVATION

DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data.

RESULTS

We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t -test, provide a systematic inference approach that compares favorably with simple t -test or fold methods, and partly compensate for the lack of replication.

Flexibility of the genetic code with respect to DNA structure

MOTIVATION

The primary function of DNA is to carry genetic information through the genetic code. DNA, however, contains a variety of other signals related, for instance, to reading frame, codon bias, pairwise codon bias, splice sites and transcription regulation, nucleosome positioning and DNA structure. Here we study the relationship between the genetic code and DNA structure and address two questions. First, to which degree does the degeneracy of the genetic code and the acceptable amino acid substitution patterns allow for the superimposition of DNA structural signals to protein coding sequences? Second, is the origin or evolution of the genetic code likely to have been constrained by DNA structure?

RESULTS

We develop an index for code flexibility with respect to DNA structure. Using five different di- or tri-nucleotide models of sequence-dependent DNA structure, we show that the standard genetic code provides a fair level of flexibility at the level of broad amino acid categories. Thus the code generally allows for the superimposition of any structural signal on any protein-coding sequence, through amino acid substitution. The flexibility observed at the level of single amino acids allows only for the superimposition of punctual and loosely positioned signals to conserved amino acid sequences. The degree of flexibility of the genetic code is low or average with respect to several classes of alternative codes. This result is consistent with the view that DNA structure is not likely to have played a significant role in the origin and evolution of the genetic code.

Matching protein beta-sheet partners by feedforward and recurrent neural networks

Predicting the secondary structure (alpha-helices, beta-sheets, coils) of proteins is an important step towards understanding their three dimensional conformations. Unlike alpha-helices that are built up from one contiguous region of the polypeptide chain, beta-sheets are more complex resulting from a combination of two or more disjoint regions. The exact nature of these long distance interactions remains unclear. Here we introduce two neural-network based methods for the prediction of amino acid partners in parallel as well as anti-parallel beta-sheets. The neural architectures predict whether two residues located at the center of two distant windows are paired or not in a beta-sheet structure. Variations on these architecture, including also profiles and ensembles, are trained and tested via five-fold cross validation using a large corpus of curated data. Prediction on both coupled and non-coupled residues currently approaches 84% accuracy, better than any previously reported method.

Unusually located hydatid cysts miming a pulmonary tumor invaliding the spine

Hydatid disease is a worldwide encountered zoonosis but at present very rare in Europe, liver and lungs being the most frequently involved sites. Bone involvement is very uncommon and the vertebral spine is the most common site of skeletal involvement (less than 1% overall). We report a case of vertebral hydatid disease with secondary pleuro-pulmonary involvement successfully treated by emergency spinal decompression followed by lung resection en bloc with chest wall and partial vertebrectomy.

Sequence analysis by additive scales: DNA structure for sequences and repeats of all lengths

MOTIVATION

DNA structure plays an important role in a variety of biological processes. Different di- and tri-nucleotide scales have been proposed to capture various aspects of DNA structure including base stacking energy, propeller twist angle, protein deformability, bendability, and position preference. Yet, a general framework for the computational analysis and prediction of DNA structure is still lacking. Such a framework should in particular address the following issues: (1) construction of sequences with extremal properties; (2) quantitative evaluation of sequences with respect to a given genomic background; (3) automatic extraction of extremal sequences and profiles from genomic databases; (4) distribution and asymptotic behavior as the length N of the sequences increases; and (5) complete analysis of correlations between scales.

RESULTS

We develop a general framework for sequence analysis based on additive scales, structural or other, that addresses all these issues. We show how to construct extremal sequences and calibrate scores for automatic genomic and database extraction. We show that distributions rapidly converge to normality as Nincreases. Pairwise correlations between scales depend both on background distribution and sequence length and rapidly converge to an analytically predictable asymptotic value. For di- and tri-nucleotide scales, normal behavior and asymptotic correlation values are attained over a characteristic window length of about 10-15 bp. With a uniform background distribution, pairwise correlations between empirically-derived scales remain relatively small and roughly constant at all lengths, except for propeller twist and protein deformability which are positively correlated. There is a positive (resp. negative) correlation between dinucleotide base stacking (resp. propeller twist and protein deformability) and AT-content that increases in magnitude with length. The framework is applied to the analysis of various DNA tandem repeats. We derive exact expressions for counting the number of repeat unit classes at all lengths. Tandem repeats are likely to result from a variety of different mechanisms, a fraction of which is likely to depend on profiles characterized by extreme structural features.

The cold dependent accumulation of COR TMC-AP3 in cereals with contrasting, frost tolerance is regulated by different mRNA expression and protein turnover

The accumulation of specific cold-regulated (COR) proteins is a component of the hardening process and different amount of COR proteins has been related to different degrees of cold tolerance. A number of different mechanisms controls the accumulation of the COR proteins in the plant cells. In this work we describe the mechanisms controlling the accumulation of the COR protein TMC-AP3, a putative chloroplastic amino acid selective channel protein [1] in barley, durum, wheat, emmer and bread wheat. Winter barley and, to less extent, winter bread wheat showed a higher cor tmc-ap3 expression at low temperature than the spring one while no significant differences were detected between the emmer and the durum. wheat genotypes. After 2 days of de-hardening the transcript level dropped down in the same way in all tested genotypes, nevertheless the decrease in protein content was genotype dependent. In all frost resistant genotypes the amount of COR TMC-AP3 after 9 days of de-hardening was higher compared with that of susceptible ones. These findings suggest that resistant and susceptible genotypes have different protein degradation rate and/or mRNA translational efficiency. Differences in the protein degradation rate were not dependent from the amino acidic sequence of the protein, being extremely similar in all tested genotypes. A genetic study based on Chinese spring/Cheyenne chromosome substitution lines showed that the turnover of TMC-AP3 is a polygenic trait controlled by a number of loci being the most important located on chromosomes 1B, 2B, 2D and 4D.

Cascading phase shift and multivalued response in counterpropagating frequency-nondegenerate parametric amplifiers

We propose and analyze a novel geometry for all-optical processing of low-power signals that is based on a frequency-nondegenerate counterpropagating parametric amplifier. The stationary response in the cascading regime exhibits multivalued solutions with enhanced nonlinear phase shifts. Implementation in a quasi-phase-matched LiNbO(3) waveguide is discussed.

Assessing the accuracy of prediction algorithms for classification: an overview

We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.

On the convergence of a clustering algorithm for protein-coding regions in microbial genomes

MOTIVATION

As the number of fully sequenced prokaryotic genomes continues to grow rapidly, computational methods for reliably detecting protein-coding regions become even more important. Audic and Claverie (1998) Proc. Natl Acad. Sci. USA, 95, 10026-10031, have proposed a clustering algorithm for protein-coding regions in microbial genomes. The algorithm is based on three Markov models of order k associated with subsequences extracted from a given genome. The parameters of the three Markov models are recursively updated by the algorithm which, in simulations, always appear to converge to a unique stable partition of the genome. The partition corresponds to three kinds of regions: (1) coding on the direct strand, (2) coding on the complementary strand, (3) non-coding.

RESULTS

Here we provide an explanation for the convergence of the algorithm by observing that it is essentially a form of the expectation maximization (EM) algorithm applied to the corresponding mixture model. We also provide a partial justification for the uniqueness of the partition based on identifiability. Other possible variations and improvements are briefly discussed.

Exploiting the past and the future in protein secondary structure prediction

MOTIVATION

Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small window avoids overfitting problems, it does not permit capturing variable long-rang information.

RESULTS

We introduce a family of novel architectures which can learn to make predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary information, expressed in terms of multiple alignments, both at the input and output levels. While our system currently achieves an overall performance close to 76% correct prediction--at least comparable to the best existing systems--the main emphasis here is on the development of new algorithmic ideas.

AVAILABILITY

The executable program for predicting protein secondary structure is available from the authors free of charge.

CONTACT

pfbaldi@ics.uci.edu, gpollast@ics.uci.edu, brunak@cbs.dtu.dk, paolo@dsi.unifi.it.