1,326 research outputs found
An HMM-based Comparative Genomic Framework for Detecting Introgression in Eukaryotes
One outcome of interspecific hybridization and subsequent effects of
evolutionary forces is introgression, which is the integration of genetic
material from one species into the genome of an individual in another species.
The evolution of several groups of eukaryotic species has involved
hybridization, and cases of adaptation through introgression have been already
established. In this work, we report on a new comparative genomic framework for
detecting introgression in genomes, called PhyloNet-HMM, which combines
phylogenetic networks, that capture reticulate evolutionary relationships among
genomes, with hidden Markov models (HMMs), that capture dependencies within
genomes. A novel aspect of our work is that it also accounts for incomplete
lineage sorting and dependence across loci.
Application of our model to variation data from chromosome 7 in the mouse
(Mus musculus domesticus) genome detects a recently reported adaptive
introgression event involving the rodent poison resistance gene Vkorc1, in
addition to other newly detected introgression regions. Based on our analysis,
it is estimated that about 12% of all sites withinchromosome 7 are of
introgressive origin (these cover about 18 Mbp of chromosome 7, and over 300
genes). Further, our model detects no introgression in two negative control
data sets. Our work provides a powerful framework for systematic analysis of
introgression while simultaneously accounting for dependence across sites,
point mutations, recombination, and ancestral polymorphism
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Many approaches to program synthesis perform a search within an enormous
space of programs to find one that satisfies a given specification. Prior works
have used neural models to guide combinatorial search algorithms, but such
approaches still explore a huge portion of the search space and quickly become
intractable as the size of the desired program increases. To tame the search
space blowup, we propose training a neural model to learn a hands-on search
policy for bottom-up synthesis, instead of relying on a combinatorial search
algorithm. Our approach, called CrossBeam, uses the neural model to choose how
to combine previously-explored programs into new programs, taking into account
the search history and partial program executions. Motivated by work in
structured prediction on learning to search, CrossBeam is trained on-policy
using data extracted from its own bottom-up searches on training tasks. We
evaluate CrossBeam in two very different domains, string manipulation and logic
programming. We observe that CrossBeam learns to search efficiently, exploring
much smaller portions of the program space compared to the state-of-the-art.Comment: Published at ICLR 202
Carbon Nanotubes in Biology and Medicine: in vitro and in vivo Detection, Imaging and Drug Delivery
Carbon nanotubes exhibit many unique intrinsic physical and chemical
properties and have been intensively explored for biological and biomedical
applications. In this review, we summarize the main results of our and other
groups in this field and clarify that surface functionalization is critical to
the behaviors of carbon nanotubes in biological systems. Ultra-sensitive
detection of biological species with carbon nanotubes can be realized after
surface passivation to inhibit the non-specific binding of bio-molecules on the
hydrophobic nanotube surface. Electrical nanosensors based on nanotubes provide
a label-free approach to biological detections. Surface enhanced Raman
spectroscopy of CNT opens up a method of protein microarray with down to 1 fM
detection sensitivity. In vitro and in vivo toxicity studies reveal that well
water soluble and serum stable nanotubes are biocompatible, non-toxic and
potentially useful for biomedical applications. In vivo biodistributions vary
with the functionalization and possibly also sizes of nanotubes, with a
tendency of accumulation in the reticuloendothelial systems, including the
liver and spleen, after intravenous administration. If well functionalized,
nanotubes may be excreted mainly through the biliary pathway in feces. Carbon
nanotube-based drug delivery has shown promises in various in vitro and in vivo
experiments including delivery pf small interfering RNA, paclitaxel and
doxorubicin. Moreover, SWNTs with various interesting intrinsic optical
properties have been used as novel photoluminance, Raman and photoacoustic
contrast agents for imaging of cells and animals. Further multidisciplinary
explorations in this field are promising and may bring new opportunities to the
realm of biomedicine.Comment: To appear in Nano Research,
http://www.springerlink.com/content/m6t5871n55841736/?p=53184a51bcfd44239b9091ee7e1ef67
Changes in Continental Freshwater Discharge from 1948 to 2004
A new dataset of historical monthly streamflow at the farthest downstream stations for the world’s 925 largest ocean-reaching rivers has been created for community use. Available new gauge records are added to a network of gauges that covers ∼80 × 106 km2 or ∼80% of global ocean-draining land areas and accounts for about 73% of global total runoff. For most of the large rivers, the record for 1948–2004 is fairly complete. Data gaps in the records are filled through linear regression using streamflow simulated by a land surface model [Community Land Model, version 3 (CLM3)] forced with observed precipitation and other atmospheric forcings that are significantly (and often strongly) correlated with the observed streamflow for most rivers. Compared with previous studies, the new dataset has improved homogeneity and enables more reliable assessments of decadal and long-term changes in continental freshwater discharge into the oceans. The model-simulated runoff ratio over drainage areas with and without gauge records is used to estimate the contribution from the areas not monitored by the gauges in deriving the total discharge into the global oceans.
Results reveal large variations in yearly streamflow for most of the world’s large rivers and for continental discharge, but only about one-third of the top 200 rivers (including the Congo, Mississippi, Yenisey, Paraná, Ganges, Columbia, Uruguay, and Niger) show statistically significant trends during 1948–2004, with the rivers having downward trends (45) outnumbering those with upward trends (19). The interannual variations are correlated with the El Niño–Southern Oscillation (ENSO) events for discharge into the Atlantic, Pacific, Indian, and global ocean as a whole. For ocean basins other than the Arctic, and for the global ocean as a whole, the discharge data show small or downward trends, which are statistically significant for the Pacific (−9.4 km3 yr−1). Precipitation is a major driver for the discharge trends and large interannual-to-decadal variations. Comparisons with the CLM3 simulation suggest that direct human influence on annual streamflow is likely small compared with climatic forcing during 1948–2004 for most of the world’s major rivers. For the Arctic drainage areas, upward trends in streamflow are not accompanied by increasing precipitation, especially over Siberia, based on available data, although recent surface warming and associated downward trends in snow cover and soil ice content over the northern high latitudes contribute to increased runoff in these regions. The results are qualitatively consistent with climate model projections but contradict an earlier report of increasing continental runoff during the recent decades based on limited records
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Search is an important technique in program synthesis that allows for
adaptive strategies such as focusing on particular search directions based on
execution results. Several prior works have demonstrated that neural models are
effective at guiding program synthesis searches. However, a common drawback of
those approaches is the inability to handle iterative loops, higher-order
functions, or lambda functions, thus limiting prior neural searches from
synthesizing longer and more general programs. We address this gap by designing
a search algorithm called LambdaBeam that can construct arbitrary lambda
functions that compose operations within a given DSL. We create semantic vector
representations of the execution behavior of the lambda functions and train a
neural policy network to choose which lambdas to construct during search, and
pass them as arguments to higher-order functions to perform looping
computations. Our experiments show that LambdaBeam outperforms neural,
symbolic, and LLM-based techniques in an integer list manipulation domain
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