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On February 1996, a receiver able to measure the electric field strength of LF radio broadcastings, with a sampling frequency of ten minutes, was put into operation in a site (AS) located in central Italy. One of the broadcasting stations selected is MCO (f=216 kHz), located in southeast France, 518 km far from the receiver. The MCO data collected since February 1996 up to September 2004 were examined and, at first, the night time data and the day time data (in winter and summer) were separated. Then, the wavelet analysis on the night and day time data was applied. The main result of the analysis was the appearance of a very clear anomaly during summer (July–August) 1998, at day time and at night time. The anomaly is a strong exaltation of the signal components with period in the 25–40 days range. Theoretical calculations of electric field strength were made and the only way to justify this anomaly seems to be the occurrence of an increase of the ground wave propagation mode of the radio signal. Such an increase could have been produced by an increase of the ground conductivity and by modifications of some parameter of the troposphere, mainly the refractive index. On 15 August 1998 a seismic sequence started with 17 earthquakes (M=2.2–4.6) on the Reatini mountains, a seismogenic zone located 30 km far from the AS receiver along the path MCO-AS. In this paper, the possibility that the previous radio anomaly can be a precursor of this seismic sequence is proposed.

Starting from 1992 geochemical data are being collected with a mean sampling frequency of three days in the form of the pH value and of the most common ions and gases in the groundwater in one deep well located in Petropavlovsk, the capital city of Kamchatka (Russia). On 1 January 1996 a strong eruption started from the Karymsky volcano, that is located about 100km far from the well, in the north-northeastern direction. At the same time, a large earthquake (M=6.9) occurred in the Karymsky area. On 5 December 1997 a very large earthquake (M=7.7) occurred offshore, at a distance of 350km from the well and towards the same direction. The analysis of the geochemical data shows clear variations in the raw temporal trends on both cases. For the first event, a clear premonitory phase appeared; for the second one, some pre-seismic variations could be revealed but permanent modifications of the chemistry of the water subsequent to the earthquake are very clear. In both cases the feature of the geochemical variations is consistent with an afflux of new water in the aquifer connected with the well and with an escape of the Carbon dioxide gas from the ground in different directions. A schematic model able to justify such a phenomenology and the connections of the geochemical variations with the previous tectonic activities is proposed.

At the beginning of 2002 an OmniPAL receiver was put into operation at the Department of Physics of Bari University (Southern Italy). The electric field strength of five VLF-LF signals transmitted from United Kingdom (f=16kHz), France (f=20.9kHz), Germany (f=23.4kHz), Iceland (f=37.5kHz) and Italy (f=54kHz) has been monitoring with a 5s sampling frequency. In a first step we reduced the amount of the data taking one datum each 10min (mean of the ±5min raw data) and then we smoothed these data by a running adjacent averaging over 7 days. Analysing the trends we obtained, we revealed at first in the signal from the Italian transmitter two clear intensity decreases in April 2002 and in August-September 2002. At these times we observed earthquakes with M=4.3 and M=5.6 respectively near the transmitter-receiver path and a precursory effect in the previous decreases appeared. Then, we noted that all of the five radio trends in the time interval March 2002-February 2003 are more disturbed than in other periods; in particular an evident simultaneous decrease appears in January-February 2003. We propose that these disturbances are related to general excitation of the margin between the African and European plates. In a second step we examined the terminator time (evening) changes for the Italian transmitter in July-September 2002, and we found significant deviations from the mean value at the end of August, which is supportive for some precursory ionospheric signature of earthquakes.

We examined the groundwater Argon content data sampled from 1988 to 2001 at two wells in Kamchatka (Russia) and anomalous increases appeared clearly during June-July 1996. On 21 June, a shallow (1km) earthquake with M=7.1 occurred at a distance less than 250km from the wells and so the previous increases could be related to this earthquake and, in particular, could be considered premonitory anomalies. In order to support this raw interpretation, we analysed the data collected in details. At first we smoothed out the high frequency fluctuations arising from the errors in a single measurement. Next we considered the known external effects on the water of a well that are the slow tectonic re-adjustment processes, the meteorology and the gravity tides and we separated these effects applying band-pass filters to the Argon content raw trends. Then we identified the largest fluctuations in these trends applying the 3 σ criterion and we found three anomalies in a case and two anomalies in other case. Comparing the time occurrence of the anomalies at the two wells we found out that a coincidence exists only in the case of the premonitory anomalies we are studying. The simultaneous appearance of well definite anomalies in the residual trends of the same parameter at two different sites supports their meaning and the possibility that they are related to some large scale effect, as the occurrence of a strong earthquake. But, other earthquakes similar to the June 1996 event took place during the Argon content measurements time and no anomaly appeared in this content. In the past, some of the authors of this paper studied the Helium content data collected in three natural springs of the Caucasus during seven years. A very similar result, that is the simultaneous appearance of clear premonitory anomalies only on the occasion of a strong (M=7.0) but shallow (2–4km) earthquake, was obtained. The correspondence with the case of the Caucasus validates the interpretation of the Kamchatkian anomalies as precursors.

During February–March 1998, we observed a significant increase (6–8 dB) in the electric field of the CZE (f = 270 kHz, Czech Republic) broadcasting station at a site named AS and located in central Italy. On 13 March 1998 an earthquake (M = 5.2) occurred in Slovenia, starting a strong seismic crisis (M = 6.0 on 12 April, M = 5.1 on 6 May). The distances of the epicentres from the radio receiver were over 400 km, but the epicentres are located in a zone that is in the middle of the CZE-AS path. Previously, we advanced the hypothesis that the increase of radio-signal electric field detected could be a precursor of the Slovenia seismic crisis. At the purpose to precise the connection between this radio anomaly and the preparatory phase of the earthquakes, we analysed in detail the data collected. At first we carried out a FFT on the data and clear harmonic components with period 0.5d, 1d, 14d, 28d and 365d were revealed. These periods are characteristic of gravity tides and therefore, it seems reasonable to consider the previous harmonics in the electric field of the radio signal as components related to tides of the atmosphere, assuming as tides of the atmosphere only the linear action of gravity tides on atmosphere. Then, we carried out band pass filters on our data and we discovered that the February–March 1998 increase is mainly related to an exalting of the harmonics with period 0.5d, 1d, 14d and 28d, i.e. of the (semi)monthly and (semi)diurnal atmospheric tidal components in the radio signal. Supposing that this exalting is produced by a corresponding exalting of the atmospheric tides we propose a model able to explain the production of such an effect during the preparatory phase of the Slovenia earthquakes.

For many years flow-rate, temperature, ions and gases content data have been collected from a natural spring located in the Koryakskiy volcano area (Kamchatka, Russia). We have investigated the correlations between the hydrogeochemical data and the areal seismicity represented by the ks values (ks is a function of magnitude and hypocentral distance) of the earthquakes. At first we smoothed the raw hydrogeochemical data using a semi-triangle weight function. Then we compared the trends of each smoothed hydrogeochemical parameter with the ks trend using a running cross-correlation function with a maximum lag of ± 30 days and the main result was that, sometimes, we found 0.7–0.4 cross-correlation coefficients with no lag for flow rate and with + (10 – 15) days lags for some ion and gas contents. The correlation is positive, i.e. flow rate and ion and gas contents increase when ks increases. This phenomenology could be explained by an underground water pumping produced by some earthquake. We advance the hypothesis that this pumping could be the response of the viscoelastic underground medium of the Koryakskiy volcano to seismic waves. So, sometimes, the supply of elastic energy of the earthquakes may provide the trigger to a catastrophic nucleation of bubbles of this material producing a new melt with a lower density which will tend to expand and cause a pressure increase. This pressure produces a more intensive circulation of underground water and an anomalous increase of the flow rate and subsequently anomalous increases in groundwater ions and gases content.

Among radio signals, low frequency (LF) radio signals lie in the band between 30–300 kHz. Monitoring equipment with the ability to measure the electric strength of such signals at field sites, were designed and assembled in Italy. From 1993 onwards, the electric field strength of the MCO (216 kHz, France) broadcasting station has been collecting measurements at two sites in central Italy that were chosen according to very low noise levels. At the end of 1996, radio signals from the CLT (189 kHz, Italy) and CZE (270 kHz, Czech Republic) broadcasting stations were included in the measurements. Meteorological data from central Italy were also collected over the same time period in order to study the influence of weather conditions on the experimental measurements. During the monitoring period, we observed some evident attenuation of the electric field strength in some of the radio signals at some of the receivers. The duration of the attenuation observed was several days, so it could possibly be related to particular meteorological conditions. On the other hand, this phenomenon might represent precursors of moderate (3.0 < M < 3.5) earthquakes that occurred near the receivers (within 50 km) along the transmitter-receiver path. In this case, it is possible that the pre-seismic processes could have produced irregularities in the troposphere, such as ducts, reflecting layers and scattering zones, so that some local troposphere defocusing of the radio signals might have occurred. These observations were related only to moderate earthquakes and in these cases, suitable meteorological conditions were probably needed to observe the effect. Between February – March 1998, we observed at one measuring site, a significant increase in the CZE electric field strength. Unfortunately, we could not use the data of the other receiver in this case, due to frequent interruptions in the data set. The increase might have been a precursor of the strong seismic sequence (M = 5.0–6.0) that occurred during March – May 1998 in Slovenia at a location over 400 km from the receiver, but lying in the middle of the transmitter-receiver path. In this case, it is possible that an ionospheric disturbance, produced by the pre-seismic processes, might have occurred.

For many years, ion and gas content data have been collected from the groundwater of three deep wells in the southern area of the Kamchatka peninsula, Russia. In the last ten years, five earthquakes with M > 6.5 have occurred within 250 km of the wells. In a previous study, we investigated the possibility that the hydrogeochemical time series contained precursors. The technique used was to assume that each signal with an amplitude of three times the standard deviation is an irregularity and we then defined anomalies as irregularities occurring simultaneously in the data for more than one parameter at each well. Using this method, we identified 11 anomalies with 8 of them being possible successes and 3 being failures as earthquake precursors. Precursors were obtained for all five earthquakes that we considered. In this paper, we allow for the cross-correlation found between the gas data sets and in some cases, between the ion data sets. No cross-correlation has been found between gas and ion content data. Any correlation undermines the idea that an anomaly might be identified from irregularities appearing simultaneously on different parameters at each site. To refine the technique, we re-examine the hydrogeochemical data and define as anomalies those irregularities occurring simultaneously only in the data of two or more uncorrelated parameters. We then restricted the analysis to the cases of just the gas content data and the ion content data. In the first case, we found 6 successes and 2 failures, and in the second case, we found only 3 successes. In the first case, the precursors appear only for three of the five earthquakes we considered, and in the second case, only for two, but these are the earthquakes nearest to the wells. Interestingly, it shows that when a strict set of rules for defining an anomaly is used, the method produces only successes and when less restrictive rules are used, earthquakes further from the well are implicated, but at the cost of false alarms being introduced.

To help maximize the impact of scientific journal articles, authors must ensure that article figures are accessible to people with color-vision deficiencies (CVDs), which affect up to 8% of males and 0.5% of females. We evaluated images published in biology- and medicine-oriented research articles between 2012 and 2022. Most included at least one color contrast that could be problematic for people with deuteranopia (‘deuteranopes’), the most common form of CVD. However, spatial distances and within-image labels frequently mitigated potential problems. Initially, we reviewed 4964 images from eLife, comparing each against a simulated version that approximated how it might appear to deuteranopes. We identified 636 (12.8%) images that we determined would be difficult for deuteranopes to interpret. Our findings suggest that the frequency of this problem has decreased over time and that articles from cell-oriented disciplines were most often problematic. We used machine learning to automate the identification of problematic images. For a hold-out test set from eLife (n=879), a convolutional neural network classified the images with an area under the precision-recall curve of 0.75. The same network classified images from PubMed Central (n=1191) with an area under the precision-recall curve of 0.39. We created a Web application (https://bioapps.byu.edu/colorblind_image_tester); users can upload images, view simulated versions, and obtain predictions. Our findings shed new light on the frequency and nature of scientific images that may be problematic for deuteranopes and motivate additional efforts to increase accessibility.

Background: Seasonal influenza vaccination rates are very low among teenagers. Objectives: We used publicly available data from the NIS-Teen annual national immunization survey to explore factors that influence the likelihood of a teen receiving their seasonal flu shot. Methods: Traditional stepwise multivariable regression was used in tandem with machine learning to determine the predictive factors in teen vaccine uptake. Results and Conclusions: Age was the largest predictor, with older teens being much less likely to be vaccinated than younger teens (97.48% compared to 41.71%, p < 0.0001). Provider participation in government programs such as Vaccines for Children and the state vaccine registry positively impacts vaccine uptake (p < 0.0001). Identifying as non-Hispanic Black was a small, negative predictor of teen vaccine uptake (78.18% unvaccinated compared to 73.78% of White teens, p < 0.0001). The state quartile for COVID-19 vaccine uptake also strongly predicted flu vaccine uptake, with the upper quartile of state COVID-19 vaccine uptake being significantly more likely to also get vaccinated for influenza (76.96%, 74.94%, 74.55%, and 72.97%, p < 0.0001). Other significant factors are the number of providers, education of the mother, poverty status, and having a mixed provider facility type. Additionally, the multivariable regression analysis revealed little difference in the predictive factors of vaccine uptake between pre- and post-pandemic datasets.

Low Frequency (LF) radio signals lie in the band 30-300 kHz. Monitoring equipment able to measure the electric strength of such signals, at field sites with very low noise levels, were designed and assembled in Italy. From 1993 onwards, the electric field strength of the MCO (216 kHz, France) broadcasting station has been measured at two sites in Central Italy. At the end of 1996, radio signals from the CLT (189 kHz, Italy) and the CZE (270 kHz, Czech Republic) broadcasting stations were included in the measurements. During this monitoring period, evident attenuation of the electric field strength in some of the radio signals was observed at some of the receivers. The duration of the attenuation observed was several days and so it could have been related to particular meteorological conditions. On the other hand, this phenomenon could also represent precursors of moderate (3.0 =M =3.5) earthquakes that occurred near the receivers (within 50 km) along the transmitter-receiver path. In this case it is possible that some local troposphere defocusing of the radio signals, produced by the pre-seismic processes, might have occurred. These observations were related only to moderate earthquakes and in these cases it may be that suitable meteorological conditions are needed to observe the effect. During February-March 1998 at one measuring site, we observed a significant increase in the CZE electric field strength. Unfortunately, the data of the other receiver could not be used in this case because of frequent interruptions in the recordings. The increase might have been a precursor of a strong earthquake (M = 5.3) that occurred on March 26, 1998 in the Umbria-Marche zone at a location over 100 km from the receiver, but which lay along the transmitter-receiver path. In this case, it is possible that an ionospheric disturbance, produced by the pre-seismic processes, might have occurred. If this pre-seismic behaviour of the LF signals could be confirmed then this type of precursor would seem capable of giving information on the direction, and perhaps even the rough location, of a forthcoming earthquake.

Computer programming is a fundamental tool for life scientists, allowing them to carry out essential research tasks. However, despite various educational efforts, learning to write code can be a challenging endeavor for students and researchers in life-sciences disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such tool-OpenAI's ChatGPT-could successfully complete programming tasks. ChatGPT solved 139 (75.5%) of the exercises on its first attempt. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have implications for life-sciences education and research. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public. For some programming tasks, researchers may be able to work in collaboration with machine-learning models to produce functional code.

Abstract Background When analyzing DNA sequence data of an individual, knowing which nucleotide was inherited from each parent can be beneficial when trying to identify certain types of DNA variants. Mendelian inheritance logic can be used to accurately phase (haplotype) the majority (67–83%) of an individual's heterozygous nucleotide positions when genotypes are available for both parents (trio). However, when all members of a trio are heterozygous at a position, Mendelian inheritance logic cannot be used to phase. For such positions, a computational phasing algorithm can be used. Existing phasing algorithms use a haplotype reference panel, sequencing reads, and/or parental genotypes to phase an individual; however, they are limited in that they can only phase certain types of variants, require a specific genotype build, require large amounts of storage capacity, and/or require long run times. We created trioPhaser to address these challenges. Results trioPhaser uses gVCF files from an individual and their parents as initial input, and then outputs a phased VCF file. Input trio data are first phased using Mendelian inheritance logic. Then, the positions that cannot be phased using inheritance information alone are phased by the SHAPEIT4 phasing algorithm. Using whole-genome sequencing data of 52 trios, we show that trioPhaser, on average, increases the total number of phased positions by 21.0% and 10.5%, respectively, when compared to the number of positions that SHAPEIT4 or Mendelian inheritance logic can phase when either is used alone. In addition, we show that the accuracy of the phased calls output by trioPhaser are similar to linked-read and read-backed phasing. Conclusion trioPhaser is a containerized software tool that uses both Mendelian inheritance logic and SHAPEIT4 to phase trios when gVCF files are available. By implementing both phasing methods, more variant positions are phased compared to what either method is able to phase alone.

Abstract Background Drug-induced liver injury (DILI) is a serious concern during drug development and the treatment of human disease. The ability to accurately predict DILI risk could yield significant improvements in drug attrition rates during drug development, in drug withdrawal rates, and in treatment outcomes. In this paper, we outline our approach to predicting DILI risk using gene-expression data from Build 02 of the Connectivity Map (CMap) as part of the 2018 Critical Assessment of Massive Data Analysis CMap Drug Safety Challenge. Results First, we used seven classification algorithms independently to predict DILI based on gene-expression values for two cell lines. Similar to what other challenge participants observed, none of these algorithms predicted liver injury on a consistent basis with high accuracy. In an attempt to improve accuracy, we aggregated predictions for six of the algorithms (excluding one that had performed exceptionally poorly) using a soft-voting method. This approach also failed to generalize well to the test set. We investigated alternative approaches—including a multi-sample normalization method, dimensionality-reduction techniques, a class-weighting scheme, and expanding the number of hyperparameter combinations used as inputs to the soft-voting method. We met limited success with each of these solutions. Conclusions We conclude that alternative methods and/or datasets will be necessary to effectively predict DILI in patients based on RNA expression levels in cell lines. Reviewers This article was reviewed by Paweł P Labaj and Aleksandra Gruca (both nominated by David P Kreil).

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool’s inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable—they can be executed on diverse computers—including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used primarily for batch processing of large datasets, especially in genomics. But it can also be used for analytical steps of a study. This article explains key concepts about CWL and software containers and provides examples for using CWL in biology research. CWL documents are text-based, so they can be created manually, without computer programming. However, ensuring that these documents conform to the CWL specification may prevent some users from adopting it. To address this gap, we created ToolJig, a Web application that enables researchers to create CWL documents interactively. ToolJig validates information provided by the user to ensure it is complete and valid. After creating a CWL tool or workflow, the user can create ‘input-object’ files, which store values for a particular invocation of a tool or workflow. In addition, ToolJig provides examples of how to execute the tool or workflow via a workflow engine. ToolJig and our examples are available at https://github.com/srp33/ToolJig.

HPRT is a housekeeping enzyme involved in recycling guanine and inosine in the purine salvage pathway. As a housekeeping gene, HPRT has been widely used as an endogenous control for molecular studies evaluating changes in gene expression. Yet, recent evidence has shown that HPRT exhibits high variability within malignant samples. We designed this study to determine whether this observed upregulation is consistently found, therefore rendering hprt an unsuitable normalization control in cancer. Utilizing protein and RNA-seq expression, we found that malignant and normal patient samples vary significantly both within the same tissue type and across organ sites. Upon staining for HPRT via immunohistochemistry, we found that expression is highly variable in malignant samples (Lung; 89.2–111.8, Breast; 66.7–98.3, Colon; 85.3–129.7, Prostate; 90.8–155.4, Pancreas; 74.1–132.1). Similarly, we observed high variability across cell lines via western blotting (p

Compound Heterozygous ( CH) variant identification requires distinguishing maternally from paternally derived nucleotides, a process that requires numerous computational tools. Using such tools often introduces unforeseen challenges such as installation procedures that are operating-system specific, software dependencies that must be installed, and formatting requirements for input files. To overcome these challenges, we developed Compound Heterozygous Variant Identification Pipeline (CompoundHetVIP), which uses a single Docker image to encapsulate commonly used software tools for file aggregation ( BCFtools or GATK4), VCF liftover ( Picard Tools), joint-genotyping ( GATK4), file conversion ( Plink2), phasing ( SHAPEIT2, Beagle, and/or Eagle2), variant normalization ( vt tools), annotation ( SnpEff), relational database generation ( GEMINI), and identification of CH, homozygous alternate, and de novo variants in a series of 13 steps. To begin using our tool, researchers need only install the Docker engine and download the CompoundHetVIP Docker image. The tools provided in CompoundHetVIP, subject to the limitations of the underlying software, can be applied to whole-genome, whole-exome, or targeted exome sequencing data of individual samples or trios (a child and both parents), using VCF or gVCF files as initial input. Each step of the pipeline produces an analysis-ready output file that can be further evaluated. To illustrate its use, we applied CompoundHetVIP to data from a publicly available Ashkenazim trio and identified two genes with a candidate CH variant and two genes with a candidate homozygous alternate variant after filtering based on user-set thresholds for global minor allele frequency, Combined Annotation Dependent Depletion, and Gene Damage Index. While this example uses genomic data from a healthy child, we anticipate that most researchers will use CompoundHetVIP to uncover missing heritability in human diseases and other phenotypes. CompoundHetVIP is open-source software and can be found at https://github.com/dmiller903/CompoundHetVIP; this repository also provides detailed, step-by-step examples.

Compound heterozygous (CH) variants occur when two recessive alleles are inherited and the variants are located at different loci within the same gene in a given individual. CH variants are important contributors to many different types of recessively inherited diseases. However, many studies overlook CH variants because identification of this type of variant requires knowing the parent of origin for each nucleotide. Using computational methods, haplotypes can be inferred using a process called “phasing,” which estimates the chromosomal origin of most nucleotides. In this paper, we used germline, phased, whole-genome sequencing (WGS) data to identify CH variants across seven pediatric diseases (adolescent idiopathic scoliosis: n = 16, congenital heart defects: n = 709, disorders of sex development: n = 79, ewing sarcoma: n = 287, neuroblastoma: n = 259, orofacial cleft: n = 107, and syndromic cranial dysinnervation: n = 172), available as parent-child trios in the Gabriella Miller Kids First Data Resource Center. Relatively little is understood about the genetic underpinnings of these diseases. We classified CH variants as “potentially damaging” based on minor allele frequencies (MAF), Combined Annotation Dependent Depletion scores, variant impact on transcription or translation, and gene-level frequencies in the disease group compared to a healthy population. For comparison, we also identified homozygous alternate (HA) variants, which affect both gene copies at a single locus; HA variants represent an alternative mechanism of recessive disease development and do not require phasing. Across all diseases, 2.6% of the samples had a potentially damaging CH variant and 16.2% had a potentially damaging HA variant. Of these samples with potentially damaging variants, the average number of genes per sample was 1 with a CH variant and 1.25 with a HA variant. Across all samples, 5.1 genes per disease had a CH variant, while 35.6 genes per disease had a HA variant; on average, only 4.3% of these variants affected common genes. Therefore, when seeking to identify potentially damaging variants of a putatively recessive disease, CH variants should be considered as potential contributors to disease development. If CH variants are excluded from analysis, important candidate genes may be overlooked.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.

The genetic underpinnings of most pediatric-cancer cases are unknown. Population-based studies use large sample sizes but have accounted for only a small proportion of the estimated heritability of pediatric cancers. Pedigree-based studies are infeasible for most human populations. One alternative is to collect genetic data from a single nuclear family and use inheritance patterns within the family to filter candidate variants. This approach can be applied to common and rare variants, including those that are private to a given family or to an affected individual. We evaluated this approach using genetic data from three nuclear families with 5, 4, and 7 children, respectively. Only one child in each nuclear family had been diagnosed with cancer, and neither parent had been affected. Diagnoses for the affected children were benign low-grade astrocytoma, Wilms tumor (stage 2), and Burkitt's lymphoma, respectively. We used whole-genome sequencing to profile normal cells from each family member and a linked-read technology for genomic phasing. For initial variant filtering, we used global minor allele frequencies, deleteriousness scores, and functional-impact annotations. Next, we used genetic variation in the unaffected siblings as a guide to filter the remaining variants. As a way to evaluate our ability to detect variant(s) that may be relevant to disease status, the corresponding author blinded the primary author to affected status; the primary author then assigned a risk score to each child. Based on this evidence, the primary author predicted which child had been affected in each family. The primary author's prediction was correct for the child who had been diagnosed with a Wilms tumor; the child with Burkitt's lymphoma had the second-highest risk score among the seven children in that family. This study demonstrates a methodology for filtering and evaluating candidate genomic variants and genes within nuclear families that may merit further exploration.

A compound heterozygous ( CH) variant occurs when a person inherits two alternate alleles, one from each parent, and these alleles occur at different positions within the same gene. Therefore, CH variant identification requires distinguishing maternally from paternally derived nucleotides, a process that requires numerous computational tools. Using such tools can be challenging and often introduce unforeseen challenges such as installation procedures that are operating-system specific, software dependencies, and format requirements for input files. To overcome these challenges, we developed Compound Heterozygous Variant Identification Pipeline (CompoundHetVIP), which uses a single Docker image to encapsulate commonly used software tools for phasing, annotating, and analyzing CH, homozygous alternate, and de novo variants in a series of 13 steps. To begin using our tool, researchers need only install the Docker engine and download the CompoundHetVIP Docker image. The tools provided in CompoundHetVIP can be applied to Illumina whole-genome sequencing data of individual samples or trios (a child and both parents), using VCF or gVCF files as initial input. Each step of the pipeline produces an analysis-ready output file that can be further evaluated. To illustrate its use, we applied CompoundHetVIP to data from a publicly available Ashkenazim trio and identified two genes with candidate CH variants and one gene with a candidate homozygous alternate variant after filtering. While this example uses genomic data from a healthy child, we anticipate that most researchers will use CompoundHetVIP to uncover missing heritability in human diseases and other phenotypes. CompoundHetVIP is open-source software and can be found at https://github.com/dmiller903/CompoundHetVIP; this repository also provides detailed, step-by-step examples.

Gastric cancer (GC) is a significant cancer-related cause of death worldwide. The most used chemotherapeutic regimen in GC is based on platinum drugs such as cisplatin (CDDP). However, CDDP resistance reduces advanced GC survival. In vitro drug-resistant cell model would help in the understanding of molecular mechanisms underlying this drug-resistance phenomenon. The aim of this study was to characterize new models of CDDP-resistant GC cell lines (AGS R-CDDP and MKN-28 R-CDDP) obtained through a stepwise increasing drug doses method, in order to understand the molecular mechanisms underlying chemoresistance as well as identify new therapeutic targets for the treatment of GC. Cell viability assays, cell death assays and the expression of resistance molecular markers confirmed that AGS R-CDDP and MKN-28 R-CDDP are reliable CDDP-resistant models. RNA-seq and bioinformatics analyses identified a total of 189 DEGs, including 178 up-regulated genes and 11 down-regulated genes, associated mainly to molecular functions involved in CDDP-resistance. DEGs were enriched in 23 metabolic pathways, among which the most enriched was the inflammation mediated by chemokine and cytokine signaling pathway. Finally, the higher mRNA expression of SERPINA1, BTC and CCL5, three up-regulated DEGs associated to CDDP resistance found by RNA-seq analysis was confirmed. In summary, this study showed that AGS R-CDDP and MKN-28 R-CDDP are reliable models of CDDP resistance because resemble many of resistant phenotype in GC, being also useful to assess potential therapeutic targets for the treatment of gastric cancers resistant to CDDP. In addition, we identified several DEGs associated with molecular functions such as binding, catalytic activity, transcription regulator activity and transporter activity, as well as signaling pathways associated with inflammation process, which could be involved in the development of CDDP resistance in GC. Further studies are necessary to clarify the role of inflammatory processes in GC resistant to CDDP and these models could be useful for these purposes.

Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term "BRCA-like" (or "BRCAness") describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with tumors being BRCA-like could provide mechanistic insights and guide development of targeted treatments. Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCA-like events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities among patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Somatic-mutation signatures of tumors having a non-germline aberration in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes (CDKN2A, CTNNA1, PALB2, PALLD, PRSS1, SDHC) also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2. Our methodology represents an objective way to identify genes that have similar downstream effects on molecular signatures when mutated, deleted, or hypermethylated.

Abstract Background Malignant tumors are typically caused by a conglomeration of genomic aberrations—including point mutations, small insertions, small deletions, and large copy-number variations. In some cases, specific chemotherapies and targeted drug treatments are effective against tumors that harbor certain genomic aberrations. However, predictive aberrations (biomarkers) have not been identified for many tumor types and treatments. One way to address this problem is to examine the downstream, transcriptional effects of genomic aberrations and to identify characteristic patterns. Even though two tumors harbor different genomic aberrations, the transcriptional effects of those aberrations may be similar. These patterns could be used to inform treatment choices. Methods We used data from 9300 tumors across 25 cancer types from The Cancer Genome Atlas. We used supervised machine learning to evaluate our ability to distinguish between tumors that had mutually exclusive genomic aberrations in specific genes. An ability to accurately distinguish between tumors with aberrations in these genes suggested that the genes have a relatively different downstream effect on transcription, and vice versa. We compared these findings against prior knowledge about signaling networks and drug responses. Results Our analysis recapitulates known relationships in cancer pathways and identifies gene pairs known to predict responses to the same treatments. For example, in lung adenocarcinomas, gene-expression profiles from tumors with somatic aberrations in EGFR or MET were negatively correlated with each other, in line with prior knowledge that MET amplification causes resistance to EGFR inhibition. In breast carcinomas, we observed high similarity between PTEN and PIK3CA, which play complementary roles in regulating cellular proliferation. In a pan-cancer analysis, we found that genomic aberrations in BRAF and VHL exhibit downstream effects that are clearly distinct from other genes. Conclusion We show that transcriptional data offer promise as a way to group genomic aberrations according to their downstream effects, and these groupings recapitulate known relationships. Our approach shows potential to help pharmacologists and clinical trialists narrow the search space for candidate gene/drug associations, including for rare mutations, and for identifying potential drug-repurposing opportunities.

In metastatic breast cancer, subclonal evolution can drive drug resistance. Here, the authors genetically and transcriptionally follow the evolution of four breast cancers over time and treatment, and suggest a phenotype-targeted treatment strategy to adapt to cancer as it evolves.

To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8–93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species.

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

Abstract The signaling events that drive familial breast cancer (FBC) risk remain poorly understood. While the majority of genomic studies have focused on genetic risk variants, known risk variants account for at most 30% of FBC cases. Considering that multiple genes may influence FBC risk, we hypothesized that a pathway‐based strategy examining different data types from multiple tissues could elucidate the biological basis for FBC. In this study, we performed integrated analyses of gene expression and exome‐sequencing data from peripheral blood mononuclear cells and showed that cell adhesion pathways are significantly and consistently dysregulated in women who develop FBC. The dysregulation of cell adhesion pathways in high‐risk women was also identified by pathway‐based profiling applied to normal breast tissue data from two independent cohorts. The results of our genomic analyses were validated in normal primary mammary epithelial cells from high‐risk and control women, using cell‐based functional assays, drug‐response assays, fluorescence microscopy, and Western blotting assays. Both genomic and cell‐based experiments indicate that cell–cell and cell–extracellular matrix adhesion processes seem to be disrupted in non‐malignant cells of women at high risk for FBC and suggest a potential role for these processes in FBC development.

The originally published version of this Article contained an error in Figure 4. In panel a, grey boxes surrounding the subclones associated with patients #2 and #4 obscured adjacent portions of the heatmap. This error has now been corrected in both the PDF and HTML versions of the Article.