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[Display omitted] •The use of handheld Raman spectroscopy shows promise for NPS identification in field.•A 1064nm source significantly reduces background fluorescence of NPS products.•Identification using a ‘first pass’ matching algorithm successfully identified an NPS in 29 products. The chemical identification of new psychoactive substances (NPS) in the field is challenging due not only to the plethora of substances available, but also as a result of the chemical complexity of products and the chemical similarity of NPS analogues. In this study, handheld Raman spectroscopy and the use of two excitation wavelengths, 785 and 1064nm, were evaluated for the identification of 60 NPS products. The products contained a range of NPS from classes including the aminoindanes, arylalkylamines, benzodiazepines, and piperidines & pyrrolidines. Identification was initially assessed using the instruments’ in built algorithm (i.e., % HQI) and then further by visual inspection of the Raman spectra. Confirmatory analysis was preformed using gas chromatography mass spectrometry. For the 60 diverse products, an NPS was successfully identified via the algorithm in 11 products (18%) using the 785nm source and 29 products (48%) using the 1064nm source. Evaluation of the Raman spectra showed that increasing the excitation wavelength from 785 to 1064nm improved this ‘first pass’ identification primarily due to a significant reduction in fluorescence, which increased S/N of the characteristic peaks of the substance identified. True positive correlations between internet products and NPS signatures ranged from 57.0 to 91.3% HQI with typical RSDs<10%. Tablet formulations and branded products were particularly challenging as a result of low NPS concentration and high chemical complexity, respectively. This study demonstrates the advantage of using a 1064nm source with handheld Raman spectroscopy for improved ‘first pass’ NPS identification when minimal spectral processing is required, such as when working in field. Future investigations will focus on the use of mixture algorithms, effect of NPS concentration, and further improvement of spectral libraries.

Alternative splicing is crucial for cancer progression and can be targeted pharmacologically, yet identifying driver exons genome-wide remains challenging. We propose identifying such exons by associating statistically gene-level cancer dependencies from knockdown viability screens with splicing profiles and gene expression. Our models predict the effects of splicing perturbations on cell proliferation from transcriptomic data, enabling in silico RNA screening and prioritizing targets for splicing-based therapies. We identified 1,073 exons impacting cell proliferation, many from genes not previously linked to cancer. Experimental validation confirms their influence on proliferation, especially in highly proliferative cancer cell lines. Integrating pharmacological screens with splicing dependencies highlights the potential driver exons affecting drug sensitivity. Our models also allow predicting treatment outcomes from tumor transcriptomes, suggesting applications in precision oncology. This study presents an approach to identifying cancer driver exon and their therapeutic potential, emphasizing alternative splicing as a cancer target. While alternative splicing is known to drive oncogenesis and be a source of potential therapeutic targets, identifying such drivers on a genome-wide scale has proven difficult. Here, the authors present a computational approach to identify potential cancer-driver exons and evaluate applicability as therapeutic targets.

Background Independent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. Results We present robustica , a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components’ signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis. Conclusion robustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.

Hair color is one of the most recognizable visual traits in European populations and is under strong genetic control. Here we report the results of a genome-wide association study meta-analysis of almost 300,000 participants of European descent. We identified 123 autosomal and one X-chromosome loci significantly associated with hair color; all but 13 are novel. Collectively, single-nucleotide polymorphisms associated with hair color within these loci explain 34.6% of red hair, 24.8% of blond hair, and 26.1% of black hair heritability in the study populations. These results confirm the polygenic nature of complex phenotypes and improve our understanding of melanin pigment metabolism in humans. Genome-wide meta-analysis identifies >100 loci associated with hair color variation in humans of European ancestry. These loci explain a large portion of the heritability of this trait & provide insights into pathways regulating hair pigmentation.

The perennial rhizomatous grass Arundo donax L. (Poaceae), the giant reed, is an exotic invasive species in several countries of Europe that is rapidly spreading in the savannas of Central Brazil, locally known as Cerrado. Allelopathy could facilitate the successful invasion of this species by hampering or suppressing the regeneration of the native vegetation. However, information on the phytotoxicity of A. donax extracts is limited.  We investigated the allelopathic potential of A. donax leaf and rhizome extracts, screened them for phytochemicals by thin-layer chromatography (TLC) and nuclear magnetic resonance (1H-NMR), and tested the extracts for antioxidant activity, antimicrobial activity, and cytotoxicity against Artemia salina. Aqueous and methanolic extracts were initially tested in germination and seedling growth bioassays using Lactuca sativa L. (Asteraceae). The aqueous extracts were then tested on five Cerrado tree species and on Megathyrsus maximus, an invasive, alien grass in the Cerrado. Extracts negatively affected germination and seedling growth of the target species. Leaf extracts were more inhibitory. Extracts did not show antioxidant and cytotoxic activity and had very low antimicrobial activity. Flavonoids, and other phenolics were detected mostly in leaves. Terpenes, which were also present in the leaves, were the main secondary metabolites in rhizomes. Alkaloids were detected by TLC in leaf methanolic extracts. However, 1H-NMR revealed the presence of indole alkaloids in methanolic extracts from rhizomes and leaves. We confirmed the allelopathic potential of this species and caution against weed control methods relying on cutting the plant back to soil level for favouring release of allelochemicals.

Language production has often been described as impaired in psychiatric diseases such as in psychosis. Nevertheless, little is known about the characteristics of linguistic difficulties and their relation with other cognitive domains in patients with a first episode of psychosis (FEP), either affective or non-affective. To deepen our comprehension of linguistic profile in FEP, 133 patients with FEP (95 non-affective, FEP-NA; 38 affective, FEP-A) and 133 healthy controls (HC) were assessed with a narrative discourse task. Speech samples were systematically analyzed with a well-established multilevel procedure investigating both micro- (lexicon, morphology, syntax) and macro-linguistic (discourse coherence, pragmatics) levels of linguistic processing. Executive functioning and IQ were also evaluated. Both linguistic and neuropsychological measures were secondarily implemented with a machine learning approach in order to explore their predictive accuracy in classifying participants as FEP or HC. Compared to HC, FEP patients showed language production difficulty at both micro- and macro-linguistic levels. As for the former, FEP produced shorter and simpler sentences and fewer words per minute, along with a reduced number of lexical fillers, compared to HC. At the macro-linguistic level, FEP performance was impaired in local coherence, which was paired with a higher percentage of utterances with semantic errors. Linguistic measures were not correlated with any neuropsychological variables. No significant differences emerged between FEP-NA and FEP-A (p≥0.02, after Bonferroni correction). Machine learning analysis showed an accuracy of group prediction of 76.36% using language features only, with semantic variables being the most impactful. Such a percentage was enhanced when paired with clinical and neuropsychological variables. Results confirm the presence of language production deficits already at the first episode of the illness, being such impairment not related to other cognitive domains. The high accuracy obtained by the linguistic set of features in classifying groups support the use of machine learning methods in neuroscience investigations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background The genetic basis of hearing loss in humans is relatively poorly understood. In recent years, experimental approaches including laboratory studies of early onset hearing loss in inbred mouse strains, or proteomic analyses of hair cells or hair bundles, have suggested new candidate molecules involved in hearing function. However, the relevance of these genes/gene products to hearing function in humans remains unknown. We investigated whether single nucleotide polymorphisms (SNPs) in the human orthologues of genes of interest arising from the above-mentioned studies correlate with hearing function in children. Methods 577 SNPs from 13 genes were each analysed by linear regression against averaged high (3, 4 and 8 kHz) or low frequency (0.5, 1 and 2 kHz) audiometry data from 4970 children in the Avon Longitudinal Study of Parents and Children (ALSPAC) birth-cohort at age eleven years. Genes found to contain SNPs with low p -values were then investigated in 3417 adults in the G-EAR study of hearing. Results Genotypic data were available in ALSPAC for a total of 577 SNPs from 13 genes of interest. Two SNPs approached sample-wide significance (pre-specified at p  = 0.00014): rs12959910 in CBP80/20-dependent translation initiation factor ( CTIF ) for averaged high frequency hearing ( p  = 0.00079, β = 0.61 dB per minor allele); and rs10492452 in L-plastin ( LCP1 ) for averaged low frequency hearing ( p  = 0.00056, β = 0.45 dB). For low frequencies, rs9567638 in LCP1 also enhanced hearing in females ( p  = 0.0011, β = −1.76 dB; males p  = 0.23, β = 0.61 dB, likelihood-ratio test p = 0.006). SNPs in LCP1 and CTIF were then examined against low and high frequency hearing data for adults in G-EAR. Although the ALSPAC results were not replicated, a SNP in LCP1 , rs17601960, is in strong LD with rs9967638, and was associated with enhanced low frequency hearing in adult females in G-EAR ( p  = 0.00084). Conclusions There was evidence to suggest that multiple SNPs in CTIF may contribute a small detrimental effect to hearing, and that a sex-specific locus in LCP1 is protective of hearing. No individual SNPs reached sample-wide significance in both ALSPAC and G-EAR. This is the first report of a possible association between LCP1 and hearing function.

BackgroundIndependent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix. ResultsWe present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. From the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. After confirming the bias introduced with Pearson distances, we created a subroutine that infers and corrects the components′ signs across ICA iterations to enable using Euclidean distance. Our subroutine effectively corrected the bias while simultaneously increasing the precision, robustness, and memory efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define a new gene expression module with the key modulators of tumor aggressiveness downregulated upon IDH1 mutation. Conclusionrobustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.Availabilityrobustica is written in Python under the open-source BSD 3-Clause license. The source code and documentation are freely available at https://github.com/CRG-CNAG/robustica . Additionally, all scripts to reproduce the work presented are available at https://github.com/MiqG/publication_robustica . Competing Interest Statement The authors have declared no competing interest. Footnotes * We included methods and an implementation section in the main text.