SUMMARY We report the construction and analysis of 4,836 heterozygous diploid deletion mutants covering 98.4% of the fission yeast genome. This resource provides a powerful tool for biotechnological and eukaryotic cell biology research. Comprehensive gene dispensability comparisons with budding yeast, the first time such studies have been possible between two eukaryotes, revealed that 83% of single copy orthologues in the two yeasts had conserved dispensability. Gene dispensability differed for certain pathways between the two yeasts, including mitochondrial translation and cell cycle checkpoint control. We show that fission yeast has more essential genes than budding yeast and that essential genes are more likely than non-essential genes to be single copy, broadly conserved and to contain introns. Growth fitness analyses determined sets of haploinsufficient and haploproficient genes for fission yeast, and comparisons with budding yeast identified specific ribosomal proteins and RNA polymerase subunits, which may act more generally to regulate eukaryotic cell growth.

Caenorhabditis elegans oocytes, like those of most animals, arrest during meiotic prophase. Sperm promote the resumption of meiosis (maturation) and contraction of smooth muscle-like gonadal sheath cells, which are required for ovulation. We show that the major sperm cytoskeletal protein (MSP) is a bipartite signal for oocyte maturation and sheath contraction. MSP also functions in sperm locomotion, playing a role analogous to actin. Thus, during evolution, MSP has acquired extracellular signaling and intracellular cytoskeletal functions for reproduction. Proteins with MSP-like domains are found in plants, fungi, and other animals, suggesting that related signaling functions may exist in other phyla.

Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.

Caenorhabditis elegans GLD-1, a KH motif containing RNA-binding protein of the GSG/STAR subfamily, controls diverse aspects of germ line development, suggesting that it may have multiple mRNA targets. We used an immunoprecipitation/subtractive hybridization/cloning strategy to identify 15 mRNAs that are putative targets of GLD-1 binding and regulation. For one target, the rme-2 yolk receptor mRNA, GLD-1 acts as a translational repressor to spatially restrict RME-2 accumulation, and thus yolk uptake, to late-stage oocytes. We found that GLD-1 binds sequences in both 5 coding and the 3 untranslated region of rme-2 mRNA. Initial characterization of the other 14 targets shows that (1) they are coexpressed with GLD-1; (2) they can have mutant/RNA-mediated interference depletion phenotypes indicating functions in germ line development or as maternal products necessary for early embryogenesis; and (3) GLD-1 may coregulate mRNAs corresponding to functionally redundant subsets of genes within two gene families. Thus, a diverse set of genes have come under GLD-1-mediated regulation to achieve normal germ line development. Previous work identified tra-2 as a GLD-1 target for germ line sex determination. Comparisons of GLD-1-mediated translational control of rme-2 and tra-2 suggests that the mechanisms may differ for distinct target mRNA species.

Advances in scattering-based optical imaging technologies offer a new approach to noninvasive point-of-care detection, diagnosis, and monitoring of cancer. Emerging photonics technologies provide a cost-effective means to image tissue in vivo with high resolution in real time. Advancing the clinical potential of these imaging strategies requires the development of optical contrast agents targeted to specific molecular signatures of disease. We describe the use of a novel class of contrast agents based on nanoshell bioconjugates for molecular imaging in living cells. Nanoshells offer significant advantages over conventional imaging probes including continuous and broad wavelength tunability, far greater scattering and absorption coefficients, increased chemical stability, and improved biocompatibility. We show that nanoshell bioconjugates can be used to effectively target and image human epidermal growth factor receptor 2 (HER2), a clinically relevant biomarker, in live human breast carcinoma cells.

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p53 is a tumor suppressor gene whose regulation is crucial to maintaining genome stability and for the apoptotic elimination of abnormal, potentially cancer-predisposing cells. C. elegans contains a primordial p53 gene, cep-1, that acts as a transcription factor necessary for DNA damage-induced apoptosis. In a genetic screen for negative regulators of CEP-1, we identified a mutation in GLD-1, a translational repressor implicated in multiple C. elegans germ cell fate decisions and related to mammalian Quaking proteins. CEP-1-dependent transcription of proapoptotic genes is upregulated in the gld-1(op236) mutant and an elevation of p53-mediated germ cell apoptosis in response to DNA damage is observed. Further, we demonstrate that GLD-1 mediates its repressive effect by directly binding to the 3'UTR of cep-1/p53 mRNA and repressing its translation. This study reveals that the regulation of cep-1/p53 translation influences DNA damage-induced apoptosis and demonstrates the physiological importance of this mechanism.

For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left-and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral-spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral-spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral-spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral-spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatiospectral filter optimization (BSSFO)].