We identified a novel regulator, Thermococcales glycolytic regulator (Tgr), functioning as both an activator and a repressor of transcription in the hyperthermophilic archaeon Thermococcus kodakaraensis KOD1. Tgr (TK1769) displays similarity (28% identical) to Pyrococcus furiosus TrmB (PF1743), a transcriptional repressor regulating the trehalose/maltose ATP-binding cassette transporter genes, but is more closely related (67%) to a TrmB paralog in P. furiosus (PF0124). Growth of a tgr disruption strain (Deltatgr) displayed a significant decrease in growth rate under gluconeogenic conditions compared with the wild-type strain, whereas comparable growth rates were observed under glycolytic conditions. A whole genome microarray analysis revealed that transcript levels of almost all genes related to glycolysis and maltodextrin metabolism were at relatively high levels in the Deltatgr mutant even under gluconeogenic conditions. The Deltatgr mutant also displayed defects in the transcriptional activation of gluconeogenic genes under these conditions, indicating that Tgr functions as both an activator and a repressor. Genes regulated by Tgr contain a previously identified sequence motif, the Thermococcales glycolytic motif (TGM). The TGM was positioned upstream of the Transcription factor B-responsive element (BRE)/TATA sequence in gluconeogenic promoters and downstream of it in glycolytic promoters. Electrophoretic mobility shift assay indicated that recombinant Tgr protein specifically binds to promoter regions containing a TGM. Tgr was released from the DNA when maltotriose was added, suggesting that this sugar is most likely the physiological effector. Our results strongly suggest that Tgr is a global transcriptional regulator that simultaneously controls, in response to sugar availability, both glycolytic and gluconeogenic metabolism in T. kodakaraensis via its direct binding to the TGM.

Considerable attention has been recently paid to improving replicability and reproducibility in life science research. This has resulted in commendable efforts to standardize a variety of reagents, assays, cell lines and other resources. However, given that microscopy is a dominant tool for biologists, comparatively little discussion has been offered regarding how the proper reporting and documentation of microscopy relevant details should be handled. Image processing is a critical step of almost any microscopy-based experiment; however, improper, or incomplete reporting of its use in the literature is pervasive. The chosen details of an image processing workflow can dramatically determine the outcome of subsequent analyses, and indeed, the overall conclusions of a study. This Review aims to illustrate how proper reporting of image processing methodology improves scientific reproducibility and strengthens the biological conclusions derived from the results.

The taste system is one of our fundamental senses, responsible for detecting and responding to sweet, bitter, umami, salty, and sour stimuli. In the tongue, the five basic tastes are mediated by separate classes of taste receptor cells each finely tuned to a single taste quality. We explored the logic of taste coding in the brain by examining how sweet, bitter, umami, and salty qualities are represented in the primary taste cortex of mice. We used in vivo two-photon calcium imaging to demonstrate topographic segregation in the functional architecture of the gustatory cortex. Each taste quality is represented in its own separate cortical field, revealing the existence of a gustotopic map in the brain. These results expose the basic logic for the central representation of taste.

Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor-filter receptive fields from a natural image ensemble in agreement with physiological experiments. Whereas the OF algorithm can be mapped onto the dynamics and synaptic plasticity in a single-layer neural network, the derived learning rule is nonlocal - the synaptic weight update depends on the activity of neurons other than just pre- and postsynaptic ones – and hence biologically implausible. Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input’s similarity matrix. Our algorithm maps onto a neural network of the same architecture as OF but using only biologically plausible local learning rules. When trained on natural images our network learns Gabor-filter receptive fields and reproduces the correlation among synaptic weights hard-wired in the OF network. Therefore, online symmetric matrix factorization may serve as an algorithmic theory of neural computation. 

Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.

A large number of degrees of freedom are required to produce a high quality focus through random scattering media. Previous demonstrations based on spatial phase modulations suffer from either a slow speed or a small number of degrees of freedom. In this work, a high speed wavefront determination technique based on spatial frequency domain wavefront modulations is proposed and experimentally demonstrated, which is capable of providing both a high operation speed and a large number of degrees of freedom. The technique was employed to focus light through a strongly scattering medium and the entire wavefront was determined in 400 milliseconds,  three orders of magnitude faster than the previous report.

We demonstrate a high throughput, large compensation range, single-prism femtosecond pulse compressor, using a single prism and two roof mirrors. The compressor has zero angular dispersion, zero spatial dispersion, zero pulse-front tilt, and unity magnification. The high efficiency is achieved by adopting two roof mirrors as the retroreflectors. We experimentally achieved ~ -14500 fs2 group delay dispersion (GDD) with 30 cm of prism tip-roof mirror prism separation, and ~90.7% system throughput with the current implementation. With better components, the throughput can be even higher.

The Caenorhabditis elegans embryo is an important model for analyzing mechanisms of cell fate specification and tissue morphogenesis. Sophisticated lineage-tracing approaches for analyzing embryogenesis have been developed but are labor intensive and do not naturally integrate morphogenetic readouts. To enable the rapid classification of developmental phenotypes, we developed a high-content method that employs two custom strains: a Germ Layer strain that expresses nuclear markers in the ectoderm, mesoderm and endoderm/pharynx; and a Morphogenesis strain that expresses markers labeling epidermal cell junctions and the neuronal cell surface. We describe a procedure that allows simultaneous live imaging of development in 80-100 embryos and provide a custom program that generates cropped, oriented image stacks of individual embryos to facilitate analysis. We demonstrate the utility of our method by perturbing 40 previously characterized developmental genes in variants of the two strains containing RNAi-sensitizing mutations. The resulting datasets yielded distinct, reproducible signature phenotypes for a broad spectrum of genes that are involved in cell fate specification and morphogenesis. In addition, our analysis provides new in vivo evidence for MBK-2 function in mesoderm fate specification and LET-381 function in elongation.

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).