Multiple Target Regions Research Articles

Abstract 2409: Circulating differential methylation allele fraction (DMAF) strongly correlates with circulating tumor DNA (ctDNA) variant allele fraction (VAF)

Abstract Measurement of ctDNA has been established as a prognostic biomarker of cancer disease burden. Emerging evidence has demonstrated that differential methylation patterns can serve as an accurate biomarker both for cancer detection and for predicting the tissue of origin. While it is important to distinguish cancer-positive from cancer-negative patients, clinical decision making increasingly depends on the exact level of the measured biomarker. To that end, we report on the correlation between ctDNA VAF and DMAF across patients with colorectal cancer (CRC). Pre-surgical plasma samples from patients with CRC (N=70) were sourced from the prospective GALAXY, observational arm of ongoing CIRCULATE-Japan study trial (UMIN000039205). Samples classified as ctDNA-positive by a tumor-informed ctDNA assay (SignateraTM) were included in this analysis and ctDNA VAF (%) was calculated. We performed deep methylation sequencing (Illumina Novaseq) across multiple target regions using two independent target sets. DMAF was calculated computationally, which estimates the fraction of differential methylation alleles for circulating cell-free DNA across target regions. Among 70 CRC patients, 68 had positive ctDNA results. Median age at testing was 64.0 years and male individuals represented 55.9% (n=38) of the cohort. Among them, 9 (13.2%) were stage I, 24 (35.3%) were stage II, 22 (32.3%) were stage III, 8 (11.7%) were stage IV, and 5 (7.3%) had unknown staging. The correlation of DMAF with ctDNA VAF was 0.894 (Spearman’s Rank Correlation Coefficient) and was independent of disease stage, histology, age, and sex. Among MSS (micro-satellite stable; n=58) and MSI-H (micro-satellite unstable n=10) patients, the DMAF correlation was 0.9 and 0.915, respectively. The correlation between two target sets evaluating DMAF with differing numbers of targeted regions was high (0.89). These data demonstrate that abundance of differential methylation in CRC patients correlates with VAF regardless of clinicopathologic features of patients with CRC. Methylation-based assays are a promising tool for cancer detection and quantifying the disease burden. Citation Format: Preethi Srinivasan, Tzu-Chun Chen, Ehsan Haghshenas, Misagh Kordi, Ilker Tunc, Wenching Chan, Omid Shams Solari, Joshua Babiarz, Yoshiaki Nakamura, Takayuki Yoshino, Alexey Aleshin, Trupti Kawli, Johannes G. Reiter. Circulating differential methylation allele fraction (DMAF) strongly correlates with circulating tumor DNA (ctDNA) variant allele fraction (VAF) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2409.

Semantic segmentation of urban land classes using a multi-scale dataset

ABSTRACT The use of remote sensing imagery for land cover and land use classification has made significant advancements in recent years. However, it becomes particularly challenging to enhance the semantic representation of high-resolution networks while dealing with uneven land categories and merging multi-scale data without compromising the accuracy of semantic segmentation. To tackle this challenge, this paper presents a novel method for classifying high-resolution remote sensing images based on a deep neural network that performs semantic segmentation of urban construction lands into five categories: vegetation, water, buildings, roads, and bare soil. The network incorporates a U-shaped high-resolution neural network and the advanced high-resolution network (HRNet) framework. The parallel storage of feature maps with different resolutions enables the exchange of information between them. The data pre-processing module addresses the issue of data imbalance in the semantic segmentation of urban construction lands, resulting in an increase in Intersection over Union (IoU) values for different land types by 3.75%-12.01%. Additionally, a target context representation module is introduced to enhance the feature representation of pixels by calculating the relationship between pixels and multiple target regions. Moreover, a polarization attention mechanism is proposed to extract the characteristics of geographical objects in all directions and achieve a stronger semantic representation. This method provides a novel approach to accurately and effectively extract information on construction lands and advance the development of monitoring algorithms for urban construction lands. To validate the proposed U-HRNet-OCR+PSA network, a comparative analysis was conducted with six classical networks, including DeepLabv3+, PSPNet, U-Net, U-Net++, HRNet, and HRNet-OCR, as well as the relatively new ViT-adapter-L, Oneformer and InternImage-H. The experiments demonstrate that the U-HRNet-OCR+PSA network achieves higher accuracy compared to the aforementioned networks. Specifically, the corresponding IoU values for the buildings, roads, vegetation, bare soil, and water in the multi-scale dataset are 89.79%, 90.05%, 94.89%, 85.91%, and 88.36%, respectively.

Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance.

Accurate segmentation of infrared images for circuit board diagnosis using an improved Deeplabv3+ network

An effective infrared image segmentation algorithm is essential for non-contact fault diagnosis of circuit boards. However, the uneven grayscale of the infrared images, multiple target regions, and large radiation noise pose challenges to achieving accurate segmentation and efficient data extraction for the interested regions. In this paper, we propose a segmentation algorithm based on the Deeplabv3+ network, using the lightweight MobileNetV2 as a replacement for the original Xception backbone network to improve computational efficiency and reduce overfitting. We also employ a composite loss function and cosine annealing learning rate to balance foreground-background segmentation and avoid local optima. Furthermore, we integrate the Convolutional Block Attention Module (CBAM) to extract and combine important spatial and channel features, allowing the algorithm to focus on identifying elements of the circuit board instead of background pixels, thereby improving segmentation accuracy. Experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance in terms of both segmentation accuracy and computational efficiency on our self-built infrared circuit board dataset, with a MIoU of 90.34%, MPA of 95.26%, and processing speed of 25.19 fps. Overall, our proposed segmentation algorithm can effectively identify the key regions of interest in infrared images of circuit boards, providing technical support for non-contact diagnosis.

An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks

In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.

Ultrasensitive capacitive sensing system for smart medical devices with ability to monitor fracture healing stages

Bone fractures are a global public health problem. A sustained increase in the number of incident cases has been observed in the last few decades, as well as the number of prevalent cases and the number of years lived with disability. Current monitoring techniques are based on imaging techniques, which are highly subjective, radioactive, expensive and unable to provide daily monitoring of fracture healing stages. The development of reliable, non-invasive and non-subjective technologies is mandatory to minimize non-union risks. Delayed healing and non-union conditions require timely medical intervention, such that preventive procedures and shortened treatment periods can be carried out. This work proposes the development of an ultrasensitive capacitive sensing system for smart implantable fixation implants with ability to effectively monitor the evolution of bone fractures. Both in vitro experimental tests and numerical simulations highlight that networks of co-surface capacitive systems are able: (i) to detect four different bone healing phases, capacitance decrease patterns occurring as the healing process progresses and (ii) to monitor the callus evolution in multiple target regions. These are very promising results that highlight the potential of capacitive technologies to minimize the individual and social burdens related to fracture management, mainly when delayed healing or non-union conditions occur.

Gene editing of Duchenne muscular dystrophy using biomineralization-based spCas9 variant nanoparticles

The CRISPR/Cas9 mediated genome editing have provided a promising strategy to correct multiple mutations of Duchenne muscular dystrophy (DMD). However, the delivery of CRISPR/Cas9 system into mammalian cell for DMD gene editing mainly relies on adeno associated virus (AAV)-mediated transport. Meanwhile, the protospacer adjacent motif (PAM) requirement of wild-typed Cas9 protein causing the target sites for exon splice acceptor site are restricted to limited regions. Here, we developed a biomineralized PAMLess Cas9 (SpRY) variant nanoparticles (Bm-SpRY NPs) for DMD gene editing in vitro and in vivo. This method described a facile synthesis of biomineralized NPs with high SpRY pDNA encapsulation efficiency. In vitro results show that the Bm-SpRY NPs have the obvious advantages of well biocompatibility and protecting SpRY pDNA from enzyme degradation and efficient delivery under high serum condition. Cell studies demonstrated that Bm-SpRY NPs enable rapid cellular uptake, endo-lysosomes escape and nucleus transport. Meanwhiles, the DMD gene editing via Bm-SpRY NPs pathway is transient process without genomic integration. We evaluated multiple target regions with different PAMs for the DMD exon 51 splice acceptor site through Bm-SpRY NPs method and found that the target region with TAG PAM has the highest editing efficiency and significant preferential mutation. In vivo results show that intramuscular injection of Bm-SpRY NPs enable DMD gene mutation in muscle tissue without tissue damage. This study may extend the advanced application of CRISPR system for DMD therapy. Statement of significanceThe gene editing technology of CRISPR/Cas9 provides an effective treatment strategy for the Duchenne muscular dystrophy (DMD) therapy. However, the delivery of CRISPR system in mammalian cell mainly relies on viral mediated transport and the NGG or NAG requirement of wild-typed Cas9 protein limits the target region in DMD gene. Here, the present study provides a biomineralized PAM Less Cas9 (SpRY) variant nanoparticles (Bm-SpRY NPs) for DMD gene editing in vitro and in vivo. This study may extend the application of CRISPR system for DMD gene therapy.

Design and Performance Test of Specific Primers to Detect Bovine DNA Fragments using Multiplex PCR Technique for Halal Authentification

Adulterating meat products with several species, including non-halal species, is often found in commercial products. Therefore, non-halal ingredients are a major source of concern for Muslims. Multiplex polymerase chain reaction (PCR) with multiple primers can detect contamination of meat components from non-halal species in a single reaction process, making it more effective and efficient. Multiplex PCR is a PCR technique that uses multiple primers and DNA samples in one reaction to amplify multiple target regions. In this study, a pair of species-specific primers encoding the Cytochrome c oxidase subunit I (CO1) gene were designed to amplify bovine DNA, tested for specificity, and applied in multiplex PCR technique together with D-loop primers for pigs, Cyt-b for rats, and 12S rRNA for dogs. The CO1 primers, along with D-loop primers for porcine, Cyt-b primers for rats, and 12S rRNA primers for dogs, can be used to detect specific bovine DNA with a size of 279 bp and sequence similarity of 96%.

Next-Generation Bisulfite Sequencing for Targeted DNA Methylation Analysis.

Bisulfite sequencing is the "gold-standard" technique for DNA methylation analysis. By combining bisulfite sequencing with high-throughput, next-generation sequencing technology, we can document methylation from many thousands of individual reads (equivalent to alleles or "cells"), for multiple target regions and from many samples simultaneously. Here, we describe a next-generation bisulfite-sequencing assay for targeted DNA methylation analysis which offers scope for the simultaneous interrogation of multiple genomic loci across numerous samples.

Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.

Multi label image retrieval based on depth hash

In recent years, hash is a popular method of image retrieval. Convolutional neural network is used to generate image features and hash codes, so as to achieve fast and effective image retrieval. In order to solve the problem of poor image retrieval effect caused by complex background of Multi-target Image, this paper proposes a hash generation method based on the combination of target region recommendation network and convolutional neural network. The RPN network is selected as the target region recommendation algorithm. After the image passes through the RPN network, multiple target regions and the four-dimensional coordinates of each region will be generated. According to the four-dimensional coordinates, the main target region will be filtered and extracted. Then, the feature of the largest target area is extracted by the convolutional neural network (GoogleNet), and the corresponding hash code is generated, which is finally retrieved in the database. Experiments are carried out on voc2012 data set and self collected data set to verify the algorithm. When the number of test images is 1000, the experimental results show that the total correct rate of the retrieval results of this method is 95.5%, which is about 5 percentage points higher than the existing methods.

Divergent Projection Patterns Revealed by Reconstruction of Individual Neurons in Orbitofrontal Cortex.

The orbitofrontal cortex (OFC) is involved in diverse brain functions via its extensive projections to multiple target regions. There is a growing understanding of the overall outputs of the OFC at the population level, but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare. Here, by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system (fMOST), we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons. We compared the whole-brain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer. We found cortical layer-dependent projections characterized by divergent patterns for information delivery. Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC, but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.

A custom amplicon sequencing approach to detect resistance associated mutations and sequence types in Mycoplasma genitalium

BackgroundMycoplasma genitalium resistance to antibiotic treatments is increasing, with very limited treatment alternatives on the horizon. Surveillance via sequencing of multiple M. genitalium loci would allow: monitoring of known antibiotic resistance mutations, associations between resistance/treatment failure and specific mutations, and strain typing for epidemiological purposes. In this study we assessed the performance of a custom amplicon sequencing approach, which negates the cost of library preparation for next generation sequencing. MethodsFifty-two M. genitalium positive samples (cervical, vaginal, anal and rectal swabs, and urine) were used. Three regions associated with M. genitalium antibiotic resistance (23S rRNA, parC and gyrA genes) were targeted, in conjunction with a locus used for differentiation of sequence types in the mgpB gene, and findings compared to Sanger sequencing. ResultsAmplicon sequencing provided adequate sequence read coverage (>30×) for the majority of samples for 23S rRNA gene (96%) and mgpB (97%), parC (78%) and gyrA (75%). Single nucleotide polymorphisms (SNPs) were characterised in samples for 23S rRNA gene (94%), parC (56%) and gyrA (4%). Unlike Sanger sequencing, mixed mutations could be identified by the amplicon sequencing method, and ratios of mutation types determined. All results, with one exception, were concordant to Sanger sequence results. Sequence diversity in the mgpB region was represented by 15 sequence types, 4 being observed in multiple samples. ConclusionsWe have demonstrated the utility of this custom amplicon sequencing approach for generating highly informative datasets with the capacity to identify and determine ratios of mixed sequences. The use of this customisable amplicon sequencing method enables cost effective, scalable amplicon sequencing of multiple target regions of interest in M. genitalium.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.

A Hybrid Many-Objective Evolutionary Algorithm With Region Preference for Decision Makers

In many-objective optimization, maintaining a good balance between convergence and diversity have turned out to be a considerable challenge for classical evolutionary algorithms. The large scale solution set required to describe the entire Pareto optimal front hinder the decision makers from finding the most satisfactory solution, whereas he/she is only interested in a limited part of the objective space. The dilemma can be handled by incorporating the preference information in the search process. In this paper, an evolutionary algorithm based on region preference is proposed for many-objective optimization. The preference model is constructed in combination of the target region and reference points. To focus the search on the preference region while maintaining well convergence and diversity within the region, a tri-level ranking criterion is introduced into the proposed algorithm, and different rank works at different phase of the search process. A fuzzy theory-based interactive approach is proposed to guide more individuals to further search into the objective space with higher preference degree and help the decision maker choose the most interested solution. The proposed algorithm has been extensively compared with other state-of-the-art preference-based algorithms on DTLZ1~ DTLZ4 test problems having 3–10 objectives. The experimental results indicate that the proposed algorithm can achieve competitive and better performance. Moreover, we extend the algorithm to handle multiple target regions.

Genetic Isolation of Hypothalamic Neurons that Regulate Context-Specific Male Social Behavior.

Nearly all animals engage in a complex assortment of social behaviors that are essential for the survival of the species. In mammals, these behaviors are regulated by sub-nuclei within the hypothalamus, but the specific cell types within these nuclei responsible for coordinating behavior in distinct contexts are only beginning to be resolved. Here, we identify a population of neurons in the ventral premammillary nucleus of the hypothalamus (PMV) that are strongly activated in male intruder mice in response to a larger resident male but that are not responsive to females. Using a combination of molecular and genetic approaches, we demonstrate that these PMV neurons regulate intruder-specific male social behavior and social novelty recognition in a manner dependent on synaptic release of the excitatory neurotransmitter glutamate. These data provide direct evidence for a unique population of neurons that regulate social behaviors in specific contexts.

Image Analysis and Processing with Applications in Proteomics and Medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Segmentation is a challenging task in computer vision with essential applications in biomedical engineering, remote sensing, robotics and automation. Typically, the target region is separated from the rest of image regions utilizing defining features including intensity, texture, color or motion cues. In this light, multiple segments are generated and the selection of the most significant segments becomes a controversial decision as it highly hinges on heuristic considerations. Moreover, the separation of the target regions is impeded by several daunting factors such as: background clutter, the presence of noise and artifacts as well as occlusions on multiple target regions. This thesis focuses on image segmentation using deformable models and specifically region-based Active Contours (ACs) because of their strong mathematical foundation and their appealing properties.

Concurrent Targeting of KRAS and AKT by MiR-4689 Is a Novel Treatment Against Mutant KRAS Colorectal Cancer.

KRAS mutations are a major cause of drug resistance to molecular-targeted therapies. Aberrant epidermal growth factor receptor (EGFR) signaling may cause dysregulation of microRNA (miRNA) and gene regulatory networks, which leads to cancer initiation and progression. To address the functional relevance of miRNAs in mutant KRAS cancers, we transfected exogenous KRASG12V into human embryonic kidney 293 and MRC5 cells with wild-type KRAS and BRAF genes, and we comprehensively profiled the dysregulated miRNAs. The result showed that mature miRNA oligonucleotide (miR)-4689, one of the significantly down-regulated miRNAs in KRASG12V overexpressed cells, was found to exhibit a potent growth-inhibitory and proapoptotic effect both in vitro and in vivo. miR-4689 expression was significantly down-regulated in cancer tissues compared to normal mucosa, and it was particularly decreased in mutant KRAS CRC tissues. miR-4689 directly targets v-ki-ras2 kirsten rat sarcoma viral oncogene homolog (KRAS) and v-akt murine thymoma viral oncogene homolog 1(AKT1), key components of two major branches in EGFR pathway, suggesting KRAS overdrives this signaling pathway through inhibition of miR-4689. Overall, this study provided additional evidence that mutant KRAS functions as a broad regulator of the EGFR signaling cascade by inhibiting miR-4689, which negatively regulates both RAS/mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT pathways. These activities indicated that miR-4689 may be a promising therapeutic agent in mutant KRAS CRC.

A High-Plex PCR Approach for Massively Parallel Sequencing

Current methods for targeted massively parallel sequencing (MPS) have several drawbacks, including limited design flexibility, expense, and protocol complexity, which restrict their application to settings involving modest target size and requiring low cost and high throughput. To address this, we have developed Hi-Plex, a PCR-MPS strategy intended for high-throughput screening of multiple genomic target regions that integrates simple, automated primer design software to control product size. Featuring permissive thermocycling conditions and clamp bias reduction, our protocol is simple, cost- and time-effective, uses readily available reagents, does not require expensive instrumentation, and requires minimal optimization. In a 60-plex assay targeting the breast cancer predisposition genes PALB2 and XRCC2, we applied Hi-Plex to 100 ng LCL-derived DNA, and 100 ng and 25 ng FFPE tumor-derived DNA. Altogether, at least 86.94% of the human genome-mapped reads were on target, and 100% of targeted amplicons were represented within 25-fold of the mean. Using 25 ng FFPE-derived DNA, 95.14% of mapped reads were on-target and relative representation ranged from 10.1-fold lower to 5.8-fold higher than the mean. These results were obtained using only the initial automatically-designed primers present in equal concentration. Hi-Plex represents a powerful new approach for screening panels of genomic target regions.

Selective Activation of Striatal Fast-Spiking Interneurons during Choice Execution

Basal ganglia circuits are essential for the organization and execution of voluntary actions. Within the striatum, fast-spiking interneurons (FSIs) are thought to tightly regulate the activity of medium-spiny projection neurons (MSNs) through feed-forward inhibition, yet few studies have investigated the functional contributions of FSIs in behaving animals. We recorded presumed MSNs and FSIs together with motor cortex and globus pallidus (GP) neurons, in rats performing a simple choice task. MSN activity was widely distributed across the task sequence, especially near reward receipt. By contrast, FSIs showed a coordinated pulse of increased activity as chosen actions were initiated, in conjunction with a sharp decrease in GP activity. Both MSNs and FSIs were direction selective, but neighboring MSNs and FSIs showed opposite selectivity. Our findings suggest that individual FSIs participate in local striatal information processing, but more global disinhibition of FSIs by GP is important for initiating chosen actions while suppressing unwanted alternatives.