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Phosphorus is essential in all cells’ structural, metabolic and regulatory functions. For fungal cells that import inorganic phosphate (Pi) up a steep concentration gradient, surface Pi transporters are critical capacitators of growth. Fungi must deploy Pi transporters that enable optimal Pi uptake in pH and Pi concentration ranges prevalent in their environments. Single, triple and quadruple mutants were used to characterize the four Pi transporters we identified for the human fungal pathogenCandida albicans, which must adapt to alkaline conditions during invasion of the host bloodstream and deep organs. A high-affinity Pi transporter, Pho84, was most efficient across the widest pH range while another, Pho89, showed high-affinity characteristics only within one pH unit of neutral. Two low-affinity Pi transporters, Pho87 and Fgr2, were active only in acidic conditions. Only Pho84 among the Pi transporters was clearly required in previously identified Pi-related functions including Target of Rapamycin Complex 1 signaling, oxidative stress resistance and hyphal growth. We used in vitro evolution and whole genome sequencing as an unbiased forward genetic approach to probe adaptation to prolonged Pi scarcity of two quadruple mutant lineages lacking all 4 Pi transporters. Lineage-specific genomic changes corresponded to divergent success of the two lineages in fitness recovery during Pi limitation. Initial, large-scale genomic alterations like aneuploidies and loss of heterozygosity eventually resolved, as populations gained small-scale mutations. Severity of some phenotypes linked to Pi starvation, like cell wall stress hypersensitivity, decreased in parallel to evolving populations’ fitness recovery in Pi scarcity, while severity of others like membrane stress responses diverged from Pi scarcity fitness. Among preliminary candidate genes for contributors to fitness recovery, those with links to TORC1 were overrepresented. Since Pi homeostasis differs substantially between fungi and humans, adaptive processes to Pi deprivation may harbor small-molecule targets that impact fungal growth, stress resistance and virulence. 

In integrated circuit design, analysis of wafer map patterns is critical to enhance yield and detect manufacturing issues. With the emergence of novel wafer map patterns, there is increasing need for robust artificial intelligence models that can both accurately classify seen patterns and while also detecting ones not seen during training, a capability known as open world classification. We develop a novel solution to this problem: WaferCap, a Deep Capsule Network designed for wafer map pattern classification and equipped with a rejection mechanism. When evaluated using the WM-811k dataset, WaferCap significantly surpasses existing methods, achieving 99\% accuracy for fully seen patterns while demonstrating robust performance in open-world settings by effectively detecting unseen wafer map patterns. 

Neuromorphic computation is based on spike trains in which the location and frequency of spikes occurring within the network guide the execution. This paper develops a frame-work to monitor the correctness of a neuromorphic program’s execution using model-based redundancy in which a software-based monitor compares discrepancies between the behavior of neurons mapped to hardware and that predicted by a corresponding mathematical model in real time. Our approach reduces the hardware overhead needed to support the monitoring infrastructure and minimizes intrusion on the executing application. Fault-injection experiments utilizing CARLSim, a high-fidelity SNN simulator, show that the framework achieves high fault coverage using parsimonious models which can operate with low computational overhead in real time. 

The paper develops a methodology for the online built-in self-testing of deep neural network (DNN) accelerators to validate the correct operation with respect to their functional specifications. The DNN of interest is realized in the hardware to perform in-memory computing using non-volatile memory cells as computational units. Assuming a functional fault model, we develop methods to generate pseudorandom and structured test patterns to detect hardware faults. We also develop a test-sequencing strategy that combines these different classes of tests to achieve high fault coverage. The testing methodology is applied to a broad class of DNNs trained to classify images from the MNIST, Fashion-MNIST, and CIFAR-10 datasets. The goal is to expose hardware faults which may lead to the incorrect classification of images. We achieve an average fault coverage of 94% for these different architectures, some of which are large and complex. 

While most fungi have the ability to reproduce sexually, multiple independent lineages have lost meiosis and developed parasexual cycles in its place. Emergence of parasexual cycles is particularly prominent in medically relevant fungi from the CUG paraphyletic group of Candida species. Since the discovery of parasex in C. albicans roughly two decades ago, it has served as the model for Candida species. Importantly, parasex in C. albicans retains hallmarks of meiosis including genetic recombination and chromosome segregation, making it a potential driver of genetic diversity. Furthermore, key meiotic genes play similar roles in C. albicans parasex and highlights parallels between these processes. Yet, the evolutionary role of parasex in Candida adaptation and the extent of resulting genotypic and phenotypic diversity remain as key knowledge gaps in this facultative reproductive program. Here, we present our current understanding of parasex, the mechanisms governing its regulation, and its relevance to Candida biology.