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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. 

Thanks to the numerous machine learning based malware detection (MLMD) research in recent years and the readily available online malware scanning system (e.g., VirusTotal), it becomes relatively easy to build a seemingly successful MLMD system using the following standard procedure: first prepare a set of ground truth data by checking with VirusTotal, then extract features from training dataset and build a machine learning detection model, and finally evaluate the model with a disjoint testing dataset. We argue that such evaluation methods do not expose the real utility of ML based malware detection in practice since the ML model is both built and tested on malware that are known at the time of training. The user could simply run them through VirusTotal just as how the researchers obtained the ground truth, instead of using the more sophisticated ML approach. However, ML based malware detection has the potential of identifying malware that has not been known at the time of training, which is the real value ML brings to this problem. We present experimentation study on how well a machine learning based malware detection system can achieve this. Our experiments showed that MLMD can consistently generate previously unknown malware knowledge, e.g., malware that is not detectable by existing malware detection systems at MLMD’s training time. Our research illustrates an ideal usage scenario for MLMD systems and demonstrates that such systems can benefit malware detection in practice. For example, by utilizing the new signals provided by the MLMD system and the detection capability of existing malware detection systems, we can more quickly uncover new malware variants or families.