Automated peptide and oligonucleotide synthesizers enabled a revolution in molecular biology and helped pave the way to modern synthetic biology. Similarly, fully automated synthetic chemistry could herald a new wave of innovation in biology and materials sciences by greatly facilitating access to known and novel molecules. Here, we report on an automated multistep chemical synthesizer, AutoSyn, that makes milligram-to-gram-scale amounts of virtually any drug-like small molecule in a matter of hours and demonstrate its versatility with the synthesis of ten known drugs. Of the FDA-approved small-molecule drugs for which we were able to compute a synthetic route, 87% are predicted to be synthesizable on AutoSyn. Moreover, AutoSyn enables digital synthesis protocols that ensure the reproducibility and transferability of synthesis protocols from one lab to another.

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The theoretical and experimental evaluation of a digital hardware correlation system for low-power ultrasonic applications is presented. The system, which incorporates dual Golay code matched filtering, is capable of 20-MHz processing rates with a signal-to-noise-ratio enhancement (SNRE) of 23 dB over a conventional pulse-echo system operating at the same peak power levels. The effects of digitization have been investigated, and a TTL (transistor transistor logic)-based hardware correlator has been developed. For many applications, low-voltage driving followed by differential detection is sufficient, permitting the system to be used in a number of power-limited environments. Sample tests conducted on three different transducers have demonstrated that the system is operational over a wide variety of probe configurations.

Tissue classification by examining sets of ultrasound parameters is an elusive goal. We report analysis of measurements of ultrasound speed, attenuation and backscatter in the range 3 to 8 MHz in breast tissues at 37 C. Statistical discriminant analysis and neural net analysis were employed. Data were acquired from 24 biopsy and 7 mastectomy specimens. Best separation of the classes normal, benign, and malignant occurred in the 18 cases where two tissue classes were present in the same specimen and parameters were corrected for within-patient mean; then 85-90% of cases in test sets were correctly classified. Most errors comprised misclassified benign cases. The neural net was comparable to discriminant analysis and slightly superior in separating normal and malignant classes.