This paper aims to explore some properties of certain third-order linear sequences which have some properties analogous to the better known second-order sequences of Fibonacci and Lucas. Historical background issues are outlined. These, together with the number and combinatorial theoretical results, provide plenty of pedagogical opportunities for further development.

We test whether and how well the presence of an embedded librarian improves the quality of student research. Students in introductory-level courses tend to have very low levels of research skills and experience. Though faculty are frustrated by this lack of skills, both students and faculty tend to have only a peripheral knowledge of the role librarians can play in helping develop their research skills. Studies suggest that embedding librarians into course instruction is the preferred method for improving students' research skills, yet the political science teaching and learning literature rarely addresses this issue, or focus on single class experiences, measuring change in student knowledge and skills from the beginning to the end of a semester. We compare two International Politics courses taught in consecutive fall semesters with different levels of librarian involvement in the class. We assess the changes in quality and use of information sources in the final research papers, both from a bibliographic perspective (looking at the number, quality, and variety of sources used) as well as an information use perspective (looking at the relevance of cited material in supporting arguments).

Thiosuccinimide-linked antibody-drug conjugates (ADCs) are susceptible to drug loss over time due to a retro-Michael reaction, which can be prevented by selecting stable conjugation positions or hydrolysis of the thiosuccinimide. Here, we investigate pyrrolobenzodiazepine (PBD) ADC drug-linkers equipped with N-phenyl maleimide functionality for stable thiol conjugation via thiosuccinimide hydrolysis. Two PBD drug-linker formats (enzyme-cleavable and non-cleavable) were evaluated following site-specific conjugation to an engineered cysteine incorporated at position T289, which is known to be unstable for N-alkyl maleimide conjugates. N-phenyl maleimide PBDs conjugated to antibodies with similar efficiencies as N-alkyl maleimide PBDs and enhanced thiosuccinimide hydrolysis for N-phenyl maleimide PBDs was confirmed by mass spectrometry, capillary isoelectric focusing, and a SYPRO Orange dye binding assay. All of the PBD ADCs were highly potent in vitro regardless of maleimide-or linker-type, exhibiting low pM EC 50 values. Thiol conjugation to N-phenyl maleimide PBD minimized the retro-Michael reaction in both rat and mouse serum. However, cleavage of the valine-alanine dipeptide in mouse serum for ADCs containing cleavable drug-linker led to drug loss regardless of maleimide type, which impacted ADC potency in tumor growth inhibition studies that were conducted in mouse models. Therapeutic improvement in mouse tumor models was realized for ADCs prepared with non-cleavable PBD drug-linkers that were conjugated through N-phenyl maleimide, where a stronger tumor growth inhibition (TGI) response was achieved when compared to the analogous N-alkyl maleimide drug-linker ADC. Altogether, our findings highlight the stability and efficacy benefits of N-phenyl maleimide functionality for ADCs that are produced with thiol-maleimide conjugation chemistry.

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This is essentially an expository paper which sheds new light on existing knowledge due to Asveld and Horadam and suggests ideas for extension and generalization based on the approaches of these authors.

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Classification of the severity of diabetic retinopathy {DR)and quantijkation of diabetic changes are vital for assessing the therapies and risk factors for this frequent complication of diabetes. A multilayer feedforward network has been developed for the classification of DR. One of its major strengths is that accurate feature extractions and accurate grading of DR lesions are not required. Another strength of this technique is its robustness as the network can also classify DR eflectively in noisy environments.

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