Darwinian evolution has become dogma; AI can rescue what is salvageable.

Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.

Biological evolution requires an emergent, self-organizing principle.

In this perspective review, we assess fundamental flaws in Darwinian evolution, including its modern versions. Fixed mutations 'explain' microevolution but not macroevolution including speciation events and the origination of all the major body plans of the Cambrian explosion. Complex, multifactorial change is required for speciation events and inevitably requires self-organization beyond what is accomplished by known mechanisms. The assembly of ribosomes and ATP synthase are specific examples. We propose their origin is a model for what is unexplained in biological evolution. Probability of evolution is modeled in Section 9 and values are absurdly improbable. Speciation and higher taxonomic changes become exponentially less probable as the number of required, genetically-based events increase. Also, the power required of the proposed selection mechanism (survival of the fittest) is nil for any biological advance requiring multiple changes, because they regularly occur in multiple generations (different genomes) and would not be selectively conserved by the concept survival of the fittest (a concept ultimately centered on the individual). Thus, survival of the fittest cannot 'explain' the origin of the millions of current and extinct species. We also focus on the inadequacies of laboratory chemistry to explain the complex, required biological self-organization seen in cells. We propose that a 'bioelectromagnetic' field/principle emerges in living cells. Synthesis by self-organization of massive molecular complexes involves biochemical responses to this emergent field/principle. There are ramifications for philosophy, science, and religion. Physics and mathematics must be more strongly integrated with biology and integration should receive dedicated funding with special emphasis for medical applications; treatment of cancer and genetic diseases are examples.

Neo-Darwinism must Mutate to survive.

Darwinian evolution is a nineteenth century descriptive concept that itself has evolved. Selection by survival of the fittest was a captivating idea. Microevolution was biologically and empirically verified by discovery of mutations. There has been limited progress to the modern synthesis. The central focus of this perspective is to provide evidence to document that selection based on survival of the fittest is insufficient for other than microevolution. Realistic probability calculations based on probabilities associated with microevolution are presented. However, macroevolution (required for all speciation events and the complexifications appearing in the Cambrian explosion) are shown to be probabilistically highly implausible (on the order of 10-50) when based on selection by survival of the fittest. We conclude that macroevolution via survival of the fittest is not salvageable by arguments for random genetic drift and other proposed mechanisms. Evolutionary biology is relevant to cancer mechanisms with significance beyond academics. We challenge evolutionary biology to advance boldly beyond the inadequacies of the modern synthesis toward a unifying theory modeled after the Grand Unified Theory in physics. This should include the possibility of a fifth force in nature. Mathematics should be rigorously applied to current and future evolutionary empirical discoveries. We present justification that molecular biology and biochemistry must evolve to aeon (life) chemistry that acknowledges the uniqueness of enzymes for life. To evolve, biological evolution must face the known deficiencies, especially the limitations of the concept survival of the fittest, and seek solutions in Eigen's concept of self-organization, Schrödinger's negentropy, and novel approaches.

Simulations of blood pressure and identification of atrial fibrillation and arterial stiffness using an extended Kalman filter with oscillometric pulsation measurements.

BACKGROUND AND OBJECTIVE: The accuracy of systolic and diastolic blood pressure levels from oscillometric devices is difficult to assess for patients with atrial fibrillation and arterial stiffness; in such cases, changes in these levels from heartbeat to heartbeat can only be known if the actual total waveform during a test can be observed. Variation in these levels affects the accuracy of the algorithms in oscillometric devices where precision is of paramount importance according to the American College of Cardiology and the American Heart Association. Recently issued guidelines lowered the definition of high blood pressure; approximately three-quarters of men over 65 years of age meet the criterion and are diagnosed as hypertensive. A review in 2019 reported that atrial fibrillation affects approximately 3% of the general population in the U.S. and is twice as common in hypertensive patients. The actual shape and details of the arterial blood pressure waveform not only reveal variations in rhythm and in systolic and diastolic levels but has also proven to be valuable in the diagnosis of arterial stiffness and atrial fibrillation which are vital to determining the physiological status of a patient's cardiovascular system. This research pertains to the identification of a patient's total arterial blood pressure waveform using only the measured pulsations in the cuff of an oscillometric device. METHOD: Empirically formulated hypothetical arterial blood pressure waveforms were simulated and assumed to be unknown. Oscillometric pulsations corresponding to these simulated unknown waveforms were used as input to a new model-based extended Kalman filter algorithm for identifying the levels, shape and features of the unknown waveforms. RESULTS: Simulations with hypothetical waveforms with varying systolic and diastolic levels and with variations in heartbeat rhythm associated arterial fibrillation and stiff arteries, demonstrate potential arterial pressure estimate accuracies of 1.5 mmHg with standard deviations on the order of 0.1 mmHg; in addition, variations in heartbeat rhythm were observed and degrees of arterial stiffness identified and quantified. CONCLUSIONS: Computational analysis of the oscillometric cuff pulsations with the extended Kalman filter can be used to detect variations in heartbeat rhythm and blood pressure levels associated atrial fibrillation, quantify arterial stiffness, and provide noninvasive continuous blood pressure monitoring without the need for electrocardiogram or photoplethysmography sensors.