EFFECT OF SHORT-STORAGE HRGCs ON DRIVER DECISION BEHAVIOR AND SAFETY CONCERNS: REAL-WORLD ANALYSIS AND EXPERIMENTAL EVIDENCE

Vehicle-train collisions at highway-rail grade crossings (HRGCs) continue to be a safety concern, and despite improvements in warnings, many of these incidents are attributed to human error. In some cases, distractions other than railroad traffic, such as HRGCs with limited space between the railroad tracks and the highway intersection, may create additional cognitive burdens for drivers. We investigated the effect of HRGC type (short-storage vs. non-short storage) on driver attention and decision-making in two studies. In Study 1, we systematically analyzed 996 incidents from 2017-2019 from the Federal Railroad Administration’s Safety database. Driver decision making and outcomes were different depending on HRGC type, with more train strikes in short storage incidents, as opposed to vehicle strikes. Study 2 was a controlled lab experiment in which drivers identified safety concerns in driving images. Drivers reported more safety concerns, and rated them more important in images of short-storage HRGCs than non-short storage HRGCs. This pattern did not depend on their rural or urban driving experience. Eye-tracking analysis found some differences in search behavior depending on the type of HRGC. This research contributes to a new area of research in rail safety, as studies comparing the two types of HRGCs have previously not been done. Interventions for non-short-storage HRGCs may not apply to short-storage HRGCs if it is found that drivers approach them differently

EXPLICIT RULE LEARNING: A COGNITIVE TUTORIAL METHOD TO TRAIN USERS OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING SYSTEMS

Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used to train humans in intelligent software systems; one of them is exemplar-based training. Although there has been some success, depending on the structure of the system, there are limitations to exemplars, which oftentimes are post hoc and case-based. Explicit Rule Learning is a global and rule-based training method that incorporates exemplars, but goes beyond specific cases. It provides learners with rich, robust mental models and the ability to transfer the learned skills to novel, previously unencountered situations. Learners are given verbalizable, probabilistic if...then statements, supplemented with exemplars. This is followed up with a series of practice problems, to which learners respond and receive immediate feedback on their correctness. The expectation is that this method will result in a refined representation of the system’s underlying principles, and a richer and more robust mental model that will enable the learner to simulate future states. Preliminary research helped to evaluate and refine Explicit Rule Learning. The final study in this research applied Explicit Rule Learning to a more real-world system, autonomous driving. The mixed-method within-subject study used a more naturalistic environment. Participants were given training material using the Explicit Rule Learning method and were subsequently tested on their ability to predict the autonomous vehicle’s actions. The results indicate that the participants trained with the Explicit Rule Learning method were more proficient at predicting the autonomous vehicle’s actions. These results, together with the results of preceding studies indicate that Explicit Rule Learning is an effective method to accelerate the proficiency of learners of intelligent software systems. Explicit Rule Learning is a low-cost training intervention that can be adapted to many intelligent software systems, including the many types of AI/ML systems in today’s world

Oral malodour : background and diagnostics

Bad breath or oral malodour can be related to gingival diseases, trimethylaminuria, various inflammation diseases of upper respiratory tract, foreign bodies in nasal cavity etc. Bad breath is usually, in 85 % to 95 % of cases, inflicted by gram negative anaerobic bacteria in tongue coating. These bacteria have a tendency of producing foul-smelling sulphur containing gases called volatile sulphur compounds or VSC. Main cause of bad breath is parodontitis or postnasal drip into posterior part of the tongue. Detecting bad breath is most efficiently done by organoleptic method. By skilled analyser the reason for oral malodour can be determined with great accuracy. For scientific study the most effective method is gas chromatography (GC) with flame photometric detector (FPD). With it almost every component of exhaled air can be detected both quantitative and qualitative. Effective chairside methods include portable sulphur monitors and saliva tests

Tanniinipitoisuudesta härkäpavun (Vicia faba L.) siemenissä

vokKirjasto Aj-

Lähdeviitteet ja pisteen paikka — ajatuksia viittauskäytänteiden kirjosta

Ohjeet ja käytännöt lähdeviitteiden merkitsemisestä vaihtelevat oppilaitoksittain. Omien käytäntöjen sijaan tulisi suosia kansainvälisiä standardeja ja ammattimaisia viitteidenhallintaohjelmia. Erityisen kiusallinen on kumottuun kansalliseen standardiin perustuva käytäntö pisteen käytöstä lähdeviitesulkeiden sisällä, jota viitteidenhallintatyökalut eivät tue eivätkä tunnista

The androgen receptor and signal-transduction pathways in hormone-refractory prostate cancer. Part 2: androgen-receptor cofactors and bypass pathways

Prostate cancer is the second leading cause of cancer related deaths in men from the western world. Treatment of prostate cancer has relied on androgen deprivation therapy for the past 50 years. Response rates are initially high (70-80%), however almost all patients develop androgen escape and subsequently die within 1-2 years. Unlike breast cancer, alternative approaches (chemotherapy and radiotherapy) do not increase survival time. The high rate of prostate cancer mortality is therefore strongly linked to both development of androgen escape and the lack of alternate therapies. AR mutations and amplifications can not explain all cases of androgen escape and post-translational modification of the AR has become an alternative theory. However recently it has been suggested that AR co-activators e.g. SRC-1 or pathways the bypass the AR (Ras/MAP kinase or PI3K/Akt) may stimulated prostate cancer progression independent of the AR. This review will focus on how AR coactivators may act to increase AR transactivation during sub-optimal DHT concentrations and also how signal transduction pathways may promote androgen escape via activation of transcription factors, e.g. AP-1, c-Myc and Myb, that induce cell proliferation or inhibit apoptosis

Androgen regulation of the androgen receptor coregulators

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Supraphysiologic Testosterone Therapy in the Treatment of Prostate Cancer: Models, Mechanisms and Questions

Since Huggins defined the androgen-sensitive nature of prostate cancer (PCa), suppression of systemic testosterone (T) has remained the most effective initial therapy for advanced disease although progression inevitably occurs. From the inception of clinical efforts to suppress androgen receptor (AR) signaling by reducing AR ligands, it was also recognized that administration of T in men with castration-resistant prostate cancer (CRPC) could result in substantial clinical responses. Data from preclinical models have reproducibly shown biphasic responses to T administration, with proliferation at low androgen concentrations and growth inhibition at supraphysiological T concentrations. Many questions regarding the biphasic response of PCa to androgen treatment remain, primarily regarding the mechanisms driving these responses and how best to exploit the biphasic phenomenon clinically. Here we review the preclinical and clinical data on high dose androgen growth repression and discuss cellular pathways and mechanisms likely to be involved in mediating this response. Although meaningful clinical responses have now been observed in men with PCa treated with high dose T, not all men respond, leading to questions regarding which tumor characteristics promote response or resistance, and highlighting the need for studies designed to determine the molecular mechanism(s) driving these responses and identify predictive biomarkers