Genome-wide analyses of light-regulated genes in Aspergillus nidulans reveal a complex interplay between different photoreceptors and novel photoreceptor functions.

Fungi sense light of different wavelengths using blue-, green-, and red-light photoreceptors. Blue light sensing requires the "white-collar" proteins with flavin as chromophore, and red light is sensed through phytochrome. Here we analyzed genome-wide gene expression changes caused by short-term, low-light intensity illumination with blue-, red- or far-red light in Aspergillus nidulans and found that more than 1100 genes were differentially regulated. The largest number of up- and downregulated genes depended on the phytochrome FphA and the attached HOG pathway. FphA and the white-collar orthologue LreA fulfill activating but also repressing functions under all light conditions and both appear to have roles in the dark. Additionally, we found about 100 genes, which are red-light induced in the absence of phytochrome, suggesting alternative red-light sensing systems. We also found blue-light induced genes in the absence of the blue-light receptor LreA. We present evidence that cryptochrome may be part of this regulatory cue, but that phytochrome is essential for the response. In addition to in vivo data showing that FphA is involved in blue-light sensing, we performed spectroscopy of purified phytochrome and show that it responds indeed to blue light.

Non-Equilibrium Wigner Function and Application to Model of Catalyzed Polymerization.

The quantum Wigner function and non-equilibrium equation for a microscopic particle in one spatial dimension (1D) subject to a potential and a heat bath at thermal equilibrium are considered by non-trivially extending a previous analysis. The non-equilibrium equation yields a general hierarchy for suitable non-equilibrium moments. A new non-trivial solution of the hierarchy combining the continued fractions and infinite series thereof is obtained and analyzed. In a short thermal wavelength regime (keeping quantum features adequate for chemical reactions), the hierarchy is approximated by a three-term one. For long times, in turn, the three-term hierarchy is replaced by a Smoluchovski equation. By extending that 1D analysis, a new model of the growth (polymerization) of a molecular chain (template or te) by binding an individual unit (an atom) and activation by a catalyst is developed in three spatial dimensions (3D). The atom, te, and catalyst move randomly as solutions in a fluid at rest in thermal equilibrium. Classical statistical mechanics describe the te and catalyst approximately. Atoms and bindings are treated quantum-mechanically. A mixed non-equilibrium quantum-classical Wigner-Liouville function and dynamical equations for the atom and for the te and catalyst, respectively, are employed. By integrating over the degrees of freedom of te and with the catalyst assumed to be near equilibrium, an approximate Smoluchowski equation is obtained for the unit. The mean first passage time (MFPT) for the atom to become bound to the te, facilitated by the catalyst, is considered. The resulting MFPT is consistent with the Arrhenius formula for rate constants in chemical reactions.

MRI and PET of Brain Tumor Neuroinflammation in the Era of Immunotherapy, From the AJR Special Series on Inflammation.

With the emergence of immune-modulating therapies, brain tumors present important diagnostic imaging challenges. These challenges include planning personalized treatment and adjudicating accurate monitoring approaches and therapeutically specific response criteria. The challenges have been due, in part reliance on nonspecific imaging metrics, such as gadolinium contrast-enhanced MRI or FDG PET, and rapidly evolving biologic understanding of neuroinflammation. The importance of the tumor immune interaction and ability to therapeutically augment inflammation to improve clinical outcomes make it necessary for radiologists to develop a working knowledge of the immune system and its role in clinical neuroimaging. The purpose of this article is to review relevant biologic concepts of the tumor microenvironment of primary and metastatic brain tumors, the interactions between the tumors and the immune system, and MRI and PET methods for imaging inflammatory elements associated with these malignancies. In recognition of the growing fields of immunotherapeutics and precision oncology, clinically translatable imaging metrics for the diagnosis and monitoring of brain tumor neuroinflammation are highlighted. Practical guidance is provided for implementing iron nanoparticle imaging, including imaging indications, protocols, interpretation, and pitfalls. A comprehensive understanding of the inflammatory mechanisms within brain tumors and their imaging features will facilitate the development of innovative noninvasive prognostic and predictive imaging strategies for precision oncology.

Characteristics of Laparoscopic Surgery for Trauma Patients and Risks of Conversion to Open Laparotomy.

BACKGROUND: The discussion is ongoing about appropriate indications for laparoscopic surgery in trauma patients. As timing and risks of conversion to laparotomy remain unclear, we aimed to elucidate characteristics of and risks for conversion following laparoscopic surgery, using a nationwide database. METHODS: A retrospective observational study was conducted, using Japanese Trauma Data Bank (2004-2018). We included adult trauma patients who underwent laparoscopic surgery as an initial surgical intervention. Conversion to laparotomy was defined as laparotomy at the initial surgery. Patient demographics, mechanism and severity of injury, injured organs, timing of surgery, and clinical outcomes were compared between patients with and without conversion. Risks for conversion were analyzed focusing on indications for laparoscopic surgery, after adjusting patient and institution characteristics. RESULTS: Among 444 patients eligible for the study, 31 required conversions to laparotomy. The number of laparoscopic surgeries gradually increased over the study period (0.5-4.5% of trauma laparotomy), without changes in conversion rates (5-10%). Patients who underwent conversion had more severe abdominal injuries compared with those who did not (AIS 3 vs 2). While length of hospital stay and in-hospital mortality were comparable, abdominal complications were higher among patients with conversion (12.9 vs. 2.9%), particularly when laparoscopy was performed for peritonitis (OR, 22.08 [5.11-95.39]). A generalized estimating equation model adjusted patient background and identified hemoperitoneum and peritoneal penetration as risks for conversion (OR, 24.07 [7.35-78.75] and 8.26 [1.20- 56.75], respectively). CONCLUSIONS: Trauma laparoscopy for hemoperitoneum and peritoneal penetration were associated with higher incidence of conversion to open laparotomy.

Facial Expression Recognition from Multi-Perspective Visual Inputs and Soft Voting.

Automatic identification of human facial expressions has many potential applications in today's connected world, from mental health monitoring to feedback for onscreen content or shop windows and sign-language prosodic identification. In this work we use visual information as input, namely, a dataset of face points delivered by a Kinect device. The most recent work on facial expression recognition uses Machine Learning techniques, to use a modular data-driven path of development instead of using human-invented ad hoc rules. In this paper, we present a Machine-Learning based method for automatic facial expression recognition that leverages information fusion architecture techniques from our previous work and soft voting. Our approach shows an average prediction performance clearly above the best state-of-the-art results for the dataset considered. These results provide further evidence of the usefulness of information fusion architectures rather than adopting the default ML approach of features aggregation.

Use of Coffee Bean Bagasse Extracts in the Brewing of Craft Beers: Optimization and Antioxidant Capacity.

Coffee bean bagasse is one of the main by-products generated by industrial coffee production. This by-product is rich in bioactive compounds such as caffeine, caffeic and chlorogenic acid, and other phenols. The aims of this work are to optimize the extraction conditions of phenolic compounds present in coffee bean bagasse and incorporate them into stout-style craft beers, as well as to determine their effect on the phenol content and antioxidant capacity. The optimal conditions for extraction were 30% ethanol, 30 °C temperature, 17.5 mL of solvent per gram of dry sample, and 30 min of sonication time. These conditions presented a total phenol content of 115.42 ± 1.04 mg GAE/g dry weight (DW), in addition to an antioxidant capacity of 39.64 ± 2.65 µMol TE/g DW in DPPH• and 55.51 ± 6.66 µMol TE/g DW for FRAP. Caffeine, caffeic and chlorogenic acids, and other minor compounds were quantified using HPLC-DAD. The coffee bean bagasse extracts were added to the stout craft beer and increased the concentration of phenolic compounds and antioxidant capacity of the beer. This work is the first report of the use of this by-product added to beers.

Death and Other Losses in the COVID-19 Pandemic in Long-Term Care Facilities for Older Adults in the Perception of Occupational Therapists: A Qualitative Study.

Long-term care facilities for older adults (LTCFs) were directly affected by the COVID-19 pandemic. This study aimed to discuss the perceptions of occupational therapists about deaths and other losses in LTCFs during the pandemic. This qualitative study is anchored in social phenomenology, and conducted in-depth interviews with eight occupational therapists who worked in LTCFs. Thus, two themes were generated after the Thematic Analyses: "The proximity of death" and "Losses associated with living and dying in a LTCF." In the first theme, the interviewees addressed the feeling of imminent death in the daily life of the LTCF, and feelings related to their own death, that of their family members and other older adults. In the second, the professionals highlighted three groups of losses: social, functional, and psychological/cognitive. These results highlighted the challenges faced by occupational therapists and can contribute to improve behavior and care for institutionalized older adults during the pandemic.

Timing of repair of blunt thoracic aortic injuries in the thoracic endovascular aortic repair era.

OBJECTIVE: Thoracic endovascular aortic repair (TEVAR) is the preferred operative treatment of blunt thoracic aortic injuries (BTAIs). Its use is associated with improved outcomes compared with open surgical repair and nonoperative management. However, the optimal time from injury to repair is unknown and remains a subject of debate across different societal practice guidelines. The purpose of this study was to evaluate national trends in the management of BTAI, with a specific focus on the impact of timing of repair on outcomes. METHODS: Using the National Trauma Data Bank, we identified adult patients with BTAI between 2012 and 2017. Patients with prehospital or emergency department cardiac arrest or incomplete data sets were excluded from analysis. Patients were classified according to timing of repair: group 1, <24 hours; and group 2, ≥24 hours. The primary outcome evaluated was in-hospital mortality; secondary outcomes included overall hospital and intensive care unit length of stay. Multivariable logistic regression was performed to identify independent predictors of mortality. RESULTS: The analysis was completed for 2821 patients who underwent TEVAR for BTAI with known operative times. The overall mortality in the patient cohort was 8.4% (238/2821); 75% of patients undergoing TEVAR were repaired within 24 hours. Mortality was more than twofold greater in group 1 compared with group 2 (9.8% [207/2118] vs 4.4% [31/703]; P = .001). This mortality benefit persisted across injury severity groups and was independent of the presence of serious extrathoracic injuries. Logistic regression analysis, adjusting for age ≥65 years, Glasgow Coma Scale score ≤8, systolic blood pressure ≤90 mm Hg at admission, and serious extrathoracic injuries, showed a higher adjusted mortality in group 1 (odds ratio, 2.54; 95% confidence interval, 1.66-3.91; P = .001). CONCLUSIONS: The majority of patients with BTAI undergo endovascular repair within 24 hours of injury. Patients undergoing delayed repair have improved survival compared with those repaired within the first 24 hours of injury in spite of similar injury patterns and severity. In patients with BTAIs without signs of imminent rupture, delaying endovascular repair beyond 24 hours after injury should be considered.

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains.

Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others' shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or "architectures" as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis's first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.

Cariology consensus for undergraduates at dental schools in the Caribbean region.

AIM: To obtain a consensus on the domains of cariology for undergraduates in dental schools in the Caribbean. MATERIALS AND METHODS: Dental school deans from the Dominican Republic (DR) and Puerto Rico (PR) were invited to participate. Afterwards, 24 cariology faculty members from the dental schools in the DR received a 40-h workshop on the current understanding of dental caries. Then, representatives from participating dental schools in the DR and PR were divided into six groups and tasked with exploring the cariology domains described in the European and Colombian consensus. For each domain, the competencies in what the dental student, upon graduation, would have to (1) be proficient in, (2) have knowledge of, (3) and be familiar with were described. Two cariology consensus meetings to work on the domains and suggest changes for the Caribbean region were done, and representatives from the DR and PR agreed on a cariology consensus. For the second stage, Caribbean English-speaking countries were invited to participate in the consensus. Meetings were held with representatives from dental schools in Trinidad and Tobago (TT) and Jamaica (J) to include their suggestions on each consensus domain. RESULTS: A total of 15 dental schools (DR = 12; PR = 1; TT = 1; and J = 1) participated; they agreed on a new consensus considering the realities of the Caribbean participating countries. CONCLUSION: A cariology consensus for undergraduates was achieved for 15 dental schools in the Caribbean region. This accomplishment will promote a shift for the Caribbean dental schools' graduates towards a current understanding of dental caries.

Choosing the Best Sensor Fusion Method: A Machine-Learning Approach.

Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method.

Virtual Sensors for Optimal Integration of Human Activity Data.

Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as "virtual sensors", in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the "signatures" of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.

Multi-Sensor Fusion for Activity Recognition-A Survey.

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.

Application of a compact trickle-bed bioreactor for the removal of odor and volatile organic compounds emitted from a wastewater treatment plant.

A compact trickle-bed bioreactor (CTBB) was tested for the removal of volatile organic compounds (VOCs) and hydrogen sulphide (H2S) present in the exhaust air of a wastewater treatment plant. At gas-flow rates varying between 2.0 and 30.0 m3/h and for specific pollutant loads up to 20 g/(m3·h), removal efficiencies for H2S and VOC were >95%. The CTBB was designed for a maximum H2S concentration of ∼200 ppm and removal efficiencies >97% were noticed. VOC concentrations were in the range of 25-240 ppmv and the removal efficiency was in the range of 85-99%. Possible consequences of an excessive pollutant overload and the time required for regenerating the microbial activity and reviving stable process conditions in the CTBB were also investigated. An increase in the H2S concentration from 400 to 600 ppmv for a few hours caused bioreactor poisoning; however, when original H2S concentrations were restored, stable CTBB operation was ascertained within 3 h.

Non-Equilibrium Liouville and Wigner Equations: Classical Statistical Mechanics and Chemical Reactions for Long Times.

We review and improve previous work on non-equilibrium classical and quantum statistical systems, subject to potentials, without ab initio dissipation. We treat classical closed three-dimensional many-particle interacting systems without any "heat bath" (h b), evolving through the Liouville equation for the non-equilibrium classical distribution W c, with initial states describing thermal equilibrium at large distances but non-equilibrium at finite distances. We use Boltzmann's Gaussian classical equilibrium distribution W c , e q, as weight function to generate orthogonal polynomials (H n's) in momenta. The moments of W c, implied by the H n's, fulfill a non-equilibrium hierarchy. Under long-term approximations, the lowest moment dominates the evolution towards thermal equilibrium. A non-increasing Liapunov function characterizes the long-term evolution towards equilibrium. Non-equilibrium chemical reactions involving two and three particles in a h b are studied classically and quantum-mechanically (by using Wigner functions W). Difficulties related to the non-positivity of W are bypassed. Equilibrium Wigner functions W e q generate orthogonal polynomials, which yield non-equilibrium moments of W and hierarchies. In regimes typical of chemical reactions (short thermal wavelength and long times), non-equilibrium hierarchies yield approximate Smoluchowski-like equations displaying dissipation and quantum effects. The study of three-particle chemical reactions is new.

Medical causes of temporary or definitive leaves from a French counterterrorist unit pre-internship.

INTRODUCTION: Each year, the French Special Weapons And Tactics team, Groupe d'Intervention de la Gendarmerie Nationale, recruits new members through a physically demanding 8-week selection process. The goal of this study is to estimate the incidence and the causes for temporary or final interruptions during this process for medical reasons. SUBJECTS, MATERIAL AND METHODS: All of the candidates for the November 2015 selection process were included in this prospective study. The number and reasons for temporary or final interruptions were documented by military general practitioners. RESULTS: The applicants were 48 law enforcement professionals (2 women, mean age 29.4 years, range 22-35). In 14 cases, a temporary interruption was required and in five cases the selection process prematurely ended. Fifty-two per cent of the temporary interruptions were due to sprains, tendinopathies, fractures or muscle tears, 11% were due to burns, wounds or subcutaneous bruises, 16% were due to cranial trauma and 21% were due to medical causes. DISCUSSION: The high prevalence of minor traumatology that we observed is similar to the ones observed in other cohorts describing initial training for military personnel in the conventional forces. However, the presence of other pathologies in our study, such as cranial trauma or medical causes, is due to the specificity of this internship selection granting access to an elite unit.

The recruitment of AMP-activated protein kinase to glycogen is regulated by autophosphorylation.

The mammalian AMP-activated protein kinase (AMPK) is an obligatory αßγ heterotrimeric complex carrying a carbohydrate-binding module (CBM) in the ß-subunit (AMPKß) capable of attaching AMPK to glycogen. Nonetheless, AMPK localizes at many different cellular compartments, implying the existence of mechanisms that prevent AMPK from glycogen binding. Cell-free carbohydrate binding assays revealed that AMPK autophosphorylation abolished its carbohydrate-binding capacity. X-ray structural data of the CBM displays the central positioning of threonine 148 within the binding pocket. Substitution of Thr-148 for a phospho-mimicking aspartate (T148D) prevents AMPK from binding to carbohydrate. Overexpression of isolated CBM or ß1-containing AMPK in cellular models revealed that wild type (WT) localizes to glycogen particles, whereas T148D shows a diffuse pattern. Pharmacological AMPK activation and glycogen degradation by glucose deprivation but not forskolin enhanced cellular Thr-148 phosphorylation. Cellular glycogen content was higher if pharmacological AMPK activation was combined with overexpression of T148D mutant relative to WT AMPK. In summary, these data show that glycogen-binding capacity of AMPKß is regulated by Thr-148 autophosphorylation with likely implications in the regulation of glycogen turnover. The findings further raise the possibility of regulated carbohydrate-binding function in a wider variety of CBM-containing proteins.

Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances.

Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce.

Performance of Apparent Diffusion Coefficient Values and Conventional MRI Features in Differentiating Tumefactive Demyelinating Lesions From Primary Brain Neoplasms.

OBJECTIVE: Tumefactive demyelinating lesions (TDLs) remain one of the most common brain lesions to mimic a brain tumor, particularly primary CNS lymphoma (PCNSL) and high-grade gliomas. The purpose of our study was to evaluate the ability of apparent diffusion coefficient (ADC) values and conventional MRI features to differentiate TDLs from PCNSLs and high-grade gliomas. MATERIALS AND METHODS: Seventy-five patients (24 patients with TDLs, 28 with PCNSLs, and 23 with high-grade gliomas) with 168 brain lesions (70 TDLs, 68 PCNSLs, and 30 high-grade gliomas) who underwent DWI before surgery or therapy were included in the study. Minimum ADC (ADC(min)) and average ADC (ADC(avg)) values were calculated for each lesion. ANOVA and ROC analyses were performed. ROC analyses were also performed for the presence of incomplete rim enhancement and for the number of lesions. Multiple-variable logistic regression with ROC analysis was then performed to evaluate performance in multiple-variable models. RESULTS: ADC(min) was statistically significantly higher (p < 0.01) in TDLs (mean, 0.886; 95% CI, 0.802-0.931) than in PCNSLs (0.547; 95% CI, 0.496-0.598) and high-grade gliomas (0.470; 95% CI, 0.385-0.555). (All ADC values in this article are reported in units of × 10(-3) mm(2)/s.) ADC(avg) was statistically significantly higher (p < 0.01) in TDLs (mean, 1.362; 95% CI, 1.268-1.456) than in PCNSLs (0.990; 95% CI, 0.919-1.061) but not in high-grade gliomas (1.216; 95% CI, 1.074-1.356). Multiple-variable models showed statistically significant individual effects and superior diagnostic performance on ROC analysis. CONCLUSION: TDLs can be diagnosed on preoperative MRI with a high degree of specificity; MRI features of incomplete rim enhancement, high ADC values, and a large number of lesions individually increase the probability and diagnostic confidence that a lesion is a TDL.

Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone.

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user's location in an indoor environment. A multivariate model is applied to find the user's location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth's magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of information.