REMOVAL OF PROTECTIONISM, FOREIGN INVESTMENT AND WELFARE IN A MODEL OF INFORMAL SECTOR

The paper develops a three-sector general equilibrium model with two informal sectors with complete mobility of labour between these sectors and with a positive relationship between wage income and labour's efficiency to show that the results relating to foreign capital inflow and removal of protectionism may be counterintuitive to the conventional wisdom. The paper is also devoted to explain why some developing countries implement tariff reforms very slowly compared to others, even after formally choosing free trade as their development strategies, in a more general fashion than the existing tariff-jumping theory.Foreign capital inflow, tariff reduction, mobility of labour, wage efficiency hypothesis, tariff-jumping theory

Acquisition, Processing, and Analysis of Video, Audio and Meteorological Data in Multi-Sensor Electronic Beehive Monitoring

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures. Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top of a super and requires no structural modifications to a standard beehive (Langstroth or Dadant beehive), thereby preserving the sacredness of the bee space without disturbing the natural beehive cycles. The system is capable of capturing videos of forager traffic through a camera placed over the landing pad. Audio of bee buzzing is also recorded through microphones attached outside just above the landing pad. The above sensors are connected to a low-cost raspberry pi computer, and the data is saved on the raspberry pi itself or an external hard drive. In this dissertation, we have developed an algorithm that analyzes those video recordings and returns the number of bees that have moved in each video. The algorithm is also able to distinguish between incoming, outgoing, and lateral bee movements. We believe this would help commercial and amateur beekeepers or even citizen scientists to observe the bee traffic near their respective hives to identify the state of the corresponding bee colonies. This information helps those mentioned above because it is believed that honeybee traffic carries information on colony behavior and phenology. Next, we analyzed the audio recordings and presented a system that can classify those recordings into bee buzzing, cricket chirping, and ambient noise. We later saw how a long–term analysis of the intensity of bee buzzing could help us understand the hive’s development through an entire beekeeping season. We also investigated the effect of local weather conditions using 21 different meteorological variables on the forager traffic. We collected the meteorological data from a weather station located on the campus of Utah State University. Through our study, we were able to show that without the use of additional costly intrusive hardware to count the bees, we can use our bee motion counting algorithm to calculate the bee motions and then use the counts to investigate the relationship between foraging activity and local weather. To ensure that our findings and algorithms can be reproduced, we have made our datasets and source codes public for interested research and citizen science communities

On Video Analysis of Omnidirectional Bee Traffic: Counting Bee Motions with Motion Detection and Image Classification

Omnidirectional bee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a given hive over a given period of time. Video bee traffic analysis has the potential to automate the assessment of omnidirectional bee traffic levels, which, in turn, may lead to a complete or partial automation of honeybee colony health assessment. In this investigation, we proposed, implemented, and partially evaluated a two-tier method for counting bee motions to estimate levels of omnidirectional bee traffic in bee traffic videos. Our method couples motion detection with image classification so that motion detection acts as a class-agnostic object location method that generates a set of regions with possible objects and each such region is classified by a class-specific classifier such as a convolutional neural network or a support vector machine or an ensemble of classifiers such as a random forest. The method has been, and is being iteratively field tested in BeePi monitors, multi-sensor electronic beehive monitoring systems, installed on live Langstroth beehives in real apiaries. Deployment of a BeePi monitor on top of a beehive does not require any structural modification of the beehive’s woodenware, and is not disruptive to natural beehive cycles. To ensure the replicability of the reported findings and to provide a performance benchmark for interested research communities and citizen scientists, we have made public our curated and labeled image datasets of 167,261 honeybee images and our omnidirectional bee traffic videos used in this investigation

Application of Digital Particle Image Velocimetry to Insect Motion: Measurement of Incoming, Outgoing, and Lateral Honeybee Traffic

The well-being of a honeybee (Apis mellifera) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed to measure directional traffic. The algorithm uses digital particle image velocimetry to compute motion vectors, analytically classifies them as incoming, outgoing, or lateral, and returns the classified vector counts as measurements of directional traffic levels. Dynamic time warping is used to compare the algorithm’s omnidirectional traffic curves to the curves produced by a previously proposed bee motion counting algorithm based on motion detection and deep learning and to the curves obtained from a human observer’s counts on four honeybee traffic videos (2976 video frames). The currently proposed algorithm not only approximates the human ground truth on par with the previously proposed algorithm in terms of omnidirectional bee motion counts but also provides estimates of directional bee traffic and does not require extensive training. An analysis of correlation vectors of consecutive image pairs with single bee motions indicates that correlation maps follow Gaussian distribution and the three-point Gaussian sub-pixel accuracy method appears feasible. Experimental evidence indicates it is reasonable to treat whole bees as tracers, because whole bee bodies and not parts thereof cause maximum motion. To ensure the replicability of the reported findings, these videos and frame-by-frame bee motion counts have been made public. The proposed algorithm is also used to investigate the incoming and outgoing traffic curves in a healthy hive on the same day and on different days on a dataset of 292 videos (216,956 video frames)

Case Report Bilateral Upper Extremity DVT in a 43-Year-Old Man: Is It Thoracic Outlet Syndrome?!

Recurrent deep venous thrombosis, involving bilateral upper extremities, is an extremely rare phenomenon. We are presenting a 43-year-old man who was diagnosed with left upper extremity deep vein thrombosis (UEDVT) and was treated with anticoagulation and surgical decompression in 2004. 9 years later, he presented with right arm swelling and was diagnosed with right UEDVT using US venous Doppler. Venogram showed compression of the subclavian vein by the first rib, diagnosing thoracic outlet syndrome (TOS). He was treated with anticoagulation and local venolysis and later by surgical decompression of the subclavian vein. Bilateral UEDVT, as mentioned above, is an extremely rare condition that is uncommonly caused by TOS. To our knowledge, we are reporting the first case of bilateral UEDVT due to TOS. Diagnosis usually starts with US venous Doppler to detect the thrombosis, followed by the gold standard venogram to locate the area of obstruction and lyse the thrombus if needed. The ultimate treatment for TOS remains surgical decompression of the vascular bundle at the thoracic outlet

Aging augments obesity-induced thymic involution and peripheral T cell exhaustion altering the “obesity paradox”

IntroductionThe incidence of obesity, a condition characterized by systemic chronic inflammation, has reached pandemic proportions and is a poor prognostic factor in many pathologic states. However, its role on immune parameters has been diverse and at times contradictory. We have previously demonstrated that obesity can result in what has been called the “obesity paradox” which results in increased T cell exhaustion, but also greater efficacy of immune checkpoint blockade in cancer treatment.MethodsThe role of obesity, particularly in the context of aging, has not been robustly explored using preclinical models. We therefore evaluated how age impacts the immune environment on T cell development and function using diet-induced obese (DIO) mice.ResultsWe observed that DIO mice initially displayed greater thymopoiesis but then developed greater thymic involution over time compared to their lean counterparts. Both aging and obesity resulted in increased T cell memory conversion combined with increased expression of T cell exhaustion markers and Treg expansion. This increased T cell immunosuppression with age then resulted in a loss of anti-tumor efficacy by immune checkpoint inhibitors (ICIs) in older DIO mice compared to the younger DIO counterparts.DiscussionThese results suggest that both aging and obesity contribute to T cell dysfunction resulting in increased thymic involution. This combined with increased T cell exhaustion and immunosuppressive parameters affects immunotherapy efficacy reducing the advantage of obesity in cancer immunotherapy responses

Tumor Inflammation, Obesity, and Proliferative Status as Biomarkers in Gastroesophageal Adenocarcinoma

Recent epidemiological studies have shown that obesity, typically measured by increased body mass index (BMI), is associated with an increased risk of gastroesophageal adenocarcinoma (GEAC), but the contributing molecular and immune mechanisms remain unknown. Since obesity is known to promote chronic inflammation, we hypothesized that obesity leads to inflammation-related immune dysfunction, which can be reversed by immune-modulating therapy. To test our hypothesis, we examined the clinical and molecular data from advanced GEAC patients. To this end, 46 GEAC tumors were evaluated for biomarkers representing tumor inflammation, cell proliferation, and PD-L1 expression. A CoxPH regression model with potential co-variates, followed by pairwise post hoc analysis, revealed that inflammation in the GEAC tumor microenvironment is associated with improved overall survival, regardless of BMI. We also observed a significant association between cell proliferation and progression-free survival in overweight individuals who received immune-modulating therapy. In conclusion, our data confirm the role of the immune system in the natural course of GEAC and its responses to immunotherapies, but do not support the role of BMI as an independent clinically relevant biomarker in this group of patients

On Video Analysis of Omnidirectional Bee Traffic: Counting Bee Motions with Motion Detection and Image Classification

Omnidirectional bee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a given hive over a given period of time. Video bee traffic analysis has the potential to automate the assessment of omnidirectional bee traffic levels, which, in turn, may lead to a complete or partial automation of honeybee colony health assessment. In this investigation, we proposed, implemented, and partially evaluated a two-tier method for counting bee motions to estimate levels of omnidirectional bee traffic in bee traffic videos. Our method couples motion detection with image classification so that motion detection acts as a class-agnostic object location method that generates a set of regions with possible objects and each such region is classified by a class-specific classifier such as a convolutional neural network or a support vector machine or an ensemble of classifiers such as a random forest. The method has been, and is being iteratively field tested in BeePi monitors, multi-sensor electronic beehive monitoring systems, installed on live Langstroth beehives in real apiaries. Deployment of a BeePi monitor on top of a beehive does not require any structural modification of the beehive’s woodenware, and is not disruptive to natural beehive cycles. To ensure the replicability of the reported findings and to provide a performance benchmark for interested research communities and citizen scientists, we have made public our curated and labeled image datasets of 167,261 honeybee images and our omnidirectional bee traffic videos used in this investigation