From the Cover: Satellites can reveal global extent of forced labor in the world’s fishing fleet

While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Forced labor in fisheries, a type of modern slavery, is increasingly recognized as a human rights crisis. The International Labor Organization (ILO) defines forced labor as “all work or service which is exacted from any person under the menace of any penalty and for which the said person has not offered himself voluntarily” (1). The ILO provides a framework of 11 forced labor risk indicators (2) that have all been documented within the fisheries sector, including indicators representative of debt-bonded labor, as well as indicators representative of servitude or slave labor such as abusive working and living conditions. In 2015, reports emerged on forced labor in Thai fisheries (3) and the role of forced labor in producing seafood imported to the United States (4). More recent reports have described the global nature of the problem (5), and there has been a call to integrate social responsibility into ocean science (6). Despite widespread condemnation and ambitious commitments, forced labor remains poorly understood in the fisheries sector. Here we show that recently available high-frequency vessel monitoring of the global industrial fishing fleet can shed new light on forced labor at a much finer resolution. We combine expertise from on-the-ground human rights practitioners and satellite vessel monitoring data for over 16,000 industrial fishing vessels to estimate 1) the number of high-risk vessels and the number of crew who may be victims working on those vessels, 2) where these vessels fish, and 3) what ports these vessels visit. This information can inform new market, policy, and enforcement interventions to combat forced labor in global fisheries. This research more generally demonstrates how remote sensing can detect forced labor abuses by observing dynamic behavior.Current estimates of forced labor in fisheries are coarse and are based on country-level statistics. Using country-level household surveys, the ILO estimated that 16 million people were victims of forced labor in 2016, with 11% of these in agriculture, forestry, or fisheries (7). The Global Slavery Index reports that the seven countries with highest slavery risk in 2018 generated 39% of global fisheries catch (3, 8), and Tickler et al. found that the United States has slavery risks of 0.2 kg per metric ton for domestic seafood and 3.1 kg per metric ton for imported seafood (9). While these studies are important for broadly understanding which countries have risk, current methods are unable to detect this problem at the level of individual fishing vessels, which will be essential for targeted interventions.We empirically examine whether vessels reported to exhibit any of the ILO indicators of forced labor behave in ways that are systematically different from other vessels, and then exploit this information using machine learning to discriminate between vessels that use forced labor from those that do not. We do so by measuring a suite of features that can be observed using satellite Automatic Identification System (AIS) vessel monitoring data made available by Global Fishing Watch (GFW) (10). There may be many behavioral correlates with forced labor that could help to differentiate between high-risk and low-risk vessels. To determine which model features to include, we first conducted a literature review of investigative journalism reports and looked for instances of forced labor case accounts that detailed specific behaviors that could be observed using vessel monitoring data. We next conducted informal phone interviews with experts from several nongovernmental organizations (NGOs) working in this field, during which we asked interviewees what observable vessel behaviors they would look for if they wanted to identify suspicious activity. The machine-learning approach we use does not assume that vessels behave in any particular way; rather, it merely uses the features identified by literature review and expert insight to exploit any observed empirical differences between vessels that use forced labor and other vessels. NGO experts and investigative journalism suggest that gaps in AIS transmission, port avoidance, transshipment, and extended time at sea may indicate the presence of forced labor (11). Certain features, like information on catch and the species being targeted, could also be helpful in discriminating between high- and low-risk behavior by providing more context on the fishing taking place. However, these data are not currently available at the vessel level on a global scale. Data on recruitment practices and vessel ownership and information on from where the crew originates could also be helpful, but, again, these data are not widely available. We arrived at a list of 27 vessel behavior and characteristic features for which we have globally available data at the vessel level (SI Appendix, Table S1, and SI Appendix).To build a predictive model for identifying high-risk vessels, we developed a training dataset that includes the behavior and characteristics of known forced-labor vessels, as well as the behavior and characteristics of other vessels. We compiled a comprehensive database of vessels that were reported to display one or more of the ILO forced labor indicators (2); these vessels are labeled as “positives.” We do not, however, know which vessels do not use forced labor (“negatives”). Rather, any vessel that we do not label as positive is “unlabeled,” and may in fact be a positive vessel that has not yet been identified or may truly be a negative vessel. This is an example of “positive-unlabeled (PU)” learning, a less straightforward problem than traditional supervised machine learning (12). We use PU learning to predict whether or not 16,261 longliner, trawler, and squid jigger fishing vessels were high-risk during each year they operated between 2012 and 2018 (“vessel-years”). We focus on this subset of vessels because they broadcasted sufficient and reliable AIS positions and because these are the only fishing gear types with documented cases of forced labor aboard vessels that broadcasted sufficient AIS data. These vessels represent 33% of the total time at sea spent by all fishing vessels operating in this time period tracked by GFW. Our PU approach leverages information from all positively labeled vessels (n = 22 unique vessels across 22 vessel-years using our baseline model assumption), but places less emphasis on unlabeled vessels given their uncertain nature (n = 16,257 unique vessels across 66,314 vessel-years using our baseline model assumption).     

Stem cell bioengineering strategies to widen the therapeutic applications of haematopoietic stem/progenitor cells from umbilical cord blood

Umbilical cord blood (UCB) transplantation has observed a significant increase in recent years, due to the unique features of UCB haematopoietic stem/progenitor cells (HSCs) for the treatment of blood‐related disorders. However, the low cell numbers available per UCB unit significantly impairs the widespread use of this source for transplantation of adult patients, resulting in graft failure, delayed engraftment and delayed immune reconstitution. In order to overcome this issue, distinct approaches are now being considered in clinical trials, such as double‐UCB transplantation, intrabone injection or ex vivo expansion. In this article the authors review the current state of the art, future trends and challenges on the ex vivo expansion of UCB HSCs, focusing on culture parameters affecting the yield and quality of the expanded HSC grafts: novel HSC selection schemes prior to cell culture, cytokine/growth factor cocktails, the impact of biochemical factors (e.g. O₂) or the addition of supportive cells, e.g. mesenchymal stem/stromal cell (MSC)‐based feeder layers) were addressed. Importantly, a critical challenge in cellular therapy is still the scalability, reproducibility and control of the expansion process, in order to meet the clinical requirements for therapeutic applications. Efficient design of bioreactor systems and operation modes are now the focus of many bioengineers, integrating the increasing 'know‐how' on HSC biology and physiology, while complying with the GMP standards for the production of cellular products, i.e. through the use of commercially available, highly controlled, disposable technologies. Copyright © 2013 John Wiley & Sons, Ltd.     

Colorectal carcinoma in different age groups

Purpose  

Although colorectal cancer is typical in the older population, tumor onset before age 40 is not infrequent. However, the behavior, characteristics, and prognosis of this disease in young patients are unclear when compared to the older population. It is believed that young patients have a poor prognosis. We hypothesized that young patients have a poor prognosis because they have advanced-stage cancer with more aggressive pathologic features.     

Gallbladder-associated ectopic liver: A rare finding during a laparoscopic cholecystectomy

INTRODUCTIONEctopic hepatic tissue is due to an uncommon failure of embryological liver development that is rarely described in the world medical literature. The incidence of ectopic liver (EL) has been reported to be anywhere from 0.24% to 0.47% as diagnosed at laparotomy or laparoscopy. We describe a case of EL adherent to the gallbladder, removed at laparoscopic cholecystectomy.PRESENTATION OF CASEA 37-year-old female was admitted for elective cholecystectomy having had an episode of acute cholecystitis provoked by gallstones. During the procedure, a 30 mm × 10 mm × 5 mm section of EL tissue attached to the anterior wall of the gallbladder was identified and removed by en-bloc excision during laparoscopic cholecystectomy. Histological examination confirmed the absence of malignant degeneration of the hepatic tissue. The patient recovered well postoperatively and was discharged the day after the operation. She was well when seen six months later.DISCUSSIONEL has been reported in several sites, such as the gallbladder, gastrohepatic ligament, adrenal glands, esophagus, and thoracic cavity. EL is often clinically silent and discovered incidentally during abdominal surgical procedures or autopsies. Because patients with ectopic liver may suffer complications such as torsion, peritoneal bleeding, fatty change, and evolution to cirrhosis or malignant degeneration to hepatocellular carcinoma, any ectopic liver tissue needs to be correctly identified and removed.CONCLUSIONDespite the rare occurrence of EL, it should be recognized and removed by the surgeon to prevent a higher risk of complications and malignant transformation.     

The presence of periodontopathogens associated with the tumour necrosis factor-alpha expression in patients with different periodontal status

The purpose of this study was to investigate the relationship between P gingivalis, T forsythia, T denticola, P intermedia, and A. actinomycetemcomitans in the sulci or pockets of patients with gingivitis (G), mild chronic periodontitis (MiCP), moderate chronic periodontitis (MoCP) and severe periodontitis (SP), and the expression of TNF-alpha in gingival tissue associated with clinical parameters. Six patients with G, 7 with MiCP, 23 with MoCP and 7 with SP were recruited. Pathogens obtained from the sulci or pockets were identified by PCR, and expression of TNF-alpha from gingival tissue was analysed Probing depth (PD), clinical attachment level (CAL) and loss of bone were recorded. P gingivalis was detected at the following rates: 16.6% in subjects with G 57.1% in MiCP 57.8 % in MoCP and 58.1% in SP (p < 0.05). P intermedia was not identified in subjects with G A. actinomycetemcomitans was only identified in subjects with MoCP (31.5%) and SP (42.8%). T denticola and T forsythia were identified in all subject groups. Bacterial combinations were identified as follows: P denticola + P intermedia and PR intermedia + T forsythia were associated (p = 0.04, p = 0.02) with the presence of TNF-alpha mRNA in 20% and 25% of subjects, respectively. P gingivalis + A. Actinomycetemcomitans andA. actinomycetemcomitans + T forsythia were associated with severe PD and CAL, respectively. The association between the presence of P intermedia and expression levels of TNF-alpha was significant (p = 0.05). These results indicate that the proportion of patients with P gingivalis increases with the progression of disease. We observed that the presence of P intermedia may trigger the expression of TNF-alpha and cause a worsening of the patient's clinical status.     

Is the wrong question being asked in infertility research?

A persistent finding is that assisted reproductive technology (ART) is associated with compromised birth outcomes, including higher risks for prematurity, low birthweight, and congenital malformations, even among singletons. Over the past decade, our research group, the Massachusetts Outcome Study of Assisted Reproductive Technology (MOSART), has evaluated pregnancy and birth outcomes among three groups of women, those women treated with ART, those with indicators of subfertility but without ART treatment, and fertile women. We have also explored the influence of infertility-related diagnoses on outcomes for women and infants. Over the course of our research, we have changed our perspective from an original focus on ART treatment parameters as the primary cause of excess morbidity to one centered instead on the underlying infertility-related diagnoses. This paper summarizes the research findings from our group that support this change in focus for infertility-based research from a primary emphasis on ART treatment to greater attention to the contribution of preexisting pathology underlying the infertility and suggests directions for future analyses.