Relative reward processing in primate striatum

Rewards are often not only valued according to their physical characteristics but also relative to other available rewards. The striatum (caudate nucleus, putamen, ventral striatum including nucleus accumbens) is involved in the organization of movement and the processing of reward information. We studied the activity of single striatal neurons in macaques that were presented with different combinations of two rewards. We found in nearly half of the investigated neurons that the processing for one reward shifted, relative to the other rewards that were available in a given trial block. The relative reward processing concerned all forms of striatal activity related to reward-predicting visual stimuli, arm movements and reception of rewards. The observed changes may provide a neural basis for the known shifts in valuation of rewarding outcomes relative to known references.     

How Can We Differentiate Local Recurrence From Heterotopic Ossification After Resection and Implantation of an Oncologic Knee Prosthesis in Patients with a Bone Sarcoma?

BackgroundHeterotopic ossification (HO) is common after total joint arthroplasty and usually does not cause diagnostic problems. However, the occurrence of HO after oncologic prostheses implantation can be troublesome as it may mimic a locally recurrent tumor. Because this distinction could have a profound impact on the surgeon and patient, it is important to distinguish the two entities; to our knowledge, no study has evaluated this after oncologic endoprosthetic reconstruction around the knee after tumor resection.Questions/purposes(1) How common is the occurrence of HO compared with local recurrence (LR) after resection of bone sarcoma and the use of an oncologic knee prosthesis? (2) Are there any factors associated with the development of HO after limb salvage procedures with an endoprosthesis? (3) What features allow the surgeon to differentiate HO from a locally recurrent tumor in this setting?Methods‏Between 2002 and 2018, we performed 409 resections of primary bone tumors followed by reconstructions with oncologic endoprostheses. Of these, 17% (71 of 409) died before 2 years and did not have HO at that time, 2% (8 of 409) were lost to follow-up before 2 years, and another 2% (10 of 409) did not have radiographs available at a minimum of 2 years after surgery (and had not developed HO before then), and so could not be analyzed, leaving 320 patients for analysis in this retrospective study. Forty-two patients were excluded; 2% (5 of 320) for a history of failed allograft reconstruction, 3% (8 of 320) for pathologic fracture at presentation, 2% (6 of 320) for inadequate or complicated biopsy, 1% (2 of 320) for stem fractures, 2% (7 of 320) for stem loosening, and 4% (14 of 320) for extracortical bone bridging, leaving 278 patients for final evaluation. Two observers analyzed AP and lateral radiographs for signs of HO at a mean follow-up of 63 ± 33 months after surgery. We defined HO as extraskeletal bone formation in soft tissues. The primary study endpoint was survivorship free from HO, as ascertained by a competing-risks estimator. To identify factors associated with HO appearance, the demographic, radiographic, clinical, pathologic, and surgical characteristics were compared between patients with HO and those who had no lesion. Characteristic features were also compared between patients with HO and those with LR to help their differentiation. Univariate analysis was used for all statistical evaluations.ResultsHO developed in 8% (21 of 278) of patients in whom oncologic knee prosthesis was implanted. LR developed in 10% (28 of 278) of the patients. According to survivorship estimates, the HO-free survival rate was not different from the LR-free survival rate at 2 years after oncologic knee reconstruction (76 ± 5% [95% CI 63 to 87] versus 74 ± 5% [95% CI 62 to 88]; p = 0.19). History of infection was more common in patients with HO than in patients with no lesion (19% [4 of 21] versus 5% [12 of 229], Odds ratio [OR] 6 [95% CI 2 to 17]; p < 0.001). The male sex was more common in the HO group as well (76% [16 of 21] versus 55% [128 of 229], OR 2 [95% CI 1 to 5]; p = 0.03). The Modular Universal Tumor and Revision System prosthesis was more frequently used in patients with HO (67% [14 of 21]) compared to those with no lesions (40% [92 of 229]; OR 2 [95% CI 1 to 5]; p = 0.02).‏ The lesion border in radiographs was ill-defined in 19% (4 of 21) of patients with HO and 100% (28 of 28) of patients with LR (OR 8 [95% CI 3 to 20]; p < 0.001). The median time to the appearance of HO was shorter than the time to LR (8 months [3 to 13] versus 16 months [11 to 21], [95% CI 10 to 13]; p < 0.001). Pain at presentation was more frequent in patients with LR than in those with HO (86% [24 of 28] versus 14% [3 of 21], OR 36 [95% CI 7 to 181]; p < 0.001).ConclusionHO may occur after the use of oncologic knee prostheses for reconstruction after tumor resection. In most patients, HO could be differentiated from local recurrence through identifying a well-defined border on radiographs. Otherwise, factors such as an earlier time of presentation and absence of pain could suggest an HO, rather than an LR.Level of EvidenceLevel III, therapeutic study.     

The relationship between occupational exposure to organic solvents and metabolic syndrome in petroleum refinery workers in Tehran,Iran

AimsThe rising prevalence of metabolic syndrome has made it a major health concern. Chronic occupational exposure to organic solvents affects different systems of the body. This study aimed to investigate the association between exposure to organic solvents and the prevalence of metabolic syndrome in petroleum refinery workers.MethodThis study was conducted in 2019–2020 on workers employed in an Iranian petroleum refinery. The demographic and occupational information on the participants was obtained using the interview method. Their height, weight, and blood pressure were measured by the occupational health team, and fasting blood samples were taken from them to measure the paraclinical parameters.ResultsIn this study, 1009 petroleum refinery workers were analyzed. The prevalence of metabolic syndrome in workers was 20.1% and it was about two times higher in exposed workers (CI 95%: 1.61–3.35) compared to non-exposed ones. Factors associated with the prevalence of metabolic syndrome include age, higher BMI, exercise, and longer exposure to organic solvents.ConclusionFindings of this study suggested that exposure to organic solvents is associated with increased prevalence of metabolic syndrome (the highest association was observed with elevated serum triglycerides). Besides, longer exposure to organic solvents increased the risk of developing metabolic syndrome.     

Size does matter: High volume breast surgeons accept smaller excision margins for wide local excision – A national survey of the surgical management of wide local excision margins in UK breast cancer patients

IntroductionOptimal margins for wide local excision (WLE) have not been clearly established. Larger margins lead to lower recurrence rates but at the expense of cosmetic appearance. NICE guidelines recommend a 2 mm margin for ductal carcinoma in-situ (DCIS), whilst the British Association of Surgical Oncology (BASO) recommend units develop local guidelines. There are presently no specific guidelines for invasive cancer. We surveyed members of the Association of Breast Surgeons (ABS) in order to establish current practice nationally. We hypothesised that larger units may accept narrower excision margins to the benefit of better cosmesis.Materials and methodsA postal questionnaire was sent to all ABS members in October 2010. This consisted of questions about the current practice of the surgeon and their unit. 481 questionnaires were posted in total, all questionnaires returned by April 2011 were analysed.ResultsQuestionnaire response rate was 60% (281). Surgeons operating on over 50 cancers per year accepted smaller margins than those operating on less than 50 (p < 0.02). Acceptable adequate anterior and radial margins ranged from 0 to 10 mm for DCIS and 0 to 5 mm for invasive cancer. A variety of approaches to re-excising anterior margins were reported.ConclusionsThis survey suggests that substantial variations exist in current practice with regard to the approach to WLE. Operator workload appears to influence what is deemed to be an acceptable margin. There is a need for standardised national and international guidelines.     

Predicting long-term dynamics of soil salinity and sodicity on a global scale

Knowledge of spatiotemporal distribution and likelihood of (re)occurrence of salt-affected soils is crucial to our understanding of land degradation and for planning effective remediation strategies in face of future climatic uncertainties. However, conventional methods used for tracking the variability of soil salinity/sodicity are extensively localized, making predictions on a global scale difficult. Here, we employ machine-learning techniques and a comprehensive set of climatic, topographic, soil, and remote sensing data to develop models capable of making predictions of soil salinity (expressed as electrical conductivity of saturated soil extract) and sodicity (measured as soil exchangeable sodium percentage) at different longitudes, latitudes, soil depths, and time periods. Using these predictive models, we provide a global-scale quantitative and gridded dataset characterizing different spatiotemporal facets of soil salinity and sodicity variability over the past four decades at a ∼1-km resolution. Analysis of this dataset reveals that a soil area of 11.73 Mkm² located in nonfrigid zones has been salt-affected with a frequency of reoccurrence in at least three-fourths of the years between 1980 and 2018, with 0.16 Mkm² of this area being croplands. Although the net changes in soil salinity/sodicity and the total area of salt-affected soils have been geographically highly variable, the continents with the highest salt-affected areas are Asia (particularly China, Kazakhstan, and Iran), Africa, and Australia. The proposed method can also be applied for quantifying the spatiotemporal variability of other dynamic soil properties, such as soil nutrients, organic carbon content, and pH.

Soil salinization is one of the main land-degrading threats influencing soil fertility, stability, and biodiversity. Saline soils are ones with excess accumulation of soluble salts in the root zone (1). On the other hand, accumulation of high levels of sodium salt relative to other exchangeable cations is the main attribute of sodic soils (2). Wind, rainfall, and parent rock weathering are the main origins of these salts in “primary” soil salinization, whereas in “secondary” soil salinization excessive salt accumulation is human-induced (3). Saline and sodic soils, or in general salt-affected soils, mostly lie across arid and semiarid climates where the dominance of evaporation over precipitation concentrates the salts in the root zone (1, 4), leading to undesirable alterations in the physical, chemical, and biological functions of the soil (5, 6). Sodicity adversely influences the soil infiltration capacity (7), increases the susceptibility of water and wind-blown erosion (8), and exposes more soil organic matter to decomposing processes (9). Soil salinity, on the other side, distresses the soil respiration, nitrogen cycle, and decomposing functionality of soil microorganisms (9, 10). Salinity stress affects the vegetation growth directly by reducing the plant water uptake (osmotic stress) and/or by deteriorating the transpiring leaves (specific ion effects) (11), in turn reducing organic input to the soil and ultimately leading to desertification of lands (12, 13). Under extreme conditions, dispersion of saline dust (8, 14), poverty, migration, and high costs of soil reclamation are long-term socioeconomic consequences of soil salinization (15).Soil salinity and sodicity levels are spatially, vertically, and temporally dynamic (15, 16), particularly at the top 0- to 30-cm soil layer which is substantially affected by governing climatic conditions. Naturally occurring events, such as flash floods, El Niño and La Niña, alternative wet and dry years, and long periods of drought can considerably affect soil salinization and accumulation/leaching of the salts in/from the root zone at daily to multiyear temporal resolutions. Similarly, anthropogenic activities like irrigation and dryland management can affect soil salinization at different temporal resolutions. Given the high dynamism in soil salinization processes, updated spatial and temporal information on the extent of salt-affected soils is indispensable for devising appropriate sustainable action programs for managing land and soil resources (6, 17–19). This information can be also valuable for enhancing our understanding of terrestrial carbon dynamics (7, 20), food security and agricultural modeling (21, 22), climate change impacts (23, 24), water resources and irrigation management (25, 26), and efficiency of organic/inorganic reclamation practices (27, 28). Several statistics on the global distribution of salt-affected soils (17–19, 29–33) have been generated based on data from soil surveys and statistical extrapolation (1, 19), yet these estimations are mainly purely spatial (17, 34), not necessarily up-to-date (15, 17), and in some cases incomparable (3, 35). Therefore, there is still a need for a methodologically consistent dataset documenting long-term variations of the soil salinity and sodicity at high spatial resolutions (36)To address this need, we focused on two target variables: ground-derived measurements of soil EC_e (the ability of a water-saturated soil paste extract to conduct electrical current, representative of salinity severity) and ESP (exchangeable sodium percentage, representative of sodicity severity). We used 42,984 and 197,988 data, respectively, scattered over time from 1980 to 2018. We trained two-part predictive models for making four-dimensional (4D) predictions of soil salinity and sodicity as target variables (longitude, latitude, soil depth, and time; see Methods). Through mapping data-driven relations between soil EC_e/ESP observations and a collection of associated predictors generated from topographic, climatic, vegetative, soil, and landscape properties of the sampling locations (SI Appendix, Table S1), these two-part models enabled us to make long-term gridded predictions of soil salinity and sodicity at new locations with available predictors’ values. Note that “prediction” refers to the estimation by the trained models of soil salinity/sodicity on a global scale from 1980 to 2018 even in locations where there is no measurement available rather than to future projection of soil salinity/sodicity on the basis of current trends. The first part of the models classified the soil into saline/sodic and nonsaline/nonsodic classes (binary classification) and the second part predicted per-class severity of the salinity/sodicity issue (regression). Meaningful statistics derived from the EC_e and ESP predictions were then used to generate univariate thematic maps of the variability of different aspects of soil salinity/sodicity between 1980 and 2018 at ∼1-km spatial resolution (30 arc-seconds; e.g., Fig. 1). These were delimited to −55° and 55° latitudes, comprising tropics, subtropics, and temperate zones (see Data Availability). We focused on the topsoil layer (or surface soil), referring to the top 30 cm of the soil profile measured from the surface.Open in a separate windowFig. 1.Variability of different aspects of soil salinity and sodicity in the western United States. (A and D) SD of annually predicted soil salinity (EC_e) and sodicity (ESP), respectively, between 1980 and 2018. (B and E) Average of annually predicted EC_e and ESP, respectively (1980 to 2018). (C and F) Change in the likelihood (θ) of soils with an EC_e ≥4 dS⋅m⁻¹ or ESP ≥6% in the period 2000 to 2018 relative to 1981 to 1999 (the likelihood is dimensionelss, calculated by dividing the number of years with EC_e ≥4 dSm⁻¹ or ESP ≥6% by the total number of years in the studied period). Positive θ indicates that the likelihood has increased and negative shows that it has decreased.