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A 1% potassium peroxymonosulphate-based environmental disinfectant (PPED) produces sodium hypochlorite when combined with sodium chloride, which functions as a disinfectant. However, little is known about the impact of hospital cleaning with PPED on hospital-onset Clostridioides difficile infection (HO-CDI).To reduce HO-CDI, we promoted antimicrobial stewardship and hospital ward cleaning with PPED: this study was conducted to evaluate their impact.We began a promotion of post-prescription review with feedback for broad-spectrum antimicrobials and hospital ward cleaning with PPED. We reviewed the ratio of HO-CDI, PPED consumption, and days of therapy (DOT) of broad-spectrum antimicrobials between July 2014 and March 2018, dividing this time into the pre-promotion (July 2014 to June 2015) and post-promotion periods (July 2015 to March 2018).Using interrupted time series analysis, an immediate significant change in HO-CDI was observed after intervention (P=0.03), although a downward trend was not observed over this period (P=0.19). Trends in PPED consumption significantly changed over this period (P=0.02). DOT of carbapenems decreased immediately after the intervention began (P0.01). A Poisson regression analysis showed that PPED consumption and DOT of carbapenems were independent factors affecting HO-CDI (P=0.039 and 0.016, respectively).We revealed that DOT of carbapenems and use of PPED were associated with the HO-CDI ratio and that both interventions reduced the rate of HO-CDI. This is the first report on the impact of hospital ward cleaning with PPED on the reduction of HO-CDI.

Background Endoscopy has been used to assess the disease activity of Crohn's disease (CD) but may not be sufficient considering the transmural nature of its inflammation. Bowel ultrasonography (BUS) has been reported to reflect disease activity of CD and long-term outcomes. We performed a prospective study to determine whether BUS can predict short-term clinical response to induction therapy. Methods Consecutive CD patients with active ulcers in endoscopy requiring induction therapy (Anti-TNF antibody, IL-12/23 p40 antibody, integrin inhibitor, prednisolone) were prospectively enrolled at a single center from October 2018 to July 2022. Short-term clinical response was defined by a reduction of Crohn’s disease activity index > 70 at week 8. Ultrasonographic findings (bowel wall thickness (BWT), colour Doppler signal (CDS), and shear wave elastography (SWE) were recorded for the most severely affected segment at baseline, week 1, and 8 and compared between responders and non-responders. Results Seventeen out of 29 patients were classified as responders at week 8. There was no difference in BWT and CDS at baseline, week 1, and 8 between responders and non-responders (Table 1). In contrast, responders had lower baseline SWE compared with non-responders (2.7±0.3 vs. 3.8±0.3, p=0.02). Adjusted by the prior exposure to biologics, the lower SWE at baseline was associated with clinical response at week 8 (adjusted odds ratio associated with 0.1 m/s increase: 0.9, p=0.018) (Table 2). SWE did not change over time regardless of response for 8 weeks. Conclusion Lower intestinal wall stiffness as measured by SWE is an independent predictor of short-term clinical response.

Background Endoscopic remission is associated with better long-term outcomes of ulcerative colitis (UC), and therefore, it is considered a treatment target. However, endoscopy is invasive and frequent monitoring is not feasible. We have reported that bowel ultrasound (BUS) is useful in predicting endoscopic and histologic severity and in determining response to treatment (Sagami S et al, APT 2020 & 2022). However, it is unclear whether BUS can predict the relapse of UC in remission. Methods We conducted a retrospective cohort study enrolling UC patients who underwent BUS from Jul 2018 to Jul 2021 during clinical remission (Patient Reported Outcome-2 ≦ 1 and no rectal bleeding) for at least 3 months and followed for 1 year. Spearman rank correlation coefficient was used to analyse the correlation between BUS and Mayo endoscopic subscore (MES) (evaluation of the most severe part of the colon). The association between BUS findings (bowel wall thickness (BWT), bowel wall flow (BWF), bowel wall stratification (BWS), enlarged lymph nodes), Milan Ultrasound Criteria (MUC = 1.4 × colonic BWT + 2 × colonic BWF (0 = absence, 1 = presence of colour Doppler signal)) (Allocca M et al, UEGJ 2021), MES, CRP and faecal calprotectin (FC) and subsequent clinical relapse was evaluated. Relapse was defined as rectal bleeding score ≧ 1, stool frequency score ≧ 2, or treatment intensification for symptoms. Results A total of 58 patients were included in the study. The median age was 46 years, 37 (63.8%) were male, 39 (67.2%) were pancolitis, and the median disease duration was 116 months. BWT and BWF were moderately correlated with MES but MUC showed a numerically highest coefficiency (0.61) when evaluated for the colon. Overall, 18 patients (31.0%) relapsed within 1 year. Neither BWT, BWF, BWS, enlarged lymph nodes, nor CRP was predictive for relapse. The log-rank test showed MUC > 6.2 (p = 0.019), MES ≥ 1 (p = 0.013), and FC ≥ 250 μg/g (p = 0.040) were associated with a shorter time to relapse. Cox proportional hazards model showed MUC > 6.2 (HR 3.22: 95%CI 1.14-9.08, p = 0.027), MES ≥1 (HR 8.70: 95%CI 1.11-68.1, p = 0.040) had a higher risk of relapse in 1 year. Sensitivity, specificity, PPV, and NPV of MUC > 6.2 for relapses of different time points (3, 6, and 12 months) showed high specificity of 0.90-0.91 and NPV of 0.74-0.90. Interestingly, 4 out of 9 patients with MUC > 6.2 relapsed within 3 months, suggesting MUC > 6.2 is a risk of short-term relapse. Conclusion BUS in UC patients in remission can predict relapse using MUC. In particular, the MUC score ≤ 6.2 is associated with a lower risk of relapse and could be a treatment-target alternative to endoscopic healing.

In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R2 ~ 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R2 of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management.

In this review, we include rotifers, copepods, and cladocerans, including other groups not usually deemed as zooplankters: i.e., protists, acari, and large branchiopods. The objectives of this study were to integrate the dispersed literature on the taxonomy and diversity of these freshwater zooplankton groups and to explain (1) how these contributions can be arranged in distinct historical periods and (2) how this knowledge has allowed the detection of exotic and threatened species. We divided the freshwater zooplankton studies in Mexico into three historical periods: the first one comprised the 1840s to the 1940s when foreign researchers carried out most studies during several expeditions. Spanish researchers promoted surveys on different zooplankton taxa at the end of this first period. The second period, from the early 1950s to the end of the 1990s of the XX century, showed a remarkably increased research activity in its last ten years only (that is, during the 1990s to 2000), represented by contributions of a new generation of Mexican zooplanktologists. This period yielded more complete zooplankton listings and detailed morphological descriptions of rotifers, cladocerans, copepods, and large branchiopods. The third period started from the year 2000 to date. During this time, listings and online faunistic baselines based on integrative taxonomy have been the primary trend. An account of exotic zooplankters and conservation issues of several native species are discussed. The results of this review show that the knowledge of the freshwater zooplankton of this country has increased significantly over the last 40 years, with at least 408 first records of species for Mexico. Currently, the knowledge of Mexican freshwater zooplankton is among the most complete in the world. However, it is estimated that only a small fraction of the true diversity has been documented.

Background Thiopurines are useful to maintain remission for ulcerative colitis (UC) and to inhibit immunogenicity in combination with biologics while their long-term use may increase risks of infections and malignancies. Therefore, the risk-benefit balance should be evaluated, and de-escalation needs to be discussed. However, optimal de-escalation strategy such as dosing and monitoring has not been well studied. Methods UC patients in clinical remission (partial Mayo score ≤ 2 and rectal bleeding subscore of 0) who de-escalated thiopurines (dose-reduction or discontinuation) were enrolled. The risk of relapse and changes in Mayo endoscopic subscore (MES) and histological findings after thiopurine de-escalation were retrospectively investigated. The correlation with relapse (treatment escalation (including topical therapy) with the exacerbation of symptoms or rectal bleeding score ≥ 1) was analyzed by logistic regression analysis. Results A total of 69 de-escalation steps (51 dose-reductions and 18 discontinuations) were conducted in 39 patients. The mean duration and dose of thiopurine use were 3.5 years and 66.6mg (converted to azathioprine). Thiopurines were used in combination with biologics in 16 (41.0%). Relapse occurred in 17/69 (24.6%) de-escalation steps (10/17 discontinuations). In 36 de-escalations remaining in remission with available data before and after de-escalations, worsening of MES occurred in 13%, whereas exacerbations of any histopathological finding occurred in 44% (crypt architectural irregularities 25%, chronic inflammatory cell infiltrate 19％, acute inflammatory cell infiltrate 16％, basal plasmacytosis 13%, mucin depletion 13%, crypt abscess 8%). Chronic inflammatory cellular infiltrate (p=0.002), basal plasmacytosis (p=0.008), and acute inflammatory cell infiltrate (p=0.039) at de-escalation were significantly associated with relapse after de-escalation. Nancy Index was also associated with relapse (p=0.011), while MES was not. Conclusion Histological worsening may precede clinical relapse thus may be useful to predict relapse after thiopurine withdrawal.

Recent years have witnessed enormous interest in the application of Unmanned Aerial Systems (UAS) for precision agriculture. This study presents a novel approach to use multi-temporal UAS data for comparison of two management practices in cotton, conventional tillage (CT) and no-tillage (NT). The plant parameters considered for the comparison are: canopy height (CH), canopy cover (CC), canopy volume (CV) and Normalized Difference Vegetation Index (NDVI). Initially, the whole study area was divided into approximately one square meter size grids. Measurements were extracted grid wise using high resolution UAS data captured ten times over whole crop growing season of the cotton. One tailed Z-test hypothesis reveals that there is a significant difference between cotton growth under CT and NT for almost all the epochs. With 95% confidence interval, the crop grown under NT found to have taller canopy, higher canopy cover, bigger biomass and higher NDVI, as compared to those under CT cropping system.

To explore the differences in mean treatment costs between home-based care and hospital-based care in enteral nutrition patients in Japan.Using claims data from September 2013 to August 2014, we analyzed patients with recorded reimbursements for enteral nutrition at home or in a hospital. Treatment costs were compared using a panel data analysis with an individual fixed effects model that adjusted for the number of comorbidities and fiscal year. Costs were compared for all patients, as well as for specific diseases (pneumonia, sequelae of cerebrovascular disease, and dementia).The study sample comprised 7,783 patients with a cumulative total of 33,751 person-months of data. The mean patient age was 84.4 years for home-based care, 83.7 years for hospital-based care. The panel data analysis found that the cost estimates for hospital-based care were consistently higher than those for home-based care; the difference in adjusted treatment costs were $4,894 for all patients, $5,315 for pneumonia patients, $4,481 for sequelae of cerebrovascular disease patients, and $4,519 for dementia patients (all P 0.001). Hospital-based care was still more expensive even when long-term care services were included in home-based care treatment cost estimates.Home-based care was consistently and substantially cheaper than hospital-based care in enteral nutrition patients in Japan.

Upland cotton (Gossypium hirsutum L.) is an important socioeconomic crop throughout most of the southern U.S. In Texas, cotton is the lead cash crop and its productivity is often limited by abiotic stress events such as drought and elevated ambient temperatures. The objective of this study was to assess the effects of 1-methylcyclopropene (1-MCP) applications triggered by canopy temperature and forecasted ambient temperatures on field-grown cotton plants. Yield and crop morphological responses to 1-MCP applications were investigated in field studies conducted during the summers of 2012 to 2014 at the Texas A&M University Field Laboratory in Burleson County, TX. Positive effects of 1-MCP were found for fruit retention in 2013 and 2014 for both irrigated and dryland studies; however, a negative impact was found in the 2012 irrigated study. By harvest, 1-MCP applications had no effect on final cotton yield or fiber quality parameters. Applications of 1-MCP affected some morphological characteristics of cotton plants; however, it did not improve crop yield.

Pigments are known to modify the spectral properties of foliage, which in turn affect the amount of radiant energy stored by the plant canopy. Studies have shown that red pigments (anthocyanin) increase leaf absorptivity of solar radiation, but little is known about their effect on canopy net radiation and temperature. We hypothesized that increased absorptivity of solar radiation caused by red pigments would result in higher canopy temperature when compared to that of a green canopy. To better understand the role of red pigments on canopy net radiation and temperature, we conducted a study where we measured leaf spectral properties, canopy spectral reflectivity, stomatal conductance, net radiation, and leaf and canopy temperature of red and green cotton (Gossypium hirsutum L.) canopies. On average, albedo of the red canopy was 0.02 lower than that of the green canopy, and most of the differences in reflected solar irradiance were in near-infrared wavelengths. Red canopy had greater net radiation than the green canopy throughout the measurement period, and that was due to its lower albedo. Red canopy was about 1 °C warmer than the green canopy on average; however, computer simulation indicates that albedo was of secondary importance in controlling canopy temperature. Contrary to our hypothesis, results suggest that lower stomatal conductance in the red leaves was the main driver of canopy temperature differences between red and green canopies.

The amphidromous species of Macrobrachium Spence Bate, 1868 occurring on the Mexican Pacific slope (M. americanum Spence Bate, 1868, M. digueti (Bouvier, 1895), M. hobbsi Nates & Villalobos in Villalobos Hiriart & Nates Rodríguez, 1990, M. occidentale Holthuis, 1950 (Holthuis, 1950b), and M. tenellum (Smith, 1871)) show a disjunct distribution, with the abscence of records along about 2000 km of the coastal margins in the northern Gulf of California. Recent morphological and genetic studies using the mitochondrial genes 16S rRNA (16S) and cytochrome oxidase I (COI) show that individuals of the putative M. digueti, M. hobbsi, and M. occidentale from the Baja California Peninsula are conspecific to their respective taxonomic entities found along the Pacific slope of mainland Mexico. We review the systematics of M. americanum from the Mexican Pacific slope and found that individuals of putative M. americanum from the Baja California Peninsula belong to the same lineage of individuals from the Pacific slope of mainland Mexico based on newly generated fragments of the mitochondrial genes 16S (491 bp) and COI (548 bp). We present a systematic account for M. americanum, including a taxonomic treatment of the studied populations with a report of their genetic identity and phylogenetic relationships.

Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

For the Mexican oniscideans of the family Armadillidae, 6 species of the genus Cubaris Brandt, 1833 and 18 species of the genus Venezillo Verhoeff, 1928 have been recorded. Of them, only Venezillo stuckchensis (Mulaik, 1960) has been registered for the Baja California Peninsula (BCP). This species was described upon a single male and no biological studies exist on this taxonomic entity since its original description in 1960. In this work, we redescribe V. stuckchensisincluding the designation of a neotype and paratopotypes using specimens from the type locality. We report this species from 5 additional wetlands of southern BCP in the state of Baja California Sur. The type locality is corrected in its formal name and its geographical location. The morphological identity of V. stuckchensis is documented using SEM micrographs, and data on its molecular identity with 16S ribosomal RNA mitochondrial gene fragments are given.

Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R2 values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0–14 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.

Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic. These factors are threatening the environmental and economic sustainability of current and future food supply systems. Scientific and technological innovations are needed more than ever to secure enough food for a fast-growing global population. Scientific advances have led to a better understanding of how various components of the agricultural system interact, from the cell to the field level. Despite incredible advances in genetic tools over the past few decades, our ability to accurately assess crop status in the field, at scale, has been severely lacking until recently. Thanks to recent advances in remote sensing and Artificial Intelligence (AI), we can now quantify field scale phenotypic information accurately and integrate the big data into predictive and prescriptive management tools. This review focuses on the use of recent technological advances in remote sensing and AI to improve the resilience of agricultural systems, and we will present a unique opportunity for the development of prescriptive tools needed to address the next decade's agricultural and human nutrition challenges.

Electron transport across a tunneling barrier is governed by intricate and diverse causes such as interface conditions, material properties, and device geometries. Here, by measuring the shot noise, we investigate electron transport in a (Ga,Mn)As/GaAs-based spin Esaki diode junction over a wide range of bias voltage. The asymmetric electronic band profile across the junction allows us to tune the types of tunneling process. By changing the bias voltage in a single device, we successively address the conventional direct tunneling, the excess current conduction through the mid-gap localized states, and the thermal excitation current conduction. These observations lead to a proper comparison of the bias dependent Fano factors. While the Fano factor is unity for the direct tunneling, it is pronouncedly reduced in the excess current region. Thus, we have succeeded in evaluating several types of conduction process with the Fano factor in a single junction.

To date 86 species of 33 genera and 16 families of Oniscidea have been recorded in Mexico. Collections of 4 academic institutions and recent field materials were revised. From 1,820 specimens, 1,417 (77.6%) were determined to belong to 6 exotic forms. A taxonomic account and a species identification key with SEM plates of these exotic entities are presented. For morphological analysis we used light and SEM microscopy. The molecular identity of 5 of the 6 forms was examined through cytochrome oxidase 1 gene (mtDNA) analyses. The exotic species determined are: Armadillidae, Cubaris murina ; Armadillidiidae, Armadillidium vulgare ; Porcellionidae, Agabiformius lentus , Porcellio laevis, P. scaber, and Porcellionides pruinosus . The exotic oniscideans were recorded from urban, rural and natural (riparian) zones in 15 states of northern Mexico, including the first formal records in Aguascalientes, Chihuahua, Durango, and Zacatecas. Armadillidium vulgare, P. laevis, and P. pruinosus often co-occurred and are the most widely distributed species in this region. Although most of the material examined was recorded from urban zones (> 50% of studied sites), A. vulgare and P. laevis were also found in riparian areas (natural zones), yet P. pruinosus was recorded mainly from riparian areas (60% of 45 lots examined).

Canopy temperature is an important variable directly linked to a plant’s water status. Recent advances in Unmanned Aerial Vehicle (UAV) and sensor technology provides a great opportunity to obtain high-quality imagery for crop monitoring and high-throughput phenotyping (HTP) applications. In this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing. Raw thermal imagery collected over a cotton field was converted to surface temperature using the Software Development Kit (SDK) provided by the sensor company. Canopy temperature map was generated using Structure from Motion (SfM), and Thermal Stress Index (TSI) was calculated for the test site. UAV temperature measurements were compared to ground measurements acquired by net radiometers and thermocouples. Temperature differences between UAV and ground measurements were less than 5%, and UAV measurements proved to be more stable. The proposed UAV system was successful in showing temperature differences between the cotton genotype. In conclusion, the system described in this study could possibly be used to monitor crop water status in a field setting, which should prove helpful for precision agriculture and crop research.

Recent advances in molecular breeding and bioinformatics have greatly accelerated the screening of large sets of genotypes. Development of field-level phenotyping, however, still lags behind and is considered by many as the main bottleneck to improved efficiency in breeding programs. Unmanned Aerial System (UAS) and sensor technology available today enables collection of data at high spatial and temporal scales, previously unobtainable using traditional airborne remote sensing technologies. Here, we propose an UAS-assisted high throughput phenotyping framework for cotton (Gossypium hirsutum L.) genotype selection. UAS data collected on July 24, 2015 were used to calculate canopy cover, and UAS data collected on August 5, 2015 were used to extract open boll related phenotypic features including number of open bolls, average area of open bolls, average diameter of open bolls, perimeter of open bolls, perimeter to area ratio. Using the extracted features, a sequential selection procedure was performed on a population of 144 entries. Entries selected from the proposed framework were compared to the highest yielding entries determined by mechanical harvest results. Experimental results indicated that the selection process increased minimum and average lint yield of the remaining population by 7.4 and 10%, respectively, and UAS-selected entries and genotypes matched 80 and 73%, respectively, the same lists ranked by actual field harvest measurements.