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Objective: To estimate the incidence and describe clinical characteristics and outcome of GBS in COVID-19 patients (COVID19-GBS) in one of the most hit regions during the first pandemic wave, Lombardia. Methods: Adult patients admitted to 20 Neurological Units between 1/3–30/4/2020 with COVID19-GBS were included as part of a multi-center study organized by the Italian society of Hospital Neuroscience (SNO). Results: Thirty-eight COVID19-GBS patients had a mean age of 60.7 years and male frequency of 86.8%. CSF albuminocytological dissociation was detected in 71.4%, and PCR for SARS-CoV-2 was negative in 19 tested patients. Based on neurophysiology, 81.8% of patients had a diagnosis of AIDP, 12.1% of AMSAN, and 6.1% of AMAN. The course was favorable in 76.3% of patients, stable in 10.5%, while 13.2% worsened, of which 3 died. The estimated occurrence rate in Lombardia ranges from 0.5 to 0.05 GBS cases per 1000 COVID-19 infections depending on whether you consider positive cases or estimated seropositive cases. When we compared GBS cases with the pre-pandemic period, we found a reduction of cases from 165 to 135 cases in the 2-month study period in Lombardia. Conclusions: We detected an increased incidence of GBS in COVID-19 patients which can reflect a higher risk of GBS in COVID-19 patients and a reduction of GBS events during the pandemic period possibly due to a lower spread of more common respiratory infectious diseases determined by an increased use of preventive measures.

The above article was published with error. The affiliation address of the authors Alberto Albanese and Tiziana De Santis has been modified to: “U.O. Neurologia, IRCCS Istituto Clinico Humanitas, Milan, Italy”.The Original article has been corrected.

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

The Atti Chiari project, collecting the first large Italian corpus of judicial acts, presents strict legal requirements as well as many peculiarities in terms of language and content; to meet them, a number of processes and tools have been designed and implemented. The first issue is the requirement to remove any personal data from the documents, without however destroying their linguistic form, nor compromising their readability. To this end, a pseudonymisation procedure has been created based on a preliminary annotation stage, which adds information right in order to remove it in different ways, according to different purposes (linguistic analysis, legal analysis, etc.). At the same time, this light annotation provides data useful not only for pseudonymization, but also for the conversion of documents, from their original presentational format into a semantic one based on TEI. Once documents have been prepared in this way, they are then centralized in a corpus, ready to be indexed for linguistic research. Given the multiple search criteria that must be combined, whatever their origin and model, a new type of search engine, designed primarily in the philological field, has been used here to obtain the required openness and granularity of metadata.

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

Posterior reversible encephalopathy cases are increasingly being reported in patients affected by COVID-19, but the largest series so far only includes 4 patients. We present a series of 6 patients diagnosed with PRES during COVID-19 hospitalized in 5 Centers in Lombardia, Italy. 5 out of the 6 patients required intensive care assistence and seizures developed at weaning from assisted ventilation. 3 out of 6 patients underwent cerebrospinal fluid analysis which was normal in all cases, with negative PCR for Sars-CoV-2 genome search. PRES occurrence may be less rare than supposed in COVID-19 patients and a high suspicion index is warranted for prompt diagnosis and treatment.

Several studies focused on the role of vitamin D (vitD) in pain chronification. This study focused on vitD level and pain chronification and extension in headache disorders. Eighty patients with primary headache underwent neurological examination, laboratory exams, including serum calcifediol 25(OH)D, and headache features assessment along with three questionnaires investigating depression, anxiety, and allodynia. The 86.8% of the population had migraine (48% episodic and 52% chronic). The 44.1% of patients had extracranial pain, and 47.6% suffered from allodynia. A vitD deficit, namely a serum 25(OH)D level <20 ng/ml, was detectable in 46.1% of the patients, and it occurred more frequently (p = 0.009) in patients suffering from chronic migraine (CM)–medication overuse migraine (MOH) (62.9%) than in episodic migraine (EM, 25.7%) or tension-type headache (TTH, 11.4%). The occurrence of extracranial pain and allodynia was higher in the CM-MOH than in the EM and in the TTH groups but was not related to the co-occurrence of vitD deficiency (Fisher's exact test p = 0.11 and p = 0.32, respectively). Our findings show that 25(OH)D deficit is also related to chronic headache, probably because of vitD anti-inflammatory and tolerogenic properties, reinforcing the idea of a neuroinflammatory mechanism underpinning migraine chronification.

Purpose of reviewSingular interventions targeting vulnerable populations of people living with HIV (PLWH) are necessary for reducing new infections and optimizing individual-level outcomes, but extant literature for PLWH who experience homelessness and unstable housing (HUH) has not been compiled. To inform implementation of clinic-based programs that improve care outcomes in this population, we present a synthetic review of key studies examining clinic-based interventions, specifically case management, patient navigation, financial incentives, and the use of mobile technology.Recent findingsResults from unimodal interventions are mixed or descriptive, are limited by inability to address related multi-modal barriers to care, and do not address major challenges to implementation. Multi-component interventions are needed, but gaps in our knowledge base may limit widespread uptake of such interventions before further data are compiled. Future research evaluating interventions for PLWH experiencing HUH should include implementation outcomes in order to facilitate adaptation across diverse clinical settings.

The mechanics and seismogenic behavior of fault zones are strongly influenced by their internal structure. In this perspective, the internal structure of the extensional seismically active Vado di Corno Fault Zone (VCFZ, Central Apennines, Italy) was quantified by combining high-resolution structural mapping with 3-D fault network analysis over ∼2 km along fault strike. The fault zone was exhumed from ∼2 km depth in carbonate rocks, accommodated 1.5–2 km of extensional throw since Early Pleistocene, and cuts through the Pliocene Omo Morto Thrust Zone (OMTZ) with partial reactivation in extension. The exceptional exposure of the footwall block allowed us to reconstruct in detail the geometry of the OMTZ and quantify the spatial arrangement of master/subsidiary faults and fault zone rocks within the extensional VCFZ. The combination of the structural map and the 3-D fault network with kinematic and topological analyses pointed out the crucial role of the older thrust geometry (i.e., lateral ramps) in controlling the along-strike segmentation and slip distribution of the VCFZ. These observations were discussed in the framework of regional extension through a slip tendency analysis and a simplified mechanical model, which suggest the activation of oblique inherited structures during the lateral propagation of the VCFZ segments. The interaction of the VCFZ with the OMTZ generated along strike and possibly downdip mechanical asperities. Considering the exhumed VCFZ as an analog for the shallow structure of other seismic sources in the Central Apennines, similar settings could play first-order control on the spatio-temporal evolution and rupture heterogeneity of earthquakes in the region.

During the COVID-19 outbreak, the Neurology and Stroke Unit (SU) of the hospital of Varese had to serve as a cerebrovascular hub, meaning that the referral area for the unit doubled. The number of beds in the SU was increased from 4 to 8. We took advantage of the temporary suspension of the out-patient clinic and reshaped our activity to guarantee the 24/7 availability of recombinant tissue Plasminogen Activator (rtPA) intravenous therapy (IVT) in the SU, and to ensure we were able to admit patients to the SU as soon as they completed endovascular treatment (EVT). In 42 days, 46 stroke patients were admitted to our hospital, and 34.7% of them underwent IVT and/or EVT, which means that we treated 0.38 patients per day; in the baseline period from 2016 to 2018, these same figures had been 23.5% and 0.23, respectively. The mean values of the door-to-first CT/MRI and the door-to-groin puncture, but not of the onset-to-door and the door-to-needle periods were slightly but significantly longer than those observed in the baseline period in 276 patients. On an individual basis, only one patient exceeded the door-to-groin puncture time limit computed from the baseline period by about 10 min. None of the patients had a major complication following the procedures. None of the patients was or became SARS-CoV2 positive. In conclusion, we were able to manage the new hub-and-spoke system safely and without significant delays. The reshaping of the SU was made possible by the significant reduction of out-patient activity. The consequences of this reduction are still unknown but eventually, this emergency will suggest ways to reconsider the management and the allocation of health system resources.

Recently WHO has declared novel coronavirus disease 2019 (COVID-19) outbreak a pandemic. Acute respiratory syndrome seems to be the most common manifestation of COVID-19. Besides pneumonia, it has been demonstrated that SARS-CoV-2 infection affects multiple organs, including brain tissues, causing different neurological manifestations, especially acute cerebrovascular disease (ischemic and hemorrhagic stroke), impaired consciousness and skeletal muscle injury. To our knowledge, among neurological disorders associated with SARS-CoV2 infection, no Posterior Reversible Encephalopathy Syndrome (PRES) has been described yet. Herein, we report a case of a 64-year old woman with COVID19 infection who developed a PRES, and we suggest that it could be explained by the disruption of the blood brain barrier induced by the cerebrovascular endothelial dysfunction caused by SARS-CoV-2.

The mechanics and seismogenic behaviour of fault zones are strongly influenced by their internal structure. In this perspective, the internal structure of the extensional seismically active Vado di Corno Fault Zone (VCFZ, Central Apennines - Italy) was quantified by combining high-resolution structural mapping with 3D fault network analysis over 2 km along fault strike. The fault zone was exhumed from 2 km depth in carbonate rocks, accommodated 1.5-2 km of extensional throw since Early-Pleistocene and cuts through the Pliocene Omo Morto Thrust Zone (OMTZ) with partial reactivation in extension. The exceptional exposure of the footwall block allowed us to reconstruct in detail the geometry of the OMTZ and quantify the spatial arrangement of master/subsidiary faults and fault zone rocks within the extensional VCFZ. The combination of the structural map and the 3D fault network with kinematic and topological analyses, pointed out the crucial role of the older thrust geometry (i.e. lateral ramps) in controlling the along-strike segmentation and slip distribution of the VCFZ. These observations were discussed in the framework of regional extension through a slip tendency analysis and a simplified mechanical model, which suggest the activation of oblique inherited structures during the lateral propagation of the VCFZ segments. The interaction of the VCFZ with the OMTZ generated along-strike and possibly down-dip mechanical asperities. Considering the exhumed VCFZ as an analogue for the shallow structure of other seismic sources in the Central Apennines, similar settings could play first-order control on the spatio-temporal evolution and rupture heterogeneity of earthquakes in the region.

The mechanics and seismogenic behaviour of fault zones are strongly influenced by their internal structure, intended as three-dimensional geometry and topology of the fault/fracture network and distribution of the fault zone rocks with related physical properties. In this perspective, the internal structure of the extensional seismically active Vado di Corno Fault Zone (Central Apennines, Italy) was quantified by combining high-resolution structural mapping with modern techniques of 3D fault network modelling over ∼2 km along fault strike. The fault zone is hosted in carbonate host rocks, was exhumed from ∼2 km depth, accommodated a normal slip of ∼1.5-2 km since Early-Pleistocene and cuts through the Pliocene Omo Morto Thrust Zone that was partially reactivated in extension. The exceptional exposure of the Vado di Corno Fault Zone footwall block allowed us to reconstruct with extreme detail the geometry of the older Omo Morto Thrust Zone and quantify the spatial arrangement of master and subsidiary faults, and fault zone rocks within the Vado di Corno Fault Zone. The combined analysis of the structural map and of a realistic 3D fault network model with kinematic, topological and slip tendency analyses, pointed out the crucial role of the older Omo Morto Thrust Zone geometry (i.e. the occurrence and position of lateral ramps) in controlling the along-strike segmentation and slip distribution of the active Vado di Corno normal fault zone. These findings were tested with a boundary element mechanical model that highlights the effect of inherited compressional features on the Vado di Corno Fault Zone internal structure and returns distributions and particularly partitioning of slip comparable with those measured in the field. Lastly, we discuss the exhumed Vado di Corno Fault Zone as an analogue for the shallow structure of many seismic sources in the Central Apennines. The mechanical interaction of the inherited Omo Morto Thrust Zone and the extensional Vado di Corno

BackgroundPeople with HIV experiencing homelessness have low rates of viral suppression, driven by sociostructural barriers and traditional care system limitations. Informed by the capability-opportunity-motivation-behavior (COM-B) model and patient preference research, we developed POP-UP, an integrated drop-in (nonappointment-based) HIV clinic with wrap-around services for persons with housing instability and viral nonsuppression in San Francisco.MethodsWe report HIV viral suppression (VS; <200 copies/mL), care engagement, and mortality at 12 months postenrollment. We used logistic regression to determine participant characteristics associated with VS.ResultsWe enrolled 112 patients with viral nonsuppression and housing instability: 52% experiencing street-homelessness, 100% with a substance use disorder, and 70% with mental health diagnoses. At 12 months postenrollment, 70% had ≥1 visit each 4-month period, although 59% had a 90-day care gap; 44% had VS, 24% had viral nonsuppression, 23% missing, and 9% died (6 overdose, 2 AIDS-associated, 2 other). No baseline characteristics were associated with VS.ConclusionsThe POP-UP low-barrier HIV care model successfully reached and retained some of our clinic's highest-risk patients. It was associated with VS improvement from 0% at baseline to 44% at 12 months among people with housing instability. Care gaps and high mortality from overdose remain major challenges to achieving optimal HIV treatment outcomes in this population.

PurposeVaccinating adults who are involved with the carceral system, particularly those aged 55 or older, is crucial to containing the COVID-19 pandemic in the USA, particularly as variants continue to emerge and spread. In this Viewpoint, the authors discuss the reasons why improving access to COVID-19 vaccine and boosters among community supervised adults, especially the aging population, is critical to mitigating the public health consequences of the COVID-19 pandemic. This study concludes by providing recommendations to enhance vaccine and booster uptake in this population, as the pandemic continues.Design/methodology/approachThis is a Viewpoint paper regarding mitigating the spread of COVID-19 by improving access to vaccine and boosters among community supervised adults, especially the aging population.FindingsA key population that has been overlooked in vaccination efforts are older adults involved in the carceral system who are living in the community (i.e. "community supervised" or people on probation or parole). Older adults on probation and parole are at high risk for SARS-CoV-2 transmission and severe disease due to numerous factors at the individual, community, social and structural levels.Originality/valueImplementation of recommendations presented in this Viewpoint will mitigate COVID-19 risk among a population that has been marginalized and overlooked, yet has been the epicenter of the COVID-19 pandemic.

BackgroundPeople living with HIV (PLWH) who experience homelessness and unstable housing (HUH) often have fragmented health care. Research that incorporates granular assessments of housing status and primary care visit adherence to understand patterns of acute care utilization can help pinpoint areas for intervention.MethodsWe collected self-reported living situation, categorized as stable (rent/own, hotel/single room occupancy), unstable (treatment/transitional program, staying with friends), or homeless (homeless shelter, outdoors/in vehicle) at an urban safety-net HIV clinic between February and August 2017 and abstracted demographic and clinical information from the medical record. Regression models evaluated the association of housing status on the frequency of acute care visits-urgent care (UC) visits, emergency department (ED) visits, and hospitalizations-and whether suboptimal primary care visit adherence (<75%) interacted with housing status on acute care visits.ResultsAmong 1198 patients, 25% experienced HUH. In adjusted models, unstable housing resulted in a statistically significant increase in the incidence rate ratio for UC visits (incidence rate ratio [IRR], 1.35; 95% confidence interval [CI], 1.10 to 1.66; P < .001), ED visits (IRR, 2.12; 95% CI, 1.44 to 3.13; P < .001), and hospitalizations (IRR, 1.75; 95% CI, 1.10 to 2.77; P = 0.018). Homelessness led to even greater increases in UC visits (IRR, 1.75; 95% CI, 1.29 to 2.39; P < .001), ED visits (IRR, 4.18; 95% CI, 2.77 to 6.30; P < .001), and hospitalizations (IRR, 3.18; 95% CI, 2.03 to 4.97; P < .001). Suboptimal visit adherence differentially impacted UC and ED visits by housing status, suggesting interaction.ConclusionsIncreased acute care visit frequency among HUH-PLWH suggests that interventions at these visits may create opportunities to improve care.

BackgroundPeople living with HIV (PLWH) who experience homelessness and unstable housing (HUH) often have fragmented health care. Research that incorporates granular assessments of housing status and primary care visit adherence to understand patterns of acute care utilization can help pinpoint areas for intervention.MethodsWe collected self-reported living situation, categorized as stable (rent/own, hotel/single room occupancy), unstable (treatment/transitional program, staying with friends), or homeless (homeless shelter, outdoors/in vehicle) at an urban safety-net HIV clinic between February and August 2017 and abstracted demographic and clinical information from the medical record. Regression models evaluated the association of housing status on the frequency of acute care visits-urgent care (UC) visits, emergency department (ED) visits, and hospitalizations-and whether suboptimal primary care visit adherence (<75%) interacted with housing status on acute care visits.ResultsAmong 1198 patients, 25% experienced HUH. In adjusted models, unstable housing resulted in a statistically significant increase in the incidence rate ratio for UC visits (incidence rate ratio [IRR], 1.35; 95% confidence interval [CI], 1.10 to 1.66; P < .001), ED visits (IRR, 2.12; 95% CI, 1.44 to 3.13; P < .001), and hospitalizations (IRR, 1.75; 95% CI, 1.10 to 2.77; P = 0.018). Homelessness led to even greater increases in UC visits (IRR, 1.75; 95% CI, 1.29 to 2.39; P < .001), ED visits (IRR, 4.18; 95% CI, 2.77 to 6.30; P < .001), and hospitalizations (IRR, 3.18; 95% CI, 2.03 to 4.97; P < .001). Suboptimal visit adherence differentially impacted UC and ED visits by housing status, suggesting interaction.ConclusionsIncreased acute care visit frequency among HUH-PLWH suggests that interventions at these visits may create opportunities to improve care.

Job loss and evictions tied to the Coronavirus Disease 2019 (COVID-19) pandemic are expected to increase homelessness significantly in the coming months. Reciprocally, homelessness and the many vulnerabilities that inevitably accompany it are driving COVID-19 outbreaks in US shelters and other congregate living situations. Unless we intervene to address homelessness, these co-existing and synergistic situations will make the current public health crisis even worse. Preventing homelessness and providing permanent affordable housing has reduced the ravages of the HIV epidemic. We must take the lessons learned in 40 years of fighting HIV to respond effectively to the COVID-19 crisis. Housing is an investment that will curb the spread of COVID-19 and help protect all of us from future pandemics.

Job loss and evictions tied to the Coronavirus Disease 2019 (COVID-19) pandemic are expected to increase homelessness significantly in the coming months. Reciprocally, homelessness and the many vulnerabilities that inevitably accompany it are driving COVID-19 outbreaks in US shelters and other congregate living situations. Unless we intervene to address homelessness, these co-existing and synergistic situations will make the current public health crisis even worse. Preventing homelessness and providing permanent affordable housing has reduced the ravages of the HIV epidemic. We must take the lessons learned in 40 years of fighting HIV to respond effectively to the COVID-19 crisis. Housing is an investment that will curb the spread of COVID-19 and help protect all of us from future pandemics.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Coronavirus disease-2019 (COVID-19) threatens to further worsen HIV outcomes among people experiencing homelessness. We conducted an interrupted time-series analysis of care engagement and viral suppression among unhoused individuals in the 'POP-UP' low-barrier, high-intensity HIV primary care program during COVID-19. Among 85 patients, care engagement and viral suppression did not decrease in the 5 months following implementation of San Francisco's 'shelter-in-place' ordinance. Low-barrier, in-person HIV care for homeless individuals may be important for maintaining HIV outcomes during COVID-19.

ObjectiveHomelessness is the greatest risk factor for HIV viremia in San Francisco. Innovative care models for people with HIV (PWH) with homelessness or unstable housing (HUH) are needed to address this inequity. We developed a novel low-barrier clinic-based program for PWH-HUH in an urban safety-net clinic ('POP-UP') and report outcomes on care engagement and viral suppression.DesignA prospective cohort study.SettingSan Francisco General Hospital HIV Clinic (Ward 86).ParticipantsWe enrolled PWH who are HUH, viraemic and for whom usual care is not working (at least one missed primary care appointment and at least two drop-in visits at Ward 86 in the last year).InterventionPOP-UP provides drop-in comprehensive primary care, housing assistance and case management, financial incentives and patient navigation with frequent contact.Main outcome measuresWe describe uptake of eligible patients into POP-UP, and cumulative incidence of antiretroviral therapy (ART) initiation, return to care and virologic suppression 6 months post-enrolment, estimated via Kaplan--Meier.ResultsOut of 192 referred patients, 152 were eligible, and 75 enrolled. All 75 were off ART and viraemic; 100% had a substance use disorder; and 77% had a mental health diagnosis. Over three-quarters restarted ART within 7 days of enrolment, and 91% returned for follow-up within 90 days. The cumulative incidence of viral suppression at 6 months was 55% (95% confidence interval 43-68).ConclusionA novel care model for PWH-HUH demonstrates early success in engaging viraemic patients in care and improving viral suppression. Low-barrier, high-contact primary care programmes offering comprehensive services and incentives may improve outcomes for this vulnerable population.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Housing instability negatively impacts outcomes in people [living] with human immunodeficiency virus (PLHIV), yet the effect of diverse living arrangements has not previously been evaluated. Using 6 dwelling types to measure housing status, we found a strong inverse association between housing instability and viral suppression across a spectrum of unstable housing arrangements.

Housing instability negatively impacts outcomes in people [living] with human immunodeficiency virus (PLHIV), yet the effect of diverse living arrangements has not previously been evaluated. Using 6 dwelling types to measure housing status, we found a strong inverse association between housing instability and viral suppression across a spectrum of unstable housing arrangements.

Throughout the new century, the issue of immigration has become one of the central questions of the public, political and media debate in many European countries. This article analyses the main Spanish and Italian laws on immigration from the mid-1980s until today, with the aim to extract their key terms and to evaluate, from a contrastive perspective, their spread and semantic evolution in the laws, as well as to look at the use and the connotation of the same terms and of their variants in the press. Additionally, the way in which the cultural and linguistic issues involved in the migration phenomenon are addressed in the laws will be examined., En muchos países europeos, a lo largo del nuevo siglo, el tema de la inmigración ha llegado a ser una de las cuestiones centrales del debate público, político y mediático. En este artículo se analizan las principales disposiciones legislativas españolas e italianas sobre la inmigración desde mediados de los años ochenta hasta hoy, con el objetivo de extraer los términos clave y de evaluar, en una óptica contrastiva, su difusión y su evolución semántica en las leyes; en paralelo, se verifica el uso y la connotación de los mismos términos y de sus variantes en la prensa. Se examina, además, cómo abordan las leyes las cuestiones culturales y lingüísticas que el fenómeno migratorio plantea.

Throughout the new century, the issue of immigration has become one of the central questions of the public, political and media debate in many European countries. This article analyses the main Spanish and Italian laws on immigration from the mid-1980s until today, with the aim to extract their key terms and to evaluate, from a contrastive perspective, their spread and semantic evolution in the laws, as well as to look at the use and the connotation of the same terms and of their variants in the press. Additionally, the way in which the cultural and linguistic issues involved in the migration phenomenon are addressed in the laws will be examined., En muchos países europeos, a lo largo del nuevo siglo, el tema de la inmigración ha llegado a ser una de las cuestiones centrales del debate público, político y mediático. En este artículo se analizan las principales disposiciones legislativas españolas e italianas sobre la inmigración desde mediados de los años ochenta hasta hoy, con el objetivo de extraer los términos clave y de evaluar, en una óptica contrastiva, su difusión y su evolución semántica en las leyes; en paralelo, se verifica el uso y la connotación de los mismos términos y de sus variantes en la prensa. Se examina, además, cómo abordan las leyes las cuestiones culturales y lingüísticas que el fenómeno migratorio plantea.

The characterization of fracture networks in rock masses has several applications in structural geology, reservoir characterization and geomechanics. In this contribution, we present a workflow that allows reconstructing the 3D fracture pattern on an outcrop starting from 2D fracture trace interpretation performed on a Digital Outcrop Model (DOM). For this purpose, we developed Python scripts based on the GoPy SKUA/Gocad plugin. The scripts automatically reconstruct fracture surfaces starting from fracture traces obtained from 3D DOM interpretation (Mittempergher et al., this meeting). Fracture surfaces are created from fracture traces with different methods depending on structural constraints and local morphology of the outcrop. Then structural parameters like length, height, azimuth and dip of single fractures, and cumulative properties of the fracture network like fracture intensity/density, intersections, etc., can be automatically extracted and analyzed with Python and particularly Numpy/Scipy tools, including advanced statistical libraries and plotting utilities. Our scripts are presented with examples on outcrop analogues in fractured carbonates

BackgroundStaphylococcus aureus is a leading cause of bacteremia, yet there remains a significant knowledge gap in the identification of relevant biomarkers that predict clinical outcomes. Heterogeneity in the host response to invasive S. aureus infection suggests that specific biomarker signatures could be utilized to differentiate patients prone to severe disease, thereby facilitating earlier implementation of more aggressive therapies.MethodsTo further elucidate the inflammatory correlates of poor clinical outcomes in patients with S. aureus bacteremia, we evaluated the association between a panel of blood proteins at initial presentation of bacteremia and disease severity outcomes using 2 cohorts of patients with S. aureus bacteremia (n = 32 and n = 124).ResultsWe identified 13 candidate proteins that were correlated with mortality and persistent bacteremia. Prognostic modeling identified interleukin (IL)-8 and CCL2 as the strongest individual predictors of mortality, with the combination of these biomarkers classifying fatal outcome with 89% sensitivity and 77% specificity (P < .0001). Baseline IL-17A levels were elevated in patients with persistent bacteremia (P < .0001), endovascular (P = .026) and metastatic tissue infections (P = .012).ConclusionsThese results demonstrate the potential utility of selected biomarkers to distinguish patients with the highest risk for treatment failure and bacteremia-related complications, providing a valuable tool for clinicians in the management of S. aureus bacteremia. Additionally, these biomarkers could identify patients with the greatest potential to benefit from novel therapies in clinical trials.

While poverty is an established barrier to achieving success at each step of the HIV care continuum, less is known about specific aspects of poverty and how they overlap with behavior in exceptionally low-income individuals who live in well-resourced areas. We considered unsuppressed viral load over 3 years among women living with HIV in San Francisco who used homeless shelters, low-income hotels and free meal programs. One-hundred twenty study participants were followed; 60% had > 1 unsuppressed viral load and 19% were unsuppressed at every visit. Across six-month intervals, the odds of unsuppressed viral load were 11% higher for every 10 nights spent sleeping on the street [Adjusted Odds Ratio (AOR) 1.11, 95% CI 1.02-1.20]; 16% higher for every 10 nights spent sleeping in a shelter (AOR/10 nights 1.16, 95% CI 1.06-1.27); 4% higher for every 10 nights spent sleeping in a single-room occupancy hotel (AOR/10 nights 1.04, 95% CI 1.02-1.07); and over threefold higher among women who experienced any recent incarceration (AOR 3.56, 95% CI 1.84-6.86). Violence and recent use of outpatient health care did not significantly predict viral suppression in adjusted analysis. While strategies to promote retention in care are important for vulnerable persons living with HIV, they are insufficient to ensure sustained viral suppression in low-income women experiencing homelessness and incarceration. Results presented here in combination with prior research linking incarceration to homelessness among women indicate that tailored interventions, which not only consider but prioritize affordable housing, are critical to achieving sustained viral suppression in low-income women living with HIV.

The Vado di Corno Fault Zone (VCFZ) is an active extensional fault cutting through carbonates in the Italian Central Apennines. The fault zone was exhumed from ∼2 km depth and accommodated a normal throw of ∼2 km since Early-Pleistocene. In the studied area, the master fault of the VCFZ dips N210/54° and juxtaposes Quaternary colluvial deposits in the hangingwall with cataclastic dolostones in the footwall. Detailed mapping of the fault zone rocks within the ∼300 m thick footwall-block evidenced the presence of five main structural units (Low Strain Damage Zone, High Strain Damage Zone, Breccia Unit, Cataclastic Unit 1 and Cataclastic Unit 2). The Breccia Unit results from the Pleistocene extensional reactivation of a pre-existing Pliocene thrust. The Cataclastic Unit 1 forms a ∼40 m thick band lining the master fault and recording in-situ shattering due to the propagation of multiple seismic ruptures. Seismic faulting is suggested also by the occurrence of mirror-like slip surfaces, highly localized sheared calcite-bearing veins and fluidized cataclasites. The VCFZ architecture compares well with seismological studies of the L'Aquila 2009 seismic sequence (mainshock MW 6.1), which imaged the reactivation of shallow-seated low-angle normal faults (Breccia Unit) cut by major high-angle normal faults (Cataclastic Units).

Background: We reported that 6-month therapy with intravenous immunoglobulin (IVIg) was more frequently effective or tolerated than intravenous methylprednisolone (IVMP) in patients with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). We now retrospectively compared the proportion of patients who eventually worsened after discontinuing therapy and the median time to clinical worsening. Methods: By March 2013, data were available from 41 of the 45 patients completing the trial with a median follow-up after therapy discontinuation of 42 months (range 1-60). Three patients withdrew during the original study and one failed to respond to either of the therapies. No patient received a diagnosis alternative to CIDP during the follow-up. Results: Twenty-eight of the 32 patients treated with IVIg (as primary or secondary therapy after failing to respond to IVMP) improved after therapy (87.5%) as compared with 13 of the 24 patients treated with IVMP as primary or secondary therapy (54.2%). After a median follow-up of 42 months (range 1-57), 24 out of 28 patients responsive to IVIg (85.7%) worsened after therapy discontinuation. The same occurred in 10 out of 13 patients (76.9%) responsive to IVMP (p=0.659) after a median follow-up of 43 months (range 7-60). Worsening occurred 1-24 months (median 4.5) after IVIg discontinuation and 1-31 months (median 14) after IVMP discontinuation (p=0.0126). Conclusions: A similarly high proportion of patients treated with IVIg or IVMP eventually relapse after therapy discontinuation but the median time to relapse was significantly longer after IVMP than IVIg. This difference may help to balance the more frequent response to IVIg than to IVMP in patients with CIDP.